When PostgreSQL executes complex queries, especially those involving joins or aggregations, it often relies on hash-based operations to process data efficiently. Operations such as Hash Join and Hash Aggregate build an in-memory hash table to quickly match or group rows. These operations are typically faster than alternatives like nested loop joins when dealing with large datasets. However, for a hash operation to perform well, the hash table must fit in memory. If it does not, PostgreSQL has to split the data into batches and temporarily write some of the data to disk, which significantly slows down query execution. To control how much memory queries can use, PostgreSQL provides the configuration parameter work_mem, which limits the memory available to individual operations such as sorts, joins, and aggregates.
The challenge is that hash operations often require slightly more memory than other operations because they must maintain buckets, store tuples, and manage additional metadata for the hash table. If PostgreSQL strictly limited hash operations to the exact work_mem value, many hash joins would spill to disk unnecessarily, even when the system still had available memory. To address this, PostgreSQL introduced another configuration parameter called hash_mem_multiplier. This parameter allows hash-based operations to use a multiple of work_mem instead of being restricted to it. In simple terms, PostgreSQL calculates the maximum memory available for a hash table by multiplying work_mem with hash_mem_multiplier. For example, if work_mem is set to 4 MB and hash_mem_multiplier is set to 2, then a hash join can use up to 8 MB of memory for building its hash table.
The purpose of this multiplier is to strike a balance between performance and memory safety. Increasing work_mem globally can be risky because that value applies to all operations in a query. A single query may contain several sorts, joins, and aggregations, and each operation could potentially consume the full work_mem. In systems with many concurrent users, raising work_mem too high can lead to excessive memory consumption. The hash_mem_multiplier parameter avoids this issue by giving additional memory only to hash-based operations, without affecting the memory limits for other types of operations like sorting. This targeted approach allows PostgreSQL to reduce unnecessary disk spills for hash joins while still maintaining predictable memory usage across the system.
In practice, the effect of hash_mem_multiplier becomes visible when analyzing query execution plans. When a hash join cannot fit its hash table into the allowed memory, PostgreSQL splits the operation into multiple batches and writes intermediate data to temporary files. Execution plans will often show information such as the number of batches used or disk usage during the hash operation. By increasing hash_mem_multiplier, the hash table may become large enough to stay entirely in memory, allowing the join to run in a single batch. This can greatly improve performance, especially for analytical queries that join large tables.
The default value of hash_mem_multiplier in PostgreSQL is 2, meaning hash operations can use twice the memory specified by work_mem. In many cases, this default works well, but workloads that involve heavy reporting or large joins may benefit from increasing it. For example, reporting systems or ERP platforms like Odoo often run complex queries that join multiple large tables to generate dashboards or pivot reports. In such scenarios, a slightly higher multiplier can reduce disk spills and improve response times.
Overall, the main purpose of hash_mem_multiplier is to give PostgreSQL more flexibility when executing hash-based operations. Instead of forcing hash joins to operate within a strict memory limit that may cause unnecessary disk activity, PostgreSQL allows them to use a controlled amount of extra memory. This helps queries run faster while still protecting the system from uncontrolled memory usage. When tuned carefully alongside work_mem, hash_mem_multiplier becomes a useful parameter for improving the performance of join-heavy workloads.