This page provides an overview of how the Rerun documentation is organized to help you find what you need.

Overview

High-level introduction to Rerun:

• What is Rerun? - Learn about Rerun, the Unified Data Layer for Physical AI
• Installing the Viewer - Get Rerun installed on your system

Getting started

Step-by-step guides to get up and running quickly:

• Log and Ingest - Learn how to log data to Rerun from your code
• Visualize - Customize the visualization to your needs
• Query and Transform - Query and export data from Rerun recordings
• Troubleshooting - Solutions to common issues

Concepts

Understanding the foundational concepts behind Rerun:

• How Does Rerun Work - How Rerun applications are structured
• Entity Component System - Rerun's data model
• Entity Paths - Organizing your data hierarchically
• Spaces and Transforms - Working with coordinate systems
• Timelines - Managing temporal data
• Blueprints - Configuring visualization layouts
• Batches - Efficiently logging collections of data
• Static Data - Data that exists across all timelines
• Query Semantics - How Rerun resolves data queries
• Annotation Context - Shared styling and labels
• Recordings - Managing recordings, application IDs, and the catalog server
• Visualizers and Overrides - Customizing rendering
• Chunks - Internal storage mechanism (advanced)

How-to guides

Practical guides for specific tasks and advanced features:

Logging data

• Logging - Advanced logging techniques
• Send Columns - Efficiently log columnar data
• Using Native Loggers - Integrate with existing logging systems
• Short-lived Entities - Handling temporary data

Visualization

• Visualization - Advanced visualization techniques
• Configure Viewer Through Code - Programmatic viewer configuration
• Fixed Window Plots - Creating time-windowed plots

Data management

• DataFrame API - Query recordings programmatically
• Get Data Out - Export data from Rerun
• MCAP Integration - Working with MCAP files
• Shared Recordings - Collaborate with recordings

Integration & deployment

• Integrations - Integrate Rerun with other tools
• Embed Rerun Viewer - Embed the viewer in your application
• Jupyter Notebooks - Use Rerun in notebooks
• Callbacks - Respond to viewer events

Performance & optimization

• Limit RAM Usage - Control memory consumption
• Optimize Chunks - Fine-tune data storage

Extending Rerun

• Extend - Add custom types and visualizations

Examples

• ROS2 Nav Turtlebot - Complete robotics example

Reference

Detailed API documentation and technical specifications:

Types

• Archetypes - Bundles of components with first-class viewer support
• Components - Individual data components used by archetypes
• Datatypes - Fundamental data structures
• Views - Available visualization view types

SDKs

• Python APIs - Python SDK reference
• Rust APIs - Rust SDK reference
• C++ APIs - C++ SDK reference
• Web Viewer API - JavaScript/TypeScript web viewer API

Viewer & CLI

• Viewer - Viewer UI overview and features
• CLI Manual - Command-line interface reference

Migration guides

• Migration - Guides for upgrading between Rerun versions

Development

Contributing to Rerun:

• Developing Rerun - How to contribute to the Rerun project

Community

• Rerun Discord - Join the community
• GitHub Repository - Source code and issue tracking
• Examples Gallery - See Rerun in action