New Book: MySQL 8 Query Performance Tuning

I have over the last few years been fortunate to have two books published through Apress, Pro MySQL NDB Cluster which I wrote together with Mikiya Okuno and MySQL Connector/Python Revealed. With the release of MySQL 8 around a year ago, I started to think of how many changes there has been in the last few MySQL versions. Since MySQL 5.6 was released as GA in early 2013, some of the major features related to performance tuning includes the Performance Schema which was greatly changed in 5.6, histograms, EXPLAIN ANALYZE, hash joins, and visual explain. Some of these are even unique in MySQL 8.

So, I was thinking that it could be interesting to write a book that focuses on performance tuning in MySQL 8. In order to try to limit the scope somewhat (which as you can see from the page count that I was not too successful with), I decided to mainly look at the topics related to query performance. I proposed this to my acquisition editor Jonathan Gennick, and he was very interested. Oracle whom I worked for at the time was also interested (thanks Adam Dixon, Victoria Reznichenko, Edwin DeSouza, and Rich Mason for supporting me and approving the project). Also thanks to the Apress editors and staff who has been involved including but not limited to Jonathan Gennick, Jill Balzano, Laura Berendson, and Arockia Rajan Dhurai.

Now around a year later, the final result is ready: MySQL 8 Query Performance Tuning. If you are interested, you can read more about the content and/or buy it at Apress, Amazon, and others book shops:

ShopPaper BookE-Book
ApressSoftcoverDRM free ePub/PDF
Amazon USSoftcoverMobi (Kindle)

Book Structure

The book is divided into six parts with a total of 27 chapters. I have attempted to keep each chapter relatively self-contained with the aim that you can use the book as a reference book. The drawback of this choice is that there is some duplication of information from time to time. An example is Chapter 18 which describes the more theoretical side of locks and how to monitor locks, and Chapter 22 which provides practical examples of investigating lock contention. Chapter 22 naturally draws on the information in Chapter 18, so some of the information is repeated. This was a deliberate choice, and I hope it helps you reduce the amount of page flipping to find the information you need.

The six parts progressively move you through the topics starting with some basic background and finishing with more solution-oriented tasks. The first part starts out discussing the methodology, benchmarks, and test data. The second part focuses on the sources of information such as the Performance Schema. The third part covers the tools such as MySQL Shell used in this book. The fourth part provides the theoretical background used in the last two parts. The fifth part focuses on analyzing queries, transactions, and locks. Finally, the sixth part discusses how to improve performance through the configuration, query optimization, replication, and caching. There are cases where some content is a little out of place, like all replication information is contained in a single chapter.


Part I: Getting Started

Part I introduces you to the concepts of MySQL query performance tuning. This includes some high-level considerations, of which some are not unique to MySQL (but are of course discussed in the context of MySQL). The four chapters are

  • Chapter 1 – MySQL Performance Tuning
    This introductory chapter covers some high-level concepts of MySQL performance tuning such as the importance of considering the whole stack and the lifecycle of a query.
  • Chapter 2 – Query Tuning Methodology
    It is important to work in an effective way to solve performance problems. This chapter introduces a methodology to work effectively and emphasizes the importance of working proactively rather than doing firefighting.
  • Chapter 3 – Benchmarking with Sysbench
    It is often necessary to use benchmarks to determine the effect of a change. This chapter introduces benchmarking in general and specifically discusses the Sysbench tool including how to create your own custom benchmarks.
  • Chapter 4 – Test Data
    The book mostly uses a few standard test databases which are introduced in this chapter.

Part II: Sources of Information

MySQL exposes information about the performance through a few sources. The Performance Schema, the sys schema, the Information Schema, and the SHOW statement are introduced in each their chapter. There are only relatively few examples of using these sources in this part; however, these four sources of information are used extensively in the remainder of the book. If you are not already familiar with them, you are strongly encouraged to read this part. Additionally, the slow query log is covered. The five chapters are

