1. Introduction
MySQL still maintained a strong database popularity growth trend in 2016. More and more customers build their applications on MySQL database, and even migrate from Oracle to MySQL. However, some customers encounter some problems in the process of using MySQL database, such as slow response time, full CPU, etc. Alibaba cloud RDS expert service team has helped cloud customers solve many urgent problems. Some common SQL problems in ApsaraDB expert diagnosis report are summarized as follows for your reference.
2. Foreword
MySQL still maintained a strong growth trend in database popularity in 2016. More and more customers build their applications on MySQL database, and even migrate from Oracle to MySQL. However, some customers encounter some problems in the process of using MySQL database, such as slow response time, full CPU, etc. Alibaba cloud RDS expert service team has helped cloud customers solve many urgent problems. Some common SQL problems in ApsaraDB expert diagnosis report are summarized as follows for your reference.
Common SQL error usage
1. LIMIT statement
Paging query is one of the most common scenarios, but it is also often the most prone to problems. For example, for the following simple statements, the general idea of DBA s is to type, name and create_ Add a composite index to the time field. In this way, conditional sorting can effectively use the index and improve the performance rapidly.
SELECT * FROM operation WHERE type = 'SQLStats' AND name = 'SlowLog' ORDER BY create_time LIMIT 1000, 10;
Well, maybe more than 90% of DBA s solve this problem, that's all. However, when the LIMIT clause becomes "LIMIT 1000000,10", programmers will still complain: why is it still slow when I only take 10 records?
You should know that the database does not know where the 1000000 record starts. Even if there is an index, it needs to be calculated from scratch. In most cases, programmers are lazy when this performance problem occurs. In scenarios such as front-end data browsing, page turning, or batch export of big data, the maximum value of the previous page can be used as a parameter as a query condition. The SQL is redesigned as follows:
SELECT * FROM operation WHERE type = 'SQLStats' AND name = 'SlowLog' AND create_time > '2017-03-16 14:00:00' ORDER BY create_time limit 10;
Under the new design, the query time is basically fixed and will not change with the increase of the amount of data.
2. Implicit conversion
Another common error in SQL statements is that the types of query variables and field definitions do not match. For example, the following statement:
mysql> explain extended SELECT * > FROM my_balance b > WHERE b.bpn = 14000000123 > AND b.isverified IS NULL ; mysql> show warnings; | Warning | 1739 | Cannot use ref access on index 'bpn' due to type or collation conversion on field 'bpn'
The field bpn is defined as varchar(20). MySQL's strategy is to convert strings into numbers and then compare them. Function on table field, index invalid.
The above situation may be the parameters automatically filled in by the application framework, rather than the programmer's original intention. Now there are many application frameworks, which are very complicated. It is convenient to use, but also be careful that it may dig holes for yourself.
3. Update and delete Association
Although MySQL 5 6 introduces the materialization feature, but it needs special attention that it is only aimed at the optimization of query statements. For update or deletion, you need to rewrite it manually as a JOIN.
For example, in the following UPDATE statement, MySQL actually executes a required subquery, and its execution time can be imagined.
UPDATE operation o SET status = 'applying' WHERE o.id IN (SELECT id FROM (SELECT o.id, o.status FROM operation o WHERE o.group = 123 AND o.status NOT IN ( 'done' ) ORDER BY o.parent, o.id LIMIT 1) t);
Execution plan:
+----+--------------------+-------+-------+---------------+---------+---------+-------+------+-----------------------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+--------------------+-------+-------+---------------+---------+---------+-------+------+-----------------------------------------------------+ | 1 | PRIMARY | o | index | | PRIMARY | 8 | | 24 | Using where; Using temporary | | 2 | DEPENDENT SUBQUERY | | | | | | | | Impossible WHERE noticed after reading const tables | | 3 | DERIVED | o | ref | idx_2,idx_5 | idx_5 | 8 | const | 1 | Using where; Using filesort | +----+--------------------+-------+-------+---------------+---------+---------+-------+------+-----------------------------------------------------+
After rewriting to JOIN, the selection mode of sub query changes from DEPENDENT SUBQUERY to DERIVED, and the execution speed is greatly accelerated, from 7 seconds to 2 milliseconds.
