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