Databases – Sheeri Dot Org

The One Where I Scream Into the Cloud

Did you know DBAs could have personalities?

Which is more exhausting – being oncall, or having a newborn?

What does a Product Manager of Technical Lineage for ETL Tools do?

How many times do I say “right?” in 36 minutes?*

For all this and more, listen to my interview on the latest Screaming in the Cloud podcast, or read the transcript at https://www.lastweekinaws.com/podcast/screaming-in-the-cloud/technical-lineage-and-careers-in-tech-with-sheeri-cabral/ or wherever you get your podcasts.

* the answer is 32

LDAP with auth_pam and Python to authenticate against MySQL

If that title looks familiar, it is because a few months ago I posted about LDAP with auth_pam and PHP to authenticate against MySQL.

The good news is that recompiling the connector for Python is a lot easier than for PHP. With PHP, the complexity was due to there being one monolithic package to recompile. The bad news is that there is a slight hitch with Python.

Skip down to the hitch and how to compile MySQLdb for use with auth_pam plugin.

As a quick reminder, here is a repeat of the background:

Background

There are two plugins that can be used. From the documentation, the two plugins are:

Full PAM plugin called auth_pam. This plugin uses dialog.so. It fully supports the PAM protocol with arbitrary communication between client and server.
Oracle-compatible PAM called auth_pam_compat. This plugin uses mysql_clear_password which is a part of Oracle MySQL client. It also has some limitations, such as, it supports only one password input. You must use -p option in order to pass the password to auth_pam_compat.

Percona’s MySQL client supports both plugins natively. That is, you can use auth_pam or auth_pam_compat and use the “mysql” tool (or “mysqldump”, or mysql_upgrade, etc.) and you are good to go. Given the choice, we would all use auth_pam, under which clients DO NOT use mysql_clear_password.

Not all clients support auth_pam, which is the main problem. Workarounds have called for using auth_pam_compat over SSL, which is a perfectly reasonable way to handle the risk of cleartext passwords – encrypt the connection.

However, what if you want to use auth_pam, and avoid cleartext passwords all together?

If you try to connect to MySQL using Python, you will receive this error: “Client does not support authentication protocol requested by server; consider upgrading MySQL client.”

Back in 2013, Percona posted about how to install and configure auth_pam and auth_pam_compat. The article explains how to recompile clients to get them to work:

The good news is that if the client uses libmysqlclient library to connect via MySQL, you can recompile the client’s source code to use the libmysqlclient library of Percona Server to make it compatible. This involves installing Percona Server development library, compiler tools, and development libraries followed by compiling and installing the client’s source code.

The Hitch

The hitch with Python is that there are a few different ways to connect to MySQL with Python. In fact, MySQL has written a Python connector, called mysql-connector-python. According to the documentation for mysql-connector-python:

It is written in pure Python and does not have any dependencies except for the Python Standard Library.

This means we cannot recompile mysql-connector-python to use the libmysqlclient library from Percona that supports auth_pam – because it does not use the libmysqlclient library.

However, mysql-connector-python is not the only way to connect MySQL to python. There is a mysqlclient-python package, which provides the MySQLdb module for connecting to MySQL. According to the documentation:

MySQLdb is a thin Python wrapper around _mysql

And the docs for the _mysql module say:

_mysql provides an interface which mostly implements the MySQL C API.

It is using the standard library, and we can recompile it. Here is a mirror of the Perl recompilation process for MySQL.

Recompiling MySQLdb to support auth_pam

Step 1

“Install Percona yum repository and Percona Server development library.” This is not a problem, do what you need to do to install Percona-Server-devel for your version.

Step 2

Install a package manager so you can build a package – optional, but useful, if you ever want to have this new client without having to recompile. As in the example, I chose the RPM package manager, so I installed rpm-build.

Step 3

“Download and install the source RPM for the client package.”

I did a web search for “mysqlclient-python source rpm” and found the rpmfind page containing many versions. If you click on the link in the “Package” column you will get to a page that has a link for the Source RPM. I chose the most recent (as of this writing) CentOS package.

