運行EXPLAIN命令時，首先應(yīng)關(guān)注連接類型、索引使用情況、掃描行數(shù)和額外信息四項核心內(nèi)容。1. 連接類型（如eq_ref、const、ref高效，ALL低效）反映表連接效率；2. 索引相關(guān)字段（key、key_len、ref）顯示索引是否被正確使用；3. rows列估算查詢掃描的行數(shù)，數(shù)值大表示潛在性能問題；4. Extra信息（如Using filesort、Using temporary需避免，Using index為理想狀態(tài)）提供優(yōu)化方向。優(yōu)化策略包括：優(yōu)先使用高效連接類型、添加或調(diào)整索引以提升查詢效率、減少不必要的臨時表和文件排序操作。

When you run an EXPLAIN command in SQL (especially in systems like MySQL or PostgreSQL), it shows you how the database plans to execute your query. Understanding this output helps you identify performance bottlenecks and optimize your queries accordingly.

Here’s a breakdown of what to look for and which columns really matter when reading the output of EXPLAIN.

1. Type of Join (type or Join Type)

This tells you how tables are joined together, and it's one of the most important indicators of query efficiency.

In MySQL, the type column shows the join type. Here’s what the values typically mean:

• system: Very fast, only one row in the table.
• const: Fast, based on a primary key or unique index lookup.
• eq_ref: Usually used when joining with a primary key or unique index — very efficient.
• ref: Uses a non-unique index; multiple rows might be scanned.
• range: Scans a range of index values — better than scanning all rows.
• index: Full scan of the index tree (not ideal).
• ALL: Full table scan — usually bad unless the table is tiny.

In PostgreSQL, this info appears more indirectly through the "Index Scan" or "Seq Scan" entries.

What to do:

• Aim for eq_ref, const, or ref types.
• Avoid ALL if possible — that means no index is being used effectively.

2. Which Indexes Are Used (key, key_len, ref)

These columns tell you whether and how indexes are helping your query.

• key: The index actually used.
• key_len: How many bytes of the index are used — useful for composite indexes.
• ref: Which column or constant was used with the index.

If key is NULL, that means no index is being used — a red flag for performance issues.

Tips:

• Make sure frequently queried columns have indexes.
• Composite indexes can help but need to match the query structure.
• If key_len is shorter than expected, maybe not the whole index is used.

3. Number of Rows Examined (rows)

The rows column gives you an estimate of how many rows MySQL thinks it needs to examine to process this part of the query.

It's not always 100% accurate, but it's a good ballpark.

What to watch out for:

• Large numbers here suggest inefficiency.
• If you're filtering with a WHERE clause and rows is still high, maybe the index isn't selective enough or isn’t being used properly.

4. Extra Information (Extra)

This column often contains critical hints about what’s going on behind the scenes.

Common messages include:

• Using filesort: Sorting done outside the index — avoid if possible.
• Using temporary: A temporary table is created during processing — also something to optimize away.
• Using where: Filtering is happening after retrieving rows — could be okay depending on context.
• Using index: Good! The query is using a covering index.

Action items:

• Try to eliminate "filesort" and "temporary" by improving indexes or restructuring queries.
• "Using index" is your friend — it means less disk I/O.

Final Notes

Understanding EXPLAIN output doesn't require memorizing every field. Focus on a few key columns — type, key, rows, and Extra. These will give you most of what you need to start optimizing.

And remember, even small changes — like adding a missing index or reordering a JOIN — can make a big difference in performance.

基本上就這些。

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