In my three previous blogs I wrote about our implementation of Fractal Tree Indexes on MongoDB, showing a 10x insertion performance increase, a 268x query performance increase, and a comparison of covered indexes and clustered indexes. The benchmarks show the difference that rich and efficient indexing can make to your MongoDB workload.

It’s one thing for us to benchmark MongoDB + TokuDB and another to measure real world performance. If you are looking for a way to improve the performance or scalability of your MongoDB deployment, we can help …

Last week I wrote about our 10x insertion performance increase with MongoDB. We’ve continued our experimental integration of Fractal Tree® Indexes into MongoDB, adding support for clustered indexes.  A clustered index stores all non-index fields as the “value” portion of the index, as opposed to a standard MongoDB index that stores a pointer to the document data.  The benefit is that indexed lookups can immediately return any requested values instead of needing to do an additional lookup (and potential disk IOs) for the requested fields.

To create a clustered index you just need to add “clustering:true” as in the following example (note that version 2 indexes are Fractal Tree Indexes): …

The challenge of handling massive data processing workloads has spawned many new innovations and techniques in the database world, from indexing innovations like our Fractal Tree® technology to a myriad of “NoSQL” solutions (here is our Chief Scientist’s perspective). Among the most popular and widely adopted NoSQL solutions is MongoDB and we became curious if our Fractal Tree indexing could offer some advantage when combined with it. The answer seems to be a strong “yes”.

Earlier in the summer we kicked off a small side project and here’s what we did: we implemented a “version 2” IndexInterface as a Fractal Tree index and ran some benchmarks. Note that our integration only affects MongoDB’s secondary indexes; primary indexes continue to rely on MongoDB’s indexing code. All the changes we made to the MongoDB source …

You might have seen my attempts at testing MySQL as a Key Value Store (KVS) (here and here), i.e. used like MongoDB, and I have had some comments to those. Above all, many wanted to know the details of my test environment. To be honest, this started off as a very simple thing, but it turned out a bigger than I thought, so I guess its reasonable to give some background. I apologize for not giving you this background before, but as I said, my initial intention was to just run bog standard MongoDB versus MySQL and see what happened.BackgroundHere at Recorded Future, when I joined about 2 …

What is Ansible?

Ansible is a configuration management and deployment system, like Puppet, Capistrano, Fabric, and Chef. Its aim is to be radically simple and let you use your existing scripts to help with cluster configuration and software deployment whenever possible. Here are the ways that Ansible differentiates itself.

Simplicity

Ansible does not include a client/server architecture with pull-based clients (although in more recent versions, it does include pull-based configuration and deployment). Rather, it uses pre-existing network infrastructure: SSH. Every company has SSH installed on their cluster servers, and Ansible simply rides on top of this infrastructure to get the code and configuration out to the nodes.

Language Agnostic

You can write modules for Ansible in …

Even though I have come late to the party of professional development, relatively speaking, I am acutely aware of the conflict that seems to pervade the developer-DBA relationship. This is what I gather about why this is: DBAs used to be paid better that developers, and often this was because they were able to reduce the overall license and hardware costs of large database installations. Both the size and proprietary nature of databases made them incredibly expensive, so paying an individual gobs of money to make sure they ran efficiently and that the data was preserved was worth it.

Several trends have changed the playing field. The first is the arrival of small, commodity server hardware that makes mainframes or large servers unnecessary, and thus the cost is pushed down dramatically for most installations, while salaries for trained database professionals remained the same.

The second is the explosion of databases that do not (in …

… Ya se que estás piantao, piantao, piantao…

For my lastest blog, a review of the MySQL, NoSQL and Cloud Conference, I’ll continue to use the tango metaphor. Balada para un loco (ballad for a crazy one) is a Piazzola classic and explains what I think of Santiago Lertora from Binlogic for single handedly putting together this event; he had to be piantao (slang for ‘crazy’) to pursue his vision to kick start the Open Source database community in South America into becoming as active as it is in the US and Europe. He was able to gather some renowned speakers such as our own Martin Farach-Colton, Sheeri Cabral from Mozilla, Max Mether and Massimo Brignoli from SkySQL, Colin Charles from Monty Program, Alejandro Kojima from the MySQL team at Oracle, Oracle ACE director Ronald Bradford, and many more. Among the firms attending, some …

The MySQL Cluster engineering team recently ran a live webinar, available now on-demand demonstrating the ClusterJ and ClusterJPA NoSQL APIs for MySQL Cluster, and how these can be used in building real-time, high scale Java-based services that require continuous availability.

Attendees asked a number of great questions during the webinar, and I thought it would be useful to share those here, so others are also able to learn more about the Java NoSQL APIs.

First, a little bit about why we developed these APIs and why they are interesting to Java developers.

ClusterJ and Cluster JPA

ClusterJ is a Java interface to MySQL Cluster that provides either a static or dynamic domain object model, similar to the data model used by JDO, …

In my previous post I covered the shard-disk paradigm's pros and cons, but the conclusion that is that it cannot really qualify as a scale-out solution, when it comes to massive OLTP, big-data, big-sessions-count and mixture of reads and writes.

Read/Write splitting is achieved when numerous replicated database servers are used for reads. This way the system can scale to cope with increase in concurrent load. This solution qualifies as a scale-out solution as it allow expansion beyond the boundaries of one DB, DB machines are shared-nothing, can be added as a slave to the replication "group" when required.


And, as a fact, read/write …

Hosted by Binlogic, the MySQL/NoSQL/Cloud Latin American Conference at the Hilton Buenos Aires in Argentina, June 26-28th, will bring together key members of the of open source database community for two intense days of technical talks and tutorials on popular open source databases like MySQL, MariaDB, and Drizzle; NoSQL databases such as MongoDB and CouchDB; and related technologies such as the Soir and Sphinx search engines.

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