Important notice: With this release, we have changed the registration keys. As usual, the new keys are available for registered users from our Customer Portal.

Monyog MySQL Monitor v8.4.0 adds more alert and logging options and enhances the MONyog API. Further, we redesigned the settings page and a number of bugs were fixed.

Changes as compared to Monyog MySQL Monitor v8.3.2 include: Features:

Added more notification channels (Slack and Pagerduty) for Monyog alerts. (Read more) Option to write Monyog alerts in the Syslog of the machine where Monyog is installed. This option is only available for Linux. Option to edit the subject line for Monyog alerts. Added MONyog API to disable notification for a monitor based on a server/tag. ( …

After upgrading some of our slaves to latest 5.7, I have found  what looks like a serious regression introduced in MySQL 5.7.
A couple weeks ago I noticed that the error log file of one of our clusters, where I had implemented my in place transparent compression of binary logs,  was literally flooded by the following error:

[ERROR] Binlog has bad magic number;  It's not a binary log file that can be used by this version of MySQL

In the above setup this is  an harmless error, and it should only happen at server startup, where mysqld opens and reads all available binary log files.  The error is due to the fact that since files are now compressed, mysqld doesn't recognize them as valid - not an issue, as only older files are compressed, and only after …

Semi-sync Replication is a plugin available for mysql which allows you to create more durable replication topologies.  For instance you can ensure that in the event of a master crash that at least one of your replicas has all transaction currently written to the master so that when you promote, you know you're not missing any data.

That's a huge simplification.

What's the downside?  Write speed.  If a transaction on your master have to wait until a replica acknowledges it has that transaction, then there is going to be some delay.  Not only that, but your network latency between the two points matters a lot.  If you want greater durability, the cost is performance.

It's important to note that the master doesn't wait until the replica actually runs the transaction on the …

Amazon Web Services (AWS) is a cloud platform that offers a wide variety of services including computing power, database storage, content delivery and other functionality that targets businesses of all sizes. One of their database solutions includes the Amazon Relational Database Service. Amazon RDS includes a number of popular RDBMSes, including Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and Microsoft SQL Server, as well as tools to manage your databases and monitor their performance.

Despite the wide range of metrics available within the Amazon RDS console, there are some very good reasons for using your own monitoring tool(s) instead or in addition to those offered by Amazon RDS. For example, familiarity with your own tool(s) or access to features that Amazon RDS does not provide would constitute two persuasive reasons for employing a local tool.

With traditional software monitoring platforms such as Monyog still enjoying …

With Halloween all but a distant memory, the time has come to turn our attention to the upcoming holiday season. First, Thanksgiving, then Black Friday and Cyber Monday, culminating in the Christmas/boxing week shopping bonanza. For business owners, this time of the year marks the long-awaited year’s end profit taking. For some DBA’s, it brings fear, trepidation, and even sleepless nights toiling away to bring systems back online.

Thankfully, this need not be the case. With a little proactive tweaking of MySQL performance variables, you can insulate your database server(s) against the onslaught of increased demand that the shopping season brings.

Tip #1: Determine the Maximum Number of MySQL Connections

A good starting estimate for the maximum number connections on MySQL is one for every five requests to your web server. A few of those five requests to your web server will be for resources like CSS style sheets, …

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 …

MySQL is not only a relational database, but can also be used as a schemaless/NOSQL document store, or a mix of both. This is realized by the JSON datatype, and is useful for any data that is hard to fit in the ”tabular” format of a traditional table.…

This post is following the story of MySQL 8.0 Performance & Scalability started with article about 2.1M QPS obtained on Read-Only workloads. The current story will cover now our progress in Read-Write workloads..
Historically our Read-Only scalability was a big pain, as Read-Only (RO) workloads were often slower than Read-Write (sounds very odd: "add Writes to your Reads to go faster", but this was our reality ;-)) -- and things were largely improved here since MySQL 5.7 where we broke 1M QPS barrier and reached 1.6M QPS for the first time. However, improving Writes or mixed Read+Writes (RW) workloads is a much more complex story..
What are the main scalability show-stoppers …

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 …

In this blog post, I’ll provide some guidance on how to choose the MySQL innodb_log_file_size.

Like many database management systems, MySQL uses logs to achieve data durability (when using the default InnoDB storage engine). This ensures that when a transaction is committed, data is not lost in the event of crash or power loss.

MySQL’s InnoDB storage engine uses a fixed size (circular) Redo log space. The size is controlled by innodb_log_file_size and innodb_log_files_in_group (default 2). You multiply those values and get the Redo log space that available to use. While technically it shouldn’t matter whether you change either the innodb_log_file_size or innodb_log_files_in_group variable to control the Redo space size, most people just work with the innodb_log_file_size and leave innodb_log_files_in_group alone.

Configuring …