PostgreSQL, a powerful open-source relational database management system, employs parallel query execution to enhance performance for certain types of queries. This article delves into the mechanics of parallel query execution, exploring how it works, when it is utilized, and the considerations involved in optimizing its performance.

How Parallel Query Works
When the PostgreSQL query optimizer determines that parallel execution is the most efficient strategy, it constructs a query plan that includes a Gather or Gather Merge node. This node serves as the coordinator for parallel execution, collecting results from parallel worker processes. The number of workers is determined by the planner and can be visualized using the EXPLAIN command.

The Gather or Gather Merge node appears at the top of the query plan tree if the entire query is to be executed in parallel. Otherwise, it is positioned where parallelism is applied within the plan. In the example provided, a parallel sequential scan on the pgbench_accounts table is executed for a query filtering rows based on the filler column.

Parallel execution involves background worker processes, and the number of workers is constrained by configuration parameters like max_parallel_workers_per_gather. If the available workers are insufficient, or if certain conditions prevent parallel execution, the query may revert to a serial execution plan.

Parallel Query Constraints

Several factors influence whether a parallel query plan is generated:

Configuration Settings:

max_parallel_workers_per_gather must be set to a value greater than zero.
The system should not be in single-user mode.

Query Characteristics:
Queries involving data modification or row locking do not support parallel plans.
Certain commands like CREATE TABLE ... AS or SELECT INTO can use parallel plans for underlying SELECT operations.
Queries marked with functions labeled as PARALLEL UNSAFE are ineligible.

Execution Context:
Nested queries or queries within parallel constructs may not use parallel plans.
Operations like DECLARE CURSOR or PL/pgSQL loops may preclude parallel execution.

Safety Measures:
The system avoids parallel plans if it suspects partial or incremental execution.
Parallel Query Execution Challenges

Even when a parallel query plan is generated, execution may face challenges:

Worker Availability:
If the total number of background workers exceeds system limits (max_worker_processes or max_parallel_workers), parallel execution may be constrained.
Client Constraints:

The client's request, such as a non-zero fetch count, may limit parallel execution. This is influenced by the extended query protocol.

Dynamic Adjustments:

To prevent suboptimal serial execution, consider adjusting max_parallel_workers_per_gatherdynamically based on the expected execution context.

Conclusion
Understanding PostgreSQL's parallel query execution is crucial for optimizing database performance. Configuring relevant parameters, adhering to parallel-safe practices, and recognizing constraints on parallelism contribute to efficient query processing. As PostgreSQL evolves, staying informed about advancements and best practices ensures effective utilization of parallel query capabilities for enhanced database performance.