1 background
As a long-term in the first-line belt group Owner And the old trial Officer , Our interview targets are basically front-line developers . Start from the technology stack of the server , The scope of the problem is mainly around the development language （Java、Go） And so on 、 Database technology 、 Cache technology 、 Message middleware 、 The use of the microservice framework .
MySQL As the mainstream data storage configuration of large factories , Of course, the most frequently asked , The key area is the use and optimization of indexes .
2 Optimization steps of index
2.1 The principle of efficient indexing
- Correctly understand and calculate the discrimination of index field , Here are the calculation rules , High resolution index , You can quickly locate data , The distinction is too low , Can't use index effectively , You may need to scan a large number of data pages , It's no different than not using an index . When we create an index , Try to select highly differentiated columns as indexes .
selecttivity = count(distinct c_name)/count(*)
- Correctly understand and calculate the field length of prefix index , Here are the rules of judgment , The appropriate length should ensure high discrimination and the most appropriate index storage capacity , Only when you are at your best , It is the index that ensures high efficiency . The length of the next cloth is 6 Is at its best .
select count(distinct left(c_name , calcul_len)) / count(*) from t_name;
mysql> SELECT
count(DISTINCT LEFT(empname, 3)) / count(*) AS sel3,
count(DISTINCT LEFT(empname, 4)) / count(*) AS sel4,
count(DISTINCT LEFT(empname, 5)) / count(*) AS sel5,
count(DISTINCT LEFT(empname, 6)) / count(*) AS sel6,
count(DISTINCT LEFT(empname, 7)) / count(*) AS sel7
FROM
emp;
+--------+--------+--------+--------+--------+
| sel3 | sel4 | sel5 | sel6 | sel7 |
+--------+--------+--------+--------+--------+
| 0.0012 | 0.0076 | 0.0400 | 0.1713 | 0.1713 |
+--------+--------+--------+--------+--------+
1 row in set
- Union index pays attention to the leftmost matching principle ： Match from left to right ,MySQL The index is matched to the right until a range query is encountered (>、<、between、like) And then stop matching . Such as depno=1 and empname>'' and job=1 , If set up (depno,empname,job) Index of order ,empname and job There is no index .
- Strategy on demand , When looking up records , Don't use it as soon as you come up *, Just take the data you need , If possible, use only index coverage , It can reduce the operation of returning table , Improve efficiency .
- Judge whether to use union index correctly （ Strategy chapter The section on the use of joint indexes describes the judgment rules ）, We can also further analyze the index push down （IPC）, Reduce the return operation , Improve efficiency .
- The principle of avoiding index invalidation ： Disable the use of functions... On index fields 、 Operator operation , Will invalidate the index . This is actually the field corresponding to the index ” Cleanliness “.
- Avoid unnecessary type conversions , When string fields are compared with numeric values, the index will be invalid .
- Fuzzy query '%value%' Will invalidate the index , Change to full scan , Because it's impossible to determine the scan range , however 'value%' The index can be used effectively .
- Index override sort field , This reduces the sorting steps , Improve query efficiency
- Expand the index as much as possible , There is no need to create a new index . Let's say it's already in the table a The index of , Now want to add (a,b) The index of , All you need to do is change the original index .
- There is no need to force index order , such as establish (depno,empno,jobno) Index of order , You can be empno = 1 and jobno = 2 and depno = 8. because MySQL The query optimizer of will optimize the order according to the actual index situation , So the sequence consistency is not enforced here . But under the same conditions, they are arranged in order , clearer , And save the processing of query optimizer .
2.2 Query optimization Analyzer - explain
explain The order should be familiar to all of you , Please refer to the official website for specific usage and field meaning explain-output, It needs to be emphasized here rows It's the core indicator , most rows Small statements must execute very quickly , Because the scanning content base is small .
So the optimization statement is basically in the optimization to reduce rows value .
