Building high availability, one step at a time

When it comes to database infrastructure, we all want it. We all strive to build a highly available setup. Redundancy is the key. We start to implement redundancy at the lowest level and continue up the stack. It starts with hardware - redundant power supplies, redundant cooling, hot-swap disks. Network layer - multiple NIC’s bonded together and connected to different switches which are using redundant routers. For storage we use disks set in RAID, which gives better performance but also redundancy. Then, on the software level, we use clustering technologies: multiple database nodes working together to implement redundancy: MySQL Cluster, Galera Cluster.

Related resources

 Galera Cluster for MySQL Tutorial

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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: …

In this blog post, I will look at disk utilization and saturation.

In my previous blog post, I wrote about CPU utilization and saturation, the practical difference between them and how different CPU utilization and saturation impact response times. Now we will look at another critical component of database performance: the storage subsystem. In this post, I will refer to the storage subsystem as “disk” (as a casual catch-all). 

The most common tool for command line IO performance monitoring is

iostat

, which shows information like this:

root@ts140i:~# iostat -x nvme0n1 5
Linux 4.4.0-89-generic (ts140i)         08/05/2017      _x86_64_        (4 CPU)
avg-cpu:  %user   %nice %system %iowait  %steal   %idle
          0.51    0.00    2.00    9.45    0.00   88.04
Device:         rrqm/s   wrqm/s …

By default for MySQL server, InnoDB Engine is getting used widely due it’s ACID support, optimized read-write performance and for many other reasons which are great significance for the database server.

In this blog post, we are going to cover the InnoDB tablespace and its features like,

• InnoDB engine tablespaces
• Tablespace Data Encryption
• Tablespace related Configuration

InnoDB engine tablespaces System tablespace:  

Common tablespace for MySQL server operations. Apart from the table data storage, InnoDB’s functionality requires looking for table metadata, storing and retrieving MVCC info to support ACID compliance and Transaction Isolation. It contains several types of information for InnoDB objects.

• Contains:
  Table Data Pages
  Table Index Pages …

SQLite is really a good tool to set up quick proof of concepts and small applications; however it’s not the most robust solution on the market for working with relational databases. In the open source community two databases take the top of the list: PostgreSQL and MySQL.

I did a small project for my studies. I was using SQLite as I didn’t need much out of it. Curious, I decided to see how the application would behave on other databases and decided to try PostgreSQL and MySQL. I had two problems to solve, and this post is about the first one: how to deal with the migrations. They were as follows:

Active Record automatically put the field id in all of its tables, that’s why it is omitted on the migrations.

In PostgreSQL it …

Join Percona Chief Evangelist Colin Charles as he covers happenings, gives pointers and provides musings on the open source database community.

We are five weeks out to the conference! The tutorials and the sessions have been released, and there’s an added bonus – you can now look at all this in a grid view: tutorials, day one and day two. Now that you can visualize what’s being offered, don’t forget to register.

If you want a discount code, feel free to email me at …

Percona announces the release of Percona Server for MySQL 5.6.37-82.2 on August 25, 2017. Download the latest version from the Percona web site or the Percona Software Repositories. You can also run Docker containers from the images in the Docker Hub repository.

Based on MySQL 5.6.37, and including all the bug fixes in it, Percona Server for MySQL …

This post is about, how I have tried to make simple Python script using mysqlx module work with MyRocks.
This is also related to pytest, as I have implemented simple pytest tests to call them from bash file.

So let’s discuss problem description:
The base problem is, by default when you create collection using Python X Plugin, the collection will have, 1 json type column called `doc` and 1 generated column from this `doc` column called `_id`.
So basically, you can not alter table engine to MyRocks because it will give an error something like:

ERROR 3106 (HY000): 'Specified storage engine' is not supported for generated columns.

The result:

Well, it can be solved by dropping generated `_id` column. Here we are encountering another issue that, if you have table with json data, please do NOT alter it to MyRocks, otherwise, you will get some weird results as described here:

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