So I downloaded the source RPM and installed it into the sources directory:
cd SRPMS wget http://vault.centos.org/7.4.1708/os/Source/SPackages/MySQL-python-1.2.5-1.el7.src.rpm cd ../SOURCES rpm -Uvh MySQL-python-1.2.5-1.el7.src.rpm

This unpacks MySQL-python-1.2.5.zip and a patch file in the SOURCES directory and puts a spec file in the SPECS directory, so this is not as complicated as the PHP version.

Step 4

“Install compilers and dependencies”.
On my host I had no work to get any requirements installed (your mileage may vary – I had installed a lot of dependencies previously in my PHP test, and used the same machine). Make sure to include the Percona Server package for the /usr/lib64/mysql/plugin/dialog.so file:
yum install Percona-Server-server-55-5.5.55-rel38.8.el6.x86_64

Step 5

“Build the RPM file”.

rpmbuild -bb rpmbuild/SPECS/MySQL-python.spec

Then I installed my new package and tested it, and it worked!
# rpm -e MySQL-python
# rpm -Uvh /root/rpmbuild/RPMS/x86_64/MySQL-python-1.2.5-1.el6.x86_64.rpm
Preparing… ########################################### [100%]
1:MySQL-python ########################################### [100%]

I will not be continuing this experiment with any other clients (e.g. not going to try for Ruby) but I welcome others to do the same!

Lesson 09: Managing Users and Privileges in MySQL

Notes/errata/updates for Chapter 9:
See the official book errata at http://tahaghoghi.com/LearningMySQL/errata.php – Chapter 9 includes pages 297 – 350.

In the fourth paragraph of this chapter, starting with “Most applications don’t need superuser privileges for day-to-day activities” they give you some reasons why you want to create users without the SUPER privilege. There are better reasons than the book gives, which are at the MySQL Manual page for the SUPER privilege.

In the section “Creating and Using New Users” (p. 300) they say “There’s no limit on password length, but we recommend using eight or fewer characters because this avoids problems with system libraries on some platforms.” You should ignore this, this book was written in 2006 and modern system libraries can handle more than 8 characters in a password. Also ignore it when they say the same thing in the section “Understanding and Changing Passwords” (p. 324).

In the section “Creating a New Remote User” at the very end (p. 214), it talks about using % as a host wildcard character. I want to point out that if there are no ACL’s set for a given host, MySQL will reject ALL connections from that host – even “telnet host 3306” will fail. So if you avoid using %, you are slightly more secure.

In the “Anonymous Users” section (p. 315), one fact that is not mentioned is that for all users, including the anonymous user, any database named “test” or that starts with “test_” can be accessed and manipulated. So an anonymous user can create tables in the “test” database (or even “test_application”) and fill it full of data, causing a denial of service when the disk eventually fills up. This fact is mentioned later in the chapter in the “Default User Configuration” section under “Linux and Mac OS X”, but it should be known earlier.

The “mysqlaccess” utility described in the section of that name (p. 320) is usually not used. These days, folks prefer the pt-show-grants tool. Here is a blog post with some examples of pt-show-grants.

In the section on “Removing Users” (p. 324), it says that if all the privileges are revoked, and a user only has GRANT USAGE, “This means the user can still connect, but has no privileges when she does.” This is untrue, as mentioned before, everyone can access and manipulate databases starting with “test”.

The section “Managing Privileges with SQL” is deprecated (p. 339-346, up to and including “Activating Privileges”). It used to be, back when this was written, that few people used the GRANT statements and more people directly manipulated the tables. These days, it’s the other way around, and due to problems like SQL injection, there are safeguards in place – for example, if you change the host of a user with an ALTER TABLE on the mysql.user table, the user will have all privileges dropped. Just about the only thing direct querying is used for, is to find who has the Super_priv variable set to ‘Y’ in the user table.

Supplemental material: I have a video presentation on security which includes ACLs and there are accompanying PDF slides.