2.2.1 Explain Output field
| Column | JSON Name | Meaning |
|---|---|---|
| id select_id | The SELECT | identifier |
| select_type | None | The SELECT type |
| table table_name | The table for the output row | |
| partitions | partitions | The matching partitions |
| type | access_type | The join type |
| possible_keys | possible_keys | The possible indexes to choose |
| key | key | The index actually chosen |
| key_len | key_length | The length of the chosen key |
| ref | ref | The columns compared to the index |
| rows | rows | Estimate of rows to be examined |
| filtered | filtered | Percentage of rows filtered by table condition |
| Extra | None | Additional information |
2.2.2 select_type enumeration
Note several core key parameters ：possible_keys、key、rows、select_type, It is very meaningful for optimization guidance .
- select_type： Represents each... In the query select The type of clause （Simple、Primary、Depend SubQuery）
- possible_keys : Pointed out that MySQL Which index can be used to find records in the table , If there is an index on the field involved in the query , Then the index will be listed , But not necessarily used by queries
- key：key Columns show MySQL Actually decide which key to use （ Indexes ）, Index not taken yes null
- rows： Express MySQL According to table statistics and index selection , Estimate the number of rows to scan
Basic steps of slow query optimization
- Run it first to see the actual time consumption , Judging whether it's really slow （ Pay attention to the settings SQL_NO_CACHE）.
- High priority strategy ：where Check the list of conditions , Conditions for locking the minimum return record table .
It's a query statement where The table with the smallest number of records returned in the table starts to look up , Each field of a single table is queried separately , See which field has the highest discrimination . The fields with high discrimination are arranged forward . - explain View execution plan , Whether or not 1 The expectations are the same （ Start with a table with fewer locked records ）
- order by limit Formal sql Statement to give priority to the sorted table
- Understand the usage scenarios of the business side , Adjust according to the use scenario .
- When adding index, refer to the ten principles of building index above
- Observations , Do not meet expectations, continue to analyze from the first step
2.3 Query case analysis
The following examples explain in detail how to analyze and optimize slow queries .
Analysis of complex query conditions
Generally speaking, we write SQL The way to do it is to It's about realizing the function , On the basis of realizing the function, guarantee MySQL The efficiency of implementation is also very important , This requires us to be right MySQL Have a very clear understanding of the execution plan and indexing rules of , Analyze the following case ：
1 mysql> select a.*,b.depname,b.memo from emp a left join
2 dep b on a.depno = b.depno where sal>100 and a.empname like 'ab%' and a.depno=106 order by a.hiredate desc ;
3 +---------+---------+---------+---------+-----+---------------------+------+------+-------+------------+----------+
4 | id | empno | empname | job | mgr | hiredate | sal | comn | depno | depname | memo |
5 +---------+---------+---------+---------+-----+---------------------+------+------+-------+------------+----------+
6 | 4976754 | 4976754 | ABijwE | SALEMAN | 1 | 2021-01-23 16:46:24 | 2000 | 400 | 106 | kDpNWugzcQ | TYlrVEkm |
7 ......
8 +---------+---------+---------+---------+-----+---------------------+------+------+-------+------------+----------+
9 744 rows in set (4.958 sec)
All in all 744 Data , But it cost 4.958 Time for , Let's take a look at the existing indexes in the current table and the analysis of index usage
1 mysql> show index from emp;
2 +-------+------------+---------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
3 | Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
4 +-------+------------+---------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
5 | emp | 0 | PRIMARY | 1 | id | A | 4952492 | NULL | NULL | | BTREE | | |
6 | emp | 1 | idx_emo_depno | 1 | depno | A | 18 | NULL | NULL | | BTREE | | |
7 +-------+------------+---------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
8 2 rows in set
9
10 mysql> explain select a.*,b.depname,b.memo from emp a left join
11 dep b on a.depno = b.depno where sal>100 and a.empname like 'ab%' and a.depno=106 order by a.hiredate desc ;
12 +----+-------------+-------+------+---------------+---------------+---------+-------+--------+-----------------------------+
13 | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
14 +----+-------------+-------+------+---------------+---------------+---------+-------+--------+-----------------------------+
15 | 1 | SIMPLE | a | ref | idx_emo_depno | idx_emo_depno | 3 | const | 974898 | Using where; Using filesort |
16 | 1 | SIMPLE | b | ref | idx_dep_depno | idx_dep_depno | 3 | const | 1 | NULL |
17 +----+-------------+-------+------+---------------+---------------+---------+-------+--------+-----------------------------+
18 2 rows in set
It can be seen that , Currently in emp There is only one index on the table except the primary key idx_emo_depno , In the department number field , The goal of the index is to filter out data under specific department numbers .
