Patrick Sullivan 46cce3de59 feat: JSON-indpendent hashing of dashboard models (#5133)
The previous mechanism for generating hashes for dashboard models
depended on hashing the JSON representation of the dashboard that was
stored in the `meta` table.

As part of the migration of dashboards out of the `meta` table and into
a dedicated `dashboards` table, this ☝️ presented some problems:

Specifically when we want to translate from the Dashboard ORM model to a
`core::meta::dashboards::Dashboard` model, we need to generate the hash
at the time of translation since `hash` is a field on
`core::meta::dashboards::Dashboard`. We can't simply generate a hash
from the JSON stored in the `dashboards.data` column because this JSON
no longer contains fields for `title`, `owner`, `description`, and
`role`. Those fields are now stored in DB columns, and changes to those
columns in the DB columns would *not* result in changes to the hash on
the JSON blob.

Therefore, rather than hashing the JSON blob, this PR implements hash
functions on the Rust types for the dashboards. Therefore changes to
fields in those Rust structs will result in changes to the hash values,
but changes to the way in which the data is stored in the DB will not
affect the hash values.

⚠️ Note that this PR is changing the hash function on the dashboard
models, so hashes generated for the same model before and after this
deployment will not be the same. My understanding is that this shouldn't
be an issue since hashes are just used as a way of checking on the UI if
the dashboard model has been updated to avoid overwriting the changes of
another user. I believe that this change to the dashboard function might
just have the effect (around the time of deployment) where it would
appear to the UI that the dashboard has been updated when, in fact, it
has not.

https://github.com/openobserve/o2-enterprise/actions/runs/11979723563

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Introduced a new type alias `OrdF64` for enhanced precision in
floating-point operations.
- Added the `Hash` trait to multiple dashboard-related structs and
enums, enabling their use in hash-based collections.

- **Bug Fixes**
- Improved error handling and assertion clarity in API testing for
folder and dashboard operations.

- **Chores**
- Standardized hashing mechanism for dashboard objects in database
operations.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
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OpenObserve

🚀 10x easier, 🚀 140x lower storage cost, 🚀 high performance, 🚀 petabyte scale - Elasticsearch/Splunk/Datadog alternative for 🚀 (logs, metrics, traces).

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OpenObserve (O2 for short) is a cloud-native observability platform built specifically for logs, metrics, traces, analytics, RUM (Real User Monitoring - Performance, Errors, Session Replay) designed to work at petabyte scale.

It is straightforward and easy to operate, in contrast to Elasticsearch, which requires understanding and tuning numerous settings. Get OpenObserve up and running in under 2 minutes.

OpenObserve serves as a seamless replacement for Elasticsearch for users who ingest data using APIs and perform searches. OpenObserve comes with its own user interface, eliminating the need for separate installation.

You can reduce your log storage costs by ~140x compared to Elasticsearch by using OpenObserve. Below, we present the results from pushing logs from our production Kubernetes cluster to both Elasticsearch and OpenObserve using Fluent Bit.

OpenObserve Vs Elasticsearch

🎥 Introduction Video

OpenObserve Introduction

🌟 Features:

  • Logs, Metrics, Traces: Comprehensive support for various data types.
  • OpenTelemetry Support: Full compatibility with OTLP for logs, metrics, and traces.
  • Real User Monitoring (RUM): Includes performance tracking, error logging, and session replay.
  • Dashboards, Reports, Alerts: Features over 18 different chart types for comprehensive data visualization for on-the-fly analysis and reporting along with alerting.
  • Pipelines: Enrich, redact, reduce, normalize data on the fly. Stream processing for logs to metrics and more.
  • Advanced Embedded GUI: Intuitive and user-friendly interface.
  • SQL and PromQL Support: Query logs and traces with SQL, and metrics with SQL and PromQL.
  • Single Binary or HA Installation: Install using a single binary for small deployments or in HA mode for large deployments.
  • Versatile Storage Options: Supports local disk, S3, MinIO, GCS, Azure Blob Storage.
  • High Availability and Clustering: Ensures reliable and scalable performance.
  • Dynamic Schema: Adapts to your data structure seamlessly.
  • Built-in Authentication: Secure and ready to use.
  • Ease of Operation: Designed for simplicity and efficiency.
  • Seamless Upgrades: Hassle-free updates.
  • Multilingual UI: Supports 11 languages, including English, Spanish, German, French, Chinese, and more.

For a full list of features, check the documentation.

⚡️ Quick start

🐳 Docker:

docker run -d \
      --name openobserve \
      -v $PWD/data:/data \
      -p 5080:5080 \
      -e ZO_ROOT_USER_EMAIL="root@example.com" \
      -e ZO_ROOT_USER_PASSWORD="Complexpass#123" \
      public.ecr.aws/zinclabs/openobserve:latest

🐙 Docker Compose:

services:
  openobserve:
    image: public.ecr.aws/zinclabs/openobserve:latest
    restart: unless-stopped
    environment:
      ZO_ROOT_USER_EMAIL: "root@example.com"
      ZO_ROOT_USER_PASSWORD: "Complexpass#123"
    ports:
      - "5080:5080"
    volumes:
      - data:/data
volumes:
  data:

For other ways to quickly install OpenObserve or use OpenObserve cloud, check quickstart documentation.

For installing OpenObserve in HA mode, check HA deployment documentation.

📷 Screenshots

Home

Home

Logs

Logs

Traces (OpenTelemetry)

Trace details page Traces using OpenTelemetry

Golden metrics based on traces Traces golden metrics

Visualizations and Dashboards

Dashboard Dashboard Create panel Map

Front end monitoring

Performance analytics Performance

Session replay Session replay

Error tracking Error tracking

Alerts

Alerts

Streams

Streams

Ingestion

Ingestion

Ingestion

SBOM

Software Bill of Materials for OpenObserve

Rust

SBOM can be found here. You can analyze it using dependency track.

In order to generate the SBOM, you can use the following commands:

Install cargo-cyclonedx:

cargo install cargo-cyclonedx

Generate the SBOM:

cargo-cyclonedx cyclonedx

JavaScript

SBOM can be found here. You can analyze it using dependency track.

In order to generate the SBOM, you can use the following commands:

Install cyclonedx-npm:

npm install --global @cyclonedx/cyclonedx-npm

Generate the SBOM:

cd web
cyclonedx-npm > sbom.json         

⚖️ License

OpenObserve is licensed under the AGPL-3.0 license. For more details, see the LICENSE.

🌍 Community

🔗 Join OpenObserve community on Slack

Slack

Easiest way to get support is to join the Slack channel.

📱 Join OpenObserve community on WeChat

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Description
Open source observability platform for logs, metrics, traces, RUM (web, android, ios), Session replay, pipelines, SLO and LLM observability. A sophisticated, simple and highly performant alternative to Datadog, Splunk, and Elasticsearch with 140x lower storage costs and single binary deployment.
Readme AGPL-3.0
1 GiB
Languages
TypeScript 39.9%
Rust 29.5%
Vue 16.9%
JavaScript 11%
Python 2.3%
Other 0.3%