Nick Dimiduk blog et al.

BlockCache Showdown

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. It’s high time the HBase community changed that! I did some benchmarking of these implementations, and these results I’d like to share with you here.

Note that this is my second post on the BlockCache. In my previous post, I provide an overview of the BlockCache in general as well as brief details about each of the implementations. I’ll assume you’ve read that one already.

BlockCache 101

Edit: The sequel post, BlockCache Showdown is now available!

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”. Memory store, implemented as the MemStore, accumulates data edits as they’re received, buffering them in memory 1. The block cache, an implementation of the BlockCache interface, keeps data blocks resident in memory after they’re read.

HBase via Hive, Part 2

"Apache Hive"

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. The whole mess was tested to work on a tiny little 5-node cluster running HDP-1.3.2, which means Hive 0.11.0 and HBase

HBase via Hive, Part 1

"Apache Hive"

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

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. This is a topic that we did not get to cover in HBase in Action, perhaps these notes will become the basis for the 2nd edition ;) These notes are applicable to Hive 0.11.x used in conjunction with HBase 0.94.x. They should be largely applicable to 0.12.x + 0.96.x, though I haven’t tested everything yet.

Data Types != Schema

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.

Edit: this entry has been cross-posted onto the Apache HBase blog. You might find more comments and discussion over there.

Cascalog’s Not So Lazy-generator

I find Cascalog’s choice of name for the lazy-generator to be a bit of a misnomer. That is, it’s not actually lazy! The lazy-generator consumes entirely your lazy-seq into a temporary tap. This necessary inconvenience results in a convenient side-effect, however.

How to Contribute to HBase and Hadoop2

In case you haven’t heard, Hadoop2 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. Early examples of such applications are rare, but two noteworthy examples are Knitting Boar and Storm on YARN. Hadoop2 will also ship a MapReduce implementation built on top of YARN that is binary compatible with applications written for MapReduce on Hadoop-1.x.

The HBase project is rearing to get onto this new platform as well. Hadoop2 will be a fully supported deployment environment for HBase 0.96 release. There are still lots of bugs to squish and the build lights aren’t green yet. That’s where you come in!