Mysql 查询优化

Question

我有一个 table，里面有将近 700 万行。这是 table 结构

 `CREATE TABLE `ERS_SALES_TRANSACTIONS` (
  `saleId` int(12) NOT NULL AUTO_INCREMENT,
  `ERS_COMPANY_CODE` int(3) DEFAULT NULL,
  `SALE_SECTION` varchar(128) DEFAULT NULL,
  `SALE_DATE` date DEFAULT NULL,
  `SALE_STOCKAGE_EXACT` int(4) DEFAULT NULL,
  `SALE_NET_AMOUNT` decimal(11,2) DEFAULT NULL, 
  `SALE_ABSOLUTE_CDATE` date DEFAULT NULL,
  PRIMARY KEY (`saleId`),
  KEY `index_location` (`ERS_COMPANY_CODE`),
  KEY `idx-erscode-salesec` (`SALE_SECTION`,`ERS_COMPANY_CODE`) USING BTREE,
  KEY `idx-saledate-section` (`SALE_DATE`,`SALE_SECTION`) USING BTREE
  KEY `idx_quick_sales_transactions` (`ERS_COMPANY_CODE`,`SALE_SECTION`,`SALE_DATE`,`SALE_STOCKAGE_EXACT`,`SALE_NET_AMOUNT`)
) ENGINE=InnoDB;

此查询执行时间超过 7 秒，有什么方法可以加快速度吗？

SELECT 
   A.SALE_SECTION,  
   SUM(IF(A.SALE_DATE BETWEEN '2016-01-16' AND '2016-04-30'
          AND A.SALE_STOCKAGE_EXACT BETWEEN 0 AND 90, A.SALE_NET_AMOUNT, 0)) AS fs1_pd1_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-01-16' AND '2016-04-30'
          AND A.SALE_STOCKAGE_EXACT BETWEEN 91 AND 180, A.SALE_NET_AMOUNT, 0)) AS fs2_pd1_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-01-16' AND '2016-04-30'
          AND A.SALE_STOCKAGE_EXACT BETWEEN 181 AND 365, A.SALE_NET_AMOUNT, 0)) AS os1_pd1_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-01-16' AND '2016-04-30'
          AND A.SALE_STOCKAGE_EXACT BETWEEN 366 AND 9999, A.SALE_NET_AMOUNT, 0)) AS os2_pd1_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-01-16' AND '2016-04-30', A.SALE_NET_AMOUNT, 0)) AS TOTAL_PD1_SALE,
   SUM(IF(A.SALE_DATE BETWEEN '2016-04-01' AND '2016-04-30'
          AND A.SALE_STOCKAGE_EXACT BETWEEN 0 AND 90, A.SALE_NET_AMOUNT, 0)) AS fs1_pd2_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-04-01' AND '2016-04-30'
          AND A.SALE_STOCKAGE_EXACT BETWEEN 91 AND 180, A.SALE_NET_AMOUNT, 0)) AS fs2_pd2_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-04-01' AND '2016-04-30'
          AND A.SALE_STOCKAGE_EXACT BETWEEN 181 AND 365, A.SALE_NET_AMOUNT, 0)) AS os1_pd2_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-04-01' AND '2016-04-30'
          AND A.SALE_STOCKAGE_EXACT BETWEEN 366 AND 9999, A.SALE_NET_AMOUNT, 0)) AS os2_pd2_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-04-01' AND '2016-04-30', A.SALE_NET_AMOUNT, 0)) AS TOTAL_PD2_SALE,
   SUM(IF(A.SALE_DATE BETWEEN '2016-05-01' AND '2016-05-31'
          AND A.SALE_ABSOLUTE_CDATE BETWEEN '2016-03-01' AND '2016-05-31', A.SALE_NET_AMOUNT, 0)) AS fs1_achived_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-05-01' AND '2016-05-31'
          AND A.SALE_ABSOLUTE_CDATE BETWEEN '2015-12-01' AND '2016-02-29', A.SALE_NET_AMOUNT, 0)) AS fs2_achived_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-05-01' AND '2016-05-31'
          AND A.SALE_ABSOLUTE_CDATE BETWEEN '2015-06-01' AND '2015-11-30', A.SALE_NET_AMOUNT, 0)) AS os1_achived_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-05-01' AND '2016-05-31'
          AND A.SALE_ABSOLUTE_CDATE BETWEEN '2006-12-26' AND '2015-05-31', A.SALE_NET_AMOUNT, 0)) AS os2_achived_sale,
   SUM(IF(A.SALE_DATE BETWEEN '2016-05-01' AND '2016-05-31', A.SALE_NET_AMOUNT, 0)) AS Total_ACHIVED_SALE
   FROM ERS_SALES_TRANSACTIONS A WHERE A.ERS_COMPANY_CODE = 48 GROUP BY A.SALE_SECTION