Topics covered:
Creating and dropping local and remote users
Different MySQL privileges
SUPER privilege
GRANT and REVOKE syntax
Hosts and wildcards
Anonymous and default users
Checking privileges
Password management
Basic user security
Resource limit controls

Reference/Quick Links for MySQL Marinate

MySQL 5.7 InnoDB Temporary Tablespace – but why?

So, recently we had a runaway query eat up all sorts of temporary table space on our machines. Several machines had several terabytes in their ibtmp1 file after this happened. So I set out to find out more about why the InnoDB temporary tablespace is used, why it is better than using regular files, which was what was used prior to MySQL 5.7, and how to make sure that runaway queries do not end up filling up disk space.

Unfortunately, the manual does not go into why ibtmp1 is better than one file per temporary query, which disappears once the query ends. There are a few sections to look into:

Temporary Table Undo Logs – has one paragraph that states that these are the undo logs for temporary tablespaces. Given that these are undo logs, my guess is that this makes MySQL more crash-safe. But that is just a guess.

There is also InnoDB Temporary Tablespace which is two paragraphs, with some more details, but again, no reasoning why.

And finally, the documentation for the innodb_temp_data_file_path system variable sheds a bit of light on the subject – It explains “Metadata about active InnoDB temporary tables is located in INFORMATION_SCHEMA.INNODB_TEMP_TABLE_INFO.”

There is a manual page on Innodb temp table info table as well, which shows both compressed and uncompressed tables – uncompressed tables live in the ibtmp tablespaces, and compressed temporary tables live in the .ibd tablespace of the compressed table in question – as shown in the “PER_TABLE_SPACE” and “IS_COMPRESSED” fields.

Sadly, the table does not give useful information such as which process ID or user is generating the data. And of course it is only the active temporary space usage at the time – if you have a large temporary tablespace but no active queries using the tablespace, INNODB_TEMP_TABLE_INFO is empty.

I can imagine a scenario with more than one long-running query using a lot of space in the temporary tablespace. But I do not see how the INNODB_TEMP_TABLE_INFO would help me determine any useful information as to which query I should kill. I guess it is useful to see if there is an active query currently using temporary tablespace, but when you have a large file with nothing in it, it is just that much more depressing.

# mysql INFORMATION_SCHEMA -p -e “SELECT * FROM INNODB_TEMP_TABLE_INFO”
Enter password:
# ls -rlth /var/lib/mysql/ibtmp1
-rw-r—– 1 mysql mysql 2.3T Oct 31 10:50 /var/lib/mysql/ibtmp1

Lesson 07: Advanced MySQL Querying

Notes/errata/updates for Chapter 7:
See the official book errata at http://tahaghoghi.com/LearningMySQL/errata.php – Chapter 7 includes pages 223 – 275.

Supplemental blog post – ORDER BY NULL – read the blog post and the comments!

GROUP BY and HAVING examples – Supplemental blog post. The example of HAVING in the text shows a use case where HAVING is the same function as WHERE. This blog posts shows examples of HAVING that you cannot do any other way.

In the section called “The GROUP BY clause”, on pages 231-232, the book says:
“you can count any column in a group, and you’ll get the same answer, so COUNT(artist_name) is the same as COUNT(*) or COUNT(artist_id).” This is not 100% true; COUNT does not count NULL values, so if you had 10 rows and 1 artist_name was NULL, COUNT(artist_name) would return 9 instead of 10. COUNT(*) counts the number of rows and would always return 10, so COUNT(*) is preferable when you intend to count the number of rows.

Also in that section, on page 233 when they show you the example:
SELECT * FROM track GROUP BY artist_id;
– Note that they explain the result is not meaningful. In most other database systems, this query would not be allowed.

In the “Advanced Joins” section, specifically on page 238 at the bottom where they say “There’s no real advantage or disadvantage in using an ON or a WHERE clause; it’s just a matter of taste.” While that’s true for the MySQL parser, it’s much easier for humans to read, and see if you missed a join condition, if you put the join conditions in an ON clause.

