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 程式師世界 >> 數據庫知識 >> MYSQL數據庫 >> 關於MYSQL數據庫 >> MySQL中分頁優化的實例詳解

MySQL中分頁優化的實例詳解

編輯:關於MYSQL數據庫

通常,我們會采用ORDER BY LIMIT start, offset 的方式來進行分頁查詢。例如下面這個SQL:

SELECT * FROM `t1` WHERE ftype=1 ORDER BY id DESC LIMIT 100, 10;

或者像下面這個不帶任何條件的分頁SQL:

SELECT * FROM `t1` ORDER BY id DESC LIMIT 100, 10;

一般而言,分頁SQL的耗時隨著 start 值的增加而急劇增加,我們來看下面這2個不同起始值的分頁SQL執行耗時:

[email protected]> SELECT * FROM `t1` WHERE ftype=1 ORDER BY id DESC LIMIT 500, 10;
…

10 rows in set (0.05 sec)


[email protected]> SELECT * FROM `t1` WHERE ftype=6 ORDER BY id DESC LIMIT 935500, 10;
…

10 rows in set (2.39 sec)

可以看到,隨著分頁數量的增加,SQL查詢耗時也有數十倍增加,顯然不科學。今天我們就來分析下,如何能優化這個分頁方案。 一般滴,想要優化分頁的終極方案就是:沒有分頁,哈哈哈~~~,不要說我講廢話,確實如此,可以把分頁算法交給Sphinx、Lucence等第三方解決方案,沒必要讓MySQL來做它不擅長的事情。 當然了,有小伙伴說,用第三方太麻煩了,我們就想用MySQL來做這個分頁,咋辦呢?莫急,且待我們慢慢分析,先看下表DDL、數據量、查詢SQL的執行計劃等信息:

[email protected]> SHOW CREATE TABLE `t1`;
CREATE TABLE `t1` (
 `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
...
 `ftype` tinyint(3) unsigned NOT NULL,
...
 PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

[email protected]> select count(*) from t1;
+----------+
| count(*) |
+----------+
| 994584 |
+----------+

[email protected]> EXPLAIN SELECT * FROM `t1` WHERE ftype=1 ORDER BY id DESC LIMIT 500, 10\G
*************************** 1. row ***************************
 id: 1
 select_type: SIMPLE
 table: t1
 type: index
possible_keys: NULL
 key: PRIMARY
 key_len: 4
 ref: NULL
 rows: 510
 Extra: Using where

[email protected]> EXPLAIN SELECT * FROM `t1` WHERE ftype=1 ORDER BY id DESC LIMIT 935500, 10\G
*************************** 1. row ***************************
 id: 1
 select_type: SIMPLE
 table: t1
 type: index
possible_keys: NULL
 key: PRIMARY
 key_len: 4
 ref: NULL
 rows: 935510
 Extra: Using where

可以看到,雖然通過主鍵索引進行掃描了,但第二個SQL需要掃描的記錄數太大了,而且需要先掃描約935510條記錄,然後再根據排序結果取10條記錄,這肯定是非常慢了。 針對這種情況,我們的優化思路就比較清晰了,有兩點:

1、盡可能從索引中直接獲取數據,避免或減少直接掃描行數據的頻率
2、盡可能減少掃描的記錄數,也就是先確定起始的范圍,再往後取N條記錄即可

據此,我們有兩種相應的改寫方法:子查詢、表連接,即下面這樣的:

#采用子查詢的方式優化,在子查詢裡先從索引獲取到最大id,然後倒序排,再取10行結果集
#注意這裡采用了2次倒序排,因此在取LIMIT的start值時,比原來的值加了10,即935510,否則結果將和原來的不一致

[email protected]> EXPLAIN SELECT * FROM (SELECT * FROM `t1` WHERE id > ( SELECT id FROM `t1` WHERE ftype=1 ORDER BY id DESC LIMIT 935510, 1) LIMIT 10) t ORDER BY id DESC\G
*************************** 1. row ***************************
 id: 1
 select_type: PRIMARY
 table: <derived2>
 type: ALL
possible_keys: NULL
 key: NULL
 key_len: NULL
 ref: NULL
 rows: 10
 Extra: Using filesort
*************************** 2. row ***************************
 id: 2
 select_type: DERIVED
 table: t1
 type: ALL
possible_keys: PRIMARY
 key: NULL
 key_len: NULL
 ref: NULL
 rows: 973192
 Extra: Using where
*************************** 3. row ***************************
 id: 3
 select_type: SUBQUERY
 table: t1
 type: index
possible_keys: NULL
 key: PRIMARY
 key_len: 4
 ref: NULL
 rows: 935511
 Extra: Using where
#采用INNER JOIN優化,JOIN子句裡也優先從索引獲取ID列表,然後直接關聯查詢獲得最終結果,這裡不需要加10
[email protected]> EXPLAIN SELECT * FROM `t1` INNER JOIN ( SELECT id FROM `t1` WHERE ftype=1 ORDER BY id DESC LIMIT 935500,10) t2 USING (id)\G
*************************** 1. row ***************************
 id: 1
 select_type: PRIMARY
 table: <derived2>
 type: ALL
possible_keys: NULL
 key: NULL
 key_len: NULL
 ref: NULL
 rows: 935510
 Extra: NULL
*************************** 2. row ***************************
 id: 1
 select_type: PRIMARY
 table: t1
 type: eq_ref
possible_keys: PRIMARY
 key: PRIMARY
 key_len: 4
 ref: t2.id
 rows: 1
 Extra: NULL
*************************** 3. row ***************************
 id: 2
 select_type: DERIVED
 table: t1
 type: index
possible_keys: NULL
 key: PRIMARY
 key_len: 4
 ref: NULL
 rows: 973192
 Extra: Using where

