Using pg_partman, an extension that automates time-based partitioning in PostgreSQL.

This supports the three types of partitioning (e.g., PARTITION BY RANGE, BY LIST, or BY HASH).

Using the PARTITION BY clause native in PostgreSQL.Hash partitioning: rows are partitioned based on the hash value of the partition key to distribute data across a fixed number of partitions evenly.ĭepending on which partitioning strategy you’re using, you can choose between different methodologies, the most common being the following:.List partitioning: partitions are defined by a list of values (e.g., by country).Range partitioning: partitions are defined by a range of values (e.g., by month, year, or an incrementing sequence).In terms of the types of partitioning, you could choose between: If the total table size or (even worse) the total index size of the unpartitioned table exceeds the amount of memory Postgres uses for cache, then the difference becomes even more significant.Īs we introduced in our previous article on partitioning, you can follow different strategies and techniques to partition your PostgreSQL tables. When you select data from a partitioned table using a WHERE clause with a time-based restriction, PostgreSQL is able to immediately discard all the partitions that aren’t relevant before it plans the query.īecause we aren’t searching through all the data, we spend less time doing I/O, and the query is faster. Partitioning Strategies in PostgreSQL: Quick OverviewĪ partitioned table comprises many non-overlapping partitions, each covering a part of your dataset. 📑 Check out our previous article on when to consider partitioning- if you haven’t already. While both approaches aim to simplify partitioning, this article explores why we believe Timescale's hypertables present compelling advantages over pg_partman. Among these partitioning methodologies, there are two that stand out as the most popular: Timescale's hypertables (optimized for time-based/range partitioning) and the pg_partman extension. Luckily, the PostgreSQL ecosystem offers a range of partitioning techniques to optimize the performance and maintenance of these datasets.

When you have data streaming into PostgreSQL constantly, sooner or later you end up with these big, slow tables. Your app gets bottlenecked user experience takes a dive. You all know the feeling: You’ve got one big table in your database, and it’s getting slower and slower.

Over partition by postgresql