  • Chapter 5 – The Performance Schema
    The main source of performance related information in MySQL is – as the name suggests – the Performance Schema. This chapter introduces the terminology, the main concepts, the organization, and the configuration.
  • Chapter 6 – The sys Schema
    The sys schema provides reports through predefined views and utilities in stored functions and programs. This chapter provides an overview of what features are available.
  • Chapter 7 – The Information Schema
    If you need metadata about the MySQL and the databases, the Information Schema is the place to look. It also includes important information for performance tuning such as information about indexes, index statistics, and histograms. This chapter provides an overview of the views available in the Information Schema.
  • Chapter 8 – SHOW Statements
    The SHOW statements are the oldest way to obtain information ranging from which queries are executing to schema information. This chapter relates the SHOW statements to the Information Schema and Performance Schema and covers in somewhat more detail the SHOW statements without counterparts in the two schemas.
  • Chapter 9 – The Slow Query Log
    The traditional way to find slow queries is to log them to the slow query log. This chapter covers how to configure the slow query log, how to read the log events, and how to aggregate the events with the mysqldump utility.

Part III: Tools

MySQL provides several tools that are useful when performing the daily work as well as specialized tasks. This part covers three tools ranging from monitoring to simple query execution. This book uses Oracle’s dedicated MySQL monitoring solution (requires commercial subscription but is also available as a trial) as an example of monitoring. Even if you are using other monitoring solutions, you are encouraged to study the examples as there will be a large overlap. These three tools are also used extensively in the remainder of the book. The three chapters in this part are

  • Chapter 10 – MySQL Enterprise Monitor
    Monitoring is one of the most important aspects of maintaining a stable and well-performing database. This chapter introduces MySQL Enterprise Monitor (MEM) and shows how you can install the trial and helps you navigate and use the graphical user interface (GUI).
  • Chapter 11 – MySQL Workbench
    MySQL provides a graphical user interface through the MySQL Workbench product. This chapter shows how you can install and use it. In this book, MySQL Workbench is particularly important for its ability to create diagrams – known as Visual Explain – representing the query execution plans.
  • Chapter 12 – MySQL Shell
    One of the newest tools around from Oracle for MySQL is MySQL Shell which is a second-generation commandline client with support for executing code in both SQL, Python, and JavaScript. This chapter gets you up to speed with MySQL Shell and teaches you about its support for using external code modules, its reporting infrastructure, and how to create custom modules, reports, and plugins.

Part IV: Schema Considerations and the Query Optimizer

In Part IV, there is a change of pace, and the focus moves to the topics more directly related to performance tuning starting with topics related to the schema, the query optimizer, and locks. The six chapters are

  • Chapter 13 – Data Types
    In relational databases, each column has a data type. This data type defines which values can be stored, which rules apply when comparing two values, how the data is stored, and more. This chapter covers the data types available in MySQL and gives guidance on how to decide which data types to use.
  • Chapter 14 – Indexes
    An index is used to locate data, and a good indexing strategy can greatly improve the performance of your queries. This chapter covers the index concepts, considerations about indexes, index types, index features, and more. It also includes a discussion on how InnoDB uses indexes and how to come up with an indexing strategy.
  • Chapter 15 – Index Statistics
    When the optimizer needs to determine how useful an index is and how many rows match a condition on an indexed value, it needs information on the data in the index. This information is index statistics. This chapter covers how index statistics work in MySQL, how to configure them, monitoring, and updating the index statistics.
  • Chapter 16 – Histograms
    If you want the optimizer to know how frequent a value occurs for a given column, you need to create a histogram. This is a new feature in MySQL 8, and this chapter covers how histograms can be used, their internals, and how to query the histogram metadata and statistics.
  • Chapter 17 – The Query Optimizer
    When you execute a query, it is the query optimizer that determines how to execute it. This chapter covers the tasks performed by the optimizer, join algorithms, join optimizations, configuration of the optimizer, and resource groups.
  • Chapter 18 – Locking Theory and Monitoring
    One of the problems that can cause the most frustration is lock contention. The first part of this chapter explains why locks are needed, lock access levels, and lock types (granularities). The second part of the chapter goes into what happens when a lock cannot be obtained, how to reduce lock contention, and where to find information about locks.

