How to Use ClickHouse and ClickTail to Monitor and Analyze MySQL Audit Logs
How to Use ClickHouse and ClickTail to Monitor and Analyze MySQL Audit Logs
If you are looking for a fast and easy way to monitor and analyze MySQL audit logs, you might want to consider using ClickHouse and ClickTail. ClickHouse is a column-oriented database management system that can handle large volumes of data and perform complex analytical queries in real time. ClickTail is a tool that can parse and load various types of logs, including MySQL audit logs, to ClickHouse.
Analyze MySQL Audit Logs with ClickHouse and ClickTail
MySQL audit logs are available with a free plugin for Percona Server for MySQL (https://www.percona.com/doc/percona-server/LATEST/management/audit_log_plugin.html). They can provide valuable insights about the activity and performance of your MySQL server, as well as help you with compliance and security requirements. However, MySQL audit logs can also get very large and difficult to process and analyze with traditional tools.
In this article, you will learn how to use ClickHouse and ClickTail to monitor and analyze MySQL audit logs. You will also learn about the benefits and features of ClickHouse and ClickTail, and how to install and configure them. You will also see some examples of queries that you can run on your MySQL audit logs using SQL.
What are ClickHouse and ClickTail?
ClickHouse is an open-source column-oriented database management system that was developed by Yandex. It is designed for online analytical processing (OLAP) and can handle petabytes of data and thousands of queries per second. It supports SQL as a query language, as well as various data types, functions, operators, and aggregation engines. It also supports distributed and replicated architectures, compression, partitioning, sharding, and high availability.
ClickTail is a tool that was developed by Altinity based on Charity Majors' Honeytail. It can parse and load various types of logs, such as MySQL slow query logs, MySQL audit logs, syslog, nginx logs, etc., to ClickHouse. It supports multiple parsers, formats, filters, backfills, and options. It can also extract all fields available in Percona Server for MySQL's audit log in JSON format.
Why use ClickHouse and ClickTail for MySQL audit log analysis?
Using ClickHouse and ClickTail for MySQL audit log analysis has some advantages over other solutions, such as:
Speed: ClickHouse can process large amounts of data and perform complex analytical queries in real time. It can scan billions of rows per second using parallel processing and vectorized execution. It can also compress data efficiently using various algorithms.
Flexibility: ClickTail can parse and load different types of logs to ClickHouse using various parsers and options. It can also handle different formats, such as JSON, CSV, TSV, etc., and apply filters and backfills.
Simplicity: ClickHouse and ClickTail are easy to install and configure. They do not require any indexes or schemas to be defined. They also use SQL as a query language, which is familiar to most database users.
Functionality: ClickHouse and ClickTail can provide rich functionality for MySQL audit log analysis. They can extract all fields available in Percona Server for MySQL's audit log in JSON format. They can also support various data types, functions, operators, aggregation engines, subqueries, joins, etc.
How to install and configure ClickHouse and ClickTail?
To install and configure ClickHouse and ClickTail, you will need to follow these steps:
Install ClickHouse: You can install ClickHouse using various methods, such as binary packages, Docker images, or source code. You can find the installation instructions for different platforms and versions on the official website: https://clickhouse.tech/docs/en/getting-started/install/.
Configure ClickHouse: You can configure ClickHouse using various files, such as config.xml, users.xml, and dictionaries. You can find the configuration instructions and options on the official website: https://clickhouse.tech/docs/en/operations/configuration-files/. For example, you might want to configure the server name, port, log level, compression method, etc.
Install ClickTail: You can install ClickTail using a binary file or a source code. You can find the installation instructions and options on the GitHub repository: https://github.com/Altinity/clicktail. For example, you might want to download the latest release from https://github.com/Altinity/clicktail/releases and extract it to a directory.
Configure ClickTail: You can configure ClickTail using a configuration file or command-line arguments. You can find the configuration instructions and options on the GitHub repository: https://github.com/Altinity/clicktail. For example, you might want to create a configuration file named clicktail.conf with the following content:
[clicktail]
dataset = 'clicktail.mysql_audit_log'
parser = 'mysqlaudit'
file = '/var/log/mysql/audit.log'
backfill = true
This configuration file tells ClickTail to parse and load MySQL audit logs from /var/log/mysql/audit.log to a dataset named clicktail.mysql_audit_log in ClickHouse using the mysqlaudit parser and backfill option.
How to start and stop ClickHouse and ClickTail?
To start and stop ClickHouse and ClickTail, you will need to follow these steps:
Start ClickHouse: You can start ClickHouse using various methods, such as systemd, init.d, or Docker. You can find the instructions for different platforms and versions on the official website: https://clickhouse.tech/docs/en/getting-started/install/. For example, you might want to use the following command to start ClickHouse as a service on Linux:
sudo service clickhouse-server start
Stop ClickHouse: You can stop ClickHouse using various methods, such as systemd, init.d, or Docker. You can find the instructions for different platforms and versions on the official website: https://clickhouse.tech/docs/en/getting-started/install/. For example, you might want to use the following command to stop ClickHouse as a service on Linux:
sudo service clickhouse-server stop
Start ClickTail: You can start ClickTail using a binary file or a source code. You can find the instructions and options on the GitHub repository: https://github.com/Altinity/clicktail. For example, you might want to use the following command to start ClickTail using a configuration file named clicktail.conf:
./clicktail --config=clicktail.conf
Stop ClickTail: You can stop ClickTail using a keyboard interrupt or a signal. For example, you might want to use the following command to stop ClickTail using Ctrl-C:
Ctrl-C
Once you have started ClickHouse and ClickTail, you will have MySQL audit logs flowing to ClickHouse in real time (and you can query them).
