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Home Database Mysql Tutorial Understanding the MySQL EXPLAIN statement for query analysis

Understanding the MySQL EXPLAIN statement for query analysis

Jul 06, 2025 am 02:51 AM
mysql explain

To troubleshoot the reasons why MySQL query is slow, it is key to use the EXPLAIN statement to analyze the execution plan. 1. First, check the type column, and the priority should be system, const, eq_ref and other efficient connection types. If ALL appears, it needs to be optimized, such as adding indexes or refactoring queries; 2. Secondly, focus on the Extra column. If "Using filesort" or "Using temporary" appears, it means that there is additional overhead, and it may be necessary to index the sorting or grouping fields; 3. Check the rows column to evaluate the number of scan rows. Too high values ??may lead to increased I/O and time. Scans can be reduced by optimizing indexes or adjusting the JOIN order; 4. Finally, in MySQL 8.0, EXPLAIN ANALYZE can be used to obtain actual execution details to help more accurately identify performance bottlenecks. Mastering these steps can help quickly locate and optimize slow query problems.

Understanding the MySQL EXPLAIN statement for query analysis

When you're trying to figure out why a MySQL query is slow, the EXPLAIN statement is one of your best tools. It shows how MySQL plans to execute a query and helps identify potential bottlenecks—like missing indexes or inefficient joins—before they become real problems.

Understanding the MySQL EXPLAIN statement for query analysis

Here are some key things to look for when using EXPLAIN .

Understanding the MySQL EXPLAIN statement for query analysis

1. Check the type column – it tells you about the join type

The type column in the output gives you an idea of ??how efficient a table access is.
You want this to be as high up the efficiency ladder as possible.

Common values ??(from best to worse):

Understanding the MySQL EXPLAIN statement for query analysis
  • system or const : Very fast, usually when querying by a primary key.
  • eq_ref : Good for joins on unique keys.
  • ref : OK, used when matching non-unique keys.
  • range : Still acceptable, used with IN , BETWEEN , or indexed conditions.
  • index : Full index scan, not great but better than scanning all rows.
  • ALL : Full table scan, often a red flag.

If you see ALL here, especially on large tables, that's a good place to start optimizing—maybe add an index or rethink the query structure.


2. Look at the Extra column – it reveals hidden costs

This column often contains important hints like whether filesors or temporary tables are being used.

Watch for:

  • "Using filesort" : MySQL needs to do an extra sort pass, which can be slow.
  • "Using temporary" : A temporary table is created, often due to complex grouping or joins.
  • "Using where" : Indicates filtering is happening after reading rows.
  • "Impossible WHERE" : Might suggest a typo or logic error in the query.

For example, if you're ordering by a non-indexed column and filtering with a WHERE , you might see both "Using filesort" and "Using where" . That combination is a hint that adding an index could help.


3. Pay attention to rows – it estimates the workload

MySQL's optimizer gives an estimate of how many rows it thinks it will need to exam. This number isn't always 100% accurate, but it gives you a ballpark idea of ??performance impact.

A high number in the rows column means:

  • More disk I/O
  • Longer execution time
  • Potential for memory pressure

If you see tens of thousands or more, especially early in the query plan, consider whether you can reduce that number by:

  • Adding better indexes
  • Filtering earlier
  • Restructuring joins

Also note that sometimes the rows count looks low, but the query is still slow. In those cases, double-check the Extra column—it might explain why the actual work is heavier than expected.


4. Use EXPLAIN with different SQL modes (like ANALYZE)

In newer versions of MySQL (8.0 ), you can use EXPLAIN ANALYZE , which runs the query and shows actual execution details. This gives you more accurate insight into what really happens—not just what the optimizer thinks will happen.

It shows:

  • How long each step actually took
  • How many rows were read
  • Whether buffers helped or hurt

This is especially useful when the estimated rows from regular EXPLAIN doesn't match reality.


Understanding EXPLAIN takes a bit of practice, but once you get the hang of reading the output, it becomes second nature. You'll start spotting issues quickly and making smarter decisions about indexing and query design.

Basically that's it.

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