In the section on Nested Queries, on page 251, it says “nested queries are hard to optimize, and so they’re almost always slower to run than the unnested alternative.” MySQL has gotten better and better at optimizing nested queries, so this statement isn’t necessarily true any more.

A “derived table”, is a nested query in the FROM Clause, as described in the section heading with that name (p. 262).

In the “Table Types” subsection (p. 267), it says that MyISAM is a good choice for storage engines, and that “you very rarely need to make any other choice in small-to medium-size applications”. However, it’s recommended to use InnoDB for better concurrency, transaction support and being safer from data corruption in a crash situation. Indeed, the default storage engine in more recent versions of MySQL is InnoDB.

In addition, the lingo has been changed since the book was written; we now use “storage engine” instead of “table type”. The examples that use CREATE TABLE or ALTER TABLE with TYPE may need to be changed to STORAGE ENGINE instead of TYPE.

Finally, you can skip the section on BDB since it has been deprecated (p. 274-5).

Topics covered:
Aliases
Join style
Joins (JOIN, INNER, COMMA, STRAIGHT, RIGHT, LEFT, NATURAL)
UNION and UNION ALL
Data aggregation (DISTINCT, GROUP BY, HAVING
Subqueries and Nested Queries (including ANY, SOME, ALL, IN, NOT IN, EXISTS, NOT EXISTS, correlated subqueries, derived tables, row subqueries)
User Variables
Transactions/locking
Table Types/Storage engines

Reference/Quick Links for MySQL Marinate

Lesson 06: Working with Database Structures

Notes/errata/updates for Chapter 6:
See the official book errata at http://tahaghoghi.com/LearningMySQL/errata.php – Chapter 6 includes pages 179 – 222.

Other notes:
At the end of the “Creating Tables” section (p.183-4), it says “We like using the underscore character to separate words, but that’s just a matter of style and taste; you could use underscores or dashes, or omit the word-separating formatting altogether.” While this is true, beware of using a dash, because MySQL will try to interpret “two-words”, thinking – is a minus sign. I recommend avoiding dashes for this reason (even though the book does this on page 215).

At the end of the “Collation and Character Sets” section (p.186), it says “When you’re creating a database, you can set the default character set and sort order for the database and its tables.” Note that the default character set for the server will set the default character set for any new databases created if a default character set is not specified; there is no change in existing databases. In turn, the default character set for the database sets the default character set for any new tables created but does not change any existing tables, and the default character set for a table determines the default character set for each column, which can be overridden by specifying a character set when defining a column.

Under the “Other Features” section it references a section called “Table Types”. This section is in chapter 7, p. 267.

Under the “Other Features” section it shows the SHOW CREATE TABLE example (p. 187). By default, MySQL shows you output in horizontal format – that is, one table row is shown as one row in the output. However, you can have MySQL show you output in vertical format, where one column is shows as one row in the output. Instead of using ; to end a query, use \G

Try it with:
SHOW CREATE TABLE artist;
vs
SHOW CREATE TABLE artist\G

And see the difference.

In the “Column Types” section on page 194, it says that “Only one TIMESTAMP column per table can be automatically set to the current date and time on insert or update.” This is not true as of MySQL version 5.6.5 and higher. As per the documentation at https://dev.mysql.com/doc/refman/5.6/en/timestamp-initialization.html: “For any TIMESTAMP or DATETIME column in a table, you can assign the current timestamp as the default value, the auto-update value, or both.”

In the section called “The AUTO_INCREMENT Feature”, on page 211, it says “If, however, we delete all the data in the table, the counter is reset to 1.” The example shows the use of TRUNCATE TABLE. Note that if you deleted all the data in the table with DELETE, such as “DELETE FROM count WHERE 1=1;”, the counter is NOT reset.

