Level: Advanced
In this session, we'll dive deep into optimizing time-series data management in PostgreSQL, combining partitioning strategies with automated maintenance for enterprise-scale applications. We'll explore practical techniques for designing high-performance time-series tables using declarative partitioning, demonstrating how to achieve optimal write throughput and query performance through strategic partition pruning. Key areas we'll cover include: 1/ Implementing efficient partition key selection for time-series data 2/ Automating partition management using pg_cron for creation and retention 3/ Optimizing bulk data ingestion through parallel query execution 4/ Real-world performance comparisons between partitioned and non-partitioned approaches 5/ Best practices for handling high-velocity data streams while maintaining query responsiveness Through live demonstrations and real-world examples, attendees will learn how to implement a robust, scalable time-series data architecture that can handle billions of rows while maintaining consistent performance. We'll also address common challenges such as partition cleanup, index management, and monitoring strategies specific for PostgreSQL.