PostgreSQL Tutorial: Performance optimization
This chapter explains how to optimize PostgreSQL performance and provides examples.
This chapter explains how to optimize PostgreSQL performance and provides examples.
Summary: In this tutorial, you’ll learn how PostgreSQL internal statistics (pg_stats) work and some optimization guides.
Summary: in this tutorial, you will learn how to tune random_page_cost setting in PostgreSQL.
As applications grow, PostgreSQL tables inevitably scale from thousands of records to hundreds of millions or even billions. Performance degradation for large tables is rarely sudden; instead, it happens through a process of silent degradation over time.
Scaling Postgres’s high-concurrency handling capability can never rely on simply throwing more hardware at the problem. Each database connection incurs a hidden system tax, including Linux-level memory and CPU overhead, as well as Postgres-level MVCC (Multi-Version Concurrency Control) and locking constraints. To handle high-performance workloads, we need to stop guessing and start precisely tuning our systems by deeply understanding these underlying limitations.
Summary: In this tutorial, you will learn how to enable atomic writes at the storage layer and disable full_page_writes to boost OLTP processing performance of PostgreSQL.
Summary: Using Huge Pages reduces the memory page table size of PostgreSQL processes and cuts down CPU overhead spent on memory management, thereby improving overall database performance.
Summary: In this tutorial, you will learn about pg_stat_io view, including byte-level I/O statistics, WAL tracking, and comprehensive real-world use cases for better database performance tuning.
Summary: in this tutorial, you will learn how PostgreSQL collects partitioned table statistics and how they affect PostgreSQL’s estimates.
Summary: in this tutorial, you will learn what’s ’estimated rows’ in EXPLAIN output all about.
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