adopt explain The analyzer can see where After the condition is gone idx_emo_depno Indexes , But it also compares 97W The data of , This shows that the discrimination of this field is not high , According to the principle of high discrimination priority , We calculate the discrimination of the three query fields in this table .
1 mysql> select count(distinct empname)/count(*),count(distinct depno)/count(*),count(distinct sal)/count(*) from emp;
2 +----------------------------------+--------------------------------+------------------------------+
3 | count(distinct empname)/count(*) | count(distinct depno)/count(*) | count(distinct sal)/count(*) |
4 +----------------------------------+--------------------------------+------------------------------+
5 | 0.1713 | 0.0000 | 0.0000 |
6 +----------------------------------+--------------------------------+------------------------------+
7 1 row in set
This is the result of the calculation ,empname The highest discrimination , Therefore, it is reasonable to establish a joint index containing these three fields , Order as follows ：empname、depno、sal;
And the query conditions are reordered , In line with the leftmost matching principle ; On the other hand, strategies based on needs , hold b.memo The fields are removed .
1 mysql> select a.*,b.depname from emp a left join
2 dep b on a.depno = b.depno where a.empname like 'ab%' and a.depno=106 and a.sal>100 order by a.hiredate desc ;
3 +---------+---------+---------+---------+-----+---------------------+------+------+-------+------------+
4 | id | empno | empname | job | mgr | hiredate | sal | comn | depno | depname |
5 +---------+---------+---------+---------+-----+---------------------+------+------+-------+------------+
6 | 4976754 | 4976754 | ABijwE | SALEMAN | 1 | 2021-01-23 16:46:24 | 2000 | 400 | 106 | kDpNWugzcQ |
7 ......
8 +---------+---------+---------+---------+-----+---------------------+------+------+-------+------------+
9 744 rows in set (0.006 sec)
Here's another question , The union index is based on the leftmost matching principle ： Must match from left to right ,MySQL The index is matched to the right until a range query is encountered (>、<、between、like) And then stop matching .
So in the sentence Execute to a.empname Field , Because of the use of like, No more indexing . In this scenario , independent empname The efficiency of index on field is similar to that of joint index .
In addition, sort fields hiredate You can also consider covering the index , It will increase efficiency accordingly .
Analysis of invalid index
There is a need , Using the user table userinfo And consumption schedules salinvest , The purpose is to make 2020 Each user in four category levels in the year （A1、A2、A3、A4） The amount of consumption on the Internet , So we have the following script ：
1 select (@rowNO := @rowNo+1) AS id,bdata.* from
2 (
3 select distinct a.usercode,a.username,
4 @A1:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A1'
5 and c.logdate between '2020-01-01' and '2020-12-31'),0) as A1,
6 @A2:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A2'
7 and c.logdate between '2020-01-01' and '2020-12-31'),0) as A2,
8 @A3:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A3'
9 and c.logdate between '2020-01-01' and '2020-12-31'),0) as A3,
10 @A4:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A4'
11 and c.logdate between '2020-01-01' and '2020-12-31'),0) as A4,
12 ,(@[email protected][email protected][email protected]) as allnum
13 from userinfo a
14 inner JOIN `salinvest` b on a.usercode = b.usercode
15 where b.logdate between '2020-01-01' and '2020-12-31'
16 order by allnum desc
17 ) as bdata,(SELECT @rowNO:=0) b;
This query seems to be ok , Although compound query is used 、 Subquery , But if the index is done right , There won't be any problem . Let's take a look at the index , There is a joint index , In line with our leftmost matching principle and high discrimination priority principle ：
1 mysql> show index from salinvest;
2 +------------+------------+------------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
3 | Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
