feat: async decode

.coderabbit.yaml

@@ -0,0 +1,15 @@
+# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
+language: "en-US"
+early_access: false
+reviews:
+  profile: "chill"
+  request_changes_workflow: false
+  high_level_summary: true
+  poem: true
+  review_status: true
+  collapse_walkthrough: false
+  auto_review:
+    enabled: false
+    drafts: false
+chat:
+  auto_reply: true

.github/CODEOWNERS

@@ -4,7 +4,7 @@
 
 * @GreptimeTeam/db-approver
 
-## [Module] Database Engine
+## [Module] Databse Engine
 /src/index @zhongzc
 /src/mito2 @evenyag @v0y4g3r @waynexia
 /src/query @evenyag

.github/actions/build-linux-artifacts/action.yml

@@ -52,7 +52,7 @@ runs:
       uses: ./.github/actions/build-greptime-binary
       with:
         base-image: ubuntu
-        features: servers/dashboard
+        features: servers/dashboard,pg_kvbackend,mysql_kvbackend
         cargo-profile: ${{ inputs.cargo-profile }}
         artifacts-dir: greptime-linux-${{ inputs.arch }}-${{ inputs.version }}
         version: ${{ inputs.version }}
@@ -70,7 +70,7 @@ runs:
       if: ${{ inputs.arch == 'amd64' && inputs.dev-mode == 'false' }} # Builds greptime for centos if the host machine is amd64.
       with:
         base-image: centos
-        features: servers/dashboard
+        features: servers/dashboard,pg_kvbackend,mysql_kvbackend
         cargo-profile: ${{ inputs.cargo-profile }}
         artifacts-dir: greptime-linux-${{ inputs.arch }}-centos-${{ inputs.version }}
         version: ${{ inputs.version }}

.github/actions/build-windows-artifacts/action.yml

@@ -47,6 +47,7 @@ runs:
       shell: pwsh
       run: make test sqlness-test
       env:
+        RUSTUP_WINDOWS_PATH_ADD_BIN: 1 # Workaround for https://github.com/nextest-rs/nextest/issues/1493
         RUST_BACKTRACE: 1
         SQLNESS_OPTS: "--preserve-state"
 

.github/actions/release-cn-artifacts/action.yaml

@@ -64,11 +64,11 @@ inputs:
   upload-max-retry-times:
     description: Max retry times for uploading artifacts to S3
     required: false
-    default: "30"
+    default: "20"
   upload-retry-timeout:
     description: Timeout for uploading artifacts to S3
     required: false
-    default: "120" # minutes
+    default: "30" # minutes
 runs:
   using: composite
   steps:

.github/actions/setup-greptimedb-cluster/action.yml

@@ -8,15 +8,15 @@ inputs:
     default: 2
     description: "Number of Datanode replicas"
   meta-replicas:
-    default: 2
+    default: 1
     description: "Number of Metasrv replicas"
-  image-registry:
+  image-registry: 
     default: "docker.io"
     description: "Image registry"
-  image-repository:
+  image-repository: 
     default: "greptime/greptimedb"
     description: "Image repository"
-  image-tag:
+  image-tag: 
     default: "latest"
     description: 'Image tag'
   etcd-endpoints:
@@ -32,12 +32,12 @@ runs:
   steps:
   - name: Install GreptimeDB operator
     uses: nick-fields/retry@v3
-    with:
+    with: 
       timeout_minutes: 3
       max_attempts: 3
       shell: bash
       command: |
-        helm repo add greptime https://greptimeteam.github.io/helm-charts/
+        helm repo add greptime https://greptimeteam.github.io/helm-charts/ 
         helm repo update
         helm upgrade \
           --install \
@@ -48,10 +48,10 @@ runs:
           --wait-for-jobs
   - name: Install GreptimeDB cluster
     shell: bash
-    run: |
+    run: | 
       helm upgrade \
         --install my-greptimedb \
-        --set meta.backendStorage.etcd.endpoints=${{ inputs.etcd-endpoints }} \
+        --set meta.etcdEndpoints=${{ inputs.etcd-endpoints }} \
         --set meta.enableRegionFailover=${{ inputs.enable-region-failover }} \
         --set image.registry=${{ inputs.image-registry }} \
         --set image.repository=${{ inputs.image-repository }}  \
@@ -59,7 +59,7 @@ runs:
         --set base.podTemplate.main.resources.requests.cpu=50m \
         --set base.podTemplate.main.resources.requests.memory=256Mi \
         --set base.podTemplate.main.resources.limits.cpu=2000m \
-        --set base.podTemplate.main.resources.limits.memory=3Gi \
+        --set base.podTemplate.main.resources.limits.memory=2Gi \
         --set frontend.replicas=${{ inputs.frontend-replicas }} \
         --set datanode.replicas=${{ inputs.datanode-replicas }} \
         --set meta.replicas=${{ inputs.meta-replicas }} \
@@ -72,7 +72,7 @@ runs:
   - name: Wait for GreptimeDB
     shell: bash
     run: |
-      while true; do
+      while true; do 
         PHASE=$(kubectl -n my-greptimedb get gtc my-greptimedb -o jsonpath='{.status.clusterPhase}')
         if [ "$PHASE" == "Running" ]; then
           echo "Cluster is ready"
@@ -86,10 +86,10 @@ runs:
   - name: Print GreptimeDB info
     if: always()
     shell: bash
-    run: |
+    run: | 
       kubectl get all --show-labels -n my-greptimedb
   - name: Describe Nodes
     if: always()
     shell: bash
-    run: |
+    run: | 
       kubectl describe nodes

.github/actions/setup-greptimedb-cluster/with-remote-wal.yaml

@@ -2,14 +2,13 @@ meta:
   configData: |-
     [runtime]
     global_rt_size = 4
-
+    
     [wal]
     provider = "kafka"
     broker_endpoints = ["kafka.kafka-cluster.svc.cluster.local:9092"]
     num_topics = 3
-    auto_prune_interval = "30s"
-    trigger_flush_threshold = 100
 
+        
     [datanode]
     [datanode.client]
     timeout = "120s"
@@ -22,7 +21,7 @@ datanode:
     [wal]
     provider = "kafka"
     broker_endpoints = ["kafka.kafka-cluster.svc.cluster.local:9092"]
-    overwrite_entry_start_id = true
+    linger = "2ms"
 frontend:
   configData: |-
     [runtime]

.github/labeler.yaml

@@ -1,15 +0,0 @@
-ci:
-  - changed-files:
-      - any-glob-to-any-file: .github/**
-
-docker:
-  - changed-files:
-      - any-glob-to-any-file: docker/**
-
-documentation:
-  - changed-files:
-      - any-glob-to-any-file: docs/**
-
-dashboard:
-  - changed-files:
-      - any-glob-to-any-file: grafana/**

.github/scripts/create-version.sh

@@ -8,25 +8,24 @@ set -e
 # - If it's a nightly build, the version is 'nightly-YYYYMMDD-$(git rev-parse --short HEAD)', like 'nightly-20230712-e5b243c'.
 # create_version ${GIHUB_EVENT_NAME} ${NEXT_RELEASE_VERSION} ${NIGHTLY_RELEASE_PREFIX}
 function create_version() {
-  # Read from environment variables.
+  # Read from envrionment variables.
   if [ -z "$GITHUB_EVENT_NAME" ]; then
-      echo "GITHUB_EVENT_NAME is empty" >&2
+      echo "GITHUB_EVENT_NAME is empty"
       exit 1
   fi
 
   if [ -z "$NEXT_RELEASE_VERSION" ]; then
-      echo "NEXT_RELEASE_VERSION is empty, use version from Cargo.toml" >&2
-      # NOTE: Need a `v` prefix for the version string.
-      export NEXT_RELEASE_VERSION=v$(grep '^version = ' Cargo.toml | cut -d '"' -f 2 | head -n 1)
+      echo "NEXT_RELEASE_VERSION is empty"
+      exit 1
   fi
 
   if [ -z "$NIGHTLY_RELEASE_PREFIX" ]; then
-      echo "NIGHTLY_RELEASE_PREFIX is empty" >&2
+      echo "NIGHTLY_RELEASE_PREFIX is empty"
       exit 1
   fi
 
   # Reuse $NEXT_RELEASE_VERSION to identify whether it's a nightly build.
-  # It will be like 'nightly-20230808-7d0d8dc6'.
+  # It will be like 'nigtly-20230808-7d0d8dc6'.
   if [ "$NEXT_RELEASE_VERSION" = nightly ]; then
     echo "$NIGHTLY_RELEASE_PREFIX-$(date "+%Y%m%d")-$(git rev-parse --short HEAD)"
     exit 0
@@ -36,7 +35,7 @@ function create_version() {
   # It will be like 'dev-2023080819-f0e7216c'.
   if [ "$NEXT_RELEASE_VERSION" = dev ]; then
     if [ -z "$COMMIT_SHA" ]; then
-      echo "COMMIT_SHA is empty in dev build" >&2
+      echo "COMMIT_SHA is empty in dev build"
       exit 1
     fi
     echo "dev-$(date "+%Y%m%d-%s")-$(echo "$COMMIT_SHA" | cut -c1-8)"
@@ -46,7 +45,7 @@ function create_version() {
   # Note: Only output 'version=xxx' to stdout when everything is ok, so that it can be used in GitHub Actions Outputs.
   if [ "$GITHUB_EVENT_NAME" = push ]; then
     if [ -z "$GITHUB_REF_NAME" ]; then
-      echo "GITHUB_REF_NAME is empty in push event" >&2
+      echo "GITHUB_REF_NAME is empty in push event"
       exit 1
     fi
     echo "$GITHUB_REF_NAME"
@@ -55,15 +54,15 @@ function create_version() {
   elif [ "$GITHUB_EVENT_NAME" = schedule ]; then
     echo "$NEXT_RELEASE_VERSION-$NIGHTLY_RELEASE_PREFIX-$(date "+%Y%m%d")"
   else
-    echo "Unsupported GITHUB_EVENT_NAME: $GITHUB_EVENT_NAME" >&2
+    echo "Unsupported GITHUB_EVENT_NAME: $GITHUB_EVENT_NAME"
     exit 1
   fi
 }
 
 # You can run as following examples:
-#  GITHUB_EVENT_NAME=push NEXT_RELEASE_VERSION=v0.4.0 NIGHTLY_RELEASE_PREFIX=nightly GITHUB_REF_NAME=v0.3.0 ./create-version.sh
-#  GITHUB_EVENT_NAME=workflow_dispatch NEXT_RELEASE_VERSION=v0.4.0 NIGHTLY_RELEASE_PREFIX=nightly ./create-version.sh
-#  GITHUB_EVENT_NAME=schedule NEXT_RELEASE_VERSION=v0.4.0 NIGHTLY_RELEASE_PREFIX=nightly ./create-version.sh
-#  GITHUB_EVENT_NAME=schedule NEXT_RELEASE_VERSION=nightly NIGHTLY_RELEASE_PREFIX=nightly ./create-version.sh
-#  GITHUB_EVENT_NAME=workflow_dispatch COMMIT_SHA=f0e7216c4bb6acce9b29a21ec2d683be2e3f984a NEXT_RELEASE_VERSION=dev NIGHTLY_RELEASE_PREFIX=nightly ./create-version.sh
+#  GITHUB_EVENT_NAME=push NEXT_RELEASE_VERSION=v0.4.0 NIGHTLY_RELEASE_PREFIX=nigtly GITHUB_REF_NAME=v0.3.0 ./create-version.sh
+#  GITHUB_EVENT_NAME=workflow_dispatch NEXT_RELEASE_VERSION=v0.4.0 NIGHTLY_RELEASE_PREFIX=nigtly ./create-version.sh
+#  GITHUB_EVENT_NAME=schedule NEXT_RELEASE_VERSION=v0.4.0 NIGHTLY_RELEASE_PREFIX=nigtly ./create-version.sh
+#  GITHUB_EVENT_NAME=schedule NEXT_RELEASE_VERSION=nightly NIGHTLY_RELEASE_PREFIX=nigtly ./create-version.sh
+#  GITHUB_EVENT_NAME=workflow_dispatch COMMIT_SHA=f0e7216c4bb6acce9b29a21ec2d683be2e3f984a NEXT_RELEASE_VERSION=dev NIGHTLY_RELEASE_PREFIX=nigtly ./create-version.sh
 create_version

.github/scripts/deploy-greptimedb.sh

@@ -10,7 +10,7 @@ GREPTIMEDB_IMAGE_TAG=${GREPTIMEDB_IMAGE_TAG:-latest}
 ETCD_CHART="oci://registry-1.docker.io/bitnamicharts/etcd"
 GREPTIME_CHART="https://greptimeteam.github.io/helm-charts/"
 
-# Create a cluster with 1 control-plane node and 5 workers.
+# Ceate a cluster with 1 control-plane node and 5 workers.
 function create_kind_cluster() {
   cat <<EOF | kind create cluster --name "${CLUSTER}" --image kindest/node:"$KUBERNETES_VERSION" --config=-
 kind: Cluster
@@ -68,7 +68,7 @@ function deploy_greptimedb_cluster() {
 
   helm install "$cluster_name" greptime/greptimedb-cluster \
     --set image.tag="$GREPTIMEDB_IMAGE_TAG" \
-    --set meta.backendStorage.etcd.endpoints="etcd.$install_namespace:2379" \
+    --set meta.etcdEndpoints="etcd.$install_namespace:2379" \
     -n "$install_namespace"
 
   # Wait for greptimedb cluster to be ready.
@@ -103,7 +103,7 @@ function deploy_greptimedb_cluster_with_s3_storage() {
 
   helm install "$cluster_name" greptime/greptimedb-cluster -n "$install_namespace" \
     --set image.tag="$GREPTIMEDB_IMAGE_TAG" \
-    --set meta.backendStorage.etcd.endpoints="etcd.$install_namespace:2379" \
+    --set meta.etcdEndpoints="etcd.$install_namespace:2379" \
     --set storage.s3.bucket="$AWS_CI_TEST_BUCKET" \
     --set storage.s3.region="$AWS_REGION" \
     --set storage.s3.root="$DATA_ROOT" \

.github/scripts/update-dev-builder-version.sh

@@ -1,37 +0,0 @@
-#!/bin/bash
-
-DEV_BUILDER_IMAGE_TAG=$1
-
-update_dev_builder_version() {
-  if [ -z "$DEV_BUILDER_IMAGE_TAG" ]; then
-    echo "Error: Should specify the dev-builder image tag"
-    exit 1
-  fi
-
-  # Configure Git configs.
-  git config --global user.email greptimedb-ci@greptime.com
-  git config --global user.name greptimedb-ci
-
-  # Checkout a new branch.
-  BRANCH_NAME="ci/update-dev-builder-$(date +%Y%m%d%H%M%S)"
-  git checkout -b $BRANCH_NAME
-
-  # Update the dev-builder image tag in the Makefile.
-  sed -i "s/DEV_BUILDER_IMAGE_TAG ?=.*/DEV_BUILDER_IMAGE_TAG ?= ${DEV_BUILDER_IMAGE_TAG}/g" Makefile
-
-  # Commit the changes.
-  git add Makefile
-  git commit -m "ci: update dev-builder image tag"
-  git push origin $BRANCH_NAME
-
-  # Create a Pull Request.
-  gh pr create \
-      --title "ci: update dev-builder image tag" \
-      --body "This PR updates the dev-builder image tag" \
-      --base main \
-      --head $BRANCH_NAME \
-      --reviewer zyy17 \
-      --reviewer daviderli614
-}
-
-update_dev_builder_version

.github/scripts/update-helm-charts-version.sh

@@ -1,46 +0,0 @@
-#!/bin/bash
-
-set -e
-
-VERSION=${VERSION}
-GITHUB_TOKEN=${GITHUB_TOKEN}
-
-update_helm_charts_version() {
-  # Configure Git configs.
-  git config --global user.email update-helm-charts-version@greptime.com
-  git config --global user.name update-helm-charts-version
-
-  # Clone helm-charts repository.
-  git clone "https://x-access-token:${GITHUB_TOKEN}@github.com/GreptimeTeam/helm-charts.git"
-  cd helm-charts
-
-  # Set default remote for gh CLI
-  gh repo set-default GreptimeTeam/helm-charts
-
-  # Checkout a new branch.
-  BRANCH_NAME="chore/greptimedb-${VERSION}"
-  git checkout -b $BRANCH_NAME
-
-  # Update version.
-  make update-version CHART=greptimedb-cluster VERSION=${VERSION}
-  make update-version CHART=greptimedb-standalone VERSION=${VERSION}
-
-  # Update docs.
-  make docs
-
-  # Commit the changes.
-  git add .
-  git commit -s -m "chore: Update GreptimeDB version to ${VERSION}"
-  git push origin $BRANCH_NAME
-
-  # Create a Pull Request.
-  gh pr create \
-    --title "chore: Update GreptimeDB version to ${VERSION}" \
-    --body "This PR updates the GreptimeDB version." \
-    --base main \
-    --head $BRANCH_NAME \
-    --reviewer zyy17 \
-    --reviewer daviderli614
-}
-
-update_helm_charts_version

.github/scripts/update-homebrew-greptme-version.sh

@@ -1,42 +0,0 @@
-#!/bin/bash
-
-set -e
-
-VERSION=${VERSION}
-GITHUB_TOKEN=${GITHUB_TOKEN}
-
-update_homebrew_greptime_version() {
-  # Configure Git configs.
-  git config --global user.email update-greptime-version@greptime.com
-  git config --global user.name update-greptime-version
-
-  # Clone helm-charts repository.
-  git clone "https://x-access-token:${GITHUB_TOKEN}@github.com/GreptimeTeam/homebrew-greptime.git"
-  cd homebrew-greptime
-
-  # Set default remote for gh CLI
-  gh repo set-default GreptimeTeam/homebrew-greptime
-
-  # Checkout a new branch.
-  BRANCH_NAME="chore/greptimedb-${VERSION}"
-  git checkout -b $BRANCH_NAME
-
-  # Update version.
-  make update-greptime-version VERSION=${VERSION}
-
-  # Commit the changes.
-  git add .
-  git commit -s -m "chore: Update GreptimeDB version to ${VERSION}"
-  git push origin $BRANCH_NAME
-
-  # Create a Pull Request.
-  gh pr create \
-    --title "chore: Update GreptimeDB version to ${VERSION}" \
-    --body "This PR updates the GreptimeDB version." \
-    --base main \
-    --head $BRANCH_NAME \
-    --reviewer zyy17 \
-    --reviewer daviderli614
-}
-
-update_homebrew_greptime_version

.github/scripts/upload-artifacts-to-s3.sh

@@ -41,7 +41,7 @@ function upload_artifacts() {
 # Updates the latest version information in AWS S3 if UPDATE_VERSION_INFO is true.
 function update_version_info() {
   if [ "$UPDATE_VERSION_INFO" == "true" ]; then
-    # If it's the official release(like v1.0.0, v1.0.1, v1.0.2, etc.), update latest-version.txt.
+    # If it's the officail release(like v1.0.0, v1.0.1, v1.0.2, etc.), update latest-version.txt.
     if [[ "$VERSION" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]]; then
       echo "Updating latest-version.txt"
       echo "$VERSION" > latest-version.txt

.github/workflows/dev-build.yml

@@ -55,11 +55,6 @@ on:
         description: Build and push images to DockerHub and ACR
         required: false
         default: true
-      upload_artifacts_to_s3:
-        type: boolean
-        description: Whether upload artifacts to s3
-        required: false
-        default: false
       cargo_profile:
         type: choice
         description: The cargo profile to use in building GreptimeDB.
@@ -243,7 +238,7 @@ jobs:
           version: ${{ needs.allocate-runners.outputs.version }}
           push-latest-tag: false # Don't push the latest tag to registry.
           dev-mode: true # Only build the standard images.
-
+          
       - name: Echo Docker image tag to step summary
         run: |
           echo "## Docker Image Tag" >> $GITHUB_STEP_SUMMARY
@@ -286,7 +281,7 @@ jobs:
           aws-cn-access-key-id: ${{ secrets.AWS_CN_ACCESS_KEY_ID }}
           aws-cn-secret-access-key: ${{ secrets.AWS_CN_SECRET_ACCESS_KEY }}
           aws-cn-region: ${{ vars.AWS_RELEASE_BUCKET_REGION }}
-          upload-to-s3: ${{ inputs.upload_artifacts_to_s3 }}
+          upload-to-s3: false
           dev-mode: true                     # Only build the standard images(exclude centos images).
           push-latest-tag: false             # Don't push the latest tag to registry.
           update-version-info: false         # Don't update the version info in S3.

.github/workflows/develop.yml

@@ -22,7 +22,6 @@ concurrency:
 
 jobs:
   check-typos-and-docs:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Check typos and docs
     runs-on: ubuntu-latest
     steps:
@@ -37,7 +36,6 @@ jobs:
           || (echo "'config/config.md' is not up-to-date, please run 'make config-docs'." && exit 1)
 
   license-header-check:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     runs-on: ubuntu-latest
     name: Check License Header
     steps:
@@ -47,7 +45,6 @@ jobs:
       - uses: korandoru/hawkeye@v5
 
   check:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Check
     runs-on: ${{ matrix.os }}
     strategy:
@@ -74,7 +71,6 @@ jobs:
         run: cargo check --locked --workspace --all-targets
 
   toml:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Toml Check
     runs-on: ubuntu-latest
     timeout-minutes: 60
@@ -89,7 +85,6 @@ jobs:
         run: taplo format --check
 
   build:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Build GreptimeDB binaries
     runs-on: ${{ matrix.os }}
     strategy:
@@ -132,7 +127,6 @@ jobs:
           version: current
 
   fuzztest:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Fuzz Test
     needs: build
     runs-on: ubuntu-latest
@@ -189,13 +183,11 @@ jobs:
           max-total-time: 120
 
   unstable-fuzztest:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Unstable Fuzz Test
     needs: build-greptime-ci
     runs-on: ubuntu-latest
     timeout-minutes: 60
     strategy:
-      fail-fast: false
       matrix:
         target: [ "unstable_fuzz_create_table_standalone" ]
     steps:
@@ -223,12 +215,12 @@ jobs:
         run: |
           sudo apt update && sudo apt install -y libfuzzer-14-dev
           cargo install cargo-fuzz cargo-gc-bin --force
-      - name: Download pre-built binary
+      - name: Download pre-built binariy
         uses: actions/download-artifact@v4
         with:
           name: bin
           path: .
-      - name: Unzip binary
+      - name: Unzip bianry
         run: |
           tar -xvf ./bin.tar.gz
           rm ./bin.tar.gz
@@ -250,14 +242,8 @@ jobs:
           name: unstable-fuzz-logs
           path: /tmp/unstable-greptime/
           retention-days: 3
-      - name: Describe pods
-        if: failure()
-        shell: bash
-        run: |
-          kubectl describe pod -n my-greptimedb
 
   build-greptime-ci:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Build GreptimeDB binary (profile-CI)
     runs-on: ${{ matrix.os }}
     strategy:
@@ -281,7 +267,7 @@ jobs:
       - name: Install cargo-gc-bin
         shell: bash
         run: cargo install cargo-gc-bin --force
-      - name: Build greptime binary
+      - name: Build greptime bianry
         shell: bash
         # `cargo gc` will invoke `cargo build` with specified args
         run: cargo gc --profile ci -- --bin greptime --features "pg_kvbackend,mysql_kvbackend"
@@ -299,13 +285,11 @@ jobs:
           version: current
 
   distributed-fuzztest:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Fuzz Test (Distributed, ${{ matrix.mode.name }}, ${{ matrix.target }})
     runs-on: ubuntu-latest
     needs:  build-greptime-ci
     timeout-minutes: 60
     strategy:
-      fail-fast: false
       matrix:
         target: [ "fuzz_create_table", "fuzz_alter_table", "fuzz_create_database", "fuzz_create_logical_table", "fuzz_alter_logical_table", "fuzz_insert", "fuzz_insert_logical_table" ]
         mode:
@@ -335,9 +319,9 @@ jobs:
         name: Setup Minio
         uses: ./.github/actions/setup-minio
       - if: matrix.mode.kafka
-        name: Setup Kafka cluster
+        name: Setup Kafka cluser
         uses: ./.github/actions/setup-kafka-cluster
-      - name: Setup Etcd cluster
+      - name: Setup Etcd cluser
         uses: ./.github/actions/setup-etcd-cluster
       # Prepares for fuzz tests
       - uses: arduino/setup-protoc@v3
@@ -410,11 +394,6 @@ jobs:
         shell: bash
         run: |
           kubectl describe nodes
-      - name: Describe pod
-        if: failure()
-        shell: bash
-        run: |
-          kubectl describe pod -n my-greptimedb
       - name: Export kind logs
         if: failure()
         shell: bash
@@ -437,13 +416,11 @@ jobs:
           docker system prune -f
 
   distributed-fuzztest-with-chaos:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Fuzz Test with Chaos (Distributed, ${{ matrix.mode.name }}, ${{ matrix.target }})
     runs-on: ubuntu-latest
     needs:  build-greptime-ci
     timeout-minutes: 60
     strategy:
-      fail-fast: false
       matrix:
         target: ["fuzz_migrate_mito_regions", "fuzz_migrate_metric_regions", "fuzz_failover_mito_regions", "fuzz_failover_metric_regions"]
         mode:
@@ -488,9 +465,9 @@ jobs:
         name: Setup Minio
         uses: ./.github/actions/setup-minio
       - if: matrix.mode.kafka
-        name: Setup Kafka cluster
+        name: Setup Kafka cluser
         uses: ./.github/actions/setup-kafka-cluster
-      - name: Setup Etcd cluster
+      - name: Setup Etcd cluser
         uses: ./.github/actions/setup-etcd-cluster
       # Prepares for fuzz tests
       - uses: arduino/setup-protoc@v3
@@ -564,11 +541,6 @@ jobs:
         shell: bash
         run: |
           kubectl describe nodes
-      - name: Describe pods
-        if: failure()
-        shell: bash
-        run: |
-          kubectl describe pod -n my-greptimedb
       - name: Export kind logs
         if: failure()
         shell: bash
@@ -591,12 +563,10 @@ jobs:
           docker system prune -f
 
   sqlness:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Sqlness Test (${{ matrix.mode.name }})
     needs: build
     runs-on: ${{ matrix.os }}
     strategy:
-      fail-fast: false
       matrix:
         os: [ ubuntu-latest ]
         mode:
@@ -606,12 +576,9 @@ jobs:
           - name: "Remote WAL"
             opts: "-w kafka -k 127.0.0.1:9092"
             kafka: true
-          - name: "PostgreSQL KvBackend"
+          - name: "Pg Kvbackend"
             opts: "--setup-pg"
             kafka: false
-          - name: "MySQL Kvbackend"
-            opts: "--setup-mysql"
-            kafka: false
     timeout-minutes: 60
     steps:
       - uses: actions/checkout@v4
@@ -639,7 +606,6 @@ jobs:
           retention-days: 3
 
   fmt:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Rustfmt
     runs-on: ubuntu-latest
     timeout-minutes: 60
@@ -657,7 +623,6 @@ jobs:
         run: make fmt-check
 
   clippy:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Clippy
     runs-on: ubuntu-latest
     timeout-minutes: 60
@@ -683,7 +648,6 @@ jobs:
         run: make clippy
 
   conflict-check:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Check for conflict
     runs-on: ubuntu-latest
     steps:
@@ -694,7 +658,7 @@ jobs:
         uses: olivernybroe/action-conflict-finder@v4.0
 
   test:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' && github.event_name != 'merge_group' }}
+    if: github.event_name != 'merge_group'
     runs-on: ubuntu-22.04-arm
     timeout-minutes: 60
     needs:  [conflict-check, clippy, fmt]
@@ -746,7 +710,7 @@ jobs:
           UNITTEST_LOG_DIR: "__unittest_logs"
 
   coverage:
-    if: ${{ github.repository == 'GreptimeTeam/greptimedb' && github.event_name == 'merge_group' }}
+    if: github.event_name == 'merge_group'
     runs-on: ubuntu-22.04-8-cores
     timeout-minutes: 60
     steps:
@@ -806,7 +770,6 @@ jobs:
           verbose: true
 
   # compat:
-  #   if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
   #   name: Compatibility Test
   #   needs: build
   #   runs-on: ubuntu-22.04

.github/workflows/grafana.yml

@@ -21,6 +21,32 @@ jobs:
         run: sudo apt-get install -y jq
 
       # Make the check.sh script executable
-      - name: Check grafana dashboards
+      - name: Make check.sh executable
+        run: chmod +x grafana/check.sh
+
+      # Run the check.sh script
+      - name: Run check.sh
+        run: ./grafana/check.sh
+
+      # Only run summary.sh for pull_request events (not for merge queues or final pushes)
+      - name: Check if this is a pull request
+        id: check-pr
         run: |
-          make check-dashboards
+          if [[ "${{ github.event_name }}" == "pull_request" ]]; then
+            echo "is_pull_request=true" >> $GITHUB_OUTPUT
+          else
+            echo "is_pull_request=false" >> $GITHUB_OUTPUT
+          fi
+
+      # Make the summary.sh script executable
+      - name: Make summary.sh executable
+        if: steps.check-pr.outputs.is_pull_request == 'true'
+        run: chmod +x grafana/summary.sh
+
+      # Run the summary.sh script and add its output to the GitHub Job Summary
+      - name: Run summary.sh and add to Job Summary
+        if: steps.check-pr.outputs.is_pull_request == 'true'
+        run: |
+          SUMMARY=$(./grafana/summary.sh)
+          echo "### Summary of Grafana Panels" >> $GITHUB_STEP_SUMMARY
+          echo "$SUMMARY" >> $GITHUB_STEP_SUMMARY

.github/workflows/nightly-ci.yml

@@ -107,6 +107,7 @@ jobs:
           CARGO_BUILD_RUSTFLAGS: "-C linker=lld-link"
           RUST_BACKTRACE: 1
           CARGO_INCREMENTAL: 0
+          RUSTUP_WINDOWS_PATH_ADD_BIN: 1 # Workaround for https://github.com/nextest-rs/nextest/issues/1493
           GT_S3_BUCKET: ${{ vars.AWS_CI_TEST_BUCKET }}
           GT_S3_ACCESS_KEY_ID: ${{ secrets.AWS_CI_TEST_ACCESS_KEY_ID }}
           GT_S3_ACCESS_KEY: ${{ secrets.AWS_CI_TEST_SECRET_ACCESS_KEY }}
@@ -117,16 +118,16 @@ jobs:
     name: Run clean build on Linux
     runs-on: ubuntu-latest
     if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
-    timeout-minutes: 45
+    timeout-minutes: 60
     steps:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
           persist-credentials: false
-      - uses: cachix/install-nix-action@v31
-      - run: nix develop --command cargo check --bin greptime
-        env:
-          CARGO_BUILD_RUSTFLAGS: "-C link-arg=-fuse-ld=mold"
+      - uses: cachix/install-nix-action@v27
+        with:
+          nix_path: nixpkgs=channel:nixos-24.11
+      - run: nix develop --command cargo build
 
   check-status:
     name: Check status

.github/workflows/pr-labeling.yaml

@@ -1,42 +0,0 @@
-name: 'PR Labeling'
-
-on:
-  pull_request_target:
-    types:
-      - opened
-      - synchronize
-      - reopened
-
-permissions:
-  contents: read
-  pull-requests: write
-  issues: write
-
-jobs:
-  labeler:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout sources
-        uses: actions/checkout@v4
-
-      - uses: actions/labeler@v5
-        with:
-          configuration-path: ".github/labeler.yaml"
-          repo-token: "${{ secrets.GITHUB_TOKEN }}"
-
-  size-label:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: pascalgn/size-label-action@v0.5.5
-        env:
-          GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
-        with:
-          sizes: >
-            {
-              "0": "XS",
-              "100": "S",
-              "300": "M",
-              "1000": "L",
-              "1500": "XL",
-              "2000": "XXL"
-            }

.github/workflows/release-dev-builder-images.yaml

@@ -24,19 +24,11 @@ on:
         description: Release dev-builder-android image
         required: false
         default: false
-      update_dev_builder_image_tag:
-        type: boolean
-        description: Update the DEV_BUILDER_IMAGE_TAG in Makefile and create a PR
-        required: false
-        default: false
 
 jobs:
   release-dev-builder-images:
     name: Release dev builder images
-    # The jobs are triggered by the following events:
-    # 1. Manually triggered workflow_dispatch event
-    # 2. Push event when the PR that modifies the `rust-toolchain.toml` or `docker/dev-builder/**` is merged to main
-    if: ${{ github.event_name == 'push' || inputs.release_dev_builder_ubuntu_image || inputs.release_dev_builder_centos_image || inputs.release_dev_builder_android_image }}
+    if: ${{ inputs.release_dev_builder_ubuntu_image || inputs.release_dev_builder_centos_image || inputs.release_dev_builder_android_image }} # Only manually trigger this job.
     runs-on: ubuntu-latest
     outputs:
       version: ${{ steps.set-version.outputs.version }}
@@ -65,9 +57,9 @@ jobs:
           version: ${{ env.VERSION }}
           dockerhub-image-registry-username: ${{ secrets.DOCKERHUB_USERNAME }}
           dockerhub-image-registry-token: ${{ secrets.DOCKERHUB_TOKEN }}
-          build-dev-builder-ubuntu: ${{ inputs.release_dev_builder_ubuntu_image || github.event_name == 'push' }}
-          build-dev-builder-centos: ${{ inputs.release_dev_builder_centos_image || github.event_name == 'push' }}
-          build-dev-builder-android: ${{ inputs.release_dev_builder_android_image || github.event_name == 'push' }}
+          build-dev-builder-ubuntu: ${{ inputs.release_dev_builder_ubuntu_image }}
+          build-dev-builder-centos: ${{ inputs.release_dev_builder_centos_image }}
+          build-dev-builder-android: ${{ inputs.release_dev_builder_android_image }}
 
   release-dev-builder-images-ecr:
     name: Release dev builder images to AWS ECR
@@ -93,7 +85,7 @@ jobs:
 
       - name: Push dev-builder-ubuntu image
         shell: bash
-        if: ${{ inputs.release_dev_builder_ubuntu_image || github.event_name == 'push' }}
+        if: ${{ inputs.release_dev_builder_ubuntu_image }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -114,7 +106,7 @@ jobs:
 
       - name: Push dev-builder-centos image
         shell: bash
-        if: ${{ inputs.release_dev_builder_centos_image || github.event_name == 'push' }}
+        if: ${{ inputs.release_dev_builder_centos_image }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -135,7 +127,7 @@ jobs:
 
       - name: Push dev-builder-android image
         shell: bash
-        if: ${{ inputs.release_dev_builder_android_image || github.event_name == 'push' }}
+        if: ${{ inputs.release_dev_builder_android_image }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -170,7 +162,7 @@ jobs:
 
       - name: Push dev-builder-ubuntu image
         shell: bash
-        if: ${{ inputs.release_dev_builder_ubuntu_image || github.event_name == 'push' }}
+        if: ${{ inputs.release_dev_builder_ubuntu_image }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -184,7 +176,7 @@ jobs:
 
       - name: Push dev-builder-centos image
         shell: bash
-        if: ${{ inputs.release_dev_builder_centos_image || github.event_name == 'push' }}
+        if: ${{ inputs.release_dev_builder_centos_image }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -198,7 +190,7 @@ jobs:
 
       - name: Push dev-builder-android image
         shell: bash
-        if: ${{ inputs.release_dev_builder_android_image || github.event_name == 'push' }}
+        if: ${{ inputs.release_dev_builder_android_image }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -209,24 +201,3 @@ jobs:
             quay.io/skopeo/stable:latest \
             copy -a docker://docker.io/$IMAGE_NAMESPACE/dev-builder-android:$IMAGE_VERSION \
             docker://$ACR_IMAGE_REGISTRY/$IMAGE_NAMESPACE/dev-builder-android:$IMAGE_VERSION
-  
-  update-dev-builder-image-tag:
-    name: Update dev-builder image tag
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-      pull-requests: write
-    if: ${{ github.event_name == 'push' || inputs.update_dev_builder_image_tag }}
-    needs: [
-      release-dev-builder-images
-    ]
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Update dev-builder image tag
-        shell: bash
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          ./.github/scripts/update-dev-builder-version.sh ${{ needs.release-dev-builder-images.outputs.version }}

.github/workflows/release.yml

@@ -88,8 +88,10 @@ env:
   # Controls whether to run tests, include unit-test, integration-test and sqlness.
   DISABLE_RUN_TESTS: ${{ inputs.skip_test || vars.DEFAULT_SKIP_TEST }}
 
-  # The scheduled version is '${{ env.NEXT_RELEASE_VERSION }}-nightly-YYYYMMDD', like v0.2.0-nightly-20230313;
+  # The scheduled version is '${{ env.NEXT_RELEASE_VERSION }}-nightly-YYYYMMDD', like v0.2.0-nigthly-20230313;
   NIGHTLY_RELEASE_PREFIX: nightly
+  # Note: The NEXT_RELEASE_VERSION should be modified manually by every formal release.
+  NEXT_RELEASE_VERSION: v0.13.0
 
 jobs:
   allocate-runners:
@@ -124,7 +126,7 @@ jobs:
 
       # The create-version will create a global variable named 'version' in the global workflows.
       # - If it's a tag push release, the version is the tag name(${{ github.ref_name }});
-      # - If it's a scheduled release, the version is '${{ env.NEXT_RELEASE_VERSION }}-nightly-$buildTime', like v0.2.0-nightly-20230313;
+      # - If it's a scheduled release, the version is '${{ env.NEXT_RELEASE_VERSION }}-nightly-$buildTime', like v0.2.0-nigthly-20230313;
       # - If it's a manual release, the version is '${{ env.NEXT_RELEASE_VERSION }}-<short-git-sha>-YYYYMMDDSS', like v0.2.0-e5b243c-2023071245;
       - name: Create version
         id: create-version
@@ -133,6 +135,7 @@ jobs:
         env:
           GITHUB_EVENT_NAME: ${{ github.event_name }}
           GITHUB_REF_NAME: ${{ github.ref_name }}
+          NEXT_RELEASE_VERSION: ${{ env.NEXT_RELEASE_VERSION }}
           NIGHTLY_RELEASE_PREFIX: ${{ env.NIGHTLY_RELEASE_PREFIX }}
 
       - name: Allocate linux-amd64 runner
@@ -314,7 +317,7 @@ jobs:
           image-registry-username: ${{ secrets.DOCKERHUB_USERNAME }}
           image-registry-password: ${{ secrets.DOCKERHUB_TOKEN }}
           version: ${{ needs.allocate-runners.outputs.version }}
-          push-latest-tag: ${{ github.ref_type == 'tag' && !contains(github.ref_name, 'nightly') && github.event_name != 'schedule' }}
+          push-latest-tag: true
 
       - name: Set build image result
         id: set-build-image-result
@@ -361,7 +364,7 @@ jobs:
           dev-mode: false
           upload-to-s3: true
           update-version-info: true
-          push-latest-tag: ${{ github.ref_type == 'tag' && !contains(github.ref_name, 'nightly') && github.event_name != 'schedule' }}
+          push-latest-tag: true
 
   publish-github-release:
     name: Create GitHub release and upload artifacts
@@ -388,7 +391,7 @@ jobs:
 
   ### Stop runners ###
   # It's very necessary to split the job of releasing runners into 'stop-linux-amd64-runner' and 'stop-linux-arm64-runner'.
-  # Because we can terminate the specified EC2 instance immediately after the job is finished without unnecessary waiting.
+  # Because we can terminate the specified EC2 instance immediately after the job is finished without uncessary waiting.
   stop-linux-amd64-runner: # It's always run as the last job in the workflow to make sure that the runner is released.
     name: Stop linux-amd64 runner
     # Only run this job when the runner is allocated.
@@ -441,10 +444,10 @@ jobs:
           aws-region: ${{ vars.EC2_RUNNER_REGION }}
           github-token: ${{ secrets.GH_PERSONAL_ACCESS_TOKEN }}
 
-  bump-downstream-repo-versions:
-    name: Bump downstream repo versions
+  bump-doc-version:
+    name: Bump doc version
     if: ${{ github.event_name == 'push' || github.event_name == 'schedule' }}
-    needs: [allocate-runners, publish-github-release]
+    needs: [allocate-runners]
     runs-on: ubuntu-latest
     # Permission reference: https://docs.github.com/en/actions/using-jobs/assigning-permissions-to-jobs
     permissions:
@@ -456,58 +459,13 @@ jobs:
           fetch-depth: 0
           persist-credentials: false
       - uses: ./.github/actions/setup-cyborg
-      - name: Bump downstream repo versions
+      - name: Bump doc version
         working-directory: cyborg
-        run: pnpm tsx bin/bump-versions.ts
+        run: pnpm tsx bin/bump-doc-version.ts
         env:
-          TARGET_REPOS: website,docs,demo
           VERSION: ${{ needs.allocate-runners.outputs.version }}
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          WEBSITE_REPO_TOKEN: ${{ secrets.WEBSITE_REPO_TOKEN }}
           DOCS_REPO_TOKEN: ${{ secrets.DOCS_REPO_TOKEN }}
-          DEMO_REPO_TOKEN: ${{ secrets.DEMO_REPO_TOKEN }}
-
-  bump-helm-charts-version:
-    name: Bump helm charts version
-    if: ${{ github.ref_type == 'tag' && !contains(github.ref_name, 'nightly') && github.event_name != 'schedule' }}
-    needs: [allocate-runners, publish-github-release]
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-      pull-requests: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Bump helm charts version
-        env:
-          GITHUB_TOKEN: ${{ secrets.HELM_CHARTS_REPO_TOKEN }}
-          VERSION: ${{ needs.allocate-runners.outputs.version }}
-        run: |
-          ./.github/scripts/update-helm-charts-version.sh
-
-  bump-homebrew-greptime-version:
-    name: Bump homebrew greptime version
-    if: ${{ github.ref_type == 'tag' && !contains(github.ref_name, 'nightly') && github.event_name != 'schedule' }}
-    needs: [allocate-runners, publish-github-release]
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-      pull-requests: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Bump homebrew greptime version
-        env:
-          GITHUB_TOKEN: ${{ secrets.HOMEBREW_GREPTIME_REPO_TOKEN }}
-          VERSION: ${{ needs.allocate-runners.outputs.version }}
-        run: |
-          ./.github/scripts/update-homebrew-greptme-version.sh
 
   notification:
     if: ${{ github.repository == 'GreptimeTeam/greptimedb' && (github.event_name == 'push' || github.event_name == 'schedule') && always() }}

.github/workflows/semantic-pull-request.yml

@@ -14,9 +14,6 @@ concurrency:
 jobs:
   check:
     runs-on: ubuntu-latest
-    permissions:
-      pull-requests: write  # Add permissions to modify PRs
-      issues: write
     timeout-minutes: 10
     steps:
       - uses: actions/checkout@v4

.gitignore

@@ -28,7 +28,6 @@ debug/
 # Logs
 **/__unittest_logs
 logs/
-!grafana/dashboards/logs/
 
 # cpython's generated python byte code
 **/__pycache__/
@@ -55,9 +54,3 @@ tests-fuzz/corpus/
 # Nix
 .direnv
 .envrc
-
-## default data home
-greptimedb_data
-
-# github
-!/.github

CONTRIBUTING.md

@@ -108,7 +108,7 @@ of what you were trying to do and what went wrong. You can also reach for help i
 The core team will be thrilled if you would like to participate in any way you like. When you are stuck, try to ask for help by filing an issue, with a detailed description of what you were trying to do and what went wrong. If you have any questions or if you would like to get involved in our community, please check out:
 
 - [GreptimeDB Community Slack](https://greptime.com/slack)
-- [GreptimeDB GitHub Discussions](https://github.com/GreptimeTeam/greptimedb/discussions)
+- [GreptimeDB Github Discussions](https://github.com/GreptimeTeam/greptimedb/discussions)
 
 Also, see some extra GreptimeDB content:
 

Cargo.toml

@@ -29,15 +29,12 @@ members = [
     "src/common/query",
     "src/common/recordbatch",
     "src/common/runtime",
-    "src/common/session",
-    "src/common/stat",
     "src/common/substrait",
     "src/common/telemetry",
     "src/common/test-util",
     "src/common/time",
     "src/common/version",
     "src/common/wal",
-    "src/common/workload",
     "src/datanode",
     "src/datatypes",
     "src/file-engine",
@@ -49,7 +46,6 @@ members = [
     "src/meta-client",
     "src/meta-srv",
     "src/metric-engine",
-    "src/mito-codec",
     "src/mito2",
     "src/object-store",
     "src/operator",
@@ -71,17 +67,16 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "0.15.0"
+version = "0.13.0"
 edition = "2021"
 license = "Apache-2.0"
 
