Understanding clustering_factor in PostgreSQL

In PostgreSQL, the clustering_factor is a statistic that provides information about how well the physical order of rows in a table or index matches the logical order defined by the clustering key. It is particularly relevant for tables that use index-organized storage or have indexes that are frequently scanned or used for range queries.

The clustering factor is a measure of data locality, indicating how close the physical order of rows is to the order of the clustering key. The lower the clustering factor, the better the data locality, meaning that the data is physically stored in a way that aligns with the order of the clustering key. This can improve the performance of certain types of queries, such as range scans or queries that access adjacent rows based on the clustering key.

The clustering factor is calculated by examining the order of index blocks and the position of table rows within those blocks. A lower clustering factor indicates that related rows are stored in close proximity to each other, reducing the number of disk I/O operations required to fetch the data. On the other hand, a higher clustering factor suggests that the physical order of rows is more scattered, requiring more I/O operations to retrieve the desired data.

To obtain the clustering factor in PostgreSQL, you can use the pg_stats system catalog view or the pgstattuple extension. Here’s an example query using pg_stats:

SELECT tablename, indexname, relpages, reltuples, relpages * reltuples AS total_rows, reltuples / relpages AS clustering_factor
FROM pg_stats
WHERE schemaname = ‘public’ — replace with your schema
AND tablename = ‘your_table’
AND attname = ‘your_clustering_key’;

In the above query, replace ‘your_table’ with the name of your table and ‘your_clustering_key’ with the name of the column that serves as the clustering key.

It’s important to note that the clustering factor is specific to a particular index and clustering key combination. Regularly monitoring and analyzing the clustering factor can help identify potential performance bottlenecks and guide decisions on index maintenance, table reorganization, or query optimizations to improve data locality and overall query performance.

About Shiv Iyer 219 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.

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