4 +------------+------------+------------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
5 | lnuminvest | 0 | PRIMARY | 1 | autocode | A | 5 | NULL | NULL | | BTREE | | |
6 | lnuminvest | 1 | idx_salinvest_complex | 1 | usercode | A | 2 | NULL | NULL | YES | BTREE | | |
7 | lnuminvest | 1 | idx_salinvest_complex | 2 | gravalue | A | 2 | NULL | NULL | YES | BTREE | | |
8 | lnuminvest | 1 | idx_salinvest_complex | 3 | logdate | A | 2 | NULL | NULL | YES | BTREE | | |
9 +------------+------------+------------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
10 4 rows in set
Let's look at the efficiency of its implementation ：
mysql> select (@rowNO := @rowNo+1) AS id,bdata.* from
(
select (@rowNO := @rowNo+1) AS id,bdata.* from
(
select distinct a.usercode,a.username,
@A1:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A1'
and c.logdate between '2020-01-01' and '2020-12-31'),0) as A1,
@A2:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A2'
and c.logdate between '2020-01-01' and '2020-12-31'),0) as A2,
@A3:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A3'
and c.logdate between '2020-01-01' and '2020-12-31'),0) as A3,
@A4:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A4'
and c.logdate between '2020-01-01' and '2020-12-31'),0) as A4,
,(@[email protected][email protected][email protected]) as allnum
from userinfo a
inner JOIN `salinvest` b on a.usercode = b.usercode
where b.logdate between '2020-01-01' and '2020-12-31'
order by allnum desc
) as bdata,(SELECT @rowNO:=0) b;
+----+------------+---------+------+------+------+------+------+--------+
| id | usercode | username | A1 | A2 | A3 | A4 |allnum
+----+------------+---------+------+------+------+------+------+--------+
| 1 | 063105015 | brand | 789.00 | 1074.50 | 998.00 | 850.00 |
......
+----+------------+---------+------+------+------+------+------+--------+
6217 rows in set (12.745 sec)
I've omitted the query results here , In fact, the result output 6000 Multiple data , In about 50W Statistics and consolidation of the data , Output 6000 Multiple data , It cost nearly 13 second , This is obviously unreasonable .
Let's analyze the reasons ：
1 mysql> explain select (@rowNO := @rowNo+1) AS id,bdata.* from
2 (
3 select distinct a.usercode,a.username,
4 @A1:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A1'
5 and c.logdate between '2020-01-01' and '2020-12-31'),0) as A1,
6 @A2:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A2'
7 and c.logdate between '2020-01-01' and '2020-12-31'),0) as A2,
8 @A3:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A3'
9 and c.logdate between '2020-01-01' and '2020-12-31'),0) as A3,
10 @A4:=IFNULL((select sum(c.ltimenum) from `salinvest` c where c.usercode=a.usercode AND c.gravalue='A4'
11 and c.logdate between '2020-01-01' and '2020-12-31'),0) as A4,
12 ,(@[email protected][email protected][email protected]) as allnum
13 from userinfo a
14 inner JOIN `salinvest` b on a.usercode = b.usercode
15 where b.logdate between '2020-01-01' and '2020-12-31'
16 order by allnum desc
17 ) as bdata,(SELECT @rowNO:=0) b;
18 +----+--------------------+------------+------------+--------+------------------------+------------------------+---------+-----------------------+------+----------+-----------------------------------------------------------+
19 | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
20 +----+--------------------+------------+------------+--------+------------------------+------------------------+---------+-----------------------+------+----------+-----------------------------------------------------------+
21 | 1 | PRIMARY | <derived8> | NULL | system | NULL | NULL | NULL | NULL | 1 | 100 | NULL |
22 | 1 | PRIMARY | <derived2> | NULL | ALL | NULL | NULL | NULL | NULL | 2 | 100 | NULL |
23 | 8 | DERIVED | NULL | NULL | NULL | NULL | NULL | NULL | NULL | NULL | NULL | No tables used |
24 | 2 | DERIVED | b | NULL | index | idx_salinvest_complex | idx_salinvest_complex | 170 | NULL | 5 | 20 | Using where; Using index; Using temporary; Using filesort |
25 | 7 | DEPENDENT SUBQUERY | c | NULL | ALL | idx_salinvest_complex | NULL | NULL | NULL | 5 | 20 | Using where |
