On Friday our CEO John Partridge was named a “Tech Luminary” in the 17th annual Innovation All Stars award, which is given jointly by Mass High Tech (MHT) and the Boston Business Journal (BBJ). As noted in MHT by the editor, Chris McIntosh, the Luminary designations “reflect deep accomplishment in various technology-related industries.”

For more than 20 years, Tokutek CEO John Partridge has worked with startups in both the Boston area and in Silicon Valley. He joined Tokutek from StreamBase Systems which John co-founded with database pioneer Dr. Michael Stonebraker. He started his career as a software developer …

If you’re a user of MySQL Workbench then you may have noticed a pocket knife icon appear in the top right hand corner – click on that and a terminal opens which gives you access to the MySQL utilities. In this post I’m focussing on the replication utilities but you can also refer to the full MySQL Utilities documentation.

What I’ll step through is how to uses these utilities to:

• Set up replication from a single master to multiple slaves
• Automatically detect the failure of the master and promote one of the slaves to be the new master
• Introduce the old master back into the topology as a new slave and then promote it to be the master again

Tutorial Video

Before going through the steps in detail here’s a demonstration of the replication utilities in action…

To get full …

We’ve recently blogged about the various MySQL Connect sessions focused on MySQL Cluster, InnoDB, the MySQL Optimizer and MySQL Replication. But we also wanted to draw your attention to some great opportunities to network and have fun! That’s also part of what makes a good conference...

MySQL Connect Reception

San Francisco Hilton - Continental Ballroom

6:30 p.m.–8:30 p.m.

A great opportunity to network with Oracle’s MySQL engineers, partners having a booth in the exhibition hall and just about everyone at MySQL Connect. Long …

Hi,

You have been using MONyog to monitor your MySQL servers. You connected your servers with MONyog and collected data to analyse later. MONyog did a great job analyzing past performance. If you know something is wrong and don’t have MONyog monitoring your MySQL, you cannot do much to fix things right then. You’ll have to first add your MySQL to MONyog and wait till data collects.

Not any more. Introducing real-time monitoring with MONyog 5.5. It tells you what’s happening on your MySQL server RIGHT NOW. It details key metrics like top 200 queries, slow queries, locked queries, locking queries, tables, databases, users, hosts and thread states, all of this in real-time as it happens. Information is retrieved from the server every one second. And the best part is that you can get started with real-time with just a click of a button without the overhead of enabling logs.

Please refer to …

Network partitioning (aka split brain) is something you don’t want to happen, but what is the problem really?

Sometimes, users set up a Galera Cluster (or Percona XtraDB Cluster) containing an even number
of nodes, e.g, four or six nodes, and then place half of the nodes in one data center and the other half in another data center. If the network link goes down between the two data centers, then the Galera cluster is partitioned and half of the nodes cannot communicate with the other half. Galera’s quorum algorithm
will not be able to select the ‘primary component’, since both sets have the same number of nodes. You then end up with 2 sets of nodes that are ‘non-primary’, and effectively, none of the nodes would be available for database transactions.

Below is a …

Have you seen Damn Cool Algorithms: Cardinality Estimation yet? If not, take a few minutes and read through it. Now, what if we try using that approach instead of COUNT(DISTINCT) in MySQL to see how many distinct values there are in a column?

I recently needed this information in real life, and the table is large with many duplicate values. The column is some 32-character hex string, a hash value that represents a session ID. I’ll call the column sess_id. I wanted to know how many distinct values it had, but I thought it would be cool (damn cool, really) to try this approach and see what happened.

I read the blog post, convinced myself that it made sense, and tried to code it. Here’s my rough translation of the algorithm into MySQL-speak. Note that I’m using crc32(), which may not be a great choice …

For some of you who situated near New York City I am happy to announce that you could attend two events related to leading Full-Text search engines in open source – Sphinx Search.

First meeting organized by NYPHP meetup on Tuesday, September 25th at IBM, 590 Madison Avenue, New York. I’ll be speaking about search services in cloud environment and distributed search tips and tricks. Event is free, please RSVP.

One week later on October 1st, I’ll be doing tutorial about MySQL and Sphinx “Full-text based services with Sphinx and MySQL” …

Have you seen Damn Cool Algorithms: Cardinality Estimation yet? If not, take a few minutes and read through it. Now, what if we try using that approach instead of COUNT(DISTINCT) in MySQL to see how many distinct values there are in a column? I recently needed this information in real life, and the table is large with many duplicate values. The column is some 32-character hex string, a hash value that represents a session ID.

With large enough infrastructure it gets a bit more and more complicated to detect whether an incident or a problem is real systems problem or a monitoring glitch. This is a story of one such investigation.

With a sufficiently large set of machines, there’re multiple graphs/sets of data to look at to understand what is going on:

• Group average – the easiest to look at for long term trends or system-wide problems
• Top-N chart – our favorite for dive-ins, looking only at top offender lines (quite often part of the offender lines end up matching tier baseline)
• Percentiles chart – knowing best performers, medians, 99% or 99.9% behavior is incredibly useful to understand medium-term dynamics and can give lots of insight where to look for problems. Is way more complicated to collect though.

Another very important property of data one looks at is collection frequency – though …

In the Matrix movie there is a scene where the heroes visit a spiritual councelor, and amongst the people in her waiting room they see a little boy, dressed like a buddhist monk, who can bend a spoon just by looking at it. When they ask him what he does to bend the spoon, the boy's answer is: "There is no spoon". And if you watch the movie to the end, you will see that he is right. (In that spirit, if this post is too long to read for you, just skip to the last paragraph for the answer.)

The title for this blog post is of course inspired by Baron's "Is automated failover the root of all evil?", which is a commentary on GitHub's detailed explanation of their recent Pacemaker-induced downtime. Baron makes a good question, but the …