metrics: more generic metrics batching mode

### Add Flight Compression Support

 - **Configuration Updates**:
   - Added `grpc.flight_compression` option to `config/config.md`, `config/datanode.example.toml`, and `config/frontend.example.toml` to specify compression modes for Arrow IPC service.

 - **Code Enhancements**:
   - Updated `FlightEncoder` in `src/common/grpc/src/flight.rs` to support compression modes.
   - Modified `RegionServer` and `DatanodeBuilder` in `src/datanode/src/datanode.rs` and `src/datanode/src/region_server.rs` to handle `FlightCompression`.
   - Integrated `FlightCompression` in `src/servers/src/grpc.rs` and `src/servers/src/grpc/flight.rs` to manage compression settings.

 - **Testing and Integration**:
   - Updated test utilities and integration tests in `tests-integration/src/grpc/flight.rs` and `tests-integration/src/test_util.rs` to include `FlightCompression`.

Signed-off-by: Lei, HUANG <mrsatangel@gmail.com>

.cargo/config.toml

@@ -3,3 +3,12 @@ linker = "aarch64-linux-gnu-gcc"
 
 [alias]
 sqlness = "run --bin sqlness-runner --"
+
+[unstable.git]
+shallow_index = true
+shallow_deps = true
+[unstable.gitoxide]
+fetch = true
+checkout = true
+list_files = true
+internal_use_git2 = false

.coderabbit.yaml

@@ -1,15 +0,0 @@
-# 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] Databse Engine
+## [Module] Database Engine
 /src/index @zhongzc
 /src/mito2 @evenyag @v0y4g3r @waynexia
 /src/query @evenyag

.github/actions/build-dev-builder-images/action.yml

@@ -41,7 +41,14 @@ runs:
         username: ${{ inputs.dockerhub-image-registry-username }}
         password: ${{ inputs.dockerhub-image-registry-token }}
 
-    - name: Build and push dev-builder-ubuntu image
+    - name: Set up qemu for multi-platform builds
+      uses: docker/setup-qemu-action@v3
+      with:
+        platforms: linux/amd64,linux/arm64
+        # The latest version will lead to segmentation fault.
+        image: tonistiigi/binfmt:qemu-v7.0.0-28
+
+    - name: Build and push dev-builder-ubuntu image # Build image for amd64 and arm64 platform.
       shell: bash
       if: ${{ inputs.build-dev-builder-ubuntu == 'true' }}
       run: |
@@ -52,7 +59,7 @@ runs:
           IMAGE_NAMESPACE=${{ inputs.dockerhub-image-namespace }} \
           DEV_BUILDER_IMAGE_TAG=${{ inputs.version }}
 
-    - name: Build and push dev-builder-centos image
+    - name: Build and push dev-builder-centos image # Only build image for amd64 platform.
       shell: bash
       if: ${{ inputs.build-dev-builder-centos == 'true' }}
       run: |
@@ -69,8 +76,7 @@ runs:
       run: |
         make dev-builder \
           BASE_IMAGE=android \
+          BUILDX_MULTI_PLATFORM_BUILD=amd64 \
           IMAGE_REGISTRY=${{ inputs.dockerhub-image-registry }} \
           IMAGE_NAMESPACE=${{ inputs.dockerhub-image-namespace }} \
-          DEV_BUILDER_IMAGE_TAG=${{ inputs.version }} && \
-
-        docker push ${{ inputs.dockerhub-image-registry }}/${{ inputs.dockerhub-image-namespace }}/dev-builder-android:${{ inputs.version }}
+          DEV_BUILDER_IMAGE_TAG=${{ inputs.version }}

.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,pg_kvbackend
+        features: servers/dashboard
         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,pg_kvbackend
+        features: servers/dashboard
         cargo-profile: ${{ inputs.cargo-profile }}
         artifacts-dir: greptime-linux-${{ inputs.arch }}-centos-${{ inputs.version }}
         version: ${{ inputs.version }}

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

@@ -47,7 +47,6 @@ 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: "20"
+    default: "30"
   upload-retry-timeout:
     description: Timeout for uploading artifacts to S3
     required: false
-    default: "30" # minutes
+    default: "120" # minutes
 runs:
   using: composite
   steps:

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

@@ -8,7 +8,7 @@ inputs:
     default: 2
     description: "Number of Datanode replicas"
   meta-replicas:
-    default: 1
+    default: 2
     description: "Number of Metasrv replicas"
   image-registry: 
     default: "docker.io"
@@ -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=2Gi \
+        --set base.podTemplate.main.resources.limits.memory=3Gi \
         --set frontend.replicas=${{ inputs.frontend-replicas }} \
         --set datanode.replicas=${{ inputs.datanode-replicas }} \
         --set meta.replicas=${{ inputs.meta-replicas }} \

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

@@ -2,13 +2,14 @@ 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"
@@ -21,7 +22,7 @@ datanode:
     [wal]
     provider = "kafka"
     broker_endpoints = ["kafka.kafka-cluster.svc.cluster.local:9092"]
-    linger = "2ms"
+    overwrite_entry_start_id = true
 frontend:
   configData: |-
     [runtime]

.github/actions/start-runner/action.yml

@@ -56,7 +56,7 @@ runs:
 
     - name: Start EC2 runner
       if: startsWith(inputs.runner, 'ec2')
-      uses: machulav/ec2-github-runner@v2
+      uses: machulav/ec2-github-runner@v2.3.8
       id: start-linux-arm64-ec2-runner
       with:
         mode: start

.github/actions/stop-runner/action.yml

@@ -33,7 +33,7 @@ runs:
 
     - name: Stop EC2 runner
       if: ${{ inputs.label && inputs.ec2-instance-id }}
-      uses: machulav/ec2-github-runner@v2
+      uses: machulav/ec2-github-runner@v2.3.8
       with:
         mode: stop
         label: ${{ inputs.label }}

.github/scripts/create-version.sh

@@ -8,24 +8,25 @@ 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} ${NIGHTLY_RELEASE_PREFIX}
 function create_version() {
-  # Read from envrionment variables.
+  # Read from environment variables.
   if [ -z "$GITHUB_EVENT_NAME" ]; then
-      echo "GITHUB_EVENT_NAME is empty"
+      echo "GITHUB_EVENT_NAME is empty" >&2
       exit 1
   fi
 
   if [ -z "$NEXT_RELEASE_VERSION" ]; then
-      echo "NEXT_RELEASE_VERSION is empty"
-      exit 1
+      echo "NEXT_RELEASE_VERSION is empty, use version from Cargo.toml" >&2
+      # NOTE: Need a `v` prefix for the version string.
+      export NEXT_RELEASE_VERSION=v$(grep '^version = ' Cargo.toml | cut -d '"' -f 2 | head -n 1)
   fi
 
   if [ -z "$NIGHTLY_RELEASE_PREFIX" ]; then
-      echo "NIGHTLY_RELEASE_PREFIX is empty"
+      echo "NIGHTLY_RELEASE_PREFIX is empty" >&2
       exit 1
   fi
 
   # Reuse $NEXT_RELEASE_VERSION to identify whether it's a nightly build.
-  # It will be like 'nigtly-20230808-7d0d8dc6'.
+  # It will be like 'nightly-20230808-7d0d8dc6'.
   if [ "$NEXT_RELEASE_VERSION" = nightly ]; then
     echo "$NIGHTLY_RELEASE_PREFIX-$(date "+%Y%m%d")-$(git rev-parse --short HEAD)"
     exit 0
@@ -35,7 +36,7 @@ function create_version() {
   # It will be like 'dev-2023080819-f0e7216c'.
   if [ "$NEXT_RELEASE_VERSION" = dev ]; then
     if [ -z "$COMMIT_SHA" ]; then
-      echo "COMMIT_SHA is empty in dev build"
+      echo "COMMIT_SHA is empty in dev build" >&2
       exit 1
     fi
     echo "dev-$(date "+%Y%m%d-%s")-$(echo "$COMMIT_SHA" | cut -c1-8)"
@@ -45,7 +46,7 @@ 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"
+      echo "GITHUB_REF_NAME is empty in push event" >&2
       exit 1
     fi
     echo "$GITHUB_REF_NAME"
@@ -54,15 +55,15 @@ function create_version() {
   elif [ "$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"
+    echo "Unsupported GITHUB_EVENT_NAME: $GITHUB_EVENT_NAME" >&2
     exit 1
   fi
 }
 
 # You can run as following examples:
-#  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
+#  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
 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/"
 
-# Ceate a cluster with 1 control-plane node and 5 workers.
+# Create 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

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

@@ -0,0 +1,37 @@
+#!/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

@@ -0,0 +1,46 @@
+#!/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 -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

@@ -0,0 +1,42 @@
+#!/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 -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 [ "$UPDATE_VERSION_INFO" == "true" ]; then
-    # If it's the officail release(like v1.0.0, v1.0.1, v1.0.2, etc.), update latest-version.txt.
+    # If it's the official release(like v1.0.0, v1.0.1, v1.0.2, etc.), update latest-version.txt.
     if [[ "$VERSION" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]]; then
       echo "Updating latest-version.txt"
       echo "$VERSION" > latest-version.txt

.github/workflows/apidoc.yml

@@ -14,7 +14,7 @@ name: Build API docs
 
 jobs:
   apidoc:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
       with:

.github/workflows/dev-build.yml

@@ -16,11 +16,11 @@ on:
         description: The runner uses to build linux-amd64 artifacts
         default: ec2-c6i.4xlarge-amd64
         options:
-          - ubuntu-20.04
-          - ubuntu-20.04-8-cores
-          - ubuntu-20.04-16-cores
-          - ubuntu-20.04-32-cores
-          - ubuntu-20.04-64-cores
+          - ubuntu-22.04
+          - ubuntu-22.04-8-cores
+          - ubuntu-22.04-16-cores
+          - ubuntu-22.04-32-cores
+          - ubuntu-22.04-64-cores
           - ec2-c6i.xlarge-amd64 # 4C8G
           - ec2-c6i.2xlarge-amd64 # 8C16G
           - ec2-c6i.4xlarge-amd64 # 16C32G
@@ -55,6 +55,11 @@ 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.
@@ -83,7 +88,7 @@ jobs:
   allocate-runners:
     name: Allocate runners
     if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     outputs:
       linux-amd64-runner: ${{ steps.start-linux-amd64-runner.outputs.label }}
       linux-arm64-runner: ${{ steps.start-linux-arm64-runner.outputs.label }}
@@ -218,7 +223,7 @@ jobs:
       build-linux-amd64-artifacts,
       build-linux-arm64-artifacts,
     ]
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     outputs:
       build-result: ${{ steps.set-build-result.outputs.build-result }}
     steps:
@@ -239,6 +244,13 @@ jobs:
           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" >> $GITHUB_STEP_SUMMARY
+          echo "Image Tag: \`${{ needs.allocate-runners.outputs.version }}\`" >> $GITHUB_STEP_SUMMARY
+          echo "Full Image Name: \`docker.io/${{ vars.IMAGE_NAMESPACE }}/${{ vars.DEV_BUILD_IMAGE_NAME }}:${{ needs.allocate-runners.outputs.version }}\`" >> $GITHUB_STEP_SUMMARY
+          echo "Pull Command: \`docker pull docker.io/${{ vars.IMAGE_NAMESPACE }}/${{ vars.DEV_BUILD_IMAGE_NAME }}:${{ needs.allocate-runners.outputs.version }}\`" >> $GITHUB_STEP_SUMMARY
+
       - name: Set build result
         id: set-build-result
         run: |
@@ -251,7 +263,7 @@ jobs:
       allocate-runners,
       release-images-to-dockerhub,
     ]
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     continue-on-error: true
     steps:
       - uses: actions/checkout@v4
@@ -274,7 +286,7 @@ 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: false
+          upload-to-s3: ${{ inputs.upload_artifacts_to_s3 }}
           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.
@@ -283,7 +295,7 @@ jobs:
     name: Stop linux-amd64 runner
     # Only run this job when the runner is allocated.
     if: ${{ always() }}
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     needs: [
       allocate-runners,
       build-linux-amd64-artifacts,
@@ -309,7 +321,7 @@ jobs:
     name: Stop linux-arm64 runner
     # Only run this job when the runner is allocated.
     if: ${{ always() }}
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     needs: [
       allocate-runners,
       build-linux-arm64-artifacts,
@@ -337,7 +349,7 @@ jobs:
     needs: [
       release-images-to-dockerhub
     ]
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     permissions:
       issues: write
 

.github/workflows/develop.yml

@@ -22,8 +22,9 @@ concurrency:
 
 jobs:
   check-typos-and-docs:
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Check typos and docs
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
         with:
@@ -36,7 +37,8 @@ jobs:
           || (echo "'config/config.md' is not up-to-date, please run 'make config-docs'." && exit 1)
 
   license-header-check:
-    runs-on: ubuntu-20.04
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
+    runs-on: ubuntu-latest
     name: Check License Header
     steps:
       - uses: actions/checkout@v4
@@ -45,11 +47,12 @@ jobs:
       - uses: korandoru/hawkeye@v5
 
   check:
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Check
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ ubuntu-20.04 ]
+        os: [ ubuntu-latest ]
     timeout-minutes: 60
     steps:
       - uses: actions/checkout@v4
@@ -71,8 +74,9 @@ jobs:
         run: cargo check --locked --workspace --all-targets
 
   toml:
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Toml Check
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     timeout-minutes: 60
     steps:
       - uses: actions/checkout@v4
@@ -85,11 +89,12 @@ jobs:
         run: taplo format --check
 
   build:
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Build GreptimeDB binaries
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ ubuntu-20.04 ]
+        os: [ ubuntu-latest ]
     timeout-minutes: 60
     steps:
       - uses: actions/checkout@v4
@@ -111,7 +116,7 @@ jobs:
       - name: Build greptime binaries
         shell: bash
         # `cargo gc` will invoke `cargo build` with specified args
-        run: cargo gc -- --bin greptime --bin sqlness-runner --features pg_kvbackend
+        run: cargo gc -- --bin greptime --bin sqlness-runner --features "pg_kvbackend,mysql_kvbackend"
       - name: Pack greptime binaries
         shell: bash
         run: |
@@ -127,6 +132,7 @@ jobs:
           version: current
 
