---
title: "PostgreSQL Query Planner Cost Estimation"
description: "An in-depth guide to understanding and tuning the PostgreSQL query planner's cost estimation model"
keywords: ["postgresql", "query planner", "cost estimation", "indexing", "parallelism", "jit"]
category: "databases"
---

The PostgreSQL query planner's cost estimation model is a critical component in determining the most efficient execution plan for a given query. At the core of this model are the sequential scan and index scan cost estimates, which are controlled by the random_page_cost and seq_page_cost configuration parameters. 

In PostgreSQL versions 10 and later, the default random_page_cost is set to 4.0, reflecting the higher random I/O costs of modern storage media compared to the legacy default of 1.0. Administrators can tune this parameter based on their specific hardware, for example setting random_page_cost to 1.1 for fast SSD storage. The effective_cache_size parameter also plays a role, representing the amount of memory available for caching data pages and improving the planner's ability to estimate the cost of index scans.

The planner considers several join strategies, including nested loop, hash join, and merge join. The choice of join strategy is heavily influenced by the estimated costs, with hash joins preferred when the driving table is small enough to fit in memory, and merge joins used when the input data is already sorted. Administrators can influence these choices by setting appropriate values for work_mem and hash_mem_multiplier.

Parallel query execution, introduced in PostgreSQL 9.6, allows the planner to leverage multiple worker processes to execute parts of a query in parallel, reducing overall query latency. The planner's cost model accounts for the potential benefits of parallelism, with the parallel_setup_cost and parallel_tuple_cost parameters controlling the estimated overhead. Administrators can tune these parameters based on their hardware and workload characteristics.

In PostgreSQL 11 and later, the query planner can leverage just-in-time (JIT) compilation to further optimize query execution. The planner will only consider JIT compilation if the estimated benefits, controlled by the jit and jit_above_cost parameters, outweigh the compilation overhead. Monitoring the actual performance impact of JIT compilation using EXPLAIN ANALYZE BUFFERS can help administrators fine-tune these settings.

Understanding and tuning the PostgreSQL query planner's cost estimation model is essential for achieving optimal database performance, especially for complex queries and high-throughput workloads. By adjusting parameters like random_page_cost, effective_cache_size, work_mem, and jit_above_cost, administrators can ensure the planner makes informed decisions and generates efficient execution plans.The PostgreSQL query planner's cost estimation model also takes into account the potential benefits of index scans compared to sequential table scans. The planner will prefer an index scan when the estimated cost is lower, which is influenced by factors like the selectivity of the query predicates, the number of index columns, and the distribution of data in the table. Administrators can monitor the planner's decisions using the EXPLAIN command and tune the index-related cost parameters, such as random_page_cost and seq_page_cost, to ensure the planner is making optimal choices.

Another important aspect of the cost estimation model is how the planner handles joins. The planner will consider different join strategies, such as nested loop, hash join, and merge join, and choose the one with the lowest estimated cost. The choice of join strategy is influenced by the size of the input tables, the availability of indexes, and the distribution of data. Administrators can tune the memory-related parameters, like work_mem and hash_mem_multiplier, to control the planner's behavior and ensure efficient join processing.

Parallel query execution, introduced in PostgreSQL 9.6, is another feature that can significantly improve query performance by leveraging multiple worker processes to execute parts of a query in parallel. The planner's cost estimation model accounts for the potential benefits of parallelism, with parameters like parallel_setup_cost and parallel_tuple_cost controlling the estimated overhead. Administrators can monitor the actual performance impact of parallel queries using the EXPLAIN ANALYZE BUFFERS command and adjust these parameters accordingly.

The introduction of just-in-time (JIT) compilation in PostgreSQL 11 and later versions has further enhanced the query planner's ability to optimize query execution. The planner will only consider JIT compilation if the estimated benefits, controlled by the jit and jit_above_cost parameters, outweigh the compilation overhead. Administrators can use the EXPLAIN ANALYZE BUFFERS command to analyze the actual performance impact of JIT compilation and fine-tune these settings based on their workload characteristics.

