哪个更快/最好?SELECT *或SELECT columnn1, colum2, column3等

我听说在编写SQL命令时使用SELECT *通常是不好的做法，因为选择您特别需要的列会更有效。

如果我需要选择表中的每一列，我应该使用

SELECT * FROM TABLE

SELECT column1, colum2, column3, etc. FROM TABLE

在这种情况下，效率真的重要吗?如果你真的需要所有的数据，我认为SELECT *在内部会更优，但我这么说并没有真正理解数据库。

我很好奇在这种情况下最好的做法是什么。

更新:我可能应该指定，我真正想要执行SELECT *的唯一情况是，当我从一个表中选择数据时，我知道总是需要检索所有列，即使添加了新列。

然而，鉴于我所看到的反应，这似乎仍然是一个坏主意，由于我曾经考虑过的许多技术原因，SELECT *不应该被使用。

当前回答

select *是一件坏事，有四个主要原因:

The most significant practical reason is that it forces the user to magically know the order in which columns will be returned. It's better to be explicit, which also protects you against the table changing, which segues nicely into... If a column name you're using changes, it's better to catch it early (at the point of the SQL call) rather than when you're trying to use the column that no longer exists (or has had its name changed, etc.) Listing the column names makes your code far more self-documented, and so probably more readable. If you're transferring over a network (or even if you aren't), columns you don't need are just waste.

2010-06-04 07:03:28

其他回答

总之，至少在PostgreSQL中，选择所有带*和不带*的列的性能几乎是一样的。

在PostgreSQL中，我创建了包含10个id_x列和1000万行的测试表，如下所示:

CREATE TABLE test AS SELECT generate_series(1, 10000000) AS id_1,
                            generate_series(1, 10000000) AS id_2,
                            generate_series(1, 10000000) AS id_3,
                            generate_series(1, 10000000) AS id_4,
                            generate_series(1, 10000000) AS id_5,
                            generate_series(1, 10000000) AS id_6,
                            generate_series(1, 10000000) AS id_7,
                            generate_series(1, 10000000) AS id_8,
                            generate_series(1, 10000000) AS id_9,
                            generate_series(1, 10000000) AS id_10;

然后，我交替运行以下2个查询共20次。*每个查询总共运行10次:

SELECT * FROM test:

SELECT id_1, id_2, id_3, id_4, id_5, id_6, id_7, id_8, id_9, id_10 FROM test;

结果> <

	Select all columns with *	Select all columns without *
1st run	12.792 seconds	12.483 seconds
2nd run	12.803 seconds	12.608 seconds
3rd run	12.537 seconds	12.549 seconds
4th run	12.512 seconds	12.457 seconds
5th run	12.570 seconds	12.487 seconds
6th run	12.508 seconds	12.493 seconds
7th run	12.432 seconds	12.475 seconds
8th run	12.532 seconds	12.489 seconds
9th run	12.532 seconds	12.452 seconds
10th run	12.437 seconds	12.477 seconds
Average	12.565 seconds	12.497 seconds