   fuzztest:
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Fuzz Test
     needs: build
     runs-on: ubuntu-latest
@@ -183,11 +189,13 @@ 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:
@@ -215,12 +223,12 @@ jobs:
         run: |
           sudo apt update && sudo apt install -y libfuzzer-14-dev
           cargo install cargo-fuzz cargo-gc-bin --force
-      - name: Download pre-built binariy
+      - name: Download pre-built binary
         uses: actions/download-artifact@v4
         with:
           name: bin
           path: .
-      - name: Unzip bianry
+      - name: Unzip binary
         run: |
           tar -xvf ./bin.tar.gz
           rm ./bin.tar.gz
@@ -242,13 +250,19 @@ 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:
       matrix:
-        os: [ ubuntu-20.04 ]
+        os: [ ubuntu-latest ]
     timeout-minutes: 60
     steps:
       - uses: actions/checkout@v4
@@ -267,10 +281,10 @@ jobs:
       - name: Install cargo-gc-bin
         shell: bash
         run: cargo install cargo-gc-bin --force
-      - name: Build greptime bianry
+      - name: Build greptime binary
         shell: bash
         # `cargo gc` will invoke `cargo build` with specified args
-        run: cargo gc --profile ci -- --bin greptime --features pg_kvbackend
+        run: cargo gc --profile ci -- --bin greptime --features "pg_kvbackend,mysql_kvbackend"
       - name: Pack greptime binary
         shell: bash
         run: |
@@ -285,11 +299,13 @@ 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:
@@ -319,9 +335,9 @@ jobs:
         name: Setup Minio
         uses: ./.github/actions/setup-minio
       - if: matrix.mode.kafka
-        name: Setup Kafka cluser
+        name: Setup Kafka cluster
         uses: ./.github/actions/setup-kafka-cluster
-      - name: Setup Etcd cluser
+      - name: Setup Etcd cluster
         uses: ./.github/actions/setup-etcd-cluster
       # Prepares for fuzz tests
       - uses: arduino/setup-protoc@v3
@@ -394,6 +410,11 @@ 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
@@ -416,11 +437,13 @@ 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:
@@ -465,9 +488,9 @@ jobs:
         name: Setup Minio
         uses: ./.github/actions/setup-minio
       - if: matrix.mode.kafka
-        name: Setup Kafka cluser
+        name: Setup Kafka cluster
         uses: ./.github/actions/setup-kafka-cluster
-      - name: Setup Etcd cluser
+      - name: Setup Etcd cluster
         uses: ./.github/actions/setup-etcd-cluster
       # Prepares for fuzz tests
       - uses: arduino/setup-protoc@v3
@@ -541,6 +564,11 @@ 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
@@ -563,12 +591,14 @@ 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-20.04 ]
+        os: [ ubuntu-latest ]
         mode:
           - name: "Basic"
             opts: ""
@@ -576,9 +606,12 @@ jobs:
           - name: "Remote WAL"
             opts: "-w kafka -k 127.0.0.1:9092"
             kafka: true
-          - name: "Pg Kvbackend"
+          - name: "PostgreSQL KvBackend"
             opts: "--setup-pg"
             kafka: false
+          - name: "MySQL Kvbackend"
+            opts: "--setup-mysql"
+            kafka: false
     timeout-minutes: 60
     steps:
       - uses: actions/checkout@v4
@@ -606,8 +639,9 @@ jobs:
           retention-days: 3
 
   fmt:
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Rustfmt
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     timeout-minutes: 60
     steps:
       - uses: actions/checkout@v4
@@ -623,8 +657,9 @@ jobs:
         run: make fmt-check
 
   clippy:
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Clippy
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     timeout-minutes: 60
     steps:
       - uses: actions/checkout@v4
@@ -648,6 +683,7 @@ jobs:
         run: make clippy
 
   conflict-check:
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
     name: Check for conflict
     runs-on: ubuntu-latest
     steps:
@@ -658,7 +694,7 @@ jobs:
         uses: olivernybroe/action-conflict-finder@v4.0
 
   test:
-    if: github.event_name != 'merge_group'
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' && github.event_name != 'merge_group' }}
     runs-on: ubuntu-22.04-arm
     timeout-minutes: 60
     needs:  [conflict-check, clippy, fmt]
@@ -687,7 +723,7 @@ jobs:
         working-directory: tests-integration/fixtures
         run: docker compose up -d --wait
       - name: Run nextest cases
-        run: cargo nextest run --workspace -F dashboard -F pg_kvbackend
+        run: cargo nextest run --workspace -F dashboard -F pg_kvbackend -F mysql_kvbackend
         env:
           CARGO_BUILD_RUSTFLAGS: "-C link-arg=-fuse-ld=mold"
           RUST_BACKTRACE: 1
@@ -704,13 +740,14 @@ jobs:
           GT_MINIO_ENDPOINT_URL: http://127.0.0.1:9000
           GT_ETCD_ENDPOINTS: http://127.0.0.1:2379
           GT_POSTGRES_ENDPOINTS: postgres://greptimedb:admin@127.0.0.1:5432/postgres
+          GT_MYSQL_ENDPOINTS: mysql://greptimedb:admin@127.0.0.1:3306/mysql
           GT_KAFKA_ENDPOINTS: 127.0.0.1:9092
           GT_KAFKA_SASL_ENDPOINTS: 127.0.0.1:9093
           UNITTEST_LOG_DIR: "__unittest_logs"
 
   coverage:
-    if: github.event_name == 'merge_group'
-    runs-on: ubuntu-20.04-8-cores
+    if: ${{ github.repository == 'GreptimeTeam/greptimedb' && github.event_name == 'merge_group' }}
+    runs-on: ubuntu-22.04-8-cores
     timeout-minutes: 60
     steps:
       - uses: actions/checkout@v4
@@ -739,7 +776,7 @@ jobs:
         working-directory: tests-integration/fixtures
         run: docker compose up -d --wait
       - name: Run nextest cases
-        run: cargo llvm-cov nextest --workspace --lcov --output-path lcov.info -F dashboard -F pg_kvbackend
+        run: cargo llvm-cov nextest --workspace --lcov --output-path lcov.info -F dashboard -F pg_kvbackend -F mysql_kvbackend
         env:
           CARGO_BUILD_RUSTFLAGS: "-C link-arg=-fuse-ld=mold"
           RUST_BACKTRACE: 1
@@ -755,6 +792,7 @@ jobs:
           GT_MINIO_ENDPOINT_URL: http://127.0.0.1:9000
           GT_ETCD_ENDPOINTS: http://127.0.0.1:2379
           GT_POSTGRES_ENDPOINTS: postgres://greptimedb:admin@127.0.0.1:5432/postgres
+          GT_MYSQL_ENDPOINTS: mysql://greptimedb:admin@127.0.0.1:3306/mysql
           GT_KAFKA_ENDPOINTS: 127.0.0.1:9092
           GT_KAFKA_SASL_ENDPOINTS: 127.0.0.1:9093
           UNITTEST_LOG_DIR: "__unittest_logs"
@@ -768,9 +806,10 @@ jobs:
           verbose: true
 
   # compat:
+  #   if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
   #   name: Compatibility Test
   #   needs: build
-  #   runs-on: ubuntu-20.04
+  #   runs-on: ubuntu-22.04
   #   timeout-minutes: 60
   #   steps:
   #     - uses: actions/checkout@v4

.github/workflows/docbot.yml

@@ -9,7 +9,7 @@ concurrency:
 
 jobs:
   docbot:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     permissions:
       pull-requests: write
       contents: read

.github/workflows/docs.yml

@@ -31,7 +31,7 @@ name: CI
 jobs:
   typos:
     name: Spell Check with Typos
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
         with:
@@ -39,7 +39,7 @@ jobs:
       - uses: crate-ci/typos@master
 
   license-header-check:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     name: Check License Header
     steps:
       - uses: actions/checkout@v4
@@ -49,29 +49,29 @@ jobs:
 
   check:
     name: Check
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
       - run: 'echo "No action required"'
 
   fmt:
     name: Rustfmt
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
       - run: 'echo "No action required"'
 
   clippy:
     name: Clippy
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
       - run: 'echo "No action required"'
 
   coverage:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
       - run: 'echo "No action required"'
 
   test:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
       - run: 'echo "No action required"'
 
@@ -80,7 +80,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ ubuntu-20.04 ]
+        os: [ ubuntu-latest ]
         mode:
           - name: "Basic"
           - name: "Remote WAL"

.github/workflows/grafana.yml

@@ -0,0 +1,26 @@
+name: Check Grafana Panels
+
+on:
+  pull_request:
+    branches:
+      - main
+    paths:
+      - 'grafana/**'  # Trigger only when files under the grafana/ directory change
+
+jobs:
+  check-panels:
+    runs-on: ubuntu-latest
+
+    steps:
+      # Check out the repository
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      # Install jq (required for the script)
+      - name: Install jq
+        run: sudo apt-get install -y jq
+
+      # Make the check.sh script executable
+      - name: Check grafana dashboards
+        run: |
+          make check-dashboards

.github/workflows/nightly-build.yml

@@ -14,11 +14,11 @@ on:
         description: The runner uses to build linux-amd64 artifacts
         default: ec2-c6i.4xlarge-amd64
         options:
-          - ubuntu-20.04
-          - ubuntu-20.04-8-cores
-          - ubuntu-20.04-16-cores
-          - ubuntu-20.04-32-cores
-          - ubuntu-20.04-64-cores
+          - ubuntu-22.04
+          - ubuntu-22.04-8-cores
+          - ubuntu-22.04-16-cores
+          - ubuntu-22.04-32-cores
+          - ubuntu-22.04-64-cores
           - ec2-c6i.xlarge-amd64 # 4C8G
           - ec2-c6i.2xlarge-amd64 # 8C16G
           - ec2-c6i.4xlarge-amd64 # 16C32G
@@ -70,7 +70,7 @@ jobs:
   allocate-runners:
     name: Allocate runners
     if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     outputs:
       linux-amd64-runner: ${{ steps.start-linux-amd64-runner.outputs.label }}
       linux-arm64-runner: ${{ steps.start-linux-arm64-runner.outputs.label }}
@@ -182,7 +182,7 @@ jobs:
       build-linux-amd64-artifacts,
       build-linux-arm64-artifacts,
     ]
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     outputs:
       nightly-build-result: ${{ steps.set-nightly-build-result.outputs.nightly-build-result }}
     steps:
@@ -214,7 +214,7 @@ jobs:
       allocate-runners,
       release-images-to-dockerhub,
     ]
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     # When we push to ACR, it's easy to fail due to some unknown network issues.
     # However, we don't want to fail the whole workflow because of this.
     # The ACR have daily sync with DockerHub, so don't worry about the image not being updated.
@@ -249,7 +249,7 @@ jobs:
     name: Stop linux-amd64 runner
     # Only run this job when the runner is allocated.
     if: ${{ always() }}
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     needs: [
       allocate-runners,
       build-linux-amd64-artifacts,
@@ -275,7 +275,7 @@ jobs:
     name: Stop linux-arm64 runner
     # Only run this job when the runner is allocated.
     if: ${{ always() }}
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     needs: [
       allocate-runners,
       build-linux-arm64-artifacts,
@@ -303,7 +303,7 @@ jobs:
     needs: [
       release-images-to-dockerhub
     ]
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     permissions:
       issues: write
     env:

.github/workflows/nightly-ci.yml

@@ -13,7 +13,7 @@ jobs:
   sqlness-test:
     name: Run sqlness test
     if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -107,7 +107,6 @@ 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 }}
@@ -118,22 +117,22 @@ jobs:
     name: Run clean build on Linux
     runs-on: ubuntu-latest
     if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
-    timeout-minutes: 60
+    timeout-minutes: 45
     steps:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
           persist-credentials: false
-      - uses: cachix/install-nix-action@v27
-        with:
-          nix_path: nixpkgs=channel:nixos-24.11
-      - run: nix develop --command cargo build
+      - uses: cachix/install-nix-action@v31
+      - run: nix develop --command cargo check --bin greptime
+        env:
+          CARGO_BUILD_RUSTFLAGS: "-C link-arg=-fuse-ld=mold"
 
   check-status:
     name: Check status
     needs: [sqlness-test, sqlness-windows, test-on-windows]
     if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     outputs:
       check-result: ${{ steps.set-check-result.outputs.check-result }}
     steps:
@@ -146,7 +145,7 @@ jobs:
     if: ${{ github.repository == 'GreptimeTeam/greptimedb' && always() }} # Not requiring successful dependent jobs, always run.
     name: Send notification to Greptime team
     needs: [check-status]
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     env:
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_DEVELOP_CHANNEL }}
     steps:

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

@@ -24,12 +24,20 @@ 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
-    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-20.04-16-cores
+    # 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 }}
+    runs-on: ubuntu-latest
     outputs:
       version: ${{ steps.set-version.outputs.version }}
     steps:
@@ -57,13 +65,13 @@ 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 }}
-          build-dev-builder-centos: ${{ inputs.release_dev_builder_centos_image }}
-          build-dev-builder-android: ${{ inputs.release_dev_builder_android_image }}
+          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' }}
 
   release-dev-builder-images-ecr:
     name: Release dev builder images to AWS ECR
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     needs: [
       release-dev-builder-images
     ]
@@ -85,7 +93,7 @@ jobs:
 
       - name: Push dev-builder-ubuntu image
         shell: bash
-        if: ${{ inputs.release_dev_builder_ubuntu_image }}
+        if: ${{ inputs.release_dev_builder_ubuntu_image || github.event_name == 'push' }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -106,7 +114,7 @@ jobs:
 
       - name: Push dev-builder-centos image
         shell: bash
-        if: ${{ inputs.release_dev_builder_centos_image }}
+        if: ${{ inputs.release_dev_builder_centos_image || github.event_name == 'push' }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -127,7 +135,7 @@ jobs:
 
       - name: Push dev-builder-android image
         shell: bash
-        if: ${{ inputs.release_dev_builder_android_image }}
+        if: ${{ inputs.release_dev_builder_android_image || github.event_name == 'push' }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -148,7 +156,7 @@ jobs:
 
   release-dev-builder-images-cn: # Note: Be careful issue: https://github.com/containers/skopeo/issues/1874 and we decide to use the latest stable skopeo container.
     name: Release dev builder images to CN region
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     needs: [
       release-dev-builder-images
     ]
@@ -162,7 +170,7 @@ jobs:
 
       - name: Push dev-builder-ubuntu image
         shell: bash
-        if: ${{ inputs.release_dev_builder_ubuntu_image }}
+        if: ${{ inputs.release_dev_builder_ubuntu_image || github.event_name == 'push' }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -176,7 +184,7 @@ jobs:
 
       - name: Push dev-builder-centos image
         shell: bash
-        if: ${{ inputs.release_dev_builder_centos_image }}
+        if: ${{ inputs.release_dev_builder_centos_image || github.event_name == 'push' }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -190,7 +198,7 @@ jobs:
 
       - name: Push dev-builder-android image
         shell: bash
-        if: ${{ inputs.release_dev_builder_android_image }}
+        if: ${{ inputs.release_dev_builder_android_image || github.event_name == 'push' }}
         env:
           IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
           IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -201,3 +209,24 @@ jobs:
             quay.io/skopeo/stable:latest \
             copy -a docker://docker.io/$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

