In the previous article we introduced Hadoop as the most popular Big Data toolset on the market today. We had just started talking about MapReduce as the major framework that makes Hadoop distinctive. So let’s continue the discussion where we left off.

MapReduce is really the key to understanding Hadoop’s parallel processing functionality as it enables data in various formats (XML, text, binary, log, SQL, ect) to be divided up and mapped out to many computers nodes and then recombined back to produce a final data set.

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Dan McKinley (Etsy) wrote an [IMHO] insightful article Why MongoDB Never Worked at Etsy.

First off, it’s important to realise that it’s not a snipe at MongoDB – it’s a fine tool.

The lessons are related to mixing multiple databases in a deployment (administration and monitoring overhead) and the acknowledgement that issues of schema design, scalability and maintenance need attention regardless of which brand or technology you pick for your database. That comes back to the old insight that migrations are rarely worth it (regardless of what you migrate to what).

I think these are indeed important considerations as they have a major impact on the ongoing costs of your entire environment (production as well as development and testing) – these days we encounter the “we’re doing this part of our application using MongoDB” …

We all know that MySQL says it is "the world's most popular open-source database". And PostgreSQL has a firm hold on second place while claiming to be, instead, "the world's most advanced open source database". But the horse that comes in third can return some money to gamblers who bet "to show". And the horse that shows momentum or gets close is worth watching for next time.

So I'll just ignore the dolphin and the elephant in the room, and go on to a harder question: who's number three?

According to Wikipedia

To find out how many times someone has expressed interest in a topic, I can go to stats.grok.se and ask how many times someone has looked at that topic's page in Wikipedia. Evil-thinking people could manipulate these numbers with ease, but until now they have …

“The most valuable thing I’ve learned in this role is that judicious use of a little bit of knowledge can go a long way. I’ve seen colleagues and other companies get caught up in the “Big Data” craze by spend hundreds of thousands of pounds sterling on a Hadoop cluster that sees a few megabytes [...]

TokuMX v1.2.1 introduces two simple new features to help you understand the performance characteristics of your database: lock diagnostics and index usage statistics. We’d like to take you through a few examples of what these features are and how to use them.

Lock Diagnostics

Since we introduced TokuMX, one of the most frequent complaints has been about “lock not granted” errors.  These arise when a long-running operation takes document-level locks, and other clients timeout while waiting to acquire the same locks.

This is a new problem in TokuMX that doesn’t exist in MongoDB, because MongoDB doesn’t have document-level locks.  It has a single lock per database, and if an operation takes a long time, it simply …

On Thursday 12th September I co-presented a webinar on how MySQL Cluster delivers the key benefits of NoSQL Data Stores without having to give up the features that people rely on from relational databases (consistency, SQL etc.). There was a particular focus on how to use the new node.js JavaScript API which was recently released as part of MySQL Cluster 7.3. If you weren’t able to attend the live event then the webinar replay is available here. If you scroll down to the bottom of this post you’ll see a summary of the Questions and Answers from the session.

The new MySQL JavaScript Connector for Node.js is designed to deliver simplicity and high performance for JavaScript users.

Enables end-to-end JavaScript development, from the …

Note that the webinar replay + transcript of the Questions and Answers is now available from here.

On Thursday 12th September I’ll be co-presenting a free webinar on how MySQL Cluster delivers the key benefits of NoSQL Data Stores without having to give up the features that people rely on from relational databases (consistency, SQL etc.). There will be particular focus on how to use the new node.js JavaScript API which was recently released as part of MySQL Cluster 7.3.  As usual the webinar is free but you should …

” One of the biggest challenges with any project of such a long duration is coping with change. There are many aspects to coping with change, including changes in requirements, changes in technology, vendor stability, changes in staffing and so on”–Jon Brumfitt. On May 14, 2009, the European Space Agency launched an Arianne 5 rocket [...]

For people used to relational databases, using NoSQL solutions such as MongoDB brings interesting challenges. One of them is schema design: while in the relational world, normalization is a good way to start, how should we design our collections when creating a new MongoDB application?

Let’s see with a simple example how we would create a data structure for MySQL (or any relational database) and for MongoDB. We will assume in this post that we want to store people information (their name) and the details from their passport (country and validity date).

Relational Design

In the relational world, the basic idea is to try to stick to the 3rd normal form and create two tables (I’ll omit indexes and foreign keys for clarity – MongoDB supports indexes but not foreign keys):

mysql> select * from people;
+----+------------+
| id | name       |
+----+------------+
|  1 | Stephane   |
|  2 | John       |
|  3 | …

“We went down the path of building Oracle NoSQL database because of explicit request from some of our largest Oracle Berkeley DB installations that wanted to move away from maintaining home grown sharding implementations and very much wanted an out of box technology that can replicate the robustness of what they had built “out of [...]