索引布尔列与日期时间列的查询性能

Performance of query on indexed Boolean column vs Datetime column(索引布尔列与日期时间列的查询性能)

问题描述

如果索引设置在 datetime 类型列，而不是 boolean 类型列(并且在该列上完成查询)，查询性能是否有显着差异?

Is there a notable difference in query performance, if the index is set on datetime type column, instead of boolean type column (and querying is done on that column)?

在我目前的设计中，我有 2 列:

In my current design I got 2 columns:

• is_active TINYINT(1)，索引
• deleted_at日期时间

• is_active TINYINT(1), indexed
• deleted_at DATETIME

查询是SELECT * FROM table WHERE is_active = 1;

如果我在 deleted_at 列上建立索引，然后运行类似 SELECT * FROM table WHERE deleted_at is null; 之类的查询，会不会更慢?p>

Would it be any slower, if I made an index on deleted_at column instead, and ran queries like this SELECT * FROM table WHERE deleted_at is null; ?

推荐答案

这是一个包含 10M 行的 MariaDB (10.0.19) 基准测试(使用 序列插件):

Here is a MariaDB (10.0.19) benchmark with 10M rows (using the sequence plugin):

drop table if exists test;
CREATE TABLE `test` (
    `id` MEDIUMINT UNSIGNED NOT NULL,
    `is_active` TINYINT UNSIGNED NOT NULL,
    `deleted_at` TIMESTAMP NULL,
    PRIMARY KEY (`id`),
    INDEX `is_active` (`is_active`),
    INDEX `deleted_at` (`deleted_at`)
) ENGINE=InnoDB
    select seq id
        , rand(1)<0.5 as is_active
        , case when rand(1)<0.5 
            then null
            else '2017-03-18' - interval floor(rand(2)*1000000) second
        end as deleted_at
    from seq_1_to_10000000;

为了测量我在执行查询后使用 set profiling=1 并运行 show profile 的时间.从分析结果中，我取 Sending data 的值，因为其他所有内容都小于 1 毫秒.

To measure the time I use set profiling=1 and run show profile after executing a query. From the profiling result I take the value of Sending data since everything else is altogether less than one msec.

TINYINT 索引:

SELECT COUNT(*) FROM test WHERE is_active = 1;

运行时间:~ 738 毫秒

TIMESTAMP索引:

SELECT COUNT(*) FROM test WHERE  deleted_at is null;

运行时间:~ 748 毫秒

索引大小:

select database_name, table_name, index_name, stat_value*@@innodb_page_size
from mysql.innodb_index_stats 
where database_name = 'tmp'
  and table_name = 'test'
  and stat_name = 'size'

结果:

database_name | table_name | index_name | stat_value*@@innodb_page_size
-----------------------------------------------------------------------
tmp           | test       | PRIMARY    | 275513344 
tmp           | test       | deleted_at | 170639360 
tmp           | test       | is_active  |  97107968 

请注意，虽然 TIMESTAMP(4 字节)是 TYNYINT(1 字节)的 4 倍，但索引大小甚至没有两倍大.但是，如果它不适合内存，则索引大小可能会很大.因此，当我将 innodb_buffer_pool_size 从 1G 更改为 50M 时，我得到以下数字:

Note that while TIMESTAMP (4 Bytes) is 4 times as long as TYNYINT (1 Byte), the index size is not even twice as large. But the index size can be significant if it doesn't fit into memory. So when i change innodb_buffer_pool_size from 1G to 50M i get the following numbers:

• TINYINT:~ 960 毫秒
• 时间戳:~ 1500 毫秒

为了更直接地解决这个问题，我对数据做了一些更改:

To address the question more directly I did some changes to the data:

• 我使用 DATETIME 而不是 TIMESTAMP
• 由于条目通常很少被删除，我使用 rand(1)<0.99(1% 已删除)而不是 rand(1)<0.5(50% 已删除))
• 表格大小从 10M 变为 1M 行.
• SELECT COUNT(*) 改为 SELECT *

• Instead of TIMESTAMP I use DATETIME
• Since entries are usually rarely deleted I use rand(1)<0.99 (1% deleted) instead of rand(1)<0.5 (50% deleted)
• Table size changed from 10M to 1M rows.
• SELECT COUNT(*) changed to SELECT *

索引大小:

index_name | stat_value*@@innodb_page_size
------------------------------------------
PRIMARY    | 25739264
deleted_at | 12075008
is_active  | 11026432

由于 99% 的 deleted_at 值为 NULL，因此索引大小没有显着差异，尽管非空 DATETIME 需要 8 个字节 (MariaDB).

Since 99% of deleted_at values are NULL there is no significant difference in index size, though a non empty DATETIME requires 8 Bytes (MariaDB).

SELECT * FROM test WHERE is_active = 1;      -- 782 msec
SELECT * FROM test WHERE deleted_at is null; -- 829 msec

删除两个索引两个查询在大约 350 毫秒内执行.删除 is_active 列后，deleted_at is null 查询将在 280 毫秒内执行.

Dropping both indexes both queries execute in about 350 msec. And dropping the is_active column the deleted_at is null query executes in 280 msec.

请注意，这仍然不是一个现实的场景.您不太可能希望从 1M 行中选择 990K 行并将其交付给用户.表格中可能还会有更多列(可能包括文本).但它表明，您可能不需要 is_active 列(如果它不添加其他信息)，并且任何索引在最好的情况下对于选择未删除的条目都是无用的.

Note that this is still not a realistic scenario. You will unlikely want to select 990K rows out of 1M and deliver it to the user. You will probably also have more columns (maybe including text) in the table. But it shows, that you probably don't need the is_active column (if it doesn't add additional information), and that any index is in best case useless for selecting non deleted entries.

然而，索引对于选择已删除的行很有用:

However an index can be usefull to select deleted rows:

SELECT * FROM test WHERE is_active = 0;

使用索引在 10 毫秒内执行，不使用索引在 170 毫秒内执行.

Executes in 10 msec with index and in 170 msec without index.

SELECT * FROM test WHERE deleted_at is not null;

使用索引在 11 毫秒内执行，不使用索引在 167 毫秒内执行.

Executes in 11 msec with index and in 167 msec without index.

删除 is_active 列，它在 4 毫秒内执行索引，在 150 毫秒内执行索引.

Dropping the is_active column it executes in 4 msec with index and in 150 msec without index.

因此，如果这种情况以某种方式适合您的数据，那么结论将是:删除 is_active 列，如果您很少选择已删除，则不要在 deleted_at 列上创建索引条目.或者根据您的需要调整基准并做出自己的结论.

So if this scenario somehow fits your data the conclusion would be: Drop the is_active column and don't create an index on deleted_at column if you are rarely selecting deleted entries. Or adjust the benchmark to your needs and make your own conclusion.

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