PostHog Performance Tuning

About

This skill provides comprehensive patterns and implementations for reducing PostHog API latency and increasing request throughput. It helps developers implement multi-level caching strategies (local and distributed), manage high-volume data requests through automatic batching with DataLoader, and optimize network overhead using connection pooling. Whether you are dealing with slow response times or scaling a high-traffic integration, this skill ensures your PostHog implementation remains responsive, efficient, and resilient to API bottlenecks.

Key Features

• HTTPS connection pooling and keep-alive configuration for lower overhead
• Automatic request batching using DataLoader to reduce API roundtrips
• Optimized cursor-based pagination patterns for large datasets
• 983 GitHub stars
• Multi-level caching strategies including LRU and Redis implementations
• Performance monitoring wrappers for real-time latency benchmarking

Use Cases

• Scaling backend data ingestion to handle high-volume event processing
• Optimizing resource usage and reducing API overhead in serverless environments
• Reducing latency in analytics dashboards powered by PostHog data