Nick Dimiduk blog et al.

HBase Root Dir on a Mac

While working on HBase bug fixes and feature development, it’s often quite convenient to test changes on a local-mode HBase. This is done by running HBase right out of your developer sandbox. Though a lot of HBase development happens on Macs these days, it’s a system designed first to run on Linux. That means there are a couple minor annoyances for non-Linux users. Let me show you how I work around one of them.

Latency Talk at Hadoop Summit

The Latency Talk Nicolas and I gave at HBaseCon has been accepted for Hadoop Summit San Jose. If you missed us at HBaseCon, you get one more opportunity! We’re speaking on June 4th at 3:25p.

See you in June!

Edit: Unfortunately, Nicolas was unable to make it so I presented solo. I hope I did his section justice.

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.