Those in the MySQL/Drizzle world have probably seen this a bunch of times before, but I think it still holds up (now five years later) as one of the best NoSQL explanations around.

It may not be surprising that there’s been a few projects over the years that I’ve worked on where we’ve had to care about stack usage (to varying degrees).

For threaded userspace applications (e.g. MySQL, Drizzle) you get a certain amount of stack per thread – and you really don’t want to bust that. For a great many years now, there’s been both a configuration parameter in MySQL to set how much stack each thread (connection) gets as well as various checks in the source code to ensure there’s enough free stack to do a particular operation (IIRC open_table is the most hairy one of this in MySQL).

For the Linux Kernel, stack usage is a relatively (in)famous problem… although by now just about every real problem has been fixed and merely mentioning it is probably just the influence of the odd grey beard hairs I’m pretending not to notice.

In a current project I’m working on, it’s also something we have …

There are many cool, new things happening with Percona Cloud Tools.  To avoid “tl;dr” I will highlight only one new feature after a brief, general announcement.  The new feature is a 3-minute MySQL monitor.  I’ll blog later about other features.

The general announcement is: Last week we quietly released a brand-new agent called percona-agent, and we added MySQL and system monitoring to Percona Cloud Tools.  We also wrote a brand-new API from the ground up.  We call it all “PCT v2″.  For you it means a better experience and more features, all still free while we’re in beta.

One new feature in Percona Cloud Tools v2 is MySQL monitoring in 3 minutes, i.e. a 3-minute MySQL monitor.  Let’s be honest about monitoring: We …

I’ve just released the 1.0.1 version of the MySQL sys schema. It contains a few bug fixes (including a contribution from Joe Grasse, thanks Joe!), and a number of new helper procedures around viewing and altering configuration for consumers and instruments, contributed by the MySQL QA team, as they’ve started to incorporate more Performance Schema trace data in to their automated testing.

Next up is the 1.1.0 release, that also includes a bunch of new host summary views that were contributed by Arnaud Adant (of the MySQL Support team). I have a number of new things in development to add as well before then though.

Let me know if there are things you’d like to see as well, maybe I can find time to work on those too.

Enjoy.

I spoke at MySQL Tech Day in Paris yesterday, it was a fun day – apart from the early and late flights in and out on the same day, my boss told me I was crazy, he was probably right.

First Dimitri presented some recent benchmark findings:

MySQL techday at Oracle @dimitrik_fr @Aurelien_LEQUOY @sfrezefond @MarkLeith @lefred … pic.twitter.com/Zy8mI9i8JK
— Olivier DASINI (@freshdaz) May 22, 2014

Then I got my turn to speak for a few hours around …

May 23, 2014 By Severalnines

If you need to deploy a database cluster behind your corporate firewall, on servers with no internet access, then this blog is for you. Most of the installation tutorials out there require servers to have an internet connection, and that would not work very well with companies under strict policies and compliance guidelines. 

Our online Galera Configurator supports deployment without Internet connection for Percona XtraDB Cluster. You would need to prepare a minimum of four hosts (three DB nodes and one management node), generate a Deployment Package from the Configurator and copy it to the ClusterControl host. In this post, we will show you how to deploy Percona XtraDB Cluster 5.5 in an environment without internet connection on CentOS 6.5 (installed with minimal installation). Our setup is …

During April’s Percona Live MySQL Conference and Expo 2014, I attended a talk on MySQL 5.7 performance an scalability given by Dimitri Kravtchuk, the Oracle MySQL benchmark specialist. He mentioned at some point that the InnoDB double write buffer was a real performance killer. For the ones that don’t know what the innodb double write buffer is, it is a disk buffer were pages are written before being written to the actual data file. Upon restart, pages in the double write buffer are rewritten to their data files if complete. This is to avoid data file corruption with half written pages. I knew it has an impact on performance, on ZFS since it is transactional I always disable it, but I never realized how important the performance impact could be. Back from PLMCE, a friend had dropped home a Dell R320 server, asking …

We are pleased to inform you that the new Continuent Tungsten 2.0.2 is now available. Continuent Tungsten keeps data available to applications 7x24 through hardware failures, maintenance, and software upgrades. It ensures business continuity and performance by keeping up-to-date data closer to the user, and increases application throughput and improves response time by load balancing SQL

MySQL 5.6 has added support for EXPLAIN FORMAT=JSON. The basic use case for that feature is that one can look at the JSON output and see more details about the query plan. More advanced/specific use cases are difficult, though. The problem is, you can’t predict what EXPLAIN FORMAT=JSON will produce. There is no documentation or any kind of convention regarding the contents of JSON document that you will get.

To make sure I’m not missing something, I looked at MySQL Workbench. MySQL Workbench has a feature called Visual Explain. If you want to use, prepare to seeing this a lot:

In Workbench 6.1.4 you get it for (almost?) any query with subquery. In Workbench 6.1.6 (released last week), some subqueries work, but it’s still easy to hit a query whose EXPLAIN JSON output confuses workbench.

Looking at the source code, this seems to be just the start of …

An article about moving data into Hadoop in real-time has just been published over at DBTA, written by me and my CEO Robert Hodges.

In the article I talk about one of the major issues for all people deploying databases in the modern heterogenous world – how do we move and migrate data effectively between entirely different database systems in a way that is efficient and usable. How do you get the data you need to the database you need it in. If your source is a transactional database, how does that data get moved into Hadoop in a way that makes the data usable to be queried by Hive, Impala or HBase?

You can read the full article here: Real-Time Data Movement: The Key to Enabling Live Analytics With Hadoop

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