Supplemental material:
Data types:
Podcast on Strings
Podcast on Numeric data types
Podcast on ENUM, SET and different SQL modes
Podcast on Times and time zones

Topics covered:
How to CREATE, DROP and ALTER databases, tables, columns and indexes
Collations and character sets
Data types
AUTO_INCREMENT

Reference/Quick Links for MySQL Marinate

MySQL Marinate – So you want to learn MySQL! – START HERE

Want to learn or refresh yourself on MySQL? MySQL Marinate is the FREE virtual self-study group is for you!

MySQL Marinate quick links if you know what it is all about.

This is for beginners – If you have no experience with MySQL, or if you are a developer that wants to learn how to administer MySQL, or an administrator that wants to learn how to query MySQL, this course is what you want. If you are not a beginner, you will likely still learn some nuances, and it will be easy and fast to do. If you have absolutely zero experience with MySQL, this is perfect for you. The first few chapters walk you through getting and installing MySQL, so all you need is a computer and the book.

The format of a virtual self-study group is as follows:
Each participant acquires the same textbook (Learning MySQL, the “butterfly O’Reilly book”, published 2007). You can acquire the textbook however you want (e.g. from the libary or from a friend, hard copy or online). Yes, the book is old, but SQL dates back to at least the 1970’s and the basics haven’t changed! There are notes and errata for each chapter so you will have updated information. The book looks like this:

O'Reilly Butterfly book picture — O’Reilly Butterfly book picture

Each participant commits to reading each chapter (we suggest one chapter per week as a good deadline), complete the exercises and post a link to the completed work.

Each participant obtains assistance by posting questions to the comments on a particular chapter.

Note: There is no classroom instruction.

How do I get started?

– Watch sheeri.com each week for the chapters to be posted.

– Get Learning MySQL
Acquire a book (the only item that may cost money). Simply acquire Learning MySQL – see if your local library has it, if someone is selling their copy, or buy it new.

– Start!
When your book arrives, start your virtual learning by reading one chapter per week. Complete the exercises; if you have any questions, comments or want to learn more in-depth, that’s what the comments for!

FAQs:
Q: Does this cover the Percona patch set or MariaDB forks?

A: This covers the basics of MySQL, which are applicable to Percona’s patched MySQL or MariaDB builds, as well as newer versions of MySQL.

Q: What do I need in order to complete the course?

A: All you need is the book and access to a computer, preferably one that you have control over. Windows, Mac OS X or Unix/Linux will work. A Chromebook or tablet is not recommended for this course.

Q: Where can I put completed assignments?

A: Completed assignments get uploaded to github. See How to Submit Homework

Q: The book was published in 2007. Isn’t that a bit old?

A: Yes! The basics are still accurate, and we will let you know what in the book is outdated. I have contacted O’Reilly, offering to produce a new edition, and they are not interested in updating the book. We will also have optional supplemental material (blog posts, videos, slides) for those who want to learn more right away. We are confident that this self-study course will make you ready to dive into other, more advanced material.

Soak it in!

Reference/Quick Links for MySQL Marinate

Cost/Benefit Analysis of a MySQL Index

We all know that if we add a MySQL index to speed up a read, we end up making writes slower. How often do we do the analysis to look at how much more work is done?

Recently, a developer came to me and wanted to add an index to a very large table (hundreds of gigabytes) to speed up a query. We did some testing on a moderately used server:

Set long_query_time to 0 and turn slow query logging on
Turn slow query logging off after 30 minutes.

Add the index (was on a single field)

Repeat the slow query logging for 30 minutes at a similar time frame (in our case, we did middle of the day usage on a Tuesday and Wednesday, when the database is heavily used).

Then I looked at the write analysis – there were no DELETEs, no UPDATEs that updated the indexed field, and no UPDATEs that used the indexed field in the filtering. There were only INSERTs, and with the help of pt-query-digest, here’s what I found:

INSERT analysis:
Query hash 0xFD7…..
Count: 2627 before, 2093 after
Exec time:
– avg – 299us before, 369us after (70us slower)
– 95% – 445 us before, 596us after
– median – 273us before, 301us after

I extrapolated the average per query to 2400 queries, and got:
**Total, based on 2400 queries – 71.76ms before, 88.56ms after, 16.8ms longer**

There was only one read query that used the indexed field for ORDER BY (or anywhere at all!), so the read analysis was also simple:

Read analysis:
Query hash 0xF94……
Count:187 before, 131 after
Exec time:
– avg – 9ms before, 8ms after. 1 ms saved
– 95% – 20ms before, 16 ms after
– median – 9ms before, 8 ms after

Again, extrapolating to average for 150 queries:
**Total, based on 150 queries: 150ms saved**

So we can see in this case, the index created a delay of 16.8 ms in a half-hour timeframe, but saved 150 ms in reads.