UPDATE operation o JOIN (SELECT o.id, o.status FROM operation o WHERE o.group = 123 AND o.status NOT IN ( 'done' ) ORDER BY o.parent, o.id LIMIT 1) t ON o.id = t.id SET status = 'applying'
The implementation plan is simplified to:
+----+-------------+-------+------+---------------+-------+---------+-------+------+-----------------------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-------+------+---------------+-------+---------+-------+------+-----------------------------------------------------+ | 1 | PRIMARY | | | | | | | | Impossible WHERE noticed after reading const tables | | 2 | DERIVED | o | ref | idx_2,idx_5 | idx_5 | 8 | const | 1 | Using where; Using filesort | +----+-------------+-------+------+---------------+-------+---------+-------+------+-----------------------------------------------------+
4. Mixed sorting
MySQL cannot use indexes for mixed sorting. However, in some scenarios, there are opportunities to use special methods to improve performance.
SELECT * FROM my_order o INNER JOIN my_appraise a ON a.orderid = o.id ORDER BY a.is_reply ASC, a.appraise_time DESC LIMIT 0, 20
The execution plan is displayed as a full table scan:
+----+-------------+-------+--------+-------------+---------+---------+---------------+---------+-+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra +----+-------------+-------+--------+-------------+---------+---------+---------------+---------+-+ | 1 | SIMPLE | a | ALL | idx_orderid | NULL | NULL | NULL | 1967647 | Using filesort | | 1 | SIMPLE | o | eq_ref | PRIMARY | PRIMARY | 122 | a.orderid | 1 | NULL | +----+-------------+-------+--------+---------+---------+---------+-----------------+---------+-+
Because is_reply has only two states: 0 and 1. After we rewrite it according to the following method, the execution time is reduced from 1.58 seconds to 2 milliseconds.
SELECT * FROM ((SELECT * FROM my_order o INNER JOIN my_appraise a ON a.orderid = o.id AND is_reply = 0 ORDER BY appraise_time DESC LIMIT 0, 20) UNION ALL (SELECT * FROM my_order o INNER JOIN my_appraise a ON a.orderid = o.id AND is_reply = 1 ORDER BY appraise_time DESC LIMIT 0, 20)) t ORDER BY is_reply ASC, appraisetime DESC LIMIT 20;
5. EXISTS statement
When MySQL treats the EXISTS clause, it still adopts the execution method of nested subquery. As shown in the following SQL statement:
SELECT * FROM my_neighbor n LEFT JOIN my_neighbor_apply sra ON n.id = sra.neighbor_id AND sra.user_id = 'xxx' WHERE n.topic_status < 4 AND EXISTS(SELECT 1 FROM message_info m WHERE n.id = m.neighbor_id AND m.inuser = 'xxx') AND n.topic_type <> 5
The implementation plan is:
+----+--------------------+-------+------+-----+------------------------------------------+---------+-------+---------+ -----+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+--------------------+-------+------+ -----+------------------------------------------+---------+-------+---------+ -----+ | 1 | PRIMARY | n | ALL | | NULL | NULL | NULL | 1086041 | Using where | | 1 | PRIMARY | sra | ref | | idx_user_id | 123 | const | 1 | Using where | | 2 | DEPENDENT SUBQUERY | m | ref | | idx_message_info | 122 | const | 1 | Using index condition; Using where | +----+--------------------+-------+------+ -----+------------------------------------------+---------+-------+---------+ -----+
Removing exists and changing it to join can avoid nested subqueries and reduce the execution time from 1.93 seconds to 1 millisecond.
