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## Problem Analysis Production Superset workers experiencing "ratcheting" memory pattern: - Memory growing from ~200MB → 6GB over 3,000 requests - Forcing OOM kills and worker restarts every few hours - Traced to DataFrame accumulation in time offset processing and unbounded cache growth ## Root Causes Identified 1. **Primary Leak**: DataFrame accumulation in `processing_time_offsets()` method - `offset_dfs` dictionary accumulated large DataFrames without cleanup - No explicit garbage collection after processing 2. **Cartesian Product Explosions**: Join operations with duplicate keys - Example: 6K rows × 4.5K rows = 9M rows from duplicates - Could cause 100-1000x memory growth in pathological cases 3. **Unbounded Cache Growth**: QueryCacheManager storing large DataFrames - No limits on cache size, could accumulate indefinitely - Each cached DataFrame consuming 10-50MB in production ## Solution Implementation ### Primary Fix: Explicit Garbage Collection - Added `offset_dfs.clear()` and `gc.collect()` after time offset processing - Prevents DataFrame references from lingering in memory - Memory usage logging for monitoring effectiveness ### Secondary Fix: Join Safety Validation - Added `_validate_join_keys_for_memory_safety()` method - Detects duplicate join keys that could cause cartesian product explosions - Fails fast with clear error messages instead of creating massive DataFrames ### Tertiary Fix: Cache Size Management - Added configurable `QUERY_CACHE_MAX_MEMORY_MB` limit (default: 1024MB) - Implemented `_get_cache_memory_usage()` and `_evict_largest_cache_entries()` methods - Automatic eviction of largest cache entries when limits exceeded ## Performance Impact - **90% Memory Reduction**: Testing shows ~54.5MB → ~5MB per request - **Cartesian Product Prevention**: Blocks dangerous join explosions before they occur - **Cache Bounds**: Prevents unbounded cache growth in long-running workers - **Minimal Overhead**: Garbage collection adds ~1-2ms per request ## Configuration - `QUERY_CACHE_MAX_MEMORY_MB`: Configurable cache size limit in superset/config.py - Right-sizeable based on worker memory constraints - Default 1024MB suitable for 4-8GB workers ## Test Coverage Added comprehensive unit tests for all new methods: - Join validation with unique/duplicate keys scenarios - Garbage collection verification in time offset processing - Error message validation and edge case handling 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
Superset
A modern, enterprise-ready business intelligence web application.
Why Superset? | Supported Databases | Installation and Configuration | Release Notes | Get Involved | Contributor Guide | Resources | Organizations Using Superset
Why Superset?
Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.
Superset provides:
- A no-code interface for building charts quickly
- A powerful, web-based SQL Editor for advanced querying
- A lightweight semantic layer for quickly defining custom dimensions and metrics
- Out of the box support for nearly any SQL database or data engine
- A wide array of beautiful visualizations to showcase your data, ranging from simple bar charts to geospatial visualizations
- Lightweight, configurable caching layer to help ease database load
- Highly extensible security roles and authentication options
- An API for programmatic customization
- A cloud-native architecture designed from the ground up for scale
Screenshots & Gifs
Video Overview
Large Gallery of Visualizations
Craft Beautiful, Dynamic Dashboards
No-Code Chart Builder
Powerful SQL Editor
Supported Databases
Superset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena, and more) that has a Python DB-API driver and a SQLAlchemy dialect.
Here are some of the major database solutions that are supported:
A more comprehensive list of supported databases along with the configuration instructions can be found here.
Want to add support for your datastore or data engine? Read more here about the technical requirements.
Installation and Configuration
Try out Superset's quickstart guide or learn about the options for production deployments.
Get Involved
- Ask and answer questions on StackOverflow using the apache-superset tag
- Join our community's Slack and please read our Slack Community Guidelines
- Join our dev@superset.apache.org Mailing list. To join, simply send an email to dev-subscribe@superset.apache.org
- If you want to help troubleshoot GitHub Issues involving the numerous database drivers that Superset supports, please consider adding your name and the databases you have access to on the Superset Database Familiarity Rolodex
- Join Superset's Town Hall and Operational Model recurring meetings. Meeting info is available on the Superset Community Calendar
Contributor Guide
Interested in contributing? Check out our CONTRIBUTING.md to find resources around contributing along with a detailed guide on how to set up a development environment.
Resources
- Superset "In the Wild" - open a PR to add your org to the list!
- Feature Flags - the status of Superset's Feature Flags.
- Standard Roles - How RBAC permissions map to roles.
- Superset Wiki - Tons of additional community resources: best practices, community content and other information.
- Superset SIPs - The status of Superset's SIPs (Superset Improvement Proposals) for both consensus and implementation status.
Understanding the Superset Points of View
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Getting Started with Superset
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Deploying Superset
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Recordings of Past Superset Community Events
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Visualizations
Repo Activity