26 | 6 | DEPENDENT SUBQUERY | c | NULL | ALL | idx_salinvest_complex | NULL | NULL | NULL | 5 | 20 | Using where |
27 | 5 | DEPENDENT SUBQUERY | c | NULL | ALL | idx_salinvest_complex | NULL | NULL | NULL | 5 | 20 | Using where |
28 | 4 | DEPENDENT SUBQUERY | c | NULL | ALL | idx_salinvest_complex | NULL | NULL | NULL | 5 | 20 | Using where |
29 +----+--------------------+------------+------------+--------+------------------------+------------------------+---------+-----------------------+------+----------+-----------------------------------------------------------+
30 9 rows in set
Look at the last four data , Look at him possible_key and Actually key, It's going to be idx_salinvest_complex Indexes , In fact, the index is empty , Why is this ？ Look at the front select_type Field , The value is DEPENDENT SUBQUERY, It's clear .
The official response to DEPENDENT SUBQUERY Explanation ： First in subquery SELECT, Depends on external queries .
What does that mean ？ It means two steps ：
First step ,MySQL according to select distinct a.usercode,a.username Get a big result set t1, That's what we've shown above 6000 user .
The second step , The big result set above t1 Each record in , Equivalent to subquery SQL Compose a new query statement ： select sum(c.ltimenum) from salinvest c where c.usercode in (select distinct a.usercode from userinfo a) .
in other words , Each subquery needs to compare 6000 Time , Hundreds of thousands of data …… Even if the index is used in both steps of the query , But it's going to be slow .
In this case , The execution efficiency of subquery is limited by the number of records of outer query , It's better to split it into two independent queries and execute them sequentially .
This slow query solution , There are a lot of solutions on the Internet , The most common method is to replace subqueries with union queries , You can check it yourself .
3 Appropriate sub database and sub table
Physical server CPU、 Memory 、 The storage device 、 Limited resources such as connections , A large number of connections perform operations at the same time in a certain period of time , It will cause the database to encounter performance bottleneck in processing . To solve this problem , Industry pioneers have fully carried forward the idea of division and rule , Split the large database table ,
Then implement better control and management , Using multiple machines at the same time CPU、 Memory 、 Storage , Provide better performance . There are two ways to achieve divide and conquer ： Split vertically and horizontally .
3.1 Vertical sub database
Vertical database is a simple logical partition , For example, establish an independent commodity Library in the database Products、 Order Library Orders, Integral Library Scores etc. .
3.2 Vertical sub table
Comparison is applicable to tables with more fields , Suppose we have a table with 100 A field , Analyze the current business execution SQL sentence , Yes 20 Fields are frequently used , And the other 80 Fewer fields to use . hold 20 Fields in the main table , Let's create another auxiliary table , Store in addition 80 A field .
3.3 Sub table in the warehouse
Split a single large capacity table according to certain policies .
3.4 Sub database and sub table
The sub database and sub table are based on the sub table in the database , Move the divided tables to different hosts and databases . Can make full use of other hosts CPU、 Memory and IO resources .
4 A complete index knowledge system
Refer to the four indexes written by the author before + There are two sub databases and tables
MySQL Break down altogether 22： Introduction and principle analysis of index
MySQL Break down altogether 23：MySQL Index implementation and use
MySQL Break down altogether 24： Building high performance indexes （ Strategy chapter ）
MySQL Break down altogether 25： Building high performance indexes （ Case analysis ）
MySQL Break down altogether 28： Sub database and sub table
MySQL Break down altogether 29： Sub library sub table Partition Function details
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