SELECT * FROM my_neighbor n INNER JOIN message_info m ON n.id = m.neighbor_id AND m.inuser = 'xxx' LEFT JOIN my_neighbor_apply sra ON n.id = sra.neighbor_id AND sra.user_id = 'xxx' WHERE n.topic_status < 4 AND n.topic_type <> 5
New implementation plan:
+----+-------------+-------+--------+ -----+------------------------------------------+---------+ -----+------+ -----+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-------+--------+ -----+------------------------------------------+---------+ -----+------+ -----+ | 1 | SIMPLE | m | ref | | idx_message_info | 122 | const | 1 | Using index condition | | 1 | SIMPLE | n | eq_ref | | PRIMARY | 122 | ighbor_id | 1 | Using where | | 1 | SIMPLE | sra | ref | | idx_user_id | 123 | const | 1 | Using where | +----+-------------+-------+--------+ -----+------------------------------------------+---------+ -----+------+ -----+
6. Push under conditions
External query conditions cannot be pushed down to complex views or sub queries:
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Aggregate sub query;
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Subquery with LIMIT;
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UNION or UNION ALL subquery;
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Sub query in the output field;
As shown in the following statement, it can be seen from the execution plan that its conditions act after the aggregate subquery:
SELECT * FROM (SELECT target, Count(*) FROM operation GROUP BY target) t WHERE target = 'rm-xxxx'
+----+-------------+------------+-------+---------------+-------------+---------+-------+------+-------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+------------+-------+---------------+-------------+---------+-------+------+-------------+ | 1 | PRIMARY | <derived2> | ref | <auto_key0> | <auto_key0> | 514 | const | 2 | Using where | | 2 | DERIVED | operation | index | idx_4 | idx_4 | 519 | NULL | 20 | Using index | +----+-------------+------------+-------+---------------+-------------+---------+-------+------+-------------+
It is determined that the query criteria can be directly pushed down and rewritten as follows:
SELECT target, Count(*) FROM operation WHERE target = 'rm-xxxx' GROUP BY target
The execution plan becomes:
+----+-------------+-----------+------+---------------+-------+---------+-------+------+--------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-----------+------+---------------+-------+---------+-------+------+--------------------+ | 1 | SIMPLE | operation | ref | idx_4 | idx_4 | 514 | const | 1 | Using where; Using index | +----+-------------+-----------+------+---------------+-------+---------+-------+------+--------------------+
For a detailed explanation that MySQL external conditions cannot be pushed down, please refer to the previous article: pushing to materialized tables under MySQL performance optimization conditions
7. Reduce the scope in advance
Start with the initial SQL statement:
SELECT * FROM my_order o LEFT JOIN my_userinfo u ON o.uid = u.uid LEFT JOIN my_productinfo p ON o.pid = p.pid WHERE ( o.display = 0 ) AND ( o.ostaus = 1 ) ORDER BY o.selltime DESC LIMIT 0, 15
The original meaning of this SQL statement is: first make a series of left connections, and then sort the first 15 records. It can also be seen from the execution plan that the number of sorting records in the last step is estimated to be 900000 and the time consumption is 12 seconds.
+----+-------------+-------+--------+---------------+---------+---------+-----------------+--------+----------------------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-------+--------+---------------+---------+---------+-----------------+--------+----------------------------------------------------+ | 1 | SIMPLE | o | ALL | NULL | NULL | NULL | NULL | 909119 | Using where; Using temporary; Using filesort | | 1 | SIMPLE | u | eq_ref | PRIMARY | PRIMARY | 4 | o.uid | 1 | NULL | | 1 | SIMPLE | p | ALL | PRIMARY | NULL | NULL | NULL | 6 | Using where; Using join buffer (Block Nested Loop) | +----+-------------+-------+--------+---------------+---------+---------+-----------------+--------+----------------------------------------------------+
Since the last WHERE condition and sorting are for the leftmost main table, you can sort my first_ Order sorting reduces the amount of data in advance, and then makes a left connection. After SQL rewriting, the execution time is reduced to about 1 ms.
