In this webinar, Baron Schwartz explains how VividCortex has used MySQL to support a large-scale, high-velocity time-series database in the AWS cloud.

If you did not have a chance to join the webinar, the slide deck is embedded below. You can also register for a recording here.

VividCortex: Building a Time-Series Database in MySQL from VividCortex

While MongoDB 2.8 introduces a formal storage engine API and brings with it the new WiredTiger storage engine, it also adds collection level locking to the existing memory mapped engine (MMAPV1) which will remain the default engine until MongoDB 3.0, so says Eliot.

The MongoDB community has been waiting a long time for collection level locking, the Jira ticket was created on June 15, 2010. When I saw the following Facebook post I got excited to give it a spin, but unfortunately the results were extremely poor using MongoDB …

In MySQL 5.6 we introduced a huge improvement in the way that index and table statistics are gathered by InnoDB and subsequently used by the Optimizer during query optimization: Persistent Statistics. Some aspects of the way that Persistent Statistics work could be improved further though, and we’d really like your input on that.

How much to sample?

The statistics are gathered by picking some pages semi-randomly, analyzing them, and deriving some conclusions about the entire table and/or index from those analyzed pages. The number of pages sampled can be specified on a per-table basis with the STATS_SAMPLE_PAGES clause. For example:

ALTER TABLE t STATS_SAMPLE_PAGES=500;

This way …

Taxonomy upgrade extras: HAProxyload balancerGalera ClusterVIPvirtual IPHigh Availabilityha

After properly installing and testing a Galera Cluster we see that the set-up is not finished yet. It needs something in front of the Galera Cluster that balances the load over all nodes.
So we install a load balancer in front of the Galera Cluster. Typically nowadays …

upload on 2014.12 [ten important tips of MySQL database design for better performance] Download this PDF

Installing Apache2 With PHP5 And MySQL Support On Fedora 21 (LAMP)

LAMP is short for Linux, Apache, MySQL, PHP. This tutorial shows how you can install an Apache2 webserver on a Fedora 21 server with PHP5 support (mod_php) and MySQL support.

Installing Apache2 With PHP5 And MySQL Support On Fedora 21 (LAMP) LAMP is short for Linux, Apache, MySQL, PHP. This tutorial shows how you can install an Apache2 webserver on a Fedora 21 server with PHP5 support (mod_php) and MySQL support.

Installing Apache2 With PHP5 And MySQL Support On Fedora 21 (LAMP) LAMP is short for Linux, Apache, MySQL, PHP. This tutorial shows how you can install an Apache2 webserver on a Fedora 21 server with PHP5 support (mod_php) and MySQL support.

There are few easy ticks to see what is stuck inside MySQL instance. All these techniques will not give you whole picture, but might help to find queries that block performance. Let’s start from what exactly doing your MySQL server right now.

Which queries are running now?

This will give you an idea what’s running right now so you can find long running queries which slowing down MySQL and/or causing replication lag:

mysql -e "SHOW PROCESSLIST" | grep -v -i "sleep"

It is more convenient than just run “SHOW PROCESSLIST” as it’s hiding all connected threads in “Sleep” state so you’ll get a clean output. Also you can get same output but updating each second:

watch -n1 'mysql -e "SHOW FULL PROCESSLIST" | grep -v -i "Sleep"'

What to look for? This is complex output but you can start with Time and State columns. When you see a query running for more …

It's easy to load multiple rows of data from a file into a MySQL table with a single LOAD DATA INFILE command, but if the file is over a certain size you may want to load it in multiple chunks. When I have a file with millions of lines I typically use pt-fifo-split to separate the file into multiple chunks in order to limit the size of each transaction. Unlike most tools in Percona Toolkit, pt-fifo-split doesn't actually interact with your database, but this is one MySQL use case where it's very helpful.

Here's a quick example in bash that illustrates how I can take a large tab-delimited file, break it into multiple 100,000 line chunks, and load the chunks into a table:

``` FLAT_FILE="/tmp/big_file.txt" …