 [workspace.lints]
+clippy.print_stdout = "warn"
+clippy.print_stderr = "warn"
 clippy.dbg_macro = "warn"
 clippy.implicit_clone = "warn"
-clippy.result_large_err = "allow"
-clippy.large_enum_variant = "allow"
-clippy.doc_overindented_list_items = "allow"
-clippy.uninlined_format_args = "allow"
+clippy.readonly_write_lock = "allow"
 rust.unknown_lints = "deny"
 rust.unexpected_cfgs = { level = "warn", check-cfg = ['cfg(tokio_unstable)'] }
 
@@ -93,20 +88,20 @@ rust.unexpected_cfgs = { level = "warn", check-cfg = ['cfg(tokio_unstable)'] }
 #
 # See for more detaiils: https://github.com/rust-lang/cargo/issues/11329
 ahash = { version = "0.8", features = ["compile-time-rng"] }
-aquamarine = "0.6"
-arrow = { version = "54.2", features = ["prettyprint"] }
-arrow-array = { version = "54.2", default-features = false, features = ["chrono-tz"] }
-arrow-flight = "54.2"
-arrow-ipc = { version = "54.2", default-features = false, features = ["lz4", "zstd"] }
-arrow-schema = { version = "54.2", features = ["serde"] }
+aquamarine = "0.3"
+arrow = { version = "53.0.0", features = ["prettyprint"] }
+arrow-array = { version = "53.0.0", default-features = false, features = ["chrono-tz"] }
+arrow-flight = "53.0"
+arrow-ipc = { version = "53.0.0", default-features = false, features = ["lz4", "zstd"] }
+arrow-schema = { version = "53.0", features = ["serde"] }
 async-stream = "0.3"
 async-trait = "0.1"
 # Remember to update axum-extra, axum-macros when updating axum
 axum = "0.8"
 axum-extra = "0.10"
-axum-macros = "0.5"
+axum-macros = "0.4"
 backon = "1"
-base64 = "0.22"
+base64 = "0.21"
 bigdecimal = "0.4.2"
 bitflags = "2.4.1"
 bytemuck = "1.12"
@@ -116,45 +111,43 @@ chrono-tz = "0.10.1"
 clap = { version = "4.4", features = ["derive"] }
 config = "0.13.0"
 crossbeam-utils = "0.8"
-dashmap = "6.1"
-datafusion = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "12c0381babd52c681043957e9d6ee083a03f7646" }
-datafusion-common = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "12c0381babd52c681043957e9d6ee083a03f7646" }
-datafusion-expr = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "12c0381babd52c681043957e9d6ee083a03f7646" }
-datafusion-functions = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "12c0381babd52c681043957e9d6ee083a03f7646" }
-datafusion-functions-aggregate-common = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "12c0381babd52c681043957e9d6ee083a03f7646" }
-datafusion-optimizer = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "12c0381babd52c681043957e9d6ee083a03f7646" }
-datafusion-physical-expr = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "12c0381babd52c681043957e9d6ee083a03f7646" }
-datafusion-physical-plan = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "12c0381babd52c681043957e9d6ee083a03f7646" }
-datafusion-sql = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "12c0381babd52c681043957e9d6ee083a03f7646" }
-datafusion-substrait = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "12c0381babd52c681043957e9d6ee083a03f7646" }
-deadpool = "0.12"
-deadpool-postgres = "0.14"
-derive_builder = "0.20"
+dashmap = "5.4"
+datafusion = { git = "https://github.com/apache/datafusion.git", rev = "2464703c84c400a09cc59277018813f0e797bb4e" }
+datafusion-common = { git = "https://github.com/apache/datafusion.git", rev = "2464703c84c400a09cc59277018813f0e797bb4e" }
+datafusion-expr = { git = "https://github.com/apache/datafusion.git", rev = "2464703c84c400a09cc59277018813f0e797bb4e" }
+datafusion-functions = { git = "https://github.com/apache/datafusion.git", rev = "2464703c84c400a09cc59277018813f0e797bb4e" }
+datafusion-optimizer = { git = "https://github.com/apache/datafusion.git", rev = "2464703c84c400a09cc59277018813f0e797bb4e" }
+datafusion-physical-expr = { git = "https://github.com/apache/datafusion.git", rev = "2464703c84c400a09cc59277018813f0e797bb4e" }
+datafusion-physical-plan = { git = "https://github.com/apache/datafusion.git", rev = "2464703c84c400a09cc59277018813f0e797bb4e" }
+datafusion-sql = { git = "https://github.com/apache/datafusion.git", rev = "2464703c84c400a09cc59277018813f0e797bb4e" }
+datafusion-substrait = { git = "https://github.com/apache/datafusion.git", rev = "2464703c84c400a09cc59277018813f0e797bb4e" }
+deadpool = "0.10"
+deadpool-postgres = "0.12"
+derive_builder = "0.12"
 dotenv = "0.15"
 etcd-client = "0.14"
+flate2 = { version = "1.1.0", default-features = false, features = ["zlib-rs"] }
 fst = "0.4.7"
 futures = "0.3"
 futures-util = "0.3"
-greptime-proto = { git = "https://github.com/GreptimeTeam/greptime-proto.git", rev = "464226cf8a4a22696503536a123d0b9e318582f4" }
+greptime-proto = { git = "https://github.com/GreptimeTeam/greptime-proto.git", rev = "c5419bbd20cb42e568ec325a4d71a3c94cc327e1" }
 hex = "0.4"
 http = "1"
 humantime = "2.1"
 humantime-serde = "1.1"
 hyper = "1.1"
 hyper-util = "0.1"
-itertools = "0.14"
+itertools = "0.10"
 jsonb = { git = "https://github.com/databendlabs/jsonb.git", rev = "8c8d2fc294a39f3ff08909d60f718639cfba3875", default-features = false }
 lazy_static = "1.4"
 local-ip-address = "0.6"
 loki-proto = { git = "https://github.com/GreptimeTeam/loki-proto.git", rev = "1434ecf23a2654025d86188fb5205e7a74b225d3" }
 meter-core = { git = "https://github.com/GreptimeTeam/greptime-meter.git", rev = "5618e779cf2bb4755b499c630fba4c35e91898cb" }
-mockall = "0.13"
+mockall = "0.11.4"
 moka = "0.12"
 nalgebra = "0.33"
-nix = { version = "0.30.1", default-features = false, features = ["event", "fs", "process"] }
-notify = "8.0"
+notify = "6.1"
 num_cpus = "1.16"
-object_store_opendal = "0.50"
 once_cell = "1.18"
 opentelemetry-proto = { version = "0.27", features = [
     "gen-tonic",
@@ -164,17 +157,15 @@ opentelemetry-proto = { version = "0.27", features = [
     "logs",
 ] }
 parking_lot = "0.12"
-parquet = { version = "54.2", default-features = false, features = ["arrow", "async", "object_store"] }
+parquet = { version = "53.0.0", default-features = false, features = ["arrow", "async", "object_store"] }
 paste = "1.0"
 pin-project = "1.0"
 prometheus = { version = "0.13.3", features = ["process"] }
-promql-parser = { git = "https://github.com/GreptimeTeam/promql-parser.git", rev = "0410e8b459dda7cb222ce9596f8bf3971bd07bd2", features = [
-    "ser",
-] }
-prost = { version = "0.13", features = ["no-recursion-limit"] }
+promql-parser = { version = "0.5", features = ["ser"] }
+prost = "0.13"
 raft-engine = { version = "0.4.1", default-features = false }
-rand = "0.9"
-ratelimit = "0.10"
+rand = "0.8"
+ratelimit = "0.9"
 regex = "1.8"
 regex-automata = "0.4"
 reqwest = { version = "0.12", default-features = false, features = [
@@ -183,39 +174,36 @@ reqwest = { version = "0.12", default-features = false, features = [
     "stream",
     "multipart",
 ] }
-rskafka = { git = "https://github.com/influxdata/rskafka.git", rev = "8dbd01ed809f5a791833a594e85b144e36e45820", features = [
+rskafka = { git = "https://github.com/influxdata/rskafka.git", rev = "75535b5ad9bae4a5dbb582c82e44dfd81ec10105", features = [
     "transport-tls",
 ] }
-rstest = "0.25"
+rstest = "0.21"
 rstest_reuse = "0.7"
 rust_decimal = "1.33"
 rustc-hash = "2.0"
-# It is worth noting that we should try to avoid using aws-lc-rs until it can be compiled on various platforms.
-rustls = { version = "0.23.25", default-features = false }
+rustls = { version = "0.23.20", default-features = false } # override by patch, see [patch.crates-io]
 serde = { version = "1.0", features = ["derive"] }
 serde_json = { version = "1.0", features = ["float_roundtrip"] }
 serde_with = "3"
-shadow-rs = "1.1"
-simd-json = "0.15"
+shadow-rs = "0.38"
 similar-asserts = "1.6.0"
 smallvec = { version = "1", features = ["serde"] }
 snafu = "0.8"
-sqlparser = { git = "https://github.com/GreptimeTeam/sqlparser-rs.git", rev = "0cf6c04490d59435ee965edd2078e8855bd8471e", features = [
-    "visitor",
-    "serde",
-] } # branch = "v0.54.x"
 sqlx = { version = "0.8", features = [
     "runtime-tokio-rustls",
     "mysql",
-    "postgres",
-    "chrono",
 ] }
-strum = { version = "0.27", features = ["derive"] }
-sysinfo = "0.33"
+sysinfo = "0.30"
+# on branch v0.52.x
+sqlparser = { git = "https://github.com/GreptimeTeam/sqlparser-rs.git", rev = "71dd86058d2af97b9925093d40c4e03360403170", features = [
+    "visitor",
+    "serde",
+] } # on branch v0.44.x
+strum = { version = "0.25", features = ["derive"] }
 tempfile = "3"
 tokio = { version = "1.40", features = ["full"] }
 tokio-postgres = "0.7"
-tokio-rustls = { version = "0.26.2", default-features = false }
+tokio-rustls = { version = "0.26.0", default-features = false } # override by patch, see [patch.crates-io]
 tokio-stream = "0.1"
 tokio-util = { version = "0.7", features = ["io-util", "compat"] }
 toml = "0.8.8"
@@ -258,13 +246,11 @@ common-procedure-test = { path = "src/common/procedure-test" }
 common-query = { path = "src/common/query" }
 common-recordbatch = { path = "src/common/recordbatch" }
 common-runtime = { path = "src/common/runtime" }
-common-session = { path = "src/common/session" }
 common-telemetry = { path = "src/common/telemetry" }
 common-test-util = { path = "src/common/test-util" }
 common-time = { path = "src/common/time" }
 common-version = { path = "src/common/version" }
 common-wal = { path = "src/common/wal" }
-common-workload = { path = "src/common/workload" }
 datanode = { path = "src/datanode" }
 datatypes = { path = "src/datatypes" }
 file-engine = { path = "src/file-engine" }
@@ -276,13 +262,9 @@ log-store = { path = "src/log-store" }
 meta-client = { path = "src/meta-client" }
 meta-srv = { path = "src/meta-srv" }
 metric-engine = { path = "src/metric-engine" }
-mito-codec = { path = "src/mito-codec" }
 mito2 = { path = "src/mito2" }
 object-store = { path = "src/object-store" }
 operator = { path = "src/operator" }
-otel-arrow-rust = { git = "https://github.com/open-telemetry/otel-arrow", rev = "5d551412d2a12e689cde4d84c14ef29e36784e51", features = [
-    "server",
-] }
 partition = { path = "src/partition" }
 pipeline = { path = "src/pipeline" }
 plugins = { path = "src/plugins" }
@@ -292,11 +274,19 @@ query = { path = "src/query" }
 servers = { path = "src/servers" }
 session = { path = "src/session" }
 sql = { path = "src/sql" }
-stat = { path = "src/common/stat" }
 store-api = { path = "src/store-api" }
 substrait = { path = "src/common/substrait" }
 table = { path = "src/table" }
 
+[patch.crates-io]
+# change all rustls dependencies to use our fork to default to `ring` to make it "just work"
+hyper-rustls = { git = "https://github.com/GreptimeTeam/hyper-rustls", rev = "a951e03" } # version = "0.27.5" with ring patch
+rustls = { git = "https://github.com/GreptimeTeam/rustls", rev = "34fd0c6" }             # version = "0.23.20" with ring patch
+tokio-rustls = { git = "https://github.com/GreptimeTeam/tokio-rustls", rev = "4604ca6" } # version = "0.26.0" with ring patch
+# This is commented, since we are not using aws-lc-sys, if we need to use it, we need to uncomment this line or use a release after this commit, or it wouldn't compile with gcc < 8.1
+# see https://github.com/aws/aws-lc-rs/pull/526
+# aws-lc-sys = { git ="https://github.com/aws/aws-lc-rs", rev = "556558441e3494af4b156ae95ebc07ebc2fd38aa" }
+
 [workspace.dependencies.meter-macros]
 git = "https://github.com/GreptimeTeam/greptime-meter.git"
 rev = "5618e779cf2bb4755b499c630fba4c35e91898cb"

Makefile

@@ -8,7 +8,7 @@ CARGO_BUILD_OPTS := --locked
 IMAGE_REGISTRY ?= docker.io
 IMAGE_NAMESPACE ?= greptime
 IMAGE_TAG ?= latest
-DEV_BUILDER_IMAGE_TAG ?= 2025-05-19-b2377d4b-20250520045554
+DEV_BUILDER_IMAGE_TAG ?= 2024-12-25-a71b93dd-20250305072908
 BUILDX_MULTI_PLATFORM_BUILD ?= false
 BUILDX_BUILDER_NAME ?= gtbuilder
 BASE_IMAGE ?= ubuntu
@@ -32,10 +32,6 @@ ifneq ($(strip $(BUILD_JOBS)),)
 	NEXTEST_OPTS += --build-jobs=${BUILD_JOBS}
 endif
 
-ifneq ($(strip $(BUILD_JOBS)),)
-	SQLNESS_OPTS += --jobs ${BUILD_JOBS}
-endif
-
 ifneq ($(strip $(CARGO_PROFILE)),)
 	CARGO_BUILD_OPTS += --profile ${CARGO_PROFILE}
 endif
@@ -197,7 +193,6 @@ fix-clippy: ## Fix clippy violations.
 fmt-check: ## Check code format.
 	cargo fmt --all -- --check
 	python3 scripts/check-snafu.py
-	python3 scripts/check-super-imports.py
 
 .PHONY: start-etcd
 start-etcd: ## Start single node etcd for testing purpose.
@@ -222,16 +217,6 @@ start-cluster: ## Start the greptimedb cluster with etcd by using docker compose
 stop-cluster: ## Stop the greptimedb cluster that created by docker compose.
 	docker compose -f ./docker/docker-compose/cluster-with-etcd.yaml stop
 
-##@ Grafana
-
-.PHONY: check-dashboards
-check-dashboards: ## Check the Grafana dashboards.
-	@./grafana/scripts/check.sh
-
-.PHONY: dashboards
-dashboards: ## Generate the Grafana dashboards for standalone mode and intermediate dashboards.
-	@./grafana/scripts/gen-dashboards.sh
-
 ##@ Docs
 config-docs: ## Generate configuration documentation from toml files.
 	docker run --rm \

README.md

@@ -6,9 +6,7 @@
   </picture>
 </p>
 
-<h2 align="center">Real-Time & Cloud-Native Observability  Database<br/>for metrics, logs, and traces</h2>
-
->  Delivers sub-second querying at PB scale and exceptional cost efficiency from edge to cloud.
+<h2 align="center">Unified & Cost-Effective Time Series Database for Metrics, Logs, and Events</h2>
 
 <div align="center">
 <h3 align="center">
@@ -51,77 +49,70 @@
 </div>
 
 - [Introduction](#introduction)
-- [⭐ Key Features](#features)
-- [Quick Comparison](#quick-comparison)
-- [Architecture](#architecture)
-- [Try GreptimeDB](#try-greptimedb)
+- [**Features: Why GreptimeDB**](#why-greptimedb)
+- [Architecture](https://docs.greptime.com/contributor-guide/overview/#architecture)
+- [Try it for free](#try-greptimedb)
 - [Getting Started](#getting-started)
-- [Build From Source](#build-from-source)
-- [Tools & Extensions](#tools--extensions)
 - [Project Status](#project-status)
-- [Community](#community)
+- [Join the community](#community)
+  - [Contributing](#contributing)
+- [Tools & Extensions](#tools--extensions)
 - [License](#license)
-- [Commercial Support](#commercial-support)
-- [Contributing](#contributing)
 - [Acknowledgement](#acknowledgement)
 
 ## Introduction
 
-**GreptimeDB** is an open-source, cloud-native database purpose-built for the unified collection and analysis of observability data (metrics, logs, and traces). Whether you’re operating on the edge, in the cloud, or across hybrid environments, GreptimeDB empowers real-time insights at massive scale — all in one system.
+**GreptimeDB** is an open-source unified & cost-effective time-series database for **Metrics**, **Logs**, and **Events** (also **Traces** in plan). You can gain real-time insights from Edge to Cloud at Any Scale.
 
-## Features
+## Why GreptimeDB
 
-|   Feature  | Description |
-| --------- | ----------- |
-| [Unified Observability Data](https://docs.greptime.com/user-guide/concepts/why-greptimedb) | Store metrics, logs, and traces as timestamped, contextual wide events. Query via [SQL](https://docs.greptime.com/user-guide/query-data/sql), [PromQL](https://docs.greptime.com/user-guide/query-data/promql), and [streaming](https://docs.greptime.com/user-guide/flow-computation/overview). |
-| [High Performance & Cost Effective](https://docs.greptime.com/user-guide/manage-data/data-index) | Written in Rust, with a distributed query engine, [rich indexing](https://docs.greptime.com/user-guide/manage-data/data-index), and optimized columnar storage, delivering sub-second responses at PB scale. |
-| [Cloud-Native Architecture](https://docs.greptime.com/user-guide/concepts/architecture) | Designed for [Kubernetes](https://docs.greptime.com/user-guide/deployments/deploy-on-kubernetes/greptimedb-operator-management), with compute/storage separation, native object storage (AWS S3, Azure Blob, etc.) and seamless cross-cloud access. |
-| [Developer-Friendly](https://docs.greptime.com/user-guide/protocols/overview) | Access via SQL/PromQL interfaces, REST API, MySQL/PostgreSQL protocols, and popular ingestion [protocols](https://docs.greptime.com/user-guide/protocols/overview). |
-| [Flexible Deployment](https://docs.greptime.com/user-guide/deployments/overview) | Deploy anywhere: edge (including ARM/[Android](https://docs.greptime.com/user-guide/deployments/run-on-android)) or cloud, with unified APIs and efficient data sync. |
+Our core developers have been building time-series data platforms for years. Based on our best practices, GreptimeDB was born to give you:
 
-Learn more in [Why GreptimeDB](https://docs.greptime.com/user-guide/concepts/why-greptimedb) and [Observability 2.0 and the Database for It](https://greptime.com/blogs/2025-04-25-greptimedb-observability2-new-database).
+* **Unified Processing of Metrics, Logs, and Events**
 
-## Quick Comparison
+  GreptimeDB unifies time series data processing by treating all data - whether metrics, logs, or events - as timestamped events with context. Users can analyze this data using either [SQL](https://docs.greptime.com/user-guide/query-data/sql) or [PromQL](https://docs.greptime.com/user-guide/query-data/promql) and leverage stream processing ([Flow](https://docs.greptime.com/user-guide/flow-computation/overview)) to enable continuous aggregation. [Read more](https://docs.greptime.com/user-guide/concepts/data-model).
 
-| Feature                         | GreptimeDB            | Traditional TSDB   | Log Stores      |
-|----------------------------------|-----------------------|--------------------|-----------------|
-| Data Types                      | Metrics, Logs, Traces | Metrics only       | Logs only       |
-| Query Language                  | SQL, PromQL, Streaming|  Custom/PromQL     | Custom/DSL      |
-| Deployment                      | Edge + Cloud          | Cloud/On-prem      | Mostly central  |
-| Indexing & Performance          | PB-Scale, Sub-second  | Varies             | Varies          |
-| Integration                     | REST, SQL, Common protocols | Varies     | Varies          |
+* **Cloud-native Distributed Database**
 
-**Performance:**
-* [GreptimeDB tops JSONBench's billion-record cold run test!](https://greptime.com/blogs/2025-03-18-jsonbench-greptimedb-performance)
-* [TSBS Benchmark](https://github.com/GreptimeTeam/greptimedb/tree/main/docs/benchmarks/tsbs)
+  Built for [Kubernetes](https://docs.greptime.com/user-guide/deployments/deploy-on-kubernetes/greptimedb-operator-management). GreptimeDB achieves seamless scalability with its [cloud-native architecture](https://docs.greptime.com/user-guide/concepts/architecture) of separated compute and storage, built on object storage (AWS S3, Azure Blob Storage, etc.) while enabling cross-cloud deployment through a unified data access layer.
 
-Read [more benchmark reports](https://docs.greptime.com/user-guide/concepts/features-that-you-concern#how-is-greptimedbs-performance-compared-to-other-solutions).
+* **Performance and Cost-effective**
 
-## Architecture
+  Written in pure Rust for superior performance and reliability. GreptimeDB features a distributed query engine with intelligent indexing to handle high cardinality data efficiently. Its optimized columnar storage achieves 50x cost efficiency on cloud object storage through advanced compression. [Benchmark reports](https://www.greptime.com/blogs/2024-09-09-report-summary).
 
-* Read the [architecture](https://docs.greptime.com/contributor-guide/overview/#architecture) document.
-* [DeepWiki](https://deepwiki.com/GreptimeTeam/greptimedb/1-overview) provides an in-depth look at GreptimeDB:
-  <img alt="GreptimeDB System Overview" src="docs/architecture.png">
+* **Cloud-Edge Collaboration**
+
+  GreptimeDB seamlessly operates across cloud and edge (ARM/Android/Linux), providing consistent APIs and control plane for unified data management and efficient synchronization. [Learn how to run on Android](https://docs.greptime.com/user-guide/deployments/run-on-android/).
+
+* **Multi-protocol Ingestion, SQL & PromQL Ready**
+
+  Widely adopted database protocols and APIs, including MySQL, PostgreSQL, InfluxDB, OpenTelemetry, Loki and Prometheus, etc.  Effortless Adoption & Seamless Migration. [Supported Protocols Overview](https://docs.greptime.com/user-guide/protocols/overview).
+
+For more detailed info please read  [Why GreptimeDB](https://docs.greptime.com/user-guide/concepts/why-greptimedb).
 
 ## Try GreptimeDB
 
 ### 1. [Live Demo](https://greptime.com/playground)
 
-Experience GreptimeDB directly in your browser.
+Try out the features of GreptimeDB right from your browser.
 
 ### 2. [GreptimeCloud](https://console.greptime.cloud/)
 
 Start instantly with a free cluster.
 
-### 3. Docker (Local Quickstart)
+### 3. Docker Image
+
+To install GreptimeDB locally, the recommended way is via Docker:
 
 ```shell
 docker pull greptime/greptimedb
 ```
 
+Start a GreptimeDB container with:
+
 ```shell
 docker run -p 127.0.0.1:4000-4003:4000-4003 \
-  -v "$(pwd)/greptimedb_data:/greptimedb_data" \
+  -v "$(pwd)/greptimedb:/tmp/greptimedb" \
   --name greptime --rm \
   greptime/greptimedb:latest standalone start \
   --http-addr 0.0.0.0:4000 \
@@ -129,90 +120,112 @@ docker run -p 127.0.0.1:4000-4003:4000-4003 \
   --mysql-addr 0.0.0.0:4002 \
   --postgres-addr 0.0.0.0:4003
 ```
-Dashboard: [http://localhost:4000/dashboard](http://localhost:4000/dashboard)
-[Full Install Guide](https://docs.greptime.com/getting-started/installation/overview)
 
-**Troubleshooting:**
-* Cannot connect to the database? Ensure that ports `4000`, `4001`, `4002`, and `4003` are not blocked by a firewall or used by other services.
-* Failed to start? Check the container logs with `docker logs greptime` for further details.
+Access the dashboard via `http://localhost:4000/dashboard`.
+
+Read more about [Installation](https://docs.greptime.com/getting-started/installation/overview) on docs.
 
 ## Getting Started
 
-- [Quickstart](https://docs.greptime.com/getting-started/quick-start)
-- [User Guide](https://docs.greptime.com/user-guide/overview)
-- [Demo Scenes](https://github.com/GreptimeTeam/demo-scene)
-- [FAQ](https://docs.greptime.com/faq-and-others/faq)
+* [Quickstart](https://docs.greptime.com/getting-started/quick-start)
+* [User Guide](https://docs.greptime.com/user-guide/overview)
+* [Demos](https://github.com/GreptimeTeam/demo-scene)
+* [FAQ](https://docs.greptime.com/faq-and-others/faq)
 
-## Build From Source
+## Build
+
+Check the prerequisite:
 
-**Prerequisites:**
 * [Rust toolchain](https://www.rust-lang.org/tools/install) (nightly)
 * [Protobuf compiler](https://grpc.io/docs/protoc-installation/) (>= 3.15)
 * C/C++ building essentials, including `gcc`/`g++`/`autoconf` and glibc library (eg. `libc6-dev` on Ubuntu and `glibc-devel` on Fedora)
 * Python toolchain (optional): Required only if using some test scripts.
 
