Cloudera Developer Blog · HBase Posts
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.
In Part 1 of this series about Apache HBase snapshots, you learned how to use the new Snapshots feature and a bit of theory behind the implementation. Now, it’s time to dive into the technical details a bit more deeply.
What is a Table?
An HBase table comprises a set of metadata information and a set of key/value pairs:
For those of you who missed the show, session video and presentation slides (as well as photos) will be available via hbasecon.com in a few weeks. (To be notified, follow @cloudera or @ClouderaEng.) Although it’s not quite as good as being there with the rest of the community, you’ll still be able to partake from the real-world experiences of Apache HBase users like Facebook, Box, Yahoo!, Salesforce.com, Pinterest, Twitter, Groupon, and more.
This is the week of Apache HBase, with HBaseCon 2013 taking place Thursday, followed by WibiData’s KijiCon on Friday. In the many conversations I’ve had with Cloudera customers over the past 18 months, I’ve noticed a trend: Those that run HBase stand out. They tend to represent a group of very sophisticated Hadoop users that are accomplishing impressive things with Big Data. They deploy HBase because they require random, real-time read/write access to the data in Hadoop. Hadoop is a core component of their data management infrastructures, and these users rely on the latest and greatest components of the Hadoop stack to satisfy their mission-critical data needs.
Today I’d like to shine a spotlight on one innovative company that is putting top engineering talent (and HBase) to work, helping to save the planet — literally.
HBaseCon 2013 is this Thursday (June 13 in San Francisco), and we can hardly wait!
Michael Stack is the chair of the Apache HBase PMC and has been a committer and project “caretaker” since 2007. Stack is a Software Engineer at Cloudera.
Apache Hadoop and HBase have quickly become industry standards for storage and analysis of Big Data in the enterprise, yet as adoption spreads, new challenges and opportunities have emerged. Today, there is a large gap — a chasm, a gorge — between the nice application model your Big Data Application builder designed and the raw, byte-based APIs provided by HBase and Hadoop. Many Big Data players have invested a lot of time and energy in bridging this gap. Cloudera, where I work, is developing the Cloudera Development Kit (CDK). Kiji, an open source framework for building Big Data Applications, is another such thriving option. A lot of thought has gone into its design. More importantly, long experience building Big Data Applications on top of Hadoop and HBase has been baked into how it all works.
Kiji provides a model and set of libraries that help you get up and running quickly.
As you may know, Apache HBase has a vibrant community and gets a lot of contributions from developers worldwide. The collaborative development effort is so active, in fact, that a new point-release comes out about every six weeks (with the current stable branch being 0.94).
At Cloudera, we’re committed to ensuring that CDH, our open source distribution of Apache Hadoop and related projects (including HBase), ships with the results of this steady progress. Thus, CDH 4.2 was rebased on 0.94.2, as compared to its predecessor CDH 4.1, which was based on 0.92.1. CDH 4.3 has moved one step further and is rebased on 0.94.6.1.