这里是解释查询

{
"data":
[
    {
        "id": 1,
        "select_type": "SIMPLE",
        "table": "A",
        "type": "ref",
        "possible_keys": "index_location,idx-erscode-salesec,idx-saledate-section",
        "key": "index_location",
        "key_len": "5",
        "ref": "const",
        "rows": 1411944,
        "Extra": "Using where; Using temporary; Using filesort"
    }
]
}

添加复合索引后，时间减少到4.03秒。这是计划

{
"data":
[
    {
        "id": 1,
        "select_type": "SIMPLE",
        "table": "A",
        "type": "ref",
        "possible_keys": "index_location,idx-erscode-salesec,idx-saledate-section,idx_quick_sales_transactions",
        "key_len": "5",
        "key": "idx_quick_sales_transactions",
        "ref": "const",
        "rows": 1306058,
        "Extra": "Using where"
    }
]

}

Answer 1

SELECT 
   sales.SALE_SECTION,
   SUM( fs1_pd1.SALE_NET_AMOUNT ) AS fs1_pd1_sale,
   SUM( fs2_pd1.SALE_NET_AMOUNT ) AS fs2_pd1_sale,
...
FROM ERS_SALES_TRANSACTIONS sales

LEFT OUTER JOIN ERS_SALES_TRANSACTIONS fs1_pd1 ON sales.ERS_COMPANY_CODE = fs1_pd1.ERS_COMPANY_CODE AND sales.SALE_SECTION = fs1_pd1.SALE_SECTION
  AND fs1_pd1.SALE_DATE BETWEEN '2016-01-16' AND '2016-04-30'
  AND fs1_pd1.SALE_STOCKAGE_EXACT BETWEEN 0 AND 90

LEFT OUTER JOIN ERS_SALES_TRANSACTIONS fs2_pd1 ON sales.ERS_COMPANY_CODE = fs2_pd1.ERS_COMPANY_CODE AND sales.SALE_SECTION = fs2_pd1.SALE_SECTION
  AND fs2_pd1.SALE_DATE BETWEEN '2016-01-16' AND '2016-04-30'
  AND fs2_pd1.SALE_STOCKAGE_EXACT BETWEEN 91 AND 180
...
   WHERE sales.ERS_COMPANY_CODE = 48
   GROUP BY sales.SALE_SECTION

这样一来，优化器就可以为查询使用多个索引。

不过，我建议首先尝试@Thorsten Kettner 推荐的复合索引，因为这可能会产生相同的效果，而且复杂性要低得多。

Answer 2

我不同意 Jimmy B 的观点。我认为您的查询看起来很完美。

根据公司 48 的记录数量，应按顺序读取完整的 table（当数量很多时，例如占所有 table 记录的 50%）或 ERS_COMPANY_CODE 应该使用（当它不是那么多时，比如说，只有所有记录的 1%）。

由于DBMS决定在ERS_COMPANY_CODE上使用索引，所以后者应该是这样。

您可以尝试通过创建组合索引来进一步加快查询速度。至少 (ERS_COMPANY_CODE , SALE_SECTION)，以便 GROUP BY 更快。最好甚至添加所有字段，这样可以从索引中收集所有数据，并且不必再访问 table 本身。

CREATE INDEX idx_quick_sales_transactions ON ERS_SALES_TRANSACTIONS
  (ERS_COMPANY_CODE, SALE_SECTION, SALE_DATE, SALE_STOCKAGE_EXACT, SALE_NET_AMOUNT);

Answer 3

不知道有没有办法加快速度。但是，您可以尝试使用索引。我会在 ERS_SALES_TRANSACTIONS(ERS_COMPANY_CODE, SALE_SECTION, SALE_DATE, SALE_NET_AMOUNT).

上推荐一个

这是查询的覆盖索引，意味着用于查询的所有列都在索引中 -- 因此数据库引擎不需要访问原始数据页。

但是，性能仍然取决于与特定公司代码匹配的行数。并且，特别是用于聚合的文件排序的性能。

Mysql 查询优化

Mysql Query Optimization

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