In the world of data management, query performance is paramount. PostgreSQL, one of the most popular open-source relational database systems, has consistently evolved to meet the growing demands of data-intensive applications. With the release of PostgreSQL 17, one of the standout features is the enhancement of parallel query execution. This feature can significantly boost query performance by leveraging multi-core processors to divide and conquer complex queries. In this blog, we’ll delve into the intricacies of parallel query execution, exploring how it works and when to use it.
Understanding Parallel Query Execution
Parallel query execution is a technique that allows PostgreSQL to break down a query into smaller tasks, which can then be executed simultaneously across multiple CPU cores. This approach is designed to reduce query execution time, especially for large datasets and complex operations.
Key Components of Parallel Query Execution
1. Query Planner and Analyzer
PostgreSQL's query planner is responsible for determining whether a query is a good candidate for parallel execution.
It evaluates the cost and potential benefits, considering factors such as dataset size, query complexity, and system resources.
2. Task Division
* Sequential Scans: The table is divided into chunks, and each chunk is assigned to a different worker process.
* Index Scans: Similarly, index scans can be divided, with each worker handling a portion of the index.
* Joins and Aggregates: Complex operations like joins and aggregates are split into smaller tasks for parallel execution.
3. Worker and Leader Processes
* Worker Processes: These are the processes that execute the divided tasks in parallel.
* Leader Process: The leader process coordinates the parallel execution, collects results from workers, and compiles the final output.
4. Tuple Queues
Results from worker processes are passed back to the leader process through tuple queues. This efficient data-sharing mechanism ensures smooth communication between processes.
Enabling Parallel Query Execution
By default, PostgreSQL does not always use parallel query execution for every query. To benefit from parallelism, certain configuration parameters must be tuned:
* max_parallel_workers_per_gather: Defines the maximum number of parallel workers that can be used for a single query.
* parallel_setup_cost: Specifies the cost threshold for initiating parallel execution.
* parallel_tuple_cost: Represents the cost of passing a tuple between worker and leader processes.
These parameters can be adjusted to optimize performance based on the workload and system capabilities.
Example of Parallel Query Execution
Let’s walk through an example to see parallel query execution in action:
Create a Table and Insert Values:
CREATE TABLE sales (id SERIAL PRIMARY KEY, region TEXT, sales NUMERIC);
INSERT INTO sales (region, sales)
SELECT 'North', generate_series(1, 1000000) * random()
UNION ALL
SELECT 'South', generate_series(1, 1000000) * random()
UNION ALL
SELECT 'East', generate_series(1, 1000000) * random()
UNION ALL
SELECT 'West', generate_series(1, 1000000) * random();
Perform a Normal Query Execution:
EXPLAIN ANALYZE
SELECT region, SUM(sales)
FROM sales
GROUP BY region;
This query will be executed normally without parallelism.
Set Up Parallel Query Execution:
SET max_parallel_workers_per_gather = 4;
SET parallel_setup_cost = 1000;
SET parallel_tuple_cost = 0.1;
Execute the Query with Parallel Execution:
EXPLAIN ANALYZE
SELECT region, SUM(sales)
FROM sales
GROUP BY region;
1. This time, the query planner will use parallel workers to divide the task, resulting in faster execution.
2. Analyze the Output: The EXPLAIN ANALYZE output will show the involvement of multiple workers and the reduced execution time, demonstrating the efficiency of parallel query execution.
Parallel Query Execution Workflow in PostgreSQL 17
Step 1: Query Parsing and Planning
Input: The user submits a query.
Action: The query goes through the parser, which converts the SQL statement into a query tree (internal representation of the query).
Action: The planner analyzes the query tree and creates an execution plan.
During this step, PostgreSQL decides whether parallel execution is appropriate based on factors like:
The complexity of the query.
Available system resources (CPU cores).
The query’s cost is relative to sequential execution.
Step 2: Determining the Need for Parallel Execution
Action: The query planner evaluates whether parallelism will reduce the execution time by comparing the cost of parallel execution against sequential execution.
The planner checks:
The size of tables being scanned (larger tables are more likely to benefit from parallelism).
Whether the query involves operations like sequential scans, joins, or aggregation that can be parallelized.
System settings like max_parallel_workers_per_gather and parallel_setup_cost.
Step 3: Parallel Query Plan Construction
Action: If parallel execution is deemed beneficial, the planner constructs a parallel query plan.
The planner will add a Parallel Gather Node or similar parallel node in the plan.
The query plan is split into parts that can be executed concurrently (e.g., scanning different parts of a large table).
Step 4: Leader Process Initiation
Action: The leader process (main query process) starts executing the query.
The leader is responsible for controlling the parallel execution and merging results from the parallel workers.
The leader sends requests to create parallel workers to execute the assigned tasks.
The leader monitors the workers and receives partial results from them.
Step 5: Parallel Worker Creation
Action: Based on the parallel query plan, parallel workers are created as background processes.
The number of parallel workers is determined by:
System resources (CPU cores).
max_parallel_workers_per_gather (the max number of workers for a query).
The workers are assigned specific tasks, such as scanning different portions of a large table or handling individual join partitions.
Step 6: Task Distribution
Action: The leader process divides the tasks:
For Sequential Scans: The table is divided into chunks, and each worker scans a different portion in parallel.
For Joins: The data from two or more tables is partitioned among workers, each handling a portion of the join.
For Aggregations: Partial results (e.g., sums or counts) are computed by workers, which will be merged later.
Step 7: Parallel Execution by Workers
Action: Each parallel worker independently processes its assigned task.
Workers operate on separate parts of the data, accessing shared memory as needed.
Each worker communicates with the leader process to report progress and send intermediate results.
If necessary, workers access disk or memory buffers and operate on their specific partitions.
Step 8: Merging Results
Action: Once the workers complete their tasks, the leader process gathers and merges their results.
In the case of aggregations, the leader combines partial aggregates from each worker to form the final result.
For joint operations, the leader will merge the results from each worker into the final joined dataset.
Sorting may be necessary if workers process data in different orders.
Step 9: Final Result Delivery
Action: After merging the results, the leader process returns the final result set to the client.
The query execution is complete, and the results are sent back to the user.
Step 10: Cleanup
Action: The leader and parallel workers clean up any resources used during the parallel execution (memory buffers, worker processes).
All temporary data created during parallel execution is removed.
PostgreSQL 17's enhanced parallel query execution is a game-changer for handling large datasets and complex queries. By leveraging multiple CPU cores, it significantly reduces query execution time, making the database more efficient and responsive. The systematic breakdown of tasks and efficient coordination between worker and leader processes ensure optimal performance gains. As data volumes continue to grow, mastering parallel query execution will be essential for developers and database administrators aiming to maximize the potential of PostgreSQL. Whether dealing with massive tables, complex joins, or intensive aggregations, the advancements in PostgreSQL 17 offer a powerful toolset to meet the demands of modern data-driven applications.