Tag Archives: developers

Introducing Apache HBase Medium Object Storage (MOB) compaction partition policies

Categories: HBase

Introduction

The Apache HBase Medium Object Storage (MOB) feature was introduced by HBASE-11339. This feature improves low latency read and write access for moderately-sized values (ideally from 100K to 10MB based on our testing results), making it well-suited for storing documents, images, and other moderately-sized objects [1]. The Apache HBase MOB feature achieves this improvement by separating IO paths for file references and MOB objects, applying different compaction policies to MOBs and thus reducing write amplification created by HBase’s compactions.

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Progress Report: Hive-on-Spark Nears Production Readiness

Categories: Cloudera Labs Hive Spark

Contributors from Intel, Cloudera, and the rest of the community have been making strong progress on the Hive-on-Spark initiative. This post provides an update.

[Editor’s note (April 20, 2016): Hive-on-Spark is now GA/shipping starting in CDH 5.7.]

Since its inception about one year ago, the community initiative to make Apache Spark a data processing engine for Apache Hive (HIVE-7292) has attracted widespread interest from developers around the world and gone through phases of rapid development,

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Docker is the New QuickStart Option for Apache Hadoop and Cloudera

Categories: CDH Ops and DevOps QuickStart VM Testing

Now there’s an even quicker “QuickStart” option for getting hands-on with the Apache Hadoop ecosystem and Cloudera’s platform: a new Docker image.

docker-logoYou might already be familiar with Cloudera’s popular QuickStart VM, a virtual image containing our distributed data processing platform. Originally intended as a demo environment, the QuickStart VM quickly evolved over time into quite a useful general-purpose environment for developers, customers, and partners. Today,

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Sustained Innovation in Apache Spark: DataFrames, Spark SQL, and MLlib

Categories: CDH Spark

Cloudera has announced support for Spark SQL/DataFrame API and MLlib. This post explains their benefits for app developers, data analysts, data engineers, and data scientists.

In July 2015, Cloudera re-affirmed its position since 2013: that Apache Spark is on course to replace MapReduce as the default general-purpose data processing engine for Apache Hadoop. Thanks to initiatives like the One Platform Initiative,

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