The Hortonworks Blog

More from Nick Dimiduk

This blog post originally appeared here and is reproduced in its entirety here. Part 1 can be found here.

The HBase BlockCache is an important structure for enabling low latency reads. As of HBase 0.96.0, there are no less than three different BlockCache implementations to choose from. But how to know when to use one over the other? There’s a little bit of guidance floating around out there, but nothing concrete.…

This blog post originally appeared here and is reproduced in its entirety here.

HBase is a distributed database built around the core concepts of an ordered write log and a log-structured merge tree. As with any database, optimized I/O is a critical concern to HBase. When possible, the priority is to not perform any I/O at all. This means that memory utilization and caching structures are of utmost importance. To this end, HBase maintains two cache structures: the “memory store” and the “block cache”.…

This is the second of two posts examining the use of Hive for interaction with HBase tables. This is a hands-on exploration so the first post isn’t required reading for consuming this one. Still, it might be good context.

“Nick!” you exclaim, “that first post had too many words and I don’t care about JIRA tickets. Show me how I use this thing!”

This is post is exactly that: a concrete, end-to-end example of consuming HBase over Hive.…

This is the first of two posts examining the use of Hive for interaction with HBase tables. The second post is here.

One of the things I’m frequently asked about is how to use HBase from Apache Hive. Not just how to do it, but what works, how well it works, and how to make good use of it. I’ve done a bit of research in this area, so hopefully this will be useful to someone besides myself.…

My work on adding data types to HBase has come along far enough that ambiguities in the conversation are finally starting to shake out. These were issues I’d hoped to address through initial design documentation and a draft specification. Unfortunately, it’s not until there’s real code implemented that the finer points are addressed in concrete. I’d like to take a step back from the code for a moment to initiate the conversation again and hopefully clarify some points about how I’ve approached this new feature.…

In case you haven’t heard, Hadoop 2.0 is on the way! There are loads more new features than I can begin to enumerate, including lots of interesting enhancements to HDFS for online applications like HBase. One of the most anticipated new features is YARN, an entirely new way to think about deploying applications across your Hadoop cluster. It’s easy to think of YARN as the infrastructure necessary to turn Hadoop into a cloud-like runtime for deploying and scaling data-centric applications.…

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