Understanding PostgreSQL EXPLAIN: A Guide to Query Performance Optimisation

PostgreSQL EXPLAIN for Analysing Query Execution Plans

When you’re looking to enhance the performance of your PostgreSQL database, EXPLAIN is an essential command to utilise. It provides insight into how your SQL queries are transformed into execution plans, highlighting areas for optimisation:

  • Execution Plan Display: Visualises the path your database takes to execute a query.
  • Performance Metrics: Gathers real-time statistics including duration and rows affected.
  • Optimisation Opportunities: Identifies slow or inefficient operations needing attention.

Using EXPLAIN effectively can lead to significantly improved database efficiency.

Understanding the Execution Plan in PostgreSQL

In PostgreSQL, an execution plan illustrates the processes a database employs to execute a query. Here’s what you need to grasp:

  • The Planner: PostgreSQL introduces a component known as the planner or query planner. This element’s duty is to determine the optimal path for the query’s execution.
  • Steps in Execution: The plan manifests as a tree, where each node represents a step in the process, such as retrieving data from a table, or joining two tables together.
  • Efficient Execution: Among the various potential paths, the planner selects the most resource-effective route to execute the query.
  • Performance Insights: By inspecting the execution plan, you can identify which parts of your query could be fine-tuned for better efficiency.

Being acquainted with the execution plan enables you to make informed adjustments to optimise how your queries run.

Understanding PostgreSQL EXPLAIN Command

When you aim to evaluate how your queries perform in PostgreSQL, the EXPLAIN command proves to be a vital tool. Utilising this command illuminates the pathway the database planner follows, including the anticipated cost of executing the query.

  • Syntax: The structure for writing this command is EXPLAIN [ ( option [, ...] ) ] SQL statement;
  • Options: Varying flags can be used to gain more detailed information:
    • ANALYZE [boolean]
    • VERBOSE [boolean]
    • COSTS [boolean]
    • SETTINGS [boolean]
    • BUFFERS [boolean]
    • WAL [boolean]
    • TIMING [boolean]
    • SUMMARY [boolean]

Through the EXPLAIN command, you ascertain the method PostgreSQL will utilise to access and combine data, whether via sequential scans or employing different join techniques for handling multiple tables. Additionally, setting specific boolean options allows you to customise the output further to include execution statistics, actual row counts, and timing for each stage in the execution plan.

Understanding Execution Plans in PostgreSQL

When attempting to optimise performance in PostgreSQL, it’s imperative to decipher the execution plan it yields for any given query. This plan is a map to how PostgreSQL will execute a query, outlining the steps and resources involved.

The execution plan begins by displaying the join type, estimated costs (encompassing both the startup cost and the cost to complete the operation), number of rows processed, and size of rows. Look for a Seq Scan, which signifies a sequential table scan where each row of a table is read one by one. The provided startup cost depicts the expense to fetch the first row, whereas total cost covers the entire operation.

Scans over tables yield insights on the operation, such as which condition joins tables. This includes hash joins or scanning methods for subsequent tables. Each node within the plan—representing steps like filters, sorting, or scans—includes details on time estimates, the total number and size of rows, the branching tree structure, and execution costs.

To understand the intricacy of the execution plan, study the indented tree format which illustrates how each step relates to another. The parent and child nodes reveal the relational hierarchy. Moreover, specific nodes like bitmap heap scans or bitmap index scans indicate specialised strategies for executing the query.

JSON or text formatting can be chosen for your output, with JSON providing a structured, machine-readable summary while the text is more readable. Delving into these metrics, such as execution time and filter conditions, provides you with a comprehensive view of the resources required for the query, and potential bottlenecks in the process.

PostgreSQL ANALYZE Option

In the context of PostgreSQL, ANALYSE represents a tool that executes an SQL statement, subsequently delivering actual performance metrics. Unlike its counterpart that only estimates the plan and costs, the ANALYSE option carries out the query, appending execution times and returned row numbers.

When working with ANALYSE, especially involving statements that alter data like DROP and UPDATE, there’s a level of caution required. To review the anticipated plan and costs sans data impact, it’s advisable to:

  • Start a transaction
  • Invoke ANALYSE within the transaction
  • Revert using ROLLBACK

For instance:


This method allows you to contrast the regular estimate with one including execution data, giving a comprehensive view of the query’s performance without making permanent changes.

Utilising PostgreSQL Explain Analyse

When executing EXPLAIN ANALYSE in PostgreSQL, you receive a detailed report on the query execution. This tool becomes valuable for pinpointing slowdowns within SQL statements and enhancing their efficiency. The breakdown includes:

  • Timing: Measures the duration of each phase in milliseconds.
  • Actual Row Count: Displays how many rows the step returned.
  • Buffers: Information on memory usage, detailing buffer hits and reads.

By scrutinising these metrics, you can compare the anticipated plan against the actual performance, identify if indexes are hit successfully, and adjust your strategies accordingly to optimise database interactions.