然後我們來對比下這2個優化後的新SQL執行時間:

[email protected]> SELECT * FROM (SELECT * FROM `t1` WHERE id > ( SELECT id FROM `t1` WHERE ftype=1 ORDER BY id DESC LIMIT 935510, 1) LIMIT 10) T ORDER BY id DESC;
...
rows in set (1.86 sec)
#采用子查詢優化,從profiling的結果來看,相比原來的那個SQL快了:28.2%

[email protected]> SELECT * FROM `t1` INNER JOIN ( SELECT id FROM `t1` WHERE ftype=1 ORDER BY id DESC LIMIT 935500,10) t2 USING (id);
...
10 rows in set (1.83 sec)
#采用INNER JOIN優化,從profiling的結果來看,相比原來的那個SQL快了:30.8%

我們再來看一個不帶過濾條件的分頁SQL對比:

#原始SQL
[email protected]> EXPLAIN SELECT * FROM `t1` ORDER BY id DESC LIMIT 935500, 10\G
*************************** 1. row ***************************
      id: 1
 select_type: SIMPLE
    table: t1
     type: index
possible_keys: NULL
     key: PRIMARY
   key_len: 4
     ref: NULL
     rows: 935510
    Extra: NULL

[email protected]> SELECT * FROM `t1` ORDER BY id DESC LIMIT 935500, 10;
...
10 rows in set (2.22 sec)

#采用子查詢優化
[email protected]> EXPLAIN SELECT * FROM (SELECT * FROM `t1` WHERE id > ( SELECT id FROM `t1` ORDER BY id DESC LIMIT 935510, 1) LIMIT 10) t ORDER BY id DESC;
*************************** 1. row ***************************
      id: 1
 select_type: PRIMARY
    table: <derived2>
     type: ALL
possible_keys: NULL
     key: NULL
   key_len: NULL
     ref: NULL
     rows: 10
    Extra: Using filesort
*************************** 2. row ***************************
      id: 2
 select_type: DERIVED
    table: t1
     type: ALL
possible_keys: PRIMARY
     key: NULL
   key_len: NULL
     ref: NULL
     rows: 973192
    Extra: Using where
*************************** 3. row ***************************
      id: 3
 select_type: SUBQUERY
    table: t1
     type: index
possible_keys: NULL
     key: PRIMARY
   key_len: 4
     ref: NULL
     rows: 935511
    Extra: Using index

[email protected]> SELECT * FROM (SELECT * FROM `t1` WHERE id > ( SELECT id FROM `t1` ORDER BY id DESC LIMIT 935510, 1) LIMIT 10) t ORDER BY id DESC;
…
10 rows in set (2.01 sec)
#采用子查詢優化,從profiling的結果來看,相比原來的那個SQL快了:10.6%


#采用INNER JOIN優化
[email protected]> EXPLAIN SELECT * FROM `t1` INNER JOIN ( SELECT id FROM `t1`ORDER BY id DESC LIMIT 935500,10) t2 USING (id)\G
*************************** 1. row ***************************
      id: 1
 select_type: PRIMARY
    table: 
     type: ALL
possible_keys: NULL
     key: NULL
   key_len: NULL
     ref: NULL
     rows: 935510
    Extra: NULL
*************************** 2. row ***************************
      id: 1
 select_type: PRIMARY
    table: t1
     type: eq_ref
possible_keys: PRIMARY
     key: PRIMARY
   key_len: 4
     ref: t1.id
     rows: 1
    Extra: NULL
*************************** 3. row ***************************
      id: 2
 select_type: DERIVED
    table: t1
     type: index
possible_keys: NULL
     key: PRIMARY
   key_len: 4
     ref: NULL
     rows: 973192
    Extra: Using index

[email protected]> SELECT * FROM `t1` INNER JOIN ( SELECT id FROM `t1`ORDER BY id DESC LIMIT 935500,10) t2 USING (id);
…
10 rows in set (1.70 sec)
#采用INNER JOIN優化,從profiling的結果來看,相比原來的那個SQL快了:30.2%

至此,我們看到采用子查詢或者INNER JOIN進行優化後,都有大幅度的提升,這個方法也同樣適用於較小的分頁,雖然LIMIT開始的 start 位置小了很多,SQL執行時間也快了很多,但采用這種方法後,帶WHERE條件的分頁分別能提高查詢效率:24.9%、156.5%,不帶WHERE條件的分頁分別提高查詢效率:554.5%、11.7%,各位可以自行進行測試驗證。單從提升比例說,還是挺可觀的,確保這些優化方法可以適用於各種分頁模式,就可以從一開始就是用。 我們來看下各種場景相應的提升比例是多少:

201558115710029.jpg (649×215)

結論:這樣看就和明顯了,尤其是針對大分頁的情況,因此我們優先推薦使用INNER JOIN方式優化分頁算法。

上述每次測試都重啟mysqld實例,並且加了SQL_NO_CACHE,以保證每次都是直接數據文件或索引文件中讀取。如果數據經過預熱後,查詢效率會一定程度提升,但但上述相應的效率提升比例還是基本一致的。

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