-**Build and Run:**
-```bash
+Build GreptimeDB binary:
+
+```shell
 make
+```
+
+Run a standalone server:
+
+```shell
 cargo run -- standalone start
 ```
 
 ## Tools & Extensions
 
-- **Kubernetes:** [GreptimeDB Operator](https://github.com/GrepTimeTeam/greptimedb-operator)
-- **Helm Charts:** [Greptime Helm Charts](https://github.com/GreptimeTeam/helm-charts)
-- **Dashboard:** [Web UI](https://github.com/GreptimeTeam/dashboard)
-- **SDKs/Ingester:** [Go](https://github.com/GreptimeTeam/greptimedb-ingester-go), [Java](https://github.com/GreptimeTeam/greptimedb-ingester-java), [C++](https://github.com/GreptimeTeam/greptimedb-ingester-cpp), [Erlang](https://github.com/GreptimeTeam/greptimedb-ingester-erl), [Rust](https://github.com/GreptimeTeam/greptimedb-ingester-rust), [JS](https://github.com/GreptimeTeam/greptimedb-ingester-js)
-- **Grafana**: [Official Dashboard](https://github.com/GreptimeTeam/greptimedb/blob/main/grafana/README.md)
+### Kubernetes
+
+- [GreptimeDB Operator](https://github.com/GrepTimeTeam/greptimedb-operator)
+
+### Dashboard
+
+- [The dashboard UI for GreptimeDB](https://github.com/GreptimeTeam/dashboard)
+
+### SDK
+
+- [GreptimeDB Go Ingester](https://github.com/GreptimeTeam/greptimedb-ingester-go)
+- [GreptimeDB Java Ingester](https://github.com/GreptimeTeam/greptimedb-ingester-java)
+- [GreptimeDB C++ Ingester](https://github.com/GreptimeTeam/greptimedb-ingester-cpp)
+- [GreptimeDB Erlang Ingester](https://github.com/GreptimeTeam/greptimedb-ingester-erl)
+- [GreptimeDB Rust Ingester](https://github.com/GreptimeTeam/greptimedb-ingester-rust)
+- [GreptimeDB JavaScript Ingester](https://github.com/GreptimeTeam/greptimedb-ingester-js)
+
+### Grafana Dashboard
+
+Our official Grafana dashboard for monitoring GreptimeDB is available at [grafana](grafana/README.md) directory.
 
 ## Project Status
 
-> **Status:** Beta.
-> **GA (v1.0):** Targeted for mid 2025.
+GreptimeDB is currently in Beta. We are targeting GA (General Availability) with v1.0 release by Early 2025.
 
-- Being used in production by early adopters
-- Stable, actively maintained, with regular releases ([version info](https://docs.greptime.com/nightly/reference/about-greptimedb-version))
-- Suitable for evaluation and pilot deployments
+While in Beta, GreptimeDB is already:
+
+* Being used in production by early adopters
+* Actively maintained with regular releases, [about version number](https://docs.greptime.com/nightly/reference/about-greptimedb-version)
+* Suitable for testing and evaluation
 
 For production use, we recommend using the latest stable release.
-[![Star History Chart](https://api.star-history.com/svg?repos=GreptimeTeam/GreptimeDB&type=Date)](https://www.star-history.com/#GreptimeTeam/GreptimeDB&Date)
-
-If you find this project useful, a ⭐ would mean a lot to us!
-<img alt="Known Users" src="https://greptime.com/logo/img/users.png"/>
 
 ## Community
 
-We invite you to engage and contribute!
+Our core team is thrilled to see you participate in any ways you like. When you are stuck, try to
+ask for help by filling an issue with a detailed description of what you were trying to do
+and what went wrong. If you have any questions or if you would like to get involved in our
+community, please check out:
 
-- [Slack](https://greptime.com/slack)
-- [Discussions](https://github.com/GreptimeTeam/greptimedb/discussions)
-- [Official Website](https://greptime.com/)
-- [Blog](https://greptime.com/blogs/)
-- [LinkedIn](https://www.linkedin.com/company/greptime/)
-- [X (Twitter)](https://X.com/greptime)
-- [YouTube](https://www.youtube.com/@greptime)
+- GreptimeDB Community on [Slack](https://greptime.com/slack)
+- GreptimeDB [GitHub Discussions forum](https://github.com/GreptimeTeam/greptimedb/discussions)
+- Greptime official [website](https://greptime.com)
 
-## License
+In addition, you may:
 
-GreptimeDB is licensed under the [Apache License 2.0](https://apache.org/licenses/LICENSE-2.0.txt).
+- View our official [Blog](https://greptime.com/blogs/)
+- Connect us with [Linkedin](https://www.linkedin.com/company/greptime/)
+- Follow us on [Twitter](https://twitter.com/greptime)
 
 ## Commercial Support
 
-Running GreptimeDB in your organization?
-We offer enterprise add-ons, services, training, and consulting.
-[Contact us](https://greptime.com/contactus) for details.
+If you are running GreptimeDB OSS in your organization, we offer additional
+enterprise add-ons, installation services, training, and consulting. [Contact
+us](https://greptime.com/contactus) and we will reach out to you with more
+detail of our commercial license.
+
+## License
+
+GreptimeDB uses the [Apache License 2.0](https://apache.org/licenses/LICENSE-2.0.txt) to strike a balance between
+open contributions and allowing you to use the software however you want.
 
 ## Contributing
 
-- Read our [Contribution Guidelines](https://github.com/GreptimeTeam/greptimedb/blob/main/CONTRIBUTING.md).
-- Explore [Internal Concepts](https://docs.greptime.com/contributor-guide/overview.html) and [DeepWiki](https://deepwiki.com/GreptimeTeam/greptimedb).
-- Pick up a [good first issue](https://github.com/GreptimeTeam/greptimedb/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) and join the #contributors [Slack](https://greptime.com/slack) channel.
+Please refer to [contribution guidelines](CONTRIBUTING.md) and [internal concepts docs](https://docs.greptime.com/contributor-guide/overview.html) for more information.
 
 ## Acknowledgement
 
-Special thanks to all contributors! See [AUTHORS.md](https://github.com/GreptimeTeam/greptimedb/blob/main/AUTHOR.md).
+Special thanks to all the contributors who have propelled GreptimeDB forward. For a complete list of contributors, please refer to [AUTHOR.md](AUTHOR.md).
 
-- Uses [Apache Arrow™](https://arrow.apache.org/) (memory model)
-- [Apache Parquet™](https://parquet.apache.org/) (file storage)
-- [Apache Arrow DataFusion™](https://arrow.apache.org/datafusion/) (query engine)
-- [Apache OpenDAL™](https://opendal.apache.org/) (data access abstraction)
+- GreptimeDB uses [Apache Arrow™](https://arrow.apache.org/) as the memory model and [Apache Parquet™](https://parquet.apache.org/) as the persistent file format.
+- GreptimeDB's query engine is powered by [Apache Arrow DataFusion™](https://arrow.apache.org/datafusion/).
+- [Apache OpenDAL™](https://opendal.apache.org) gives GreptimeDB a very general and elegant data access abstraction layer.
+- GreptimeDB's meta service is based on [etcd](https://etcd.io/).

config/config.md

@@ -12,6 +12,7 @@
 
 | Key | Type | Default | Descriptions |
 | --- | -----| ------- | ----------- |
+| `mode` | String | `standalone` | The running mode of the datanode. It can be `standalone` or `distributed`. |
 | `default_timezone` | String | Unset | The default timezone of the server. |
 | `init_regions_in_background` | Bool | `false` | Initialize all regions in the background during the startup.<br/>By default, it provides services after all regions have been initialized. |
 | `init_regions_parallelism` | Integer | `16` | Parallelism of initializing regions. |
@@ -23,11 +24,10 @@
 | `runtime.compact_rt_size` | Integer | `4` | The number of threads to execute the runtime for global write operations. |
 | `http` | -- | -- | The HTTP server options. |
 | `http.addr` | String | `127.0.0.1:4000` | The address to bind the HTTP server. |
-| `http.timeout` | String | `0s` | HTTP request timeout. Set to 0 to disable timeout. |
+| `http.timeout` | String | `30s` | HTTP request timeout. Set to 0 to disable timeout. |
 | `http.body_limit` | String | `64MB` | HTTP request body limit.<br/>The following units are supported: `B`, `KB`, `KiB`, `MB`, `MiB`, `GB`, `GiB`, `TB`, `TiB`, `PB`, `PiB`.<br/>Set to 0 to disable limit. |
 | `http.enable_cors` | Bool | `true` | HTTP CORS support, it's turned on by default<br/>This allows browser to access http APIs without CORS restrictions |
 | `http.cors_allowed_origins` | Array | Unset | Customize allowed origins for HTTP CORS. |
-| `http.prom_validation_mode` | String | `strict` | Whether to enable validation for Prometheus remote write requests.<br/>Available options:<br/>- strict: deny invalid UTF-8 strings (default).<br/>- lossy: allow invalid UTF-8 strings, replace invalid characters with REPLACEMENT_CHARACTER(U+FFFD).<br/>- unchecked: do not valid strings. |
 | `grpc` | -- | -- | The gRPC server options. |
 | `grpc.bind_addr` | String | `127.0.0.1:4001` | The address to bind the gRPC server. |
 | `grpc.runtime_size` | Integer | `8` | The number of server worker threads. |
@@ -86,6 +86,10 @@
 | `wal.create_topic_timeout` | String | `30s` | Above which a topic creation operation will be cancelled.<br/>**It's only used when the provider is `kafka`**. |
 | `wal.max_batch_bytes` | String | `1MB` | The max size of a single producer batch.<br/>Warning: Kafka has a default limit of 1MB per message in a topic.<br/>**It's only used when the provider is `kafka`**. |
 | `wal.consumer_wait_timeout` | String | `100ms` | The consumer wait timeout.<br/>**It's only used when the provider is `kafka`**. |
+| `wal.backoff_init` | String | `500ms` | The initial backoff delay.<br/>**It's only used when the provider is `kafka`**. |
+| `wal.backoff_max` | String | `10s` | The maximum backoff delay.<br/>**It's only used when the provider is `kafka`**. |
+| `wal.backoff_base` | Integer | `2` | The exponential backoff rate, i.e. next backoff = base * current backoff.<br/>**It's only used when the provider is `kafka`**. |
+| `wal.backoff_deadline` | String | `5mins` | The deadline of retries.<br/>**It's only used when the provider is `kafka`**. |
 | `wal.overwrite_entry_start_id` | Bool | `false` | Ignore missing entries during read WAL.<br/>**It's only used when the provider is `kafka`**.<br/><br/>This option ensures that when Kafka messages are deleted, the system<br/>can still successfully replay memtable data without throwing an<br/>out-of-range error.<br/>However, enabling this option might lead to unexpected data loss,<br/>as the system will skip over missing entries instead of treating<br/>them as critical errors. |
 | `metadata_store` | -- | -- | Metadata storage options. |
 | `metadata_store.file_size` | String | `64MB` | The size of the metadata store log file. |
@@ -94,13 +98,10 @@
 | `procedure` | -- | -- | Procedure storage options. |
 | `procedure.max_retry_times` | Integer | `3` | Procedure max retry time. |
 | `procedure.retry_delay` | String | `500ms` | Initial retry delay of procedures, increases exponentially |
-| `procedure.max_running_procedures` | Integer | `128` | Max running procedures.<br/>The maximum number of procedures that can be running at the same time.<br/>If the number of running procedures exceeds this limit, the procedure will be rejected. |
 | `flow` | -- | -- | flow engine options. |
 | `flow.num_workers` | Integer | `0` | The number of flow worker in flownode.<br/>Not setting(or set to 0) this value will use the number of CPU cores divided by 2. |
-| `query` | -- | -- | The query engine options. |
-| `query.parallelism` | Integer | `0` | Parallelism of the query engine.<br/>Default to 0, which means the number of CPU cores. |
 | `storage` | -- | -- | The data storage options. |
-| `storage.data_home` | String | `./greptimedb_data` | The working home directory. |
+| `storage.data_home` | String | `/tmp/greptimedb/` | The working home directory. |
 | `storage.type` | String | `File` | The storage type used to store the data.<br/>- `File`: the data is stored in the local file system.<br/>- `S3`: the data is stored in the S3 object storage.<br/>- `Gcs`: the data is stored in the Google Cloud Storage.<br/>- `Azblob`: the data is stored in the Azure Blob Storage.<br/>- `Oss`: the data is stored in the Aliyun OSS. |
 | `storage.cache_path` | String | Unset | Read cache configuration for object storage such as 'S3' etc, it's configured by default when using object storage. It is recommended to configure it when using object storage for better performance.<br/>A local file directory, defaults to `{data_home}`. An empty string means disabling. |
 | `storage.cache_capacity` | String | Unset | The local file cache capacity in bytes. If your disk space is sufficient, it is recommended to set it larger. |
@@ -123,7 +124,6 @@
 | `storage.http_client.connect_timeout` | String | `30s` | The timeout for only the connect phase of a http client. |
 | `storage.http_client.timeout` | String | `30s` | The total request timeout, applied from when the request starts connecting until the response body has finished.<br/>Also considered a total deadline. |
 | `storage.http_client.pool_idle_timeout` | String | `90s` | The timeout for idle sockets being kept-alive. |
-| `storage.http_client.skip_ssl_validation` | Bool | `false` | To skip the ssl verification<br/>**Security Notice**: Setting `skip_ssl_validation = true` disables certificate verification, making connections vulnerable to man-in-the-middle attacks. Only use this in development or trusted private networks. |
 | `[[region_engine]]` | -- | -- | The region engine options. You can configure multiple region engines. |
 | `region_engine.mito` | -- | -- | The Mito engine options. |
 | `region_engine.mito.num_workers` | Integer | `8` | Number of region workers. |
@@ -156,7 +156,6 @@
 | `region_engine.mito.index.metadata_cache_size` | String | `64MiB` | Cache size for inverted index metadata. |
 | `region_engine.mito.index.content_cache_size` | String | `128MiB` | Cache size for inverted index content. |
 | `region_engine.mito.index.content_cache_page_size` | String | `64KiB` | Page size for inverted index content cache. |
-| `region_engine.mito.index.result_cache_size` | String | `128MiB` | Cache size for index result. |
 | `region_engine.mito.inverted_index` | -- | -- | The options for inverted index in Mito engine. |
 | `region_engine.mito.inverted_index.create_on_flush` | String | `auto` | Whether to create the index on flush.<br/>- `auto`: automatically (default)<br/>- `disable`: never |
 | `region_engine.mito.inverted_index.create_on_compaction` | String | `auto` | Whether to create the index on compaction.<br/>- `auto`: automatically (default)<br/>- `disable`: never |
@@ -182,28 +181,26 @@
 | `region_engine.metric` | -- | -- | Metric engine options. |
 | `region_engine.metric.experimental_sparse_primary_key_encoding` | Bool | `false` | Whether to enable the experimental sparse primary key encoding. |
 | `logging` | -- | -- | The logging options. |
-| `logging.dir` | String | `./greptimedb_data/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
+| `logging.dir` | String | `/tmp/greptimedb/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
 | `logging.level` | String | Unset | The log level. Can be `info`/`debug`/`warn`/`error`. |
 | `logging.enable_otlp_tracing` | Bool | `false` | Enable OTLP tracing. |
-| `logging.otlp_endpoint` | String | `http://localhost:4318` | The OTLP tracing endpoint. |
+| `logging.otlp_endpoint` | String | `http://localhost:4317` | The OTLP tracing endpoint. |
 | `logging.append_stdout` | Bool | `true` | Whether to append logs to stdout. |
 | `logging.log_format` | String | `text` | The log format. Can be `text`/`json`. |
 | `logging.max_log_files` | Integer | `720` | The maximum amount of log files. |
-| `logging.otlp_export_protocol` | String | `http` | The OTLP tracing export protocol. Can be `grpc`/`http`. |
 | `logging.tracing_sample_ratio` | -- | -- | The percentage of tracing will be sampled and exported.<br/>Valid range `[0, 1]`, 1 means all traces are sampled, 0 means all traces are not sampled, the default value is 1.<br/>ratio > 1 are treated as 1. Fractions < 0 are treated as 0 |
 | `logging.tracing_sample_ratio.default_ratio` | Float | `1.0` | -- |
-| `slow_query` | -- | -- | The slow query log options. |
-| `slow_query.enable` | Bool | `false` | Whether to enable slow query log. |
-| `slow_query.record_type` | String | Unset | The record type of slow queries. It can be `system_table` or `log`. |
-| `slow_query.threshold` | String | Unset | The threshold of slow query. |
-| `slow_query.sample_ratio` | Float | Unset | The sampling ratio of slow query log. The value should be in the range of (0, 1]. |
-| `export_metrics` | -- | -- | The standalone can export its metrics and send to Prometheus compatible service (e.g. `greptimedb`) from remote-write API.<br/>This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape. |
+| `logging.slow_query` | -- | -- | The slow query log options. |
+| `logging.slow_query.enable` | Bool | `false` | Whether to enable slow query log. |
+| `logging.slow_query.threshold` | String | Unset | The threshold of slow query. |
+| `logging.slow_query.sample_ratio` | Float | Unset | The sampling ratio of slow query log. The value should be in the range of (0, 1]. |
+| `export_metrics` | -- | -- | The datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.<br/>This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape. |
 | `export_metrics.enable` | Bool | `false` | whether enable export metrics. |
 | `export_metrics.write_interval` | String | `30s` | The interval of export metrics. |
 | `export_metrics.self_import` | -- | -- | For `standalone` mode, `self_import` is recommended to collect metrics generated by itself<br/>You must create the database before enabling it. |
 | `export_metrics.self_import.db` | String | Unset | -- |
 | `export_metrics.remote_write` | -- | -- | -- |
-| `export_metrics.remote_write.url` | String | `""` | The prometheus remote write endpoint that the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`. |
+| `export_metrics.remote_write.url` | String | `""` | The url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`. |
 | `export_metrics.remote_write.headers` | InlineTable | -- | HTTP headers of Prometheus remote-write carry. |
 | `tracing` | -- | -- | The tracing options. Only effect when compiled with `tokio-console` feature. |
 | `tracing.tokio_console_addr` | String | Unset | The tokio console address. |
@@ -225,16 +222,14 @@
 | `heartbeat.retry_interval` | String | `3s` | Interval for retrying to send heartbeat messages to the metasrv. |
 | `http` | -- | -- | The HTTP server options. |
 | `http.addr` | String | `127.0.0.1:4000` | The address to bind the HTTP server. |
-| `http.timeout` | String | `0s` | HTTP request timeout. Set to 0 to disable timeout. |
+| `http.timeout` | String | `30s` | HTTP request timeout. Set to 0 to disable timeout. |
 | `http.body_limit` | String | `64MB` | HTTP request body limit.<br/>The following units are supported: `B`, `KB`, `KiB`, `MB`, `MiB`, `GB`, `GiB`, `TB`, `TiB`, `PB`, `PiB`.<br/>Set to 0 to disable limit. |
 | `http.enable_cors` | Bool | `true` | HTTP CORS support, it's turned on by default<br/>This allows browser to access http APIs without CORS restrictions |
 | `http.cors_allowed_origins` | Array | Unset | Customize allowed origins for HTTP CORS. |
-| `http.prom_validation_mode` | String | `strict` | Whether to enable validation for Prometheus remote write requests.<br/>Available options:<br/>- strict: deny invalid UTF-8 strings (default).<br/>- lossy: allow invalid UTF-8 strings, replace invalid characters with REPLACEMENT_CHARACTER(U+FFFD).<br/>- unchecked: do not valid strings. |
 | `grpc` | -- | -- | The gRPC server options. |
 | `grpc.bind_addr` | String | `127.0.0.1:4001` | The address to bind the gRPC server. |
 | `grpc.server_addr` | String | `127.0.0.1:4001` | The address advertised to the metasrv, and used for connections from outside the host.<br/>If left empty or unset, the server will automatically use the IP address of the first network interface<br/>on the host, with the same port number as the one specified in `grpc.bind_addr`. |
 | `grpc.runtime_size` | Integer | `8` | The number of server worker threads. |
-| `grpc.flight_compression` | String | `arrow_ipc` | Compression mode for frontend side Arrow IPC service. Available options:<br/>- `none`: disable all compression<br/>- `transport`: only enable gRPC transport compression (zstd)<br/>- `arrow_ipc`: only enable Arrow IPC compression (lz4)<br/>- `all`: enable all compression.<br/>Default to `none` |
 | `grpc.tls` | -- | -- | gRPC server TLS options, see `mysql.tls` section. |
 | `grpc.tls.mode` | String | `disable` | TLS mode. |
 | `grpc.tls.cert_path` | String | Unset | Certificate file path. |
@@ -279,34 +274,31 @@
 | `meta_client.metadata_cache_max_capacity` | Integer | `100000` | The configuration about the cache of the metadata. |
 | `meta_client.metadata_cache_ttl` | String | `10m` | TTL of the metadata cache. |
 | `meta_client.metadata_cache_tti` | String | `5m` | -- |
-| `query` | -- | -- | The query engine options. |
-| `query.parallelism` | Integer | `0` | Parallelism of the query engine.<br/>Default to 0, which means the number of CPU cores. |
 | `datanode` | -- | -- | Datanode options. |
 | `datanode.client` | -- | -- | Datanode client options. |
 | `datanode.client.connect_timeout` | String | `10s` | -- |
 | `datanode.client.tcp_nodelay` | Bool | `true` | -- |
 | `logging` | -- | -- | The logging options. |
-| `logging.dir` | String | `./greptimedb_data/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
+| `logging.dir` | String | `/tmp/greptimedb/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
 | `logging.level` | String | Unset | The log level. Can be `info`/`debug`/`warn`/`error`. |
 | `logging.enable_otlp_tracing` | Bool | `false` | Enable OTLP tracing. |
-| `logging.otlp_endpoint` | String | `http://localhost:4318` | The OTLP tracing endpoint. |
+| `logging.otlp_endpoint` | String | `http://localhost:4317` | The OTLP tracing endpoint. |
 | `logging.append_stdout` | Bool | `true` | Whether to append logs to stdout. |
 | `logging.log_format` | String | `text` | The log format. Can be `text`/`json`. |
 | `logging.max_log_files` | Integer | `720` | The maximum amount of log files. |
-| `logging.otlp_export_protocol` | String | `http` | The OTLP tracing export protocol. Can be `grpc`/`http`. |
 | `logging.tracing_sample_ratio` | -- | -- | The percentage of tracing will be sampled and exported.<br/>Valid range `[0, 1]`, 1 means all traces are sampled, 0 means all traces are not sampled, the default value is 1.<br/>ratio > 1 are treated as 1. Fractions < 0 are treated as 0 |
 | `logging.tracing_sample_ratio.default_ratio` | Float | `1.0` | -- |
-| `slow_query` | -- | -- | The slow query log options. |
-| `slow_query.enable` | Bool | `true` | Whether to enable slow query log. |
-| `slow_query.record_type` | String | `system_table` | The record type of slow queries. It can be `system_table` or `log`.<br/>If `system_table` is selected, the slow queries will be recorded in a system table `greptime_private.slow_queries`.<br/>If `log` is selected, the slow queries will be logged in a log file `greptimedb-slow-queries.*`. |
-| `slow_query.threshold` | String | `30s` | The threshold of slow query. It can be human readable time string, for example: `10s`, `100ms`, `1s`. |
-| `slow_query.sample_ratio` | Float | `1.0` | The sampling ratio of slow query log. The value should be in the range of (0, 1]. For example, `0.1` means 10% of the slow queries will be logged and `1.0` means all slow queries will be logged. |
-| `slow_query.ttl` | String | `30d` | The TTL of the `slow_queries` system table. Default is `30d` when `record_type` is `system_table`. |
-| `export_metrics` | -- | -- | The frontend can export its metrics and send to Prometheus compatible service (e.g. `greptimedb` itself) from remote-write API.<br/>This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape. |
+| `logging.slow_query` | -- | -- | The slow query log options. |
+| `logging.slow_query.enable` | Bool | `false` | Whether to enable slow query log. |
+| `logging.slow_query.threshold` | String | Unset | The threshold of slow query. |
+| `logging.slow_query.sample_ratio` | Float | Unset | The sampling ratio of slow query log. The value should be in the range of (0, 1]. |
+| `export_metrics` | -- | -- | The datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.<br/>This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape. |
 | `export_metrics.enable` | Bool | `false` | whether enable export metrics. |
 | `export_metrics.write_interval` | String | `30s` | The interval of export metrics. |
+| `export_metrics.self_import` | -- | -- | For `standalone` mode, `self_import` is recommend to collect metrics generated by itself<br/>You must create the database before enabling it. |
+| `export_metrics.self_import.db` | String | Unset | -- |
 | `export_metrics.remote_write` | -- | -- | -- |
-| `export_metrics.remote_write.url` | String | `""` | The prometheus remote write endpoint that the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`. |
+| `export_metrics.remote_write.url` | String | `""` | The url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`. |
 | `export_metrics.remote_write.headers` | InlineTable | -- | HTTP headers of Prometheus remote-write carry. |
 | `tracing` | -- | -- | The tracing options. Only effect when compiled with `tokio-console` feature. |
 | `tracing.tokio_console_addr` | String | Unset | The tokio console address. |
@@ -316,37 +308,26 @@
 
 | Key | Type | Default | Descriptions |
 | --- | -----| ------- | ----------- |
-| `data_home` | String | `./greptimedb_data` | The working home directory. |
+| `data_home` | String | `/tmp/metasrv/` | The working home directory. |
+| `bind_addr` | String | `127.0.0.1:3002` | The bind address of metasrv. |
+| `server_addr` | String | `127.0.0.1:3002` | The communication server address for the frontend and datanode to connect to metasrv.<br/>If left empty or unset, the server will automatically use the IP address of the first network interface<br/>on the host, with the same port number as the one specified in `bind_addr`. |
 | `store_addrs` | Array | -- | Store server address default to etcd store.<br/>For postgres store, the format is:<br/>"password=password dbname=postgres user=postgres host=localhost port=5432"<br/>For etcd store, the format is:<br/>"127.0.0.1:2379" |
 | `store_key_prefix` | String | `""` | If it's not empty, the metasrv will store all data with this key prefix. |
-| `backend` | String | `etcd_store` | The datastore for meta server.<br/>Available values:<br/>- `etcd_store` (default value)<br/>- `memory_store`<br/>- `postgres_store`<br/>- `mysql_store` |
+| `backend` | String | `etcd_store` | The datastore for meta server.<br/>Available values:<br/>- `etcd_store` (default value)<br/>- `memory_store`<br/>- `postgres_store` |
 | `meta_table_name` | String | `greptime_metakv` | Table name in RDS to store metadata. Effect when using a RDS kvbackend.<br/>**Only used when backend is `postgres_store`.** |
 | `meta_election_lock_id` | Integer | `1` | Advisory lock id in PostgreSQL for election. Effect when using PostgreSQL as kvbackend<br/>Only used when backend is `postgres_store`. |
 | `selector` | String | `round_robin` | Datanode selector type.<br/>- `round_robin` (default value)<br/>- `lease_based`<br/>- `load_based`<br/>For details, please see "https://docs.greptime.com/developer-guide/metasrv/selector". |
 | `use_memory_store` | Bool | `false` | Store data in memory. |
 | `enable_region_failover` | Bool | `false` | Whether to enable region failover.<br/>This feature is only available on GreptimeDB running on cluster mode and<br/>- Using Remote WAL<br/>- Using shared storage (e.g., s3). |
-| `region_failure_detector_initialization_delay` | String | `10m` | Delay before initializing region failure detectors.<br/>This delay helps prevent premature initialization of region failure detectors in cases where<br/>cluster maintenance mode is enabled right after metasrv starts, especially when the cluster<br/>is not deployed via the recommended GreptimeDB Operator. Without this delay, early detector registration<br/>may trigger unnecessary region failovers during datanode startup. |
-| `allow_region_failover_on_local_wal` | Bool | `false` | Whether to allow region failover on local WAL.<br/>**This option is not recommended to be set to true, because it may lead to data loss during failover.** |
 | `node_max_idle_time` | String | `24hours` | Max allowed idle time before removing node info from metasrv memory. |
 | `enable_telemetry` | Bool | `true` | Whether to enable greptimedb telemetry. Enabled by default. |
 | `runtime` | -- | -- | The runtime options. |
 | `runtime.global_rt_size` | Integer | `8` | The number of threads to execute the runtime for global read operations. |
 | `runtime.compact_rt_size` | Integer | `4` | The number of threads to execute the runtime for global write operations. |
-| `grpc` | -- | -- | The gRPC server options. |
-| `grpc.bind_addr` | String | `127.0.0.1:3002` | The address to bind the gRPC server. |
-| `grpc.server_addr` | String | `127.0.0.1:3002` | The communication server address for the frontend and datanode to connect to metasrv.<br/>If left empty or unset, the server will automatically use the IP address of the first network interface<br/>on the host, with the same port number as the one specified in `bind_addr`. |
-| `grpc.runtime_size` | Integer | `8` | The number of server worker threads. |
-| `grpc.max_recv_message_size` | String | `512MB` | The maximum receive message size for gRPC server. |
-| `grpc.max_send_message_size` | String | `512MB` | The maximum send message size for gRPC server. |
-| `http` | -- | -- | The HTTP server options. |
-| `http.addr` | String | `127.0.0.1:4000` | The address to bind the HTTP server. |
-| `http.timeout` | String | `0s` | HTTP request timeout. Set to 0 to disable timeout. |
-| `http.body_limit` | String | `64MB` | HTTP request body limit.<br/>The following units are supported: `B`, `KB`, `KiB`, `MB`, `MiB`, `GB`, `GiB`, `TB`, `TiB`, `PB`, `PiB`.<br/>Set to 0 to disable limit. |
 | `procedure` | -- | -- | Procedure storage options. |
 | `procedure.max_retry_times` | Integer | `12` | Procedure max retry time. |
 | `procedure.retry_delay` | String | `500ms` | Initial retry delay of procedures, increases exponentially |
 | `procedure.max_metadata_value_size` | String | `1500KiB` | Auto split large value<br/>GreptimeDB procedure uses etcd as the default metadata storage backend.<br/>The etcd the maximum size of any request is 1.5 MiB<br/>1500KiB = 1536KiB (1.5MiB) - 36KiB (reserved size of key)<br/>Comments out the `max_metadata_value_size`, for don't split large value (no limit). |
-| `procedure.max_running_procedures` | Integer | `128` | Max running procedures.<br/>The maximum number of procedures that can be running at the same time.<br/>If the number of running procedures exceeds this limit, the procedure will be rejected. |
 | `failure_detector` | -- | -- | -- |
 | `failure_detector.threshold` | Float | `8.0` | The threshold value used by the failure detector to determine failure conditions. |
 | `failure_detector.min_std_deviation` | String | `100ms` | The minimum standard deviation of the heartbeat intervals, used to calculate acceptable variations. |
@@ -361,30 +342,36 @@
 | `wal.provider` | String | `raft_engine` | -- |
 | `wal.broker_endpoints` | Array | -- | The broker endpoints of the Kafka cluster. |
 | `wal.auto_create_topics` | Bool | `true` | Automatically create topics for WAL.<br/>Set to `true` to automatically create topics for WAL.<br/>Otherwise, use topics named `topic_name_prefix_[0..num_topics)` |
-| `wal.auto_prune_interval` | String | `0s` | Interval of automatically WAL pruning.<br/>Set to `0s` to disable automatically WAL pruning which delete unused remote WAL entries periodically. |
-| `wal.trigger_flush_threshold` | Integer | `0` | The threshold to trigger a flush operation of a region in automatically WAL pruning.<br/>Metasrv will send a flush request to flush the region when:<br/>`trigger_flush_threshold` + `prunable_entry_id` < `max_prunable_entry_id`<br/>where:<br/>- `prunable_entry_id` is the maximum entry id that can be pruned of the region.<br/>- `max_prunable_entry_id` is the maximum prunable entry id among all regions in the same topic.<br/>Set to `0` to disable the flush operation. |
-| `wal.auto_prune_parallelism` | Integer | `10` | Concurrent task limit for automatically WAL pruning. |
 | `wal.num_topics` | Integer | `64` | Number of topics. |
 | `wal.selector_type` | String | `round_robin` | Topic selector type.<br/>Available selector types:<br/>- `round_robin` (default) |
 | `wal.topic_name_prefix` | String | `greptimedb_wal_topic` | A Kafka topic is constructed by concatenating `topic_name_prefix` and `topic_id`.<br/>Only accepts strings that match the following regular expression pattern:<br/>[a-zA-Z_:-][a-zA-Z0-9_:\-\.@#]*<br/>i.g., greptimedb_wal_topic_0, greptimedb_wal_topic_1. |
 | `wal.replication_factor` | Integer | `1` | Expected number of replicas of each partition. |
 | `wal.create_topic_timeout` | String | `30s` | Above which a topic creation operation will be cancelled. |
+| `wal.backoff_init` | String | `500ms` | The initial backoff for kafka clients. |
+| `wal.backoff_max` | String | `10s` | The maximum backoff for kafka clients. |
+| `wal.backoff_base` | Integer | `2` | Exponential backoff rate, i.e. next backoff = base * current backoff. |
+| `wal.backoff_deadline` | String | `5mins` | Stop reconnecting if the total wait time reaches the deadline. If this config is missing, the reconnecting won't terminate. |
 | `logging` | -- | -- | The logging options. |
-| `logging.dir` | String | `./greptimedb_data/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
+| `logging.dir` | String | `/tmp/greptimedb/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
 | `logging.level` | String | Unset | The log level. Can be `info`/`debug`/`warn`/`error`. |
 | `logging.enable_otlp_tracing` | Bool | `false` | Enable OTLP tracing. |
-| `logging.otlp_endpoint` | String | `http://localhost:4318` | The OTLP tracing endpoint. |
+| `logging.otlp_endpoint` | String | `http://localhost:4317` | The OTLP tracing endpoint. |
 | `logging.append_stdout` | Bool | `true` | Whether to append logs to stdout. |
 | `logging.log_format` | String | `text` | The log format. Can be `text`/`json`. |
 | `logging.max_log_files` | Integer | `720` | The maximum amount of log files. |
-| `logging.otlp_export_protocol` | String | `http` | The OTLP tracing export protocol. Can be `grpc`/`http`. |
 | `logging.tracing_sample_ratio` | -- | -- | The percentage of tracing will be sampled and exported.<br/>Valid range `[0, 1]`, 1 means all traces are sampled, 0 means all traces are not sampled, the default value is 1.<br/>ratio > 1 are treated as 1. Fractions < 0 are treated as 0 |
 | `logging.tracing_sample_ratio.default_ratio` | Float | `1.0` | -- |
-| `export_metrics` | -- | -- | The metasrv can export its metrics and send to Prometheus compatible service (e.g. `greptimedb` itself) from remote-write API.<br/>This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape. |
+| `logging.slow_query` | -- | -- | The slow query log options. |
+| `logging.slow_query.enable` | Bool | `false` | Whether to enable slow query log. |
+| `logging.slow_query.threshold` | String | Unset | The threshold of slow query. |
+| `logging.slow_query.sample_ratio` | Float | Unset | The sampling ratio of slow query log. The value should be in the range of (0, 1]. |
+| `export_metrics` | -- | -- | The datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.<br/>This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape. |
 | `export_metrics.enable` | Bool | `false` | whether enable export metrics. |
 | `export_metrics.write_interval` | String | `30s` | The interval of export metrics. |
+| `export_metrics.self_import` | -- | -- | For `standalone` mode, `self_import` is recommend to collect metrics generated by itself<br/>You must create the database before enabling it. |
+| `export_metrics.self_import.db` | String | Unset | -- |
 | `export_metrics.remote_write` | -- | -- | -- |
-| `export_metrics.remote_write.url` | String | `""` | The prometheus remote write endpoint that the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`. |
+| `export_metrics.remote_write.url` | String | `""` | The url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`. |
 | `export_metrics.remote_write.headers` | InlineTable | -- | HTTP headers of Prometheus remote-write carry. |
 | `tracing` | -- | -- | The tracing options. Only effect when compiled with `tokio-console` feature. |
 | `tracing.tokio_console_addr` | String | Unset | The tokio console address. |
@@ -394,6 +381,7 @@
 
 | Key | Type | Default | Descriptions |
 | --- | -----| ------- | ----------- |
+| `mode` | String | `standalone` | The running mode of the datanode. It can be `standalone` or `distributed`. |
 | `node_id` | Integer | Unset | The datanode identifier and should be unique in the cluster. |
 | `require_lease_before_startup` | Bool | `false` | Start services after regions have obtained leases.<br/>It will block the datanode start if it can't receive leases in the heartbeat from metasrv. |
 | `init_regions_in_background` | Bool | `false` | Initialize all regions in the background during the startup.<br/>By default, it provides services after all regions have been initialized. |
@@ -402,7 +390,7 @@
 | `enable_telemetry` | Bool | `true` | Enable telemetry to collect anonymous usage data. Enabled by default. |
 | `http` | -- | -- | The HTTP server options. |
 | `http.addr` | String | `127.0.0.1:4000` | The address to bind the HTTP server. |
-| `http.timeout` | String | `0s` | HTTP request timeout. Set to 0 to disable timeout. |
+| `http.timeout` | String | `30s` | HTTP request timeout. Set to 0 to disable timeout. |
 | `http.body_limit` | String | `64MB` | HTTP request body limit.<br/>The following units are supported: `B`, `KB`, `KiB`, `MB`, `MiB`, `GB`, `GiB`, `TB`, `TiB`, `PB`, `PiB`.<br/>Set to 0 to disable limit. |
 | `grpc` | -- | -- | The gRPC server options. |
 | `grpc.bind_addr` | String | `127.0.0.1:3001` | The address to bind the gRPC server. |
@@ -410,7 +398,6 @@
 | `grpc.runtime_size` | Integer | `8` | The number of server worker threads. |
 | `grpc.max_recv_message_size` | String | `512MB` | The maximum receive message size for gRPC server. |
 | `grpc.max_send_message_size` | String | `512MB` | The maximum send message size for gRPC server. |
-| `grpc.flight_compression` | String | `arrow_ipc` | Compression mode for datanode side Arrow IPC service. Available options:<br/>- `none`: disable all compression<br/>- `transport`: only enable gRPC transport compression (zstd)<br/>- `arrow_ipc`: only enable Arrow IPC compression (lz4)<br/>- `all`: enable all compression.<br/>Default to `none` |
 | `grpc.tls` | -- | -- | gRPC server TLS options, see `mysql.tls` section. |
 | `grpc.tls.mode` | String | `disable` | TLS mode. |
 | `grpc.tls.cert_path` | String | Unset | Certificate file path. |
@@ -447,13 +434,15 @@
 | `wal.broker_endpoints` | Array | -- | The Kafka broker endpoints.<br/>**It's only used when the provider is `kafka`**. |
 | `wal.max_batch_bytes` | String | `1MB` | The max size of a single producer batch.<br/>Warning: Kafka has a default limit of 1MB per message in a topic.<br/>**It's only used when the provider is `kafka`**. |
 | `wal.consumer_wait_timeout` | String | `100ms` | The consumer wait timeout.<br/>**It's only used when the provider is `kafka`**. |
+| `wal.backoff_init` | String | `500ms` | The initial backoff delay.<br/>**It's only used when the provider is `kafka`**. |
+| `wal.backoff_max` | String | `10s` | The maximum backoff delay.<br/>**It's only used when the provider is `kafka`**. |
+| `wal.backoff_base` | Integer | `2` | The exponential backoff rate, i.e. next backoff = base * current backoff.<br/>**It's only used when the provider is `kafka`**. |
+| `wal.backoff_deadline` | String | `5mins` | The deadline of retries.<br/>**It's only used when the provider is `kafka`**. |
 | `wal.create_index` | Bool | `true` | Whether to enable WAL index creation.<br/>**It's only used when the provider is `kafka`**. |
 | `wal.dump_index_interval` | String | `60s` | The interval for dumping WAL indexes.<br/>**It's only used when the provider is `kafka`**. |
 | `wal.overwrite_entry_start_id` | Bool | `false` | Ignore missing entries during read WAL.<br/>**It's only used when the provider is `kafka`**.<br/><br/>This option ensures that when Kafka messages are deleted, the system<br/>can still successfully replay memtable data without throwing an<br/>out-of-range error.<br/>However, enabling this option might lead to unexpected data loss,<br/>as the system will skip over missing entries instead of treating<br/>them as critical errors. |
-| `query` | -- | -- | The query engine options. |
-| `query.parallelism` | Integer | `0` | Parallelism of the query engine.<br/>Default to 0, which means the number of CPU cores. |
 | `storage` | -- | -- | The data storage options. |
-| `storage.data_home` | String | `./greptimedb_data` | The working home directory. |
+| `storage.data_home` | String | `/tmp/greptimedb/` | The working home directory. |
 | `storage.type` | String | `File` | The storage type used to store the data.<br/>- `File`: the data is stored in the local file system.<br/>- `S3`: the data is stored in the S3 object storage.<br/>- `Gcs`: the data is stored in the Google Cloud Storage.<br/>- `Azblob`: the data is stored in the Azure Blob Storage.<br/>- `Oss`: the data is stored in the Aliyun OSS. |
 | `storage.cache_path` | String | Unset | Read cache configuration for object storage such as 'S3' etc, it's configured by default when using object storage. It is recommended to configure it when using object storage for better performance.<br/>A local file directory, defaults to `{data_home}`. An empty string means disabling. |
 | `storage.cache_capacity` | String | Unset | The local file cache capacity in bytes. If your disk space is sufficient, it is recommended to set it larger. |
@@ -476,7 +465,6 @@
 | `storage.http_client.connect_timeout` | String | `30s` | The timeout for only the connect phase of a http client. |
 | `storage.http_client.timeout` | String | `30s` | The total request timeout, applied from when the request starts connecting until the response body has finished.<br/>Also considered a total deadline. |
 | `storage.http_client.pool_idle_timeout` | String | `90s` | The timeout for idle sockets being kept-alive. |
-| `storage.http_client.skip_ssl_validation` | Bool | `false` | To skip the ssl verification<br/>**Security Notice**: Setting `skip_ssl_validation = true` disables certificate verification, making connections vulnerable to man-in-the-middle attacks. Only use this in development or trusted private networks. |
 | `[[region_engine]]` | -- | -- | The region engine options. You can configure multiple region engines. |
 | `region_engine.mito` | -- | -- | The Mito engine options. |
 | `region_engine.mito.num_workers` | Integer | `8` | Number of region workers. |
@@ -509,7 +497,6 @@
 | `region_engine.mito.index.metadata_cache_size` | String | `64MiB` | Cache size for inverted index metadata. |
 | `region_engine.mito.index.content_cache_size` | String | `128MiB` | Cache size for inverted index content. |
 | `region_engine.mito.index.content_cache_page_size` | String | `64KiB` | Page size for inverted index content cache. |
-| `region_engine.mito.index.result_cache_size` | String | `128MiB` | Cache size for index result. |
 | `region_engine.mito.inverted_index` | -- | -- | The options for inverted index in Mito engine. |
 | `region_engine.mito.inverted_index.create_on_flush` | String | `auto` | Whether to create the index on flush.<br/>- `auto`: automatically (default)<br/>- `disable`: never |
 | `region_engine.mito.inverted_index.create_on_compaction` | String | `auto` | Whether to create the index on compaction.<br/>- `auto`: automatically (default)<br/>- `disable`: never |
@@ -535,21 +522,26 @@
 | `region_engine.metric` | -- | -- | Metric engine options. |
 | `region_engine.metric.experimental_sparse_primary_key_encoding` | Bool | `false` | Whether to enable the experimental sparse primary key encoding. |
 | `logging` | -- | -- | The logging options. |
-| `logging.dir` | String | `./greptimedb_data/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
+| `logging.dir` | String | `/tmp/greptimedb/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
 | `logging.level` | String | Unset | The log level. Can be `info`/`debug`/`warn`/`error`. |
 | `logging.enable_otlp_tracing` | Bool | `false` | Enable OTLP tracing. |
-| `logging.otlp_endpoint` | String | `http://localhost:4318` | The OTLP tracing endpoint. |
+| `logging.otlp_endpoint` | String | `http://localhost:4317` | The OTLP tracing endpoint. |
 | `logging.append_stdout` | Bool | `true` | Whether to append logs to stdout. |
 | `logging.log_format` | String | `text` | The log format. Can be `text`/`json`. |
 | `logging.max_log_files` | Integer | `720` | The maximum amount of log files. |
-| `logging.otlp_export_protocol` | String | `http` | The OTLP tracing export protocol. Can be `grpc`/`http`. |
 | `logging.tracing_sample_ratio` | -- | -- | The percentage of tracing will be sampled and exported.<br/>Valid range `[0, 1]`, 1 means all traces are sampled, 0 means all traces are not sampled, the default value is 1.<br/>ratio > 1 are treated as 1. Fractions < 0 are treated as 0 |
 | `logging.tracing_sample_ratio.default_ratio` | Float | `1.0` | -- |
-| `export_metrics` | -- | -- | The datanode can export its metrics and send to Prometheus compatible service (e.g. `greptimedb` itself) from remote-write API.<br/>This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape. |
+| `logging.slow_query` | -- | -- | The slow query log options. |
+| `logging.slow_query.enable` | Bool | `false` | Whether to enable slow query log. |
+| `logging.slow_query.threshold` | String | Unset | The threshold of slow query. |
+| `logging.slow_query.sample_ratio` | Float | Unset | The sampling ratio of slow query log. The value should be in the range of (0, 1]. |
+| `export_metrics` | -- | -- | The datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.<br/>This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape. |
 | `export_metrics.enable` | Bool | `false` | whether enable export metrics. |
 | `export_metrics.write_interval` | String | `30s` | The interval of export metrics. |
+| `export_metrics.self_import` | -- | -- | For `standalone` mode, `self_import` is recommend to collect metrics generated by itself<br/>You must create the database before enabling it. |
+| `export_metrics.self_import.db` | String | Unset | -- |
 | `export_metrics.remote_write` | -- | -- | -- |
-| `export_metrics.remote_write.url` | String | `""` | The prometheus remote write endpoint that the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`. |
+| `export_metrics.remote_write.url` | String | `""` | The url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`. |
 | `export_metrics.remote_write.headers` | InlineTable | -- | HTTP headers of Prometheus remote-write carry. |
 | `tracing` | -- | -- | The tracing options. Only effect when compiled with `tokio-console` feature. |
 | `tracing.tokio_console_addr` | String | Unset | The tokio console address. |
@@ -559,6 +551,7 @@
 
 | Key | Type | Default | Descriptions |
 | --- | -----| ------- | ----------- |
+| `mode` | String | `distributed` | The running mode of the flownode. It can be `standalone` or `distributed`. |
 | `node_id` | Integer | Unset | The flownode identifier and should be unique in the cluster. |
 | `flow` | -- | -- | flow engine options. |
 | `flow.num_workers` | Integer | `0` | The number of flow worker in flownode.<br/>Not setting(or set to 0) this value will use the number of CPU cores divided by 2. |
@@ -570,7 +563,7 @@
 | `grpc.max_send_message_size` | String | `512MB` | The maximum send message size for gRPC server. |
 | `http` | -- | -- | The HTTP server options. |
 | `http.addr` | String | `127.0.0.1:4000` | The address to bind the HTTP server. |
-| `http.timeout` | String | `0s` | HTTP request timeout. Set to 0 to disable timeout. |
+| `http.timeout` | String | `30s` | HTTP request timeout. Set to 0 to disable timeout. |
 | `http.body_limit` | String | `64MB` | HTTP request body limit.<br/>The following units are supported: `B`, `KB`, `KiB`, `MB`, `MiB`, `GB`, `GiB`, `TB`, `TiB`, `PB`, `PiB`.<br/>Set to 0 to disable limit. |
 | `meta_client` | -- | -- | The metasrv client options. |
 | `meta_client.metasrv_addrs` | Array | -- | The addresses of the metasrv. |
@@ -586,15 +579,18 @@
 | `heartbeat.interval` | String | `3s` | Interval for sending heartbeat messages to the metasrv. |
 | `heartbeat.retry_interval` | String | `3s` | Interval for retrying to send heartbeat messages to the metasrv. |
 | `logging` | -- | -- | The logging options. |
-| `logging.dir` | String | `./greptimedb_data/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
+| `logging.dir` | String | `/tmp/greptimedb/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
 | `logging.level` | String | Unset | The log level. Can be `info`/`debug`/`warn`/`error`. |
 | `logging.enable_otlp_tracing` | Bool | `false` | Enable OTLP tracing. |
-| `logging.otlp_endpoint` | String | `http://localhost:4318` | The OTLP tracing endpoint. |
+| `logging.otlp_endpoint` | String | `http://localhost:4317` | The OTLP tracing endpoint. |
 | `logging.append_stdout` | Bool | `true` | Whether to append logs to stdout. |
 | `logging.log_format` | String | `text` | The log format. Can be `text`/`json`. |
 | `logging.max_log_files` | Integer | `720` | The maximum amount of log files. |
-| `logging.otlp_export_protocol` | String | `http` | The OTLP tracing export protocol. Can be `grpc`/`http`. |
 | `logging.tracing_sample_ratio` | -- | -- | The percentage of tracing will be sampled and exported.<br/>Valid range `[0, 1]`, 1 means all traces are sampled, 0 means all traces are not sampled, the default value is 1.<br/>ratio > 1 are treated as 1. Fractions < 0 are treated as 0 |
 | `logging.tracing_sample_ratio.default_ratio` | Float | `1.0` | -- |
+| `logging.slow_query` | -- | -- | The slow query log options. |
+| `logging.slow_query.enable` | Bool | `false` | Whether to enable slow query log. |
+| `logging.slow_query.threshold` | String | Unset | The threshold of slow query. |
+| `logging.slow_query.sample_ratio` | Float | Unset | The sampling ratio of slow query log. The value should be in the range of (0, 1]. |
 | `tracing` | -- | -- | The tracing options. Only effect when compiled with `tokio-console` feature. |
 | `tracing.tokio_console_addr` | String | Unset | The tokio console address. |

config/datanode.example.toml

@@ -1,3 +1,6 @@
+## The running mode of the datanode. It can be `standalone` or `distributed`.
+mode = "standalone"
+
 ## The datanode identifier and should be unique in the cluster.
 ## @toml2docs:none-default
 node_id = 42
@@ -24,7 +27,7 @@ max_concurrent_queries = 0
 ## The address to bind the HTTP server.
 addr = "127.0.0.1:4000"
 ## HTTP request timeout. Set to 0 to disable timeout.
-timeout = "0s"
+timeout = "30s"
 ## HTTP request body limit.
 ## The following units are supported: `B`, `KB`, `KiB`, `MB`, `MiB`, `GB`, `GiB`, `TB`, `TiB`, `PB`, `PiB`.
 ## Set to 0 to disable limit.
@@ -44,13 +47,6 @@ runtime_size = 8
 max_recv_message_size = "512MB"
 ## The maximum send message size for gRPC server.
 max_send_message_size = "512MB"
-## Compression mode for datanode side Arrow IPC service. Available options:
-## - `none`: disable all compression
-## - `transport`: only enable gRPC transport compression (zstd)
-## - `arrow_ipc`: only enable Arrow IPC compression (lz4)
-## - `all`: enable all compression.
-## Default to `none`
-flight_compression = "arrow_ipc"
 
 ## gRPC server TLS options, see `mysql.tls` section.
 [grpc.tls]
@@ -123,7 +119,7 @@ provider = "raft_engine"
 ## The directory to store the WAL files.
 ## **It's only used when the provider is `raft_engine`**.
 ## @toml2docs:none-default
-dir = "./greptimedb_data/wal"
+dir = "/tmp/greptimedb/wal"
 
 ## The size of the WAL segment file.
 ## **It's only used when the provider is `raft_engine`**.
@@ -173,6 +169,22 @@ max_batch_bytes = "1MB"
 ## **It's only used when the provider is `kafka`**.
 consumer_wait_timeout = "100ms"
 
+## The initial backoff delay.
+## **It's only used when the provider is `kafka`**.
+backoff_init = "500ms"
+
+## The maximum backoff delay.
+## **It's only used when the provider is `kafka`**.
+backoff_max = "10s"
+
+## The exponential backoff rate, i.e. next backoff = base * current backoff.
+## **It's only used when the provider is `kafka`**.
+backoff_base = 2
+
+## The deadline of retries.
+## **It's only used when the provider is `kafka`**.
+backoff_deadline = "5mins"
+
 ## Whether to enable WAL index creation.
 ## **It's only used when the provider is `kafka`**.
 create_index = true
@@ -250,16 +262,10 @@ overwrite_entry_start_id = false
 # credential = "base64-credential"
 # endpoint = "https://storage.googleapis.com"
 
-## The query engine options.
-[query]
-## Parallelism of the query engine.
-## Default to 0, which means the number of CPU cores.
-parallelism = 0
-
 ## The data storage options.
 [storage]
 ## The working home directory.
-data_home = "./greptimedb_data"
+data_home = "/tmp/greptimedb/"
 
 ## The storage type used to store the data.
 ## - `File`: the data is stored in the local file system.
@@ -367,10 +373,6 @@ timeout = "30s"
 ## The timeout for idle sockets being kept-alive.
 pool_idle_timeout = "90s"
 
-## To skip the ssl verification
-## **Security Notice**: Setting `skip_ssl_validation = true` disables certificate verification, making connections vulnerable to man-in-the-middle attacks. Only use this in development or trusted private networks.
-skip_ssl_validation = false
-
 # Custom storage options
 # [[storage.providers]]
 # name = "S3"
@@ -510,9 +512,6 @@ content_cache_size = "128MiB"
 ## Page size for inverted index content cache.
 content_cache_page_size = "64KiB"
 
-## Cache size for index result.
-result_cache_size = "128MiB"
-
 ## The options for inverted index in Mito engine.
 [region_engine.mito.inverted_index]
 
@@ -619,7 +618,7 @@ experimental_sparse_primary_key_encoding = false
 ## The logging options.
 [logging]
 ## The directory to store the log files. If set to empty, logs will not be written to files.
-dir = "./greptimedb_data/logs"
+dir = "/tmp/greptimedb/logs"
 
 ## The log level. Can be `info`/`debug`/`warn`/`error`.
 ## @toml2docs:none-default
@@ -629,7 +628,7 @@ level = "info"
 enable_otlp_tracing = false
 
 ## The OTLP tracing endpoint.
-otlp_endpoint = "http://localhost:4318"
+otlp_endpoint = "http://localhost:4317"
 
 ## Whether to append logs to stdout.
 append_stdout = true
@@ -640,25 +639,43 @@ log_format = "text"
 ## The maximum amount of log files.
 max_log_files = 720
 
-## The OTLP tracing export protocol. Can be `grpc`/`http`.
-otlp_export_protocol = "http"
-
 ## The percentage of tracing will be sampled and exported.
 ## Valid range `[0, 1]`, 1 means all traces are sampled, 0 means all traces are not sampled, the default value is 1.
 ## ratio > 1 are treated as 1. Fractions < 0 are treated as 0
 [logging.tracing_sample_ratio]
 default_ratio = 1.0
 
-## The datanode can export its metrics and send to Prometheus compatible service (e.g. `greptimedb` itself) from remote-write API.
+## The slow query log options.
+[logging.slow_query]
+## Whether to enable slow query log.
+enable = false
+
+## The threshold of slow query.
+## @toml2docs:none-default
+threshold = "10s"
+
+## The sampling ratio of slow query log. The value should be in the range of (0, 1].
+## @toml2docs:none-default
+sample_ratio = 1.0
+
+## The datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.
 ## This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape.
 [export_metrics]
+
 ## whether enable export metrics.
 enable = false
+
 ## The interval of export metrics.
 write_interval = "30s"
 
+## For `standalone` mode, `self_import` is recommend to collect metrics generated by itself
+## You must create the database before enabling it.
+[export_metrics.self_import]
+## @toml2docs:none-default
+db = "greptime_metrics"
+
 [export_metrics.remote_write]
-## The prometheus remote write endpoint that the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
+## The url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
 url = ""
 
 ## HTTP headers of Prometheus remote-write carry.

config/flownode.example.toml

@@ -1,3 +1,6 @@
+## The running mode of the flownode. It can be `standalone` or `distributed`.
+mode = "distributed"
+
 ## The flownode identifier and should be unique in the cluster.
 ## @toml2docs:none-default
 node_id = 14
@@ -27,7 +30,7 @@ max_send_message_size = "512MB"
 ## The address to bind the HTTP server.
 addr = "127.0.0.1:4000"
 ## HTTP request timeout. Set to 0 to disable timeout.
-timeout = "0s"
+timeout = "30s"
 ## HTTP request body limit.
 ## The following units are supported: `B`, `KB`, `KiB`, `MB`, `MiB`, `GB`, `GiB`, `TB`, `TiB`, `PB`, `PiB`.
 ## Set to 0 to disable limit.
@@ -73,7 +76,7 @@ retry_interval = "3s"
 ## The logging options.
 [logging]
 ## The directory to store the log files. If set to empty, logs will not be written to files.
-dir = "./greptimedb_data/logs"
+dir = "/tmp/greptimedb/logs"
 
 ## The log level. Can be `info`/`debug`/`warn`/`error`.
 ## @toml2docs:none-default
@@ -83,7 +86,7 @@ level = "info"
 enable_otlp_tracing = false
 
 ## The OTLP tracing endpoint.
-otlp_endpoint = "http://localhost:4318"
+otlp_endpoint = "http://localhost:4317"
 
 ## Whether to append logs to stdout.
 append_stdout = true
@@ -94,17 +97,28 @@ log_format = "text"
 ## The maximum amount of log files.
 max_log_files = 720
 
-## The OTLP tracing export protocol. Can be `grpc`/`http`.
-otlp_export_protocol = "http"
-
 ## The percentage of tracing will be sampled and exported.
 ## Valid range `[0, 1]`, 1 means all traces are sampled, 0 means all traces are not sampled, the default value is 1.
 ## ratio > 1 are treated as 1. Fractions < 0 are treated as 0
 [logging.tracing_sample_ratio]
 default_ratio = 1.0
 
+## The slow query log options.
+[logging.slow_query]
+## Whether to enable slow query log.
+enable = false
+
+## The threshold of slow query.
+## @toml2docs:none-default
+threshold = "10s"
+
+## The sampling ratio of slow query log. The value should be in the range of (0, 1].
+## @toml2docs:none-default
+sample_ratio = 1.0
+
 ## The tracing options. Only effect when compiled with `tokio-console` feature.
 #+ [tracing]
 ## The tokio console address.
 ## @toml2docs:none-default
 #+ tokio_console_addr = "127.0.0.1"
+

config/frontend.example.toml

@@ -26,7 +26,7 @@ retry_interval = "3s"
 ## The address to bind the HTTP server.
 addr = "127.0.0.1:4000"
 ## HTTP request timeout. Set to 0 to disable timeout.
-timeout = "0s"
+timeout = "30s"
 ## HTTP request body limit.
 ## The following units are supported: `B`, `KB`, `KiB`, `MB`, `MiB`, `GB`, `GiB`, `TB`, `TiB`, `PB`, `PiB`.
 ## Set to 0 to disable limit.
@@ -37,12 +37,6 @@ enable_cors = true
 ## Customize allowed origins for HTTP CORS.
 ## @toml2docs:none-default
 cors_allowed_origins = ["https://example.com"]
-## Whether to enable validation for Prometheus remote write requests.
-## Available options:
-## - strict: deny invalid UTF-8 strings (default).
-## - lossy: allow invalid UTF-8 strings, replace invalid characters with REPLACEMENT_CHARACTER(U+FFFD).
-## - unchecked: do not valid strings.
-prom_validation_mode = "strict"
 
 ## The gRPC server options.
 [grpc]
@@ -54,13 +48,6 @@ bind_addr = "127.0.0.1:4001"
 server_addr = "127.0.0.1:4001"
 ## The number of server worker threads.
 runtime_size = 8
-## Compression mode for frontend side Arrow IPC service. Available options:
-## - `none`: disable all compression
-## - `transport`: only enable gRPC transport compression (zstd)
-## - `arrow_ipc`: only enable Arrow IPC compression (lz4)
-## - `all`: enable all compression.
-## Default to `none`
-flight_compression = "arrow_ipc"
 
 ## gRPC server TLS options, see `mysql.tls` section.
 [grpc.tls]
@@ -192,12 +179,6 @@ metadata_cache_ttl = "10m"
 # TTI of the metadata cache.
 metadata_cache_tti = "5m"
 
-## The query engine options.
-[query]
-## Parallelism of the query engine.
-## Default to 0, which means the number of CPU cores.
-parallelism = 0
-
 ## Datanode options.
 [datanode]
 ## Datanode client options.
@@ -208,7 +189,7 @@ tcp_nodelay = true
 ## The logging options.
 [logging]
 ## The directory to store the log files. If set to empty, logs will not be written to files.
-dir = "./greptimedb_data/logs"
+dir = "/tmp/greptimedb/logs"
 
 ## The log level. Can be `info`/`debug`/`warn`/`error`.
 ## @toml2docs:none-default
@@ -218,7 +199,7 @@ level = "info"
 enable_otlp_tracing = false
 
 ## The OTLP tracing endpoint.
-otlp_endpoint = "http://localhost:4318"
+otlp_endpoint = "http://localhost:4317"
 
 ## Whether to append logs to stdout.
 append_stdout = true
@@ -229,9 +210,6 @@ log_format = "text"
 ## The maximum amount of log files.
 max_log_files = 720
 
-## The OTLP tracing export protocol. Can be `grpc`/`http`.
-otlp_export_protocol = "http"
-
 ## The percentage of tracing will be sampled and exported.
 ## Valid range `[0, 1]`, 1 means all traces are sampled, 0 means all traces are not sampled, the default value is 1.
 ## ratio > 1 are treated as 1. Fractions < 0 are treated as 0
@@ -239,34 +217,36 @@ otlp_export_protocol = "http"
 default_ratio = 1.0
 
 ## The slow query log options.
-[slow_query]
+[logging.slow_query]
 ## Whether to enable slow query log.
-enable = true
+enable = false
 
-## The record type of slow queries. It can be `system_table` or `log`.
-## If `system_table` is selected, the slow queries will be recorded in a system table `greptime_private.slow_queries`.
-## If `log` is selected, the slow queries will be logged in a log file `greptimedb-slow-queries.*`.
-record_type = "system_table"
+## The threshold of slow query.
+## @toml2docs:none-default
+threshold = "10s"
 
-## The threshold of slow query. It can be human readable time string, for example: `10s`, `100ms`, `1s`.
-threshold = "30s"
-
-## The sampling ratio of slow query log. The value should be in the range of (0, 1]. For example, `0.1` means 10% of the slow queries will be logged and `1.0` means all slow queries will be logged.
+## The sampling ratio of slow query log. The value should be in the range of (0, 1].
+## @toml2docs:none-default
 sample_ratio = 1.0
 
-## The TTL of the `slow_queries` system table. Default is `30d` when `record_type` is `system_table`.
-ttl = "30d"
-
-## The frontend can export its metrics and send to Prometheus compatible service (e.g. `greptimedb` itself) from remote-write API.
+## The datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.
 ## This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape.
 [export_metrics]
+
 ## whether enable export metrics.
 enable = false
+
 ## The interval of export metrics.
 write_interval = "30s"
 
+## For `standalone` mode, `self_import` is recommend to collect metrics generated by itself
+## You must create the database before enabling it.
+[export_metrics.self_import]
+## @toml2docs:none-default
+db = "greptime_metrics"
+
 [export_metrics.remote_write]
-## The prometheus remote write endpoint that the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
+## The url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
 url = ""
 
 ## HTTP headers of Prometheus remote-write carry.

config/metasrv.example.toml

@@ -1,5 +1,13 @@
 ## The working home directory.
-data_home = "./greptimedb_data"
+data_home = "/tmp/metasrv/"
+
+## The bind address of metasrv.
+bind_addr = "127.0.0.1:3002"
+
+## The communication server address for the frontend and datanode to connect to metasrv.
+## If left empty or unset, the server will automatically use the IP address of the first network interface
+## on the host, with the same port number as the one specified in `bind_addr`.
+server_addr = "127.0.0.1:3002"
 
 ## Store server address default to etcd store.
 ## For postgres store, the format is:
@@ -16,7 +24,6 @@ store_key_prefix = ""
 ## - `etcd_store` (default value)
 ## - `memory_store`
 ## - `postgres_store`
-## - `mysql_store`
 backend = "etcd_store"
 
 ## Table name in RDS to store metadata. Effect when using a RDS kvbackend.
@@ -43,17 +50,6 @@ use_memory_store = false
 ## - Using shared storage (e.g., s3).
 enable_region_failover = false
 
-## Delay before initializing region failure detectors.
-## This delay helps prevent premature initialization of region failure detectors in cases where
-## cluster maintenance mode is enabled right after metasrv starts, especially when the cluster
-## is not deployed via the recommended GreptimeDB Operator. Without this delay, early detector registration
-## may trigger unnecessary region failovers during datanode startup.
-region_failure_detector_initialization_delay = '10m'
-
-## Whether to allow region failover on local WAL.
-## **This option is not recommended to be set to true, because it may lead to data loss during failover.**
-allow_region_failover_on_local_wal = false
-
 ## Max allowed idle time before removing node info from metasrv memory.
 node_max_idle_time = "24hours"
 
@@ -67,32 +63,6 @@ node_max_idle_time = "24hours"
 ## The number of threads to execute the runtime for global write operations.
 #+ compact_rt_size = 4
 
-## The gRPC server options.
-[grpc]
-## The address to bind the gRPC server.
-bind_addr = "127.0.0.1:3002"
-## The communication server address for the frontend and datanode to connect to metasrv.
-## If left empty or unset, the server will automatically use the IP address of the first network interface
-## on the host, with the same port number as the one specified in `bind_addr`.
-server_addr = "127.0.0.1:3002"
-## The number of server worker threads.
-runtime_size = 8
-## The maximum receive message size for gRPC server.
-max_recv_message_size = "512MB"
-## The maximum send message size for gRPC server.
-max_send_message_size = "512MB"
-
-## The HTTP server options.
-[http]
-## The address to bind the HTTP server.
-addr = "127.0.0.1:4000"
-## HTTP request timeout. Set to 0 to disable timeout.
-timeout = "0s"
-## HTTP request body limit.
-## The following units are supported: `B`, `KB`, `KiB`, `MB`, `MiB`, `GB`, `GiB`, `TB`, `TiB`, `PB`, `PiB`.
-## Set to 0 to disable limit.
-body_limit = "64MB"
-
 ## Procedure storage options.
 [procedure]
 
@@ -109,11 +79,6 @@ retry_delay = "500ms"
 ## Comments out the `max_metadata_value_size`, for don't split large value (no limit).
 max_metadata_value_size = "1500KiB"
 
-## Max running procedures.
-## The maximum number of procedures that can be running at the same time.
-## If the number of running procedures exceeds this limit, the procedure will be rejected.
-max_running_procedures = 128
-
 # Failure detectors options.
 [failure_detector]
 
@@ -160,22 +125,6 @@ broker_endpoints = ["127.0.0.1:9092"]
 ## Otherwise, use topics named `topic_name_prefix_[0..num_topics)`
 auto_create_topics = true
 
-## Interval of automatically WAL pruning.
-## Set to `0s` to disable automatically WAL pruning which delete unused remote WAL entries periodically.
-auto_prune_interval = "0s"
-
-## The threshold to trigger a flush operation of a region in automatically WAL pruning.
-## Metasrv will send a flush request to flush the region when:
-## `trigger_flush_threshold` + `prunable_entry_id` < `max_prunable_entry_id`
-## where:
-## - `prunable_entry_id` is the maximum entry id that can be pruned of the region.
-## - `max_prunable_entry_id` is the maximum prunable entry id among all regions in the same topic.
-## Set to `0` to disable the flush operation.
-trigger_flush_threshold = 0
-
-## Concurrent task limit for automatically WAL pruning.
-auto_prune_parallelism = 10
-
 ## Number of topics.
 num_topics = 64
 
@@ -195,6 +144,17 @@ replication_factor = 1
 
 ## Above which a topic creation operation will be cancelled.
 create_topic_timeout = "30s"
+## The initial backoff for kafka clients.
+backoff_init = "500ms"
+
+## The maximum backoff for kafka clients.
+backoff_max = "10s"
+
+## Exponential backoff rate, i.e. next backoff = base * current backoff.
+backoff_base = 2
+
+## Stop reconnecting if the total wait time reaches the deadline. If this config is missing, the reconnecting won't terminate.
+backoff_deadline = "5mins"
 
 # The Kafka SASL configuration.
 # **It's only used when the provider is `kafka`**.
@@ -217,7 +177,7 @@ create_topic_timeout = "30s"
 ## The logging options.
 [logging]
 ## The directory to store the log files. If set to empty, logs will not be written to files.
-dir = "./greptimedb_data/logs"
+dir = "/tmp/greptimedb/logs"
 
 ## The log level. Can be `info`/`debug`/`warn`/`error`.
 ## @toml2docs:none-default
@@ -227,7 +187,7 @@ level = "info"
 enable_otlp_tracing = false
 
 ## The OTLP tracing endpoint.
-otlp_endpoint = "http://localhost:4318"
+otlp_endpoint = "http://localhost:4317"
 
 ## Whether to append logs to stdout.
 append_stdout = true
@@ -238,25 +198,43 @@ log_format = "text"
 ## The maximum amount of log files.
 max_log_files = 720
 
-## The OTLP tracing export protocol. Can be `grpc`/`http`.
-otlp_export_protocol = "http"
-
 ## The percentage of tracing will be sampled and exported.
 ## Valid range `[0, 1]`, 1 means all traces are sampled, 0 means all traces are not sampled, the default value is 1.
 ## ratio > 1 are treated as 1. Fractions < 0 are treated as 0
 [logging.tracing_sample_ratio]
 default_ratio = 1.0
 
-## The metasrv can export its metrics and send to Prometheus compatible service (e.g. `greptimedb` itself) from remote-write API.
+## The slow query log options.
+[logging.slow_query]
+## Whether to enable slow query log.
+enable = false
+
+## The threshold of slow query.
+## @toml2docs:none-default
+threshold = "10s"
+
+## The sampling ratio of slow query log. The value should be in the range of (0, 1].
+## @toml2docs:none-default
+sample_ratio = 1.0
+
+## The datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.
 ## This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape.
 [export_metrics]
+
 ## whether enable export metrics.
 enable = false
+
 ## The interval of export metrics.
 write_interval = "30s"
 
+## For `standalone` mode, `self_import` is recommend to collect metrics generated by itself
+## You must create the database before enabling it.
+[export_metrics.self_import]
+## @toml2docs:none-default
+db = "greptime_metrics"
+
 [export_metrics.remote_write]
-## The prometheus remote write endpoint that the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
+## The url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
 url = ""
 
 ## HTTP headers of Prometheus remote-write carry.

config/standalone.example.toml

@@ -1,3 +1,6 @@
+## The running mode of the datanode. It can be `standalone` or `distributed`.
+mode = "standalone"
+
 ## The default timezone of the server.
 ## @toml2docs:none-default
 default_timezone = "UTC"
@@ -31,7 +34,7 @@ max_concurrent_queries = 0
 ## The address to bind the HTTP server.
 addr = "127.0.0.1:4000"
 ## HTTP request timeout. Set to 0 to disable timeout.
-timeout = "0s"
+timeout = "30s"
 ## HTTP request body limit.
 ## The following units are supported: `B`, `KB`, `KiB`, `MB`, `MiB`, `GB`, `GiB`, `TB`, `TiB`, `PB`, `PiB`.
 ## Set to 0 to disable limit.
@@ -43,13 +46,6 @@ enable_cors = true
 ## @toml2docs:none-default
 cors_allowed_origins = ["https://example.com"]
 
-## Whether to enable validation for Prometheus remote write requests.
-## Available options:
-## - strict: deny invalid UTF-8 strings (default).
-## - lossy: allow invalid UTF-8 strings, replace invalid characters with REPLACEMENT_CHARACTER(U+FFFD).
-## - unchecked: do not valid strings.
-prom_validation_mode = "strict"
-
 ## The gRPC server options.
 [grpc]
 ## The address to bind the gRPC server.
@@ -168,7 +164,7 @@ provider = "raft_engine"
 ## The directory to store the WAL files.
 ## **It's only used when the provider is `raft_engine`**.
 ## @toml2docs:none-default
-dir = "./greptimedb_data/wal"
+dir = "/tmp/greptimedb/wal"
 
 ## The size of the WAL segment file.
 ## **It's only used when the provider is `raft_engine`**.
@@ -246,6 +242,22 @@ max_batch_bytes = "1MB"
 ## **It's only used when the provider is `kafka`**.
 consumer_wait_timeout = "100ms"
 
+## The initial backoff delay.
+## **It's only used when the provider is `kafka`**.
+backoff_init = "500ms"
+
+## The maximum backoff delay.
+## **It's only used when the provider is `kafka`**.
+backoff_max = "10s"
+
+## The exponential backoff rate, i.e. next backoff = base * current backoff.
+## **It's only used when the provider is `kafka`**.
+backoff_base = 2
+
+## The deadline of retries.
+## **It's only used when the provider is `kafka`**.
+backoff_deadline = "5mins"
+
 ## Ignore missing entries during read WAL.
 ## **It's only used when the provider is `kafka`**.
 ##
@@ -290,10 +302,6 @@ purge_interval = "1m"
 max_retry_times = 3
 ## Initial retry delay of procedures, increases exponentially
 retry_delay = "500ms"
-## Max running procedures.
-## The maximum number of procedures that can be running at the same time.
-## If the number of running procedures exceeds this limit, the procedure will be rejected.
-max_running_procedures = 128
 
 ## flow engine options.
 [flow]
@@ -341,16 +349,10 @@ max_running_procedures = 128
 # credential = "base64-credential"
 # endpoint = "https://storage.googleapis.com"
 
-## The query engine options.
-[query]
-## Parallelism of the query engine.
-## Default to 0, which means the number of CPU cores.
-parallelism = 0
-
 ## The data storage options.
 [storage]
 ## The working home directory.
-data_home = "./greptimedb_data"
+data_home = "/tmp/greptimedb/"
 
 ## The storage type used to store the data.
 ## - `File`: the data is stored in the local file system.
@@ -458,10 +460,6 @@ timeout = "30s"
 ## The timeout for idle sockets being kept-alive.
 pool_idle_timeout = "90s"
 
-## To skip the ssl verification
-## **Security Notice**: Setting `skip_ssl_validation = true` disables certificate verification, making connections vulnerable to man-in-the-middle attacks. Only use this in development or trusted private networks.
-skip_ssl_validation = false
-
 # Custom storage options
 # [[storage.providers]]
 # name = "S3"
@@ -601,9 +599,6 @@ content_cache_size = "128MiB"
 ## Page size for inverted index content cache.
 content_cache_page_size = "64KiB"
 
-## Cache size for index result.
-result_cache_size = "128MiB"
-
 ## The options for inverted index in Mito engine.
 [region_engine.mito.inverted_index]
 
@@ -710,7 +705,7 @@ experimental_sparse_primary_key_encoding = false
 ## The logging options.
 [logging]
 ## The directory to store the log files. If set to empty, logs will not be written to files.
-dir = "./greptimedb_data/logs"
+dir = "/tmp/greptimedb/logs"
 
 ## The log level. Can be `info`/`debug`/`warn`/`error`.
 ## @toml2docs:none-default
@@ -720,7 +715,7 @@ level = "info"
 enable_otlp_tracing = false
 
 ## The OTLP tracing endpoint.
-otlp_endpoint = "http://localhost:4318"
+otlp_endpoint = "http://localhost:4317"
 
 ## Whether to append logs to stdout.
 append_stdout = true
@@ -731,9 +726,6 @@ log_format = "text"
 ## The maximum amount of log files.
 max_log_files = 720
 
-## The OTLP tracing export protocol. Can be `grpc`/`http`.
-otlp_export_protocol = "http"
-
 ## The percentage of tracing will be sampled and exported.
 ## Valid range `[0, 1]`, 1 means all traces are sampled, 0 means all traces are not sampled, the default value is 1.
 ## ratio > 1 are treated as 1. Fractions < 0 are treated as 0
@@ -741,27 +733,25 @@ otlp_export_protocol = "http"
 default_ratio = 1.0
 
 ## The slow query log options.
-[slow_query]
+[logging.slow_query]
 ## Whether to enable slow query log.
-#+ enable = false
-
-## The record type of slow queries. It can be `system_table` or `log`.
-## @toml2docs:none-default
-#+ record_type = "system_table"
+enable = false
 
 ## The threshold of slow query.
 ## @toml2docs:none-default
-#+ threshold = "10s"
+threshold = "10s"
 
 ## The sampling ratio of slow query log. The value should be in the range of (0, 1].
 ## @toml2docs:none-default
-#+ sample_ratio = 1.0
+sample_ratio = 1.0
 
-## The standalone can export its metrics and send to Prometheus compatible service (e.g. `greptimedb`) from remote-write API.
+## The datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.
 ## This is only used for `greptimedb` to export its own metrics internally. It's different from prometheus scrape.
 [export_metrics]
+
 ## whether enable export metrics.
 enable = false
+
 ## The interval of export metrics.
 write_interval = "30s"
 
@@ -772,7 +762,7 @@ write_interval = "30s"
 db = "greptime_metrics"
 
 [export_metrics.remote_write]
-## The prometheus remote write endpoint that the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
+## The url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
 url = ""
 
 ## HTTP headers of Prometheus remote-write carry.

cyborg/bin/bump-doc-version.ts

@@ -0,0 +1,75 @@
+/*
+ * Copyright 2023 Greptime Team
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import * as core from "@actions/core";
+import {obtainClient} from "@/common";
+
+async function triggerWorkflow(workflowId: string, version: string) {
+  const docsClient = obtainClient("DOCS_REPO_TOKEN")
+  try {
+    await docsClient.rest.actions.createWorkflowDispatch({
+      owner: "GreptimeTeam",
+      repo: "docs",
+      workflow_id: workflowId,
+      ref: "main",
+      inputs: {
+        version,
+      },
+    });
+    console.log(`Successfully triggered ${workflowId} workflow with version ${version}`);
+  } catch (error) {
+    core.setFailed(`Failed to trigger workflow: ${error.message}`);
+  }
+}
+
+function determineWorkflow(version: string): [string, string] {
+  // Check if it's a nightly version
+  if (version.includes('nightly')) {
+    return ['bump-nightly-version.yml', version];
+  }
+
+  const parts = version.split('.');
+
+  if (parts.length !== 3) {
+    throw new Error('Invalid version format');
+  }
+
+  // If patch version (last number) is 0, it's a major version
+  // Return only major.minor version
+  if (parts[2] === '0') {
+    return ['bump-version.yml', `${parts[0]}.${parts[1]}`];
+  }
+
+  // Otherwise it's a patch version, use full version
+  return ['bump-patch-version.yml', version];
+}
+
+const version = process.env.VERSION;
+if (!version) {
+  core.setFailed("VERSION environment variable is required");
+  process.exit(1);
+}
+
+// Remove 'v' prefix if exists
+const cleanVersion = version.startsWith('v') ? version.slice(1) : version;
+
+try {
+  const [workflowId, apiVersion] = determineWorkflow(cleanVersion);
+  triggerWorkflow(workflowId, apiVersion);
+} catch (error) {
+  core.setFailed(`Error processing version: ${error.message}`);
+  process.exit(1);
+}

cyborg/bin/bump-versions.ts

@@ -1,156 +0,0 @@
-/*
- * Copyright 2023 Greptime Team
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-import * as core from "@actions/core";
-import {obtainClient} from "@/common";
-
-interface RepoConfig {
-  tokenEnv: string;
-  repo: string;
-  workflowLogic: (version: string) => [string, string] | null;
-}
-
-const REPO_CONFIGS: Record<string, RepoConfig> = {
-  website: {
-    tokenEnv: "WEBSITE_REPO_TOKEN",
-    repo: "website",
-    workflowLogic: (version: string) => {
-      // Skip nightly versions for website
-      if (version.includes('nightly')) {
-        console.log('Nightly version detected for website, skipping workflow trigger.');
-        return null;
-      }
-      return ['bump-patch-version.yml', version];
-    }
-  },
-  demo: {
-    tokenEnv: "DEMO_REPO_TOKEN",
-    repo: "demo-scene",
-    workflowLogic: (version: string) => {
-      // Skip nightly versions for demo
-      if (version.includes('nightly')) {
-        console.log('Nightly version detected for demo, skipping workflow trigger.');
-        return null;
-      }
-      return ['bump-patch-version.yml', version];
-    }
-  },
-  docs: {
-    tokenEnv: "DOCS_REPO_TOKEN",
-    repo: "docs",
-    workflowLogic: (version: string) => {
-      // Check if it's a nightly version
-      if (version.includes('nightly')) {
-        return ['bump-nightly-version.yml', version];
-      }
-
-      const parts = version.split('.');
-      if (parts.length !== 3) {
-        throw new Error('Invalid version format');
-      }
-
-      // If patch version (last number) is 0, it's a major version
-      // Return only major.minor version
-      if (parts[2] === '0') {
-        return ['bump-version.yml', `${parts[0]}.${parts[1]}`];
-      }
-
-      // Otherwise it's a patch version, use full version
-      return ['bump-patch-version.yml', version];
-    }
-  }
-};
-
-async function triggerWorkflow(repoConfig: RepoConfig, workflowId: string, version: string) {
-  const client = obtainClient(repoConfig.tokenEnv);
-  try {
-    await client.rest.actions.createWorkflowDispatch({
-      owner: "GreptimeTeam",
-      repo: repoConfig.repo,
-      workflow_id: workflowId,
-      ref: "main",
-      inputs: {
-        version,
-      },
-    });
-    console.log(`Successfully triggered ${workflowId} workflow for ${repoConfig.repo} with version ${version}`);
-  } catch (error) {
-    core.setFailed(`Failed to trigger workflow for ${repoConfig.repo}: ${error.message}`);
-    throw error;
-  }
-}
-
-async function processRepo(repoName: string, version: string) {
-  const repoConfig = REPO_CONFIGS[repoName];
-  if (!repoConfig) {
-    throw new Error(`Unknown repository: ${repoName}`);
-  }
-
-  try {
-    const workflowResult = repoConfig.workflowLogic(version);
-    if (workflowResult === null) {
-      // Skip this repo (e.g., nightly version for website)
-      return;
-    }
-
-    const [workflowId, apiVersion] = workflowResult;
-    await triggerWorkflow(repoConfig, workflowId, apiVersion);
-  } catch (error) {
-    core.setFailed(`Error processing ${repoName} with version ${version}: ${error.message}`);
-    throw error;
-  }
-}
-
-async function main() {
-  const version = process.env.VERSION;
-  if (!version) {
-    core.setFailed("VERSION environment variable is required");
-    process.exit(1);
-  }
-
-  // Remove 'v' prefix if exists
-  const cleanVersion = version.startsWith('v') ? version.slice(1) : version;
-
-  // Get target repositories from environment variable
-  // Default to both if not specified
-  const targetRepos = process.env.TARGET_REPOS?.split(',').map(repo => repo.trim()) || ['website', 'docs'];
-
-  console.log(`Processing version ${cleanVersion} for repositories: ${targetRepos.join(', ')}`);
-
-  const errors: string[] = [];
-
-  // Process each repository
-  for (const repo of targetRepos) {
-    try {
-      await processRepo(repo, cleanVersion);
-    } catch (error) {
-      errors.push(`${repo}: ${error.message}`);
-    }
-  }
-
-  if (errors.length > 0) {
-    core.setFailed(`Failed to process some repositories: ${errors.join('; ')}`);
-    process.exit(1);
-  }
-
-  console.log('All repositories processed successfully');
-}
-
-// Execute main function
-main().catch((error) => {
-  core.setFailed(`Unexpected error: ${error.message}`);
-  process.exit(1);
-});

docker/docker-compose/cluster-with-etcd.yaml

@@ -25,7 +25,7 @@ services:
       - --initial-cluster-state=new
       - *etcd_initial_cluster_token
     volumes:
-      - ./greptimedb-cluster-docker-compose/etcd0:/var/lib/etcd
+      - /tmp/greptimedb-cluster-docker-compose/etcd0:/var/lib/etcd
     healthcheck:
       test: [ "CMD", "etcdctl", "--endpoints=http://etcd0:2379", "endpoint", "health" ]
       interval: 5s
@@ -68,13 +68,12 @@ services:
       - datanode
       - start
       - --node-id=0
-      - --data-home=/greptimedb_data
       - --rpc-bind-addr=0.0.0.0:3001
       - --rpc-server-addr=datanode0:3001
       - --metasrv-addrs=metasrv:3002
       - --http-addr=0.0.0.0:5000
     volumes:
-      - ./greptimedb-cluster-docker-compose/datanode0:/greptimedb_data
+      - /tmp/greptimedb-cluster-docker-compose/datanode0:/tmp/greptimedb
     healthcheck:
       test: [ "CMD", "curl", "-fv", "http://datanode0:5000/health" ]
       interval: 5s

docs/how-to/how-to-change-log-level-on-the-fly.md

@@ -11,6 +11,6 @@ And database will reply with something like:
 Log Level changed from Some("info") to "trace,flow=debug"%
 ```
 
