How EXPLAIN and EXPLAIN ANALYZE Improve PostgreSQL Performance – Understanding PostgreSQL Execution Plans

PostgreSQL is known for its powerful query processing capabilities, but sometimes even the most efficient queries can run slowly if not properly optimized.

Understanding how PostgreSQL executes queries is key to troubleshooting and improving database performance.

In this article, we’ll dive into how you can use the EXPLAIN and EXPLAIN ANALYZE commands to gain insights into query performance and make data-driven decisions to optimize your queries for better efficiency.

What Are PostgreSQL Execution Plans?

When PostgreSQL executes a SQL query, it generates a query execution plan. The execution plan is a detailed strategy that outlines how PostgreSQL intends to retrieve the requested data.

It contains vital information about the operations the database will perform, including how it will access data, join tables, apply filters, and handle sorting.

A good execution plan ensures that PostgreSQL performs queries in the most efficient manner possible, while a poorly optimized plan can lead to slow performance. Understanding how to interpret and analyze these execution plans is crucial for performance tuning.

How EXPLAIN and EXPLAIN ANALYZE Help

PostgreSQL provides two commands, EXPLAIN and EXPLAIN ANALYZE, to give you visibility into query execution plans.

• EXPLAIN: This command shows you the query execution plan without actually running the query. It provides a high-level overview of how PostgreSQL intends to execute a query, along with the estimated costs, number of rows, and other statistics.
• EXPLAIN ANALYZE: Unlike EXPLAIN, this command not only shows the execution plan but also actually runs the query. It provides detailed statistics about the actual execution of the query, including the time taken for each operation, the number of rows processed, memory usage, and I/O operations.

By using these commands together, you can gain insights into both the predicted and actual performance of your queries, helping you identify performance bottlenecks and optimization opportunities.

Using EXPLAIN to Decode the Query Plan

Let’s start by looking at how to use EXPLAIN to view the query execution plan.

Example Query

EXPLAIN SELECT * FROM employees WHERE department = 'Sales';

Sample Output

Seq Scan on employees  (cost=0.00..35.50 rows=5 width=64)
  Filter: (department = 'Sales'::text)

Explanation of the Output

• Seq Scan: This indicates that PostgreSQL will perform a sequential scan of the employees table, which means it will scan every row in the table to find those where the department column equals ‘Sales’.
• Cost: The cost value represents the estimated cost of performing this operation. The 0.00 value is the startup cost (before the first row is retrieved), and 35.50 is the total cost (after all rows have been processed).
• Rows: The rows=5 estimate tells us that PostgreSQL expects to find 5 rows matching the condition.
• Filter: The filter shows the condition (department = ‘Sales’) being applied to the rows.

While EXPLAIN provides valuable information about how the query is planned to be executed, it doesn’t tell you how long the operations actually take or how many rows are processed. This is where EXPLAIN ANALYZE comes into play.

Using EXPLAIN ANALYZE to Measure Actual Performance

EXPLAIN ANALYZE goes a step further by running the query and providing real-time statistics.

Example Query

EXPLAIN ANALYZE SELECT * FROM employees WHERE department = 'Sales';

Sample Output

Seq Scan on employees  (cost=0.00..35.50 rows=5 width=64) (actual time=0.015..0.028 rows=5 loops=1)
  Filter: (department = 'Sales'::text)
  Rows Removed by Filter: 95
Planning Time: 0.123 ms
Execution Time: 0.043 ms

Explanation of the Output

• Actual Time: The actual time tells you how long the query actually took to process each row. In this case, the scan took between 0.015ms and 0.028ms per row.
• Rows: The actual number of rows returned by the query (rows=5) matches the estimate from EXPLAIN.
• Rows Removed by Filter: This shows the number of rows that were scanned but did not match the filter condition (department = ‘Sales’).
• Planning Time: The time spent planning the query before it started executing (in this case, 0.123ms).
• Execution Time: The total time it took to execute the query (0.043ms).

By comparing the estimated values from EXPLAIN with the actual values from EXPLAIN ANALYZE, you can identify discrepancies. If the actual execution time is significantly higher than the estimated time, it could indicate an optimization opportunity.

Key Performance Metrics to Watch for in EXPLAIN ANALYZE

When using EXPLAIN ANALYZE, keep an eye on the following performance metrics:

• Execution Time: This shows the total time for query execution. If this is high, the query is likely a bottleneck.
• Rows Processed: If the number of rows processed is much higher than expected, it might indicate inefficiencies like full table scans or missing indexes.
• I/O Operations: Excessive disk I/O (like frequent reads and writes) can signal the need for indexing or query optimization.
• Memory Usage: For operations like sorting and hashing, high memory usage may indicate that increasing the work_mem parameter could help.

Optimizing Queries Using EXPLAIN and EXPLAIN ANALYZE

Once you’ve identified performance bottlenecks using EXPLAIN and EXPLAIN ANALYZE, you can start optimizing your queries. Here are some common strategies:

• Indexing: If you notice that sequential scans are being performed on large tables, consider adding an index on frequently queried columns (e.g., department in the above example).

  CREATE INDEX idx_department ON employees(department);

• Query Rewriting: Sometimes, small changes in how a query is written can make a big difference. For example, switching from a JOIN to a WHERE EXISTS clause or using more selective filters can reduce the number of rows processed.
• Analyze and Vacuum: If you have missing or outdated statistics, running ANALYZE and VACUUM can help PostgreSQL generate more accurate query plans and remove table bloat.

  VACUUM ANALYZE employees;

• Use Efficient Joins: Use the most efficient join method based on your query. For instance, if you’re joining large tables, consider using a hash join instead of a nested loop join.

Conclusion

PostgreSQL’s EXPLAIN and EXPLAIN ANALYZE commands are indispensable tools for understanding and improving query performance.

By carefully analyzing the execution plans and comparing estimated versus actual query times, you can identify performance bottlenecks, optimize query plans, and ensure your database runs efficiently. Regularly using these tools will help you make data-driven decisions and maintain a high-performance PostgreSQL database.