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Postgres FM

BRIN indexes

Jul 21, 2022
The podcast discusses the importance of BRIN indexes in Postgres and compares their performance to B-tree indexes. The concept of correlation in indexes is explored, along with the use of hidden column CTID to determine correlation. The podcast also emphasizes the need for indexes to reduce I/O operations and highlights the potential of the improved BRIN index for UID versions data. Additionally, the importance of row numbers and upgrade recommendations are discussed.
36:07

Podcast summary created with Snipd AI

Quick takeaways

  • BRIN indexes offer a smaller index size compared to B-trees and are suitable for tables with a log or telemetry pattern.
  • Experimentation with one's own data and queries is crucial for accurate performance evaluation of BRIN indexes and considering specific query requirements.

Deep dives

Brain indexes and their advantages

Brain indexes offer a smaller index size compared to B-trees, making them attractive for tables with large volumes of data. However, they may not be as efficient as B-trees for point searches where only a few rows need to be retrieved. Brain indexes are more suitable for tables with a log or telemetry pattern, where data is inserted or updated frequently. It is important to note that brain indexes require a strong correlation with the physical storage layout, so reindexing or clustering the table may be necessary for optimal performance. The inclusion of new improvements in Postgres 14, such as the min max multi operator class, allows brain indexes to support different types of correlations and have better overall performance.

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