SELECT * FROM ( SELECT * FROM my_order o WHERE ( o.display = 0 ) AND ( o.ostaus = 1 ) ORDER BY o.selltime DESC LIMIT 0, 15 ) o LEFT JOIN my_userinfo u ON o.uid = u.uid LEFT JOIN my_productinfo p ON o.pid = p.pid ORDER BY o.selltime DESC limit 0, 15
Recheck the execution plan: after materializing the sub query (select_type=DERIVED), participate in the JOIN. Although the estimated row scan is still 900000, the actual execution time becomes very small after using the index and LIMIT clause.
+----+-------------+------------+--------+---------------+---------+---------+-------+--------+----------------------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+------------+--------+---------------+---------+---------+-------+--------+----------------------------------------------------+ | 1 | PRIMARY | <derived2> | ALL | NULL | NULL | NULL | NULL | 15 | Using temporary; Using filesort | | 1 | PRIMARY | u | eq_ref | PRIMARY | PRIMARY | 4 | o.uid | 1 | NULL | | 1 | PRIMARY | p | ALL | PRIMARY | NULL | NULL | NULL | 6 | Using where; Using join buffer (Block Nested Loop) | | 2 | DERIVED | o | index | NULL | idx_1 | 5 | NULL | 909112 | Using where | +----+-------------+------------+--------+---------------+---------+---------+-------+--------+----------------------------------------------------+
8. Push down intermediate result set
Let's look at the following example that has been preliminarily optimized (the main table in the left connection takes precedence over the query criteria):
SELECT a.*, c.allocated FROM ( SELECT resourceid FROM my_distribute d WHERE isdelete = 0 AND cusmanagercode = '1234567' ORDER BY salecode limit 20) a LEFT JOIN ( SELECT resourcesid， sum(ifnull(allocation, 0) * 12345) allocated FROM my_resources GROUP BY resourcesid) c ON a.resourceid = c.resourcesid
Are there any other problems with this statement? It is not difficult to see that sub query c is a full table aggregate query. When the number of tables is particularly large, the performance of the whole statement will be degraded.
In fact, for sub query c, the final result set of the left connection only cares about the data that can match the resourceid of the main table. Therefore, we can rewrite the following statement to reduce the execution time from 2 seconds to 2 milliseconds.
SELECT a.*, c.allocated FROM ( SELECT resourceid FROM my_distribute d WHERE isdelete = 0 AND cusmanagercode = '1234567' ORDER BY salecode limit 20) a LEFT JOIN ( SELECT resourcesid， sum(ifnull(allocation, 0) * 12345) allocated FROM my_resources r, ( SELECT resourceid FROM my_distribute d WHERE isdelete = 0 AND cusmanagercode = '1234567' ORDER BY salecode limit 20) a WHERE r.resourcesid = a.resourcesid GROUP BY resourcesid) c ON a.resourceid = c.resourcesid
But the subquery a appears many times in our SQL statement. This writing method not only has additional overhead, but also makes the whole sentence more complicated. Rewrite again using the WITH statement:
WITH a AS ( SELECT resourceid FROM my_distribute d WHERE isdelete = 0 AND cusmanagercode = '1234567' ORDER BY salecode limit 20) SELECT a.*, c.allocated FROM a LEFT JOIN ( SELECT resourcesid， sum(ifnull(allocation, 0) * 12345) allocated FROM my_resources r, a WHERE r.resourcesid = a.resourcesid GROUP BY resourcesid) c ON a.resourceid = c.resourcesid
AliSQL is about to launch WITH syntax. Please look forward to it.
3. Summary
The database compiler generates an execution plan, which determines the actual execution mode of SQL. However, the compiler only serves the best, and the compilers of all databases are not perfect. Most of the scenarios mentioned above also have performance problems in other databases. Only by understanding the characteristics of database compiler can we avoid its shortcomings and write high-performance SQL statements.
When programmers design data models and write SQL statements, they should bring the idea or consciousness of algorithms into them. Develop the habit of using WITH statements when writing complex SQL statements. Simple and clear SQL statements can also reduce the burden of the database.
If you encounter difficulties in using cloud database (not limited to SQL problems), you should always seek the help of alicloud original expert services.
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