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Kalyanasundaram Somasundaram
207
courses/level101/databases_sql/lab.md
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207
courses/level101/databases_sql/lab.md
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**Prerequisites**
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Install Docker
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**Setup**
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Create a working directory named sos or something similar, and cd into it.
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Enter the following into a file named my.cnf under a directory named custom.
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```
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sos $ cat custom/my.cnf
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[mysqld]
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# These settings apply to MySQL server
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# You can set port, socket path, buffer size etc.
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# Below, we are configuring slow query settings
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slow_query_log=1
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slow_query_log_file=/var/log/mysqlslow.log
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long_query_time=1
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```
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Start a container and enable slow query log with the following:
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```
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sos $ docker run --name db -v custom:/etc/mysql/conf.d -e MYSQL_ROOT_PASSWORD=realsecret -d mysql:8
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sos $ docker cp custom/my.cnf $(docker ps -qf "name=db"):/etc/mysql/conf.d/custom.cnf
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sos $ docker restart $(docker ps -qf "name=db")
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```
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Import a sample database
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```
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sos $ git clone git@github.com:datacharmer/test_db.git
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sos $ docker cp test_db $(docker ps -qf "name=db"):/home/test_db/
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sos $ docker exec -it $(docker ps -qf "name=db") bash
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root@3ab5b18b0c7d:/# cd /home/test_db/
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root@3ab5b18b0c7d:/# mysql -uroot -prealsecret mysql < employees.sql
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root@3ab5b18b0c7d:/etc# touch /var/log/mysqlslow.log
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root@3ab5b18b0c7d:/etc# chown mysql:mysql /var/log/mysqlslow.log
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```
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_Workshop 1: Run some sample queries_
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Run the following
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```
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$ mysql -uroot -prealsecret mysql
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mysql>
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# inspect DBs and tables
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# the last 4 are MySQL internal DBs
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mysql> show databases;
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+--------------------+
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| Database |
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+--------------------+
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| employees |
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| information_schema |
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| mysql |
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| performance_schema |
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| sys |
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+--------------------+
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> use employees;
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mysql> show tables;
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+----------------------+
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| Tables_in_employees |
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+----------------------+
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| current_dept_emp |
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| departments |
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| dept_emp |
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| dept_emp_latest_date |
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| dept_manager |
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| employees |
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| salaries |
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| titles |
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+----------------------+
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# read a few rows
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mysql> select * from employees limit 5;
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# filter data by conditions
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mysql> select count(*) from employees where gender = 'M' limit 5;
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# find count of particular data
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mysql> select count(*) from employees where first_name = 'Sachin';
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```
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_Workshop 2: Use explain and explain analyze to profile a query, identify and add indexes required for improving performance_
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```
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# View all indexes on table
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#(\G is to output horizontally, replace it with a ; to get table output)
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mysql> show index from employees from employees\G
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*************************** 1. row ***************************
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Table: employees
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Non_unique: 0
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Key_name: PRIMARY
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Seq_in_index: 1
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Column_name: emp_no
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Collation: A
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Cardinality: 299113
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Sub_part: NULL
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Packed: NULL
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Null:
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Index_type: BTREE
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Comment:
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Index_comment:
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Visible: YES
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Expression: NULL
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# This query uses an index, idenitfied by 'key' field
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# By prefixing explain keyword to the command,
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# we get query plan (including key used)
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mysql> explain select * from employees where emp_no < 10005\G
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*************************** 1. row ***************************
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id: 1
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select_type: SIMPLE
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table: employees
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partitions: NULL
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type: range
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possible_keys: PRIMARY
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key: PRIMARY
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key_len: 4
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ref: NULL
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rows: 4
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filtered: 100.00
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Extra: Using where
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# Compare that to the next query which does not utilize any index
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mysql> explain select first_name, last_name from employees where first_name = 'Sachin'\G
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*************************** 1. row ***************************
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id: 1
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select_type: SIMPLE
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table: employees
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partitions: NULL
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type: ALL
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possible_keys: NULL
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key: NULL
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key_len: NULL
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ref: NULL
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rows: 299113
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filtered: 10.00
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Extra: Using where
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# Let's see how much time this query takes
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mysql> explain analyze select first_name, last_name from employees where first_name = 'Sachin'\G
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*************************** 1. row ***************************
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EXPLAIN: -> Filter: (employees.first_name = 'Sachin') (cost=30143.55 rows=29911) (actual time=28.284..3952.428 rows=232 loops=1)
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-> Table scan on employees (cost=30143.55 rows=299113) (actual time=0.095..1996.092 rows=300024 loops=1)
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# Cost(estimated by query planner) is 30143.55
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# actual time=28.284ms for first row, 3952.428 for all rows
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# Now lets try adding an index and running the query again
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mysql> create index idx_firstname on employees(first_name);
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Query OK, 0 rows affected (1.25 sec)
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Records: 0 Duplicates: 0 Warnings: 0
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mysql> explain analyze select first_name, last_name from employees where first_name = 'Sachin';
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+--------------------------------------------------------------------------------------------------------------------------------------------+
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| EXPLAIN |
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+--------------------------------------------------------------------------------------------------------------------------------------------+
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| -> Index lookup on employees using idx_firstname (first_name='Sachin') (cost=81.20 rows=232) (actual time=0.551..2.934 rows=232 loops=1)
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+--------------------------------------------------------------------------------------------------------------------------------------------+
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1 row in set (0.01 sec)
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# Actual time=0.551ms for first row
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# 2.934ms for all rows. A huge improvement!
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# Also notice that the query involves only an index lookup,
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# and no table scan (reading all rows of table)
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# ..which vastly reduces load on the DB.
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```
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_Workshop 3: Identify slow queries on a MySQL server_
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```
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# Run the command below in two terminal tabs to open two shells into the container.
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docker exec -it $(docker ps -qf "name=db") bash
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# Open a mysql prompt in one of them and execute this command
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# We have configured to log queries that take longer than 1s,
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# so this sleep(3) will be logged
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mysql -uroot -prealsecret mysql
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mysql> select sleep(3);
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# Now, in the other terminal, tail the slow log to find details about the query
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root@62c92c89234d:/etc# tail -f /var/log/mysqlslow.log
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/usr/sbin/mysqld, Version: 8.0.21 (MySQL Community Server - GPL). started with:
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Tcp port: 3306 Unix socket: /var/run/mysqld/mysqld.sock
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Time Id Command Argument
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# Time: 2020-11-26T14:53:44.822348Z
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# User@Host: root[root] @ localhost [] Id: 9
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# Query_time: 5.404938 Lock_time: 0.000000 Rows_sent: 1 Rows_examined: 1
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use employees;
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# Time: 2020-11-26T14:53:58.015736Z
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# User@Host: root[root] @ localhost [] Id: 9
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# Query_time: 10.000225 Lock_time: 0.000000 Rows_sent: 1 Rows_examined: 1
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SET timestamp=1606402428;
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select sleep(3);
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```
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These were simulated examples with minimal complexity. In real life, the queries would be much more complex and the explain/analyze and slow query logs would have more details.
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