The source of this post is in this gist on nbviewer.

After reading about the difference between MySQL Connector/Python and MySQLdb on this blog post I wondered how the C Extension option in Connector/Python would perform.

If you want to run the code yourself you'll need: Jupyter/IPython, Python 3, Requests, MySQLdb, Connector/Python, Matplotlib, Pandas and MySQL.

In [1]:

%matplotlib notebook

In [2]:

import random
import gzip
import time

import pandas as pd
import matplotlib.pyplot as plt
import requests
import mysql.connector
import MySQLdb
for imp in [mysql.connector, MySQLdb]:
    print('Using {imp} {version}'.format(imp=imp.__name__, version=imp.__version__))
print('C Extension for …

I lost count of the number of times I had customers come to me for a regular MySQL performance check-up saying “no, we don’t have any performance problems right now”, just to find they do actually have them, they’re just not aware of it. It’s no big surprise this happens when you’re not using your system as actively as your users are.

So let me share with you one trick I’m using to quickly check if the system I’m on has any problems that need troubleshooting. Actually, it’s dead simple. And no, it’s not a slow query log, it’s Threads_running. Let me show you.

Wait, what’s wrong with the slow query log?

Nothing at all. MySQL’s slow query log is a great tool when you’re looking for slow queries. Or when you’re fishing for things to optimize – either to reduce the resource consumption, or to increase speed for queries you didn’t know are slow.

And it’s …

When MySQL becomes too slow (or too unstable), temptation usually is to tweak the MySQL configuration file. Indeed, it’s a good place to start. But if you ever looked at the available configuration options, you know things can get messy – MySQL now has over 450 configuration variables for your consideration, that are not classified in any way, and neither of them are included in the stock my.cnf. It’s hard to know where to start!

I’m hoping that this blog post will help you overcome the anxiety of tuning MySQL, whether you’re setting up a new server, or tuning an already running server for better performance.

Don’t do it the way rookies do it

During the last 9 years I’ve spent at Percona working as a MySQL performance and scalability consultant, I found that customers often use the trial and error approach when tuning MySQL configuration: they change a few things and check if it …

Performance Schema has been with us for a while now. Over the years, it has gone a long way from being a curiosity disabled by default to becoming a sophisticated diagnostic tool you may want to enable permanently in your production database.
MySQL 5.7 introduced some exciting Performance Schema features and the first one I'm going to look at is the instrumentation for server memory usage. Have I Got Data For You MySQL 5.7.9 (GA) supports the following memory summary tables (per documentation) that let you look at server memory usage from different angles:

• "memory_summary_by_account_by_event_name" summarizes events for a given account.
• "memory_summary_by_host_by_event_name" summarizes events for a given host.
• "memory_summary_by_thread_by_event_name" summarizes events for a given thread and event name.
…

MySQL introduces several improvements related to partitioning. Most importantly, work has been done to move InnoDB towards native partitioning support, which not only "paves the way for better overall partitioning" (source) in the future but already comes with measurable performance benefits.
This article focuses on memory usage in heavily partitioned InnoDB databases.
Background Over the last few weeks I've been (un)lucky enough to work with several MySQL databases suffering from stability issues due to extreme memory pressure. All databases belonged to the same DBA team, who tend to apply complex partitioning schemes even to very small tables. Discussion as to whether or not this approach is correct is beyond the scope of this article but the situation encouraged …

Amazon EBS volumes come with a very cool feature called "lazy loading". In a nutshell: if a volume is created from an existing snapshot, it can become available almost immediately without waiting for all data to be restored. This allows for extremely fast provisioning of large data sets as long as you don't explicitly require the entire data set to be present before you start using it.
When an EBS volume is restored from snapshot, its blocks are fetched from Amazon S3. It happens either lazily in the background or explicitly on demand (think of a pagefault-like mechanism) and of course, fetching pieces of data from Amazon S3 is going to be one-two orders of magnitude slower than reading blocks directly from a volume.
In this short article, I will try to give you an idea of how this may impact the crash recovery time of your MySQL databases. Why talk about this? Depending on the workload and data set layout, crash recovery of a MySQL …

It goes without saying that crash recovery of busy MySQL servers (and many other RDBMS for that matter) is not an extremely quick process. In MySQL context, one of the worst case scenarios is when the server is used for multi-tenant application hosting i.e. when the MySQL instance contains hundreds or thousands of schemas and (tens/hundreds of) thousands of tablespaces. In such scenario, the server may spend a considerable amount of time in the tablespace discovery phase, during which MySQL builds a mapping between tablespace IDs and names of actual tablespace files on disk.
MySQL 5.7 promises to put an end to tablespace discovery. The documentation lists the following improvements introduced in versions 5.7.5 and up:

• Elimination of file system scans prior to redo log application. The MLOG_FILE_NAME redo log …

Previous episodes:

• MySQL-Docker operations. - Part 1: Getting started with MySQL in Docker.
• MySQL-Docker operations. - Part 2: Customizing MySQL in Docker.
• MySQL-Docker operations. - Part 3: MySQL replication in Docker.

We're going to explore the choices and the differences between various types of deployments. We will consider four use cases:

1. [Friendly]: Testing an application on a server where a different version of the same application is already installed (examples: a Python app requiring many …

The MySQL family has grown with the introduction of the Router, which brings high-availability and Fabric integration to all MySQL clients independently of any specific connector support for them. This blog focuses on the throughput of the Connection Routing plugin for Router and evaluates the overhead it may bring to application performance compared to direct connection.…

With MySQL 5.7 becoming GA it’s a good time to highlight how much performance has improved in replication since the 5.6 era. A previous blog post focused on the performance of the multi-threaded slave applier and on this one the target is the semi-synchronous replication plug-in (SemiSYNC), whose performance has improved greatly.…