Profiling PostgreSQL Queries with dbForge Studio

Optimising your PostgreSQL queries is streamlined using dbForge Studio’s Query Profiler. Here’s how you employ this tool effectively:

  • Visualise your execution plan through a detailed diagram to understand the paths your queries take.
  • Evaluate Wait Statistics for potential delays and performance hits.
  • The Plan Tree visualisation helps you trace the operations your query performs.
  • Identify resource-intensive operations with the Top Operations List.
  • Monitor Table I/O to analyse the read and write operations.
  • Use the side-by-side comparison feature to assess diverse profiling outcomes.

This approach eliminates the need for in-depth manual analysis of EXPLAIN ANALYZE results, affording a more intuitive understanding of where enhancements are necessary. For comprehensive query analysis, tools like pgAdmin, Explain.depesz.com, and pg_stat_statements extension offer additional insights, but dbForge Studio’s Query Profiler simplifies the performance tuning process with its user-oriented interface.

Enhancing Query Efficiency

In improving SQL query performance, start by evaluating your system’s hardware and software configurations for potential upgrades in efficiency. This precursory step ensures subsequent optimisation efforts are impactful.

Focus on the queries that run sluggishly; these provide the greatest benefit from optimisation. Refining indexing is often your initial move. When you introduce an index on a column, queries involving that column become swifter, as the index acts like a shortcut to the desired data.

Utilise SELECT, UPDATE, DELETE, INSERT, and CREATE TABLE judiciously — careful structuring can minimize processing resources. Consider applying:

  • WHERE clause refinements
  • Precise JOIN types, such as INNER JOIN
  • Efficient use of subqueries and CTEs (Common Table Expressions)

Apply aggregate functions (COUNT, SUM, etc.) with an ORDER BY clause only as necessary. The LIMIT keyword can also help reduce the volume of input data handled. Regular maintenance like the VACUUM command in PostgreSQL can also aid in keeping query performance optimal.


In your pursuit of high-performing data analytics and optimising the functionality of data-centric software, understanding the effectiveness of your queries is crucial. While there are numerous techniques for monitoring query performance, these processes can be intricate, often necessitating a high degree of technical knowledge to interpret. Enter dbForge Studio’s Query Profiling tool, which sets the standard with its advanced features for tracking query efficacy. This tool simplifies the analysis with an intuitive display of performance metrics, aiding you in honing the speed and efficiency of your queries.

  • Performance Monitoring: Track and improve data query efficiency with ease.
  • Advanced Features: Benefit from state-of-the-art tools tailored for detailed analysis.
  • Intuitive Display: View key metrics and indicators in a user-friendly format.

Frequently Asked Questions

Understanding PostgreSQL’s EXPLAIN Command Output

When you issue the EXPLAIN command in PostgreSQL, it returns a detailed execution plan of a given SQL statement. This output comprises information on how the database will access the tables and retrieve the required data. You’ll see whether it uses an index scan or a sequential scan, and for queries involving multiple joins, the type of join algorithm employed.


The primary difference between EXPLAIN and EXPLAIN ANALYSE in PostgreSQL is that EXPLAIN provides estimated costs of a query plan, while EXPLAIN ANALYSE both executes the query and provides actual execution time and processing statistics. This execution provides a more accurate understanding of a query’s performance.

Visualising the Execution Plan from EXPLAIN ANALYSE

To visualise an execution plan from EXPLAIN ANALYSE, you can use graphical tools that parse the verbose output and display it in an easily interpretable format. These tools can help you detect potential bottlenecks and understand the sequence of operations.

Utilising Cost Estimates from EXPLAIN for Query Optimisation

The cost estimates provided by PostgreSQL’s EXPLAIN give insights into how the database will execute a query. Lower costs typically reflect more efficient operations. By analysing these estimates, you may identify opportunities to streamline queries, such as by adding an index or rewriting the query to reduce computational complexity.

Conducting an Analysis of a PostgreSQL Table

Analysing a PostgreSQL table with EXPLAIN ANALYSE shows real-time costs and row execution counts at each stage of the query plan. To perform this analysis, preface your SELECT, UPDATE, or DELETE statement with EXPLAIN ANALYSE, taking caution as this will execute the query.

Best Practices for Performance Improvement with EXPLAIN

To enhance performance while working with PostgreSQL’s EXPLAIN plan analyser, ensure that:

  • Use appropriate indexes: Look for sequential scans which could benefit from indexing.
  • Analyse join types: Refine your joins if necessary, selecting the most efficient join types.
  • Review row counts: Compare estimated row counts against actual counts to detect inconsistencies.
  • Monitor sorts and aggregations: Optimise memory usage for sorts and aggregations to prevent slowdowns.

Avoid running EXPLAIN ANALYSE on large update or delete operations in a production environment, as this might affect performance due to the actual execution of the query.

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