-The data is a string in the format of `global_level,module1=level1,module2=level2,...` that follows the same rule of `RUST_LOG`. 
+The data is a string in the format of `global_level,module1=level1,module2=level2,...` that follow the same rule of `RUST_LOG`. 
 
 The module is the module name of the log, and the level is the log level. The log level can be one of the following: `trace`, `debug`, `info`, `warn`, `error`, `off`(case insensitive).

docs/how-to/how-to-implement-sql-statement.md

@@ -14,7 +14,7 @@ impl SqlQueryHandler for Instance {
 ```
 
 Normally, when a SQL query arrives at GreptimeDB, the `do_query` method will be called. After some parsing work, the SQL
-will be fed into `StatementExecutor`:
+will be feed into `StatementExecutor`:
 
 ```rust
 // in Frontend Instance:
@@ -27,7 +27,7 @@ an example.
 
 Now, what if the statements should be handled differently for GreptimeDB Standalone and Cluster? You can see there's
 a `SqlStatementExecutor` field in `StatementExecutor`. Each GreptimeDB Standalone and Cluster has its own implementation
-of `SqlStatementExecutor`. If you are going to implement the statements differently in the two modes (
+of `SqlStatementExecutor`. If you are going to implement the statements differently in the two mode (
 like `CREATE TABLE`), you have to implement them in their own `SqlStatementExecutor`s.
 
 Summarize as the diagram below:

docs/how-to/how-to-profile-memory.md

@@ -1,6 +1,6 @@
 # Profile memory usage of GreptimeDB
 
-This crate provides an easy approach to dump memory profiling info. A set of ready to use scripts is provided in [docs/how-to/memory-profile-scripts](./memory-profile-scripts/scripts).
+This crate provides an easy approach to dump memory profiling info.
 
 ## Prerequisites
 ### jemalloc
@@ -44,10 +44,6 @@ Dump memory profiling data through HTTP API:
 
 ```bash
 curl -X POST localhost:4000/debug/prof/mem > greptime.hprof
-# or output flamegraph directly
-curl -X POST "localhost:4000/debug/prof/mem?output=flamegraph" > greptime.svg
-# or output pprof format
-curl -X POST "localhost:4000/debug/prof/mem?output=proto" > greptime.pprof
 ```
 
 You can periodically dump profiling data and compare them to find the delta memory usage.

docs/how-to/how-to-write-aggregate-function.md

@@ -1,8 +1,8 @@
 Currently, our query engine is based on DataFusion, so all aggregate function is executed by DataFusion, through its UDAF interface. You can find DataFusion's UDAF example [here](https://github.com/apache/arrow-datafusion/blob/arrow2/datafusion-examples/examples/simple_udaf.rs). Basically, we provide the same way as DataFusion to write aggregate functions: both are centered in a struct called "Accumulator" to accumulates states along the way in aggregation.
 
-However, DataFusion's UDAF implementation has a huge restriction, that it requires user to provide a concrete "Accumulator". Take `Median` aggregate function for example, to aggregate a `u32` datatype column, you have to write a `MedianU32`, and use `SELECT MEDIANU32(x)` in SQL. `MedianU32` cannot be used to aggregate a `i32` datatype column. Or, there's another way: you can use a special type that can hold all kinds of data (like our `Value` enum or Arrow's `ScalarValue`), and `match` all the way up to do aggregate calculations. It might work, though rather tedious. (But I think it's DataFusion's preferred way to write UDAF.)
+However, DataFusion's UDAF implementation has a huge restriction, that it requires user to provide a concrete "Accumulator". Take `Median` aggregate function for example, to aggregate a `u32` datatype column, you have to write a `MedianU32`, and use `SELECT MEDIANU32(x)` in SQL. `MedianU32` cannot be used to aggregate a `i32` datatype column. Or, there's another way: you can use a special type that can hold all kinds of data (like our `Value` enum or Arrow's `ScalarValue`), and `match` all the way up to do aggregate calculations. It might work, though rather tedious. (But I think it's DataFusion's prefer way to write UDAF.)
 
-So is there a way we can make an aggregate function that automatically match the input data's type? For example, a `Median` aggregator that can work on both `u32` column and `i32`? The answer is yes until we find a way to bypass DataFusion's restriction, a restriction that DataFusion simply doesn't pass the input data's type when creating an Accumulator.
+So is there a way we can make an aggregate function that automatically match the input data's type? For example, a `Median` aggregator that can work on both `u32` column and `i32`? The answer is yes until we found a way to bypassing DataFusion's restriction, a restriction that DataFusion simply don't pass the input data's type when creating an Accumulator.
 
 > There's an example in `my_sum_udaf_example.rs`, take that as quick start.
 
@@ -16,7 +16,7 @@ You must first define a struct that will be used to create your accumulator. For
 struct MySumAccumulatorCreator {}
 ```
 
-Attribute macro `#[as_aggr_func_creator]` and derive macro `#[derive(Debug, AggrFuncTypeStore)]` must both be annotated on the struct. They work together to provide a storage of aggregate function's input data types, which are needed for creating generic accumulator later.  
+Attribute macro `#[as_aggr_func_creator]` and derive macro `#[derive(Debug, AggrFuncTypeStore)]` must both annotated on the struct. They work together to provide a storage of aggregate function's input data types, which are needed for creating generic accumulator later.  
 
 > Note that the `as_aggr_func_creator` macro will add fields to the struct, so the struct cannot be defined as an empty struct without field like `struct Foo;`, neither as a new type like `struct Foo(bar)`.
 
@@ -32,11 +32,11 @@ pub trait AggregateFunctionCreator: Send + Sync + Debug {
 
 You can use input data's type in methods that return output type and state types (just invoke `input_types()`).
 
-The output type is aggregate function's output data's type. For example, `SUM` aggregate function's output type is `u64` for a `u32` datatype column. The state types are accumulator's internal states' types. Take `AVG` aggregate function on a `i32` column as example, its state types are `i64` (for sum) and `u64` (for count).
+The output type is aggregate function's output data's type. For example, `SUM` aggregate function's output type is `u64` for a `u32` datatype column. The state types are accumulator's internal states' types. Take `AVG` aggregate function on a `i32` column as example, it's state types are `i64` (for sum) and `u64` (for count).
 
 The `creator` function is where you define how an accumulator (that will be used in DataFusion) is created. You define "how" to create the accumulator (instead of "what" to create), using the input data's type as arguments. With input datatype known, you can create accumulator generically.
 
-# 2. Impl `Accumulator` trait for your accumulator.
+# 2. Impl `Accumulator` trait for you accumulator.
 
 The accumulator is where you store the aggregate calculation states and evaluate a result. You must impl `Accumulator` trait for it. The trait's definition is:
 
@@ -49,7 +49,7 @@ pub trait Accumulator: Send + Sync + Debug {
 }
 ```
 
-The DataFusion basically executes aggregate like this:
+The DataFusion basically execute aggregate like this:
 
 1. Partitioning all input data for aggregate. Create an accumulator for each part.
 2. Call `update_batch` on each accumulator with partitioned data, to let you update your aggregate calculation.
@@ -57,16 +57,16 @@ The DataFusion basically executes aggregate like this:
 4. Call `merge_batch` to merge all accumulator's internal state to one.
 5. Execute `evaluate` on the chosen one to get the final calculation result.
 
-Once you know the meaning of each method, you can easily write your accumulator. You can refer to `Median` accumulator or `SUM` accumulator defined in file `my_sum_udaf_example.rs` for more details.
+Once you know the meaning of each method, you can easily write your accumulator. You can refer to `Median` accumulator or `SUM` accumulator defined in  file `my_sum_udaf_example.rs` for more details.
 
 # 3. Register your aggregate function to our query engine.
 
 You can call `register_aggregate_function` method in query engine to register your aggregate function. To do that, you have to new an instance of struct `AggregateFunctionMeta`. The struct has three fields, first is the name of your aggregate function's name. The function name is case-sensitive due to DataFusion's restriction. We strongly recommend using lowercase for your name. If you have to use uppercase name, wrap your aggregate function with quotation marks. For example, if you define an aggregate function named "my_aggr", you can use "`SELECT MY_AGGR(x)`"; if you define "my_AGGR", you have to use "`SELECT "my_AGGR"(x)`".
 
-The second field is arg_counts ,the count of the arguments. Like accumulator `percentile`, calculating the p_number of the column. We need to input the value of column and the value of p to calculate, and so the count of the arguments is two.
+The second field is arg_counts ,the count of the arguments. Like accumulator `percentile`, calculating the p_number of the column. We need to input the value of column and the value of p to cacalate, and so the count of the arguments is two.
 
 The third field is a function about how to create your accumulator creator that you defined in step 1 above. Create creator, that's a bit intertwined, but it is how we make DataFusion use a newly created aggregate function each time it executes a SQL, preventing the stored input types from affecting each other. The key detail can be starting looking at our `DfContextProviderAdapter` struct's `get_aggregate_meta` method.
 
 # (Optional) 4. Make your aggregate function automatically registered.
 
-If you've written a great aggregate function that wants to let everyone use it, you can make it automatically register to our query engine at start time. It's quick and simple, just refer to the `AggregateFunctions::register` function in `common/function/src/scalars/aggregate/mod.rs`.
+If you've written a great aggregate function that want to let everyone use it, you can make it automatically registered to our query engine at start time. It's quick simple, just refer to the `AggregateFunctions::register` function in `common/function/src/scalars/aggregate/mod.rs`.

docs/how-to/how-to-write-fuzz-tests.md

@@ -3,7 +3,7 @@
 This document introduces how to write fuzz tests in GreptimeDB.
 
 ## What is a fuzz test
-Fuzz test is tool that leverages deterministic random generation to assist in finding bugs. The goal of fuzz tests is to identify inputs generated by the fuzzer that cause system panics, crashes, or unexpected behaviors to occur. And we are using the [cargo-fuzz](https://github.com/rust-fuzz/cargo-fuzz) to run our fuzz test targets.
+Fuzz test is tool that leverage deterministic random generation to assist in finding bugs. The goal of fuzz tests is to identify inputs generated by the fuzzer that cause system panics, crashes, or unexpected behaviors to occur. And we are using the [cargo-fuzz](https://github.com/rust-fuzz/cargo-fuzz) to run our fuzz test targets. 
 
 ## Why we need them
 - Find bugs by leveraging random generation
@@ -13,7 +13,7 @@ Fuzz test is tool that leverages deterministic random generation to assist in fi
 All fuzz test-related resources are located in the `/tests-fuzz` directory.
 There are two types of resources: (1) fundamental components and (2) test targets.
 
-### Fundamental components
+### Fundamental components 
 They are located in the `/tests-fuzz/src` directory. The fundamental components define how to generate SQLs (including dialects for different protocols) and validate execution results (e.g., column attribute validation), etc.
 
 ### Test targets
@@ -21,25 +21,25 @@ They are located in the `/tests-fuzz/targets` directory, with each file represen
 
 Figure 1 illustrates the fundamental components of the fuzz test provide the ability to generate random SQLs. It utilizes a Random Number Generator (Rng) to generate the Intermediate Representation (IR), then employs a DialectTranslator to produce specified dialects for different protocols. Finally, the fuzz tests send the generated SQL via the specified protocol and verify that the execution results meet expectations.
 ```
-                            Rng
-                             |
-                             |
-                             v
-                       ExprGenerator
-                             |
-                             |
-                             v
-               Intermediate representation (IR)
-                             |
-                             |
-      +----------------------+----------------------+
-      |                      |                      |
-      v                      v                      v
+                            Rng                                 
+                             |                                  
+                             |                                  
+                             v                                  
+                       ExprGenerator                            
+                             |                                  
+                             |                                  
+                             v                                  
+               Intermediate representation (IR)                 
+                             |                                  
+                             |                                  
+      +----------------------+----------------------+           
+      |                      |                      |           
+      v                      v                      v           
 MySQLTranslator    PostgreSQLTranslator   OtherDialectTranslator
-      |                      |                      |
-      |                      |                      |
-      v                      v                      v
-SQL(MySQL Dialect)         .....                  .....
+      |                      |                      |           
+      |                      |                      |           
+      v                      v                      v           
+SQL(MySQL Dialect)         .....                  .....         
       |
       |
       v
@@ -133,4 +133,4 @@ fuzz_target!(|input: FuzzInput| {
     cargo fuzz run <fuzz-target> --fuzz-dir tests-fuzz
 ```
 
-For more details, please refer to this [document](/tests-fuzz/README.md).
+For more details, please refer to this [document](/tests-fuzz/README.md).

docs/how-to/memory-profile-scripts/scripts/README.md

@@ -1,52 +0,0 @@
-# Memory Analysis Process
-This section will guide you through the process of analyzing memory usage for greptimedb.
-
-1. Get the `jeprof` tool script, see the next section("Getting the `jeprof` tool") for details.
-
-2. After starting `greptimedb`(with env var `MALLOC_CONF=prof:true`), execute the `dump.sh` script with the PID of the `greptimedb` process as an argument. This continuously monitors memory usage and captures profiles when exceeding thresholds (e.g. +20MB within 10 minutes). Outputs `greptime-{timestamp}.gprof` files.
-
-3. With 2-3 gprof files, run `gen_flamegraph.sh` in the same environment to generate flame graphs showing memory allocation call stacks.
-
-4.  **NOTE:** The `gen_flamegraph.sh` script requires `jeprof` and optionally `flamegraph.pl` to be in the current directory. If needed to gen flamegraph now, run the `get_flamegraph_tool.sh` script, which downloads the flame graph generation tool `flamegraph.pl` to the current directory.
-    The usage of `gen_flamegraph.sh` is:
-
-    `Usage: ./gen_flamegraph.sh <binary_path> <gprof_directory>`
-    where `<binary_path>` is the path to the greptimedb binary, `<gprof_directory>` is the directory containing the gprof files(the directory `dump.sh` is dumping profiles to).
-    Example call: `./gen_flamegraph.sh ./greptime .`
-
-    Generating the flame graph might take a few minutes. The generated flame graphs are located in the `<gprof_directory>/flamegraphs` directory. Or if no `flamegraph.pl` is found, it will only contain `.collapse` files which is also fine.
-5.  You can send the generated flame graphs(the entire folder of `<gprof_directory>/flamegraphs`) to developers for further analysis.
-
-
-## Getting the `jeprof` tool
-there are three ways to get `jeprof`, list in here from simple to complex, using any one of those methods is ok, as long as it's the same environment as the `greptimedb` will be running on:
-1. If you are compiling greptimedb from source, then `jeprof` is already produced during compilation. After running `cargo build`, execute `find_compiled_jeprof.sh`. This will copy `jeprof` to the current directory.
-2. Or, if you have the Rust toolchain installed locally, simply follow these commands:
-```bash
-cargo new get_jeprof
-cd get_jeprof
-```
-Then add this line to `Cargo.toml`:
-```toml
-[dependencies]
-tikv-jemalloc-ctl = { version = "0.6", features = ["use_std", "stats"] }
-```
-then run:
-```bash
-cargo build
-```
-after that the `jeprof` tool is produced. Now run `find_compiled_jeprof.sh` in current directory, it will copy the `jeprof` tool to the current directory.
-
-3. compile jemalloc from source
-you can first clone this repo, and checkout to this commit:
-```bash
-git clone https://github.com/tikv/jemalloc.git
-cd jemalloc
-git checkout e13ca993e8ccb9ba9847cc330696e02839f328f7
-```
-then run:
-```bash
-./configure
-make
-```
-and `jeprof` is in `.bin/` directory. Copy it to the current directory.

docs/how-to/memory-profile-scripts/scripts/dump.sh

@@ -1,78 +0,0 @@
-#!/bin/bash
-
-# Monitors greptime process memory usage every 10 minutes
-# Triggers memory profile capture via `curl -X POST localhost:4000/debug/prof/mem > greptime-{timestamp}.gprof`
-# when memory increases by more than 20MB since last check
-# Generated profiles can be analyzed using flame graphs as described in `how-to-profile-memory.md`
-# (jeprof is compiled with the database - see documentation)
-# Alternative: Share binaries + profiles for analysis (Docker images preferred)
-
-# Threshold in Kilobytes (20 MB)
-threshold_kb=$((20 * 1024))
-sleep_interval=$((10 * 60))
-
-# Variable to store the last measured memory usage in KB
-last_mem_kb=0
-
-echo "Starting memory monitoring for 'greptime' process..."
-
-while true; do
-
-    # Check if PID is provided as an argument
-    if [ -z "$1" ]; then
-        echo "$(date): PID must be provided as a command-line argument."
-        exit 1
-    fi
-
-    pid="$1"
-
-    # Validate that the PID is a number
-    if ! [[ "$pid" =~ ^[0-9]+$ ]]; then
-        echo "$(date): Invalid PID: '$pid'. PID must be a number."
-        exit 1
-    fi
-
-    # Get the current Resident Set Size (RSS) in Kilobytes
-    current_mem_kb=$(ps -o rss= -p "$pid")
-
-    # Check if ps command was successful and returned a number
-    if ! [[ "$current_mem_kb" =~ ^[0-9]+$ ]]; then
-        echo "$(date): Failed to get memory usage for PID $pid. Skipping check."
-        # Keep last_mem_kb to avoid false positives if the process briefly becomes unreadable.
-        continue
-    fi
-
-    echo "$(date): Current memory usage for PID $pid: ${current_mem_kb} KB"
-
-    # Compare with the last measurement
-    # if it's the first run, also do a baseline dump just to make sure we can dump
-    
-    diff_kb=$((current_mem_kb - last_mem_kb))
-    echo "$(date): Memory usage change since last check: ${diff_kb} KB"
-
-    if [ "$diff_kb" -gt "$threshold_kb" ]; then
-        echo "$(date): Memory increase (${diff_kb} KB) exceeded threshold (${threshold_kb} KB). Dumping profile..."
-        timestamp=$(date +%Y%m%d%H%M%S)
-        profile_file="greptime-${timestamp}.gprof"
-        # Execute curl and capture output to file
-        if curl -sf -X POST localhost:4000/debug/prof/mem > "$profile_file"; then
-            echo "$(date): Memory profile saved to $profile_file"
-        else
-            echo "$(date): Failed to dump memory profile (curl exit code: $?)."
-            # Remove the potentially empty/failed profile file
-            rm -f "$profile_file"
-        fi
-    else
-            echo "$(date): Memory increase (${diff_kb} KB) is within the threshold (${threshold_kb} KB)."
-    fi
-    
-
-    # Update the last memory usage
-    last_mem_kb=$current_mem_kb
-    
-    # Wait for 5 minutes
-    echo "$(date): Sleeping for $sleep_interval seconds..."
-    sleep $sleep_interval
-done
-
-echo "Memory monitoring script stopped." # This line might not be reached in normal operation

docs/how-to/memory-profile-scripts/scripts/find_compiled_jeprof.sh

@@ -1,15 +0,0 @@
-#!/bin/bash
-
-# Locates compiled jeprof binary (memory analysis tool) after cargo build
-# Copies it to current directory from target/ build directories
-
-JPROF_PATH=$(find . -name 'jeprof' -print -quit)
-if [ -n "$JPROF_PATH" ]; then
-  echo "Found jeprof at $JPROF_PATH"
-  cp "$JPROF_PATH" .
-  chmod +x jeprof
-  echo "Copied jeprof to current directory and made it executable."
-else
-  echo "jeprof not found"
-  exit 1
-fi

docs/how-to/memory-profile-scripts/scripts/gen_flamegraph.sh

@@ -1,89 +0,0 @@
-#!/bin/bash
-
-# Generate flame graphs from a series of `.gprof` files
-# First argument: Path to the binary executable
-# Second argument: Path to directory containing gprof files
-# Requires `jeprof` and `flamegraph.pl` in current directory
-# What this script essentially does is:
-# ./jeprof <binary> <gprof> --collapse | ./flamegraph.pl > <output>
-# For differential analysis between consecutive profiles:
-# ./jeprof <binary> --base <gprof1> <gprof2> --collapse | ./flamegraph.pl > <output_diff>
-
-set -e # Exit immediately if a command exits with a non-zero status.
-
-# Check for required tools
-if [ ! -f "./jeprof" ]; then
-    echo "Error: jeprof not found in the current directory."
-    exit 1
-fi
-
-if [ ! -f "./flamegraph.pl" ]; then
-    echo "Error: flamegraph.pl not found in the current directory."
-    exit 1
-fi
-
-# Check arguments
-if [ "$#" -ne 2 ]; then
-    echo "Usage: $0 <binary_path> <gprof_directory>"
-    exit 1
-fi
-
-BINARY_PATH=$1
-GPROF_DIR=$2
-OUTPUT_DIR="${GPROF_DIR}/flamegraphs" # Store outputs in a subdirectory
-
-if [ ! -f "$BINARY_PATH" ]; then
-    echo "Error: Binary file not found at $BINARY_PATH"
-    exit 1
-fi
-
-if [ ! -d "$GPROF_DIR" ]; then
-    echo "Error: gprof directory not found at $GPROF_DIR"
-    exit 1
-fi
-
-mkdir -p "$OUTPUT_DIR"
-echo "Generating flamegraphs in $OUTPUT_DIR"
-
-# Find and sort gprof files
-# Use find + sort -V for natural sort of version numbers if present in filenames
-# Use null-terminated strings for safety with find/xargs/sort
-mapfile -d $'\0' gprof_files < <(find "$GPROF_DIR" -maxdepth 1 -name '*.gprof' -print0 | sort -zV)
-
-if [ ${#gprof_files[@]} -eq 0 ]; then
-    echo "No .gprof files found in $GPROF_DIR"
-    exit 0
-fi
-
-prev_gprof=""
-
-# Generate flamegraphs
-for gprof_file in "${gprof_files[@]}"; do
-    # Skip empty entries if any
-    if [ -z "$gprof_file" ]; then
-        continue
-    fi
-
-    filename=$(basename "$gprof_file" .gprof)
-    output_collapse="${OUTPUT_DIR}/${filename}.collapse"
-    output_svg="${OUTPUT_DIR}/${filename}.svg"
-    echo "Generating collapse file for $gprof_file -> $output_collapse"
-    ./jeprof "$BINARY_PATH" "$gprof_file" --collapse > "$output_collapse"
-    echo "Generating flamegraph for $gprof_file -> $output_svg"
-    ./flamegraph.pl "$output_collapse" > "$output_svg" || true
-
-    # Generate diff flamegraph if not the first file
-    if [ -n "$prev_gprof" ]; then
-        prev_filename=$(basename "$prev_gprof" .gprof)
-        diff_output_collapse="${OUTPUT_DIR}/${prev_filename}_vs_${filename}_diff.collapse"
-        diff_output_svg="${OUTPUT_DIR}/${prev_filename}_vs_${filename}_diff.svg"
-        echo "Generating diff collapse file for $prev_gprof vs $gprof_file -> $diff_output_collapse"
-        ./jeprof "$BINARY_PATH" --base "$prev_gprof" "$gprof_file" --collapse > "$diff_output_collapse"
-        echo "Generating diff flamegraph for $prev_gprof vs $gprof_file -> $diff_output_svg"
-        ./flamegraph.pl "$diff_output_collapse" > "$diff_output_svg" || true
-    fi
-
-    prev_gprof="$gprof_file"
-done
-
-echo "Flamegraph generation complete."

docs/how-to/memory-profile-scripts/scripts/gen_from_collapse.sh

@@ -1,44 +0,0 @@
-#!/bin/bash
-
-# Generate flame graphs from .collapse files
-# Argument: Path to directory containing collapse files
-# Requires `flamegraph.pl` in current directory
-
-# Check if flamegraph.pl exists
-if [ ! -f "./flamegraph.pl" ]; then
-    echo "Error: flamegraph.pl not found in the current directory."
-    exit 1
-fi
-
-# Check if directory argument is provided
-if [ -z "$1" ]; then
-    echo "Usage: $0 <collapse_directory>"
-    exit 1
-fi
-
-COLLAPSE_DIR=$1
-
-# Check if the provided argument is a directory
-if [ ! -d "$COLLAPSE_DIR" ]; then
-    echo "Error: '$COLLAPSE_DIR' is not a valid directory."
-    exit 1
-fi
-
-echo "Generating flame graphs from collapse files in '$COLLAPSE_DIR'..."
-
-# Find and process each .collapse file
-find "$COLLAPSE_DIR" -maxdepth 1 -name "*.collapse" -print0 | while IFS= read -r -d $'\0' collapse_file; do
-    if [ -f "$collapse_file" ]; then
-        # Construct the output SVG filename
-        svg_file="${collapse_file%.collapse}.svg"
-        echo "Generating $svg_file from $collapse_file..."
-        ./flamegraph.pl "$collapse_file" > "$svg_file"
-        if [ $? -ne 0 ]; then
-            echo "Error generating flame graph for $collapse_file"
-        else
-            echo "Successfully generated $svg_file"
-        fi
-    fi
-done
-
-echo "Flame graph generation complete."