It is also impressive that the write index added very little time – 70 microseconds – but saved so much time – 1 millisecond – that there were 16 times the number of writes than reads, but we still had huge improvement, especially given the cost.

I cannot make a blanket statement, that this kind of index will always have this kind of profile – very tiny write cost for a very large read savings – but I am glad I did this analysis and would love to do it more in the future, to see what the real costs and savings are.

Query Reviews (part 2): pt-query-digest

Query reviews (part 1): Overview

The 1st post in the series gave an overview of what a query review is and the value they can bring you. So now let’s talk about how one is done, specifically, how to do a query review using pt-query-digest.

The point of a query review is that it is a comprehensive review of queries. Imagine if you could get a list of all queries that run on your system, and then you systematically looked at each query to determine if it is optimized. That is the basic concept behind a query review.

So, how do you get a list of queries?

pt-query-digest can use a slow query log, binary log, general log or tcpdump. I usually use a slow query log with long_query_time set to 0, so I can capture all the successful queries and their timings. If this is too much overhead, consider using Percona Server’s log_slow_rate_limit and log_slow_rate_type parameters to only log every nth session/query. This means that if you have 5000 queries per second, you can set the slow logging rate to every 100th query, and reduce the write overhead for the slow query log to 50 queries per second (instead of all 5000 queries).

So you have your log, now what? Well, we need to process it. The –type option is where you set what your log type is (binlog, genlog, slowlog, tcpdump). Default is slowlog.

By default, pt-query-digest will give you a report of the top 95% worst queries. You can change that with the –limit parameter – note that –limit just limits the output; pt-query-digest still processes all the queries in the log file. If –limit is followed by an integer, it will limit the output to the top X queries; if it’s followed by a percentage (e.g. 10%) it will output the top percentage of queries.

As this is a query review of all queries, we will want to set the limit to 100%.

There are a lot of other options that pt-query-digest has, but many of them are there so we can distill and get queries that meet a certain criteria. The point of a query review is to look at ALL queries, so we do not need to use those options.

In fact, the only other options we need are related to the review itself. Because a review is systematic, we need a place to store information related to the review. How about a database for that? In fact, pt-query-digest has a –review option that takes parameters to store the information into a table.

Here is the command I recently used to start a query review. It was run from the shell commandline, and I used –no-report because I did not want anything other than the table and its rows created:
[sheeri.cabral@localhost]$ pt-query-digest --no-report --type slowlog --limit 100% --review h=localhost,u=sheeri.cabral,D=test,t=query_review --create-review-table --ask-pass mysql_slow.log

You can see that –review has a number of arguments, comma-separated, to identify a table on a host to put the queries into. I used the –create-review-table flag to create the table, since it did not already exist, and –ask-pass because I do not type in passwords in a shell command.

pt-query-digest then spends some time analyzing the file then creating and populating the table. Here’s a sample row in the table:

*************************** 1. row ***************************
checksum: 11038208160389475830
fingerprint: show global status like ?
sample: show global status like ‘innodb_deadlocks’
first_seen: 2017-06-03 11:20:59
last_seen: 2017-06-03 11:32:15
reviewed_by: NULL
reviewed_on: NULL
comments: NULL

The checksum and fingerprint are ways to make the query portable, no matter what values are used. The fingerprint takes out all the differences among iterations of the query, and puts ? in its place. So if you have a query that’s used over and over, like
SELECT first_name FROM customers WHERE id in (1,2,3)
the fingerprint would look like
SELECT first_name FROM customers WHERE id in (?+)

The sample provides a way for us to copy and paste into an EXPLAIN (or my favorite, EXPLAIN FORMAT=JSON) statement, so that we can assess the query.

