The pitfalls of partitioning Postgres yourself
Blog post from Hatchet
Hatchet, a durable queue built on Postgres, faced significant challenges related to data partitioning and query performance. Initially, Hatchet stored hundreds of millions of tasks daily in a single Postgres table, which led to index bloat and inefficient storage management. To address this, Hatchet implemented a custom time-based partitioning system to manage large volumes of data, focusing particularly on tables storing task data and event histories. Despite initial success, performance issues arose when queries on partitioned tables began to slow down significantly, primarily due to incorrect query statistics caused by the autovacuum process not analyzing partitioned parent tables. Expert intervention revealed that manually running ANALYZE on these tables resolved the issue, highlighting the importance of maintaining accurate query statistics for partitioned tables. This experience underscored the need for strategic partition management practices, such as using DETACH PARTITION...CONCURRENTLY to minimize lock contention and manually analyzing parent tables to ensure optimal query planning.
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