The Hortonworks Blog

Posts categorized by : Apache Hadoop

The third installment of the Hortonworks executive video series features Arun C. Murthy, co-founder of Hortonworks and VP of Apache Hadoop for the Apache Software Foundation. In this video, Arun shares his view of the power of Apache Hadoop and provides some insight into the future direction of MapReduce, including the ability to support alternate programming paradigms.

As part of Big Data Week, Dan Harvey of the London Hadoop User Group organised an afternoon session for the usergroup, which we were glad to sponsor, along with Canonical and Facegroup. I had the pleasure of presenting my view of the current and future status of Apache Hadoop to an audience that ranged from those curious about Hadoop to heavy users.

Every talk of the day was excellent, from the use cases by Datasift, Mendeley and MusicMetric, to the talk by Francine Bennett of MastodonC on the CO2 footprint of different cloud computing infrastructures, including a live dashboard on the current CO2/hour of many cloud infrastructure sites.…

Another important milestone for Apache Pig was reached this week with the release of Pig 0.10. The purpose of this blog is to summarize the new features in Pig 0.10.

Boolean Data Type

Pig 0.10 introduces boolean data type as a first-class Pig data type. Users can use the keyword “boolean” anywhere where a data type is expected, such as load-as clause, type cast clause, etc.

Here are some sample use cases:

a = load ‘input’ as (a0:boolean, a1:tuple(a10:boolean, a11:int), a2);

b = foreach a generate a0, a1, (boolean)a2;

c = group b by a2; — group by a boolean field

When loading boolean data using PigStorage, Pig expects the text “true” (ignore case) for a true value, and “false” (ignore case) for a false value; while other values map to null.…

This blog covers our on-going work on Snapshots in Apache Hadoop HDFS. In this blog, I will cover the motivations for the work, a high level design and some of the design choices we made. Having seen snapshots in use with various filesystems, I believe that adding snapshots to Apache Hadoop will be hugely valuable to the Hadoop community. With luck this work will be available to Hadoop users in late 2012 or 2013.…

We just released the second video in the Hortonworks Executive Series. This one features Matt Foley, Test and Release Engineering Manager for Hortonworks.

In this video, Matt provides an overview of Hortonworks Data Platform (HDP), including a summary of the Apache Hadoop components included in the distribution and the testing processes involved in the release process. Matt also provides an overview of Apache Ambari, an open source project that is adding monitoring and management capabilities to Apache Hadoop.…

I’m pleased to announce the first in a series of videos featuring Hortonworks founders and executives sharing their thoughts on how Apache Hadoop is being extended to become the next generation enterprise data platform. Over the coming weeks and months, you will be hearing from folks such as Matt Foley, Arun Murthy, Sanjay Radia and Alan Gates, just to name a few.

The first video features Shaun Connolly, Hortonworks VP of Corporate Strategy, talking about the Hortonworks vision for Apache Hadoop.…

We reached a significant milestone in HDFS: the Namenode HA branch was merged into the trunk. With this merge, HDFS trunk now supports HOT failover.

Significant enhancements were completed to make HOT Failover work:

  • Configuration changes for HA
  • Notion of active and standby states were added to the Namenode
  • Client-side redirection
  • Standby processing journal from Active
  • Dual block reports to Active and Standby

We have extensively tested HOT manual failover in our labs over the last few months.…

Today we announced  that we were delivering on our earlier promise to help Microsoft bring Apache Hadoop to Windows. I’m pleased to share that Microsoft, with our collaboration and guidance, has now submitted a series of patches to Apache aimed at overcoming the challenges of running Apache Hadoop in Windows Server environments.

These patches, once vetted and approved by the community, will become part of the core Hadoop code base. They will also become available in the two major Apache Hadoop branches: hadoop-1.0 (the current stable branch, which is available as part of Hortonworks Data Platform v1.0) and hadoop-0.23 (the next generation of Apache Hadoop, which will be available as part of Hortonworks Data Platform v2.0).…

A very short while ago, Vinod blogged about some of the significant improvements in Hadoop.Next (a.k.a hadoop-0.23.1).

To recap, the Hortonworks and Yahoo! teams have done a huge amount of work to test, validate and benchmark Hadoop.Next, the next generation of Apache Hadoop that includes HDFS Federation, NextGen MapReduce (a.k.a. YARN) and many other significant features and performance improvements.

Today, I’m very excited to announce that the Apache Hadoop community voted to release hadoop-0.23.1 and it’s now available for all to use!…

I’ve been surprised by a couple of recent articles highlighting our recent leadership change.  These articles imply that our business model may be changing. Let me be clear, WE ARE NOT CHANGING OUR BUSINESS MODEL. We are committed to providing training and support of a 100% open source distribution of Apache Hadoop and related projects.

What has changed?

Rob Bearden has agreed to take on the role of CEO. I am moving from CEO to the role of CTO.…

Hadoop RPC is the primary communication mechanism between the nodes in an Apache Hadoop cluster. Maintaining wire compatibility, as new features are added to Apache Hadoop, has been a significant challenge with the current RPC architecture. In this blog, I highlight the architectural improvement in Hadoop RPC and how it enables wire compatibility and rolling upgrades.

Challenges for Wire Compatibility

Earlier Hadoop RPC used Writable serialization that made it difficult to evolve the protocols while maintaining wire compatibility.…

For those of you new to Apache ZooKeeper, it is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. To learn more about ZooKeeper, please visit the Apache ZooKeeper homepage.

As part of stabilizing Apache ZooKeeper 3.4 branch, ZooKeeper 3.4.3 has just been released. It is a bug fix release on the 3.4 branch and fixes 17 issues out of which 1 is very critical and can cause data inconsistency (ZOOKEEPER-1367).…

In our previous blogs and webinars we have discussed the significant improvements and architectural changes coming to Apache Hadoop .Next (0.23). To recap, the major ones are:

  • Federation for Scaling HDFS – HDFS has undergone a transformation to separate Namespace management from the Block (storage) management to allow for significant scaling of the filesystem. In previous architectures, they were intertwined in the NameNode.
  • NextGen MapReduce (aka YARN) – MapReduce has undergone a complete overhaul in hadoop-0.23, including a fundamental change to split up the major functionalities of the JobTracker, resource management and job scheduling/monitoring into separate daemons.

Today we announced our plans to release a public preview of the Hortonworks Data Platform (HDP) version 2. HDP v2 will leverage Apache Hadoop 0.23, which is the first major update to Hadoop in more than three years. Among other advancements, HDP v2 will include the NextGen MapReduce architecture, HDFS NameNode HA and HDFS Federation. It will also include the most up-to-date stable components including HCatalog, HBase, Hive and Pig; all fully integrated and tested at scale.…

Congratulations! The Hadoop Community has given itself a big holiday present: Release 1.0.0! This release has been six years in the making, and has involved:

  • Hard work and cooperation from dozens of software developers and contributors from across the industry, including of course Doug Cutting and Mike Cafarella’s early work in Nutch and the founding Hadoop team at Yahoo, Doug, Owen O’Malley and many others, with leadership from Eric14.  Special thanks to all the Hadoop committers.
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