@@ -18,11 +18,11 @@ on:
         description: The runner uses to build linux-amd64 artifacts
         default: ec2-c6i.4xlarge-amd64
         options:
-          - ubuntu-20.04
-          - ubuntu-20.04-8-cores
-          - ubuntu-20.04-16-cores
-          - ubuntu-20.04-32-cores
-          - ubuntu-20.04-64-cores
+          - ubuntu-22.04
+          - ubuntu-22.04-8-cores
+          - ubuntu-22.04-16-cores
+          - ubuntu-22.04-32-cores
+          - ubuntu-22.04-64-cores
           - ec2-c6i.xlarge-amd64 # 4C8G
           - ec2-c6i.2xlarge-amd64 # 8C16G
           - ec2-c6i.4xlarge-amd64 # 16C32G
@@ -88,16 +88,14 @@ 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 '${{ env.NEXT_RELEASE_VERSION }}-nightly-YYYYMMDD', like v0.2.0-nigthly-20230313;
+  # The scheduled version is '${{ env.NEXT_RELEASE_VERSION }}-nightly-YYYYMMDD', like v0.2.0-nightly-20230313;
   NIGHTLY_RELEASE_PREFIX: nightly
-  # Note: The NEXT_RELEASE_VERSION should be modified manually by every formal release.
-  NEXT_RELEASE_VERSION: v0.12.0
 
 jobs:
   allocate-runners:
     name: Allocate runners
     if: ${{ github.repository == 'GreptimeTeam/greptimedb' }}
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     outputs:
       linux-amd64-runner: ${{ steps.start-linux-amd64-runner.outputs.label }}
       linux-arm64-runner: ${{ steps.start-linux-arm64-runner.outputs.label }}
@@ -126,7 +124,7 @@ 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-$buildTime', like v0.2.0-nigthly-20230313;
+      # - If it's a scheduled release, the version is '${{ env.NEXT_RELEASE_VERSION }}-nightly-$buildTime', like v0.2.0-nightly-20230313;
       # - If it's a manual release, the version is '${{ env.NEXT_RELEASE_VERSION }}-<short-git-sha>-YYYYMMDDSS', like v0.2.0-e5b243c-2023071245;
       - name: Create version
         id: create-version
@@ -135,7 +133,6 @@ 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
@@ -299,7 +296,7 @@ jobs:
       build-linux-amd64-artifacts,
       build-linux-arm64-artifacts,
     ]
-    runs-on: ubuntu-2004-16-cores
+    runs-on: ubuntu-latest
     outputs:
       build-image-result: ${{ steps.set-build-image-result.outputs.build-image-result }}
     steps:
@@ -317,7 +314,7 @@ jobs:
           image-registry-username: ${{ secrets.DOCKERHUB_USERNAME }}
           image-registry-password: ${{ secrets.DOCKERHUB_TOKEN }}
           version: ${{ needs.allocate-runners.outputs.version }}
-          push-latest-tag: true
+          push-latest-tag: ${{ github.ref_type == 'tag' && !contains(github.ref_name, 'nightly') && github.event_name != 'schedule' }}
 
       - name: Set build image result
         id: set-build-image-result
@@ -335,7 +332,7 @@ jobs:
       build-windows-artifacts,
       release-images-to-dockerhub,
     ]
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     # When we push to ACR, it's easy to fail due to some unknown network issues.
     # However, we don't want to fail the whole workflow because of this.
     # The ACR have daily sync with DockerHub, so don't worry about the image not being updated.
@@ -364,7 +361,7 @@ jobs:
           dev-mode: false
           upload-to-s3: true
           update-version-info: true
-          push-latest-tag: true
+          push-latest-tag: ${{ github.ref_type == 'tag' && !contains(github.ref_name, 'nightly') && github.event_name != 'schedule' }}
 
   publish-github-release:
     name: Create GitHub release and upload artifacts
@@ -377,7 +374,7 @@ jobs:
       build-windows-artifacts,
       release-images-to-dockerhub,
     ]
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
         with:
@@ -391,12 +388,12 @@ 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 uncessary waiting.
+  # Because we can terminate the specified EC2 instance immediately after the job is finished without unnecessary 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.
     if: ${{ always() }}
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     needs: [
       allocate-runners,
       build-linux-amd64-artifacts,
@@ -422,7 +419,7 @@ jobs:
     name: Stop linux-arm64 runner
     # Only run this job when the runner is allocated.
     if: ${{ always() }}
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     needs: [
       allocate-runners,
       build-linux-arm64-artifacts,
@@ -444,11 +441,11 @@ jobs:
           aws-region: ${{ vars.EC2_RUNNER_REGION }}
           github-token: ${{ secrets.GH_PERSONAL_ACCESS_TOKEN }}
 
-  bump-doc-version:
-    name: Bump doc version
+  bump-downstream-repo-versions:
+    name: Bump downstream repo versions
     if: ${{ github.event_name == 'push' || github.event_name == 'schedule' }}
-    needs: [allocate-runners]
-    runs-on: ubuntu-20.04
+    needs: [allocate-runners, publish-github-release]
+    runs-on: ubuntu-latest
     # Permission reference: https://docs.github.com/en/actions/using-jobs/assigning-permissions-to-jobs
     permissions:
       issues: write # Allows the action to create issues for cyborg.
@@ -459,13 +456,58 @@ jobs:
           fetch-depth: 0
           persist-credentials: false
       - uses: ./.github/actions/setup-cyborg
-      - name: Bump doc version
+      - name: Bump downstream repo versions
         working-directory: cyborg
-        run: pnpm tsx bin/bump-doc-version.ts
+        run: pnpm tsx bin/bump-versions.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() }}
@@ -475,7 +517,7 @@ jobs:
       build-macos-artifacts,
       build-windows-artifacts,
     ]
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     # Permission reference: https://docs.github.com/en/actions/using-jobs/assigning-permissions-to-jobs
     permissions:
       issues: write # Allows the action to create issues for cyborg.

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

@@ -13,7 +13,10 @@ concurrency:
 
 jobs:
   check:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: write  # Add permissions to modify PRs
+      issues: write
     timeout-minutes: 10
     steps:
       - uses: actions/checkout@v4

.gitignore

@@ -28,6 +28,7 @@ debug/
 # Logs
 **/__unittest_logs
 logs/
+!grafana/dashboards/logs/
 
 # cpython's generated python byte code
 **/__pycache__/
@@ -54,3 +55,9 @@ 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

@@ -29,12 +29,15 @@ 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",
@@ -67,16 +70,17 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "0.12.0"
+version = "0.15.0"
 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.readonly_write_lock = "allow"
+clippy.result_large_err = "allow"
+clippy.large_enum_variant = "allow"
+clippy.doc_overindented_list_items = "allow"
+clippy.uninlined_format_args = "allow"
 rust.unknown_lints = "deny"
 rust.unexpected_cfgs = { level = "warn", check-cfg = ['cfg(tokio_unstable)'] }
 
@@ -88,20 +92,20 @@ 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 = "0.3"
-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"] }
+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"] }
 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 = "0.4"
+axum-macros = "0.5"
 backon = "1"
-base64 = "0.21"
+base64 = "0.22"
 bigdecimal = "0.4.2"
 bitflags = "2.4.1"
 bytemuck = "1.12"
@@ -111,42 +115,44 @@ chrono-tz = "0.10.1"
 clap = { version = "4.4", features = ["derive"] }
 config = "0.13.0"
 crossbeam-utils = "0.8"
-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"
+dashmap = "6.1"
+datafusion = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
+datafusion-common = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
+datafusion-expr = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
+datafusion-functions = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
+datafusion-optimizer = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
+datafusion-physical-expr = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
+datafusion-physical-plan = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
+datafusion-sql = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
+datafusion-substrait = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
+deadpool = "0.12"
+deadpool-postgres = "0.14"
+derive_builder = "0.20"
 dotenv = "0.15"
 etcd-client = "0.14"
 fst = "0.4.7"
 futures = "0.3"
 futures-util = "0.3"
-greptime-proto = { git = "https://github.com/GreptimeTeam/greptime-proto.git", rev = "072ce580502e015df1a6b03a185b60309a7c2a7a" }
+greptime-proto = { git = "https://github.com/GreptimeTeam/greptime-proto.git", rev = "2dca1dc67862d7b410838aef81232274c019b3f6" }
 hex = "0.4"
 http = "1"
 humantime = "2.1"
 humantime-serde = "1.1"
 hyper = "1.1"
 hyper-util = "0.1"
-itertools = "0.10"
+itertools = "0.14"
 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 = "0.11.4"
+mockall = "0.13"
 moka = "0.12"
 nalgebra = "0.33"
-notify = "6.1"
+nix = { version = "0.30.1", default-features = false, features = ["event", "fs", "process"] }
+notify = "8.0"
 num_cpus = "1.16"
+object_store_opendal = "0.50"
 once_cell = "1.18"
 opentelemetry-proto = { version = "0.27", features = [
     "gen-tonic",
@@ -156,17 +162,17 @@ opentelemetry-proto = { version = "0.27", features = [
     "logs",
 ] }
 parking_lot = "0.12"
-parquet = { version = "53.0.0", default-features = false, features = ["arrow", "async", "object_store"] }
+parquet = { version = "54.2", 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", features = [
+promql-parser = { git = "https://github.com/GreptimeTeam/promql-parser.git", rev = "0410e8b459dda7cb222ce9596f8bf3971bd07bd2", features = [
     "ser",
-], rev = "27abb8e16003a50c720f00d6c85f41f5fa2a2a8e" }
-prost = "0.13"
+] }
+prost = { version = "0.13", features = ["no-recursion-limit"] }
 raft-engine = { version = "0.4.1", default-features = false }
-rand = "0.8"
-ratelimit = "0.9"
+rand = "0.9"
+ratelimit = "0.10"
 regex = "1.8"
 regex-automata = "0.4"
 reqwest = { version = "0.12", default-features = false, features = [
@@ -175,32 +181,39 @@ reqwest = { version = "0.12", default-features = false, features = [
     "stream",
     "multipart",
 ] }
-rskafka = { git = "https://github.com/influxdata/rskafka.git", rev = "75535b5ad9bae4a5dbb582c82e44dfd81ec10105", features = [
+rskafka = { git = "https://github.com/influxdata/rskafka.git", rev = "8dbd01ed809f5a791833a594e85b144e36e45820", features = [
     "transport-tls",
 ] }
-rstest = "0.21"
+rstest = "0.25"
 rstest_reuse = "0.7"
 rust_decimal = "1.33"
 rustc-hash = "2.0"
-rustls = { version = "0.23.20", default-features = false } # override by patch, see [patch.crates-io]
+# 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 }
 serde = { version = "1.0", features = ["derive"] }
 serde_json = { version = "1.0", features = ["float_roundtrip"] }
 serde_with = "3"
-shadow-rs = "0.38"
+shadow-rs = "1.1"
+simd-json = "0.15"
 similar-asserts = "1.6.0"
 smallvec = { version = "1", features = ["serde"] }
 snafu = "0.8"
-sysinfo = "0.30"
-# on branch v0.52.x
-sqlparser = { git = "https://github.com/GreptimeTeam/sqlparser-rs.git", rev = "71dd86058d2af97b9925093d40c4e03360403170", features = [
+sqlparser = { git = "https://github.com/GreptimeTeam/sqlparser-rs.git", rev = "0cf6c04490d59435ee965edd2078e8855bd8471e", features = [
     "visitor",
     "serde",
-] } # on branch v0.44.x
-strum = { version = "0.25", features = ["derive"] }
+] } # branch = "v0.54.x"
+sqlx = { version = "0.8", features = [
+    "runtime-tokio-rustls",
+    "mysql",
+    "postgres",
+    "chrono",
+] }
+strum = { version = "0.27", features = ["derive"] }
+sysinfo = "0.33"
 tempfile = "3"
 tokio = { version = "1.40", features = ["full"] }
 tokio-postgres = "0.7"
-tokio-rustls = { version = "0.26.0", default-features = false } # override by patch, see [patch.crates-io]
+tokio-rustls = { version = "0.26.2", default-features = false }
 tokio-stream = "0.1"
 tokio-util = { version = "0.7", features = ["io-util", "compat"] }
 toml = "0.8.8"
@@ -243,11 +256,13 @@ 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" }
@@ -262,6 +277,9 @@ metric-engine = { path = "src/metric-engine" }
 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" }
@@ -271,19 +289,11 @@ 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 ?= 2024-12-25-9d0fa5d5-20250124085746
+DEV_BUILDER_IMAGE_TAG ?= 2025-05-19-b2377d4b-20250520045554
 BUILDX_MULTI_PLATFORM_BUILD ?= false
 BUILDX_BUILDER_NAME ?= gtbuilder
 BASE_IMAGE ?= ubuntu
@@ -32,6 +32,10 @@ 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 ${CARGO_PROFILE}
 endif
@@ -60,6 +64,8 @@ ifeq ($(BUILDX_MULTI_PLATFORM_BUILD), all)
 	BUILDX_MULTI_PLATFORM_BUILD_OPTS := --platform linux/amd64,linux/arm64 --push
 else ifeq ($(BUILDX_MULTI_PLATFORM_BUILD), amd64)
 	BUILDX_MULTI_PLATFORM_BUILD_OPTS := --platform linux/amd64 --push
+else ifeq ($(BUILDX_MULTI_PLATFORM_BUILD), arm64)
+	BUILDX_MULTI_PLATFORM_BUILD_OPTS := --platform linux/arm64 --push
 else
 	BUILDX_MULTI_PLATFORM_BUILD_OPTS := -o type=docker
 endif
@@ -191,6 +197,7 @@ 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.
@@ -215,6 +222,16 @@ 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

@@ -6,7 +6,9 @@
   </picture>
 </p>
 
-<h2 align="center">Unified & Cost-Effective Time Series Database for Metrics, Logs, and Events</h2>
+<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.
 
 <div align="center">
 <h3 align="center">
@@ -49,70 +51,77 @@
 </div>
 
 - [Introduction](#introduction)
-- [**Features: Why GreptimeDB**](#why-greptimedb)
-- [Architecture](https://docs.greptime.com/contributor-guide/overview/#architecture)
-- [Try it for free](#try-greptimedb)
+- [⭐ Key Features](#features)
+- [Quick Comparison](#quick-comparison)
+- [Architecture](#architecture)
+- [Try GreptimeDB](#try-greptimedb)
 - [Getting Started](#getting-started)
-- [Project Status](#project-status)
-- [Join the community](#community)
-  - [Contributing](#contributing)
+- [Build From Source](#build-from-source)
 - [Tools & Extensions](#tools--extensions)
+- [Project Status](#project-status)
+- [Community](#community)
 - [License](#license)
+- [Commercial Support](#commercial-support)
+- [Contributing](#contributing)
 - [Acknowledgement](#acknowledgement)
 
 ## Introduction
 
-**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.
+**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.
 
-## Why GreptimeDB
+## Features
 
-Our core developers have been building time-series data platforms for years. Based on our best practices, GreptimeDB was born to give you:
+|   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. |
 
-* **Unified Processing of Metrics, Logs, and Events**
+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).
 
-  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).
+## Quick Comparison
 
-* **Cloud-native Distributed Database**
+| 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          |
 
-  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.
+**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)
 
-* **Performance and Cost-effective**
+Read [more benchmark reports](https://docs.greptime.com/user-guide/concepts/features-that-you-concern#how-is-greptimedbs-performance-compared-to-other-solutions).
 