docs/how-to/memory-profile-scripts/scripts/get_flamegraph_tool.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-
-# Download flamegraph.pl to current directory - this is the flame graph generation tool script
-
-curl https://raw.githubusercontent.com/brendangregg/FlameGraph/master/flamegraph.pl > ./flamegraph.pl
-chmod +x ./flamegraph.pl

docs/rfcs/2025-02-06-remote-wal-purge.md

@@ -1,77 +0,0 @@
----
-Feature Name: Remote WAL Purge
-Tracking Issue: https://github.com/GreptimeTeam/greptimedb/issues/5474
-Date: 2025-02-06
-Author: "Yuhan Wang <profsyb@gmail.com>"
----
-
-# Summary
-
-This RFC proposes a method for purging remote WAL in the database.
-
-# Motivation
-
-Currently only local wal entries are purged when flushing, while remote wal does nothing.
-
-# Details
-
-```mermaid
-sequenceDiagram
-    Region0->>Kafka: Last entry id of the topic in use
-    Region0->>WALPruner: Heartbeat with last entry id
-    WALPruner->>+WALPruner: Time Loop
-    WALPruner->>+ProcedureManager: Submit purge procedure
-    ProcedureManager->>Region0: Flush request
-    ProcedureManager->>Kafka: Prune WAL entries
-    Region0->>Region0: Flush
-```
-
-## Steps
-
-### Before purge
-
-Before purging remote WAL, metasrv needs to know:
-
-1. `last_entry_id` of each region.
-2. `kafka_topic_last_entry_id` which is the last entry id of the topic in use. Can be lazily updated and needed when region has empty memtable.
-3. Kafka topics that each region uses.
-
-The states are maintained through:
-1. Heartbeat: Datanode sends `last_entry_id` to metasrv in heartbeat. As for regions with empty memtable, `last_entry_id` should equals to `kafka_topic_last_entry_id`.
-2. Metasrv maintains a topic-region map to know which region uses which topic.
-
-`kafka_topic_last_entry_id` will be maintained by the region itself. Region will update the value after `k` heartbeats if the memtable is empty.
-
-### Purge procedure
-
-We can better handle locks utilizing current procedure. It's quite similar to the region migration procedure.
-
-After a period of time, metasrv will submit a purge procedure to ProcedureManager. The purge will apply to all topics.
-
-The procedure is divided into following stages:
-
-1. Preparation:
-   - Retrieve `last_entry_id` of each region kvbackend.
-   - Choose regions that have a relatively small `last_entry_id` as candidate regions, which means we need to send a flush request to these regions.
-2. Communication:
-   - Send flush requests to candidate regions.
-3. Purge:
-   - Choose proper entry id to delete for each topic. The entry should be the smallest `last_entry_id - 1` among all regions. 
-   - Delete legacy entries in Kafka.
-   - Store the `last_purged_entry_id` in kvbackend. It should be locked to prevent other regions from replaying the purged entries.
-
-### After purge
-
-After purge, there may be some regions that have `last_entry_id` smaller than the entry we just deleted. It's legal since we only delete the entries that are not needed anymore.
-
-When restarting a region, it should query the `last_purged_entry_id` from metasrv and replay from `min(last_entry_id, last_purged_entry_id)`.
-
-### Error handling
-
-No persisted states are needed since all states are maintained in kvbackend.
-
-Retry when failed to retrieving metadata from kvbackend.
-
-# Alternatives
-
-Purge time can depend on the size of the WAL entries instead of a fixed period of time, which may be more efficient.

flake.lock

@@ -8,11 +8,11 @@
         "rust-analyzer-src": "rust-analyzer-src"
       },
       "locked": {
-        "lastModified": 1745735608,
-        "narHash": "sha256-L0jzm815XBFfF2wCFmR+M1CF+beIEFj6SxlqVKF59Ec=",
+        "lastModified": 1737613896,
+        "narHash": "sha256-ldqXIglq74C7yKMFUzrS9xMT/EVs26vZpOD68Sh7OcU=",
         "owner": "nix-community",
         "repo": "fenix",
-        "rev": "c39a78eba6ed2a022cc3218db90d485077101496",
+        "rev": "303a062fdd8e89f233db05868468975d17855d80",
         "type": "github"
       },
       "original": {
@@ -41,16 +41,16 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1748162331,
-        "narHash": "sha256-rqc2RKYTxP3tbjA+PB3VMRQNnjesrT0pEofXQTrMsS8=",
+        "lastModified": 1737569578,
+        "narHash": "sha256-6qY0pk2QmUtBT9Mywdvif0i/CLVgpCjMUn6g9vB+f3M=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "7c43f080a7f28b2774f3b3f43234ca11661bf334",
+        "rev": "47addd76727f42d351590c905d9d1905ca895b82",
         "type": "github"
       },
       "original": {
         "owner": "NixOS",
-        "ref": "nixos-25.05",
+        "ref": "nixos-24.11",
         "repo": "nixpkgs",
         "type": "github"
       }
@@ -65,11 +65,11 @@
     "rust-analyzer-src": {
       "flake": false,
       "locked": {
-        "lastModified": 1745694049,
-        "narHash": "sha256-fxvRYH/tS7hGQeg9zCVh5RBcSWT+JGJet7RA8Ss+rC0=",
+        "lastModified": 1737581772,
+        "narHash": "sha256-t1P2Pe3FAX9TlJsCZbmJ3wn+C4qr6aSMypAOu8WNsN0=",
         "owner": "rust-lang",
         "repo": "rust-analyzer",
-        "rev": "d8887c0758bbd2d5f752d5bd405d4491e90e7ed6",
+        "rev": "582af7ee9c8d84f5d534272fc7de9f292bd849be",
         "type": "github"
       },
       "original": {

flake.nix

@@ -2,7 +2,7 @@
   description = "Development environment flake";
 
   inputs = {
-    nixpkgs.url = "github:NixOS/nixpkgs/nixos-25.05";
+    nixpkgs.url = "github:NixOS/nixpkgs/nixos-24.11";
     fenix = {
       url = "github:nix-community/fenix";
       inputs.nixpkgs.follows = "nixpkgs";
@@ -21,7 +21,7 @@
         lib = nixpkgs.lib;
         rustToolchain = fenix.packages.${system}.fromToolchainName {
           name = (lib.importTOML ./rust-toolchain.toml).toolchain.channel;
-          sha256 = "sha256-tJJr8oqX3YD+ohhPK7jlt/7kvKBnBqJVjYtoFr520d4=";
+          sha256 = "sha256-f/CVA1EC61EWbh0SjaRNhLL0Ypx2ObupbzigZp8NmL4=";
         };
       in
       {
@@ -51,7 +51,6 @@
           ];
 
           LD_LIBRARY_PATH = pkgs.lib.makeLibraryPath buildInputs;
-          NIX_HARDENING_ENABLE = "";
         };
       });
 }

grafana/README.md

@@ -1,122 +1,61 @@
-# Grafana dashboards for GreptimeDB
+Grafana dashboard for GreptimeDB
+--------------------------------
 
-## Overview
+GreptimeDB's official Grafana dashboard.
 
-This repository contains Grafana dashboards for visualizing metrics and logs of GreptimeDB instances running in either cluster or standalone mode. **The Grafana version should be greater than 9.0**.
+Status notify: we are still working on this config. It's expected to change frequently in the recent days. Please feel free to submit your feedback and/or contribution to this dashboard 🤗
 
-We highly recommend using the self-monitoring feature provided by [GreptimeDB Operator](https://github.com/GrepTimeTeam/greptimedb-operator) to automatically collect metrics and logs from your GreptimeDB instances and store them in a dedicated GreptimeDB instance.
-
-- **Metrics Dashboards**
-
-  - `dashboards/metrics/cluster/dashboard.json`: The Grafana dashboard for the GreptimeDB cluster. Read the [dashboard.md](./dashboards/metrics/cluster/dashboard.md) for more details.
-
-  - `dashboards/metrics/standalone/dashboard.json`: The Grafana dashboard for the standalone GreptimeDB instance. **It's generated from the `cluster/dashboard.json` by removing the instance filter through the `make dashboards` command**. Read the [dashboard.md](./dashboards/metrics/standalone/dashboard.md) for more details.
-
-- **Logs Dashboard**
-
-  The `dashboards/logs/dashboard.json` provides a comprehensive Grafana dashboard for visualizing GreptimeDB logs. To utilize this dashboard effectively, you need to collect logs in JSON format from your GreptimeDB instances and store them in a dedicated GreptimeDB instance.
-
-  For proper integration, the logs table must adhere to the following schema design with the table name `_gt_logs`:
-
-  ```sql
-  CREATE TABLE IF NOT EXISTS `_gt_logs` (
-    `pod_ip` STRING NULL,
-    `namespace` STRING NULL,
-    `cluster` STRING NULL,
-    `file` STRING NULL,
-    `module_path` STRING NULL,
-    `level` STRING NULL,
-    `target` STRING NULL,
-    `role` STRING NULL,
-    `pod` STRING NULL SKIPPING INDEX WITH(granularity = '10240', type = 'BLOOM'),
-    `message` STRING NULL FULLTEXT INDEX WITH(analyzer = 'English', backend = 'bloom', case_sensitive = 'false'),
-    `err` STRING NULL FULLTEXT INDEX WITH(analyzer = 'English', backend = 'bloom', case_sensitive = 'false'),
-    `timestamp` TIMESTAMP(9) NOT NULL,
-    TIME INDEX (`timestamp`),
-    PRIMARY KEY (`level`, `target`, `role`)
-  )
-    ENGINE=mito
-  WITH (
-    append_mode = 'true'
-  )
-  ```
-
-## Development
-
-As GreptimeDB evolves rapidly, metrics may change over time. We welcome your feedback and contributions to improve these dashboards 🤗
-
-To modify the metrics dashboards, simply edit the `dashboards/metrics/cluster/dashboard.json` file and run the `make dashboards` command. This will automatically generate the updated `dashboards/metrics/standalone/dashboard.json` and other related files.
-
-For easier dashboard maintenance, we utilize the [`dac`](https://github.com/zyy17/dac) tool to generate human-readable intermediate dashboards and documentation:
-
-- `dashboards/metrics/cluster/dashboard.yaml`: The intermediate dashboard file for the GreptimeDB cluster.
-- `dashboards/metrics/standalone/dashboard.yaml`: The intermediate dashboard file for standalone GreptimeDB instances.
-
-## Data Sources
-
-The following data sources are used to fetch metrics and logs:
-
-- **`${metrics}`**: Prometheus data source for providing the GreptimeDB metrics.
-- **`${logs}`**: MySQL data source for providing the GreptimeDB logs.
-- **`${information_schema}`**: MySQL data source for providing the information schema of the current instance and used for the `overview` panel. It is the MySQL port of the current monitored instance.
-
-## Instance Filters
-
-To deploy the dashboards for multiple scenarios (K8s, bare metal, etc.), we prefer to use the `instance` label when filtering instances.
-
-Additionally, we recommend including the `pod` label in the legend to make it easier to identify each instance, even though this field will be empty in bare metal scenarios.
-
-For example, the following query is recommended:
-
-```promql
-sum(process_resident_memory_bytes{instance=~"$datanode"}) by (instance, pod)
-```
-
-And the legend will be like: `[{{instance}}]-[{{ pod }}]`.
-
-## Deployment
-
-### (Recommended) Helm Chart
-
-If you use the [Helm Chart](https://github.com/GreptimeTeam/helm-charts) to deploy a GreptimeDB cluster, you can enable self-monitoring by setting the following values in your Helm chart:
+If you use Helm [chart](https://github.com/GreptimeTeam/helm-charts) to deploy GreptimeDB cluster, you can enable self-monitoring by setting the following values in your Helm chart:
 
 - `monitoring.enabled=true`: Deploys a standalone GreptimeDB instance dedicated to monitoring the cluster;
 - `grafana.enabled=true`: Deploys Grafana and automatically imports the monitoring dashboard;
 
-The standalone GreptimeDB instance will collect metrics from your cluster, and the dashboard will be available in the Grafana UI. For detailed deployment instructions, please refer to our [Kubernetes deployment guide](https://docs.greptime.com/user-guide/deployments-administration/deploy-on-kubernetes/getting-started).
+The standalone GreptimeDB instance will collect metrics from your cluster and the dashboard will be available in the Grafana UI. For detailed deployment instructions, please refer to our [Kubernetes deployment guide](https://docs.greptime.com/nightly/user-guide/deployments/deploy-on-kubernetes/getting-started).
 
-### Self-host Prometheus and import dashboards manually
+# How to use
 
-1. **Configure Prometheus to scrape the cluster**
+## `greptimedb.json`
 
-   The following is an example configuration(**Please modify it according to your actual situation**):
+Open Grafana Dashboard page, choose `New` -> `Import`. And upload `greptimedb.json` file.
 
-    ```yml
-    # example config
-    # only to indicate how to assign labels to each target
-    # modify yours accordingly
-    scrape_configs:
-      - job_name: metasrv
-        static_configs:
-        - targets: ['<metasrv-ip>:<port>']
+## `greptimedb-cluster.json`
 
-      - job_name: datanode
-        static_configs:
-        - targets: ['<datanode0-ip>:<port>', '<datanode1-ip>:<port>', '<datanode2-ip>:<port>']
+This cluster dashboard provides a comprehensive view of incoming requests, response statuses, and internal activities such as flush and compaction, with a layered structure from frontend to datanode. Designed with a focus on alert functionality, its primary aim is to highlight any anomalies in metrics, allowing users to quickly pinpoint the cause of errors.
 
-      - job_name: frontend
-        static_configs:
-        - targets: ['<frontend-ip>:<port>']
-    ```
+We use Prometheus to scrape off metrics from nodes in GreptimeDB cluster, Grafana to visualize the diagram. Any compatible stack should work too.
 
-2. **Configure the data sources in Grafana**
+__Note__: This dashboard is still in an early stage of development. Any issue or advice on improvement is welcomed.
 
-   You need to add two data sources in Grafana:
+### Configuration
 
-   - Prometheus: It is the Prometheus instance that scrapes the GreptimeDB metrics.
-   - Information Schema: It is the MySQL port of the current monitored instance. The dashboard will use this datasource to show the information schema of the current instance.
+Please ensure the following configuration before importing the dashboard into Grafana.
 
-3. **Import the dashboards based on your deployment scenario**
+__1. Prometheus scrape config__
 
-   - **Cluster**: Import the `dashboards/metrics/cluster/dashboard.json` dashboard.
-   - **Standalone**: Import the `dashboards/metrics/standalone/dashboard.json` dashboard.
+Configure Prometheus to scrape the cluster.
+
+```yml
+# example config
+# only to indicate how to assign labels to each target
+# modify yours accordingly
+scrape_configs:
+  - job_name: metasrv
+    static_configs:
+    - targets: ['<metasrv-ip>:<port>']
+
+  - job_name: datanode
+    static_configs:
+    - targets: ['<datanode0-ip>:<port>', '<datanode1-ip>:<port>', '<datanode2-ip>:<port>']
+
+  - job_name: frontend
+    static_configs:
+    - targets: ['<frontend-ip>:<port>']
+```
+
+__2. Grafana config__
+
+Create a Prometheus data source in Grafana before using this dashboard. We use `datasource` as a variable in Grafana dashboard so that multiple environments are supported.
+
+### Usage
+
+Use `datasource` or `instance` on the upper-left corner to filter data from certain node.

grafana/check.sh

@@ -0,0 +1,19 @@
+#!/usr/bin/env bash
+
+BASEDIR=$(dirname "$0")
+
+# Use jq to check for panels with empty or missing descriptions
+invalid_panels=$(cat $BASEDIR/greptimedb-cluster.json | jq -r '
+  .panels[]
+  | select((.type == "stats" or .type == "timeseries") and (.description == "" or .description == null))
+')
+
+# Check if any invalid panels were found
+if [[ -n "$invalid_panels" ]]; then
+  echo "Error: The following panels have empty or missing descriptions:"
+  echo "$invalid_panels"
+  exit 1
+else
+  echo "All panels with type 'stats' or 'timeseries' have valid descriptions."
+  exit 0
+fi

grafana/dashboards/logs/dashboard.json

@@ -1,292 +0,0 @@
-{
-  "annotations": {
-    "list": [
-      {
-        "builtIn": 1,
-        "datasource": {
-          "type": "grafana",
-          "uid": "-- Grafana --"
-        },
-        "enable": true,
-        "hide": true,
-        "iconColor": "rgba(0, 211, 255, 1)",
-        "name": "Annotations & Alerts",
-        "type": "dashboard"
-      }
-    ]
-  },
-  "editable": true,
-  "fiscalYearStartMonth": 0,
-  "graphTooltip": 0,
-  "id": 12,
-  "links": [],
-  "panels": [
-    {
-      "datasource": {
-        "default": false,
-        "type": "mysql",
-        "uid": "${datasource}"
-      },
-      "fieldConfig": {
-        "defaults": {},
-        "overrides": []
-      },
-      "gridPos": {
-        "h": 20,
-        "w": 24,
-        "x": 0,
-        "y": 0
-      },
-      "id": 1,
-      "options": {
-        "dedupStrategy": "none",
-        "enableInfiniteScrolling": true,
-        "enableLogDetails": true,
-        "prettifyLogMessage": false,
-        "showCommonLabels": false,
-        "showLabels": false,
-        "showTime": true,
-        "sortOrder": "Descending",
-        "wrapLogMessage": false
-      },
-      "pluginVersion": "11.6.0",
-      "targets": [
-        {
-          "dataset": "greptime_private",
-          "datasource": {
-            "type": "mysql",
-            "uid": "${datasource}"
-          },
-          "editorMode": "code",
-          "format": "table",
-          "rawQuery": true,
-          "rawSql": "SELECT `timestamp`, CONCAT('[', `level`, ']', ' ', '<', `target`, '>', ' ', `message`),\n  `role`,\n  `pod`,\n  `pod_ip`,\n  `namespace`,\n  `cluster`,\n  `err`,\n  `file`,\n  `module_path`\nFROM\n  `_gt_logs`\nWHERE\n  (\n    \"$level\" = \"'all'\"\n    OR `level` IN ($level)\n  ) \n  AND (\n    \"$role\" = \"'all'\"\n    OR `role` IN ($role)\n  )\n  AND (\n    \"$pod\" = \"\"\n    OR `pod` = '$pod'\n  )\n  AND (\n    \"$target\" = \"\"\n    OR `target` = '$target'\n  )\n  AND (\n    \"$search\" = \"\"\n    OR matches_term(`message`, '$search')\n  )\n  AND (\n    \"$exclude\" = \"\"\n    OR NOT matches_term(`message`, '$exclude')\n  )\n  AND $__timeFilter(`timestamp`)\nORDER BY `timestamp` DESC\nLIMIT $limit;\n",
-          "refId": "A",
-          "sql": {
-            "columns": [
-              {
-                "parameters": [],
-                "type": "function"
-              }
-            ],
-            "groupBy": [
-              {
-                "property": {
-                  "type": "string"
-                },
-                "type": "groupBy"
-              }
-            ],
-            "limit": 50
-          }
-        }
-      ],
-      "title": "Logs",
-      "type": "logs"
-    }
-  ],
-  "preload": false,
-  "refresh": "",
-  "schemaVersion": 41,
-  "tags": [],
-  "templating": {
-    "list": [
-      {
-        "current": {
-          "text": "logs",
-          "value": "P98F38F12DB221A8C"
-        },
-        "includeAll": false,
-        "name": "datasource",
-        "options": [],
-        "query": "mysql",
-        "refresh": 1,
-        "regex": "",
-        "type": "datasource"
-      },
-      {
-        "allValue": "'all'",
-        "current": {
-          "text": [
-            "$__all"
-          ],
-          "value": [
-            "$__all"
-          ]
-        },
-        "includeAll": true,
-        "label": "level",
-        "multi": true,
-        "name": "level",
-        "options": [
-          {
-            "selected": false,
-            "text": "INFO",
-            "value": "INFO"
-          },
-          {
-            "selected": false,
-            "text": "ERROR",
-            "value": "ERROR"
-          },
-          {
-            "selected": false,
-            "text": "WARN",
-            "value": "WARN"
-          },
-          {
-            "selected": false,
-            "text": "DEBUG",
-            "value": "DEBUG"
-          },
-          {
-            "selected": false,
-            "text": "TRACE",
-            "value": "TRACE"
-          }
-        ],
-        "query": "INFO,ERROR,WARN,DEBUG,TRACE",
-        "type": "custom"
-      },
-      {
-        "allValue": "'all'",
-        "current": {
-          "text": [
-            "$__all"
-          ],
-          "value": [
-            "$__all"
-          ]
-        },
-        "includeAll": true,
-        "label": "role",
-        "multi": true,
-        "name": "role",
-        "options": [
-          {
-            "selected": false,
-            "text": "datanode",
-            "value": "datanode"
-          },
-          {
-            "selected": false,
-            "text": "frontend",
-            "value": "frontend"
-          },
-          {
-            "selected": false,
-            "text": "meta",
-            "value": "meta"
-          }
-        ],
-        "query": "datanode,frontend,meta",
-        "type": "custom"
-      },
-      {
-        "current": {
-          "text": "",
-          "value": ""
-        },
-        "label": "pod",
-        "name": "pod",
-        "options": [
-          {
-            "selected": true,
-            "text": "",
-            "value": ""
-          }
-        ],
-        "query": "",
-        "type": "textbox"
-      },
-      {
-        "current": {
-          "text": "",
-          "value": ""
-        },
-        "label": "target",
-        "name": "target",
-        "options": [
-          {
-            "selected": true,
-            "text": "",
-            "value": ""
-          }
-        ],
-        "query": "",
-        "type": "textbox"
-      },
-      {
-        "current": {
-          "text": "",
-          "value": ""
-        },
-        "label": "search",
-        "name": "search",
-        "options": [
-          {
-            "selected": true,
-            "text": "",
-            "value": ""
-          }
-        ],
-        "query": "",
-        "type": "textbox"
-      },
-      {
-        "current": {
-          "text": "",
-          "value": ""
-        },
-        "label": "exclude",
-        "name": "exclude",
-        "options": [
-          {
-            "selected": true,
-            "text": "",
-            "value": ""
-          }
-        ],
-        "query": "",
-        "type": "textbox"
-      },
-      {
-        "current": {
-          "text": "2000",
-          "value": "2000"
-        },
-        "includeAll": false,
-        "label": "limit",
-        "name": "limit",
-        "options": [
-          {
-            "selected": true,
-            "text": "2000",
-            "value": "2000"
-          },
-          {
-            "selected": false,
-            "text": "5000",
-            "value": "5000"
-          },
-          {
-            "selected": false,
-            "text": "8000",
-            "value": "8000"
-          }
-        ],
-        "query": "2000,5000,8000",
-        "type": "custom"
-      }
-    ]
-  },
-  "time": {
-    "from": "now-6h",
-    "to": "now"
-  },
-  "timepicker": {},
-  "timezone": "browser",
-  "title": "GreptimeDB Logs",
-  "uid": "edx5veo4rd3wge2",
-  "version": 1
-}

grafana/dashboards/metrics/cluster/dashboard.md

@@ -1,112 +0,0 @@
-# Overview
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Uptime | `time() - process_start_time_seconds` | `stat` | The start time of GreptimeDB. | `prometheus` | `s` | `__auto` |
-| Version | `SELECT pkg_version FROM information_schema.build_info` | `stat` | GreptimeDB version. | `mysql` | -- | -- |
-| Total Ingestion Rate | `sum(rate(greptime_table_operator_ingest_rows[$__rate_interval]))` | `stat` | Total ingestion rate. | `prometheus` | `rowsps` | `__auto` |
-| Total Storage Size | `select SUM(disk_size) from information_schema.region_statistics;` | `stat` | Total number of data file size. | `mysql` | `decbytes` | -- |
-| Total Rows | `select SUM(region_rows) from information_schema.region_statistics;` | `stat` | Total number of data rows in the cluster. Calculated by sum of rows from each region. | `mysql` | `sishort` | -- |
-| Deployment | `SELECT count(*) as datanode FROM information_schema.cluster_info WHERE peer_type = 'DATANODE';`<br/>`SELECT count(*) as frontend FROM information_schema.cluster_info WHERE peer_type = 'FRONTEND';`<br/>`SELECT count(*) as metasrv FROM information_schema.cluster_info WHERE peer_type = 'METASRV';`<br/>`SELECT count(*) as flownode FROM information_schema.cluster_info WHERE peer_type = 'FLOWNODE';` | `stat` | The deployment topology of GreptimeDB. | `mysql` | -- | -- |
-| Database Resources | `SELECT COUNT(*) as databases FROM information_schema.schemata WHERE schema_name NOT IN ('greptime_private', 'information_schema')`<br/>`SELECT COUNT(*) as tables FROM information_schema.tables WHERE table_schema != 'information_schema'`<br/>`SELECT COUNT(region_id) as regions FROM information_schema.region_peers`<br/>`SELECT COUNT(*) as flows FROM information_schema.flows` | `stat` | The number of the key resources in GreptimeDB. | `mysql` | -- | -- |
-| Data Size | `SELECT SUM(memtable_size) * 0.42825 as WAL FROM information_schema.region_statistics;`<br/>`SELECT SUM(index_size) as index FROM information_schema.region_statistics;`<br/>`SELECT SUM(manifest_size) as manifest FROM information_schema.region_statistics;` | `stat` | The data size of wal/index/manifest in the GreptimeDB. | `mysql` | `decbytes` | -- |
-# Ingestion
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Total Ingestion Rate | `sum(rate(greptime_table_operator_ingest_rows{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | Total ingestion rate.<br/><br/>Here we listed 3 primary protocols:<br/><br/>- Prometheus remote write<br/>- Greptime's gRPC API (when using our ingest SDK)<br/>- Log ingestion http API<br/> | `prometheus` | `rowsps` | `ingestion` |
-| Ingestion Rate by Type | `sum(rate(greptime_servers_http_logs_ingestion_counter[$__rate_interval]))`<br/>`sum(rate(greptime_servers_prometheus_remote_write_samples[$__rate_interval]))` | `timeseries` | Total ingestion rate.<br/><br/>Here we listed 3 primary protocols:<br/><br/>- Prometheus remote write<br/>- Greptime's gRPC API (when using our ingest SDK)<br/>- Log ingestion http API<br/> | `prometheus` | `rowsps` | `http-logs` |
-# Queries
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Total Query Rate | `sum (rate(greptime_servers_mysql_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))`<br/>`sum (rate(greptime_servers_postgres_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))`<br/>`sum (rate(greptime_servers_http_promql_elapsed_counte{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | Total rate of query API calls by protocol. This metric is collected from frontends.<br/><br/>Here we listed 3 main protocols:<br/>- MySQL<br/>- Postgres<br/>- Prometheus API<br/><br/>Note that there are some other minor query APIs like /sql are not included | `prometheus` | `reqps` | `mysql` |
-# Resources
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Datanode Memory per Instance | `sum(process_resident_memory_bytes{instance=~"$datanode"}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{instance}}]-[{{ pod }}]` |
-| Datanode CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{instance=~"$datanode"}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
-| Frontend Memory per Instance | `sum(process_resident_memory_bytes{instance=~"$frontend"}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]` |
-| Frontend CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{instance=~"$frontend"}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]-cpu` |
-| Metasrv Memory per Instance | `sum(process_resident_memory_bytes{instance=~"$metasrv"}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]-resident` |
-| Metasrv CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{instance=~"$metasrv"}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
-| Flownode Memory per Instance | `sum(process_resident_memory_bytes{instance=~"$flownode"}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]` |
-| Flownode CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{instance=~"$flownode"}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
-# Frontend Requests
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| HTTP QPS per Instance | `sum by(instance, pod, path, method, code) (rate(greptime_servers_http_requests_elapsed_count{instance=~"$frontend",path!~"/health\|/metrics"}[$__rate_interval]))` | `timeseries` | HTTP QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]` |
-| HTTP P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, path, method, code) (rate(greptime_servers_http_requests_elapsed_bucket{instance=~"$frontend",path!~"/health\|/metrics"}[$__rate_interval])))` | `timeseries` | HTTP P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99` |
-| gRPC QPS per Instance | `sum by(instance, pod, path, code) (rate(greptime_servers_grpc_requests_elapsed_count{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | gRPC QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{code}}]` |
-| gRPC P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, path, code) (rate(greptime_servers_grpc_requests_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))` | `timeseries` | gRPC P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99` |
-| MySQL QPS per Instance | `sum by(pod, instance)(rate(greptime_servers_mysql_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | MySQL QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]` |
-| MySQL P99 per Instance | `histogram_quantile(0.99, sum by(pod, instance, le) (rate(greptime_servers_mysql_query_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))` | `timeseries` | MySQL P99 per Instance. | `prometheus` | `s` | `[{{ instance }}]-[{{ pod }}]-p99` |
-| PostgreSQL QPS per Instance | `sum by(pod, instance)(rate(greptime_servers_postgres_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | PostgreSQL QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]` |
-| PostgreSQL P99 per Instance | `histogram_quantile(0.99, sum by(pod,instance,le) (rate(greptime_servers_postgres_query_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))` | `timeseries` | PostgreSQL P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-p99` |
-# Frontend to Datanode
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Ingest Rows per Instance | `sum by(instance, pod)(rate(greptime_table_operator_ingest_rows{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | Ingestion rate by row as in each frontend | `prometheus` | `rowsps` | `[{{instance}}]-[{{pod}}]` |
-| Region Call QPS per Instance | `sum by(instance, pod, request_type) (rate(greptime_grpc_region_request_count{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | Region Call QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{request_type}}]` |
-| Region Call P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, request_type) (rate(greptime_grpc_region_request_bucket{instance=~"$frontend"}[$__rate_interval])))` | `timeseries` | Region Call P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{request_type}}]` |
-| Frontend Handle Bulk Insert Elapsed Time  | `sum by(instance, pod, stage) (rate(greptime_table_operator_handle_bulk_insert_sum[$__rate_interval]))/sum by(instance, pod, stage) (rate(greptime_table_operator_handle_bulk_insert_count[$__rate_interval]))`<br/>`histogram_quantile(0.99, sum by(instance, pod, stage, le) (rate(greptime_table_operator_handle_bulk_insert_bucket[$__rate_interval])))` | `timeseries` | Per-stage time for frontend to handle bulk insert requests | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-AVG` |
-# Mito Engine
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Request OPS per Instance | `sum by(instance, pod, type) (rate(greptime_mito_handle_request_elapsed_count{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Request QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
-| Request P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, type) (rate(greptime_mito_handle_request_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))` | `timeseries` | Request P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
-| Write Buffer per Instance | `greptime_mito_write_buffer_bytes{instance=~"$datanode"}` | `timeseries` | Write Buffer per Instance. | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]` |
-| Write Rows per Instance | `sum by (instance, pod) (rate(greptime_mito_write_rows_total{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Ingestion size by row counts. | `prometheus` | `rowsps` | `[{{instance}}]-[{{pod}}]` |
-| Flush OPS per Instance | `sum by(instance, pod, reason) (rate(greptime_mito_flush_requests_total{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Flush QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{reason}}]` |
-| Write Stall per Instance | `sum by(instance, pod) (greptime_mito_write_stall_total{instance=~"$datanode"})` | `timeseries` | Write Stall per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]` |
-| Read Stage OPS per Instance | `sum by(instance, pod) (rate(greptime_mito_read_stage_elapsed_count{instance=~"$datanode", stage="total"}[$__rate_interval]))` | `timeseries` | Read Stage OPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]` |
-| Read Stage P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_read_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))` | `timeseries` | Read Stage P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]` |
-| Write Stage P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_write_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))` | `timeseries` | Write Stage P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]` |
-| Compaction OPS per Instance | `sum by(instance, pod) (rate(greptime_mito_compaction_total_elapsed_count{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Compaction OPS per Instance. | `prometheus` | `ops` | `[{{ instance }}]-[{{pod}}]` |
-| Compaction Elapsed Time per Instance by Stage | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_compaction_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))`<br/>`sum by(instance, pod, stage) (rate(greptime_mito_compaction_stage_elapsed_sum{instance=~"$datanode"}[$__rate_interval]))/sum by(instance, pod, stage) (rate(greptime_mito_compaction_stage_elapsed_count{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Compaction latency by stage | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-p99` |
-| Compaction P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le,stage) (rate(greptime_mito_compaction_total_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))` | `timeseries` | Compaction P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-compaction` |
-| WAL write size | `histogram_quantile(0.95, sum by(le,instance, pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))`<br/>`histogram_quantile(0.99, sum by(le,instance,pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))`<br/>`sum by (instance, pod)(rate(raft_engine_write_size_sum[$__rate_interval]))` | `timeseries` | Write-ahead logs write size as bytes. This chart includes stats of p95 and p99 size by instance, total WAL write rate. | `prometheus` | `bytes` | `[{{instance}}]-[{{pod}}]-req-size-p95` |
-| Cached Bytes per Instance | `greptime_mito_cache_bytes{instance=~"$datanode"}` | `timeseries` | Cached Bytes per Instance. | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
-| Inflight Compaction | `greptime_mito_inflight_compaction_count` | `timeseries` | Ongoing compaction task count | `prometheus` | `none` | `[{{instance}}]-[{{pod}}]` |
-| WAL sync duration seconds | `histogram_quantile(0.99, sum by(le, type, node, instance, pod) (rate(raft_engine_sync_log_duration_seconds_bucket[$__rate_interval])))` | `timeseries` | Raft engine (local disk) log store sync latency, p99 | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-p99` |
-| Log Store op duration seconds | `histogram_quantile(0.99, sum by(le,logstore,optype,instance, pod) (rate(greptime_logstore_op_elapsed_bucket[$__rate_interval])))` | `timeseries` | Write-ahead log operations latency at p99 | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{logstore}}]-[{{optype}}]-p99` |
-| Inflight Flush | `greptime_mito_inflight_flush_count` | `timeseries` | Ongoing flush task count | `prometheus` | `none` | `[{{instance}}]-[{{pod}}]` |
-| Compaction Input/Output Bytes | `sum by(instance, pod) (greptime_mito_compaction_input_bytes)`<br/>`sum by(instance, pod) (greptime_mito_compaction_output_bytes)` | `timeseries` | Compaction oinput output bytes | `prometheus` | `bytes` | `[{{instance}}]-[{{pod}}]-input` |
-| Region Worker Handle Bulk Insert Requests | `histogram_quantile(0.95, sum by(le,instance, stage, pod) (rate(greptime_region_worker_handle_write_bucket[$__rate_interval])))`<br/>`sum by(instance, stage, pod) (rate(greptime_region_worker_handle_write_sum[$__rate_interval]))/sum by(instance, stage, pod) (rate(greptime_region_worker_handle_write_count[$__rate_interval]))` | `timeseries` | Per-stage elapsed time for region worker to handle bulk insert region requests. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-P95` |
-| Active Series and Field Builders Count | `sum by(instance, pod) (greptime_mito_memtable_active_series_count)`<br/>`sum by(instance, pod) (greptime_mito_memtable_field_builder_count)` | `timeseries` | Compaction oinput output bytes | `prometheus` | `none` | `[{{instance}}]-[{{pod}}]-series` |
-| Region Worker Convert Requests | `histogram_quantile(0.95, sum by(le, instance, stage, pod) (rate(greptime_datanode_convert_region_request_bucket[$__rate_interval])))`<br/>`sum by(le,instance, stage, pod) (rate(greptime_datanode_convert_region_request_sum[$__rate_interval]))/sum by(le,instance, stage, pod) (rate(greptime_datanode_convert_region_request_count[$__rate_interval]))` | `timeseries` | Per-stage elapsed time for region worker to decode requests. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-P95` |
-# OpenDAL
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| QPS per Instance | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
-| Read QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="read"}[$__rate_interval]))` | `timeseries` | Read QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
-| Read P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode",operation="read"}[$__rate_interval])))` | `timeseries` | Read P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-{{scheme}}` |
-| Write QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="write"}[$__rate_interval]))` | `timeseries` | Write QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-{{scheme}}` |
-| Write P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation="write"}[$__rate_interval])))` | `timeseries` | Write P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
-| List QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="list"}[$__rate_interval]))` | `timeseries` | List QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
-| List P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation="list"}[$__rate_interval])))` | `timeseries` | List P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
-| Other Requests per Instance | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode",operation!~"read\|write\|list\|stat"}[$__rate_interval]))` | `timeseries` | Other Requests per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
-| Other Request P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme, operation) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation!~"read\|write\|list"}[$__rate_interval])))` | `timeseries` | Other Request P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
-| Opendal traffic | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_bytes_sum{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Total traffic as in bytes by instance and operation | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
-| OpenDAL errors per Instance | `sum by(instance, pod, scheme, operation, error) (rate(opendal_operation_errors_total{instance=~"$datanode", error!="NotFound"}[$__rate_interval]))` | `timeseries` | OpenDAL error counts per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]-[{{error}}]` |
-# Metasrv
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Region migration datanode | `greptime_meta_region_migration_stat{datanode_type="src"}`<br/>`greptime_meta_region_migration_stat{datanode_type="desc"}` | `status-history` | Counter of region migration by source and destination | `prometheus` | -- | `from-datanode-{{datanode_id}}` |
-| Region migration error | `greptime_meta_region_migration_error` | `timeseries` | Counter of region migration error | `prometheus` | `none` | `{{pod}}-{{state}}-{{error_type}}` |
-| Datanode load | `greptime_datanode_load` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `binBps` | `Datanode-{{datanode_id}}-writeload` |
-| Rate of SQL Executions (RDS) | `rate(greptime_meta_rds_pg_sql_execute_elapsed_ms_count[$__rate_interval])` | `timeseries` | Displays the rate of SQL executions processed by the Meta service using the RDS backend. | `prometheus` | `none` | `{{pod}} {{op}} {{type}} {{result}} ` |
-| SQL Execution Latency (RDS) | `histogram_quantile(0.90, sum by(pod, op, type, result, le) (rate(greptime_meta_rds_pg_sql_execute_elapsed_ms_bucket[$__rate_interval])))` | `timeseries` | Measures the response time of SQL executions via the RDS backend.  | `prometheus` | `ms` | `{{pod}} {{op}} {{type}} {{result}} p90` |
-| Handler Execution Latency | `histogram_quantile(0.90, sum by(pod, le, name) (
-  rate(greptime_meta_handler_execute_bucket[$__rate_interval])
-))` | `timeseries` | Shows latency of Meta handlers by pod and handler name, useful for monitoring handler performance and detecting latency spikes.<br/> | `prometheus` | `s` | `{{pod}} {{name}} p90` |
-| Heartbeat Packet Size | `histogram_quantile(0.9, sum by(pod, le) (greptime_meta_heartbeat_stat_memory_size_bucket))` | `timeseries` | Shows p90 heartbeat message sizes, helping track network usage and identify anomalies in heartbeat payload.<br/> | `prometheus` | `bytes` | `{{pod}}` |
-| Meta Heartbeat Receive Rate | `rate(greptime_meta_heartbeat_rate[$__rate_interval])` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `s` | `{{pod}}` |
-| Meta KV Ops Latency | `histogram_quantile(0.99, sum by(pod, le, op, target) (greptime_meta_kv_request_elapsed_bucket))` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `s` | `{{pod}}-{{op}} p99` |
-| Rate of meta KV Ops | `rate(greptime_meta_kv_request_elapsed_count[$__rate_interval])` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `none` | `{{pod}}-{{op}} p99` |
-| DDL Latency | `histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_tables_bucket))`<br/>`histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_table))`<br/>`histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_view))`<br/>`histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_flow))`<br/>`histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_drop_table))`<br/>`histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_alter_table))` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `s` | `CreateLogicalTables-{{step}} p90` |
-# Flownode
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Flow Ingest / Output Rate | `sum by(instance, pod, direction) (rate(greptime_flow_processed_rows[$__rate_interval]))` | `timeseries` | Flow Ingest / Output Rate. | `prometheus` | -- | `[{{pod}}]-[{{instance}}]-[{{direction}}]` |
-| Flow Ingest Latency | `histogram_quantile(0.95, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))`<br/>`histogram_quantile(0.99, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))` | `timeseries` | Flow Ingest Latency. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-p95` |
-| Flow Operation Latency | `histogram_quantile(0.95, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))`<br/>`histogram_quantile(0.99, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))` | `timeseries` | Flow Operation Latency. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{type}}]-p95` |
-| Flow Buffer Size per Instance | `greptime_flow_input_buf_size` | `timeseries` | Flow Buffer Size per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}]` |
-| Flow Processing Error per Instance | `sum by(instance,pod,code) (rate(greptime_flow_errors[$__rate_interval]))` | `timeseries` | Flow Processing Error per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{code}}]` |

