Partitioning your Postgres tables can make your database faster and your life worse at the same time. The key isn't learning how to partition, it's knowing when the operational wins justify the schema constraints.
Your events table has 400 million rows. Deletes of old data take an hour and bloat the table. A DELETE FROM events WHERE created_at < ... locks rows the app still wants. Someone on the team says the word "partitioning" in standup, and now you have a project.
Partitioning is one of the few Postgres features that can make your database faster and your life worse at the same time. It splits one logical table into many physical ones. The planner can skip whole partitions. Dropping old data becomes a metadata operation instead of a row-by-row delete. That part is real. The cost is that partitioning changes the rules around foreign keys, unique constraints, and how the planner reasons about your queries. Get those wrong and you ship a slower database with a harder schema.
So the question isn't "how do I partition." It's "should I."
What partitioning actually is
A partitioned table is a parent that holds no rows. Every row lives in a child partition chosen by a key. You query the parent. Postgres routes reads and writes to the right children. Three strategies ship in the box.
Range splits by a continuous key, almost always a timestamp or a monotonic ID:
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