要排查MySQL查詢慢的原因，使用EXPLAIN語(yǔ)句分析執(zhí)行計(jì)劃是關(guān)鍵。 1. 首先查看type列，優(yōu)先應(yīng)為system、const、eq_ref等高效連接類型，若出現(xiàn)ALL則需優(yōu)化，如添加索引或重構(gòu)查詢；2. 其次關(guān)注Extra列，若出現(xiàn)“Using filesort”或“Using temporary”表示存在額外開(kāi)銷，可能需要對(duì)排序或分組字段加索引；3. 查看rows列評(píng)估掃描行數(shù)，數(shù)值過(guò)高可能導(dǎo)致I/O和時(shí)間增加，可通過(guò)優(yōu)化索引或調(diào)整JOIN順序減少掃描；4. 最後在MySQL 8.0 中可使用EXPLAIN ANALYZE獲取實(shí)際執(zhí)行詳情，幫助更準(zhǔn)確地識(shí)別性能瓶頸。掌握這些步驟有助於快速定位並優(yōu)化慢查詢問(wèn)題。

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.

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

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):

• 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 filesorts 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 examine. 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.

基本上就這些。

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