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EXPLAIN and EXPLAIN ANALYZE

Learn about EXPLAIN and EXPLAIN ANALYZE as part of PostgreSQL Database Design and Optimization

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Understanding PostgreSQL Query Optimization with EXPLAIN and EXPLAIN ANALYZE

In PostgreSQL, efficiently retrieving data is crucial for application performance. Query optimization is the process of finding the most efficient way to execute a SQL query. PostgreSQL provides powerful tools,

code
EXPLAIN
and
code
EXPLAIN ANALYZE
, to help developers and database administrators understand how the database plans to execute a query and how long it actually takes.

What is EXPLAIN?

code
EXPLAIN
is a command that shows the execution plan that the PostgreSQL query planner generates for a given SQL statement. It reveals the sequence of operations the database will perform, such as table scans, index usage, joins, and sorts. This plan is a prediction of how the query will run, based on statistics and cost estimates.

EXPLAIN shows the *planned* execution of a query.

It's like a roadmap for your SQL query, detailing the steps the database intends to take without actually running the query.

When you run EXPLAIN SELECT * FROM users WHERE id = 1;, PostgreSQL outputs a tree-like structure. Each node in the tree represents an operation (e.g., Seq Scan, Index Scan, Nested Loop Join). The planner assigns a cost to each operation, estimating the resources (CPU, I/O) required. These costs are relative and help the planner choose the cheapest plan. Understanding these costs and operations is key to identifying potential bottlenecks.

What is EXPLAIN ANALYZE?

code
EXPLAIN ANALYZE
goes a step further than
code
EXPLAIN
. It not only shows the execution plan but also executes the query and then displays the actual runtime statistics for each node in the plan. This includes the actual time spent, the number of rows processed, and other metrics.

EXPLAIN ANALYZE shows the *actual* execution of a query.

This command runs your query and provides real-world performance data, revealing discrepancies between the planner's estimates and reality.

By running EXPLAIN ANALYZE SELECT * FROM users WHERE id = 1;, you get the same plan structure as EXPLAIN, but with added information like actual time=0.015..0.016 rows=1 loops=1. The actual time shows the start and total time for that node, rows shows the actual number of rows output by that node, and loops indicates how many times the node was executed. Comparing the estimated costs/rows with the actual values is vital for identifying inaccurate statistics or suboptimal plan choices.

Key Differences and When to Use Them

FeatureEXPLAINEXPLAIN ANALYZE
ExecutionDoes not execute the query.Executes the query.
OutputShows the estimated execution plan and costs.Shows the estimated plan AND actual runtime statistics.
Use CaseTo understand the planner's strategy without side effects (e.g., for UPDATE, DELETE, INSERT).To diagnose performance issues, verify index usage, and identify slow operations.
Data ModificationSafe for DML statements (INSERT, UPDATE, DELETE) as it doesn't change data.Executes DML statements, which can modify data. Use with caution on production systems.

Always use EXPLAIN ANALYZE on a test or development environment first, especially for queries that modify data, as it will execute the query.

Interpreting the Output

When examining the output of

code
EXPLAIN
or
code
EXPLAIN ANALYZE
, look for the following:

  • Sequential Scans (Seq Scan): Often indicate that an index might be missing or not used effectively for a selective query. If the table is small, a Seq Scan can be efficient.
  • Index Scans (Index Scan, Bitmap Heap Scan): Generally good signs if the index is being used appropriately. Check if the
    code
    rows
    scanned by the index are significantly fewer than the total rows in the table.
  • Join Methods (Nested Loop, Hash Join, Merge Join): The choice of join method depends on the data size and whether indexes are available. Nested Loops can be slow for large datasets without good indexing.
  • Sorts: Large sorts can be expensive, especially if they spill to disk. Consider adding indexes that can provide pre-sorted data.
  • Cost Estimates vs. Actual Time: Significant discrepancies between estimated costs and actual execution times often point to outdated table statistics or complex query logic that the planner struggles to estimate accurately.

The output of EXPLAIN ANALYZE is a tree structure representing the query execution plan. Each node in the tree is an operation. The planner estimates the cost of each operation and sums them up to get the total estimated cost. EXPLAIN ANALYZE adds actual execution time and row counts to each node, allowing for comparison between estimates and reality. For example, a 'Seq Scan' node might show a high 'actual time' if the table is large and no index is used, while the planner might have estimated a low cost assuming a smaller table or efficient I/O.

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Advanced Options

Both

code
EXPLAIN
and
code
EXPLAIN ANALYZE
support several options to provide more detailed information:

  • code
    FORMAT
    : Can be
    code
    TEXT
    (default),
    code
    XML
    ,
    code
    JSON
    , or
    code
    YAML
    .
    code
    JSON
    is particularly useful for programmatic analysis.
  • code
    VERBOSE
    : Displays additional information about the plan, such as the output columns of each node.
  • code
    COSTS
    : Includes cost estimates (enabled by default).
  • code
    BUFFERS
    : When used with
    code
    ANALYZE
    , shows information about buffer usage (shared, local, temporary).
  • code
    TIMING
    : Shows actual startup time and total time spent in each node (enabled by default with
    code
    ANALYZE
    ).
What is the primary difference between EXPLAIN and EXPLAIN ANALYZE?

EXPLAIN shows the planned execution, while EXPLAIN ANALYZE executes the query and shows the actual runtime statistics.

Why is it important to compare estimated costs with actual times in EXPLAIN ANALYZE output?

Discrepancies can indicate outdated statistics, inefficient query plans, or issues with hardware/system configuration that the planner didn't account for.

Learning Resources

PostgreSQL Documentation: EXPLAIN(documentation)

The official PostgreSQL documentation for the EXPLAIN command, detailing its syntax, options, and output.

PostgreSQL EXPLAIN ANALYZE: A Deep Dive(blog)

A comprehensive blog post explaining how to use EXPLAIN ANALYZE and interpret its output for performance tuning.

Understanding PostgreSQL EXPLAIN Output(blog)

This article breaks down the common elements found in EXPLAIN output and provides practical tips for analysis.

PostgreSQL Query Planning and Optimization(documentation)

The official documentation section on how the PostgreSQL query planner works and the principles of optimization.

Visualizing PostgreSQL EXPLAIN Plans(documentation)

A web-based tool to paste EXPLAIN output and visualize it in a more readable, hierarchical format.

PostgreSQL EXPLAIN ANALYZE: A Practical Guide(blog)

A practical guide focusing on how to use EXPLAIN ANALYZE to identify and fix performance bottlenecks in PostgreSQL queries.

PostgreSQL Performance Tuning: EXPLAIN ANALYZE(blog)

An EnterpriseDB blog post offering insights into performance tuning using EXPLAIN ANALYZE with real-world examples.

PostgreSQL EXPLAIN ANALYZE with BUFFERS and WAL(blog)

This article delves into using the BUFFERS option with EXPLAIN ANALYZE to understand I/O performance and WAL activity.

SQL Query Optimization Techniques(wikipedia)

A general overview of query optimization concepts in database systems, providing context for PostgreSQL-specific tools.

PostgreSQL EXPLAIN ANALYZE JSON Output Example(documentation)

An example from the PostgreSQL documentation showcasing the JSON output format of EXPLAIN ANALYZE, useful for programmatic analysis.