-  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).
+## Architecture
 
-* **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).
+* 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">
 
 ## Try GreptimeDB
 
 ### 1. [Live Demo](https://greptime.com/playground)
 
-Try out the features of GreptimeDB right from your browser.
+Experience GreptimeDB directly in your browser.
 
 ### 2. [GreptimeCloud](https://console.greptime.cloud/)
 
 Start instantly with a free cluster.
 
-### 3. Docker Image
-
-To install GreptimeDB locally, the recommended way is via Docker:
+### 3. Docker (Local Quickstart)
 
 ```shell
 docker pull greptime/greptimedb
 ```
 
-Start a GreptimeDB container with:
-
 ```shell
 docker run -p 127.0.0.1:4000-4003:4000-4003 \
-  -v "$(pwd)/greptimedb:/tmp/greptimedb" \
+  -v "$(pwd)/greptimedb_data:/greptimedb_data" \
   --name greptime --rm \
   greptime/greptimedb:latest standalone start \
   --http-addr 0.0.0.0:4000 \
@@ -120,112 +129,89 @@ 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)
 
-Access the dashboard via `http://localhost:4000/dashboard`.
-
-Read more about [Installation](https://docs.greptime.com/getting-started/installation/overview) on docs.
+**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.
 
 ## Getting Started
 
-* [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)
+- [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)
 
-## Build
-
-Check the prerequisite:
+## Build From Source
 
+**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 GreptimeDB binary:
-
-```shell
+**Build and Run:**
+```bash
 make
-```
-
-Run a standalone server:
-
-```shell
 cargo run -- standalone start
 ```
 
 ## Tools & Extensions
 
-### 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.
+- **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)
 
 ## Project Status
 
-GreptimeDB is currently in Beta. We are targeting GA (General Availability) with v1.0 release by Early 2025.
+> **Status:** Beta.
+> **GA (v1.0):** Targeted for mid 2025.
 
-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
+- 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
 
 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
 
-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:
+We invite you to engage and contribute!
 
-- GreptimeDB Community on [Slack](https://greptime.com/slack)
-- GreptimeDB [GitHub Discussions forum](https://github.com/GreptimeTeam/greptimedb/discussions)
-- Greptime official [website](https://greptime.com)
-
-In addition, you may:
-
-- 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
-
-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.
+- [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/)
+- [Twitter](https://twitter.com/greptime)
 
 ## 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.
+GreptimeDB is licensed under the [Apache License 2.0](https://apache.org/licenses/LICENSE-2.0.txt).
+
+## Commercial Support
+
+Running GreptimeDB in your organization?
+We offer enterprise add-ons, services, training, and consulting.
+[Contact us](https://greptime.com/contactus) for details.
 
 ## Contributing
 
-Please refer to [contribution guidelines](CONTRIBUTING.md) and [internal concepts docs](https://docs.greptime.com/contributor-guide/overview.html) for more information.
+- 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.
 
 ## Acknowledgement
 
-Special thanks to all the contributors who have propelled GreptimeDB forward. For a complete list of contributors, please refer to [AUTHOR.md](AUTHOR.md).
+Special thanks to all contributors! See [AUTHORS.md](https://github.com/GreptimeTeam/greptimedb/blob/main/AUTHOR.md).
 
-- 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/).
+- 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)

config/config.md

@@ -12,7 +12,6 @@
 
 | 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. |
@@ -24,10 +23,11 @@
 | `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 | `30s` | HTTP request timeout. Set to 0 to disable timeout. |
+| `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. |
 | `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. |
@@ -86,10 +86,6 @@
 | `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. |
@@ -98,10 +94,13 @@
 | `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 | `/tmp/greptimedb/` | The working home directory. |
+| `storage.data_home` | String | `./greptimedb_data` | 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. |
@@ -156,6 +155,7 @@
 | `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 |
@@ -181,7 +181,7 @@
 | `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 | `/tmp/greptimedb/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
+| `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.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:4317` | The OTLP tracing endpoint. |
@@ -190,17 +190,18 @@
 | `logging.max_log_files` | Integer | `720` | The maximum amount of log files. |
 | `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]. |
-| `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. |
+| `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. |
 | `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 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.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.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. |
@@ -222,14 +223,16 @@
 | `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 | `30s` | HTTP request timeout. Set to 0 to disable timeout. |
+| `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. |
 | `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. |
 | `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. |
@@ -274,12 +277,14 @@
 | `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 | `/tmp/greptimedb/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
+| `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.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:4317` | The OTLP tracing endpoint. |
@@ -288,17 +293,17 @@
 | `logging.max_log_files` | Integer | `720` | The maximum amount of log files. |
 | `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]. |
-| `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. |
+| `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. |
 | `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 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.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.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. |
@@ -308,25 +313,36 @@
 
 | Key | Type | Default | Descriptions |
 | --- | -----| ------- | ----------- |
-| `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`. |
+| `data_home` | String | `./greptimedb_data` | The working home directory. |
 | `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` |
+| `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` |
 | `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). |
+| `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. |
@@ -341,17 +357,16 @@
 | `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 | `/tmp/greptimedb/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
+| `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.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:4317` | The OTLP tracing endpoint. |
@@ -360,17 +375,11 @@
 | `logging.max_log_files` | Integer | `720` | The maximum amount of log files. |
 | `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]. |
-| `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` | -- | -- | 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. |
 | `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 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.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.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. |
@@ -380,7 +389,6 @@
 
 | 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. |
@@ -389,7 +397,7 @@
 | `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 | `30s` | HTTP request timeout. Set to 0 to disable timeout. |
+| `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. |
 | `grpc` | -- | -- | The gRPC server options. |
 | `grpc.bind_addr` | String | `127.0.0.1:3001` | The address to bind the gRPC server. |
@@ -397,6 +405,7 @@
 | `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. |
 | `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. |
@@ -433,15 +442,13 @@
 | `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 | `/tmp/greptimedb/` | The working home directory. |
+| `storage.data_home` | String | `./greptimedb_data` | 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. |
@@ -496,6 +503,7 @@
 | `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 |
@@ -521,7 +529,7 @@
 | `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 | `/tmp/greptimedb/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
+| `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.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:4317` | The OTLP tracing endpoint. |
@@ -530,17 +538,11 @@
 | `logging.max_log_files` | Integer | `720` | The maximum amount of log files. |
 | `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]. |
-| `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` | -- | -- | 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. |
 | `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 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.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.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. |
@@ -550,7 +552,6 @@
 
 | 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. |
@@ -562,7 +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 | `30s` | HTTP request timeout. Set to 0 to disable timeout. |
+| `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. |
 | `meta_client` | -- | -- | The metasrv client options. |
 | `meta_client.metasrv_addrs` | Array | -- | The addresses of the metasrv. |
@@ -578,7 +579,7 @@
 | `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 | `/tmp/greptimedb/logs` | The directory to store the log files. If set to empty, logs will not be written to files. |
+| `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.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:4317` | The OTLP tracing endpoint. |
@@ -587,9 +588,5 @@
 | `logging.max_log_files` | Integer | `720` | The maximum amount of log files. |
 | `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

@@ -1,6 +1,3 @@
-## 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
@@ -27,7 +24,7 @@ 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 = "30s"
+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.
@@ -47,6 +44,12 @@ 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.
+flight_compression = "arrow_ipc"
 
 ## gRPC server TLS options, see `mysql.tls` section.
 [grpc.tls]
@@ -119,7 +122,7 @@ provider = "raft_engine"
 ## The directory to store the WAL files.
 ## **It's only used when the provider is `raft_engine`**.
 ## @toml2docs:none-default
-dir = "/tmp/greptimedb/wal"
+dir = "./greptimedb_data/wal"
 
 ## The size of the WAL segment file.
 ## **It's only used when the provider is `raft_engine`**.
@@ -169,22 +172,6 @@ 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
@@ -231,6 +218,7 @@ overwrite_entry_start_id = false
 # secret_access_key = "123456"
 # endpoint = "https://s3.amazonaws.com"
 # region = "us-west-2"
+# enable_virtual_host_style = false
 
 # Example of using Oss as the storage.
 # [storage]
@@ -261,10 +249,16 @@ 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 = "/tmp/greptimedb/"
+data_home = "./greptimedb_data"
 
 ## The storage type used to store the data.
 ## - `File`: the data is stored in the local file system.
@@ -511,6 +505,9 @@ 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]
 
@@ -617,7 +614,7 @@ 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 = "/tmp/greptimedb/logs"
+dir = "./greptimedb_data/logs"
 
 ## The log level. Can be `info`/`debug`/`warn`/`error`.
 ## @toml2docs:none-default
@@ -644,37 +641,16 @@ max_log_files = 720
 [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 datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.
+## The datanode can export its metrics and send to Prometheus compatible service (e.g. `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 url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
+## 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`.
 url = ""
 
 ## HTTP headers of Prometheus remote-write carry.

config/flownode.example.toml

@@ -1,6 +1,3 @@
-## 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
@@ -30,7 +27,7 @@ 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 = "30s"
+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.
@@ -76,7 +73,7 @@ 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 = "/tmp/greptimedb/logs"
+dir = "./greptimedb_data/logs"
 
 ## The log level. Can be `info`/`debug`/`warn`/`error`.
 ## @toml2docs:none-default
@@ -103,22 +100,8 @@ max_log_files = 720
 [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 = "30s"
+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.
@@ -37,6 +37,12 @@ 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]
@@ -48,6 +54,12 @@ 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.
+flight_compression = "arrow_ipc"
 
 ## gRPC server TLS options, see `mysql.tls` section.
 [grpc.tls]
@@ -179,6 +191,12 @@ 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.
@@ -189,7 +207,7 @@ 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 = "/tmp/greptimedb/logs"
+dir = "./greptimedb_data/logs"
 
 ## The log level. Can be `info`/`debug`/`warn`/`error`.
 ## @toml2docs:none-default
@@ -217,36 +235,34 @@ max_log_files = 720
 default_ratio = 1.0
 
 ## The slow query log options.
-[logging.slow_query]
+[slow_query]
 ## Whether to enable slow query log.
-enable = false
+enable = true
 
-## The threshold of slow query.
-## @toml2docs:none-default
-threshold = "10s"
+## 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 sampling ratio of slow query log. The value should be in the range of (0, 1].
-## @toml2docs:none-default
+## 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.
 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.
+## 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.
 ## 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 url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
+## 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`.
 url = ""
 
 ## HTTP headers of Prometheus remote-write carry.

config/metasrv.example.toml

@@ -1,13 +1,5 @@
 ## The working home directory.
-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"
+data_home = "./greptimedb_data"
 
 ## Store server address default to etcd store.
 ## For postgres store, the format is:
@@ -24,6 +16,7 @@ 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.
@@ -50,6 +43,13 @@ use_memory_store = false
 ## - Using shared storage (e.g., s3).
 enable_region_failover = false
 
+## 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"
+
 ## Whether to enable greptimedb telemetry. Enabled by default.
 #+ enable_telemetry = true
 
@@ -60,6 +60,32 @@ enable_region_failover = false
 ## 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]
 
@@ -76,6 +102,11 @@ 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]
 
@@ -122,6 +153,22 @@ 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
 
@@ -141,17 +188,6 @@ 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`**.
@@ -174,7 +210,7 @@ backoff_deadline = "5mins"
 ## The logging options.
 [logging]
 ## The directory to store the log files. If set to empty, logs will not be written to files.
-dir = "/tmp/greptimedb/logs"
+dir = "./greptimedb_data/logs"
 
 ## The log level. Can be `info`/`debug`/`warn`/`error`.
 ## @toml2docs:none-default
@@ -201,37 +237,16 @@ max_log_files = 720
 [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 datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.
+## The metasrv can export its metrics and send to Prometheus compatible service (e.g. `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 url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
+## 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`.
 url = ""
 
 ## HTTP headers of Prometheus remote-write carry.

config/standalone.example.toml

@@ -1,6 +1,3 @@
-## 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"
@@ -34,7 +31,7 @@ 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 = "30s"
+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.
@@ -46,6 +43,13 @@ 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.
@@ -164,7 +168,7 @@ provider = "raft_engine"
 ## The directory to store the WAL files.
 ## **It's only used when the provider is `raft_engine`**.
 ## @toml2docs:none-default
-dir = "/tmp/greptimedb/wal"
+dir = "./greptimedb_data/wal"
 
 ## The size of the WAL segment file.
 ## **It's only used when the provider is `raft_engine`**.
@@ -242,22 +246,6 @@ 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`**.
 ##
@@ -302,6 +290,10 @@ 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]
@@ -318,6 +310,7 @@ retry_delay = "500ms"
 # secret_access_key = "123456"
 # endpoint = "https://s3.amazonaws.com"
 # region = "us-west-2"
+# enable_virtual_host_style = false
 
 # Example of using Oss as the storage.
 # [storage]
@@ -348,10 +341,16 @@ retry_delay = "500ms"
 # 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 = "/tmp/greptimedb/"
+data_home = "./greptimedb_data"
 
 ## The storage type used to store the data.
 ## - `File`: the data is stored in the local file system.
@@ -598,6 +597,9 @@ 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]
 
@@ -704,7 +706,7 @@ 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 = "/tmp/greptimedb/logs"
+dir = "./greptimedb_data/logs"
 
 ## The log level. Can be `info`/`debug`/`warn`/`error`.
 ## @toml2docs:none-default
@@ -732,25 +734,27 @@ max_log_files = 720
 default_ratio = 1.0
 
 ## The slow query log options.
-[logging.slow_query]
+[slow_query]
 ## Whether to enable slow query log.
-enable = false
+#+ enable = false
+
+## The record type of slow queries. It can be `system_table` or `log`.
+## @toml2docs:none-default
+#+ record_type = "system_table"
 
 ## 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 datanode can export its metrics and send to Prometheus compatible service (e.g. send to `greptimedb` itself) from remote-write API.
+## The standalone can export its metrics and send to Prometheus compatible service (e.g. `greptimedb`) 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"
 
@@ -761,7 +765,7 @@ write_interval = "30s"
 db = "greptime_metrics"
 
 [export_metrics.remote_write]
-## The url the metrics send to. The url example can be: `http://127.0.0.1:4000/v1/prometheus/write?db=greptime_metrics`.
+## 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`.
 url = ""
 
 ## HTTP headers of Prometheus remote-write carry.

cyborg/bin/bump-doc-version.ts

@@ -1,75 +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";
-
-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

@@ -0,0 +1,156 @@
+/*
+ * 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/buildx/ubuntu/Dockerfile

@@ -1,4 +1,4 @@
-FROM ubuntu:20.04 as builder
+FROM ubuntu:22.04 as builder
 
 ARG CARGO_PROFILE
 ARG FEATURES

docker/ci/ubuntu/Dockerfile.fuzztests

@@ -1,4 +1,4 @@
-FROM ubuntu:22.04
+FROM ubuntu:latest
 
 # The binary name of GreptimeDB executable.
 # Defaults to "greptime", but sometimes in other projects it might be different.

