At BackType, we are heavy users of Hadoop. We use it to run computations on our 30TB datastore of social data. We've even open-sourced some significant projects that are built on top of Hadoop.

Unfortunately, Hadoop has problems. It's sloppily implemented and requires all sorts of arcane knowledge to operate it. We would be the first to try out a replacement for Hadoop if a viable alternative existed. In this post, we'll look at some of the darker aspects of Hadoop.

Jelly is a scripting and templating language from Apache's Jakarta project. It is similar to Ant, in that scripts are XML, and each tag maps to a Java class, but has a more sophisticated internal pipeline model for tag interaction, much like JSP taglibs. See the Jelly website for more details.

JIRA comes with a number of Jelly tags implementing core operations in JIRA. This provides a scriptable interface to JIRA. There are many possible uses for JIRA Jelly tags, the most common being importing data into JIRA from other systems, and automating common administrative tasks (see the examples below).

The easy way to program your smart phone. m helps to gain full control of its features and realize its potential. mShell combines an easy to learn programming language with a rich phone specific function library. This closes the gap between power and accessibility, serving as a glue between the phone's components.

BrowserCouch is an attempt at an in-browser MapReduce implementation. It's written entirely in JavaScript and intended to work on all browsers, gracefully upgrading when support for better efficiency or feature set is detected.

Not coincidentally, this library is intended to mimic the functionality of CouchDB on the client-side, and may even support integration with CouchDB in the future.

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To learn how to use BrowserCouch, check out the work-in-progress tutorial.

So what's wrong with using the Mac as a development machine for Milo, a Python application backed by PostgreSQL and Redis (or any web project, for that matter)? Well, sacred cow, here come the spears.

scikits.learn is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages (numpy, scipy, matplotlib). It aims to provide simple and efficient solutions to learning problems that are accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering.

Given our development processes we found the average productivity of a single Django developer to be equivalent to the output generated by two C# ASP.NET developers. Given equal-sized teams, Django allowed our developers to be twice as productive as our ASP.NET team.

I suspect these results may actually reflect a lower bound of the productivity differences. It should be noted that about half of the Team Python developers, while fluent in Python, had not used Django before. They quickly learned Django, but it is possible this fluency disparity may have caused an unintended bias in results–handicapping overall Django velocity.

This class is the part of Mail::DKIM responsible for generating signatures for a given message. You create an object of this class, specifying the parameters of the signature you wish to create, or specifying a callback function so that the signature parameters can be determined later. Next, you feed it the entire message using "PRINT()", completing with "CLOSE()". Finally, use the "signatures()" method to access the generated signatures.

Django applications can be tuned to consume more or less memory. Consider the following strategies to reduce your Django application’s memory consumption, but note that some configuration changes—such as allocating fewer processes or maximum requests—may have a negative impact on overall performance. You may want to experiment with different combinations of configuration values to suit your memory and performance needs.

Pattern is a web mining module for the Python programming language.

It bundles tools for data retrieval (Google + Twitter + Wikipedia API, web spider, HTML DOM parser), text analysis (rule-based shallow parser, WordNet interface, syntactical + semantical n-gram search algorithm, tf-idf + cosine similarity + LSA metrics) and data visualization (graph networks).

The module is bundled with 30+ example scripts.