Tuning Process Global Area in PostgreSQL

In PostgreSQL, the process global area (PGA) is managed by the server’s memory allocator and is used to store data structures associated with each client’s session, such as query plans and sorts.

To tune the PGA in PostgreSQL, you can adjust the following configuration parameters in the postgresql.conf file:

  1. work_mem: This parameter sets the amount of memory that each client can use for sorts and hash tables. Increasing this parameter can improve performance for queries that involve large sorts or hash joins.
  2. maintenance_work_mem: This parameter sets the amount of memory that can be used for maintenance operations like vacuuming and indexing. Increasing this parameter can improve the speed of these operations.
  3. max_worker_processes: This parameter sets the maximum number of worker processes that can be created to handle parallel queries. Increasing this parameter can improve the performance of parallel queries, but will also increase memory usage.
  4. shared_buffers: This parameter sets the amount of memory that can be used for caching data in memory. Increasing this parameter can improve the speed of queries that access frequently used data.

It is important to note that tuning the PGA involves a trade-off between performance and memory usage. Therefore, it is important to monitor system performance and adjust the configuration parameters accordingly to ensure optimal performance while avoiding excessive memory usage.

Which query operations are memory intensive in PostgreSQL?

In PostgreSQL, certain query operations can be memory-intensive, particularly when dealing with large amounts of data. Some of these operations include:

  1. Sorting: Sorting large datasets can require a significant amount of memory. The more data being sorted, the more memory will be required.
  2. Hashing: Hash joins and aggregations can be memory-intensive because they require building hash tables in memory. The amount of memory required will depend on the size of the data being hashed.
  3. Bitmap Index Scans: Bitmap index scans can be memory-intensive because they require building bitmaps in memory.
  4. Materializing subqueries: If a subquery is materialized, it will be stored in memory, and the amount of memory required will depend on the size of the subquery.
  5. Window Functions: Window functions can be memory-intensive because they require keeping track of a sliding window of rows as the query progresses.

To optimize the memory usage of these operations, you can adjust the configuration parameters discussed in my previous answer. Additionally, you can use query optimization techniques such as reducing the amount of data being sorted or filtered by using indexes, partitioning tables, or rewriting queries to use more efficient methods.

Monitoring Process Global Area activities in PostgreSQL

You can use the pg_stat_activity view to monitor the Process Global Area (PGA) in PostgreSQL using the following query:

SELECT pid, usename, application_name, client_addr, client_port, query, query_start, state, state_change, backend_start, xact_start, query_duration, max(query_mem) AS max_query_mem
FROM pg_stat_activity
WHERE state NOT LIKE ‘idle%’
GROUP BY pid, usename, application_name, client_addr, client_port, query, query_start, state, state_change, backend_start, xact_start, query_duration
ORDER BY max_query_mem DESC;

This query selects the process ID (pid), username (usename), application name (application_name), client IP address (client_addr), client port (client_port), query text (query), query start time (query_start), connection state (state), state change time (state_change), backend start time (backend_start), transaction start time (xact_start), and query duration (query_duration) from the pg_stat_activity view for all active connections that are not in idle state.

In addition, the max() function is used to select the maximum value of query_mem for each connection, which provides an estimate of the amount of memory allocated to the connection’s PGA.

The results are then grouped by the selected columns and sorted in descending order of the maximum query memory (max_query_mem) to highlight the connections that are using the most memory.

This query can help you monitor and troubleshoot memory usage in PostgreSQL and identify any active connections that are using excessive memory.

How expensive do queries in PostgreSQL create performance bottlenecks for Process Global Area?

Expensive queries in PostgreSQL can create performance bottlenecks in the Process Global Area (PGA) in several ways:

  1. Memory allocation: Expensive queries can require a significant amount of memory to execute, including memory for sorting, hashing, and storing intermediate results. This memory is allocated from the PGA and can cause the PGA to become full or exceed its allocated size.
  2. Memory fragmentation: When the PGA becomes full, memory allocation requests may result in memory fragmentation, where free memory is scattered across the PGA in small, unusable chunks. This can cause memory allocation requests to take longer, as the database must search for available free memory and potentially perform expensive memory compaction operations to free up contiguous memory.
  3. Cache pressure: Expensive queries can also put pressure on the shared buffer cache, which is used to store frequently accessed data in memory to reduce the need for disk I/O. When the shared buffer cache becomes full, PostgreSQL must evict some data to make room for new data. This can result in increased disk I/O and decreased query performance.
About Shiv Iyer 456 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.