grafana/dashboards/metrics/cluster/dashboard.yaml

@@ -1,958 +0,0 @@
-groups:
-    - title: Overview
-      panels:
-        - title: Uptime
-          type: stat
-          description: The start time of GreptimeDB.
-          unit: s
-          queries:
-            - expr: time() - process_start_time_seconds
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: __auto
-        - title: Version
-          type: stat
-          description: GreptimeDB version.
-          queries:
-            - expr: SELECT pkg_version FROM information_schema.build_info
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-        - title: Total Ingestion Rate
-          type: stat
-          description: Total ingestion rate.
-          unit: rowsps
-          queries:
-            - expr: sum(rate(greptime_table_operator_ingest_rows[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: __auto
-        - title: Total Storage Size
-          type: stat
-          description: Total number of data file size.
-          unit: decbytes
-          queries:
-            - expr: select SUM(disk_size) from information_schema.region_statistics;
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-        - title: Total Rows
-          type: stat
-          description: Total number of data rows in the cluster. Calculated by sum of rows from each region.
-          unit: sishort
-          queries:
-            - expr: select SUM(region_rows) from information_schema.region_statistics;
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-        - title: Deployment
-          type: stat
-          description: The deployment topology of GreptimeDB.
-          queries:
-            - expr: SELECT count(*) as datanode FROM information_schema.cluster_info WHERE peer_type = 'DATANODE';
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT count(*) as frontend FROM information_schema.cluster_info WHERE peer_type = 'FRONTEND';
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT count(*) as metasrv FROM information_schema.cluster_info WHERE peer_type = 'METASRV';
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT count(*) as flownode FROM information_schema.cluster_info WHERE peer_type = 'FLOWNODE';
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-        - title: Database Resources
-          type: stat
-          description: The number of the key resources in GreptimeDB.
-          queries:
-            - expr: SELECT COUNT(*) as databases FROM information_schema.schemata WHERE schema_name NOT IN ('greptime_private', 'information_schema')
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT COUNT(*) as tables FROM information_schema.tables WHERE table_schema != 'information_schema'
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT COUNT(region_id) as regions FROM information_schema.region_peers
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT COUNT(*) as flows FROM information_schema.flows
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-        - title: Data Size
-          type: stat
-          description: The data size of wal/index/manifest in the GreptimeDB.
-          unit: decbytes
-          queries:
-            - expr: SELECT SUM(memtable_size) * 0.42825 as WAL FROM information_schema.region_statistics;
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT SUM(index_size) as index FROM information_schema.region_statistics;
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT SUM(manifest_size) as manifest FROM information_schema.region_statistics;
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-    - title: Ingestion
-      panels:
-        - title: Total Ingestion Rate
-          type: timeseries
-          description: |
-            Total ingestion rate.
-
-            Here we listed 3 primary protocols:
-
-            - Prometheus remote write
-            - Greptime's gRPC API (when using our ingest SDK)
-            - Log ingestion http API
-          unit: rowsps
-          queries:
-            - expr: sum(rate(greptime_table_operator_ingest_rows{instance=~"$frontend"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: ingestion
-        - title: Ingestion Rate by Type
-          type: timeseries
-          description: |
-            Total ingestion rate.
-
-            Here we listed 3 primary protocols:
-
-            - Prometheus remote write
-            - Greptime's gRPC API (when using our ingest SDK)
-            - Log ingestion http API
-          unit: rowsps
-          queries:
-            - expr: sum(rate(greptime_servers_http_logs_ingestion_counter[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: http-logs
-            - expr: sum(rate(greptime_servers_prometheus_remote_write_samples[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: prometheus-remote-write
-    - title: Queries
-      panels:
-        - title: Total Query Rate
-          type: timeseries
-          description: |-
-            Total rate of query API calls by protocol. This metric is collected from frontends.
-
-            Here we listed 3 main protocols:
-            - MySQL
-            - Postgres
-            - Prometheus API
-
-            Note that there are some other minor query APIs like /sql are not included
-          unit: reqps
-          queries:
-            - expr: sum (rate(greptime_servers_mysql_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: mysql
-            - expr: sum (rate(greptime_servers_postgres_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: pg
-            - expr: sum (rate(greptime_servers_http_promql_elapsed_counte{instance=~"$frontend"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: promql
-    - title: Resources
-      panels:
-        - title: Datanode Memory per Instance
-          type: timeseries
-          description: Current memory usage by instance
-          unit: decbytes
-          queries:
-            - expr: sum(process_resident_memory_bytes{instance=~"$datanode"}) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{ pod }}]'
-        - title: Datanode CPU Usage per Instance
-          type: timeseries
-          description: Current cpu usage by instance
-          unit: none
-          queries:
-            - expr: sum(rate(process_cpu_seconds_total{instance=~"$datanode"}[$__rate_interval]) * 1000) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]'
-        - title: Frontend Memory per Instance
-          type: timeseries
-          description: Current memory usage by instance
-          unit: decbytes
-          queries:
-            - expr: sum(process_resident_memory_bytes{instance=~"$frontend"}) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]'
-        - title: Frontend CPU Usage per Instance
-          type: timeseries
-          description: Current cpu usage by instance
-          unit: none
-          queries:
-            - expr: sum(rate(process_cpu_seconds_total{instance=~"$frontend"}[$__rate_interval]) * 1000) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]-cpu'
-        - title: Metasrv Memory per Instance
-          type: timeseries
-          description: Current memory usage by instance
-          unit: decbytes
-          queries:
-            - expr: sum(process_resident_memory_bytes{instance=~"$metasrv"}) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]-resident'
-        - title: Metasrv CPU Usage per Instance
-          type: timeseries
-          description: Current cpu usage by instance
-          unit: none
-          queries:
-            - expr: sum(rate(process_cpu_seconds_total{instance=~"$metasrv"}[$__rate_interval]) * 1000) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]'
-        - title: Flownode Memory per Instance
-          type: timeseries
-          description: Current memory usage by instance
-          unit: decbytes
-          queries:
-            - expr: sum(process_resident_memory_bytes{instance=~"$flownode"}) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]'
-        - title: Flownode CPU Usage per Instance
-          type: timeseries
-          description: Current cpu usage by instance
-          unit: none
-          queries:
-            - expr: sum(rate(process_cpu_seconds_total{instance=~"$flownode"}[$__rate_interval]) * 1000) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]'
-    - title: Frontend Requests
-      panels:
-        - title: HTTP QPS per Instance
-          type: timeseries
-          description: HTTP QPS per Instance.
-          unit: reqps
-          queries:
-            - expr: sum by(instance, pod, path, method, code) (rate(greptime_servers_http_requests_elapsed_count{instance=~"$frontend",path!~"/health|/metrics"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]'
-        - title: HTTP P99 per Instance
-          type: timeseries
-          description: HTTP P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, path, method, code) (rate(greptime_servers_http_requests_elapsed_bucket{instance=~"$frontend",path!~"/health|/metrics"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99'
-        - title: gRPC QPS per Instance
-          type: timeseries
-          description: gRPC QPS per Instance.
-          unit: reqps
-          queries:
-            - expr: sum by(instance, pod, path, code) (rate(greptime_servers_grpc_requests_elapsed_count{instance=~"$frontend"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{code}}]'
-        - title: gRPC P99 per Instance
-          type: timeseries
-          description: gRPC P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, path, code) (rate(greptime_servers_grpc_requests_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99'
-        - title: MySQL QPS per Instance
-          type: timeseries
-          description: MySQL QPS per Instance.
-          unit: reqps
-          queries:
-            - expr: sum by(pod, instance)(rate(greptime_servers_mysql_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: MySQL P99 per Instance
-          type: timeseries
-          description: MySQL P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(pod, instance, le) (rate(greptime_servers_mysql_query_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]-p99'
-        - title: PostgreSQL QPS per Instance
-          type: timeseries
-          description: PostgreSQL QPS per Instance.
-          unit: reqps
-          queries:
-            - expr: sum by(pod, instance)(rate(greptime_servers_postgres_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: PostgreSQL P99 per Instance
-          type: timeseries
-          description: PostgreSQL P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(pod,instance,le) (rate(greptime_servers_postgres_query_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-p99'
-    - title: Frontend to Datanode
-      panels:
-        - title: Ingest Rows per Instance
-          type: timeseries
-          description: Ingestion rate by row as in each frontend
-          unit: rowsps
-          queries:
-            - expr: sum by(instance, pod)(rate(greptime_table_operator_ingest_rows{instance=~"$frontend"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Region Call QPS per Instance
-          type: timeseries
-          description: Region Call QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, request_type) (rate(greptime_grpc_region_request_count{instance=~"$frontend"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{request_type}}]'
-        - title: Region Call P99 per Instance
-          type: timeseries
-          description: Region Call P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, request_type) (rate(greptime_grpc_region_request_bucket{instance=~"$frontend"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{request_type}}]'
-        - title: 'Frontend Handle Bulk Insert Elapsed Time '
-          type: timeseries
-          description: Per-stage time for frontend to handle bulk insert requests
-          unit: s
-          queries:
-            - expr: sum by(instance, pod, stage) (rate(greptime_table_operator_handle_bulk_insert_sum[$__rate_interval]))/sum by(instance, pod, stage) (rate(greptime_table_operator_handle_bulk_insert_count[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-AVG'
-            - expr: histogram_quantile(0.99, sum by(instance, pod, stage, le) (rate(greptime_table_operator_handle_bulk_insert_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-P95'
-    - title: Mito Engine
-      panels:
-        - title: Request OPS per Instance
-          type: timeseries
-          description: Request QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, type) (rate(greptime_mito_handle_request_elapsed_count{instance=~"$datanode"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
-        - title: Request P99 per Instance
-          type: timeseries
-          description: Request P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, type) (rate(greptime_mito_handle_request_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
-        - title: Write Buffer per Instance
-          type: timeseries
-          description: Write Buffer per Instance.
-          unit: decbytes
-          queries:
-            - expr: greptime_mito_write_buffer_bytes{instance=~"$datanode"}
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Write Rows per Instance
-          type: timeseries
-          description: Ingestion size by row counts.
-          unit: rowsps
-          queries:
-            - expr: sum by (instance, pod) (rate(greptime_mito_write_rows_total{instance=~"$datanode"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Flush OPS per Instance
-          type: timeseries
-          description: Flush QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, reason) (rate(greptime_mito_flush_requests_total{instance=~"$datanode"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{reason}}]'
-        - title: Write Stall per Instance
-          type: timeseries
-          description: Write Stall per Instance.
-          queries:
-            - expr: sum by(instance, pod) (greptime_mito_write_stall_total{instance=~"$datanode"})
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Read Stage OPS per Instance
-          type: timeseries
-          description: Read Stage OPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod) (rate(greptime_mito_read_stage_elapsed_count{instance=~"$datanode", stage="total"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Read Stage P99 per Instance
-          type: timeseries
-          description: Read Stage P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_read_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]'
-        - title: Write Stage P99 per Instance
-          type: timeseries
-          description: Write Stage P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_write_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]'
-        - title: Compaction OPS per Instance
-          type: timeseries
-          description: Compaction OPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod) (rate(greptime_mito_compaction_total_elapsed_count{instance=~"$datanode"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{pod}}]'
-        - title: Compaction Elapsed Time per Instance by Stage
-          type: timeseries
-          description: Compaction latency by stage
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_compaction_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-p99'
-            - expr: sum by(instance, pod, stage) (rate(greptime_mito_compaction_stage_elapsed_sum{instance=~"$datanode"}[$__rate_interval]))/sum by(instance, pod, stage) (rate(greptime_mito_compaction_stage_elapsed_count{instance=~"$datanode"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-avg'
-        - title: Compaction P99 per Instance
-          type: timeseries
-          description: Compaction P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le,stage) (rate(greptime_mito_compaction_total_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-compaction'
-        - title: WAL write size
-          type: timeseries
-          description: Write-ahead logs write size as bytes. This chart includes stats of p95 and p99 size by instance, total WAL write rate.
-          unit: bytes
-          queries:
-            - expr: histogram_quantile(0.95, sum by(le,instance, pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-req-size-p95'
-            - expr: histogram_quantile(0.99, sum by(le,instance,pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-req-size-p99'
-            - expr: sum by (instance, pod)(rate(raft_engine_write_size_sum[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-throughput'
-        - title: Cached Bytes per Instance
-          type: timeseries
-          description: Cached Bytes per Instance.
-          unit: decbytes
-          queries:
-            - expr: greptime_mito_cache_bytes{instance=~"$datanode"}
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
-        - title: Inflight Compaction
-          type: timeseries
-          description: Ongoing compaction task count
-          unit: none
-          queries:
-            - expr: greptime_mito_inflight_compaction_count
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: WAL sync duration seconds
-          type: timeseries
-          description: Raft engine (local disk) log store sync latency, p99
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(le, type, node, instance, pod) (rate(raft_engine_sync_log_duration_seconds_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-p99'
-        - title: Log Store op duration seconds
-          type: timeseries
-          description: Write-ahead log operations latency at p99
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(le,logstore,optype,instance, pod) (rate(greptime_logstore_op_elapsed_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{logstore}}]-[{{optype}}]-p99'
-        - title: Inflight Flush
-          type: timeseries
-          description: Ongoing flush task count
-          unit: none
-          queries:
-            - expr: greptime_mito_inflight_flush_count
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Compaction Input/Output Bytes
-          type: timeseries
-          description: Compaction oinput output bytes
-          unit: bytes
-          queries:
-            - expr: sum by(instance, pod) (greptime_mito_compaction_input_bytes)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-input'
-            - expr: sum by(instance, pod) (greptime_mito_compaction_output_bytes)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-output'
-        - title: Region Worker Handle Bulk Insert Requests
-          type: timeseries
-          description: Per-stage elapsed time for region worker to handle bulk insert region requests.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.95, sum by(le,instance, stage, pod) (rate(greptime_region_worker_handle_write_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-P95'
-            - expr: sum by(instance, stage, pod) (rate(greptime_region_worker_handle_write_sum[$__rate_interval]))/sum by(instance, stage, pod) (rate(greptime_region_worker_handle_write_count[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-AVG'
-        - title: Active Series and Field Builders Count
-          type: timeseries
-          description: Compaction oinput output bytes
-          unit: none
-          queries:
-            - expr: sum by(instance, pod) (greptime_mito_memtable_active_series_count)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-series'
-            - expr: sum by(instance, pod) (greptime_mito_memtable_field_builder_count)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-field_builders'
-        - title: Region Worker Convert Requests
-          type: timeseries
-          description: Per-stage elapsed time for region worker to decode requests.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.95, sum by(le, instance, stage, pod) (rate(greptime_datanode_convert_region_request_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-P95'
-            - expr: sum by(le,instance, stage, pod) (rate(greptime_datanode_convert_region_request_sum[$__rate_interval]))/sum by(le,instance, stage, pod) (rate(greptime_datanode_convert_region_request_count[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-AVG'
-    - title: OpenDAL
-      panels:
-        - title: QPS per Instance
-          type: timeseries
-          description: QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
-        - title: Read QPS per Instance
-          type: timeseries
-          description: Read QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="read"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
-        - title: Read P99 per Instance
-          type: timeseries
-          description: Read P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode",operation="read"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-{{scheme}}'
-        - title: Write QPS per Instance
-          type: timeseries
-          description: Write QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="write"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-{{scheme}}'
-        - title: Write P99 per Instance
-          type: timeseries
-          description: Write P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation="write"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
-        - title: List QPS per Instance
-          type: timeseries
-          description: List QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="list"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
-        - title: List P99 per Instance
-          type: timeseries
-          description: List P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation="list"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
-        - title: Other Requests per Instance
-          type: timeseries
-          description: Other Requests per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode",operation!~"read|write|list|stat"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
-        - title: Other Request P99 per Instance
-          type: timeseries
-          description: Other Request P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme, operation) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation!~"read|write|list"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
-        - title: Opendal traffic
-          type: timeseries
-          description: Total traffic as in bytes by instance and operation
-          unit: decbytes
-          queries:
-            - expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_bytes_sum{instance=~"$datanode"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
-        - title: OpenDAL errors per Instance
-          type: timeseries
-          description: OpenDAL error counts per Instance.
-          queries:
-            - expr: sum by(instance, pod, scheme, operation, error) (rate(opendal_operation_errors_total{instance=~"$datanode", error!="NotFound"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]-[{{error}}]'
-    - title: Metasrv
-      panels:
-        - title: Region migration datanode
-          type: status-history
-          description: Counter of region migration by source and destination
-          queries:
-            - expr: greptime_meta_region_migration_stat{datanode_type="src"}
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: from-datanode-{{datanode_id}}
-            - expr: greptime_meta_region_migration_stat{datanode_type="desc"}
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: to-datanode-{{datanode_id}}
-        - title: Region migration error
-          type: timeseries
-          description: Counter of region migration error
-          unit: none
-          queries:
-            - expr: greptime_meta_region_migration_error
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}}-{{state}}-{{error_type}}'
-        - title: Datanode load
-          type: timeseries
-          description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
-          unit: binBps
-          queries:
-            - expr: greptime_datanode_load
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: Datanode-{{datanode_id}}-writeload
-        - title: Rate of SQL Executions (RDS)
-          type: timeseries
-          description: Displays the rate of SQL executions processed by the Meta service using the RDS backend.
-          unit: none
-          queries:
-            - expr: rate(greptime_meta_rds_pg_sql_execute_elapsed_ms_count[$__rate_interval])
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}} {{op}} {{type}} {{result}} '
-        - title: SQL Execution Latency (RDS)
-          type: timeseries
-          description: 'Measures the response time of SQL executions via the RDS backend. '
-          unit: ms
-          queries:
-            - expr: histogram_quantile(0.90, sum by(pod, op, type, result, le) (rate(greptime_meta_rds_pg_sql_execute_elapsed_ms_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}} {{op}} {{type}} {{result}} p90'
-        - title: Handler Execution Latency
-          type: timeseries
-          description: |
-            Shows latency of Meta handlers by pod and handler name, useful for monitoring handler performance and detecting latency spikes.
-          unit: s
-          queries:
-            - expr: |-
-                histogram_quantile(0.90, sum by(pod, le, name) (
-                  rate(greptime_meta_handler_execute_bucket[$__rate_interval])
-                ))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}} {{name}} p90'
-        - title: Heartbeat Packet Size
-          type: timeseries
-          description: |
-            Shows p90 heartbeat message sizes, helping track network usage and identify anomalies in heartbeat payload.
-          unit: bytes
-          queries:
-            - expr: histogram_quantile(0.9, sum by(pod, le) (greptime_meta_heartbeat_stat_memory_size_bucket))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}}'
-        - title: Meta Heartbeat Receive Rate
-          type: timeseries
-          description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
-          unit: s
-          queries:
-            - expr: rate(greptime_meta_heartbeat_rate[$__rate_interval])
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}}'
-        - title: Meta KV Ops Latency
-          type: timeseries
-          description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(pod, le, op, target) (greptime_meta_kv_request_elapsed_bucket))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}}-{{op}} p99'
-        - title: Rate of meta KV Ops
-          type: timeseries
-          description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
-          unit: none
-          queries:
-            - expr: rate(greptime_meta_kv_request_elapsed_count[$__rate_interval])
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}}-{{op}} p99'
-        - title: DDL Latency
-          type: timeseries
-          description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_tables_bucket))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: CreateLogicalTables-{{step}} p90
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_table))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: CreateTable-{{step}} p90
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_view))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: CreateView-{{step}} p90
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_flow))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: CreateFlow-{{step}} p90
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_drop_table))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: DropTable-{{step}} p90
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_alter_table))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: AlterTable-{{step}} p90
-    - title: Flownode
-      panels:
-        - title: Flow Ingest / Output Rate
-          type: timeseries
-          description: Flow Ingest / Output Rate.
-          queries:
-            - expr: sum by(instance, pod, direction) (rate(greptime_flow_processed_rows[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{pod}}]-[{{instance}}]-[{{direction}}]'
-        - title: Flow Ingest Latency
-          type: timeseries
-          description: Flow Ingest Latency.
-          queries:
-            - expr: histogram_quantile(0.95, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-p95'
-            - expr: histogram_quantile(0.99, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-p99'
-        - title: Flow Operation Latency
-          type: timeseries
-          description: Flow Operation Latency.
-          queries:
-            - expr: histogram_quantile(0.95, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]-p95'
-            - expr: histogram_quantile(0.99, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]-p99'
-        - title: Flow Buffer Size per Instance
-          type: timeseries
-          description: Flow Buffer Size per Instance.
-          queries:
-            - expr: greptime_flow_input_buf_size
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}]'
-        - title: Flow Processing Error per Instance
-          type: timeseries
-          description: Flow Processing Error per Instance.
-          queries:
-            - expr: sum by(instance,pod,code) (rate(greptime_flow_errors[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{code}}]'

grafana/dashboards/metrics/standalone/dashboard.md

@@ -1,112 +0,0 @@
-# Overview
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Uptime | `time() - process_start_time_seconds` | `stat` | The start time of GreptimeDB. | `prometheus` | `s` | `__auto` |
-| Version | `SELECT pkg_version FROM information_schema.build_info` | `stat` | GreptimeDB version. | `mysql` | -- | -- |
-| Total Ingestion Rate | `sum(rate(greptime_table_operator_ingest_rows[$__rate_interval]))` | `stat` | Total ingestion rate. | `prometheus` | `rowsps` | `__auto` |
-| Total Storage Size | `select SUM(disk_size) from information_schema.region_statistics;` | `stat` | Total number of data file size. | `mysql` | `decbytes` | -- |
-| Total Rows | `select SUM(region_rows) from information_schema.region_statistics;` | `stat` | Total number of data rows in the cluster. Calculated by sum of rows from each region. | `mysql` | `sishort` | -- |
-| Deployment | `SELECT count(*) as datanode FROM information_schema.cluster_info WHERE peer_type = 'DATANODE';`<br/>`SELECT count(*) as frontend FROM information_schema.cluster_info WHERE peer_type = 'FRONTEND';`<br/>`SELECT count(*) as metasrv FROM information_schema.cluster_info WHERE peer_type = 'METASRV';`<br/>`SELECT count(*) as flownode FROM information_schema.cluster_info WHERE peer_type = 'FLOWNODE';` | `stat` | The deployment topology of GreptimeDB. | `mysql` | -- | -- |
-| Database Resources | `SELECT COUNT(*) as databases FROM information_schema.schemata WHERE schema_name NOT IN ('greptime_private', 'information_schema')`<br/>`SELECT COUNT(*) as tables FROM information_schema.tables WHERE table_schema != 'information_schema'`<br/>`SELECT COUNT(region_id) as regions FROM information_schema.region_peers`<br/>`SELECT COUNT(*) as flows FROM information_schema.flows` | `stat` | The number of the key resources in GreptimeDB. | `mysql` | -- | -- |
-| Data Size | `SELECT SUM(memtable_size) * 0.42825 as WAL FROM information_schema.region_statistics;`<br/>`SELECT SUM(index_size) as index FROM information_schema.region_statistics;`<br/>`SELECT SUM(manifest_size) as manifest FROM information_schema.region_statistics;` | `stat` | The data size of wal/index/manifest in the GreptimeDB. | `mysql` | `decbytes` | -- |
-# Ingestion
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Total Ingestion Rate | `sum(rate(greptime_table_operator_ingest_rows{}[$__rate_interval]))` | `timeseries` | Total ingestion rate.<br/><br/>Here we listed 3 primary protocols:<br/><br/>- Prometheus remote write<br/>- Greptime's gRPC API (when using our ingest SDK)<br/>- Log ingestion http API<br/> | `prometheus` | `rowsps` | `ingestion` |
-| Ingestion Rate by Type | `sum(rate(greptime_servers_http_logs_ingestion_counter[$__rate_interval]))`<br/>`sum(rate(greptime_servers_prometheus_remote_write_samples[$__rate_interval]))` | `timeseries` | Total ingestion rate.<br/><br/>Here we listed 3 primary protocols:<br/><br/>- Prometheus remote write<br/>- Greptime's gRPC API (when using our ingest SDK)<br/>- Log ingestion http API<br/> | `prometheus` | `rowsps` | `http-logs` |
-# Queries
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Total Query Rate | `sum (rate(greptime_servers_mysql_query_elapsed_count{}[$__rate_interval]))`<br/>`sum (rate(greptime_servers_postgres_query_elapsed_count{}[$__rate_interval]))`<br/>`sum (rate(greptime_servers_http_promql_elapsed_counte{}[$__rate_interval]))` | `timeseries` | Total rate of query API calls by protocol. This metric is collected from frontends.<br/><br/>Here we listed 3 main protocols:<br/>- MySQL<br/>- Postgres<br/>- Prometheus API<br/><br/>Note that there are some other minor query APIs like /sql are not included | `prometheus` | `reqps` | `mysql` |
-# Resources
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Datanode Memory per Instance | `sum(process_resident_memory_bytes{}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{instance}}]-[{{ pod }}]` |
-| Datanode CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
-| Frontend Memory per Instance | `sum(process_resident_memory_bytes{}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]` |
-| Frontend CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]-cpu` |
-| Metasrv Memory per Instance | `sum(process_resident_memory_bytes{}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]-resident` |
-| Metasrv CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
-| Flownode Memory per Instance | `sum(process_resident_memory_bytes{}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]` |
-| Flownode CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
-# Frontend Requests
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| HTTP QPS per Instance | `sum by(instance, pod, path, method, code) (rate(greptime_servers_http_requests_elapsed_count{path!~"/health\|/metrics"}[$__rate_interval]))` | `timeseries` | HTTP QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]` |
-| HTTP P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, path, method, code) (rate(greptime_servers_http_requests_elapsed_bucket{path!~"/health\|/metrics"}[$__rate_interval])))` | `timeseries` | HTTP P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99` |
-| gRPC QPS per Instance | `sum by(instance, pod, path, code) (rate(greptime_servers_grpc_requests_elapsed_count{}[$__rate_interval]))` | `timeseries` | gRPC QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{code}}]` |
-| gRPC P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, path, code) (rate(greptime_servers_grpc_requests_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | gRPC P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99` |
-| MySQL QPS per Instance | `sum by(pod, instance)(rate(greptime_servers_mysql_query_elapsed_count{}[$__rate_interval]))` | `timeseries` | MySQL QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]` |
-| MySQL P99 per Instance | `histogram_quantile(0.99, sum by(pod, instance, le) (rate(greptime_servers_mysql_query_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | MySQL P99 per Instance. | `prometheus` | `s` | `[{{ instance }}]-[{{ pod }}]-p99` |
-| PostgreSQL QPS per Instance | `sum by(pod, instance)(rate(greptime_servers_postgres_query_elapsed_count{}[$__rate_interval]))` | `timeseries` | PostgreSQL QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]` |
-| PostgreSQL P99 per Instance | `histogram_quantile(0.99, sum by(pod,instance,le) (rate(greptime_servers_postgres_query_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | PostgreSQL P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-p99` |
-# Frontend to Datanode
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Ingest Rows per Instance | `sum by(instance, pod)(rate(greptime_table_operator_ingest_rows{}[$__rate_interval]))` | `timeseries` | Ingestion rate by row as in each frontend | `prometheus` | `rowsps` | `[{{instance}}]-[{{pod}}]` |
-| Region Call QPS per Instance | `sum by(instance, pod, request_type) (rate(greptime_grpc_region_request_count{}[$__rate_interval]))` | `timeseries` | Region Call QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{request_type}}]` |
-| Region Call P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, request_type) (rate(greptime_grpc_region_request_bucket{}[$__rate_interval])))` | `timeseries` | Region Call P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{request_type}}]` |
-| Frontend Handle Bulk Insert Elapsed Time  | `sum by(instance, pod, stage) (rate(greptime_table_operator_handle_bulk_insert_sum[$__rate_interval]))/sum by(instance, pod, stage) (rate(greptime_table_operator_handle_bulk_insert_count[$__rate_interval]))`<br/>`histogram_quantile(0.99, sum by(instance, pod, stage, le) (rate(greptime_table_operator_handle_bulk_insert_bucket[$__rate_interval])))` | `timeseries` | Per-stage time for frontend to handle bulk insert requests | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-AVG` |
-# Mito Engine
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Request OPS per Instance | `sum by(instance, pod, type) (rate(greptime_mito_handle_request_elapsed_count{}[$__rate_interval]))` | `timeseries` | Request QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
-| Request P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, type) (rate(greptime_mito_handle_request_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | Request P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
-| Write Buffer per Instance | `greptime_mito_write_buffer_bytes{}` | `timeseries` | Write Buffer per Instance. | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]` |
-| Write Rows per Instance | `sum by (instance, pod) (rate(greptime_mito_write_rows_total{}[$__rate_interval]))` | `timeseries` | Ingestion size by row counts. | `prometheus` | `rowsps` | `[{{instance}}]-[{{pod}}]` |
-| Flush OPS per Instance | `sum by(instance, pod, reason) (rate(greptime_mito_flush_requests_total{}[$__rate_interval]))` | `timeseries` | Flush QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{reason}}]` |
-| Write Stall per Instance | `sum by(instance, pod) (greptime_mito_write_stall_total{})` | `timeseries` | Write Stall per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]` |
-| Read Stage OPS per Instance | `sum by(instance, pod) (rate(greptime_mito_read_stage_elapsed_count{ stage="total"}[$__rate_interval]))` | `timeseries` | Read Stage OPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]` |
-| Read Stage P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_read_stage_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | Read Stage P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]` |
-| Write Stage P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_write_stage_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | Write Stage P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]` |
-| Compaction OPS per Instance | `sum by(instance, pod) (rate(greptime_mito_compaction_total_elapsed_count{}[$__rate_interval]))` | `timeseries` | Compaction OPS per Instance. | `prometheus` | `ops` | `[{{ instance }}]-[{{pod}}]` |
-| Compaction Elapsed Time per Instance by Stage | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_compaction_stage_elapsed_bucket{}[$__rate_interval])))`<br/>`sum by(instance, pod, stage) (rate(greptime_mito_compaction_stage_elapsed_sum{}[$__rate_interval]))/sum by(instance, pod, stage) (rate(greptime_mito_compaction_stage_elapsed_count{}[$__rate_interval]))` | `timeseries` | Compaction latency by stage | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-p99` |
-| Compaction P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le,stage) (rate(greptime_mito_compaction_total_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | Compaction P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-compaction` |
-| WAL write size | `histogram_quantile(0.95, sum by(le,instance, pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))`<br/>`histogram_quantile(0.99, sum by(le,instance,pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))`<br/>`sum by (instance, pod)(rate(raft_engine_write_size_sum[$__rate_interval]))` | `timeseries` | Write-ahead logs write size as bytes. This chart includes stats of p95 and p99 size by instance, total WAL write rate. | `prometheus` | `bytes` | `[{{instance}}]-[{{pod}}]-req-size-p95` |
-| Cached Bytes per Instance | `greptime_mito_cache_bytes{}` | `timeseries` | Cached Bytes per Instance. | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
-| Inflight Compaction | `greptime_mito_inflight_compaction_count` | `timeseries` | Ongoing compaction task count | `prometheus` | `none` | `[{{instance}}]-[{{pod}}]` |
-| WAL sync duration seconds | `histogram_quantile(0.99, sum by(le, type, node, instance, pod) (rate(raft_engine_sync_log_duration_seconds_bucket[$__rate_interval])))` | `timeseries` | Raft engine (local disk) log store sync latency, p99 | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-p99` |
-| Log Store op duration seconds | `histogram_quantile(0.99, sum by(le,logstore,optype,instance, pod) (rate(greptime_logstore_op_elapsed_bucket[$__rate_interval])))` | `timeseries` | Write-ahead log operations latency at p99 | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{logstore}}]-[{{optype}}]-p99` |
-| Inflight Flush | `greptime_mito_inflight_flush_count` | `timeseries` | Ongoing flush task count | `prometheus` | `none` | `[{{instance}}]-[{{pod}}]` |
-| Compaction Input/Output Bytes | `sum by(instance, pod) (greptime_mito_compaction_input_bytes)`<br/>`sum by(instance, pod) (greptime_mito_compaction_output_bytes)` | `timeseries` | Compaction oinput output bytes | `prometheus` | `bytes` | `[{{instance}}]-[{{pod}}]-input` |
-| Region Worker Handle Bulk Insert Requests | `histogram_quantile(0.95, sum by(le,instance, stage, pod) (rate(greptime_region_worker_handle_write_bucket[$__rate_interval])))`<br/>`sum by(instance, stage, pod) (rate(greptime_region_worker_handle_write_sum[$__rate_interval]))/sum by(instance, stage, pod) (rate(greptime_region_worker_handle_write_count[$__rate_interval]))` | `timeseries` | Per-stage elapsed time for region worker to handle bulk insert region requests. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-P95` |
-| Active Series and Field Builders Count | `sum by(instance, pod) (greptime_mito_memtable_active_series_count)`<br/>`sum by(instance, pod) (greptime_mito_memtable_field_builder_count)` | `timeseries` | Compaction oinput output bytes | `prometheus` | `none` | `[{{instance}}]-[{{pod}}]-series` |
-| Region Worker Convert Requests | `histogram_quantile(0.95, sum by(le, instance, stage, pod) (rate(greptime_datanode_convert_region_request_bucket[$__rate_interval])))`<br/>`sum by(le,instance, stage, pod) (rate(greptime_datanode_convert_region_request_sum[$__rate_interval]))/sum by(le,instance, stage, pod) (rate(greptime_datanode_convert_region_request_count[$__rate_interval]))` | `timeseries` | Per-stage elapsed time for region worker to decode requests. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-P95` |
-# OpenDAL
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| QPS per Instance | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{}[$__rate_interval]))` | `timeseries` | QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
-| Read QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="read"}[$__rate_interval]))` | `timeseries` | Read QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
-| Read P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{operation="read"}[$__rate_interval])))` | `timeseries` | Read P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-{{scheme}}` |
-| Write QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="write"}[$__rate_interval]))` | `timeseries` | Write QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-{{scheme}}` |
-| Write P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{ operation="write"}[$__rate_interval])))` | `timeseries` | Write P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
-| List QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="list"}[$__rate_interval]))` | `timeseries` | List QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
-| List P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{ operation="list"}[$__rate_interval])))` | `timeseries` | List P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
-| Other Requests per Instance | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{operation!~"read\|write\|list\|stat"}[$__rate_interval]))` | `timeseries` | Other Requests per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
-| Other Request P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme, operation) (rate(opendal_operation_duration_seconds_bucket{ operation!~"read\|write\|list"}[$__rate_interval])))` | `timeseries` | Other Request P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
-| Opendal traffic | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_bytes_sum{}[$__rate_interval]))` | `timeseries` | Total traffic as in bytes by instance and operation | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
-| OpenDAL errors per Instance | `sum by(instance, pod, scheme, operation, error) (rate(opendal_operation_errors_total{ error!="NotFound"}[$__rate_interval]))` | `timeseries` | OpenDAL error counts per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]-[{{error}}]` |
-# Metasrv
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Region migration datanode | `greptime_meta_region_migration_stat{datanode_type="src"}`<br/>`greptime_meta_region_migration_stat{datanode_type="desc"}` | `status-history` | Counter of region migration by source and destination | `prometheus` | -- | `from-datanode-{{datanode_id}}` |
-| Region migration error | `greptime_meta_region_migration_error` | `timeseries` | Counter of region migration error | `prometheus` | `none` | `{{pod}}-{{state}}-{{error_type}}` |
-| Datanode load | `greptime_datanode_load` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `binBps` | `Datanode-{{datanode_id}}-writeload` |
-| Rate of SQL Executions (RDS) | `rate(greptime_meta_rds_pg_sql_execute_elapsed_ms_count[$__rate_interval])` | `timeseries` | Displays the rate of SQL executions processed by the Meta service using the RDS backend. | `prometheus` | `none` | `{{pod}} {{op}} {{type}} {{result}} ` |
-| SQL Execution Latency (RDS) | `histogram_quantile(0.90, sum by(pod, op, type, result, le) (rate(greptime_meta_rds_pg_sql_execute_elapsed_ms_bucket[$__rate_interval])))` | `timeseries` | Measures the response time of SQL executions via the RDS backend.  | `prometheus` | `ms` | `{{pod}} {{op}} {{type}} {{result}} p90` |
-| Handler Execution Latency | `histogram_quantile(0.90, sum by(pod, le, name) (
-  rate(greptime_meta_handler_execute_bucket[$__rate_interval])
-))` | `timeseries` | Shows latency of Meta handlers by pod and handler name, useful for monitoring handler performance and detecting latency spikes.<br/> | `prometheus` | `s` | `{{pod}} {{name}} p90` |
-| Heartbeat Packet Size | `histogram_quantile(0.9, sum by(pod, le) (greptime_meta_heartbeat_stat_memory_size_bucket))` | `timeseries` | Shows p90 heartbeat message sizes, helping track network usage and identify anomalies in heartbeat payload.<br/> | `prometheus` | `bytes` | `{{pod}}` |
-| Meta Heartbeat Receive Rate | `rate(greptime_meta_heartbeat_rate[$__rate_interval])` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `s` | `{{pod}}` |
-| Meta KV Ops Latency | `histogram_quantile(0.99, sum by(pod, le, op, target) (greptime_meta_kv_request_elapsed_bucket))` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `s` | `{{pod}}-{{op}} p99` |
-| Rate of meta KV Ops | `rate(greptime_meta_kv_request_elapsed_count[$__rate_interval])` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `none` | `{{pod}}-{{op}} p99` |
-| DDL Latency | `histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_tables_bucket))`<br/>`histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_table))`<br/>`histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_view))`<br/>`histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_flow))`<br/>`histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_drop_table))`<br/>`histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_alter_table))` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `s` | `CreateLogicalTables-{{step}} p90` |
-# Flownode
-| Title | Query | Type | Description | Datasource | Unit | Legend Format |
-| --- | --- | --- | --- | --- | --- | --- |
-| Flow Ingest / Output Rate | `sum by(instance, pod, direction) (rate(greptime_flow_processed_rows[$__rate_interval]))` | `timeseries` | Flow Ingest / Output Rate. | `prometheus` | -- | `[{{pod}}]-[{{instance}}]-[{{direction}}]` |
-| Flow Ingest Latency | `histogram_quantile(0.95, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))`<br/>`histogram_quantile(0.99, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))` | `timeseries` | Flow Ingest Latency. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-p95` |
-| Flow Operation Latency | `histogram_quantile(0.95, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))`<br/>`histogram_quantile(0.99, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))` | `timeseries` | Flow Operation Latency. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{type}}]-p95` |
-| Flow Buffer Size per Instance | `greptime_flow_input_buf_size` | `timeseries` | Flow Buffer Size per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}]` |
-| Flow Processing Error per Instance | `sum by(instance,pod,code) (rate(greptime_flow_errors[$__rate_interval]))` | `timeseries` | Flow Processing Error per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{code}}]` |

