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Table of Contents
1. Key fields in EXPLAIN output
2. How to determine whether a query is efficient?
3. Common problems and optimization suggestions in practical applications
No indexing is used
Low efficiency of multi-table association
Poor sorting and grouping efficiency
Home Database Mysql Tutorial Understanding MySQL Explain Plan Output for Query Analysis

Understanding MySQL Explain Plan Output for Query Analysis

Aug 04, 2025 pm 12:22 PM

To analyze the performance of MySQL query, you can determine whether the query is efficient by using the execution plan output by the EXPLAIN command. 1. Focus on the type field to avoid ALL type full table scanning, ideally ref, eq_ref or range; 2. Check the key field to confirm whether the appropriate index is used. If it is not used, it is necessary to optimize the SQL or index design; 3. The smaller the value of the rows field, the better, and the estimated number of scanned rows can improve performance; 4. When Using filesort or Using temporary appears in the Extra field, it should be optimized, such as adding indexes or adjusting queries. Mastering these core fields and combining them with actual scenario analysis can effectively discover and solve query performance bottlenecks.

Understanding MySQL Explain Plan Output for Query Analysis

When you analyze MySQL query performance, the execution plan of the EXPLAIN command output is a very practical tool. It can tell you how the query is performed, whether there are any indexes used, whether there are full table scanning and other issues. Understanding this information can help you optimize slow queries and improve the overall performance of the database.

Understanding MySQL Explain Plan Output for Query Analysis

1. Key fields in EXPLAIN output

When you run EXPLAIN SELECT ... statement, you will see several columns of output, the most important of which are the following:

  • id : indicates the order of operations in the query. The same id represents operations at the same level, while different ids are executed from large to small according to the number.
  • select_type : Indicates whether this query is a simple query, a subquery, or a joint query, etc.
  • table : Which table is involved in this current operation?
  • type : connection type, which is one of the key indicators for judging query efficiency. Common ones include ALL (full table scan), index (traversal index), range (range lookup), ref (non-unique index matching), etc.
  • possible_keys and key : The index that may be used and the index that is actually used.
  • rows : MySQL estimates the number of rows to be scanned, the smaller the value, the better.
  • Extra : Extra information, such as whether file sorting, temporary tables, etc., if Using filesort or Using temporary , it usually means that further optimization can be achieved.

2. How to determine whether a query is efficient?

When looking at the output of EXPLAIN , there are several core points that can help you determine whether the query is efficient:

Understanding MySQL Explain Plan Output for Query Analysis
  • type field : Try to avoid ALL types, which means MySQL is doing full table scanning. Ideally you should see types like ref , eq_ref or range .
  • key field : Make sure the query uses the appropriate index. If the index is not used, check the SQL writing or index design even if there is a value in possible_keys .
  • rows field : The smaller this number, the better. If your table has millions of data but rows are displayed as thousands or even tens of thousands, then this query is worth optimizing.
  • Extra field : If Using filesort or Using temporary appears, it means that MySQL has done additional processing on memory or disk, affecting performance. It can be avoided by adding appropriate indexes or tuning SQL.

For example, if you see a combination like this:

 type: ALL, key: NULL, rows: 1000000, Extra: Using where; Using filesort

It is basically confirmed that this is a slow query that needs to be optimized.

Understanding MySQL Explain Plan Output for Query Analysis

3. Common problems and optimization suggestions in practical applications

No indexing is used

  • The query condition field has no index.
  • Using functions or expressions causes index failure, such as WHERE YEAR(create_time) = 2023 .
  • OR is used in the query, and some conditions are not supported by index.

? Optimization method:

  • Add an index to commonly used query fields.
  • Avoid calculating or function operations on fields in WHERE conditions.
  • Consider splitting OR queries, or using alternative methods such as IN uniformly.

Low efficiency of multi-table association

  • When multi-table JOIN, driver table selection is inappropriate.
  • The associated field has no index.
  • Too many fields are returned, which affects performance.

? Optimization method:

  • Make sure that the fields that are JOIN have indexes.
  • Minimize the number of tables in JOIN.
  • Only take the required fields and avoid SELECT * .

Poor sorting and grouping efficiency

  • No index sorting is used, resulting in Using filesort .
  • Grouped fields have no indexes or have too many duplicate values.

? Optimization method:

  • Creates an appropriate composite index for the ORDER BY and GROUP BY fields.
  • Try to use overwrite indexes to avoid back-table queries.

Basically that's it. As long as you master the core fields of EXPLAIN output and analyze them in combination with actual SQL scenarios, you can find out the bottlenecks of most query performance. What is not complicated but easy to ignore is that many times the problem is not the SQL writing itself, but the index design is unreasonable or the statistical information is inaccurate, which is also worth checking regularly.

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