This article explains the impact of excessive indexes on Postgres performance, detailing how they slow down writes and reads, waste disk space, and increase maintenance overhead. It emphasizes the importance of regularly dropping unused and redundant indexes to optimize database efficiency.

This article explores creative database optimization techniques in PostgreSQL, focusing on scenarios that bypass full table scans and reduce index size. It emphasizes using check constraints and function-based indexing to improve query performance without unnecessary overhead.

This article explains how PostgreSQL indexes work and their impact on query performance. It covers the types of indexes available, how data is stored, and the trade-offs in using indexes, including costs related to disk space, write operations, and memory usage.

This article explains the new skip scan feature in PostgreSQL 18, which improves query performance by allowing the database to bypass unnecessary index entries. It details the setup process, how btree indexes work, and provides examples showing significant performance gains.

Aiven has released PostgreSQL 18, which features significant performance improvements and new functionalities like asynchronous I/O, enhanced JOIN and GROUP BY operations, and parallel GIN index creation. This version allows more flexibility in schema evolution and smarter indexing with skip scans. Users can try PostgreSQL 18 with a free trial at Aiven.

PostgreSQL 18 introduces significant improvements to the btree_gist extension, primarily through the implementation of sortsupport, which enhances index building efficiency. These updates enable better performance for use cases such as nearest-neighbour search and exclusion constraints, offering notable gains in query throughput compared to previous versions.

The article explores the use of custom ICU collations with PostgreSQL's citext data type, highlighting performance comparisons between equality, range, and pattern matching operations. It concludes that while custom collations are superior for equality and range queries, citext is more practical for pattern matching until better index support for nondeterministic collations is achieved.

PostgreSQL's Index Only Scan enhances query performance by allowing data retrieval without accessing the table heap, thus eliminating unnecessary delays. It requires specific index types and query conditions to function effectively, and the concept of a covering index, which includes fields in the index, further optimizes this process. Understanding these features is crucial for backend developers working with PostgreSQL databases.

Data types significantly influence the performance and efficiency of indexing in PostgreSQL. The article explores how different data types, such as integers, floating points, and text, affect the time required to create indexes, emphasizing the importance of choosing the right data type for optimal performance.

Understanding when to rebuild PostgreSQL indexes is crucial for maintaining database performance. The decision depends on index type, bloat levels, and performance metrics, with recommendations to use the `pgstattuple` extension to assess index health before initiating a rebuild. Regular automatic rebuilds are generally unnecessary and can waste resources.