Part V: Query Analysis

With the information from Part IV, you are now ready to start analyzing queries. This includes finding the queries for further analysis and then analyzing the query using EXPLAIN or the Performance Schema. You also need to consider how transactions work and investigate lock contention when you have two or more queries fighting for the same locks. The four chapters are

  • Chapter 19 – Finding Candidate Queries for Optimization
    Whether part of the daily maintenance or during an emergency, you need to find the queries that you need to analyze and potentially optimize. This chapter shows how you can use the Performance Schema, the sys schema, MySQL Workbench, your monitoring solution, and the slow query log to find the queries that are worth looking into.
  • Chapter 20 – Analyzing Queries
    Once you have a candidate query, you need to analyze why it is slow or impacts the system too much. The main tool is the EXPLAIN statement which provides information about the query plan chosen by the optimizer. How to generate and read – including examples – the query plans using EXPLAIN is the main focus of the chapter. You can also use the optimizer trace to get more information on how the optimizer arrived at the selected query plan. An alternative way to analyze queries is to use the Performance Schema and sys schema to break queries down into smaller parts.
  • Chapter 21 – Transactions
    InnoDB executes everything as a transaction, and transactions is an important concept. Proper use of transactions ensures atomicity, consistency, and isolation. However, transactions can also be the cause of severe performance and lock problems. This chapter discusses how transactions can become a problem and how to analyze them.
  • Chapter 22 – Diagnosing Lock Contention
    This chapter goes through four scenarios with lock contention (flush locks, metadata locks, record-level locks, and deadlocks) and discusses the symptoms, the cause, how to set up the scenario, the investigation, the solution, and how to prevent problems.

Part VI: Improving Queries

You have found your problem queries and analyzed them and their transaction to understand why they are underperforming. But how do you improve the queries? This chapter goes through the most important configuration options not covered elsewhere, how to change the query plan, schema changes and bulk loading, replication, and caching as means to improve the performance. The five chapters are

  • Chapter 23 – Configuration
    MySQL requires resources when executing a query. This chapter covers the best practices for configuring these resources and the most important configuration options that are not covered in other discussions. There is also an overview of the data lifecycle in InnoDB as background for the discussion of configuring InnoDB.
  • Chapter 24 – Change the Query Plan
    While the optimizer usually does a good job at finding the optimal query execution plan, you will from time to time have to help it on its way. It may be that you end up with full table scans because no indexes exist or the existing indexes cannot be used. You may also wish to improve the index usage, or you may need to rewrite complex conditions or entire queries. This chapter covers these scenarios as well as shows how you can use the SKIP LOCKED clause to implement a queue system.
  • Chapter 25 – DDL and Bulk Data Load
    When you perform schema changes or load large data sets into the system, you ask MySQL to perform a large amount of work. This chapter discusses how you can improve the performance of such tasks including using the parallel data load feature of MySQL Shell. There is also a section on general data load considerations which also applies to data modifications in general and shows the difference between sequential and random order inserts. That discussion is followed by considerations on what this means for the choice of primary key.
  • Chapter 26 – Replication
    The ability to replicate between instances is a popular feature in MySQL. From a performance point of view, replication has two sides: you need to ensure replication performs well, and you can use replication to improve performance. This chapter discusses both sides of the coin including covering the Performance Schema tables that can be used to monitor replication.
  • Chapter 27 – Caching
    One way to improve the performance of queries is to not execute them at all, or at least avoid executing part of the query. This chapter discusses how you can use caching tables to reduce the complexity of queries and how you can use Memcached, the MySQL InnoDB Memcached plugin, and ProxySQL to avoid executing the queries altogether.

I hope you will enjoy the book.

Connector/Python C Extension Prepared Statement Support

MySQL Connector/Python 8 made the C Extension the default for the platform/Python version combinations supporting it. One thing that was missing from the C Extension implementation (unless you used the _mysql_connector module) was support for prepared statements. That has been taken care of with the release of version 8.0.17.

The two main advantages of using prepared statements are security and performance. The security comes in as you can pass query parameters and have them applied server-side, so you are sure they are quoted and escaped correctly taking the data type into consideration. The performance benefit happens, when you execute the same query (except for the parameters) several times as MySQL will prepare it only for the first execution and then reuse the prepared statement – that is where the name comes from.