So then we can go through the process of optimizing the query. In the end, this query has nothing to tweak to optimize, so I update the reviewed_on date, the reviewed_by person, and the comments:

mysql> UPDATE test.query_review set reviewed_on=NOW(), reviewed_by='sheeri.cabral', comments='no mechanism to optimize' WHERE checksum=11038208160389475830; Query OK, 1 row affected (0.00 sec) Rows matched: 1 Changed: 1 Warnings: 0

On to the next query – we shall get the next query that has not yet been reviewed:
mysql> select * from test.query_review where reviewed_on is null limit 1\G

If you have already done some query reviews, your WHERE clause may look something like where reviewed_on is null OR reviewed_on < NOW()-interval 6 month.

And then look at that query for optimization. Lather, rinse, repeat. This is a GREAT way to get familiar with how developers (and ORMs) are writing queries.

Some tricks and tips – first take a look at all the queries less than 50 characters or so – you can easily update those to be all set to reviewed, with whatever message you want.
mysql> select fingerprint from query_review where length(sample)<50; +----------------------------------------------+ | fingerprint | +----------------------------------------------+ | administrator command: Ping | | set session `wait_timeout` = ? | | show tables | | rollback | | select * from information_schema.processlist | | select @@session.tx_isolation | | show status | | start transaction | | select user() | | show query_response_time | | set autocommit=? | | show full processlist | | show databases | | administrator command: Statistics | | show plugins | | show slave status | | commit | | set names ? | | show global status like ? | | administrator command: Quit | | show /*!? global */ status | | set names utf? | | select @@version_comment limit ? | | show engine innodb status | | select database() | +----------------------------------------------+ 25 rows in set (0.00 sec)

One great feature is that you can add columns to the table. For example, maybe you want to add an “indexes” column to the table, and list the index or indexes used. Then after the query review is complete, you can look at all the indexes in use, and see if there is an index defined in a table that is NOT in use.

You can review all the queries and run a query review every 6 months or every year, to look at any new queries that have popped up, or queries that have been removed (note first_seen and last_seen in the table).

You can also see how the query performance changed over time using the –history flag to pt-query-digest, which can populate a table with statistics about each query. But that is a topic for another post!

Query reviews are excellent ways to look comprehensively at your queries, instead of just the “top 10” slow, locking, most frequent, etc. queries. The EXPLAINing is long and slow work but the results are worth it!

Why does the MySQL optimizer not do what I think it should?

In May, I presented two talks – one called “Are you getting the best out of your indexes?” and “Optimizing Queries Using EXPLAIN”. I now have slides and video for both of them.

The first talk about indexing should probably be titled “Why is MySQL doing this?!!?!!?” It gives insight into why the MySQL optimizer chooses indexes that you do not expect; especially when it does not use an index you expect it to.

The talk has something for everyone – for beginners it explains B-trees and how they work, and for the more seasoned DBA it explains concepts like average value group size, and how the optimizer uses those concepts applied to metadata to make decisions.

Slides are at http://technocation.org/files/doc/2017_05_MySQLindexes.pdf.
Click the slide image below to go to the video at https://www.youtube.com/watch?v=e39-UfxQCCs Slide from MySQL indexing talk

The EXPLAIN talk goes through everything in EXPLAIN – both the regular and JSON formats – and describes what the fields mean, and how you can use them to figure out how to best optimize your query. There are examples that show where you can find red flags, so that when you EXPLAIN your own queries, you can be better prepared for gotchas. The EXPLAIN talk references the indexing talk in a few places (both talks were given to the same audience, about a week apart), so I highly recommend you watch that one first.

Slides are at http://technocation.org/files/doc/2017_05_EXPLAIN.pdf.
Click the slide image below to go to the video at https://www.youtube.com/watch?v=OlclCoWXplg