The MySQL Performance Schema is a gold mine of valuable data. Among the many nuggets you can extract from it is an historical report of transactions: how long a transaction took to execute, what queries were executed in it (with query metrics), and idle time between queries. Mining this information is not trivial, but it’s fun and this blog post shows how to start.

The MySQL Performance Schema is a gold mine of valuable data. Among the many nuggets you can extract from it is an historical report of transactions: how long a transaction took to execute, what queries were executed in it (with query metrics), and idle time between queries. Mining this information is not trivial, but it’s fun and this blog post shows how to start.

The MySQL Performance Schema is a gold mine of valuable data. Among the many nuggets you can extract from it is an historical report of transactions: how long a transaction took to execute, what queries were executed in it (with query metrics), and idle time between queries. Mining this information is not trivial, but it’s fun and this blog post shows how to start.

How to inspect and report MySQL transactions using the Performance Schema

How to inspect and report MySQL transactions using the Performance Schema

For most people (including me until recently) database XA transactions are a fuzzy concept. In over eight years with Percona, I have never had to deal with XA transactions. Then a few weeks ago I got two customers having issues with XA transactions. That deserves a post.

XA 101

What are XA transactions? XA transactions are useful when you need to coordinate a transaction between different systems. The simplest example could be simply two storage engines within MySQL. Basically, it follows this sequence:

1. XA START
2. Some SQL statements
3. XA END
4. XA PREPARE
5. XA COMMIT or ROLLBACK

Once prepared, the XA transaction survives a MySQL crash. Upon restart, you’ll see something like this in the MySQL error log:

2017-08-23T14:53:54.189068Z 0 [Note] …

Introduction As previously explained, CDC (Change Data Capture) is one of the best ways to interconnect an OLTP database system with other systems like Data Warehouse, Caches, Spark or Hadoop. Debezium is an open source project developed by Red Hat which aims to simplify this process by allowing you to extract changes from various database … Continue reading How to extract change data events from MySQL to Kafka using Debezium →

Introduction While doing my High-Performance Java Persistence training, I came to realize that it’s worth explaining how a relational database works, as otherwise, it is very difficult to grasp many transaction-related concepts like atomicity, durability, and checkpoints. In this post, I’m going to give a high-level explanation of how a relational database works internally while … Continue reading How does a relational database work →

In my previous post, we saw the usage of MySQL Group Replication (MGR) in single-primary mode. We know that Oracle does not recommends using MGR in multi-primary mode, but there is so much in the documentation and in presentations about MGR behavior in multi-primary, that I feel I should really give it a try, and especially compare this technology with the already existing multiple master solution introduced in 5.7: multi-source replication.

Installation

To this extent, I will set up two clusters using MySQL-Sandbox. The instructions for MGR in …

How InnoDB initializes AUTO_INCREMENT counters is actually not a bug, but a documented mechanism. There were some complaints and even people who lost data over this. To initialize an auto-increment counter after a server restart, InnoDB executes the equivalent of the following statement on the first insert into a table containing an AUTO_INCREMENT column. SELECT MAX(ai_col) FROM table_name FOR