Join Percona’s Chief Evangelist Colin Charles on Thursday, November 3, 2016, at 10 am PDT/ 1:00pm EDT (UTC-7) as he presents “The MySQL Ecosystem in 2016.”

MySQL is a unique adult (now 21 years old) in many ways. It supports plugins. It supports storage engines. It is also owned by Oracle, thus birthing two branches of the popular opensource database: Percona Server and MariaDB Server. It also once spawned a fork: Drizzle. Lately, a consortium of web scale users (think a chunk of the top ten sites out there) have spawned WebScaleSQL.

You’re a busy DBA having to maintain this mix of technologies. Or you’re a CIO planning to choose one branch. How do you go about picking? Supporting multiple databases? Find out more in this talk. We will also take a deep-dive into what feature differences exist between MySQL/Percona …

Test cases that do not perform a proper cleanup turn out to be a nightmare for test engineers or anyone who is tasked with the job of monitoring regression test results. It leads to test failures that are difficult to analyse and fix.…

This blog post discusses an information script for the Percona Monitoring and Management (PMM) tool.

In recent news, we announced the fresh-of-the-press Percona Monitoring and Management (or PMM for short) platform. Given the interaction of the different components that together make up PMM, I developed a script that helps provide you information about the status of your PMM installation.

You can use this script yourself, or one of our support might point you to this page to obtain the information they need to troubleshoot an issue you are experiencing.

You will likely want to execute this script once on the PMM …

Uber Engineering’s Schemaless storage system powers some of the biggest services at Uber, such as Mezzanine. Schemaless is a scalable and highly available datastore on top of MySQL¹ clusters. Managing these clusters was fairly easy when we had …

The post Dockerizing MySQL at Uber Engineering appeared first on Uber Engineering Blog.

This blog post is a result of reading book.
To be clear -> “Expert MySQL” by Charles Bell -> http://www.apress.com/gp/book/9781430246596
This book explain internals of MySQL based on 5.6.x version. So relatively new and really good resource to use.

So the task is:
Chapter 8, “Extending MySQL High Availability” -> page 310, “Extending Replication”.
Where we should add new command -> “SQLCOM_STOP_SLAVES” executed on master and causing to STOP IO_THREAD on all slaves.
The basic idea is to run “STOP ALL SLAVES” on master, which in turn will appear on binary log and then it will applied on slaves.

Great
MySQL version is 5.6.32.

So, as book advice go to sql/lex.h. “We are adding the new symbol for the command. This file contains the symbol array stored in …

I have been following Facebook's MyRocks project (and Mark Callaghan's blog) for a long time. The idea of an LSM based engine for MySQL is actually a great idea.
We all know that InnoDB sucks at INSERTs.  BTree in general sucks when it's about insertion speed, and the more rows you insert, the more it sucks at it. There are many blog posts on the web that shows the insert speed degradation in InnoDB when the amount of rows in the table grows. Things get much worse faster if your primary key is a random key, for example an UUID.
We hit this problem with our caching servers (yes, we do caching with MySQL!), and in order to be able to scale these servers up we moved since a couple years to the TokuDB engine with great success. TokuDB is based on fractal tree technology, and guarantees the same insert speed, no matter the number of rows you have in the table; furthermore, it …

The details on this issue are here:
https://github.com/facebook/mysql-5.6/issues/369

This test is very simple. I loaded the SSB (star schema benchmark) data for scale factor 20 (12GB raw data), added indexes, and tried to count the rows in the table.

After loading data and creating indexes, the .rocksdb data directory is 17GB in size.

A full table scan "count(*)" query takes less than four minutes, sometimes reading over 1M rows per second, but when scanning the index to accomplish the same count, the database can only scan around 2000 rows per second. The four minute query would take an estimated 1000 minutes, a 250x difference.

I have eliminated the type of CRC32 function (SSE vs non-SSE) by forcing the hardware SSE function by patching the code.

There seem to be problems with any queries …

The details on this issue are here:
https://github.com/facebook/mysql-5.6/issues/369

This test is very simple. I loaded the SSB (star schema benchmark) data for scale factor 20 (12GB raw data), added indexes, and tried to count the rows in the table.

After loading data and creating indexes, the .rocksdb data directory is 17GB in size.

A full table scan "count(*)" query takes less than four minutes, sometimes reading over 1M rows per second, but when scanning the index to accomplish the same count, the database can only scan around 2000 rows per second. The four minute query would take an estimated 1000 minutes, a 250x difference.

I have eliminated the type of CRC32 function (SSE vs non-SSE) by forcing the hardware SSE function by patching the code.

There seem to be problems with any queries …

So I tried to do my first set of benchmarks and testing on RocksDB today, but I ran into a problem and had to file a bug:
https://github.com/facebook/mysql-5.6/issues/365

MySQL @ Facebook RocksDB appears to store at least 2x the size of the volume of changes in a transaction. I don't know how much space for the row + overhead there is in each transcation, so I'm just going to say 2x the raw size of the data changed in the transaction, as approximation. I am not sure how this works for updates either, that is whether old/new row information is maintained. If old/row data is maintained, then a pure update workload you would need 4x the ram for the given transactional changes. My bulk load was 12GB of raw data, so it failed as I have only 12GB of RAM in my test system.

The workaround (as suggested in the bug) is to set two configuration …

So I tried to do my first set of benchmarks and testing on RocksDB today, but I ran into a problem and had to file a bug:
https://github.com/facebook/mysql-5.6/issues/365

MySQL @ Facebook RocksDB appears to store at least 2x the size of the volume of changes in a transaction. I don't know how much space for the row + overhead there is in each transcation, so I'm just going to say 2x the raw size of the data changed in the transaction, as approximation. I am not sure how this works for updates either, that is whether old/new row information is maintained. If old/row data is maintained, then a pure update workload you would need 4x the ram for the given transactional changes. My bulk load was 12GB of raw data, so it failed as I have only 12GB of RAM in my test system.

The workaround (as suggested in the bug) is to set two configuration …