The Hortonworks Blog

Posts categorized by : HDP 2

Thanks to all those who joined in person and virtually for the Apache Ambari Meetup at Hortonworks this week. We talked tech, we saw demos, we laughed, we cried, we ate pizza.

The central theme of the night was the newly added support for Hadoop 2. Ambari now has:

  • Hadoop 2 Stack: Ambari adds support for installing, managing and monitoring a Hadoop 2 Stack.
  • NameNode HA: Configure NameNode High Availability based on QJM support built-into HDFS2
  • YARN: Ambari manages YARN Service lifecycle and automatically deploys the MapReduce2 framework.

This post is the third in our series on the motivations, architecture and performance gains of Apache Tez for data processing in Hadoop. The series has the following posts:

Apache Tez models data processing as a dataflow graph, with the vertices in the graph representing processing of data and edges representing movement of data between the processing.…

YARN and the Hortonworks Data Platform 2.0 enables one Hadoop cluster to share data and analytical processing capabilities across the Enterprise organization. Organizations can use the Hortonworks Data Platform 2.0 to:

  • Pool all enterprise data into one scalable and reliable storage platform
  • Enable all analytical processing IN the data platform
  • Provide access to this data and processing across all business units

The Capacity Scheduler (CS) ensures that groups of users and applications will get a guaranteed share of the cluster, while maximizing overall utilization of the cluster.…

Albert Einstein is credited with saying that he doesn’t worry about the future because it would arrive soon enough. We don’t worry the future either — we focus on building it. And today, we are delighted to release the Hortonworks Data Platform 2.0 Beta Sandbox. This is the single-node VM based on the HDP 2.0 Beta release. This release is in the easy-to-use Sandbox form factor and allow you to easily work with a stable, reliable v2 of Hadoop.…

This post is the second in our series on the motivations, architecture and performance gains of Apache Tez for data processing in Hadoop. The series has the following posts:

Overview

Apache Tez models data processing as a dataflow graph, with the vertices in the graph representing processing of data and edges representing movement of data between the processing.…

With HDP 1.3 and HDP 2.0 Beta, we introduced the ability to create snapshots to protect important enterprise data sets from user or application errors.

HDFS Snapshots are read-only point-in-time copies of the file system. Snapshots can be taken on a subtree of the file system or the entire file system and are:

  • Performant and Reliable: Snapshot creation is atomic and instantaneous, no matter the size or depth of the directory subtree
  • Scalable: Snapshots do not create extra copies of blocks on the file system.

This post is the first in our series on the motivations, architecture and performance gains of Apache Tez for data processing in Hadoop. The series has the following posts:

In this post we introduce the motivation behind Apache Tez (http://incubator.apache.org/projects/tez.html) and provide some background around the basic design principles for the project.…

As part of HDP 2.0 Beta, YARN takes the resource management capabilities that were in MapReduce and packages them so they can be used by new engines.  This also streamlines MapReduce to do what it does best, process data.  With YARN, you can now run multiple applications in Hadoop, all sharing a common resource management.

In this blog post we’ll walk through how to plan for and configure processing capacity in your enterprise HDP 2.0 cluster deployment.…

The upcoming Hive 0.12 is set to bring some great new advancements in the storage layer in the forms of higher compression and better query performance.

Higher Compression

ORCFile was introduced in Hive 0.11 and offered excellent compression, delivered through a number of techniques including run-length encoding, dictionary encoding for strings and bitmap encoding.

This focus on efficiency leads to some impressive compression ratios. This picture shows the sizes of the TPC-DS dataset at Scale 500 in various encodings.…

The Stinger Initiative is Hortonworks’ community-facing roadmap laying out the investments Hortonworks is making to improve Hive performance 100x and evolve Hive to SQL compliance to simplify migrating SQL workloads to Hive.

We launched the Stinger Initiative along with Apache Tez to evolve Hadoop beyond its MapReduce roots into a data processing platform that satisfies the need for both interactive query AND petabyte scale processing. We believe it’s more feasible to evolve Hadoop to cover interactive needs rather than move traditional architectures into the era of big data.…

We hosted a webinar on YARN a couple of weeks ago (see the slides and playback here). As you might expect, there was a lot of great questions and here is a set of answers to those questions.

Our next YARN-oriented Office Hours online on Sept 11th at 2pm PST. Join us on Meetup!

Who is using YARN and what benefits have they received from it?

On great public example of in production use of YARN, is at Yahoo!.…

Another week, another release…  Following the release of Apache Hadoop 2.0 beta last week, we are excited to release the beta of Hortonworks Data Platform 2.0, the first commercial release of the stable YARN API and protocols on which new applications can now be built.

For our customers this is a great opportunity to ensure the release meets expectations and provides a vehicle to voice feedback that will work to improve Hadoop and shape its roadmap. …

It’s my great pleasure to announce that the Apache Hadoop community has declared Hadoop 2.x as Beta with the vote closing over the weekend for the hadoop-2.1.0-beta release.

As noted in the announcement to the mailing lists, this is a significant milestone across multiple dimensions: not only is the release chock-full of significant features (see below), it also represents a very stable set of APIs and protocols on which we can continue to build for the future.…

Before I was a developer of Hadoop, I was a user of Hadoop.  I was responsible for operation and maintenance of multiple Hadoop clusters, so it’s very satisfying when I get the opportunity to implement features that make life easier for operations staff.

Have you ever wondered what’s happening during a namenode restart?  A new feature coming in HDP 2.0 will give operators greater visibility into this critical process.  This is a feature that would have been very useful to me in my prior role.…

Four years ago, Arun Murthy entered a JIRA ticket (MAPREDUCE -279) that outlined a re-architecture of the original MapReduce.  In the ticket, he outlined a set of capabilities that allowed processes to better share resources and an architecture that would allow Hadoop to extend beyond batch data processing.

It turned out that this ticket was prescient of true enterprise requirements for Hadoop. As enterprise adoption accelerated, it became even clearer that multiple processing models – moving beyond batch – was critical for Hadoop to broaden its applicability for mainstream usage in the modern enterprise architecture.…

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