Overall, understanding and tuning the PostgreSQL query planner's cost estimation model is crucial for achieving optimal database performance, especially for complex queries and high-throughput workloads. By adjusting parameters like random_page_cost, effective_cache_size, work_mem, and jit_above_cost, administrators can ensure the planner makes informed decisions and generates efficient execution plans that leverage the full capabilities of the PostgreSQL database.The PostgreSQL query planner's cost estimation model also takes into account the impact of index scans versus sequential table scans. When evaluating query plans, the planner will prefer an index scan if the estimated cost is lower than a full table scan. This cost estimation is influenced by factors such as the selectivity of the query predicates, the number of index columns, and the distribution of data within the table. Administrators can monitor the planner's decisions using the EXPLAIN command and tune the index-related cost parameters, like random_page_cost and seq_page_cost, to ensure the planner is making optimal choices for their specific workload.

Another key aspect of the cost estimation model is the planner's handling of join strategies. The PostgreSQL query planner will consider different join algorithms, including nested loop, hash join, and merge join, and select the one with the lowest estimated cost. The choice of join strategy is influenced by the size of the input tables, the availability of indexes, and the distribution of data. Administrators can tune memory-related parameters, such as work_mem and hash_mem_multiplier, to control the planner's behavior and ensure efficient join processing for their workload.

Parallel query execution, introduced in PostgreSQL 9.6, is a powerful feature that can significantly improve query performance by leveraging multiple worker processes to execute parts of a query in parallel. The planner's cost estimation model accounts for the potential benefits of parallelism, with parameters like parallel_setup_cost and parallel_tuple_cost controlling the estimated overhead. Administrators can monitor the actual performance impact of parallel queries using the EXPLAIN ANALYZE BUFFERS command and adjust these parameters accordingly to optimize their system's parallel processing capabilities.

The introduction of just-in-time (JIT) compilation in PostgreSQL 11 and later versions has further enhanced the query planner's ability to optimize query execution. The planner will only consider JIT compilation if the estimated benefits, controlled by the jit and jit_above_cost parameters, outweigh the compilation overhead. Administrators can use the EXPLAIN ANALYZE BUFFERS command to analyze the actual performance impact of JIT compilation and fine-tune these settings based on their specific workload characteristics and hardware capabilities.

Overall, understanding and tuning the PostgreSQL query planner's cost estimation model is essential for achieving optimal database performance, especially for complex queries and high-throughput workloads. By adjusting parameters like random_page_cost, effective_cache_size, work_mem, and jit_above_cost, administrators can ensure the planner makes informed decisions and generates efficient execution plans that leverage the full potential of the PostgreSQL database.The PostgreSQL query planner's cost estimation model also takes into account the potential impact of index scans versus sequential table scans. When evaluating query plans, the planner will prefer an index scan if the estimated cost is lower than a full table scan. This cost estimation is influenced by factors such as the selectivity of the query predicates, the number of index columns, and the distribution of data within the table. Administrators can monitor the planner's decisions using the EXPLAIN command and tune the index-related cost parameters, like random_page_cost and seq_page_cost, to ensure the planner is making optimal choices for their specific workload.

Another key aspect of the cost estimation model is the planner's handling of join strategies. The PostgreSQL query planner will consider different join algorithms, including nested loop, hash join, and merge join, and select the one with the lowest estimated cost. The choice of join strategy is influenced by the size of the input tables, the availability of indexes, and the distribution of data. Administrators can tune memory-related parameters, such as work_mem and hash_mem_multiplier, to control the planner's behavior and ensure efficient join processing for their workload.

Parallel query execution, introduced in PostgreSQL 9.6, is a powerful feature that can significantly improve query performance by leveraging multiple worker processes to execute parts of a query in parallel. The planner's cost estimation model accounts for the potential benefits of parallelism, with parameters like parallel_setup_cost and parallel_tuple_cost controlling the estimated overhead. Administrators can monitor the actual performance impact of parallel queries using the EXPLAIN ANALYZE BUFFERS command and adjust these parameters accordingly to optimize their system's parallel processing capabilities.

The introduction of just-in-time (JIT) compilation in PostgreSQL 11 and later versions has further enhanced the query planner's ability to optimize query execution. The planner will only consider JIT compilation if the estimated benefits, controlled by the jit and jit_above_cost parameters, outweigh the compilation overhead. Administrators can use the EXPLAIN ANALYZE BUFFERS command to analyze the actual performance impact of JIT compilation and fine-tune these settings based on their specific workload characteristics and hardware capabilities.

In addition to the previously discussed aspects of the cost estimation model, the PostgreSQL query planner also considers the potential impact of materialized views and partial indexes on query performance. Materialized views can significantly reduce the cost of complex queries by pre-computing the results, while partial indexes can provide targeted indexing for specific query predicates. Administrators can leverage these features to further optimize the planner's cost estimates and generate more efficient execution plans.