You use the prepared statements with the C Extension in the same way as for the pure Python implementation – by setting the prepared argument to True when creating a cursor. The simplest way to explain is to show an example.

import mysql.connector

connect_args = {
    "user": "root",
    "host": "localhost",
    "port": 3306,
    "password": "password",
    "use_pure": False,

db = mysql.connector.connect(**connect_args)
cursor = db.cursor(prepared=True)

sql = "SELECT * FROM WHERE ID = %s"
city_ids = [130, 456, 3805]

print("  ID  Name            Country  District         Popluation")
print("-" * 58)
fmt = "{0:4d}  {1:14s}  {2:^7s}  {3:15s}  {4:10d}"
for city_id in city_ids:
    cursor.execute(sql, (city_id,))
    city = cursor.fetchone()


In the connection arguments, use_pure is set to False. Since that is the default, it is not needed, but it has been added here to make it explicit that the C Extension is used.


Do not hardcode the connection arguments in your programs. It is done here to keep the example simple, but it is both insecure and inflexible to do in real programs.

When the cursor is created in line 12, prepared is set to True making it a prepared statement cursor. To verify that, the cursor is printed in the next line.

You create the statement by adding the string %s as a placeholder where you want to add the parameters to the query. You can then keep executing the query. In the example, the query is executed for three different IDs. (Yes, for this example, all three cities could have been fetched in one query, but imagine this query is used as part of a larger application where the three cities are not required at the same time. This could for example be for three independent user requests.) The parameter is provided as a tuple to the execute() method of the cursor. The output of the program is:

CMySQLCursorPrepared: (Nothing executed yet)

  ID  Name            Country  District         Popluation
 130  Sydney            AUS    New South Wales     3276207
 456  London            GBR    England             7285000
3805  San Francisco     USA    California           776733

Notice that the cursor uses the class CMySQLCursorPrepared, which is the prepared statement cursor class for the C Extension.

MySQL Connector/Python Revealed


If you want to learn more about MySQL Connector/Python, then I have written MySQL Connector/Python Revealed published by Apress. The book both covers the traditional Python Database API (PEP 249) and the X DevAPI which is new as of MySQL 8.

The book is available from Apress (print and DRM free ePub+PDF), Amazon (print and Kindle), Barnes & Noble (print), and others.

Have fun coding.

More Statistics for Slow Queries: log_slow_extra

The slow query log is the trusted old method of recording slow query, so the database administrator can determine which queries are in the most need for optimization. Since MySQL 5.6, it has to some extend been overshadowed by the Performance Schema which has lower overhead and thus allows collecting statistics about all queries. The slow query log has one major advantage though: the data is persisted. In MySQL 8.0.14 which was recently released, there is an improvement for the slow query log: additional statistics about the recorded queries.

The slow query log with log_slow_extra enabled.
The slow query log with log_slow_extra enabled.


Thanks for Facebook for contributing a patch for the new feature.

The additional information is not recorded by default. To enable the feature, enable the log_slow_extra option:

mysql> SET PERSIST log_slow_extra = ON;
Query OK, 0 rows affected (0.05 sec)

Here, SET PERSIST is used, so the configuration change is persisted when MySQL is restarted. If you just want to try the feature, you can use SET GLOBAL, then decide later whether you want to keep it enabled.

That is all that is required. You can now execute a “slow” query and take a look at the recorded data. An easy way to execute a slow query is to execute DO SLEEP(...) where you can replace ... with the number of seconds you want to sleep. However that is not a very interesting query. Another option is to lower long_query_time to ensure your query is captured. If you set long_query_time to zero, all queries are recorded:

mysql> SET SESSION long_query_time = 0.0;
Query OK, 0 rows affected (0.01 sec)

Here SET SESSION is used so only the current connection is affected.

Be Aware

The slow query log does have overhead. It is for most systems not recommended to set long_query_time to zero for all connections all the time. Doing so can cause performance problems.

Finally, execute a query:

mysql> SELECT * FROM ORDER BY Population DESC LIMIT 10;
| ID   | Name             | CountryCode | District         | Population |
| 1024 | Mumbai (Bombay)  | IND         | Maharashtra      |   10500000 |
| 2331 | Seoul            | KOR         | Seoul            |    9981619 |
|  206 | São Paulo        | BRA         | São Paulo        |    9968485 |
| 1890 | Shanghai         | CHN         | Shanghai         |    9696300 |
|  939 | Jakarta          | IDN         | Jakarta Raya     |    9604900 |
| 2822 | Karachi          | PAK         | Sindh            |    9269265 |
| 3357 | Istanbul         | TUR         | Istanbul         |    8787958 |
| 2515 | Ciudad de México | MEX         | Distrito Federal |    8591309 |
| 3580 | Moscow           | RUS         | Moscow (City)    |    8389200 |
| 3793 | New York         | USA         | New York         |    8008278 |
10 rows in set (0.04 sec)