grafana/dashboards/metrics/standalone/dashboard.yaml

@@ -1,958 +0,0 @@
-groups:
-    - title: Overview
-      panels:
-        - title: Uptime
-          type: stat
-          description: The start time of GreptimeDB.
-          unit: s
-          queries:
-            - expr: time() - process_start_time_seconds
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: __auto
-        - title: Version
-          type: stat
-          description: GreptimeDB version.
-          queries:
-            - expr: SELECT pkg_version FROM information_schema.build_info
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-        - title: Total Ingestion Rate
-          type: stat
-          description: Total ingestion rate.
-          unit: rowsps
-          queries:
-            - expr: sum(rate(greptime_table_operator_ingest_rows[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: __auto
-        - title: Total Storage Size
-          type: stat
-          description: Total number of data file size.
-          unit: decbytes
-          queries:
-            - expr: select SUM(disk_size) from information_schema.region_statistics;
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-        - title: Total Rows
-          type: stat
-          description: Total number of data rows in the cluster. Calculated by sum of rows from each region.
-          unit: sishort
-          queries:
-            - expr: select SUM(region_rows) from information_schema.region_statistics;
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-        - title: Deployment
-          type: stat
-          description: The deployment topology of GreptimeDB.
-          queries:
-            - expr: SELECT count(*) as datanode FROM information_schema.cluster_info WHERE peer_type = 'DATANODE';
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT count(*) as frontend FROM information_schema.cluster_info WHERE peer_type = 'FRONTEND';
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT count(*) as metasrv FROM information_schema.cluster_info WHERE peer_type = 'METASRV';
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT count(*) as flownode FROM information_schema.cluster_info WHERE peer_type = 'FLOWNODE';
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-        - title: Database Resources
-          type: stat
-          description: The number of the key resources in GreptimeDB.
-          queries:
-            - expr: SELECT COUNT(*) as databases FROM information_schema.schemata WHERE schema_name NOT IN ('greptime_private', 'information_schema')
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT COUNT(*) as tables FROM information_schema.tables WHERE table_schema != 'information_schema'
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT COUNT(region_id) as regions FROM information_schema.region_peers
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT COUNT(*) as flows FROM information_schema.flows
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-        - title: Data Size
-          type: stat
-          description: The data size of wal/index/manifest in the GreptimeDB.
-          unit: decbytes
-          queries:
-            - expr: SELECT SUM(memtable_size) * 0.42825 as WAL FROM information_schema.region_statistics;
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT SUM(index_size) as index FROM information_schema.region_statistics;
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-            - expr: SELECT SUM(manifest_size) as manifest FROM information_schema.region_statistics;
-              datasource:
-                type: mysql
-                uid: ${information_schema}
-    - title: Ingestion
-      panels:
-        - title: Total Ingestion Rate
-          type: timeseries
-          description: |
-            Total ingestion rate.
-
-            Here we listed 3 primary protocols:
-
-            - Prometheus remote write
-            - Greptime's gRPC API (when using our ingest SDK)
-            - Log ingestion http API
-          unit: rowsps
-          queries:
-            - expr: sum(rate(greptime_table_operator_ingest_rows{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: ingestion
-        - title: Ingestion Rate by Type
-          type: timeseries
-          description: |
-            Total ingestion rate.
-
-            Here we listed 3 primary protocols:
-
-            - Prometheus remote write
-            - Greptime's gRPC API (when using our ingest SDK)
-            - Log ingestion http API
-          unit: rowsps
-          queries:
-            - expr: sum(rate(greptime_servers_http_logs_ingestion_counter[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: http-logs
-            - expr: sum(rate(greptime_servers_prometheus_remote_write_samples[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: prometheus-remote-write
-    - title: Queries
-      panels:
-        - title: Total Query Rate
-          type: timeseries
-          description: |-
-            Total rate of query API calls by protocol. This metric is collected from frontends.
-
-            Here we listed 3 main protocols:
-            - MySQL
-            - Postgres
-            - Prometheus API
-
-            Note that there are some other minor query APIs like /sql are not included
-          unit: reqps
-          queries:
-            - expr: sum (rate(greptime_servers_mysql_query_elapsed_count{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: mysql
-            - expr: sum (rate(greptime_servers_postgres_query_elapsed_count{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: pg
-            - expr: sum (rate(greptime_servers_http_promql_elapsed_counte{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: promql
-    - title: Resources
-      panels:
-        - title: Datanode Memory per Instance
-          type: timeseries
-          description: Current memory usage by instance
-          unit: decbytes
-          queries:
-            - expr: sum(process_resident_memory_bytes{}) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{ pod }}]'
-        - title: Datanode CPU Usage per Instance
-          type: timeseries
-          description: Current cpu usage by instance
-          unit: none
-          queries:
-            - expr: sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]'
-        - title: Frontend Memory per Instance
-          type: timeseries
-          description: Current memory usage by instance
-          unit: decbytes
-          queries:
-            - expr: sum(process_resident_memory_bytes{}) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]'
-        - title: Frontend CPU Usage per Instance
-          type: timeseries
-          description: Current cpu usage by instance
-          unit: none
-          queries:
-            - expr: sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]-cpu'
-        - title: Metasrv Memory per Instance
-          type: timeseries
-          description: Current memory usage by instance
-          unit: decbytes
-          queries:
-            - expr: sum(process_resident_memory_bytes{}) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]-resident'
-        - title: Metasrv CPU Usage per Instance
-          type: timeseries
-          description: Current cpu usage by instance
-          unit: none
-          queries:
-            - expr: sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]'
-        - title: Flownode Memory per Instance
-          type: timeseries
-          description: Current memory usage by instance
-          unit: decbytes
-          queries:
-            - expr: sum(process_resident_memory_bytes{}) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]'
-        - title: Flownode CPU Usage per Instance
-          type: timeseries
-          description: Current cpu usage by instance
-          unit: none
-          queries:
-            - expr: sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]'
-    - title: Frontend Requests
-      panels:
-        - title: HTTP QPS per Instance
-          type: timeseries
-          description: HTTP QPS per Instance.
-          unit: reqps
-          queries:
-            - expr: sum by(instance, pod, path, method, code) (rate(greptime_servers_http_requests_elapsed_count{path!~"/health|/metrics"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]'
-        - title: HTTP P99 per Instance
-          type: timeseries
-          description: HTTP P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, path, method, code) (rate(greptime_servers_http_requests_elapsed_bucket{path!~"/health|/metrics"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99'
-        - title: gRPC QPS per Instance
-          type: timeseries
-          description: gRPC QPS per Instance.
-          unit: reqps
-          queries:
-            - expr: sum by(instance, pod, path, code) (rate(greptime_servers_grpc_requests_elapsed_count{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{code}}]'
-        - title: gRPC P99 per Instance
-          type: timeseries
-          description: gRPC P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, path, code) (rate(greptime_servers_grpc_requests_elapsed_bucket{}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99'
-        - title: MySQL QPS per Instance
-          type: timeseries
-          description: MySQL QPS per Instance.
-          unit: reqps
-          queries:
-            - expr: sum by(pod, instance)(rate(greptime_servers_mysql_query_elapsed_count{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: MySQL P99 per Instance
-          type: timeseries
-          description: MySQL P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(pod, instance, le) (rate(greptime_servers_mysql_query_elapsed_bucket{}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{ pod }}]-p99'
-        - title: PostgreSQL QPS per Instance
-          type: timeseries
-          description: PostgreSQL QPS per Instance.
-          unit: reqps
-          queries:
-            - expr: sum by(pod, instance)(rate(greptime_servers_postgres_query_elapsed_count{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: PostgreSQL P99 per Instance
-          type: timeseries
-          description: PostgreSQL P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(pod,instance,le) (rate(greptime_servers_postgres_query_elapsed_bucket{}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-p99'
-    - title: Frontend to Datanode
-      panels:
-        - title: Ingest Rows per Instance
-          type: timeseries
-          description: Ingestion rate by row as in each frontend
-          unit: rowsps
-          queries:
-            - expr: sum by(instance, pod)(rate(greptime_table_operator_ingest_rows{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Region Call QPS per Instance
-          type: timeseries
-          description: Region Call QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, request_type) (rate(greptime_grpc_region_request_count{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{request_type}}]'
-        - title: Region Call P99 per Instance
-          type: timeseries
-          description: Region Call P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, request_type) (rate(greptime_grpc_region_request_bucket{}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{request_type}}]'
-        - title: 'Frontend Handle Bulk Insert Elapsed Time '
-          type: timeseries
-          description: Per-stage time for frontend to handle bulk insert requests
-          unit: s
-          queries:
-            - expr: sum by(instance, pod, stage) (rate(greptime_table_operator_handle_bulk_insert_sum[$__rate_interval]))/sum by(instance, pod, stage) (rate(greptime_table_operator_handle_bulk_insert_count[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-AVG'
-            - expr: histogram_quantile(0.99, sum by(instance, pod, stage, le) (rate(greptime_table_operator_handle_bulk_insert_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-P95'
-    - title: Mito Engine
-      panels:
-        - title: Request OPS per Instance
-          type: timeseries
-          description: Request QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, type) (rate(greptime_mito_handle_request_elapsed_count{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
-        - title: Request P99 per Instance
-          type: timeseries
-          description: Request P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, type) (rate(greptime_mito_handle_request_elapsed_bucket{}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
-        - title: Write Buffer per Instance
-          type: timeseries
-          description: Write Buffer per Instance.
-          unit: decbytes
-          queries:
-            - expr: greptime_mito_write_buffer_bytes{}
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Write Rows per Instance
-          type: timeseries
-          description: Ingestion size by row counts.
-          unit: rowsps
-          queries:
-            - expr: sum by (instance, pod) (rate(greptime_mito_write_rows_total{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Flush OPS per Instance
-          type: timeseries
-          description: Flush QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, reason) (rate(greptime_mito_flush_requests_total{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{reason}}]'
-        - title: Write Stall per Instance
-          type: timeseries
-          description: Write Stall per Instance.
-          queries:
-            - expr: sum by(instance, pod) (greptime_mito_write_stall_total{})
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Read Stage OPS per Instance
-          type: timeseries
-          description: Read Stage OPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod) (rate(greptime_mito_read_stage_elapsed_count{ stage="total"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Read Stage P99 per Instance
-          type: timeseries
-          description: Read Stage P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_read_stage_elapsed_bucket{}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]'
-        - title: Write Stage P99 per Instance
-          type: timeseries
-          description: Write Stage P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_write_stage_elapsed_bucket{}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]'
-        - title: Compaction OPS per Instance
-          type: timeseries
-          description: Compaction OPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod) (rate(greptime_mito_compaction_total_elapsed_count{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{ instance }}]-[{{pod}}]'
-        - title: Compaction Elapsed Time per Instance by Stage
-          type: timeseries
-          description: Compaction latency by stage
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_compaction_stage_elapsed_bucket{}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-p99'
-            - expr: sum by(instance, pod, stage) (rate(greptime_mito_compaction_stage_elapsed_sum{}[$__rate_interval]))/sum by(instance, pod, stage) (rate(greptime_mito_compaction_stage_elapsed_count{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-avg'
-        - title: Compaction P99 per Instance
-          type: timeseries
-          description: Compaction P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le,stage) (rate(greptime_mito_compaction_total_elapsed_bucket{}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-compaction'
-        - title: WAL write size
-          type: timeseries
-          description: Write-ahead logs write size as bytes. This chart includes stats of p95 and p99 size by instance, total WAL write rate.
-          unit: bytes
-          queries:
-            - expr: histogram_quantile(0.95, sum by(le,instance, pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-req-size-p95'
-            - expr: histogram_quantile(0.99, sum by(le,instance,pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-req-size-p99'
-            - expr: sum by (instance, pod)(rate(raft_engine_write_size_sum[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-throughput'
-        - title: Cached Bytes per Instance
-          type: timeseries
-          description: Cached Bytes per Instance.
-          unit: decbytes
-          queries:
-            - expr: greptime_mito_cache_bytes{}
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
-        - title: Inflight Compaction
-          type: timeseries
-          description: Ongoing compaction task count
-          unit: none
-          queries:
-            - expr: greptime_mito_inflight_compaction_count
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: WAL sync duration seconds
-          type: timeseries
-          description: Raft engine (local disk) log store sync latency, p99
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(le, type, node, instance, pod) (rate(raft_engine_sync_log_duration_seconds_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-p99'
-        - title: Log Store op duration seconds
-          type: timeseries
-          description: Write-ahead log operations latency at p99
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(le,logstore,optype,instance, pod) (rate(greptime_logstore_op_elapsed_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{logstore}}]-[{{optype}}]-p99'
-        - title: Inflight Flush
-          type: timeseries
-          description: Ongoing flush task count
-          unit: none
-          queries:
-            - expr: greptime_mito_inflight_flush_count
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]'
-        - title: Compaction Input/Output Bytes
-          type: timeseries
-          description: Compaction oinput output bytes
-          unit: bytes
-          queries:
-            - expr: sum by(instance, pod) (greptime_mito_compaction_input_bytes)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-input'
-            - expr: sum by(instance, pod) (greptime_mito_compaction_output_bytes)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-output'
-        - title: Region Worker Handle Bulk Insert Requests
-          type: timeseries
-          description: Per-stage elapsed time for region worker to handle bulk insert region requests.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.95, sum by(le,instance, stage, pod) (rate(greptime_region_worker_handle_write_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-P95'
-            - expr: sum by(instance, stage, pod) (rate(greptime_region_worker_handle_write_sum[$__rate_interval]))/sum by(instance, stage, pod) (rate(greptime_region_worker_handle_write_count[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-AVG'
-        - title: Active Series and Field Builders Count
-          type: timeseries
-          description: Compaction oinput output bytes
-          unit: none
-          queries:
-            - expr: sum by(instance, pod) (greptime_mito_memtable_active_series_count)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-series'
-            - expr: sum by(instance, pod) (greptime_mito_memtable_field_builder_count)
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-field_builders'
-        - title: Region Worker Convert Requests
-          type: timeseries
-          description: Per-stage elapsed time for region worker to decode requests.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.95, sum by(le, instance, stage, pod) (rate(greptime_datanode_convert_region_request_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-P95'
-            - expr: sum by(le,instance, stage, pod) (rate(greptime_datanode_convert_region_request_sum[$__rate_interval]))/sum by(le,instance, stage, pod) (rate(greptime_datanode_convert_region_request_count[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-AVG'
-    - title: OpenDAL
-      panels:
-        - title: QPS per Instance
-          type: timeseries
-          description: QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
-        - title: Read QPS per Instance
-          type: timeseries
-          description: Read QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="read"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
-        - title: Read P99 per Instance
-          type: timeseries
-          description: Read P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{operation="read"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-{{scheme}}'
-        - title: Write QPS per Instance
-          type: timeseries
-          description: Write QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="write"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-{{scheme}}'
-        - title: Write P99 per Instance
-          type: timeseries
-          description: Write P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{ operation="write"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
-        - title: List QPS per Instance
-          type: timeseries
-          description: List QPS per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="list"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
-        - title: List P99 per Instance
-          type: timeseries
-          description: List P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{ operation="list"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
-        - title: Other Requests per Instance
-          type: timeseries
-          description: Other Requests per Instance.
-          unit: ops
-          queries:
-            - expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{operation!~"read|write|list|stat"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
-        - title: Other Request P99 per Instance
-          type: timeseries
-          description: Other Request P99 per Instance.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme, operation) (rate(opendal_operation_duration_seconds_bucket{ operation!~"read|write|list"}[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
-        - title: Opendal traffic
-          type: timeseries
-          description: Total traffic as in bytes by instance and operation
-          unit: decbytes
-          queries:
-            - expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_bytes_sum{}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
-        - title: OpenDAL errors per Instance
-          type: timeseries
-          description: OpenDAL error counts per Instance.
-          queries:
-            - expr: sum by(instance, pod, scheme, operation, error) (rate(opendal_operation_errors_total{ error!="NotFound"}[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]-[{{error}}]'
-    - title: Metasrv
-      panels:
-        - title: Region migration datanode
-          type: status-history
-          description: Counter of region migration by source and destination
-          queries:
-            - expr: greptime_meta_region_migration_stat{datanode_type="src"}
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: from-datanode-{{datanode_id}}
-            - expr: greptime_meta_region_migration_stat{datanode_type="desc"}
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: to-datanode-{{datanode_id}}
-        - title: Region migration error
-          type: timeseries
-          description: Counter of region migration error
-          unit: none
-          queries:
-            - expr: greptime_meta_region_migration_error
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}}-{{state}}-{{error_type}}'
-        - title: Datanode load
-          type: timeseries
-          description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
-          unit: binBps
-          queries:
-            - expr: greptime_datanode_load
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: Datanode-{{datanode_id}}-writeload
-        - title: Rate of SQL Executions (RDS)
-          type: timeseries
-          description: Displays the rate of SQL executions processed by the Meta service using the RDS backend.
-          unit: none
-          queries:
-            - expr: rate(greptime_meta_rds_pg_sql_execute_elapsed_ms_count[$__rate_interval])
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}} {{op}} {{type}} {{result}} '
-        - title: SQL Execution Latency (RDS)
-          type: timeseries
-          description: 'Measures the response time of SQL executions via the RDS backend. '
-          unit: ms
-          queries:
-            - expr: histogram_quantile(0.90, sum by(pod, op, type, result, le) (rate(greptime_meta_rds_pg_sql_execute_elapsed_ms_bucket[$__rate_interval])))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}} {{op}} {{type}} {{result}} p90'
-        - title: Handler Execution Latency
-          type: timeseries
-          description: |
-            Shows latency of Meta handlers by pod and handler name, useful for monitoring handler performance and detecting latency spikes.
-          unit: s
-          queries:
-            - expr: |-
-                histogram_quantile(0.90, sum by(pod, le, name) (
-                  rate(greptime_meta_handler_execute_bucket[$__rate_interval])
-                ))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}} {{name}} p90'
-        - title: Heartbeat Packet Size
-          type: timeseries
-          description: |
-            Shows p90 heartbeat message sizes, helping track network usage and identify anomalies in heartbeat payload.
-          unit: bytes
-          queries:
-            - expr: histogram_quantile(0.9, sum by(pod, le) (greptime_meta_heartbeat_stat_memory_size_bucket))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}}'
-        - title: Meta Heartbeat Receive Rate
-          type: timeseries
-          description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
-          unit: s
-          queries:
-            - expr: rate(greptime_meta_heartbeat_rate[$__rate_interval])
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}}'
-        - title: Meta KV Ops Latency
-          type: timeseries
-          description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.99, sum by(pod, le, op, target) (greptime_meta_kv_request_elapsed_bucket))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}}-{{op}} p99'
-        - title: Rate of meta KV Ops
-          type: timeseries
-          description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
-          unit: none
-          queries:
-            - expr: rate(greptime_meta_kv_request_elapsed_count[$__rate_interval])
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '{{pod}}-{{op}} p99'
-        - title: DDL Latency
-          type: timeseries
-          description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
-          unit: s
-          queries:
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_tables_bucket))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: CreateLogicalTables-{{step}} p90
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_table))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: CreateTable-{{step}} p90
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_view))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: CreateView-{{step}} p90
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_create_flow))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: CreateFlow-{{step}} p90
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_drop_table))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: DropTable-{{step}} p90
-            - expr: histogram_quantile(0.9, sum by(le, pod, step) (greptime_meta_procedure_alter_table))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: AlterTable-{{step}} p90
-    - title: Flownode
-      panels:
-        - title: Flow Ingest / Output Rate
-          type: timeseries
-          description: Flow Ingest / Output Rate.
-          queries:
-            - expr: sum by(instance, pod, direction) (rate(greptime_flow_processed_rows[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{pod}}]-[{{instance}}]-[{{direction}}]'
-        - title: Flow Ingest Latency
-          type: timeseries
-          description: Flow Ingest Latency.
-          queries:
-            - expr: histogram_quantile(0.95, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-p95'
-            - expr: histogram_quantile(0.99, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-p99'
-        - title: Flow Operation Latency
-          type: timeseries
-          description: Flow Operation Latency.
-          queries:
-            - expr: histogram_quantile(0.95, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]-p95'
-            - expr: histogram_quantile(0.99, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]-p99'
-        - title: Flow Buffer Size per Instance
-          type: timeseries
-          description: Flow Buffer Size per Instance.
-          queries:
-            - expr: greptime_flow_input_buf_size
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}]'
-        - title: Flow Processing Error per Instance
-          type: timeseries
-          description: Flow Processing Error per Instance.
-          queries:
-            - expr: sum by(instance,pod,code) (rate(greptime_flow_errors[$__rate_interval]))
-              datasource:
-                type: prometheus
-                uid: ${metrics}
-              legendFormat: '[{{instance}}]-[{{pod}}]-[{{code}}]'

grafana/scripts/check.sh

@@ -1,54 +0,0 @@
-#!/usr/bin/env bash
-
-DASHBOARD_DIR=${1:-grafana/dashboards/metrics}
-
-check_dashboard_description() {
-  for dashboard in $(find $DASHBOARD_DIR -name "*.json"); do
-    echo "Checking $dashboard description"
-
-    # Use jq to check for panels with empty or missing descriptions
-    invalid_panels=$(cat $dashboard | jq -r '
-      .panels[]
-    | select((.type == "stats" or .type == "timeseries") and (.description == "" or .description == null))')
-
-    # Check if any invalid panels were found
-    if [[ -n "$invalid_panels" ]]; then
-      echo "Error: The following panels have empty or missing descriptions:"
-      echo "$invalid_panels"
-      exit 1
-    else
-      echo "All panels with type 'stats' or 'timeseries' have valid descriptions."
-    fi
-  done
-}
-
-check_dashboards_generation() {
-  ./grafana/scripts/gen-dashboards.sh
-
-  if [[ -n "$(git diff --name-only grafana/dashboards/metrics)" ]]; then
-    echo "Error: The dashboards are not generated correctly. You should execute the `make dashboards` command."
-    exit 1
-  fi
-}
-
-check_datasource() {
-  for dashboard in $(find $DASHBOARD_DIR -name "*.json"); do
-    echo "Checking $dashboard datasource"
-    jq -r '.panels[] | select(.type != "row") | .targets[] | [.datasource.type, .datasource.uid] | @tsv' $dashboard | while read -r type uid; do
-    # if the datasource is prometheus, check if the uid is ${metrics}
-    if [[ "$type" == "prometheus" && "$uid" != "\${metrics}" ]]; then
-      echo "Error: The datasource uid of $dashboard is not valid. It should be \${metrics}, got $uid"
-      exit 1
-    fi
-    # if the datasource is mysql, check if the uid is ${information_schema}
-    if [[ "$type" == "mysql" && "$uid" != "\${information_schema}" ]]; then
-      echo "Error: The datasource uid of $dashboard is not valid. It should be \${information_schema}, got $uid"
-      exit 1
-    fi
-    done
-  done
-}
-
-check_dashboards_generation
-check_dashboard_description
-check_datasource

grafana/scripts/gen-dashboards.sh

@@ -1,25 +0,0 @@
-#! /usr/bin/env bash
-
-CLUSTER_DASHBOARD_DIR=${1:-grafana/dashboards/metrics/cluster}
-STANDALONE_DASHBOARD_DIR=${2:-grafana/dashboards/metrics/standalone}
-DAC_IMAGE=ghcr.io/zyy17/dac:20250423-522bd35
-
-remove_instance_filters() {
-  # Remove the instance filters for the standalone dashboards.
-  sed -E 's/instance=~\\"(\$datanode|\$frontend|\$metasrv|\$flownode)\\",?//g' "$CLUSTER_DASHBOARD_DIR/dashboard.json" > "$STANDALONE_DASHBOARD_DIR/dashboard.json"
-}
-
-generate_intermediate_dashboards_and_docs() {
-  docker run -v ${PWD}:/greptimedb --rm ${DAC_IMAGE} \
-    -i /greptimedb/$CLUSTER_DASHBOARD_DIR/dashboard.json \
-    -o /greptimedb/$CLUSTER_DASHBOARD_DIR/dashboard.yaml \
-    -m /greptimedb/$CLUSTER_DASHBOARD_DIR/dashboard.md
-
-  docker run -v ${PWD}:/greptimedb --rm ${DAC_IMAGE} \
-    -i /greptimedb/$STANDALONE_DASHBOARD_DIR/dashboard.json \
-    -o /greptimedb/$STANDALONE_DASHBOARD_DIR/dashboard.yaml \
-    -m /greptimedb/$STANDALONE_DASHBOARD_DIR/dashboard.md
-}
-
-remove_instance_filters
-generate_intermediate_dashboards_and_docs

grafana/summary.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+
+BASEDIR=$(dirname "$0")
+echo '| Title | Description | Expressions |
+|---|---|---|'
+
+cat $BASEDIR/greptimedb-cluster.json | jq -r '
+  .panels |
+  map(select(.type == "stat" or .type == "timeseries")) |
+  .[] | "| \(.title) | \(.description | gsub("\n"; "<br>")) | \(.targets | map(.expr // .rawSql | "`\(.|gsub("\n"; "<br>"))`")  | join("<br>")) |"
+'

licenserc.toml

@@ -26,14 +26,6 @@ excludes = [
     "src/common/base/src/secrets.rs",
     "src/servers/src/repeated_field.rs",
     "src/servers/src/http/test_helpers.rs",
-    # enterprise
-    "src/common/meta/src/rpc/ddl/trigger.rs",
-    "src/operator/src/expr_helper/trigger.rs",
-    "src/sql/src/statements/create/trigger.rs",
-    "src/sql/src/statements/show/trigger.rs",
-    "src/sql/src/statements/drop/trigger.rs",
-    "src/sql/src/parsers/create_parser/trigger.rs",
-    "src/sql/src/parsers/show_parser/trigger.rs",
 ]
 
 [properties]

rust-toolchain.toml

@@ -1,2 +1,2 @@
 [toolchain]
-channel = "nightly-2025-05-19"
+channel = "nightly-2024-12-25"

scripts/check-super-imports.py

@@ -1,74 +0,0 @@
-# Copyright 2023 Greptime Team
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import re
-from multiprocessing import Pool
-
-
-def find_rust_files(directory):
-    rust_files = []
-    for root, _, files in os.walk(directory):
-        # Skip files with "test" in the path
-        if "test" in root.lower():
-            continue
-
-        for file in files:
-            # Skip files with "test" in the filename
-            if "test" in file.lower():
-                continue
-
-            if file.endswith(".rs"):
-                rust_files.append(os.path.join(root, file))
-    return rust_files
-
-
-def check_file_for_super_import(file_path):
-    with open(file_path, "r") as file:
-        lines = file.readlines()
-
-    violations = []
-    for line_number, line in enumerate(lines, 1):
-        # Check for "use super::" without leading tab
-        if line.startswith("use super::"):
-            violations.append((line_number, line.strip()))
-
-    if violations:
-        return file_path, violations
-    return None
-
-
-def main():
-    rust_files = find_rust_files(".")
-
-    with Pool() as pool:
-        results = pool.map(check_file_for_super_import, rust_files)
-
-    # Filter out None results
-    violations = [result for result in results if result]
-
-    if violations:
-        print("Found 'use super::' without leading tab in the following files:")
-        counter = 1
-        for file_path, file_violations in violations:
-            for line_number, line in file_violations:
-                print(f"{counter:>5} {file_path}:{line_number} - {line}")
-                counter += 1
-        raise SystemExit(1)
-    else:
-        print("No 'use super::' without leading tab found. All files are compliant.")
-
-
-if __name__ == "__main__":
-    main()

src/api/src/helper.rs

@@ -514,7 +514,6 @@ fn query_request_type(request: &QueryRequest) -> &'static str {
         Some(Query::Sql(_)) => "query.sql",
         Some(Query::LogicalPlan(_)) => "query.logical_plan",
         Some(Query::PromRangeQuery(_)) => "query.prom_range",
-        Some(Query::InsertIntoPlan(_)) => "query.insert_into_plan",
         None => "query.empty",
     }
 }
@@ -1050,7 +1049,7 @@ pub fn value_to_grpc_value(value: Value) -> GrpcValue {
             Value::Int64(v) => Some(ValueData::I64Value(v)),
             Value::Float32(v) => Some(ValueData::F32Value(*v)),
             Value::Float64(v) => Some(ValueData::F64Value(*v)),
-            Value::String(v) => Some(ValueData::StringValue(v.into_string())),
+            Value::String(v) => Some(ValueData::StringValue(v.as_utf8().to_string())),
             Value::Binary(v) => Some(ValueData::BinaryValue(v.to_vec())),
             Value::Date(v) => Some(ValueData::DateValue(v.val())),
             Value::Timestamp(v) => Some(match v.unit() {

src/api/src/region.rs

@@ -22,7 +22,6 @@ use greptime_proto::v1::region::RegionResponse as RegionResponseV1;
 pub struct RegionResponse {
     pub affected_rows: AffectedRows,
     pub extensions: HashMap<String, Vec<u8>>,
-    pub metadata: Vec<u8>,
 }
 
 impl RegionResponse {
@@ -30,7 +29,6 @@ impl RegionResponse {
         Self {
             affected_rows: region_response.affected_rows as _,
             extensions: region_response.extensions,
-            metadata: region_response.metadata,
         }
     }
 
@@ -39,16 +37,6 @@ impl RegionResponse {
         Self {
             affected_rows,
             extensions: Default::default(),
-            metadata: Vec::new(),
-        }
-    }
-
-    /// Creates one response with metadata.
-    pub fn from_metadata(metadata: Vec<u8>) -> Self {
-        Self {
-            affected_rows: 0,
-            extensions: Default::default(),
-            metadata,
         }
     }
 }

src/api/src/v1/column_def.rs

@@ -15,13 +15,10 @@
 use std::collections::HashMap;
 
 use datatypes::schema::{
-    ColumnDefaultConstraint, ColumnSchema, FulltextAnalyzer, FulltextBackend, FulltextOptions,
-    SkippingIndexOptions, SkippingIndexType, COMMENT_KEY, FULLTEXT_KEY, INVERTED_INDEX_KEY,
-    SKIPPING_INDEX_KEY,
-};
-use greptime_proto::v1::{
-    Analyzer, FulltextBackend as PbFulltextBackend, SkippingIndexType as PbSkippingIndexType,
+    ColumnDefaultConstraint, ColumnSchema, FulltextAnalyzer, FulltextOptions, SkippingIndexOptions,
+    SkippingIndexType, COMMENT_KEY, FULLTEXT_KEY, INVERTED_INDEX_KEY, SKIPPING_INDEX_KEY,
 };
+use greptime_proto::v1::{Analyzer, SkippingIndexType as PbSkippingIndexType};
 use snafu::ResultExt;
 
 use crate::error::{self, Result};
@@ -145,21 +142,13 @@ pub fn options_from_inverted() -> ColumnOptions {
 }
 
 /// Tries to construct a `FulltextAnalyzer` from the given analyzer.
-pub fn as_fulltext_option_analyzer(analyzer: Analyzer) -> FulltextAnalyzer {
+pub fn as_fulltext_option(analyzer: Analyzer) -> FulltextAnalyzer {
     match analyzer {
         Analyzer::English => FulltextAnalyzer::English,
         Analyzer::Chinese => FulltextAnalyzer::Chinese,
     }
 }
 
-/// Tries to construct a `FulltextBackend` from the given backend.
-pub fn as_fulltext_option_backend(backend: PbFulltextBackend) -> FulltextBackend {
-    match backend {
-        PbFulltextBackend::Bloom => FulltextBackend::Bloom,
-        PbFulltextBackend::Tantivy => FulltextBackend::Tantivy,
-    }
-}
-
 /// Tries to construct a `SkippingIndexType` from the given skipping index type.
 pub fn as_skipping_index_type(skipping_index_type: PbSkippingIndexType) -> SkippingIndexType {
     match skipping_index_type {
@@ -171,7 +160,7 @@ pub fn as_skipping_index_type(skipping_index_type: PbSkippingIndexType) -> Skipp
 mod tests {
 
     use datatypes::data_type::ConcreteDataType;
-    use datatypes::schema::{FulltextAnalyzer, FulltextBackend};
+    use datatypes::schema::FulltextAnalyzer;
 
     use super::*;
     use crate::v1::ColumnDataType;
@@ -230,14 +219,13 @@ mod tests {
                 enable: true,
                 analyzer: FulltextAnalyzer::English,
                 case_sensitive: false,
-                backend: FulltextBackend::Bloom,
             })
             .unwrap();
         schema.set_inverted_index(true);
         let options = options_from_column_schema(&schema).unwrap();
         assert_eq!(
             options.options.get(FULLTEXT_GRPC_KEY).unwrap(),
-            "{\"enable\":true,\"analyzer\":\"English\",\"case-sensitive\":false,\"backend\":\"bloom\"}"
+            "{\"enable\":true,\"analyzer\":\"English\",\"case-sensitive\":false}"
         );
         assert_eq!(
             options.options.get(INVERTED_INDEX_GRPC_KEY).unwrap(),
@@ -251,12 +239,11 @@ mod tests {
             enable: true,
             analyzer: FulltextAnalyzer::English,
             case_sensitive: false,
-            backend: FulltextBackend::Bloom,
         };
         let options = options_from_fulltext(&fulltext).unwrap().unwrap();
         assert_eq!(
             options.options.get(FULLTEXT_GRPC_KEY).unwrap(),
-            "{\"enable\":true,\"analyzer\":\"English\",\"case-sensitive\":false,\"backend\":\"bloom\"}"
+            "{\"enable\":true,\"analyzer\":\"English\",\"case-sensitive\":false}"
         );
     }
 

src/auth/src/common.rs

@@ -36,7 +36,7 @@ pub fn userinfo_by_name(username: Option<String>) -> UserInfoRef {
 }
 
 pub fn user_provider_from_option(opt: &String) -> Result<UserProviderRef> {
-    let (name, content) = opt.split_once(':').with_context(|| InvalidConfigSnafu {
+    let (name, content) = opt.split_once(':').context(InvalidConfigSnafu {
         value: opt.to_string(),
         msg: "UserProviderOption must be in format `<option>:<value>`",
     })?;
@@ -57,24 +57,6 @@ pub fn user_provider_from_option(opt: &String) -> Result<UserProviderRef> {
     }
 }
 
-pub fn static_user_provider_from_option(opt: &String) -> Result<StaticUserProvider> {
-    let (name, content) = opt.split_once(':').with_context(|| InvalidConfigSnafu {
-        value: opt.to_string(),
-        msg: "UserProviderOption must be in format `<option>:<value>`",
-    })?;
-    match name {
-        STATIC_USER_PROVIDER => {
-            let provider = StaticUserProvider::new(content)?;
-            Ok(provider)
-        }
-        _ => InvalidConfigSnafu {
-            value: name.to_string(),
-            msg: format!("Invalid UserProviderOption, expect only {STATIC_USER_PROVIDER}"),
-        }
-        .fail(),
-    }
-}
-
 type Username<'a> = &'a str;
 type HostOrIp<'a> = &'a str;
 

src/auth/src/error.rs

@@ -38,14 +38,6 @@ pub enum Error {
         location: Location,
     },
 
-    #[snafu(display("Failed to convert to utf8"))]
-    FromUtf8 {
-        #[snafu(source)]
-        error: std::string::FromUtf8Error,
-        #[snafu(implicit)]
-        location: Location,
-    },
-
     #[snafu(display("Authentication source failure"))]
     AuthBackend {
         #[snafu(implicit)]
@@ -93,7 +85,7 @@ impl ErrorExt for Error {
     fn status_code(&self) -> StatusCode {
         match self {
             Error::InvalidConfig { .. } => StatusCode::InvalidArguments,
-            Error::IllegalParam { .. } | Error::FromUtf8 { .. } => StatusCode::InvalidArguments,
+            Error::IllegalParam { .. } => StatusCode::InvalidArguments,
             Error::FileWatch { .. } => StatusCode::InvalidArguments,
             Error::InternalState { .. } => StatusCode::Unexpected,
             Error::Io { .. } => StatusCode::StorageUnavailable,

src/auth/src/lib.rs

@@ -22,12 +22,10 @@ mod user_provider;
 pub mod tests;
 
 pub use common::{
-    auth_mysql, static_user_provider_from_option, user_provider_from_option, userinfo_by_name,
-    HashedPassword, Identity, Password,
+    auth_mysql, user_provider_from_option, userinfo_by_name, HashedPassword, Identity, Password,
 };
 pub use permission::{PermissionChecker, PermissionReq, PermissionResp};
 pub use user_info::UserInfo;
-pub use user_provider::static_user_provider::StaticUserProvider;
 pub use user_provider::UserProvider;
 
 /// pub type alias

src/auth/src/user_provider/static_user_provider.rs

@@ -15,15 +15,15 @@
 use std::collections::HashMap;
 
 use async_trait::async_trait;
-use snafu::{OptionExt, ResultExt};
+use snafu::OptionExt;
 
-use crate::error::{FromUtf8Snafu, InvalidConfigSnafu, Result};
+use crate::error::{InvalidConfigSnafu, Result};
 use crate::user_provider::{authenticate_with_credential, load_credential_from_file};
 use crate::{Identity, Password, UserInfoRef, UserProvider};
 
 pub(crate) const STATIC_USER_PROVIDER: &str = "static_user_provider";
 
-pub struct StaticUserProvider {
+pub(crate) struct StaticUserProvider {
     users: HashMap<String, Vec<u8>>,
 }
 
@@ -60,18 +60,6 @@ impl StaticUserProvider {
                 .fail(),
         }
     }
-
-    /// Return a random username/password pair
-    /// This is useful for invoking from other components in the cluster
-    pub fn get_one_user_pwd(&self) -> Result<(String, String)> {
-        let kv = self.users.iter().next().context(InvalidConfigSnafu {
-            value: "",
-            msg: "Expect at least one pair of username and password",
-        })?;
-        let username = kv.0;
-        let pwd = String::from_utf8(kv.1.clone()).context(FromUtf8Snafu)?;
-        Ok((username.clone(), pwd))
-    }
 }
 
 #[async_trait]

src/catalog/Cargo.toml

@@ -17,10 +17,8 @@ arrow-schema.workspace = true
 async-stream.workspace = true
 async-trait.workspace = true
 bytes.workspace = true
-common-base.workspace = true
 common-catalog.workspace = true
 common-error.workspace = true
-common-frontend.workspace = true
 common-macro.workspace = true
 common-meta.workspace = true
 common-procedure.workspace = true

src/catalog/src/error.rs

@@ -277,26 +277,6 @@ pub enum Error {
         #[snafu(implicit)]
         location: Location,
     },
-
-    #[snafu(display("Failed to invoke frontend services"))]
-    InvokeFrontend {
-        source: common_frontend::error::Error,
-        #[snafu(implicit)]
-        location: Location,
-    },
-
-    #[snafu(display("Meta client is not provided"))]
-    MetaClientMissing {
-        #[snafu(implicit)]
-        location: Location,
-    },
-
-    #[snafu(display("Failed to find frontend node: {}", addr))]
-    FrontendNotFound {
-        addr: String,
-        #[snafu(implicit)]
-        location: Location,
-    },
 }
 
 impl Error {
@@ -365,10 +345,6 @@ impl ErrorExt for Error {
             Error::GetViewCache { source, .. } | Error::GetTableCache { source, .. } => {
                 source.status_code()
             }
-            Error::InvokeFrontend { source, .. } => source.status_code(),
-            Error::FrontendNotFound { .. } | Error::MetaClientMissing { .. } => {
-                StatusCode::Unexpected
-            }
         }
     }
 

src/catalog/src/kvbackend/manager.rs

@@ -22,9 +22,7 @@ use common_catalog::consts::{
     PG_CATALOG_NAME,
 };
 use common_error::ext::BoxedError;
-use common_meta::cache::{
-    LayeredCacheRegistryRef, TableRoute, TableRouteCacheRef, ViewInfoCacheRef,
-};
+use common_meta::cache::{LayeredCacheRegistryRef, ViewInfoCacheRef};
 use common_meta::key::catalog_name::CatalogNameKey;
 use common_meta::key::flow::FlowMetadataManager;
 use common_meta::key::schema_name::SchemaNameKey;
@@ -53,7 +51,6 @@ use crate::error::{
 };
 use crate::information_schema::{InformationExtensionRef, InformationSchemaProvider};
 use crate::kvbackend::TableCacheRef;
-use crate::process_manager::ProcessManagerRef;
 use crate::system_schema::pg_catalog::PGCatalogProvider;
 use crate::system_schema::SystemSchemaProvider;
 use crate::CatalogManager;
@@ -87,7 +84,6 @@ impl KvBackendCatalogManager {
         backend: KvBackendRef,
         cache_registry: LayeredCacheRegistryRef,
         procedure_manager: Option<ProcedureManagerRef>,
-        process_manager: Option<ProcessManagerRef>,
     ) -> Arc<Self> {
         Arc::new_cyclic(|me| Self {
             information_extension,
@@ -106,14 +102,12 @@ impl KvBackendCatalogManager {
                     DEFAULT_CATALOG_NAME.to_string(),
                     me.clone(),
                     Arc::new(FlowMetadataManager::new(backend.clone())),
-                    process_manager.clone(),
                 )),
                 pg_catalog_provider: Arc::new(PGCatalogProvider::new(
                     DEFAULT_CATALOG_NAME.to_string(),
                     me.clone(),
                 )),
                 backend,
-                process_manager,
             },
             cache_registry,
             procedure_manager,
@@ -268,68 +262,16 @@ impl CatalogManager for KvBackendCatalogManager {
         let table_cache: TableCacheRef = self.cache_registry.get().context(CacheNotFoundSnafu {
             name: "table_cache",
         })?;
-        let table_route_cache: TableRouteCacheRef =
-            self.cache_registry.get().context(CacheNotFoundSnafu {
-                name: "table_route_cache",
-            })?;
-        let table = table_cache
+        if let Some(table) = table_cache
             .get_by_ref(&TableName {
                 catalog_name: catalog_name.to_string(),
                 schema_name: schema_name.to_string(),
                 table_name: table_name.to_string(),
             })
             .await
-            .context(GetTableCacheSnafu)?;
-
-        // Override logical table's partition key indices with physical table's.
-        if let Some(table) = &table
-            && let Some(table_route_value) = table_route_cache
-                .get(table.table_info().table_id())
-                .await
-                .context(TableMetadataManagerSnafu)?
-            && let TableRoute::Logical(logical_route) = &*table_route_value
-            && let Some(physical_table_info_value) = self
-                .table_metadata_manager
-                .table_info_manager()
-                .get(logical_route.physical_table_id())
-                .await
-                .context(TableMetadataManagerSnafu)?
+            .context(GetTableCacheSnafu)?
         {
-            let mut new_table_info = (*table.table_info()).clone();
-            // Gather all column names from the logical table
-            let logical_column_names: std::collections::HashSet<_> = new_table_info
-                .meta
-                .schema
-                .column_schemas()
-                .iter()
-                .map(|col| &col.name)
-                .collect();
-
-            // Only preserve partition key indices where the corresponding columns exist in logical table
-            new_table_info.meta.partition_key_indices = physical_table_info_value
-                .table_info
-                .meta
-                .partition_key_indices
-                .iter()
-                .filter(|&&index| {
-                    if let Some(physical_column) = physical_table_info_value
-                        .table_info
-                        .meta
-                        .schema
-                        .column_schemas
-                        .get(index)
-                    {
-                        logical_column_names.contains(&physical_column.name)
-                    } else {
-                        false
-                    }
-                })
-                .cloned()
-                .collect();
-
-            let new_table = DistTable::table(Arc::new(new_table_info));
-
-            return Ok(Some(new_table));
+            return Ok(Some(table));
         }
 
         if channel == Channel::Postgres {
@@ -342,7 +284,7 @@ impl CatalogManager for KvBackendCatalogManager {
             }
         }
 
-        Ok(table)
+        return Ok(None);
     }
 
     async fn tables_by_ids(
@@ -477,7 +419,6 @@ struct SystemCatalog {
     information_schema_provider: Arc<InformationSchemaProvider>,
     pg_catalog_provider: Arc<PGCatalogProvider>,
     backend: KvBackendRef,
-    process_manager: Option<ProcessManagerRef>,
 }
 
 impl SystemCatalog {
@@ -545,7 +486,6 @@ impl SystemCatalog {
                         catalog.to_string(),
                         self.catalog_manager.clone(),
                         Arc::new(FlowMetadataManager::new(self.backend.clone())),
-                        self.process_manager.clone(),
                     ))
                 });
             information_schema_provider.table(table_name)

src/catalog/src/lib.rs

@@ -14,7 +14,6 @@
 
 #![feature(assert_matches)]
 #![feature(try_blocks)]
-#![feature(let_chains)]
 
 use std::any::Any;
 use std::fmt::{Debug, Formatter};
@@ -41,7 +40,6 @@ pub mod information_schema {
     pub use crate::system_schema::information_schema::*;
 }
 
-pub mod process_manager;
 pub mod table_source;
 
 #[async_trait::async_trait]

src/catalog/src/memory/manager.rs

@@ -356,7 +356,6 @@ impl MemoryCatalogManager {
             catalog,
             Arc::downgrade(self) as Weak<dyn CatalogManager>,
             Arc::new(FlowMetadataManager::new(Arc::new(MemoryKvBackend::new()))),
-            None, // we don't need ProcessManager on regions server.
         );
         let information_schema = information_schema_provider.tables().clone();
 

src/catalog/src/metrics.rs

@@ -34,20 +34,4 @@ lazy_static! {
         register_histogram!("greptime_catalog_kv_get", "catalog kv get").unwrap();
     pub static ref METRIC_CATALOG_KV_BATCH_GET: Histogram =
         register_histogram!("greptime_catalog_kv_batch_get", "catalog kv batch get").unwrap();
-
-    /// Count of running process in each catalog.
-    pub static ref PROCESS_LIST_COUNT: IntGaugeVec = register_int_gauge_vec!(
-        "greptime_process_list_count",
-        "Running process count per catalog",
-        &["catalog"]
-    )
-    .unwrap();
-
-    /// Count of killed process in each catalog.
-    pub static ref PROCESS_KILL_COUNT: IntCounterVec = register_int_counter_vec!(
-        "greptime_process_kill_count",
-        "Completed kill process requests count",
-        &["catalog"]
-    )
-    .unwrap();
 }

src/catalog/src/process_manager.rs

@@ -1,494 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::collections::hash_map::Entry;
-use std::collections::HashMap;
-use std::fmt::{Debug, Formatter};
-use std::sync::atomic::{AtomicU32, Ordering};
-use std::sync::{Arc, RwLock};
-
-use api::v1::frontend::{KillProcessRequest, ListProcessRequest, ProcessInfo};
-use common_base::cancellation::CancellationHandle;
-use common_frontend::selector::{FrontendSelector, MetaClientSelector};
-use common_telemetry::{debug, info};
-use common_time::util::current_time_millis;
-use meta_client::MetaClientRef;
-use snafu::{ensure, OptionExt, ResultExt};
-
-use crate::error;
-use crate::metrics::{PROCESS_KILL_COUNT, PROCESS_LIST_COUNT};
-
-pub type ProcessId = u32;
-pub type ProcessManagerRef = Arc<ProcessManager>;
-
-/// Query process manager.
-pub struct ProcessManager {
-    /// Local frontend server address,
-    server_addr: String,
-    /// Next process id for local queries.
-    next_id: AtomicU32,
-    /// Running process per catalog.
-    catalogs: RwLock<HashMap<String, HashMap<ProcessId, CancellableProcess>>>,
-    /// Frontend selector to locate frontend nodes.
-    frontend_selector: Option<MetaClientSelector>,
-}
-
-impl ProcessManager {
-    /// Create a [ProcessManager] instance with server address and kv client.
-    pub fn new(server_addr: String, meta_client: Option<MetaClientRef>) -> Self {
-        let frontend_selector = meta_client.map(MetaClientSelector::new);
-        Self {
-            server_addr,
-            next_id: Default::default(),
-            catalogs: Default::default(),
-            frontend_selector,
-        }
-    }
-}
-
-impl ProcessManager {
-    /// Registers a submitted query. Use the provided id if present.
-    #[must_use]
-    pub fn register_query(
-        self: &Arc<Self>,
-        catalog: String,
-        schemas: Vec<String>,
-        query: String,
-        client: String,
-        query_id: Option<ProcessId>,
-    ) -> Ticket {
-        let id = query_id.unwrap_or_else(|| self.next_id.fetch_add(1, Ordering::Relaxed));
-        let process = ProcessInfo {
-            id,
-            catalog: catalog.clone(),
-            schemas,
-            query,
-            start_timestamp: current_time_millis(),
-            client,
-            frontend: self.server_addr.clone(),
-        };
-        let cancellation_handle = Arc::new(CancellationHandle::default());
-        let cancellable_process = CancellableProcess::new(cancellation_handle.clone(), process);
-
-        self.catalogs
-            .write()
-            .unwrap()
-            .entry(catalog.clone())
-            .or_default()
-            .insert(id, cancellable_process);
-
-        Ticket {
-            catalog,
-            manager: self.clone(),
-            id,
-            cancellation_handle,
-        }
-    }
-
-    /// Generates the next process id.
-    pub fn next_id(&self) -> u32 {
-        self.next_id.fetch_add(1, Ordering::Relaxed)
-    }
-
-    /// De-register a query from process list.
-    pub fn deregister_query(&self, catalog: String, id: ProcessId) {
-        if let Entry::Occupied(mut o) = self.catalogs.write().unwrap().entry(catalog) {
-            let process = o.get_mut().remove(&id);
-            debug!("Deregister process: {:?}", process);
-            if o.get().is_empty() {
-                o.remove();
-            }
-        }
-    }
-
-    /// List local running processes in given catalog.
-    pub fn local_processes(&self, catalog: Option<&str>) -> error::Result<Vec<ProcessInfo>> {
-        let catalogs = self.catalogs.read().unwrap();
-        let result = if let Some(catalog) = catalog {
-            if let Some(catalogs) = catalogs.get(catalog) {
-                catalogs.values().map(|p| p.process.clone()).collect()
-            } else {
-                vec![]
-            }
-        } else {
-            catalogs
-                .values()
-                .flat_map(|v| v.values().map(|p| p.process.clone()))
-                .collect()
-        };
-        Ok(result)
-    }
-
-    pub async fn list_all_processes(
-        &self,
-        catalog: Option<&str>,
-    ) -> error::Result<Vec<ProcessInfo>> {
-        let mut processes = vec![];
-        if let Some(remote_frontend_selector) = self.frontend_selector.as_ref() {
-            let frontends = remote_frontend_selector
-                .select(|node| node.peer.addr != self.server_addr)
-                .await
-                .context(error::InvokeFrontendSnafu)?;
-            for mut f in frontends {
-                processes.extend(
-                    f.list_process(ListProcessRequest {
-                        catalog: catalog.unwrap_or_default().to_string(),
-                    })
-                    .await
-                    .context(error::InvokeFrontendSnafu)?
-                    .processes,
-                );
-            }
-        }
-        processes.extend(self.local_processes(catalog)?);
-        Ok(processes)
-    }
-
-    /// Kills query with provided catalog and id.
-    pub async fn kill_process(
-        &self,
-        server_addr: String,
-        catalog: String,
-        id: ProcessId,
-    ) -> error::Result<bool> {
-        if server_addr == self.server_addr {
-            self.kill_local_process(catalog, id).await
-        } else {
-            let mut nodes = self
-                .frontend_selector
-                .as_ref()
-                .context(error::MetaClientMissingSnafu)?
-                .select(|node| node.peer.addr == server_addr)
-                .await
-                .context(error::InvokeFrontendSnafu)?;
-            ensure!(
-                !nodes.is_empty(),
-                error::FrontendNotFoundSnafu { addr: server_addr }
-            );
-
-            let request = KillProcessRequest {
-                server_addr,
-                catalog,
-                process_id: id,
-            };
-            nodes[0]
-                .kill_process(request)
-                .await
-                .context(error::InvokeFrontendSnafu)?;
-            Ok(true)
-        }
-    }
-
-    /// Kills local query with provided catalog and id.
-    pub async fn kill_local_process(&self, catalog: String, id: ProcessId) -> error::Result<bool> {
-        if let Some(catalogs) = self.catalogs.write().unwrap().get_mut(&catalog) {
-            if let Some(process) = catalogs.remove(&id) {
-                process.handle.cancel();
-                info!(
-                    "Killed process, catalog: {}, id: {:?}",
-                    process.process.catalog, process.process.id
-                );
-                PROCESS_KILL_COUNT.with_label_values(&[&catalog]).inc();
-                Ok(true)
-            } else {
-                debug!("Failed to kill process, id not found: {}", id);
-                Ok(false)
-            }
-        } else {
-            debug!("Failed to kill process, catalog not found: {}", catalog);
-            Ok(false)
-        }
-    }
-}
-
-pub struct Ticket {
-    pub(crate) catalog: String,
-    pub(crate) manager: ProcessManagerRef,
-    pub(crate) id: ProcessId,
-    pub cancellation_handle: Arc<CancellationHandle>,
-}
-
-impl Drop for Ticket {
-    fn drop(&mut self) {
-        self.manager
-            .deregister_query(std::mem::take(&mut self.catalog), self.id);
-    }
-}
-
-struct CancellableProcess {
-    handle: Arc<CancellationHandle>,
-    process: ProcessInfo,
-}
-
-impl Drop for CancellableProcess {
-    fn drop(&mut self) {
-        PROCESS_LIST_COUNT
-            .with_label_values(&[&self.process.catalog])
-            .dec();
-    }
-}
-
-impl CancellableProcess {
-    fn new(handle: Arc<CancellationHandle>, process: ProcessInfo) -> Self {
-        PROCESS_LIST_COUNT
-            .with_label_values(&[&process.catalog])
-            .inc();
-        Self { handle, process }
-    }
-}
-
-impl Debug for CancellableProcess {
-    fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
-        f.debug_struct("CancellableProcess")
-            .field("cancelled", &self.handle.is_cancelled())
-            .field("process", &self.process)
-            .finish()
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use std::sync::Arc;
-
-    use crate::process_manager::ProcessManager;
-
-    #[tokio::test]
-    async fn test_register_query() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-        let ticket = process_manager.clone().register_query(
-            "public".to_string(),
-            vec!["test".to_string()],
-            "SELECT * FROM table".to_string(),
-            "".to_string(),
-            None,
-        );
-
-        let running_processes = process_manager.local_processes(None).unwrap();
-        assert_eq!(running_processes.len(), 1);
-        assert_eq!(&running_processes[0].frontend, "127.0.0.1:8000");
-        assert_eq!(running_processes[0].id, ticket.id);
-        assert_eq!(&running_processes[0].query, "SELECT * FROM table");
-
-        drop(ticket);
-        assert_eq!(process_manager.local_processes(None).unwrap().len(), 0);
-    }
-
-    #[tokio::test]
-    async fn test_register_query_with_custom_id() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-        let custom_id = 12345;
-
-        let ticket = process_manager.clone().register_query(
-            "public".to_string(),
-            vec!["test".to_string()],
-            "SELECT * FROM table".to_string(),
-            "client1".to_string(),
-            Some(custom_id),
-        );
-
-        assert_eq!(ticket.id, custom_id);
-
-        let running_processes = process_manager.local_processes(None).unwrap();
-        assert_eq!(running_processes.len(), 1);
-        assert_eq!(running_processes[0].id, custom_id);
-        assert_eq!(&running_processes[0].client, "client1");
-    }
-
-    #[tokio::test]
-    async fn test_multiple_queries_same_catalog() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-
-        let ticket1 = process_manager.clone().register_query(
-            "public".to_string(),
-            vec!["schema1".to_string()],
-            "SELECT * FROM table1".to_string(),
-            "client1".to_string(),
-            None,
-        );
-
-        let ticket2 = process_manager.clone().register_query(
-            "public".to_string(),
-            vec!["schema2".to_string()],
-            "SELECT * FROM table2".to_string(),
-            "client2".to_string(),
-            None,
-        );
-
-        let running_processes = process_manager.local_processes(Some("public")).unwrap();
-        assert_eq!(running_processes.len(), 2);
-
-        // Verify both processes are present
-        let ids: Vec<u32> = running_processes.iter().map(|p| p.id).collect();
-        assert!(ids.contains(&ticket1.id));
-        assert!(ids.contains(&ticket2.id));
-    }
-
-    #[tokio::test]
-    async fn test_multiple_catalogs() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-
-        let _ticket1 = process_manager.clone().register_query(
-            "catalog1".to_string(),
-            vec!["schema1".to_string()],
-            "SELECT * FROM table1".to_string(),
-            "client1".to_string(),
-            None,
-        );
-
-        let _ticket2 = process_manager.clone().register_query(
-            "catalog2".to_string(),
-            vec!["schema2".to_string()],
-            "SELECT * FROM table2".to_string(),
-            "client2".to_string(),
-            None,
-        );
-
-        // Test listing processes for specific catalog
-        let catalog1_processes = process_manager.local_processes(Some("catalog1")).unwrap();
-        assert_eq!(catalog1_processes.len(), 1);
-        assert_eq!(&catalog1_processes[0].catalog, "catalog1");
-
-        let catalog2_processes = process_manager.local_processes(Some("catalog2")).unwrap();
-        assert_eq!(catalog2_processes.len(), 1);
-        assert_eq!(&catalog2_processes[0].catalog, "catalog2");
-
-        // Test listing all processes
-        let all_processes = process_manager.local_processes(None).unwrap();
-        assert_eq!(all_processes.len(), 2);
-    }
-
-    #[tokio::test]
-    async fn test_deregister_query() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-
-        let ticket = process_manager.clone().register_query(
-            "public".to_string(),
-            vec!["test".to_string()],
-            "SELECT * FROM table".to_string(),
-            "client1".to_string(),
-            None,
-        );
-        assert_eq!(process_manager.local_processes(None).unwrap().len(), 1);
-        process_manager.deregister_query("public".to_string(), ticket.id);
-        assert_eq!(process_manager.local_processes(None).unwrap().len(), 0);
-    }
-
-    #[tokio::test]
-    async fn test_cancellation_handle() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-
-        let ticket = process_manager.clone().register_query(
-            "public".to_string(),
-            vec!["test".to_string()],
-            "SELECT * FROM table".to_string(),
-            "client1".to_string(),
-            None,
-        );
-
-        assert!(!ticket.cancellation_handle.is_cancelled());
-        ticket.cancellation_handle.cancel();
-        assert!(ticket.cancellation_handle.is_cancelled());
-    }
-
-    #[tokio::test]
-    async fn test_kill_local_process() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-
-        let ticket = process_manager.clone().register_query(
-            "public".to_string(),
-            vec!["test".to_string()],
-            "SELECT * FROM table".to_string(),
-            "client1".to_string(),
-            None,
-        );
-        assert!(!ticket.cancellation_handle.is_cancelled());
-        let killed = process_manager
-            .kill_process(
-                "127.0.0.1:8000".to_string(),
-                "public".to_string(),
-                ticket.id,
-            )
-            .await
-            .unwrap();
-
-        assert!(killed);
-        assert_eq!(process_manager.local_processes(None).unwrap().len(), 0);
-    }
-
-    #[tokio::test]
-    async fn test_kill_nonexistent_process() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-        let killed = process_manager
-            .kill_process("127.0.0.1:8000".to_string(), "public".to_string(), 999)
-            .await
-            .unwrap();
-        assert!(!killed);
-    }
-
-    #[tokio::test]
-    async fn test_kill_process_nonexistent_catalog() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-        let killed = process_manager
-            .kill_process("127.0.0.1:8000".to_string(), "nonexistent".to_string(), 1)
-            .await
-            .unwrap();
-        assert!(!killed);
-    }
-
-    #[tokio::test]
-    async fn test_process_info_fields() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-
-        let _ticket = process_manager.clone().register_query(
-            "test_catalog".to_string(),
-            vec!["schema1".to_string(), "schema2".to_string()],
-            "SELECT COUNT(*) FROM users WHERE age > 18".to_string(),
-            "test_client".to_string(),
-            Some(42),
-        );
-
-        let processes = process_manager.local_processes(None).unwrap();
-        assert_eq!(processes.len(), 1);
-
-        let process = &processes[0];
-        assert_eq!(process.id, 42);
-        assert_eq!(&process.catalog, "test_catalog");
-        assert_eq!(process.schemas, vec!["schema1", "schema2"]);
-        assert_eq!(&process.query, "SELECT COUNT(*) FROM users WHERE age > 18");
-        assert_eq!(&process.client, "test_client");
-        assert_eq!(&process.frontend, "127.0.0.1:8000");
-        assert!(process.start_timestamp > 0);
-    }
-
-    #[tokio::test]
-    async fn test_ticket_drop_deregisters_process() {
-        let process_manager = Arc::new(ProcessManager::new("127.0.0.1:8000".to_string(), None));
-
-        {
-            let _ticket = process_manager.clone().register_query(
-                "public".to_string(),
-                vec!["test".to_string()],
-                "SELECT * FROM table".to_string(),
-                "client1".to_string(),
-                None,
-            );
-
-            // Process should be registered
-            assert_eq!(process_manager.local_processes(None).unwrap().len(), 1);
-        } // ticket goes out of scope here
-
-        // Process should be automatically deregistered
-        assert_eq!(process_manager.local_processes(None).unwrap().len(), 0);
-    }
-}