docker/dev-builder/ubuntu/Dockerfile

@@ -1,4 +1,4 @@
-FROM ubuntu:20.04
+FROM ubuntu:22.04
 
 # The root path under which contains all the dependencies to build this Dockerfile.
 ARG DOCKER_BUILD_ROOT=.
@@ -41,7 +41,7 @@ RUN mv protoc3/include/* /usr/local/include/
 # and the repositories are pulled from trusted sources (still us, of course). Doing so does not violate the intention
 # of the Git's addition to the "safe.directory" at the first place (see the commit message here:
 # https://github.com/git/git/commit/8959555cee7ec045958f9b6dd62e541affb7e7d9).
-# There's also another solution to this, that we add the desired submodules to the safe directory, instead of using 
+# There's also another solution to this, that we add the desired submodules to the safe directory, instead of using
 # wildcard here. However, that requires the git's config files and the submodules all owned by the very same user.
 # It's troublesome to do this since the dev build runs in Docker, which is under user "root"; while outside the Docker,
 # it can be a different user that have prepared the submodules.

docker/dev-builder/ubuntu/Dockerfile-18.10

@@ -1,51 +0,0 @@
-# Use the legacy glibc 2.28.
-FROM ubuntu:18.10
-
-ENV LANG en_US.utf8
-WORKDIR /greptimedb
-
-# Use old-releases.ubuntu.com to avoid 404s: https://help.ubuntu.com/community/EOLUpgrades.
-RUN echo "deb http://old-releases.ubuntu.com/ubuntu/ cosmic main restricted universe multiverse\n\
-deb http://old-releases.ubuntu.com/ubuntu/ cosmic-updates main restricted universe multiverse\n\
-deb http://old-releases.ubuntu.com/ubuntu/ cosmic-security main restricted universe multiverse" > /etc/apt/sources.list
-
-# Install dependencies.
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y \
-    libssl-dev \
-    tzdata \
-    curl \
-    ca-certificates \
-    git \
-    build-essential \
-    unzip \
-    pkg-config
-
-# Install protoc.
-ENV PROTOC_VERSION=29.3
-RUN if [ "$(uname -m)" = "x86_64" ]; then \
-        PROTOC_ZIP=protoc-${PROTOC_VERSION}-linux-x86_64.zip; \
-    elif [ "$(uname -m)" = "aarch64" ]; then \
-        PROTOC_ZIP=protoc-${PROTOC_VERSION}-linux-aarch_64.zip; \
-    else \
-        echo "Unsupported architecture"; exit 1; \
-    fi && \
-    curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOC_VERSION}/${PROTOC_ZIP} && \
-    unzip -o ${PROTOC_ZIP} -d /usr/local bin/protoc && \
-    unzip -o ${PROTOC_ZIP} -d /usr/local 'include/*' && \
-    rm -f ${PROTOC_ZIP}
-
-# Install Rust.
-SHELL ["/bin/bash", "-c"]
-RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- --no-modify-path --default-toolchain none -y
-ENV PATH /root/.cargo/bin/:$PATH
-
-# Install Rust toolchains.
-ARG RUST_TOOLCHAIN
-RUN rustup toolchain install ${RUST_TOOLCHAIN}
-
-# Install cargo-binstall with a specific version to adapt the current rust toolchain.
-# Note: if we use the latest version, we may encounter the following `use of unstable library feature 'io_error_downcast'` error.
-RUN cargo install cargo-binstall --version 1.6.6 --locked
-
-# Install nextest.
-RUN cargo binstall cargo-nextest --no-confirm

docker/dev-builder/ubuntu/Dockerfile-20.04

@@ -0,0 +1,66 @@
+FROM ubuntu:20.04
+
+# The root path under which contains all the dependencies to build this Dockerfile.
+ARG DOCKER_BUILD_ROOT=.
+
+ENV LANG en_US.utf8
+WORKDIR /greptimedb
+
+RUN apt-get update && \
+    DEBIAN_FRONTEND=noninteractive apt-get install -y software-properties-common
+# Install dependencies.
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y \
+    libssl-dev \
+    tzdata \
+    curl \
+    unzip \
+    ca-certificates \
+    git \
+    build-essential \
+    pkg-config
+
+ARG TARGETPLATFORM
+RUN echo "target platform: $TARGETPLATFORM"
+
+ARG PROTOBUF_VERSION=29.3
+
+# Install protobuf, because the one in the apt is too old (v3.12).
+RUN if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
+    curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOBUF_VERSION}/protoc-${PROTOBUF_VERSION}-linux-aarch_64.zip && \
+    unzip protoc-${PROTOBUF_VERSION}-linux-aarch_64.zip -d protoc3; \
+elif [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
+    curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOBUF_VERSION}/protoc-${PROTOBUF_VERSION}-linux-x86_64.zip && \
+    unzip protoc-${PROTOBUF_VERSION}-linux-x86_64.zip -d protoc3; \
+fi
+RUN mv protoc3/bin/* /usr/local/bin/
+RUN mv protoc3/include/* /usr/local/include/
+
+# Silence all `safe.directory` warnings, to avoid the "detect dubious repository" error when building with submodules.
+# Disabling the safe directory check here won't pose extra security issues, because in our usage for this dev build
+# image, we use it solely on our own environment (that github action's VM, or ECS created dynamically by ourselves),
+# and the repositories are pulled from trusted sources (still us, of course). Doing so does not violate the intention
+# of the Git's addition to the "safe.directory" at the first place (see the commit message here:
+# https://github.com/git/git/commit/8959555cee7ec045958f9b6dd62e541affb7e7d9).
+# There's also another solution to this, that we add the desired submodules to the safe directory, instead of using 
+# wildcard here. However, that requires the git's config files and the submodules all owned by the very same user.
+# It's troublesome to do this since the dev build runs in Docker, which is under user "root"; while outside the Docker,
+# it can be a different user that have prepared the submodules.
+RUN git config --global --add safe.directory '*'
+
+# Install Rust.
+SHELL ["/bin/bash", "-c"]
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- --no-modify-path --default-toolchain none -y
+ENV PATH /root/.cargo/bin/:$PATH
+
+# Install Rust toolchains.
+ARG RUST_TOOLCHAIN
+RUN rustup toolchain install ${RUST_TOOLCHAIN}
+
+# Install cargo-binstall with a specific version to adapt the current rust toolchain.
+# Note: if we use the latest version, we may encounter the following `use of unstable library feature 'io_error_downcast'` error.
+# compile from source take too long, so we use the precompiled binary instead
+COPY $DOCKER_BUILD_ROOT/docker/dev-builder/binstall/pull_binstall.sh /usr/local/bin/pull_binstall.sh
+RUN chmod +x /usr/local/bin/pull_binstall.sh && /usr/local/bin/pull_binstall.sh
+
+# Install nextest.
+RUN cargo binstall cargo-nextest --no-confirm

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

@@ -25,7 +25,7 @@ services:
       - --initial-cluster-state=new
       - *etcd_initial_cluster_token
     volumes:
-      - /tmp/greptimedb-cluster-docker-compose/etcd0:/var/lib/etcd
+      - ./greptimedb-cluster-docker-compose/etcd0:/var/lib/etcd
     healthcheck:
       test: [ "CMD", "etcdctl", "--endpoints=http://etcd0:2379", "endpoint", "health" ]
       interval: 5s
@@ -68,12 +68,13 @@ 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:
-      - /tmp/greptimedb-cluster-docker-compose/datanode0:/tmp/greptimedb
+      - ./greptimedb-cluster-docker-compose/datanode0:/greptimedb_data
     healthcheck:
       test: [ "CMD", "curl", "-fv", "http://datanode0:5000/health" ]
       interval: 5s

docs/benchmarks/tsbs/v0.12.0.md

@@ -0,0 +1,40 @@
+# TSBS benchmark - v0.12.0
+
+## Environment
+
+### Amazon EC2
+
+|         |                         |
+|---------|-------------------------|
+| Machine | c5d.2xlarge             |
+| CPU     | 8 core                  |
+| Memory  | 16GB                    |
+| Disk    | 100GB (GP3)             |
+| OS      | Ubuntu Server 24.04 LTS |
+
+## Write performance
+
+| Environment     | Ingest rate (rows/s) |
+|-----------------|----------------------|
+| EC2 c5d.2xlarge | 326839.28            |
+
+## Query performance
+
+| Query type            | EC2 c5d.2xlarge (ms) |
+|-----------------------|----------------------|
+| cpu-max-all-1         | 12.46                |
+| cpu-max-all-8         | 24.20                |
+| double-groupby-1      | 673.08               |
+| double-groupby-5      | 963.99               |
+| double-groupby-all    | 1330.05              |
+| groupby-orderby-limit | 952.46               |
+| high-cpu-1            | 5.08                 |
+| high-cpu-all          | 4638.57              |
+| lastpoint             | 591.02               |
+| single-groupby-1-1-1  | 4.06                 |
+| single-groupby-1-1-12 | 4.73                 |
+| single-groupby-1-8-1  | 8.23                 |
+| single-groupby-5-1-1  | 4.61                 |
+| single-groupby-5-1-12 | 5.61                 |
+| single-groupby-5-8-1  | 9.74                 |
+

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 follow the same rule of `RUST_LOG`. 
+The data is a string in the format of `global_level,module1=level1,module2=level2,...` that follows 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 feed into `StatementExecutor`:
+will be fed 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 mode (
+of `SqlStatementExecutor`. If you are going to implement the statements differently in the two modes (
 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.
+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).
 
 ## Prerequisites
 ### jemalloc
@@ -44,6 +44,10 @@ 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 prefer 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 preferred 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 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.
+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.
 
 > 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 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 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.  
 
 > 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, it's 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, its 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 you accumulator.
+# 2. Impl `Accumulator` trait for your 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 execute aggregate like this:
+The DataFusion basically executes 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 execute 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 cacalate, 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 calculate, 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 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`.
+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`.

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 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. 
+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.
 
 ## Why we need them
 - Find bugs by leveraging random generation
@@ -13,7 +13,7 @@ Fuzz test is tool that leverage deterministic random generation to assist in fin
 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

@@ -0,0 +1,52 @@
+# 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

@@ -0,0 +1,78 @@
+#!/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

@@ -0,0 +1,15 @@
+#!/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

@@ -0,0 +1,89 @@
+#!/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

@@ -0,0 +1,44 @@
+#!/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

@@ -0,0 +1,6 @@
+#!/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

@@ -0,0 +1,77 @@
+---
+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": 1737613896,
-        "narHash": "sha256-ldqXIglq74C7yKMFUzrS9xMT/EVs26vZpOD68Sh7OcU=",
+        "lastModified": 1745735608,
+        "narHash": "sha256-L0jzm815XBFfF2wCFmR+M1CF+beIEFj6SxlqVKF59Ec=",
         "owner": "nix-community",
         "repo": "fenix",
-        "rev": "303a062fdd8e89f233db05868468975d17855d80",
+        "rev": "c39a78eba6ed2a022cc3218db90d485077101496",
         "type": "github"
       },
       "original": {
@@ -41,16 +41,16 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1737569578,
-        "narHash": "sha256-6qY0pk2QmUtBT9Mywdvif0i/CLVgpCjMUn6g9vB+f3M=",
+        "lastModified": 1748162331,
+        "narHash": "sha256-rqc2RKYTxP3tbjA+PB3VMRQNnjesrT0pEofXQTrMsS8=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "47addd76727f42d351590c905d9d1905ca895b82",
+        "rev": "7c43f080a7f28b2774f3b3f43234ca11661bf334",
         "type": "github"
       },
       "original": {
         "owner": "NixOS",
-        "ref": "nixos-24.11",
+        "ref": "nixos-25.05",
         "repo": "nixpkgs",
         "type": "github"
       }
@@ -65,11 +65,11 @@
     "rust-analyzer-src": {
       "flake": false,
       "locked": {
-        "lastModified": 1737581772,
-        "narHash": "sha256-t1P2Pe3FAX9TlJsCZbmJ3wn+C4qr6aSMypAOu8WNsN0=",
+        "lastModified": 1745694049,
+        "narHash": "sha256-fxvRYH/tS7hGQeg9zCVh5RBcSWT+JGJet7RA8Ss+rC0=",
         "owner": "rust-lang",
         "repo": "rust-analyzer",
-        "rev": "582af7ee9c8d84f5d534272fc7de9f292bd849be",
+        "rev": "d8887c0758bbd2d5f752d5bd405d4491e90e7ed6",
         "type": "github"
       },
       "original": {

flake.nix

@@ -2,7 +2,7 @@
   description = "Development environment flake";
 
   inputs = {
-    nixpkgs.url = "github:NixOS/nixpkgs/nixos-24.11";
+    nixpkgs.url = "github:NixOS/nixpkgs/nixos-25.05";
     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-f/CVA1EC61EWbh0SjaRNhLL0Ypx2ObupbzigZp8NmL4=";
+          sha256 = "sha256-tJJr8oqX3YD+ohhPK7jlt/7kvKBnBqJVjYtoFr520d4=";
         };
       in
       {
@@ -51,6 +51,7 @@
           ];
 
           LD_LIBRARY_PATH = pkgs.lib.makeLibraryPath buildInputs;
+          NIX_HARDENING_ENABLE = "";
         };
       });
 }

grafana/README.md

@@ -1,61 +1,122 @@
-Grafana dashboard for GreptimeDB
---------------------------------
+# Grafana dashboards for GreptimeDB
 
-GreptimeDB's official Grafana dashboard.
+## Overview
 
-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 🤗
+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**.
 
-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:
+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:
 
 - `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/nightly/user-guide/deployments/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).
 
-# How to use
+### Self-host Prometheus and import dashboards manually
 
-## `greptimedb.json`
+1. **Configure Prometheus to scrape the cluster**
 
-Open Grafana Dashboard page, choose `New` -> `Import`. And upload `greptimedb.json` file.
+   The following is an example configuration(**Please modify it according to your actual situation**):
 
-## `greptimedb-cluster.json`
+    ```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>']
 
-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: datanode
+        static_configs:
+        - targets: ['<datanode0-ip>:<port>', '<datanode1-ip>:<port>', '<datanode2-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.
+      - job_name: frontend
+        static_configs:
+        - targets: ['<frontend-ip>:<port>']
+    ```
 
-__Note__: This dashboard is still in an early stage of development. Any issue or advice on improvement is welcomed.
+2. **Configure the data sources in Grafana**
 
-### Configuration
+   You need to add two data sources in Grafana:
 
-Please ensure the following configuration before importing the dashboard into Grafana.
+   - 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.
 
