Date: 2025-09-29
Time: 14:30–15:20
Room: Hub One
Level: Advanced
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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.
The following slides have been made available for this session: