Hash Partitioning

Hash partitioning is a method of dividing a large table into smaller, more manageable partitions based on a hash function applied to a specified column, typically the ID column. It offers unique advantages for certain use cases, but it also comes with limitations.

Key points:

  • Data distribution: Rows are assigned to partitions based on the hash value of their ID and a modulus-remainder calculation. For example, if partitioning by HASH(ID) with MODULUS 64 and REMAINDER 1, rows with hash(ID) % 64 == 1 would go into the corresponding partition.

  • Query requirements: Hash partitioning works best when most queries include a WHERE hashed_column = ? condition, as this allows PostgreSQL to quickly identify the relevant partition.

  • ID uniqueness: It’s the only partitioning method (aside from complex list partitioning) that can guarantee ID uniqueness across multiple partitions at the database level.

Upfront decisions:

  • The number of partitions must be chosen before table creation and cannot be easily added later. This makes it crucial to anticipate future data growth.

Unsupported query types:

  • Range queries (WHERE id BETWEEN ? AND ?) and lookups by other keys (WHERE other_id = ?) are not directly supported on hash-partitioned tables.


  • Choose a large number of partitions to accommodate future growth.
  • Ensure application queries align with hash partitioning requirements.
  • Evaluate alternatives like range partitioning or list partitioning if range queries or lookups by other keys are essential.

In summary, hash partitioning is a valuable tool for specific scenarios, particularly when ID uniqueness across partitions is crucial. However, it’s essential to carefully consider its limitations and query patterns before implementation.