This post is a sister post to MySQL Master Replication Crash Safety Part #5: making things faster without reducing durability.  There is no introduction or conclusion to this post, only landing sections: reading this post without its context is not not recommended. You should start with the main post and come back here for more details.

And this Part #5 of the series has many sub-parts.  So far,

This post is a sister post to MySQL Master Replication Crash Safety Part #4: benchmarks of high and low durability.  There are no introduction or conclusion to this post, only landing sections: reading this post without its context is not recommended. You should start with the main post and come back here for more details.

Environment

My benchmark environment is composed of three vms in

Continuing on the series of blog posts about MySQL High Availability, today we will talk about stale reads and how to overcome this issue.

The Problem

Stale reads is a read operation that fetches an incorrect value from a source that has not synchronized an update operation to the value (source Wiktionary).

A practical scenario is when your application applies INSERT or UPDATE data to your master/writer node, and has to read it immediately after. If this particular read is served from another server in the replication/cluster topology, the data is either not there yet (in case of an INSERT) or it still provides the old value (in case of an UPDATE).

If your application or part of your application …

Uber is committed to delivering safer and more reliable transportation across our global markets. To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks…

The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog.

In this blog post, we’ll look at how ioping can be used with other tools to understand and troubleshoot storage performance, specifically as it relates to MySQL workloads.

I recently ran into ioping, a nice little utility by Konstantin Khlebnikov that checks storage latency.  

For me, the main beauty of ioping is its simplicity and familiarity. It takes after the ubiquitous ping tool, but “pings” the storage instead of the network device.

First, let’s talk about what this tool isn’t: it isn’t a benchmark tool to stress load your storage as heavily as possible. For that, you can use iozone or sysbench (among many others). This also isn’t a tool for looking at …

Yesterday was Melbourne Cup day in Australia – the biggest annual horse race event in the country, and in the state of Victoria it’s even a public holiday.

Open Query does work for Luxbet (part of Tabcorp), and Melbourne Cup day is by far their biggest day of the year in terms of traffic. It’s not just a big spike, there’s orders of magnitude difference so you can really say that the rest of the year is downright quiet (in relative terms). So, a very interesting load pattern.

Since last year Luxbet has upgraded from stock MySQL to MariaDB, and with our input made some other infrastructure modifications including moving to a pure solid state storage (FusionIO) solution as a SAN just won’t deliver the resilience and performance required. This may seem odd, but remember that a) …

In computer performance, we’re especially concerned about latency outliers: very slow database queries, application requests, disk I/O, etc. The term “outlier” is subjective: there is no rigid mathematical definition. From [Grubbs 69]:

An outlying observation, or “outlier,” is one that appears to deviate markedly from other members of the sample in which it occurs.

Outliers are commonly detected by comparing the maximum value in a data set to a custom threshold, such as 50 or 100 ms for disk I/O. This requires the metric to be well understood beforehand, as is usually the case for application latency and other key metrics. However, we are also often faced with a large number of unfamiliar metrics, where we don’t know the thresholds in advance.

There are a number of …

Nice article about SimCity outage and ways to defend databases: http://www.mysqlperformanceblog.com/2013/03/16/simcity-outages-traffic-control-and-thread-pool-for-mysql/

The graphs showing throughput with and without the thread pool are taken from the benchmark performed by Oracle and taken from here:
http://www.mysql.com/products/enterprise/scalability.html

The main take away is this graph (all rights reserved to Oracle, picture original URL):

Scalability is where throughput can grow and grow, as demand grows. I need to get more from the database, the question …

I’ve done a minor update to the hdlatency tool (get it from Launchpad), it now has a –quick option to have it only do its tests with 16KB blocks rather than a whole range of sizes. This is much quicker, and 16KB is the InnoDB page size so it’s the most relevant for MySQL/MariaDB deployments.

However, I didn’t just remove the other stuff, because it can be very helpful in tracking down problems and putting misconceptions to rest. On SANs (and local RAID of course) you have things like block sizes and stripe sizes, and opinions on what might be faster. Interestingly, the real world doesn’t always agree with the opinions.

We Mark Callaghan correctly pointed out when I first published it, hdlatency does not provide anything new in terms of functionality, the db IO tests of sysbench cover it all. A key advantage of hdlatency is that it doesn’t have any …