-__1. Prometheus scrape config__
+3. **Import the dashboards based on your deployment scenario**
 
-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.
+   - **Cluster**: Import the `dashboards/metrics/cluster/dashboard.json` dashboard.
+   - **Standalone**: Import the `dashboards/metrics/standalone/dashboard.json` dashboard.

grafana/dashboards/logs/dashboard.json

@@ -0,0 +1,292 @@
+{
+  "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

@@ -0,0 +1,111 @@
+# 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[$__rate_interval]))` | `timeseries` | Per-stage elapsed time for region worker to handle bulk insert region requests. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-P95` |
+| Region Worker Convert Requests | `histogram_quantile(0.95, sum by(le, instance, stage, pod) (rate(greptime_datanode_convert_region_request_bucket[$__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

@@ -0,0 +1,943 @@
+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: ${metrics}
+              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-AVG'
+        - title: Region Worker Convert Requests
+          type: timeseries
+          description: Per-stage elapsed time for region worker to decode requests.
+          unit: s
+          queries:
+            - expr: histogram_quantile(0.95, sum by(le, instance, stage, pod) (rate(greptime_datanode_convert_region_request_bucket[$__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

@@ -0,0 +1,111 @@
+# 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[$__rate_interval]))` | `timeseries` | Per-stage elapsed time for region worker to handle bulk insert region requests. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-P95` |
+| Region Worker Convert Requests | `histogram_quantile(0.95, sum by(le, instance, stage, pod) (rate(greptime_datanode_convert_region_request_bucket[$__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

@@ -0,0 +1,943 @@
+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: ${metrics}
+              legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-AVG'
+        - title: Region Worker Convert Requests
+          type: timeseries
+          description: Per-stage elapsed time for region worker to decode requests.
+          unit: s
+          queries:
+            - expr: histogram_quantile(0.95, sum by(le, instance, stage, pod) (rate(greptime_datanode_convert_region_request_bucket[$__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

@@ -0,0 +1,54 @@
+#!/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

@@ -0,0 +1,25 @@
+#! /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

licenserc.toml

@@ -26,6 +26,13 @@ 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/parsers/create_parser/trigger.rs",
+    "src/sql/src/parsers/show_parser/trigger.rs",
 ]
 
 [properties]

rust-toolchain.toml

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

scripts/check-super-imports.py

@@ -0,0 +1,74 @@
+# 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

@@ -19,9 +19,7 @@ use common_decimal::decimal128::{DECIMAL128_DEFAULT_SCALE, DECIMAL128_MAX_PRECIS
 use common_decimal::Decimal128;
 use common_time::time::Time;
 use common_time::timestamp::TimeUnit;
-use common_time::{
-    Date, DateTime, IntervalDayTime, IntervalMonthDayNano, IntervalYearMonth, Timestamp,
-};
+use common_time::{Date, IntervalDayTime, IntervalMonthDayNano, IntervalYearMonth, Timestamp};
 use datatypes::prelude::{ConcreteDataType, ValueRef};
 use datatypes::scalars::ScalarVector;
 use datatypes::types::{
@@ -29,8 +27,8 @@ use datatypes::types::{
 };
 use datatypes::value::{OrderedF32, OrderedF64, Value};
 use datatypes::vectors::{
-    BinaryVector, BooleanVector, DateTimeVector, DateVector, Decimal128Vector, Float32Vector,
-    Float64Vector, Int32Vector, Int64Vector, IntervalDayTimeVector, IntervalMonthDayNanoVector,
+    BinaryVector, BooleanVector, DateVector, Decimal128Vector, Float32Vector, Float64Vector,
+    Int32Vector, Int64Vector, IntervalDayTimeVector, IntervalMonthDayNanoVector,
     IntervalYearMonthVector, PrimitiveVector, StringVector, TimeMicrosecondVector,
     TimeMillisecondVector, TimeNanosecondVector, TimeSecondVector, TimestampMicrosecondVector,
     TimestampMillisecondVector, TimestampNanosecondVector, TimestampSecondVector, UInt32Vector,
@@ -118,7 +116,7 @@ impl From<ColumnDataTypeWrapper> for ConcreteDataType {
             ColumnDataType::Json => ConcreteDataType::json_datatype(),
             ColumnDataType::String => ConcreteDataType::string_datatype(),
             ColumnDataType::Date => ConcreteDataType::date_datatype(),
-            ColumnDataType::Datetime => ConcreteDataType::datetime_datatype(),
+            ColumnDataType::Datetime => ConcreteDataType::timestamp_microsecond_datatype(),
             ColumnDataType::TimestampSecond => ConcreteDataType::timestamp_second_datatype(),
             ColumnDataType::TimestampMillisecond => {
                 ConcreteDataType::timestamp_millisecond_datatype()
@@ -271,7 +269,6 @@ impl TryFrom<ConcreteDataType> for ColumnDataTypeWrapper {
             ConcreteDataType::Binary(_) => ColumnDataType::Binary,
             ConcreteDataType::String(_) => ColumnDataType::String,
             ConcreteDataType::Date(_) => ColumnDataType::Date,
-            ConcreteDataType::DateTime(_) => ColumnDataType::Datetime,
             ConcreteDataType::Timestamp(t) => match t {
                 TimestampType::Second(_) => ColumnDataType::TimestampSecond,
                 TimestampType::Millisecond(_) => ColumnDataType::TimestampMillisecond,
@@ -476,7 +473,6 @@ pub fn push_vals(column: &mut Column, origin_count: usize, vector: VectorRef) {
         Value::String(val) => values.string_values.push(val.as_utf8().to_string()),
         Value::Binary(val) => values.binary_values.push(val.to_vec()),
         Value::Date(val) => values.date_values.push(val.val()),
-        Value::DateTime(val) => values.datetime_values.push(val.val()),
         Value::Timestamp(val) => match val.unit() {
             TimeUnit::Second => values.timestamp_second_values.push(val.value()),
             TimeUnit::Millisecond => values.timestamp_millisecond_values.push(val.value()),
@@ -518,6 +514,7 @@ 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",
     }
 }
@@ -577,12 +574,11 @@ pub fn pb_value_to_value_ref<'a>(
         ValueData::BinaryValue(bytes) => ValueRef::Binary(bytes.as_slice()),
         ValueData::StringValue(string) => ValueRef::String(string.as_str()),
         ValueData::DateValue(d) => ValueRef::Date(Date::from(*d)),
-        ValueData::DatetimeValue(d) => ValueRef::DateTime(DateTime::new(*d)),
         ValueData::TimestampSecondValue(t) => ValueRef::Timestamp(Timestamp::new_second(*t)),
         ValueData::TimestampMillisecondValue(t) => {
             ValueRef::Timestamp(Timestamp::new_millisecond(*t))
         }
-        ValueData::TimestampMicrosecondValue(t) => {
+        ValueData::DatetimeValue(t) | ValueData::TimestampMicrosecondValue(t) => {
             ValueRef::Timestamp(Timestamp::new_microsecond(*t))
         }
         ValueData::TimestampNanosecondValue(t) => {
@@ -651,7 +647,6 @@ pub fn pb_values_to_vector_ref(data_type: &ConcreteDataType, values: Values) ->
         ConcreteDataType::Binary(_) => Arc::new(BinaryVector::from(values.binary_values)),
         ConcreteDataType::String(_) => Arc::new(StringVector::from_vec(values.string_values)),
         ConcreteDataType::Date(_) => Arc::new(DateVector::from_vec(values.date_values)),
-        ConcreteDataType::DateTime(_) => Arc::new(DateTimeVector::from_vec(values.datetime_values)),
         ConcreteDataType::Timestamp(unit) => match unit {
             TimestampType::Second(_) => Arc::new(TimestampSecondVector::from_vec(
                 values.timestamp_second_values,
@@ -787,11 +782,6 @@ pub fn pb_values_to_values(data_type: &ConcreteDataType, values: Values) -> Vec<
             .into_iter()
             .map(|val| val.into())
             .collect(),
-        ConcreteDataType::DateTime(_) => values
-            .datetime_values
-            .into_iter()
-            .map(|v| Value::DateTime(v.into()))
-            .collect(),
         ConcreteDataType::Date(_) => values
             .date_values
             .into_iter()
@@ -947,9 +937,6 @@ pub fn to_proto_value(value: Value) -> Option<v1::Value> {
         Value::Date(v) => v1::Value {
             value_data: Some(ValueData::DateValue(v.val())),
         },
-        Value::DateTime(v) => v1::Value {
-            value_data: Some(ValueData::DatetimeValue(v.val())),
-        },
         Value::Timestamp(v) => match v.unit() {
             TimeUnit::Second => v1::Value {
                 value_data: Some(ValueData::TimestampSecondValue(v.value())),
@@ -1063,10 +1050,9 @@ 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.as_utf8().to_string())),
+            Value::String(v) => Some(ValueData::StringValue(v.into_string())),
             Value::Binary(v) => Some(ValueData::BinaryValue(v.to_vec())),
             Value::Date(v) => Some(ValueData::DateValue(v.val())),
-            Value::DateTime(v) => Some(ValueData::DatetimeValue(v.val())),
             Value::Timestamp(v) => Some(match v.unit() {
                 TimeUnit::Second => ValueData::TimestampSecondValue(v.value()),
                 TimeUnit::Millisecond => ValueData::TimestampMillisecondValue(v.value()),
@@ -1248,7 +1234,7 @@ mod tests {
             ColumnDataTypeWrapper::date_datatype().into()
         );
         assert_eq!(
-            ConcreteDataType::datetime_datatype(),
+            ConcreteDataType::timestamp_microsecond_datatype(),
             ColumnDataTypeWrapper::datetime_datatype().into()
         );
         assert_eq!(
@@ -1339,10 +1325,6 @@ mod tests {
             ColumnDataTypeWrapper::date_datatype(),
             ConcreteDataType::date_datatype().try_into().unwrap()
         );
-        assert_eq!(
-            ColumnDataTypeWrapper::datetime_datatype(),
-            ConcreteDataType::datetime_datatype().try_into().unwrap()
-        );
         assert_eq!(
             ColumnDataTypeWrapper::timestamp_millisecond_datatype(),
             ConcreteDataType::timestamp_millisecond_datatype()
@@ -1830,17 +1812,6 @@ mod tests {
         ]
     );
 
-    test_convert_values!(
-        datetime,
-        vec![1.into(), 2.into(), 3.into()],
-        datetime,
-        vec![
-            Value::DateTime(1.into()),
-            Value::DateTime(2.into()),
-            Value::DateTime(3.into())
-        ]
-    );
-
     #[test]
     fn test_vectors_to_rows_for_different_types() {
         let boolean_vec = BooleanVector::from_vec(vec![true, false, true]);

src/api/src/v1/column_def.rs

@@ -15,10 +15,13 @@
 use std::collections::HashMap;
 
 use datatypes::schema::{
-    ColumnDefaultConstraint, ColumnSchema, FulltextAnalyzer, FulltextOptions, SkippingIndexType,
-    COMMENT_KEY, FULLTEXT_KEY, INVERTED_INDEX_KEY, SKIPPING_INDEX_KEY,
+    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,
 };
-use greptime_proto::v1::{Analyzer, SkippingIndexType as PbSkippingIndexType};
 use snafu::ResultExt;
 
 use crate::error::{self, Result};
@@ -103,6 +106,13 @@ pub fn contains_fulltext(options: &Option<ColumnOptions>) -> bool {
         .is_some_and(|o| o.options.contains_key(FULLTEXT_GRPC_KEY))
 }
 
+/// Checks if the `ColumnOptions` contains skipping index options.
+pub fn contains_skipping(options: &Option<ColumnOptions>) -> bool {
+    options
+        .as_ref()
+        .is_some_and(|o| o.options.contains_key(SKIPPING_INDEX_GRPC_KEY))
+}
+
 /// Tries to construct a `ColumnOptions` from the given `FulltextOptions`.
 pub fn options_from_fulltext(fulltext: &FulltextOptions) -> Result<Option<ColumnOptions>> {
     let mut options = ColumnOptions::default();
@@ -113,14 +123,43 @@ pub fn options_from_fulltext(fulltext: &FulltextOptions) -> Result<Option<Column
     Ok((!options.options.is_empty()).then_some(options))
 }
 
+/// Tries to construct a `ColumnOptions` from the given `SkippingIndexOptions`.
+pub fn options_from_skipping(skipping: &SkippingIndexOptions) -> Result<Option<ColumnOptions>> {
+    let mut options = ColumnOptions::default();
+
+    let v = serde_json::to_string(skipping).context(error::SerializeJsonSnafu)?;
+    options
+        .options
+        .insert(SKIPPING_INDEX_GRPC_KEY.to_string(), v);
+
+    Ok((!options.options.is_empty()).then_some(options))
+}
+
+/// Tries to construct a `ColumnOptions` for inverted index.
+pub fn options_from_inverted() -> ColumnOptions {
+    let mut options = ColumnOptions::default();
+    options
+        .options
+        .insert(INVERTED_INDEX_GRPC_KEY.to_string(), "true".to_string());
+    options
+}
+
 /// Tries to construct a `FulltextAnalyzer` from the given analyzer.
-pub fn as_fulltext_option(analyzer: Analyzer) -> FulltextAnalyzer {
+pub fn as_fulltext_option_analyzer(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 {
@@ -132,7 +171,7 @@ pub fn as_skipping_index_type(skipping_index_type: PbSkippingIndexType) -> Skipp
 mod tests {
 
     use datatypes::data_type::ConcreteDataType;
-    use datatypes::schema::FulltextAnalyzer;
+    use datatypes::schema::{FulltextAnalyzer, FulltextBackend};
 
     use super::*;
     use crate::v1::ColumnDataType;
@@ -191,13 +230,14 @@ 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}"
+            "{\"enable\":true,\"analyzer\":\"English\",\"case-sensitive\":false,\"backend\":\"bloom\"}"
         );
         assert_eq!(
             options.options.get(INVERTED_INDEX_GRPC_KEY).unwrap(),
@@ -211,11 +251,12 @@ 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}"
+            "{\"enable\":true,\"analyzer\":\"English\",\"case-sensitive\":false,\"backend\":\"bloom\"}"
         );
     }
 

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(':').context(InvalidConfigSnafu {
+    let (name, content) = opt.split_once(':').with_context(|| InvalidConfigSnafu {
         value: opt.to_string(),
         msg: "UserProviderOption must be in format `<option>:<value>`",
     })?;
@@ -57,6 +57,24 @@ 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

@@ -38,6 +38,14 @@ 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)]
@@ -85,7 +93,7 @@ impl ErrorExt for Error {
     fn status_code(&self) -> StatusCode {
         match self {
             Error::InvalidConfig { .. } => StatusCode::InvalidArguments,
-            Error::IllegalParam { .. } => StatusCode::InvalidArguments,
+            Error::IllegalParam { .. } | Error::FromUtf8 { .. } => StatusCode::InvalidArguments,
             Error::FileWatch { .. } => StatusCode::InvalidArguments,
             Error::InternalState { .. } => StatusCode::Unexpected,
             Error::Io { .. } => StatusCode::StorageUnavailable,

src/auth/src/lib.rs

@@ -22,10 +22,12 @@ mod user_provider;
 pub mod tests;
 
 pub use common::{
-    auth_mysql, user_provider_from_option, userinfo_by_name, HashedPassword, Identity, Password,
+    auth_mysql, static_user_provider_from_option, 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;
+use snafu::{OptionExt, ResultExt};
 