The resulting slow query log record is:

# Time: 2019-01-31T07:24:20.518505Z
# User@Host: root[root] @ localhost [::1]  Id:    15
# Query_time: 0.001827  Lock_time: 0.000087 Rows_sent: 10  Rows_examined: 4089 Thread_id: 15 Errno: 0 Killed: 0 Bytes_received: 0 Bytes_sent: 694 Read_first: 1 Read_last: 0 Read_key: 1 Read_next: 0 Read_prev: 0 Read_rnd: 0 Read_rnd_next: 4080 Sort_merge_passes: 0 Sort_range_count: 0 Sort_rows: 10 Sort_scan_count: 1 Created_tmp_disk_tables: 0 Created_tmp_tables: 0 Start: 2019-01-31T07:24:20.516678Z End: 2019-01-31T07:24:20.518505Z
SET timestamp=1548919460;

The extra data can be a little hard to see due to the formatting of the blog – the extra fields are:

  • Thread_id: 15
  • Errno: 0
  • Killed: 0
  • Bytes_received: 0
  • Bytes_sent: 694
  • Read_first: 1
  • Read_last: 0
  • Read_key: 1
  • Read_next: 0
  • Read_prev: 0
  • Read_rnd: 0
  • Read_rnd_next: 4080
  • Sort_merge_passes: 0
  • Sort_range_count: 0
  • Sort_rows: 10
  • Sort_scan_count: 1
  • Created_tmp_disk_tables: 0
  • Created_tmp_tables: 0
  • Start: 2019-01-31T07:24:20.516678Z
  • End: 2019-01-31T07:24:20.518505Z

As comparison, here is the information for the same query with log_slow_extra = OFF:

# Time: 2019-01-31T07:24:06.100447Z
# User@Host: root[root] @ localhost [::1]  Id:    15
# Query_time: 0.002286  Lock_time: 0.000133 Rows_sent: 10  Rows_examined: 4089
SET timestamp=1548919446;


Install MySQL Enteprise Monitor (MEM) 3.0 Using Limited Resources

MySQL Enterprise Monitor (MEM) is the monitoring solution offered as part of MySQL Enterprise Edition and MySQL Cluster Carrier Grade Edition. In this blog, I will not go into details of the features of MEM, but rather focus on the case where you want to install MEM 3.0 to try it out on your personal desktop or laptop.

A trial version (the full product, but the can only be used for 30 days) is available from Oracle’s Software Delivery Cloud. If you are a MySQL customer, it is recommended that you download MEM from My Oracle Support (MOS).

Once you have downloaded and extracted the installation binaries, you can start the installation. You have the choice between using a GUI, text based, and unattended install. Here I will use the GUI install, but if you want to try one of the other install options, launch the installer with the –help option to get more information.

MEM 3.0 consists of two parts:

  • The Service Manager
  • Agents

Only the Service Manager is required, so that will be the first thing to install. As this is intended to show how you can test MEM, I will use a normal user rather than root for the installation.

It is also recommended to take a look at the MEM 3.0 Reference Manual.

Installation Wizard

When launching the installer the first screen asks which language to use – you have the choice of English and Japanese:

Installing the MEM 3.0 Service Manager - Step 1: Choose language
Step 1: Choose language

Next is an information screen that you should ensure you keep track of the usernames and passwords entered during the installation process:

Installing the MEM 3.0 Service Manager - Step 2: Remember the usernames and passwords entered during the installation process
Step 2: Remember the usernames and passwords entered during the installation process

The actual install process is now ready to start:

Installing the MEM 3.0 Service Manager - Step 3: Ready to start the actual install process
Step 3: Ready to start the actual install process

The following steps are to configure the installation – the first of which is to decide where to install the Service Manager:

Installing the MEM 3.0 Service Manager - Step 4: Choose the installation directory
Step 4: Choose the installation directory

The Service Manager will need three ports:

