Cloudera Engineering Blog · HBase Posts
In December 2012, we described how an internal application built on CDH called Cloudera Support Interface (CSI), which drastically improves Cloudera’s ability to optimally support our customers, is a unique and instructive use case for Apache Hadoop. In this post, we’ll follow up by describing two new differentiating CSI capabilities that have made Cloudera Support yet more responsive for customers:
Apache HBase is all about giving you random, real-time, read/write access to your Big Data, but how do you efficiently get that data into HBase in the first place? Intuitively, a new user will try to do that via the client APIs or by using a MapReduce job with TableOutputFormat, but those approaches are problematic, as you will learn below. Instead, the HBase bulk loading feature is much easier to use and can insert the same amount of data more quickly.
This blog post will introduce the basic concepts of the bulk loading feature, present two use cases, and propose two examples.
Overview of Bulk Loading
Those people have two main options: One is the Thrift interface (the more lightweight and hence faster of the two options), and the other is the REST interface (aka Stargate). A REST interface uses HTTP verbs to perform an action. By using HTTP, a REST interface offers a much wider array of languages and programs that can access the interface. (If you’d like more information about the REST interface, you can go to my series of how-to’s about it.)
The following post was originally published by the Hue Team at the Hue blog in a slightly different form.
In this post, we’ll take a look at the new Apache HBase Browser App added in Hue 2.5 and which has improved significantly since then. To get the Hue HBase browser, grab Hue via CDH 4.4 packages, via Cloudera Manager, or build it directly from GitHub.
While Apache HBase adoption for building end-user applications has skyrocketed, many of those applications (and many apps generally) have not been well-tested. In this post, you’ll learn some of the ways this testing can easily be done.
We will start with unit testing via JUnit, then move on to using Mockito and Apache MRUnit, and then to using an HBase mini-cluster for integration testing. (The HBase codebase itself is tested via a mini-cluster, so why not tap into that for upstream applications, as well?)
Apache HBase supports three primary client APIs that developers can use to bind applications with HBase: the Java API, the REST API, and the Thrift API. Therefore, as developers build apps against HBase, it’s very important for them to be aware of the compatibility guidelines with respect to CDH.
This blog post will describe the efforts that go into protecting the experience of a developer using the Java API. Through its testing work, Cloudera allows developers to write code and sleep well at night, knowing that their code will remain compatible through supported upgrade paths.
The ecosystem is evolving at a rapid pace – so rapidly, that important developments are often passing through the public attention zone too quickly. Thus, we think it might be helpful to bring you a digest (by no means complete!) of our favorite highlights on a regular basis. (This effort, by the way, has different goals than the fine Hadoop Weekly newsletter, which has a more expansive view – and which you should subscribe to immediately, as far as we’re concerned.)
Find the first installment below. Although the time period reflected here is obviously more than a month long, we have some catching up to do before we can move to a truly monthly cadence.
For those people new to Apache HBase (version 0.90 and later), the configuration of network ports used by the system can be a little overwhelming.
In this blog post, you will learn all the TCP ports used by the different HBase processes and how and why they are used (all in one place) — to help administrators troubleshoot and set up firewall settings, and help new developers how to debug.
This how-to is the third in a series that explores the use of the Apache HBase REST interface. Part 1 covered HBase REST fundamentals, some Python caveats, and table administration. Part 2 showed you how to insert multiple rows simultaneously using XML and JSON. Part 3 below will show how to get multiple rows using XML and JSON.
Getting Rows with XML
GET verb, you can retrieve a single row or a group of rows based on their row keys. (You can read more about the multiple value URL format here.) Here we are going to use the simple wildcard character or asterisk (*) to get all rows that start with a specific string. In this example, we can load every line of Shakespeare’s comedies with “shakespeare-comedies-*”. This also requires that our row key(s) be laid out by “AUTHOR-WORK-LINENUMBER”.
Thanks to Steven Noels, SVP of Products for NGDATA, for the guest post below.
NGDATA builds and sells Lily, the next-generation Customer Intelligence Platform that helps enterprise marketing teams collect and store customer interaction data in order to profile, segment, and present better offers. We designed Lily from the ground up to run on Apache HBase and Apache Solr. Combining these technologies with our deep marketing segmentation expertise and unique machine learning techniques we’re able to deliver interactive data management, real-time statistical calculations, faceted search views of customers, offers, interactions and the permutations they each inspire.