src/catalog/src/system_schema/information_schema.rs

@@ -19,8 +19,7 @@ mod information_memory_table;
 pub mod key_column_usage;
 mod partitions;
 mod procedure_info;
-pub mod process_list;
-pub mod region_peers;
+mod region_peers;
 mod region_statistics;
 mod runtime_metrics;
 pub mod schemata;
@@ -43,7 +42,6 @@ use common_recordbatch::SendableRecordBatchStream;
 use datatypes::schema::SchemaRef;
 use lazy_static::lazy_static;
 use paste::paste;
-use process_list::InformationSchemaProcessList;
 use store_api::storage::{ScanRequest, TableId};
 use table::metadata::TableType;
 use table::TableRef;
@@ -51,8 +49,8 @@ pub use table_names::*;
 use views::InformationSchemaViews;
 
 use self::columns::InformationSchemaColumns;
+use super::{SystemSchemaProviderInner, SystemTable, SystemTableRef};
 use crate::error::{Error, Result};
-use crate::process_manager::ProcessManagerRef;
 use crate::system_schema::information_schema::cluster_info::InformationSchemaClusterInfo;
 use crate::system_schema::information_schema::flows::InformationSchemaFlows;
 use crate::system_schema::information_schema::information_memory_table::get_schema_columns;
@@ -65,9 +63,7 @@ use crate::system_schema::information_schema::table_constraints::InformationSche
 use crate::system_schema::information_schema::tables::InformationSchemaTables;
 use crate::system_schema::memory_table::MemoryTable;
 pub(crate) use crate::system_schema::predicate::Predicates;
-use crate::system_schema::{
-    SystemSchemaProvider, SystemSchemaProviderInner, SystemTable, SystemTableRef,
-};
+use crate::system_schema::SystemSchemaProvider;
 use crate::CatalogManager;
 
 lazy_static! {
@@ -116,7 +112,6 @@ macro_rules! setup_memory_table {
 pub struct InformationSchemaProvider {
     catalog_name: String,
     catalog_manager: Weak<dyn CatalogManager>,
-    process_manager: Option<ProcessManagerRef>,
     flow_metadata_manager: Arc<FlowMetadataManager>,
     tables: HashMap<String, TableRef>,
 }
@@ -211,10 +206,6 @@ impl SystemSchemaProviderInner for InformationSchemaProvider {
                     self.catalog_manager.clone(),
                 ),
             ) as _),
-            PROCESS_LIST => self
-                .process_manager
-                .as_ref()
-                .map(|p| Arc::new(InformationSchemaProcessList::new(p.clone())) as _),
             _ => None,
         }
     }
@@ -225,13 +216,11 @@ impl InformationSchemaProvider {
         catalog_name: String,
         catalog_manager: Weak<dyn CatalogManager>,
         flow_metadata_manager: Arc<FlowMetadataManager>,
-        process_manager: Option<ProcessManagerRef>,
     ) -> Self {
         let mut provider = Self {
             catalog_name,
             catalog_manager,
             flow_metadata_manager,
-            process_manager,
             tables: HashMap::new(),
         };
 
@@ -287,9 +276,6 @@ impl InformationSchemaProvider {
             self.build_table(TABLE_CONSTRAINTS).unwrap(),
         );
         tables.insert(FLOWS.to_string(), self.build_table(FLOWS).unwrap());
-        if let Some(process_list) = self.build_table(PROCESS_LIST) {
-            tables.insert(PROCESS_LIST.to_string(), process_list);
-        }
         // Add memory tables
         for name in MEMORY_TABLES.iter() {
             tables.insert((*name).to_string(), self.build_table(name).expect(name));

src/catalog/src/system_schema/information_schema/cluster_info.rs

@@ -36,8 +36,9 @@ use datatypes::vectors::{
 use snafu::ResultExt;
 use store_api::storage::{ScanRequest, TableId};
 
+use super::CLUSTER_INFO;
 use crate::error::{CreateRecordBatchSnafu, InternalSnafu, Result};
-use crate::system_schema::information_schema::{InformationTable, Predicates, CLUSTER_INFO};
+use crate::system_schema::information_schema::{InformationTable, Predicates};
 use crate::system_schema::utils;
 use crate::CatalogManager;
 

src/catalog/src/system_schema/information_schema/columns.rs

@@ -38,11 +38,11 @@ use snafu::{OptionExt, ResultExt};
 use sql::statements;
 use store_api::storage::{ScanRequest, TableId};
 
+use super::{InformationTable, COLUMNS};
 use crate::error::{
     CreateRecordBatchSnafu, InternalSnafu, Result, UpgradeWeakCatalogManagerRefSnafu,
 };
 use crate::information_schema::Predicates;
-use crate::system_schema::information_schema::{InformationTable, COLUMNS};
 use crate::CatalogManager;
 
 #[derive(Debug)]
@@ -56,8 +56,6 @@ pub const TABLE_CATALOG: &str = "table_catalog";
 pub const TABLE_SCHEMA: &str = "table_schema";
 pub const TABLE_NAME: &str = "table_name";
 pub const COLUMN_NAME: &str = "column_name";
-pub const REGION_ID: &str = "region_id";
-pub const PEER_ID: &str = "peer_id";
 const ORDINAL_POSITION: &str = "ordinal_position";
 const CHARACTER_MAXIMUM_LENGTH: &str = "character_maximum_length";
 const CHARACTER_OCTET_LENGTH: &str = "character_octet_length";

src/catalog/src/system_schema/information_schema/information_memory_table.rs

@@ -18,7 +18,7 @@ use common_catalog::consts::{METRIC_ENGINE, MITO_ENGINE};
 use datatypes::schema::{Schema, SchemaRef};
 use datatypes::vectors::{Int64Vector, StringVector, VectorRef};
 
-use crate::system_schema::information_schema::table_names::*;
+use super::table_names::*;
 use crate::system_schema::utils::tables::{
     bigint_column, string_column, string_columns, timestamp_micro_column,
 };

src/catalog/src/system_schema/information_schema/key_column_usage.rs

@@ -24,17 +24,18 @@ use datafusion::physical_plan::stream::RecordBatchStreamAdapter as DfRecordBatch
 use datafusion::physical_plan::streaming::PartitionStream as DfPartitionStream;
 use datafusion::physical_plan::SendableRecordBatchStream as DfSendableRecordBatchStream;
 use datatypes::prelude::{ConcreteDataType, MutableVector, ScalarVectorBuilder, VectorRef};
-use datatypes::schema::{ColumnSchema, FulltextBackend, Schema, SchemaRef};
+use datatypes::schema::{ColumnSchema, Schema, SchemaRef};
 use datatypes::value::Value;
 use datatypes::vectors::{ConstantVector, StringVector, StringVectorBuilder, UInt32VectorBuilder};
 use futures_util::TryStreamExt;
 use snafu::{OptionExt, ResultExt};
 use store_api::storage::{ScanRequest, TableId};
 
+use super::KEY_COLUMN_USAGE;
 use crate::error::{
     CreateRecordBatchSnafu, InternalSnafu, Result, UpgradeWeakCatalogManagerRefSnafu,
 };
-use crate::system_schema::information_schema::{InformationTable, Predicates, KEY_COLUMN_USAGE};
+use crate::system_schema::information_schema::{InformationTable, Predicates};
 use crate::CatalogManager;
 
 pub const CONSTRAINT_SCHEMA: &str = "constraint_schema";
@@ -47,38 +48,20 @@ pub const TABLE_SCHEMA: &str = "table_schema";
 pub const TABLE_NAME: &str = "table_name";
 pub const COLUMN_NAME: &str = "column_name";
 pub const ORDINAL_POSITION: &str = "ordinal_position";
-/// The type of the index.
-pub const GREPTIME_INDEX_TYPE: &str = "greptime_index_type";
 const INIT_CAPACITY: usize = 42;
 
-/// Time index constraint name
-pub(crate) const CONSTRAINT_NAME_TIME_INDEX: &str = "TIME INDEX";
-
 /// Primary key constraint name
-pub(crate) const CONSTRAINT_NAME_PRI: &str = "PRIMARY";
-/// Primary key index type
-pub(crate) const INDEX_TYPE_PRI: &str = "greptime-primary-key-v1";
-
+pub(crate) const PRI_CONSTRAINT_NAME: &str = "PRIMARY";
+/// Time index constraint name
+pub(crate) const TIME_INDEX_CONSTRAINT_NAME: &str = "TIME INDEX";
 /// Inverted index constraint name
-pub(crate) const CONSTRAINT_NAME_INVERTED_INDEX: &str = "INVERTED INDEX";
-/// Inverted index type
-pub(crate) const INDEX_TYPE_INVERTED_INDEX: &str = "greptime-inverted-index-v1";
-
+pub(crate) const INVERTED_INDEX_CONSTRAINT_NAME: &str = "INVERTED INDEX";
 /// Fulltext index constraint name
-pub(crate) const CONSTRAINT_NAME_FULLTEXT_INDEX: &str = "FULLTEXT INDEX";
-/// Fulltext index v1 type
-pub(crate) const INDEX_TYPE_FULLTEXT_TANTIVY: &str = "greptime-fulltext-index-v1";
-/// Fulltext index bloom type
-pub(crate) const INDEX_TYPE_FULLTEXT_BLOOM: &str = "greptime-fulltext-index-bloom";
-
+pub(crate) const FULLTEXT_INDEX_CONSTRAINT_NAME: &str = "FULLTEXT INDEX";
 /// Skipping index constraint name
-pub(crate) const CONSTRAINT_NAME_SKIPPING_INDEX: &str = "SKIPPING INDEX";
-/// Skipping index type
-pub(crate) const INDEX_TYPE_SKIPPING_INDEX: &str = "greptime-bloom-filter-v1";
+pub(crate) const SKIPPING_INDEX_CONSTRAINT_NAME: &str = "SKIPPING INDEX";
 
 /// The virtual table implementation for `information_schema.KEY_COLUMN_USAGE`.
-///
-/// Provides an extra column `greptime_index_type` for the index type of the key column.
 #[derive(Debug)]
 pub(super) struct InformationSchemaKeyColumnUsage {
     schema: SchemaRef,
@@ -138,11 +121,6 @@ impl InformationSchemaKeyColumnUsage {
                 ConcreteDataType::string_datatype(),
                 true,
             ),
-            ColumnSchema::new(
-                GREPTIME_INDEX_TYPE,
-                ConcreteDataType::string_datatype(),
-                true,
-            ),
         ]))
     }
 
@@ -207,7 +185,6 @@ struct InformationSchemaKeyColumnUsageBuilder {
     column_name: StringVectorBuilder,
     ordinal_position: UInt32VectorBuilder,
     position_in_unique_constraint: UInt32VectorBuilder,
-    greptime_index_type: StringVectorBuilder,
 }
 
 impl InformationSchemaKeyColumnUsageBuilder {
@@ -230,7 +207,6 @@ impl InformationSchemaKeyColumnUsageBuilder {
             column_name: StringVectorBuilder::with_capacity(INIT_CAPACITY),
             ordinal_position: UInt32VectorBuilder::with_capacity(INIT_CAPACITY),
             position_in_unique_constraint: UInt32VectorBuilder::with_capacity(INIT_CAPACITY),
-            greptime_index_type: StringVectorBuilder::with_capacity(INIT_CAPACITY),
         }
     }
 
@@ -254,47 +230,34 @@ impl InformationSchemaKeyColumnUsageBuilder {
 
                 for (idx, column) in schema.column_schemas().iter().enumerate() {
                     let mut constraints = vec![];
-                    let mut greptime_index_type = vec![];
                     if column.is_time_index() {
                         self.add_key_column_usage(
                             &predicates,
                             &schema_name,
-                            CONSTRAINT_NAME_TIME_INDEX,
+                            TIME_INDEX_CONSTRAINT_NAME,
                             &catalog_name,
                             &schema_name,
                             table_name,
                             &column.name,
                             1, //always 1 for time index
-                            "",
                         );
                     }
                     // TODO(dimbtp): foreign key constraint not supported yet
                     if keys.contains(&idx) {
-                        constraints.push(CONSTRAINT_NAME_PRI);
-                        greptime_index_type.push(INDEX_TYPE_PRI);
+                        constraints.push(PRI_CONSTRAINT_NAME);
                     }
                     if column.is_inverted_indexed() {
-                        constraints.push(CONSTRAINT_NAME_INVERTED_INDEX);
-                        greptime_index_type.push(INDEX_TYPE_INVERTED_INDEX);
+                        constraints.push(INVERTED_INDEX_CONSTRAINT_NAME);
                     }
-                    if let Ok(Some(options)) = column.fulltext_options() {
-                        if options.enable {
-                            constraints.push(CONSTRAINT_NAME_FULLTEXT_INDEX);
-                            let index_type = match options.backend {
-                                FulltextBackend::Bloom => INDEX_TYPE_FULLTEXT_BLOOM,
-                                FulltextBackend::Tantivy => INDEX_TYPE_FULLTEXT_TANTIVY,
-                            };
-                            greptime_index_type.push(index_type);
-                        }
+                    if column.is_fulltext_indexed() {
+                        constraints.push(FULLTEXT_INDEX_CONSTRAINT_NAME);
                     }
                     if column.is_skipping_indexed() {
-                        constraints.push(CONSTRAINT_NAME_SKIPPING_INDEX);
-                        greptime_index_type.push(INDEX_TYPE_SKIPPING_INDEX);
+                        constraints.push(SKIPPING_INDEX_CONSTRAINT_NAME);
                     }
 
                     if !constraints.is_empty() {
                         let aggregated_constraints = constraints.join(", ");
-                        let aggregated_index_types = greptime_index_type.join(", ");
                         self.add_key_column_usage(
                             &predicates,
                             &schema_name,
@@ -304,7 +267,6 @@ impl InformationSchemaKeyColumnUsageBuilder {
                             table_name,
                             &column.name,
                             idx as u32 + 1,
-                            &aggregated_index_types,
                         );
                     }
                 }
@@ -327,7 +289,6 @@ impl InformationSchemaKeyColumnUsageBuilder {
         table_name: &str,
         column_name: &str,
         ordinal_position: u32,
-        index_types: &str,
     ) {
         let row = [
             (CONSTRAINT_SCHEMA, &Value::from(constraint_schema)),
@@ -337,7 +298,6 @@ impl InformationSchemaKeyColumnUsageBuilder {
             (TABLE_NAME, &Value::from(table_name)),
             (COLUMN_NAME, &Value::from(column_name)),
             (ORDINAL_POSITION, &Value::from(ordinal_position)),
-            (GREPTIME_INDEX_TYPE, &Value::from(index_types)),
         ];
 
         if !predicates.eval(&row) {
@@ -354,7 +314,6 @@ impl InformationSchemaKeyColumnUsageBuilder {
         self.column_name.push(Some(column_name));
         self.ordinal_position.push(Some(ordinal_position));
         self.position_in_unique_constraint.push(None);
-        self.greptime_index_type.push(Some(index_types));
     }
 
     fn finish(&mut self) -> Result<RecordBatch> {
@@ -378,7 +337,6 @@ impl InformationSchemaKeyColumnUsageBuilder {
             null_string_vector.clone(),
             null_string_vector.clone(),
             null_string_vector,
-            Arc::new(self.greptime_index_type.finish()),
         ];
         RecordBatch::new(self.schema.clone(), columns).context(CreateRecordBatchSnafu)
     }

src/catalog/src/system_schema/information_schema/partitions.rs

@@ -39,12 +39,13 @@ use snafu::{OptionExt, ResultExt};
 use store_api::storage::{ScanRequest, TableId};
 use table::metadata::{TableInfo, TableType};
 
+use super::PARTITIONS;
 use crate::error::{
     CreateRecordBatchSnafu, FindPartitionsSnafu, InternalSnafu, PartitionManagerNotFoundSnafu,
     Result, UpgradeWeakCatalogManagerRefSnafu,
 };
 use crate::kvbackend::KvBackendCatalogManager;
-use crate::system_schema::information_schema::{InformationTable, Predicates, PARTITIONS};
+use crate::system_schema::information_schema::{InformationTable, Predicates};
 use crate::CatalogManager;
 
 const TABLE_CATALOG: &str = "table_catalog";

src/catalog/src/system_schema/information_schema/procedure_info.rs

@@ -33,8 +33,9 @@ use datatypes::vectors::{StringVectorBuilder, TimestampMillisecondVectorBuilder}
 use snafu::ResultExt;
 use store_api::storage::{ScanRequest, TableId};
 
+use super::PROCEDURE_INFO;
 use crate::error::{CreateRecordBatchSnafu, InternalSnafu, Result};
-use crate::system_schema::information_schema::{InformationTable, Predicates, PROCEDURE_INFO};
+use crate::system_schema::information_schema::{InformationTable, Predicates};
 use crate::system_schema::utils;
 use crate::CatalogManager;
 

src/catalog/src/system_schema/information_schema/process_list.rs

@@ -1,189 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::sync::Arc;
-
-use common_catalog::consts::INFORMATION_SCHEMA_PROCESS_LIST_TABLE_ID;
-use common_error::ext::BoxedError;
-use common_frontend::DisplayProcessId;
-use common_recordbatch::adapter::RecordBatchStreamAdapter;
-use common_recordbatch::{RecordBatch, SendableRecordBatchStream};
-use common_time::util::current_time_millis;
-use common_time::{Duration, Timestamp};
-use datafusion::physical_plan::stream::RecordBatchStreamAdapter as DfRecordBatchStreamAdapter;
-use datatypes::prelude::ConcreteDataType as CDT;
-use datatypes::scalars::ScalarVectorBuilder;
-use datatypes::schema::{ColumnSchema, Schema, SchemaRef};
-use datatypes::value::Value;
-use datatypes::vectors::{
-    DurationMillisecondVectorBuilder, StringVectorBuilder, TimestampMillisecondVectorBuilder,
-    VectorRef,
-};
-use snafu::ResultExt;
-use store_api::storage::{ScanRequest, TableId};
-
-use crate::error::{self, InternalSnafu};
-use crate::information_schema::Predicates;
-use crate::process_manager::ProcessManagerRef;
-use crate::system_schema::information_schema::InformationTable;
-
-/// Column names of `information_schema.process_list`
-pub const ID: &str = "id";
-pub const CATALOG: &str = "catalog";
-pub const SCHEMAS: &str = "schemas";
-pub const QUERY: &str = "query";
-pub const CLIENT: &str = "client";
-pub const FRONTEND: &str = "frontend";
-pub const START_TIMESTAMP: &str = "start_timestamp";
-pub const ELAPSED_TIME: &str = "elapsed_time";
-
-/// `information_schema.process_list` table implementation that tracks running
-/// queries in current cluster.
-pub struct InformationSchemaProcessList {
-    schema: SchemaRef,
-    process_manager: ProcessManagerRef,
-}
-
-impl InformationSchemaProcessList {
-    pub fn new(process_manager: ProcessManagerRef) -> Self {
-        Self {
-            schema: Self::schema(),
-            process_manager,
-        }
-    }
-
-    fn schema() -> SchemaRef {
-        Arc::new(Schema::new(vec![
-            ColumnSchema::new(ID, CDT::string_datatype(), false),
-            ColumnSchema::new(CATALOG, CDT::string_datatype(), false),
-            ColumnSchema::new(SCHEMAS, CDT::string_datatype(), false),
-            ColumnSchema::new(QUERY, CDT::string_datatype(), false),
-            ColumnSchema::new(CLIENT, CDT::string_datatype(), false),
-            ColumnSchema::new(FRONTEND, CDT::string_datatype(), false),
-            ColumnSchema::new(
-                START_TIMESTAMP,
-                CDT::timestamp_millisecond_datatype(),
-                false,
-            ),
-            ColumnSchema::new(ELAPSED_TIME, CDT::duration_millisecond_datatype(), false),
-        ]))
-    }
-}
-
-impl InformationTable for InformationSchemaProcessList {
-    fn table_id(&self) -> TableId {
-        INFORMATION_SCHEMA_PROCESS_LIST_TABLE_ID
-    }
-
-    fn table_name(&self) -> &'static str {
-        "process_list"
-    }
-
-    fn schema(&self) -> SchemaRef {
-        self.schema.clone()
-    }
-
-    fn to_stream(&self, request: ScanRequest) -> error::Result<SendableRecordBatchStream> {
-        let process_manager = self.process_manager.clone();
-        let stream = Box::pin(DfRecordBatchStreamAdapter::new(
-            self.schema.arrow_schema().clone(),
-            futures::stream::once(async move {
-                make_process_list(process_manager, request)
-                    .await
-                    .map(RecordBatch::into_df_record_batch)
-                    .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))
-            }),
-        ));
-
-        Ok(Box::pin(
-            RecordBatchStreamAdapter::try_new(stream)
-                .map_err(BoxedError::new)
-                .context(InternalSnafu)?,
-        ))
-    }
-}
-
-/// Build running process list.
-async fn make_process_list(
-    process_manager: ProcessManagerRef,
-    request: ScanRequest,
-) -> error::Result<RecordBatch> {
-    let predicates = Predicates::from_scan_request(&Some(request));
-    let current_time = current_time_millis();
-    // todo(hl): find a way to extract user catalog to filter queries from other users.
-    let queries = process_manager.list_all_processes(None).await?;
-
-    let mut id_builder = StringVectorBuilder::with_capacity(queries.len());
-    let mut catalog_builder = StringVectorBuilder::with_capacity(queries.len());
-    let mut schemas_builder = StringVectorBuilder::with_capacity(queries.len());
-    let mut query_builder = StringVectorBuilder::with_capacity(queries.len());
-    let mut client_builder = StringVectorBuilder::with_capacity(queries.len());
-    let mut frontend_builder = StringVectorBuilder::with_capacity(queries.len());
-    let mut start_time_builder = TimestampMillisecondVectorBuilder::with_capacity(queries.len());
-    let mut elapsed_time_builder = DurationMillisecondVectorBuilder::with_capacity(queries.len());
-
-    for process in queries {
-        let display_id = DisplayProcessId {
-            server_addr: process.frontend.to_string(),
-            id: process.id,
-        }
-        .to_string();
-        let schemas = process.schemas.join(",");
-        let id = Value::from(display_id);
-        let catalog = Value::from(process.catalog);
-        let schemas = Value::from(schemas);
-        let query = Value::from(process.query);
-        let client = Value::from(process.client);
-        let frontend = Value::from(process.frontend);
-        let start_timestamp = Value::from(Timestamp::new_millisecond(process.start_timestamp));
-        let elapsed_time = Value::from(Duration::new_millisecond(
-            current_time - process.start_timestamp,
-        ));
-        let row = [
-            (ID, &id),
-            (CATALOG, &catalog),
-            (SCHEMAS, &schemas),
-            (QUERY, &query),
-            (CLIENT, &client),
-            (FRONTEND, &frontend),
-            (START_TIMESTAMP, &start_timestamp),
-            (ELAPSED_TIME, &elapsed_time),
-        ];
-        if predicates.eval(&row) {
-            id_builder.push(id.as_string().as_deref());
-            catalog_builder.push(catalog.as_string().as_deref());
-            schemas_builder.push(schemas.as_string().as_deref());
-            query_builder.push(query.as_string().as_deref());
-            client_builder.push(client.as_string().as_deref());
-            frontend_builder.push(frontend.as_string().as_deref());
-            start_time_builder.push(start_timestamp.as_timestamp().map(|t| t.value().into()));
-            elapsed_time_builder.push(elapsed_time.as_duration().map(|d| d.value().into()));
-        }
-    }
-
-    RecordBatch::new(
-        InformationSchemaProcessList::schema(),
-        vec![
-            Arc::new(id_builder.finish()) as VectorRef,
-            Arc::new(catalog_builder.finish()) as VectorRef,
-            Arc::new(schemas_builder.finish()) as VectorRef,
-            Arc::new(query_builder.finish()) as VectorRef,
-            Arc::new(client_builder.finish()) as VectorRef,
-            Arc::new(frontend_builder.finish()) as VectorRef,
-            Arc::new(start_time_builder.finish()) as VectorRef,
-            Arc::new(elapsed_time_builder.finish()) as VectorRef,
-        ],
-    )
-    .context(error::CreateRecordBatchSnafu)
-}

src/catalog/src/system_schema/information_schema/region_peers.rs

@@ -21,7 +21,6 @@ use common_error::ext::BoxedError;
 use common_meta::rpc::router::RegionRoute;
 use common_recordbatch::adapter::RecordBatchStreamAdapter;
 use common_recordbatch::{RecordBatch, SendableRecordBatchStream};
-use datafusion::common::HashMap;
 use datafusion::execution::TaskContext;
 use datafusion::physical_plan::stream::RecordBatchStreamAdapter as DfRecordBatchStreamAdapter;
 use datafusion::physical_plan::streaming::PartitionStream as DfPartitionStream;
@@ -35,30 +34,25 @@ use snafu::{OptionExt, ResultExt};
 use store_api::storage::{RegionId, ScanRequest, TableId};
 use table::metadata::TableType;
 
+use super::REGION_PEERS;
 use crate::error::{
     CreateRecordBatchSnafu, FindRegionRoutesSnafu, InternalSnafu, Result,
     UpgradeWeakCatalogManagerRefSnafu,
 };
 use crate::kvbackend::KvBackendCatalogManager;
-use crate::system_schema::information_schema::{InformationTable, Predicates, REGION_PEERS};
+use crate::system_schema::information_schema::{InformationTable, Predicates};
 use crate::CatalogManager;
 
-pub const TABLE_CATALOG: &str = "table_catalog";
-pub const TABLE_SCHEMA: &str = "table_schema";
-pub const TABLE_NAME: &str = "table_name";
-pub const REGION_ID: &str = "region_id";
-pub const PEER_ID: &str = "peer_id";
+const REGION_ID: &str = "region_id";
+const PEER_ID: &str = "peer_id";
 const PEER_ADDR: &str = "peer_addr";
-pub const IS_LEADER: &str = "is_leader";
+const IS_LEADER: &str = "is_leader";
 const STATUS: &str = "status";
 const DOWN_SECONDS: &str = "down_seconds";
 const INIT_CAPACITY: usize = 42;
 
 /// The `REGION_PEERS` table provides information about the region distribution and routes. Including fields:
 ///
-/// - `table_catalog`: the table catalog name
-/// - `table_schema`: the table schema name
-/// - `table_name`: the table name
 /// - `region_id`: the region id
 /// - `peer_id`: the region storage datanode peer id
 /// - `peer_addr`: the region storage datanode gRPC peer address
@@ -83,9 +77,6 @@ impl InformationSchemaRegionPeers {
 
     pub(crate) fn schema() -> SchemaRef {
         Arc::new(Schema::new(vec![
-            ColumnSchema::new(TABLE_CATALOG, ConcreteDataType::string_datatype(), false),
-            ColumnSchema::new(TABLE_SCHEMA, ConcreteDataType::string_datatype(), false),
-            ColumnSchema::new(TABLE_NAME, ConcreteDataType::string_datatype(), false),
             ColumnSchema::new(REGION_ID, ConcreteDataType::uint64_datatype(), false),
             ColumnSchema::new(PEER_ID, ConcreteDataType::uint64_datatype(), true),
             ColumnSchema::new(PEER_ADDR, ConcreteDataType::string_datatype(), true),
@@ -143,9 +134,6 @@ struct InformationSchemaRegionPeersBuilder {
     catalog_name: String,
     catalog_manager: Weak<dyn CatalogManager>,
 
-    table_catalogs: StringVectorBuilder,
-    table_schemas: StringVectorBuilder,
-    table_names: StringVectorBuilder,
     region_ids: UInt64VectorBuilder,
     peer_ids: UInt64VectorBuilder,
     peer_addrs: StringVectorBuilder,
@@ -164,9 +152,6 @@ impl InformationSchemaRegionPeersBuilder {
             schema,
             catalog_name,
             catalog_manager,
-            table_catalogs: StringVectorBuilder::with_capacity(INIT_CAPACITY),
-            table_schemas: StringVectorBuilder::with_capacity(INIT_CAPACITY),
-            table_names: StringVectorBuilder::with_capacity(INIT_CAPACITY),
             region_ids: UInt64VectorBuilder::with_capacity(INIT_CAPACITY),
             peer_ids: UInt64VectorBuilder::with_capacity(INIT_CAPACITY),
             peer_addrs: StringVectorBuilder::with_capacity(INIT_CAPACITY),
@@ -192,28 +177,24 @@ impl InformationSchemaRegionPeersBuilder {
         let predicates = Predicates::from_scan_request(&request);
 
         for schema_name in catalog_manager.schema_names(&catalog_name, None).await? {
-            let table_stream = catalog_manager
+            let table_id_stream = catalog_manager
                 .tables(&catalog_name, &schema_name, None)
                 .try_filter_map(|t| async move {
                     let table_info = t.table_info();
                     if table_info.table_type == TableType::Temporary {
                         Ok(None)
                     } else {
-                        Ok(Some((
-                            table_info.ident.table_id,
-                            table_info.name.to_string(),
-                        )))
+                        Ok(Some(table_info.ident.table_id))
                     }
                 });
 
             const BATCH_SIZE: usize = 128;
 
-            // Split tables into chunks
-            let mut table_chunks = pin!(table_stream.ready_chunks(BATCH_SIZE));
+            // Split table ids into chunks
+            let mut table_id_chunks = pin!(table_id_stream.ready_chunks(BATCH_SIZE));
 
-            while let Some(tables) = table_chunks.next().await {
-                let tables = tables.into_iter().collect::<Result<HashMap<_, _>>>()?;
-                let table_ids = tables.keys().cloned().collect::<Vec<_>>();
+            while let Some(table_ids) = table_id_chunks.next().await {
+                let table_ids = table_ids.into_iter().collect::<Result<Vec<_>>>()?;
 
                 let table_routes = if let Some(partition_manager) = &partition_manager {
                     partition_manager
@@ -225,16 +206,7 @@ impl InformationSchemaRegionPeersBuilder {
                 };
 
                 for (table_id, routes) in table_routes {
-                    // Safety: table_id is guaranteed to be in the map
-                    let table_name = tables.get(&table_id).unwrap();
-                    self.add_region_peers(
-                        &catalog_name,
-                        &schema_name,
-                        table_name,
-                        &predicates,
-                        table_id,
-                        &routes,
-                    );
+                    self.add_region_peers(&predicates, table_id, &routes);
                 }
             }
         }
@@ -244,9 +216,6 @@ impl InformationSchemaRegionPeersBuilder {
 
     fn add_region_peers(
         &mut self,
-        table_catalog: &str,
-        table_schema: &str,
-        table_name: &str,
         predicates: &Predicates,
         table_id: TableId,
         routes: &[RegionRoute],
@@ -262,20 +231,13 @@ impl InformationSchemaRegionPeersBuilder {
                 Some("ALIVE".to_string())
             };
 
-            let row = [
-                (TABLE_CATALOG, &Value::from(table_catalog)),
-                (TABLE_SCHEMA, &Value::from(table_schema)),
-                (TABLE_NAME, &Value::from(table_name)),
-                (REGION_ID, &Value::from(region_id)),
-            ];
+            let row = [(REGION_ID, &Value::from(region_id))];
 
             if !predicates.eval(&row) {
                 return;
             }
 
-            self.table_catalogs.push(Some(table_catalog));
-            self.table_schemas.push(Some(table_schema));
-            self.table_names.push(Some(table_name));
+            // TODO(dennis): adds followers.
             self.region_ids.push(Some(region_id));
             self.peer_ids.push(peer_id);
             self.peer_addrs.push(peer_addr.as_deref());
@@ -283,26 +245,11 @@ impl InformationSchemaRegionPeersBuilder {
             self.statuses.push(state.as_deref());
             self.down_seconds
                 .push(route.leader_down_millis().map(|m| m / 1000));
-
-            for follower in &route.follower_peers {
-                self.table_catalogs.push(Some(table_catalog));
-                self.table_schemas.push(Some(table_schema));
-                self.table_names.push(Some(table_name));
-                self.region_ids.push(Some(region_id));
-                self.peer_ids.push(Some(follower.id));
-                self.peer_addrs.push(Some(follower.addr.as_str()));
-                self.is_leaders.push(Some("No"));
-                self.statuses.push(None);
-                self.down_seconds.push(None);
-            }
         }
     }
 
     fn finish(&mut self) -> Result<RecordBatch> {
         let columns: Vec<VectorRef> = vec![
-            Arc::new(self.table_catalogs.finish()),
-            Arc::new(self.table_schemas.finish()),
-            Arc::new(self.table_names.finish()),
             Arc::new(self.region_ids.finish()),
             Arc::new(self.peer_ids.finish()),
             Arc::new(self.peer_addrs.finish()),

src/catalog/src/system_schema/information_schema/region_statistics.rs

@@ -30,9 +30,9 @@ use datatypes::vectors::{StringVectorBuilder, UInt32VectorBuilder, UInt64VectorB
 use snafu::ResultExt;
 use store_api::storage::{ScanRequest, TableId};
 
+use super::{InformationTable, REGION_STATISTICS};
 use crate::error::{CreateRecordBatchSnafu, InternalSnafu, Result};
 use crate::information_schema::Predicates;
-use crate::system_schema::information_schema::{InformationTable, REGION_STATISTICS};
 use crate::system_schema::utils;
 use crate::CatalogManager;
 

src/catalog/src/system_schema/information_schema/runtime_metrics.rs

@@ -35,8 +35,8 @@ use itertools::Itertools;
 use snafu::ResultExt;
 use store_api::storage::{ScanRequest, TableId};
 
+use super::{InformationTable, RUNTIME_METRICS};
 use crate::error::{CreateRecordBatchSnafu, InternalSnafu, Result};
-use crate::system_schema::information_schema::{InformationTable, RUNTIME_METRICS};
 
 #[derive(Debug)]
 pub(super) struct InformationSchemaMetrics {

src/catalog/src/system_schema/information_schema/schemata.rs

@@ -31,11 +31,12 @@ use datatypes::vectors::StringVectorBuilder;
 use snafu::{OptionExt, ResultExt};
 use store_api::storage::{ScanRequest, TableId};
 
+use super::SCHEMATA;
 use crate::error::{
     CreateRecordBatchSnafu, InternalSnafu, Result, TableMetadataManagerSnafu,
     UpgradeWeakCatalogManagerRefSnafu,
 };
-use crate::system_schema::information_schema::{InformationTable, Predicates, SCHEMATA};
+use crate::system_schema::information_schema::{InformationTable, Predicates};
 use crate::system_schema::utils;
 use crate::CatalogManager;
 

src/catalog/src/system_schema/information_schema/table_constraints.rs

@@ -32,14 +32,14 @@ use futures::TryStreamExt;
 use snafu::{OptionExt, ResultExt};
 use store_api::storage::{ScanRequest, TableId};
 
+use super::{InformationTable, TABLE_CONSTRAINTS};
 use crate::error::{
     CreateRecordBatchSnafu, InternalSnafu, Result, UpgradeWeakCatalogManagerRefSnafu,
 };
 use crate::information_schema::key_column_usage::{
-    CONSTRAINT_NAME_PRI, CONSTRAINT_NAME_TIME_INDEX,
+    PRI_CONSTRAINT_NAME, TIME_INDEX_CONSTRAINT_NAME,
 };
 use crate::information_schema::Predicates;
-use crate::system_schema::information_schema::{InformationTable, TABLE_CONSTRAINTS};
 use crate::CatalogManager;
 
 /// The `TABLE_CONSTRAINTS` table describes which tables have constraints.
@@ -188,7 +188,7 @@ impl InformationSchemaTableConstraintsBuilder {
                     self.add_table_constraint(
                         &predicates,
                         &schema_name,
-                        CONSTRAINT_NAME_TIME_INDEX,
+                        TIME_INDEX_CONSTRAINT_NAME,
                         &schema_name,
                         &table.table_info().name,
                         TIME_INDEX_CONSTRAINT_TYPE,
@@ -199,7 +199,7 @@ impl InformationSchemaTableConstraintsBuilder {
                     self.add_table_constraint(
                         &predicates,
                         &schema_name,
-                        CONSTRAINT_NAME_PRI,
+                        PRI_CONSTRAINT_NAME,
                         &schema_name,
                         &table.table_info().name,
                         PRI_KEY_CONSTRAINT_TYPE,

src/catalog/src/system_schema/information_schema/table_names.rs

@@ -47,4 +47,3 @@ pub const VIEWS: &str = "views";
 pub const FLOWS: &str = "flows";
 pub const PROCEDURE_INFO: &str = "procedure_info";
 pub const REGION_STATISTICS: &str = "region_statistics";
-pub const PROCESS_LIST: &str = "process_list";

src/catalog/src/system_schema/information_schema/tables.rs

@@ -38,10 +38,11 @@ use snafu::{OptionExt, ResultExt};
 use store_api::storage::{RegionId, ScanRequest, TableId};
 use table::metadata::{TableInfo, TableType};
 
+use super::TABLES;
 use crate::error::{
     CreateRecordBatchSnafu, InternalSnafu, Result, UpgradeWeakCatalogManagerRefSnafu,
 };
-use crate::system_schema::information_schema::{InformationTable, Predicates, TABLES};
+use crate::system_schema::information_schema::{InformationTable, Predicates};
 use crate::system_schema::utils;
 use crate::CatalogManager;
 

src/catalog/src/system_schema/information_schema/views.rs

@@ -32,12 +32,13 @@ use snafu::{OptionExt, ResultExt};
 use store_api::storage::{ScanRequest, TableId};
 use table::metadata::TableType;
 
+use super::VIEWS;
 use crate::error::{
     CastManagerSnafu, CreateRecordBatchSnafu, GetViewCacheSnafu, InternalSnafu, Result,
     UpgradeWeakCatalogManagerRefSnafu, ViewInfoNotFoundSnafu,
 };
 use crate::kvbackend::KvBackendCatalogManager;
-use crate::system_schema::information_schema::{InformationTable, Predicates, VIEWS};
+use crate::system_schema::information_schema::{InformationTable, Predicates};
 use crate::CatalogManager;
 const INIT_CAPACITY: usize = 42;
 

src/catalog/src/system_schema/memory_table.rs

@@ -29,8 +29,8 @@ use datatypes::vectors::VectorRef;
 use snafu::ResultExt;
 use store_api::storage::{ScanRequest, TableId};
 
+use super::SystemTable;
 use crate::error::{CreateRecordBatchSnafu, InternalSnafu, Result};
-use crate::system_schema::SystemTable;
 
 /// A memory table with specified schema and columns.
 #[derive(Debug)]