  • Tomcat Server Port: For the web UI when using non-SSL connections
  • Tomcat Shutdown Port: Used internally to shutdown the web server
  • Tomcat SSL Port: For the web UI when using SSL connections
Installing the MEM 3.0 Service Manager - Step 5: Choose the port numbers
Step 5: Choose the port numbers

The Service Manager uses a MySQL database to store the data collected. The next screen allows you to choose between using the MySQL database bundled with MEM or an existing one. Using the bundled instance has the advantage that MEM will configure it automatically and upgrades can be easier, however it will mean running two MySQL instances if you already have MySQL installed. For a test instance using the bundled instance also has the advantage that it’s easy to uninstall the whole installation again, so we will use the bundled instance in this example:

Installing the MEM 3.0 Service Manager - Step 6: Choose whether to use the bundled MySQL database or an existing one
Step 6: Choose whether to use the bundled MySQL database or an existing one

The final of the pre-installation configuration is to choose the username and password to use for the connection to the MySQL database. This is the username and password that you were reminded of earlier to make sure you remember:

Installing the MEM 3.0 Service Manager - Step 7: Specify username and password for the Service Manager to connect to the MySQL database storing the data collected through the monitoring
Step 7: Specify username and password for the Service Manager to connect to the MySQL database storing the data collected through the monitoring

Next a note that because we have chosen to install the Service Manager as a non-root user, it will not be started automatically when the server is restarted:

Installing the MEM 3.0 Service Manager - Step 8: Info that MEM will not start automatically when not installed as root
Step 8: Info that MEM will not start automatically when not installed as root

Ready to Install:

Installing the MEM 3.0 Service Manager - Step 9: Configuration completed
Step 9: Configuration completed – ready to install

The Service Manager is now being installed – this will take a little while as it both includes copying all the files in place as well as configuring and starting the web server and the bundled MySQL database:

Installing the MEM 3.0 Service Manager - Step 10: Installation is in progress
Step 10: Installation is in progress

MEM includes an uninstall script if you need to remove the Service Manager again:

Installing the MEM 3.0 Service Manager - Step 11: Files are now installed
Step 11: Files are now installed

To improve security MEM 3.0 by default uses SSL. The installation process adds a self-signed certificate, but you can choose to install your own certificate later:

Installing the MEM 3.0 Service Manager - Step 12: Information that MEM is using SSL with a self-signed certificate by default
Step 12: Information that MEM is using SSL with a self-signed certificate by default

The installer is now done and it is time to launch the UI:

Installing the MEM 3.0 Service Manager - Step 13: Ready to launch the Service Manager
Step 13: Ready to launch the Service Manager

Unattended Install

If you go through the installation process often, it is more convenient to use the unattended installation as it allows you to automate the installation. To perform the same installation as above using the unattended installation you can execute:

shell$ ./mysqlmonitor- \
            --mode unattended --unattendedmodeui none \
            --installdir /home/jesper/mysql/enterprise/monitor \
            --adminuser service_manager --adminpassword mypassword \
            --dbport 13306 --mysql_installation_type bundled \
            --tomcatport 18080 --tomcatshutdownport 18005 \
            --tomcatsslport 18443

As several of the values are the default ones, you can skip some of the options, but they are included here for completeness.

When the above command completes, continue with the post-installation configuration as you would have done using the installation wizard.

Post-Installation Configuration

Once the Service Manager UI has opened in your browser there is a little post-installation configuration to take care of:

  • You need to create an admin user for the web UI
  • You need to create a user agents can use if you choose to install any agents
  • You can choose whether to have the Service Manager automatically check for updates and how for how long time to keep historical data
Installing the MEM 3.0 Service Manager - Step 14: Post installation configuration
Step 14: Post installation configuration

The last step if to choose your timezone and which locale to use in the user interface:

Installing the MEM 3.0 Service Manager - Step 15: Choose timezone and the locale
Step 15: Choose timezone and the locale

After this you are ready to play around with the monitor. If you choose to install agents, the steps are similar.

Reducing Memory Footprint

One thing to be aware of though is that the default configuration used by the Service Manager is aimed at a small to medium production installation. If you for example installed MEM in a virtual machine or computer with limited memory available, the default configuration may not be optimal.

