Cloudera Engineering Blog · HBase Posts

Sneak Preview: HBaseCon 2015 Ecosystem Track

This year’s HBaseCon Ecosystem track covers projects that are complementary to HBase (with a focus on SQL) such as Apache Phoenix, Apache Kylin, and Trafodion.

In this post, I’ll give you a window into the HBaseCon 2015′s (May 7 in San Francisco) Ecosystem track.

Sneak Preview: HBaseCon 2015 Development & Internals Track

This year’s HBaseCon Development & Internals track covers new features in HBase 1.0, what’s to come in 2.0, best practices for tuning, and more.

In this post, I’ll give you a window into the HBaseCon 2015′s (May 7 in San Francisco) Development & Internals track.

Sneak Preview: HBaseCon 2015 Operations Track

This year’s HBaseCon Operations track features some of the world’s largest and most impressive operators.

In this post, I’ll give you a window into the HBaseCon 2015′s (May 7 in San Francisco) Operations track.

Sneak Preview: HBaseCon 2015 General Session

As is its tradition, this year’s HBaseCon General Session includes keynotes about the world’s most awesome HBase deployments.

It’s Spring, which also means that it’s HBaseCon season—the time when the Apache HBase community gathers for its annual ritual.

Apache HBase 1.0 is Released

The Cloudera HBase Team are proud to be members of Apache HBase’s model community and are currently AWOL, busy celebrating the release of the milestone Apache HBase 1.0. The following, from release manager Enis Soztutar, was published today in the ASF’s blog.

 

HBaseCon 2015: Call for Papers and Early Bird Registration

HBaseCon 2015 is ON, people! Book Thursday, May 7, in your calendars.

If you’re a developer in Silicon Valley, you probably already know that since its debut in 2012, HBaseCon has been one of the best developer community conferences out there. If you’re not, this is a great opportunity to learn that for yourself: HBaseCon 2015 will occur on Thurs., May 7, 2015, at the Westin St. Francis on Union Square in San Francisco.

New in Cloudera Labs: SparkOnHBase

As we progressively move from MapReduce to Spark, we shouldn’t have to give up good HBase integration. Hence the newest Cloudera Labs project, SparkOnHBase!

Apache Spark is making a huge impact across our industry, changing the way we think about batch processing and stream processing. However, as we progressively migrate from MapReduce toward Spark, we shouldn’t have to “give up” anything. One of those capabilities we need to retain is the ability to interact with Apache HBase.

New in CDH 5.2: Improvements for Running Multiple Workloads on a Single HBase Cluster

These new Apache HBase features in CDH 5.2 make multi-tenant environments easier to manage.

Historically, Apache HBase treats all tables, users, and workloads with equal weight. This approach is sufficient for a single workload, but when multiple users and multiple workloads were applied on the same cluster or table, conflicts can arise. Fortunately, starting with HBase in CDH 5.2 (HBase 0.98 + backports), workloads and users can now be prioritized.

Tuning Java Garbage Collection for HBase

This guest post from Intel Java performance architect Eric Kaczmarek (originally published here) explores how to tune Java garbage collection (GC) for Apache HBase focusing on 100% YCSB reads.

Apache HBase is an Apache open source project offering NoSQL data storage. Often used together with HDFS, HBase is widely used across the world. Well-known users include Facebook, Twitter, Yahoo, and more. From the developer’s perspective, HBase is a “distributed, versioned, non-relational database modeled after Google’s Bigtable, a distributed storage system for structured data”. HBase can easily handle very high throughput by either scaling up (i.e., deployment on a larger server) or scaling out (i.e., deployment on more servers).

NoSQL in a Hadoop World

The number of powerful data query tools in the Apache Hadoop ecosystem can be confusing, but understanding a few simple things about your needs usually makes the choice easy. 

Ah, the good old days. I recall vividly that in 2007, I was faced to store 1 billion XML documents and make them accessible as well as searchable. I had few choices on a given shoestring budget: build something one my own (it was the rage back then—and still is), use an existing open source database like PostgreSQL or MySQL, or try this thing that Google built successfully and that was now implemented in open source under the Apache umbrella: Hadoop.

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