How to perform and interpret different types of queries on MySQL audit logs?
Once you have MySQL audit logs loaded to ClickHouse using ClickTail, you can perform and interpret different types of queries on them using SQL. You can use any SQL client that supports ClickHouse, such as clickhouse-client, DBeaver, or Tabix. You can also use the ClickHouse web interface or the HTTP API.
The queries that you can run on MySQL audit logs depend on your analysis goals and needs. For example, you might want to:
Check the status and size of the audit log dataset
Filter and aggregate the audit log events by various criteria
Join the audit log events with other tables or datasets
Compute statistics and metrics on the audit log events
Visualize and export the query results
Here are some examples of queries that you can run on MySQL audit logs using SQL:
Check the status and size of the audit log dataset
To check the status and size of the audit log dataset, you can use the system.tables table, which contains information about all tables in ClickHouse. For example, you can use the following query to get the number of rows, bytes, and parts in the clicktail.mysql_audit_log dataset:
SELECT
name,
rows,
bytes,
parts
FROM system.tables
WHERE name = 'mysql_audit_log'
The query result might look like this:
+-----------------+---------+----------+-------+
name rows bytes parts
+-----------------+---------+----------+-------+
mysql_audit_log 46197504 18482000 1
+-----------------+---------+----------+-------+
This means that the dataset has 46,197,504 rows, 18,482,000 bytes, and 1 part.
Filter and aggregate the audit log events by various criteria
To filter and aggregate the audit log events by various criteria, you can use the WHERE and GROUP BY clauses, as well as various functions and operators. For example, you can use the following query to get the count and percentage of events by command class:
SELECT
command_class,
count(*) AS count,
round(count(*) * 100 / sum(count(*)) OVER (), 2) AS percentage
FROM mysql_audit_log
GROUP BY command_class
ORDER BY count DESC
The query result might look like this:
+---------------+---------+------------+
command_class count percentage
+---------------+---------+------------+
select 23098752 50.00
insert 11549376 25.00
update 11549376 25.00
+---------------+---------+------------+
This means that half of the events are select commands, and a quarter of them are insert or update commands.
Join the audit log events with other tables or datasets
To join the audit log events with other tables or datasets, you can use the JOIN clause, as well as various join types and conditions. For example, you can use the following query to join the audit log events with the student table, which contains information about the students who borrowed books:
SELECT
a.user,
a.command_class,
a.sqltext,
s.name,
s.email
FROM mysql_audit_log AS a
JOIN student AS s
ON a.user = s.student_num
WHERE a.command_class = 'select'
LIMIT 10
The query result might look like this:
+-------+---------------+----------------------------------+--------+------------------+
user command_class sqltext name email
+-------+---------------+----------------------------------+--------+------------------+
1001 select SELECT * FROM book WHERE id = 10 Alice alice@gmail.com
1002 select SELECT * FROM book WHERE id = 20 Bob bob@gmail.com
1003 select SELECT * FROM book WHERE id = 30 Carol carol@gmail.com
1004 select SELECT * FROM book WHERE id = 40 Dave dave@gmail.com
1005 select SELECT * FROM book WHERE id = 50 Eve eve@gmail.com
1006 select SELECT * FROM book WHERE id = 60 Frank frank@gmail.com
1007 select SELECT * FROM book WHERE id = 70 Grace grace@gmail.com
1008 select SELECT * FROM book WHERE id = 80 Harry harry@gmail.com
1009 select SELECT * FROM book WHERE id = 90 Irene irene@gmail.com
1010 select SELECT * FROM book WHERE id = 100 Jack jack@gmail.com
+-------+---------------+----------------------------------+--------+------------------+
This means that these are the students who ran select queries on the book table and their names and emails.
Conclusion
In this article, you have learned how to use ClickHouse and ClickTail to monitor and analyze MySQL audit logs. You have also learned about the benefits and features of ClickHouse and ClickTail, and how to install and configure them. You have also seen some examples of queries that you can run on MySQL audit logs using SQL.
ClickHouse and ClickTail are powerful and flexible tools for MySQL audit log analysis. They can handle large volumes of data and perform complex analytical queries in real time. They can also parse and load different types of logs to ClickHouse using various parsers and options. They can also support various data types, functions, operators, aggregation engines, subqueries, joins, etc.
We hope that this article has helped you to understand and appreciate ClickHouse and ClickTail, and that you will find them useful for your MySQL audit log analysis projects. b99f773239
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