With the installation above just completed, the memory usage of the web server (Tomcat) and the bundled MySQL database is:

15653 jesper    20   0 3794m 924m  13m S  1.0 23.4   4:46.01 java
15586 jesper    20   0 3425m 608m 9588 S  2.6 15.4   2:23.88 mysqld

So around 1.5G resident memory. That is without adding any additional agents and/or MySQL instances to monitor.

So when I use MEM for testing, the first thing I do is to change a few configuration values to reduce the memory usage. The configuration options I change are located in two files (paths are given relative to the installation directory):

  • mysql/my.cnf – options related to the bundled MySQL database
  • apache-tomcat/bin/ – options related to the web server


For the MySQL database I change two setting:

  • innodb_buffer_pool_size – this is by default 768M, but if you only monitor a couple of MySQL instances, something like 100M is enough
  • table_definition_cache – this is by default 2048. One side effect of the value being so large is that the auto-sizing of the Performance Schema considers the installation as a large instance, so the Performance Schema tables are made large. Reducing the value to 800 is enough for testing and will reduce the memory usage of the Performance Schema with several hundred megabytes.

So my mysql/my.cnf looks something like this after the changes:

# Main Caches
table_definition_cache = 800

# InnoDB
innodb_buffer_pool_size = 100M


It is only necessary to edit one line in the Tomcat configuration file – the default settings are:

JAVA_OPTS="-Xmx768M -Xms768M -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/home/jesper/mysql/enterprise/monitor/apache-tomcat/temp -XX:+UseParallelOldGC -XX:MaxPermSize=512M"

I change that to:

JAVA_OPTS="-Xmx256M -Xms256M -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/home/jesper/mysql/enterprise/monitor/apache-tomcat/temp -XX:+UseParallelOldGC -XX:MaxPermSize=200M"

I.e. the three setting I have changed are:

  • -Xmx (maximum  heap size) from 768M to 256M
  • -Xms (minimum heap size) from 768M to 256M
  • -XX:MaxPermSize from 512M to 200M

Enabling the New Configurations

It requires restarting the Service Manager to make the new configurations take effect. You can do the restart by going into the installation directory and execute:

monitor$ ./ restart
Using CATALINA_BASE:   /home/jesper/mysql/enterprise/monitor/apache-tomcat
Using CATALINA_HOME:   /home/jesper/mysql/enterprise/monitor/apache-tomcat
Using CATALINA_TMPDIR: /home/jesper/mysql/enterprise/monitor/apache-tomcat/temp
Using JRE_HOME:        /home/jesper/mysql/enterprise/monitor/java
Using CLASSPATH:       /home/jesper/mysql/enterprise/monitor/apache-tomcat/bin/bootstrap.jar:/home/jesper/mysql/enterprise/monitor/apache-tomcat/bin/tomcat-juli.jar
Using CATALINA_PID:    /home/jesper/mysql/enterprise/monitor/apache-tomcat/temp/
Stopping tomcat service . [ OK ]
Stopping mysql service . [ OK ]
./ : mysql  started
131128 13:58:59 mysqld_safe Logging to '/home/jesper/mysql/enterprise/monitor/mysql/runtime/mysqld.log'.
131128 13:58:59 mysqld_safe Starting mysqld daemon with databases from /home/jesper/mysql/enterprise/monitor/mysql/data/
Using CATALINA_BASE:   /home/jesper/mysql/enterprise/monitor/apache-tomcat
Using CATALINA_HOME:   /home/jesper/mysql/enterprise/monitor/apache-tomcat
Using CATALINA_TMPDIR: /home/jesper/mysql/enterprise/monitor/apache-tomcat/temp
Using JRE_HOME:        /home/jesper/mysql/enterprise/monitor/java
Using CLASSPATH:       /home/jesper/mysql/enterprise/monitor/apache-tomcat/bin/bootstrap.jar:/home/jesper/mysql/enterprise/monitor/apache-tomcat/bin/tomcat-juli.jar
Using CATALINA_PID:    /home/jesper/mysql/enterprise/monitor/apache-tomcat/temp/

The memory usage is now somewhat smaller:

19521 jesper    20   0 2847m 544m  15m S  2.0 13.8   1:51.61 java
19484 jesper    20   0 2407m 160m 9296 S  0.3  4.1   0:09.21 mysqld