-use crate::error::{InvalidConfigSnafu, Result};
+use crate::error::{FromUtf8Snafu, 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(crate) struct StaticUserProvider {
+pub struct StaticUserProvider {
     users: HashMap<String, Vec<u8>>,
 }
 
@@ -60,6 +60,18 @@ 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/src/kvbackend/manager.rs

@@ -38,6 +38,7 @@ use partition::manager::{PartitionRuleManager, PartitionRuleManagerRef};
 use session::context::{Channel, QueryContext};
 use snafu::prelude::*;
 use table::dist_table::DistTable;
+use table::metadata::TableId;
 use table::table::numbers::{NumbersTable, NUMBERS_TABLE_NAME};
 use table::table_name::TableName;
 use table::TableRef;
@@ -286,6 +287,28 @@ impl CatalogManager for KvBackendCatalogManager {
         return Ok(None);
     }
 
+    async fn tables_by_ids(
+        &self,
+        catalog: &str,
+        schema: &str,
+        table_ids: &[TableId],
+    ) -> Result<Vec<TableRef>> {
+        let table_info_values = self
+            .table_metadata_manager
+            .table_info_manager()
+            .batch_get(table_ids)
+            .await
+            .context(TableMetadataManagerSnafu)?;
+
+        let tables = table_info_values
+            .into_values()
+            .filter(|t| t.table_info.catalog_name == catalog && t.table_info.schema_name == schema)
+            .map(build_table)
+            .collect::<Result<Vec<_>>>()?;
+
+        Ok(tables)
+    }
+
     fn tables<'a>(
         &'a self,
         catalog: &'a str,

src/catalog/src/lib.rs

@@ -87,6 +87,14 @@ pub trait CatalogManager: Send + Sync {
         query_ctx: Option<&QueryContext>,
     ) -> Result<Option<TableRef>>;
 
+    /// Returns the tables by table ids.
+    async fn tables_by_ids(
+        &self,
+        catalog: &str,
+        schema: &str,
+        table_ids: &[TableId],
+    ) -> Result<Vec<TableRef>>;
+
     /// Returns all tables with a stream by catalog and schema.
     fn tables<'a>(
         &'a self,

src/catalog/src/memory/manager.rs

@@ -14,7 +14,7 @@
 
 use std::any::Any;
 use std::collections::hash_map::Entry;
-use std::collections::HashMap;
+use std::collections::{HashMap, HashSet};
 use std::sync::{Arc, RwLock, Weak};
 
 use async_stream::{stream, try_stream};
@@ -28,6 +28,7 @@ use common_meta::kv_backend::memory::MemoryKvBackend;
 use futures_util::stream::BoxStream;
 use session::context::QueryContext;
 use snafu::OptionExt;
+use table::metadata::TableId;
 use table::TableRef;
 
 use crate::error::{CatalogNotFoundSnafu, Result, SchemaNotFoundSnafu, TableExistsSnafu};
@@ -143,6 +144,33 @@ impl CatalogManager for MemoryCatalogManager {
         Ok(result)
     }
 
+    async fn tables_by_ids(
+        &self,
+        catalog: &str,
+        schema: &str,
+        table_ids: &[TableId],
+    ) -> Result<Vec<TableRef>> {
+        let catalogs = self.catalogs.read().unwrap();
+
+        let schemas = catalogs.get(catalog).context(CatalogNotFoundSnafu {
+            catalog_name: catalog,
+        })?;
+
+        let tables = schemas
+            .get(schema)
+            .context(SchemaNotFoundSnafu { catalog, schema })?;
+
+        let filter_ids: HashSet<_> = table_ids.iter().collect();
+        // It is very inefficient, but we do not need to optimize it since it will not be called in `MemoryCatalogManager`.
+        let tables = tables
+            .values()
+            .filter(|t| filter_ids.contains(&t.table_info().table_id()))
+            .cloned()
+            .collect::<Vec<_>>();
+
+        Ok(tables)
+    }
+
     fn tables<'a>(
         &'a self,
         catalog: &'a str,

src/catalog/src/system_schema.rs

@@ -77,7 +77,7 @@ trait SystemSchemaProviderInner {
     fn system_table(&self, name: &str) -> Option<SystemTableRef>;
 
     fn table_info(catalog_name: String, table: &SystemTableRef) -> TableInfoRef {
-        let table_meta = TableMetaBuilder::default()
+        let table_meta = TableMetaBuilder::empty()
             .schema(table.schema())
             .primary_key_indices(vec![])
             .next_column_id(0)

src/catalog/src/system_schema/information_schema.rs

@@ -19,7 +19,7 @@ mod information_memory_table;
 pub mod key_column_usage;
 mod partitions;
 mod procedure_info;
-mod region_peers;
+pub mod region_peers;
 mod region_statistics;
 mod runtime_metrics;
 pub mod schemata;
@@ -49,7 +49,6 @@ pub use table_names::*;
 use views::InformationSchemaViews;
 
 use self::columns::InformationSchemaColumns;
-use super::{SystemSchemaProviderInner, SystemTable, SystemTableRef};
 use crate::error::{Error, Result};
 use crate::system_schema::information_schema::cluster_info::InformationSchemaClusterInfo;
 use crate::system_schema::information_schema::flows::InformationSchemaFlows;
@@ -63,7 +62,9 @@ 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;
+use crate::system_schema::{
+    SystemSchemaProvider, SystemSchemaProviderInner, SystemTable, SystemTableRef,
+};
 use crate::CatalogManager;
 
 lazy_static! {

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

@@ -36,9 +36,8 @@ 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};
+use crate::system_schema::information_schema::{InformationTable, Predicates, CLUSTER_INFO};
 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)]
@@ -56,6 +56,8 @@ 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";
@@ -365,10 +367,6 @@ impl InformationSchemaColumnsBuilder {
             self.numeric_scales.push(None);
 
             match &column_schema.data_type {
-                ConcreteDataType::DateTime(datetime_type) => {
-                    self.datetime_precisions
-                        .push(Some(datetime_type.precision() as i64));
-                }
                 ConcreteDataType::Timestamp(ts_type) => {
                     self.datetime_precisions
                         .push(Some(ts_type.precision() as i64));

src/catalog/src/system_schema/information_schema/flows.rs

@@ -28,16 +28,19 @@ use datafusion::physical_plan::streaming::PartitionStream as DfPartitionStream;
 use datatypes::prelude::ConcreteDataType as CDT;
 use datatypes::scalars::ScalarVectorBuilder;
 use datatypes::schema::{ColumnSchema, Schema, SchemaRef};
+use datatypes::timestamp::TimestampMillisecond;
 use datatypes::value::Value;
 use datatypes::vectors::{
-    Int64VectorBuilder, StringVectorBuilder, UInt32VectorBuilder, UInt64VectorBuilder, VectorRef,
+    Int64VectorBuilder, StringVectorBuilder, TimestampMillisecondVectorBuilder,
+    UInt32VectorBuilder, UInt64VectorBuilder, VectorRef,
 };
 use futures::TryStreamExt;
 use snafu::{OptionExt, ResultExt};
 use store_api::storage::{ScanRequest, TableId};
 
 use crate::error::{
-    CreateRecordBatchSnafu, FlowInfoNotFoundSnafu, InternalSnafu, JsonSnafu, ListFlowsSnafu, Result,
+    CreateRecordBatchSnafu, FlowInfoNotFoundSnafu, InternalSnafu, JsonSnafu, ListFlowsSnafu,
+    Result, UpgradeWeakCatalogManagerRefSnafu,
 };
 use crate::information_schema::{Predicates, FLOWS};
 use crate::system_schema::information_schema::InformationTable;
@@ -59,6 +62,10 @@ pub const SOURCE_TABLE_IDS: &str = "source_table_ids";
 pub const SINK_TABLE_NAME: &str = "sink_table_name";
 pub const FLOWNODE_IDS: &str = "flownode_ids";
 pub const OPTIONS: &str = "options";
+pub const CREATED_TIME: &str = "created_time";
+pub const UPDATED_TIME: &str = "updated_time";
+pub const LAST_EXECUTION_TIME: &str = "last_execution_time";
+pub const SOURCE_TABLE_NAMES: &str = "source_table_names";
 
 /// The `information_schema.flows` to provides information about flows in databases.
 #[derive(Debug)]
@@ -99,6 +106,14 @@ impl InformationSchemaFlows {
                 (SINK_TABLE_NAME, CDT::string_datatype(), false),
                 (FLOWNODE_IDS, CDT::string_datatype(), true),
                 (OPTIONS, CDT::string_datatype(), true),
+                (CREATED_TIME, CDT::timestamp_millisecond_datatype(), false),
+                (UPDATED_TIME, CDT::timestamp_millisecond_datatype(), false),
+                (
+                    LAST_EXECUTION_TIME,
+                    CDT::timestamp_millisecond_datatype(),
+                    true,
+                ),
+                (SOURCE_TABLE_NAMES, CDT::string_datatype(), true),
             ]
             .into_iter()
             .map(|(name, ty, nullable)| ColumnSchema::new(name, ty, nullable))
@@ -170,6 +185,10 @@ struct InformationSchemaFlowsBuilder {
     sink_table_names: StringVectorBuilder,
     flownode_id_groups: StringVectorBuilder,
     option_groups: StringVectorBuilder,
+    created_time: TimestampMillisecondVectorBuilder,
+    updated_time: TimestampMillisecondVectorBuilder,
+    last_execution_time: TimestampMillisecondVectorBuilder,
+    source_table_names: StringVectorBuilder,
 }
 
 impl InformationSchemaFlowsBuilder {
@@ -196,6 +215,10 @@ impl InformationSchemaFlowsBuilder {
             sink_table_names: StringVectorBuilder::with_capacity(INIT_CAPACITY),
             flownode_id_groups: StringVectorBuilder::with_capacity(INIT_CAPACITY),
             option_groups: StringVectorBuilder::with_capacity(INIT_CAPACITY),
+            created_time: TimestampMillisecondVectorBuilder::with_capacity(INIT_CAPACITY),
+            updated_time: TimestampMillisecondVectorBuilder::with_capacity(INIT_CAPACITY),
+            last_execution_time: TimestampMillisecondVectorBuilder::with_capacity(INIT_CAPACITY),
+            source_table_names: StringVectorBuilder::with_capacity(INIT_CAPACITY),
         }
     }
 
@@ -235,13 +258,14 @@ impl InformationSchemaFlowsBuilder {
                     catalog_name: catalog_name.to_string(),
                     flow_name: flow_name.to_string(),
                 })?;
-            self.add_flow(&predicates, flow_id.flow_id(), flow_info, &flow_stat)?;
+            self.add_flow(&predicates, flow_id.flow_id(), flow_info, &flow_stat)
+                .await?;
         }
 
         self.finish()
     }
 
-    fn add_flow(
+    async fn add_flow(
         &mut self,
         predicates: &Predicates,
         flow_id: FlowId,
@@ -290,6 +314,36 @@ impl InformationSchemaFlowsBuilder {
                     input: format!("{:?}", flow_info.options()),
                 },
             )?));
+        self.created_time
+            .push(Some(flow_info.created_time().timestamp_millis().into()));
+        self.updated_time
+            .push(Some(flow_info.updated_time().timestamp_millis().into()));
+        self.last_execution_time
+            .push(flow_stat.as_ref().and_then(|state| {
+                state
+                    .last_exec_time_map
+                    .get(&flow_id)
+                    .map(|v| TimestampMillisecond::new(*v))
+            }));
+
+        let mut source_table_names = vec![];
+        let catalog_name = self.catalog_name.clone();
+        let catalog_manager = self
+            .catalog_manager
+            .upgrade()
+            .context(UpgradeWeakCatalogManagerRefSnafu)?;
+        for schema_name in catalog_manager.schema_names(&catalog_name, None).await? {
+            source_table_names.extend(
+                catalog_manager
+                    .tables_by_ids(&catalog_name, &schema_name, flow_info.source_table_ids())
+                    .await?
+                    .into_iter()
+                    .map(|table| table.table_info().full_table_name()),
+            );
+        }
+
+        let source_table_names = source_table_names.join(",");
+        self.source_table_names.push(Some(&source_table_names));
 
         Ok(())
     }
@@ -307,6 +361,10 @@ impl InformationSchemaFlowsBuilder {
             Arc::new(self.sink_table_names.finish()),
             Arc::new(self.flownode_id_groups.finish()),
             Arc::new(self.option_groups.finish()),
+            Arc::new(self.created_time.finish()),
+            Arc::new(self.updated_time.finish()),
+            Arc::new(self.last_execution_time.finish()),
+            Arc::new(self.source_table_names.finish()),
         ];
         RecordBatch::new(self.schema.clone(), columns).context(CreateRecordBatchSnafu)
     }

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

@@ -18,9 +18,9 @@ use common_catalog::consts::{METRIC_ENGINE, MITO_ENGINE};
 use datatypes::schema::{Schema, SchemaRef};
 use datatypes::vectors::{Int64Vector, StringVector, VectorRef};
 
