A unified observability platform for logs, metrics, and traces, built for high-volume environments.

Website • Documentation • Rust API Docs • Grafana Plugin • Presentations

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Micromegas is an observability system designed to provide unified insights into complex applications. It allows you to collect and analyze logs, metrics, and traces in a single, scalable database. Our goal is to help you spend less time reproducing bugs and more time understanding and improving your software's quality and performance.

Objectives

• Empower developers with comprehensive insights, eliminating time-consuming bug reproduction.
• Quantify issue frequency and severity to allow better priority management.
• Provide detailed traces based on high-frequency telemetry to enable a deep understanding of every issue.

Key Features

• 🚀 Unified Observability: Store and query logs, metrics, and traces together to get a complete picture of your application's behavior.
• ⚡ Low-Overhead Instrumentation: Client-side instrumentation adds minimal overhead, averaging just 20 ns per event in the calling thread.
• 🌊 High-Frequency Data Collection: Built to handle up to 100,000 events per second from a single instrumented process.
• ☁️ Scalable & Cloud-Native: The backend is designed to scale horizontally, capable of ingesting data from millions of concurrent processes using object storage (S3) and PostgreSQL.
• 💰 Cost-Efficient by Design: Keep costs low with tail sampling and on-demand ETL. Raw data is stored cheaply and only processed when you need to query it.
• 🔍 Powerful SQL Interface: Query your data using a powerful and familiar SQL interface, powered by Apache DataFusion and accessible via Apache Arrow FlightSQL.
• 🔐 Enterprise Authentication: Secure your data with OIDC authentication supporting both human users (browser-based login) and service accounts (OAuth 2.0 client credentials).

How It Works

Micromegas consists of several key components:

1. Instrumentation Libraries: Lightweight libraries for your applications (available in Rust and Unreal Engine) to send telemetry data. See Optimism for an example Bevy project using Micromegas.
2. Ingestion Service (telemetry-ingestion-srv): A scalable service that receives telemetry data and writes it to blob storage.
3. Analytics Service (flight-sql-srv): A DataFusion-powered service that exposes a FlightSQL endpoint for running queries against your data.
4. PostgreSQL Database: Stores metadata about processes, streams, and data blocks, keeping the object storage indexable and fast to query.
5. Object Storage (S3/GCS): Stores all raw telemetry payloads and materialized query results in Parquet format.

Cost-Effectiveness

Unlike traditional observability platforms with opaque and often escalating costs, Micromegas offers a transparent and orders of magnitude more efficient solution. With Micromegas, you can afford to record billions of events without relying heavily on sampling, gaining a complete and accurate picture of your systems. By leveraging your own cloud infrastructure, Micromegas drastically reduces your observability spend, especially at scale.

Discover how Micromegas achieves this unparalleled cost efficiency and compare it with traditional solutions in our detailed Cost Effectiveness document.

Presentations

Learn more about Micromegas through our technical presentations:

• Interactive Notebooks for Observability (February 2026) - Composable notebook cells with an in-browser query engine
• Unified Observability for Games (January 2026) - Why a unified architecture is easier to use and more powerful
• High-Frequency Observability: Cost-Efficient Telemetry at Scale (October 2025) - How to record more data for less money with tail sampling and lakehouse architecture
• Design Presentation (February 2025) - Architecture and design principles

Getting Started

To get started with Micromegas, please refer to the Getting Started guide.

Current Status & Roadmap

v0.24.0 (April 2026)

• parse_block table UDF for generic block inspection with transit-to-JSONB conversion
• Unified diff output in micromegas-screens plan/apply
• Flamechart WASD zoom fix for Chrome key-release edge cases
• Sub-microsecond flamechart span durations in nanoseconds
• Notebook variable URL desync and datasource revert fixes
• DataFusion 52.5, rand 0.9 migration, pyarrow ^23
• 20+ Dependabot security updates across Rust, Python, and JS dependencies

v0.23.0 (March 2026)

• JSONB array UDFs: jsonb_array_elements (UDTF) and jsonb_array_length (scalar)
• CSV table provider with auto-discovery via MICROMEGAS_STATIC_TABLES_URL
• FlightSqlServer builder and LakehouseContext::from_env() convenience APIs
• Screens-as-code CLI (micromegas-screens) with Terraform-inspired workflow
• Object store env var credential support for S3/GCS/Azure
• Bearer token auth for analytics-web-srv
• Notebook cell selection macros in table column overrides
• DataFusion 52.4.0

v0.22.0 (March 2026)

• Flame graph cell type with Three.js WebGL rendering
• Async span depth fixes with SpanContextFuture
• Default system properties (exe, hostname, CPU, memory, OS) on process metadata
• JSONPath UDFs (jsonb_path_query, jsonb_path_query_first) for JSONB columns
• Interactive row selection in table cells with $cell.selected.column macros
• process_spans table function for cross-thread and async span analysis
• Database migration: unique indexes on processes, streams, and blocks
• DataFusion 52.3

v0.21.0 (February 2026)

• Cross-cell notebook queries running DataFusion in the browser via WASM
• Horizontal group cell with drag-and-drop reordering
• WASM tracing support for micromegas-tracing and micromegas-telemetry-sink
• micromegas-datafusion-extensions crate — shared WASM-compatible JSONB and histogram UDFs
• Python CLI: micromegas-query and micromegas-logout via pip install micromegas
• LZ4 Arrow IPC compression for smaller network transfers

v0.20.0 (February 2026)

• Client-side WASM query execution with DataFusion in the browser
• Swimlane, property timeline, and Perfetto export cell types
• Drag-to-zoom on charts, config diff modal, per-cell data sources
• Client-side Perfetto trace generation with gzip compression
• Parquet content cache and parallelized query planning

v0.19.0 (January 2026)

• User-defined screens — JSON-configured notebooks, tables, and dashboards
• Analytics web app with syntax-highlighted SQL editors
• expand_histogram table function

For the full history, see CHANGELOG.md.

Contributing

We welcome contributions from the community! If you're interested in helping improve Micromegas, please see our Contribution Guidelines for more details on how to get involved.

Whether it's bug reports, feature requests, or code contributions, your input is valuable.

License

Micromegas is licensed under the Apache License, Version 2.0.