-use super::table_names::*;
+use crate::system_schema::information_schema::table_names::*;
 use crate::system_schema::utils::tables::{
-    bigint_column, datetime_column, string_column, string_columns,
+    bigint_column, string_column, string_columns, timestamp_micro_column,
 };
 
 const NO_VALUE: &str = "NO";
@@ -163,17 +163,17 @@ pub(super) fn get_schema_columns(table_name: &str) -> (SchemaRef, Vec<VectorRef>
                 string_column("EVENT_BODY"),
                 string_column("EVENT_DEFINITION"),
                 string_column("EVENT_TYPE"),
-                datetime_column("EXECUTE_AT"),
+                timestamp_micro_column("EXECUTE_AT"),
                 bigint_column("INTERVAL_VALUE"),
                 string_column("INTERVAL_FIELD"),
                 string_column("SQL_MODE"),
-                datetime_column("STARTS"),
-                datetime_column("ENDS"),
+                timestamp_micro_column("STARTS"),
+                timestamp_micro_column("ENDS"),
                 string_column("STATUS"),
                 string_column("ON_COMPLETION"),
-                datetime_column("CREATED"),
-                datetime_column("LAST_ALTERED"),
-                datetime_column("LAST_EXECUTED"),
+                timestamp_micro_column("CREATED"),
+                timestamp_micro_column("LAST_ALTERED"),
+                timestamp_micro_column("LAST_EXECUTED"),
                 string_column("EVENT_COMMENT"),
                 bigint_column("ORIGINATOR"),
                 string_column("CHARACTER_SET_CLIENT"),
@@ -204,10 +204,10 @@ pub(super) fn get_schema_columns(table_name: &str) -> (SchemaRef, Vec<VectorRef>
                 bigint_column("INITIAL_SIZE"),
                 bigint_column("MAXIMUM_SIZE"),
                 bigint_column("AUTOEXTEND_SIZE"),
-                datetime_column("CREATION_TIME"),
-                datetime_column("LAST_UPDATE_TIME"),
-                datetime_column("LAST_ACCESS_TIME"),
-                datetime_column("RECOVER_TIME"),
+                timestamp_micro_column("CREATION_TIME"),
+                timestamp_micro_column("LAST_UPDATE_TIME"),
+                timestamp_micro_column("LAST_ACCESS_TIME"),
+                timestamp_micro_column("RECOVER_TIME"),
                 bigint_column("TRANSACTION_COUNTER"),
                 string_column("VERSION"),
                 string_column("ROW_FORMAT"),
@@ -217,9 +217,9 @@ pub(super) fn get_schema_columns(table_name: &str) -> (SchemaRef, Vec<VectorRef>
                 bigint_column("MAX_DATA_LENGTH"),
                 bigint_column("INDEX_LENGTH"),
                 bigint_column("DATA_FREE"),
-                datetime_column("CREATE_TIME"),
-                datetime_column("UPDATE_TIME"),
-                datetime_column("CHECK_TIME"),
+                timestamp_micro_column("CREATE_TIME"),
+                timestamp_micro_column("UPDATE_TIME"),
+                timestamp_micro_column("CHECK_TIME"),
                 string_column("CHECKSUM"),
                 string_column("STATUS"),
                 string_column("EXTRA"),
@@ -330,8 +330,8 @@ pub(super) fn get_schema_columns(table_name: &str) -> (SchemaRef, Vec<VectorRef>
                 string_column("SQL_DATA_ACCESS"),
                 string_column("SQL_PATH"),
                 string_column("SECURITY_TYPE"),
-                datetime_column("CREATED"),
-                datetime_column("LAST_ALTERED"),
+                timestamp_micro_column("CREATED"),
+                timestamp_micro_column("LAST_ALTERED"),
                 string_column("SQL_MODE"),
                 string_column("ROUTINE_COMMENT"),
                 string_column("DEFINER"),
@@ -383,7 +383,7 @@ pub(super) fn get_schema_columns(table_name: &str) -> (SchemaRef, Vec<VectorRef>
                 string_column("ACTION_REFERENCE_NEW_TABLE"),
                 string_column("ACTION_REFERENCE_OLD_ROW"),
                 string_column("ACTION_REFERENCE_NEW_ROW"),
-                datetime_column("CREATED"),
+                timestamp_micro_column("CREATED"),
                 string_column("SQL_MODE"),
                 string_column("DEFINER"),
                 string_column("CHARACTER_SET_CLIENT"),

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

@@ -24,18 +24,17 @@ 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, Schema, SchemaRef};
+use datatypes::schema::{ColumnSchema, FulltextBackend, 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};
+use crate::system_schema::information_schema::{InformationTable, Predicates, KEY_COLUMN_USAGE};
 use crate::CatalogManager;
 
 pub const CONSTRAINT_SCHEMA: &str = "constraint_schema";
@@ -48,20 +47,38 @@ 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;
 
-/// Primary key constraint name
-pub(crate) const PRI_CONSTRAINT_NAME: &str = "PRIMARY";
 /// Time index constraint name
-pub(crate) const TIME_INDEX_CONSTRAINT_NAME: &str = "TIME INDEX";
+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";
+
 /// Inverted index constraint name
-pub(crate) const INVERTED_INDEX_CONSTRAINT_NAME: &str = "INVERTED INDEX";
+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";
+
 /// Fulltext index constraint name
-pub(crate) const FULLTEXT_INDEX_CONSTRAINT_NAME: &str = "FULLTEXT INDEX";
+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";
+
 /// Skipping index constraint name
-pub(crate) const SKIPPING_INDEX_CONSTRAINT_NAME: &str = "SKIPPING INDEX";
+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";
 
 /// 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,
@@ -121,6 +138,11 @@ impl InformationSchemaKeyColumnUsage {
                 ConcreteDataType::string_datatype(),
                 true,
             ),
+            ColumnSchema::new(
+                GREPTIME_INDEX_TYPE,
+                ConcreteDataType::string_datatype(),
+                true,
+            ),
         ]))
     }
 
@@ -185,6 +207,7 @@ struct InformationSchemaKeyColumnUsageBuilder {
     column_name: StringVectorBuilder,
     ordinal_position: UInt32VectorBuilder,
     position_in_unique_constraint: UInt32VectorBuilder,
+    greptime_index_type: StringVectorBuilder,
 }
 
 impl InformationSchemaKeyColumnUsageBuilder {
@@ -207,6 +230,7 @@ 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),
         }
     }
 
@@ -230,34 +254,47 @@ 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,
-                            TIME_INDEX_CONSTRAINT_NAME,
+                            CONSTRAINT_NAME_TIME_INDEX,
                             &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(PRI_CONSTRAINT_NAME);
+                        constraints.push(CONSTRAINT_NAME_PRI);
+                        greptime_index_type.push(INDEX_TYPE_PRI);
                     }
                     if column.is_inverted_indexed() {
-                        constraints.push(INVERTED_INDEX_CONSTRAINT_NAME);
+                        constraints.push(CONSTRAINT_NAME_INVERTED_INDEX);
+                        greptime_index_type.push(INDEX_TYPE_INVERTED_INDEX);
                     }
-                    if column.is_fulltext_indexed() {
-                        constraints.push(FULLTEXT_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_skipping_indexed() {
-                        constraints.push(SKIPPING_INDEX_CONSTRAINT_NAME);
+                        constraints.push(CONSTRAINT_NAME_SKIPPING_INDEX);
+                        greptime_index_type.push(INDEX_TYPE_SKIPPING_INDEX);
                     }
 
                     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,
@@ -267,6 +304,7 @@ impl InformationSchemaKeyColumnUsageBuilder {
                             table_name,
                             &column.name,
                             idx as u32 + 1,
+                            &aggregated_index_types,
                         );
                     }
                 }
@@ -289,6 +327,7 @@ impl InformationSchemaKeyColumnUsageBuilder {
         table_name: &str,
         column_name: &str,
         ordinal_position: u32,
+        index_types: &str,
     ) {
         let row = [
             (CONSTRAINT_SCHEMA, &Value::from(constraint_schema)),
@@ -298,6 +337,7 @@ 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) {
@@ -314,6 +354,7 @@ 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> {
@@ -337,6 +378,7 @@ 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

@@ -20,17 +20,18 @@ use common_catalog::consts::INFORMATION_SCHEMA_PARTITIONS_TABLE_ID;
 use common_error::ext::BoxedError;
 use common_recordbatch::adapter::RecordBatchStreamAdapter;
 use common_recordbatch::{RecordBatch, SendableRecordBatchStream};
-use common_time::datetime::DateTime;
 use datafusion::execution::TaskContext;
 use datafusion::physical_plan::stream::RecordBatchStreamAdapter as DfRecordBatchStreamAdapter;
 use datafusion::physical_plan::streaming::PartitionStream as DfPartitionStream;
 use datafusion::physical_plan::SendableRecordBatchStream as DfSendableRecordBatchStream;
 use datatypes::prelude::{ConcreteDataType, ScalarVectorBuilder, VectorRef};
 use datatypes::schema::{ColumnSchema, Schema, SchemaRef};
+use datatypes::timestamp::TimestampMicrosecond;
 use datatypes::value::Value;
 use datatypes::vectors::{
-    ConstantVector, DateTimeVector, DateTimeVectorBuilder, Int64Vector, Int64VectorBuilder,
-    MutableVector, StringVector, StringVectorBuilder, UInt64VectorBuilder,
+    ConstantVector, Int64Vector, Int64VectorBuilder, MutableVector, StringVector,
+    StringVectorBuilder, TimestampMicrosecondVector, TimestampMicrosecondVectorBuilder,
+    UInt64VectorBuilder,
 };
 use futures::{StreamExt, TryStreamExt};
 use partition::manager::PartitionInfo;
@@ -38,13 +39,12 @@ 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};
+use crate::system_schema::information_schema::{InformationTable, Predicates, PARTITIONS};
 use crate::CatalogManager;
 
 const TABLE_CATALOG: &str = "table_catalog";
@@ -127,9 +127,21 @@ impl InformationSchemaPartitions {
             ColumnSchema::new("max_data_length", ConcreteDataType::int64_datatype(), true),
             ColumnSchema::new("index_length", ConcreteDataType::int64_datatype(), true),
             ColumnSchema::new("data_free", ConcreteDataType::int64_datatype(), true),
-            ColumnSchema::new("create_time", ConcreteDataType::datetime_datatype(), true),
-            ColumnSchema::new("update_time", ConcreteDataType::datetime_datatype(), true),
-            ColumnSchema::new("check_time", ConcreteDataType::datetime_datatype(), true),
+            ColumnSchema::new(
+                "create_time",
+                ConcreteDataType::timestamp_microsecond_datatype(),
+                true,
+            ),
+            ColumnSchema::new(
+                "update_time",
+                ConcreteDataType::timestamp_microsecond_datatype(),
+                true,
+            ),
+            ColumnSchema::new(
+                "check_time",
+                ConcreteDataType::timestamp_microsecond_datatype(),
+                true,
+            ),
             ColumnSchema::new("checksum", ConcreteDataType::int64_datatype(), true),
             ColumnSchema::new(
                 "partition_comment",
@@ -200,7 +212,7 @@ struct InformationSchemaPartitionsBuilder {
     partition_names: StringVectorBuilder,
     partition_ordinal_positions: Int64VectorBuilder,
     partition_expressions: StringVectorBuilder,
-    create_times: DateTimeVectorBuilder,
+    create_times: TimestampMicrosecondVectorBuilder,
     partition_ids: UInt64VectorBuilder,
 }
 
@@ -220,7 +232,7 @@ impl InformationSchemaPartitionsBuilder {
             partition_names: StringVectorBuilder::with_capacity(INIT_CAPACITY),
             partition_ordinal_positions: Int64VectorBuilder::with_capacity(INIT_CAPACITY),
             partition_expressions: StringVectorBuilder::with_capacity(INIT_CAPACITY),
-            create_times: DateTimeVectorBuilder::with_capacity(INIT_CAPACITY),
+            create_times: TimestampMicrosecondVectorBuilder::with_capacity(INIT_CAPACITY),
             partition_ids: UInt64VectorBuilder::with_capacity(INIT_CAPACITY),
         }
     }
@@ -324,7 +336,7 @@ impl InformationSchemaPartitionsBuilder {
             };
 
             self.partition_expressions.push(expressions.as_deref());
-            self.create_times.push(Some(DateTime::from(
+            self.create_times.push(Some(TimestampMicrosecond::from(
                 table_info.meta.created_on.timestamp_millis(),
             )));
             self.partition_ids.push(Some(partition.id.as_u64()));
@@ -342,8 +354,8 @@ impl InformationSchemaPartitionsBuilder {
             Arc::new(Int64Vector::from(vec![None])),
             rows_num,
         ));
-        let null_datetime_vector = Arc::new(ConstantVector::new(
-            Arc::new(DateTimeVector::from(vec![None])),
+        let null_timestampmicrosecond_vector = Arc::new(ConstantVector::new(
+            Arc::new(TimestampMicrosecondVector::from(vec![None])),
             rows_num,
         ));
         let partition_methods = Arc::new(ConstantVector::new(
@@ -373,8 +385,8 @@ impl InformationSchemaPartitionsBuilder {
             null_i64_vector.clone(),
             Arc::new(self.create_times.finish()),
             // TODO(dennis): supports update_time
-            null_datetime_vector.clone(),
-            null_datetime_vector,
+            null_timestampmicrosecond_vector.clone(),
+            null_timestampmicrosecond_vector,
             null_i64_vector,
             null_string_vector.clone(),
             null_string_vector.clone(),

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

@@ -33,9 +33,8 @@ 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};
+use crate::system_schema::information_schema::{InformationTable, Predicates, PROCEDURE_INFO};
 use crate::system_schema::utils;
 use crate::CatalogManager;
 

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

@@ -21,6 +21,7 @@ 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;
@@ -34,25 +35,30 @@ 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};
+use crate::system_schema::information_schema::{InformationTable, Predicates, REGION_PEERS};
 use crate::CatalogManager;
 
-const REGION_ID: &str = "region_id";
-const PEER_ID: &str = "peer_id";
+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 PEER_ADDR: &str = "peer_addr";
-const IS_LEADER: &str = "is_leader";
+pub 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
@@ -77,6 +83,9 @@ 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),
@@ -134,6 +143,9 @@ 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,
@@ -152,6 +164,9 @@ 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),
@@ -177,24 +192,28 @@ impl InformationSchemaRegionPeersBuilder {
         let predicates = Predicates::from_scan_request(&request);
 
         for schema_name in catalog_manager.schema_names(&catalog_name, None).await? {
-            let table_id_stream = catalog_manager
+            let table_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))
+                        Ok(Some((
+                            table_info.ident.table_id,
+                            table_info.name.to_string(),
+                        )))
                     }
                 });
 
             const BATCH_SIZE: usize = 128;
 
-            // Split table ids into chunks
-            let mut table_id_chunks = pin!(table_id_stream.ready_chunks(BATCH_SIZE));
+            // Split tables into chunks
+            let mut table_chunks = pin!(table_stream.ready_chunks(BATCH_SIZE));
 
-            while let Some(table_ids) = table_id_chunks.next().await {
-                let table_ids = table_ids.into_iter().collect::<Result<Vec<_>>>()?;
+            while let Some(tables) = table_chunks.next().await {
+                let tables = tables.into_iter().collect::<Result<HashMap<_, _>>>()?;
+                let table_ids = tables.keys().cloned().collect::<Vec<_>>();
 
                 let table_routes = if let Some(partition_manager) = &partition_manager {
                     partition_manager
@@ -206,7 +225,16 @@ impl InformationSchemaRegionPeersBuilder {
                 };
 
                 for (table_id, routes) in table_routes {
-                    self.add_region_peers(&predicates, table_id, &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,
+                    );
                 }
             }
         }
@@ -216,6 +244,9 @@ impl InformationSchemaRegionPeersBuilder {
 
     fn add_region_peers(
         &mut self,
+        table_catalog: &str,
+        table_schema: &str,
+        table_name: &str,
         predicates: &Predicates,
         table_id: TableId,
         routes: &[RegionRoute],
@@ -231,13 +262,20 @@ impl InformationSchemaRegionPeersBuilder {
                 Some("ALIVE".to_string())
             };
 
-            let row = [(REGION_ID, &Value::from(region_id))];
+            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)),
+            ];
 
             if !predicates.eval(&row) {
                 return;
             }
 
-            // TODO(dennis): adds followers.
+            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(peer_id);
             self.peer_addrs.push(peer_addr.as_deref());
@@ -245,11 +283,26 @@ 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 {