The Hortonworks Blog

There have been many Apache Hadoop-related announcements the past few weeks, making it difficult to separate the signal from the marketing noise. One thing is crystal clear however… there is a large and growing appetite for Enterprise Hadoop because it helps unlock new insights and business opportunities in a way that was not previously technologically or economically feasible.

Enterprise and Open Source are NOT Mutually Exclusive

Dan Woods from Forbes, recently penned an article entitled “Why SQL Matters, the Limits of Open Source, and Other Lessons of EMC Greenplum’s Pivotal HD” where he paints a picture of enterprise and open source in opposite corners.…

 

For several years now Apache Hadoop has been fueling the fast growing big data market and has become the defacto platform for Big Data deployments and the technology foundation for an explosion of new analytic applications. Many organizations turn to Hadoop to help tame the vast amounts of new data they are collecting but in order to do so with Hadoop they have had to use servers running the Linux operating system.…

Cat Miller is an engineer at Mortar Data, a Hadoop-as-a-service provider, and creator of mortar, an open source framework for data processing.

Introduction

For anyone who came of programming age before cloud computing burst its way into the technology scene, data analysis has long been synonymous with SQL. A slightly awkward, declarative language whose production can more resemble logic puzzle solving than coding, SQL and the relational databases it builds on have been the pervasive standard for how to deal with data.…

Introduction

Hortonworks hosted the second Apache Hadoop YARN meetup at Hortonworks office in Palo Alto on last Friday (22 February 2013). Following the success with the first one, this meetup continues to enjoy a good attendance from the YARN community. About 40 joined the meetup in person and nearly another 30 attended via phone/webex.

Meetup sessions Update from Yahoo!

The Yahoo! grid team responsible for YARN rollout on their clusters gave an update of the current deployments and their state.…

 

In Derrick Harris’ article on GigaOM entitled “EMC to Hadoop competition: See ya, wouldn’t wanna be ya.”, EMC unveiled their new Pivotal HD offering which effectively re-architects the Greenplum analytic database so it sits on top of the Hadoop Distributed File System (HDFS). Scott Yara, Greenplum cofounder, is excited about the new product. Since a key focus for us at Hortonworks is to deeply integrate Hadoop with other data systems (a la our efforts with Teradata, Microsoft, MarkLogic, and others), I’m always excited to see data system providers like Greenplum decide to store their data natively in HDFS.…

Apache Pig version 0.11 was released last week. An Apache Pig blog post summarized the release. New features include:

  • A DateTime datatype, documentation here.
  • A RANK function, documentation here.
  • A CUBE operator, documentation here.
  • Groovy UDFs, documentation here.

And many improvements. Oink it up for Pig 0.11! Hortonworks’ Daniel Dai gave a talk on Pig 0.11 at Strata NY, check it out:…

Last week, the HBase community released 0.94.5, which is the most stable release of HBase so far. The release includes 76 jira issues resolved, with 61 bug fixes, 8 improvements, and 2 new features.

Most of the bug fixes went against the REST server, replication, region assignment, secure client, flaky unit tests, 0.92 compatibility and various stability improvements. Some of the interesting patches in this release are: [HBASE-3996] – Support multiple tables and scanners as input to the mapper in map/reduce jobs [HBASE-5416] – Improve performance of scans with some kind of filters.…

We are now less than a month away from the kickoff of Hadoop Summit Europe, taking place March 20-21 in Amsterdam. The excitement from the community is really starting to grow and pass sales are far ahead of where we expected. Much of the buzz is tied directly to the content that will be presented during the conference.

In all, there were be 42 breakout sessions with presenters from more than 20 companies, including representatives from Adobe, eBay, Facebook, HSBC, LinkedIn, Twitter and Yahoo!.…

YARN is part of the next generation Hadoop cluster compute environment. It creates a generic and flexible resource management framework to administer the compute resources in a Hadoop cluster. The YARN application framework allows multiple applications to negotiate resources for themselves and perform their application specific computations on a shared cluster. Thus, resource allocation lies at the heart of YARN.

YARN ultimately opens up Hadoop to additional compute frameworks, like Tez, so that an application can optimize compute for their specific requirements.…

 

Last week, we outlined our approach for delivering an enterprise viable Apache Hadoop distribution in the open.  Simply put: we believe the fastest way to innovate is to do our work within the open source community, introduce enterprise feature requirements into that public domain, and to work diligently to progress existing open source projects and incubate new projects to meet those needs.

In support of our approach, this week we’ve announced the submission of two new incubation projects to the Apache Software foundation together with the launch of the “Stinger Initiative”, all aimed at enhancing the security and performance of Hadoop applications.  …

 

MapReduce has served us well.  For years it has been THE processing engine for Hadoop and has been the backbone upon which a huge amount of value has been created.  While it is here to stay, new paradigms are also needed in order to enable Hadoop to serve an even greater number of usage patterns.  A key and emerging example is the need for interactive query, which today is challenged by the batch-oriented nature of MapReduce. …

 

UPDATE: Since this article was posted, the Stinger initiative has continued to drive to the goal of 100x Faster Hive. You can read the latest information at http://hortonworks.com/stinger

Introduced by Facebook in 2007, Apache Hive and its HiveQL interface has become the de facto SQL interface for Hadoop.  Today, companies of all types and sizes use Hive to access Hadoop data in a familiar way and to extend value to their organization or customers either directly or though a broad ecosystem of existing BI tools that rely on this key proven interface. …

 

Back in the day, in order to secure a Hadoop cluster all you needed was a firewall that restricted network access to only authorized users. This eventually evolved into a more robust security layer in Hadoop… a layer that could augment firewall access with strong authentication. Enter Kerberos.  Around 2008, Owen O’Malley and a team of committers led this first foray into security and today, Kerberos is still the primary way to secure a Hadoop cluster.…

 

As the Release Manager for hadoop-2.x, I’m very pleased to announce the next major milestone for the Apache Hadoop community, the release of hadoop-2.0.3-alpha!

2.0 Enhancements in this Alpha Release

This release delivers significant major enhancements and stability over previous releases in hadoop-2.x series. Notably, it includes:

  • QJM for HDFS HA for NameNode (HDFS-3077) and related stability fixes to HDFS HA
  • Multi-resource scheduling (CPU and memory) for YARN (YARN-2, YARN-3 & friends)
  • YARN ResourceManager Restart (YARN-230)
  • Significant stability at scale for YARN (over 30,000 nodes and 14 million applications so far, at time of release – see more details from folks at Yahoo! 

Pig can easily stuff Redis full of data. To do so, we’ll need to convert our data to JSON. We’ve previously talked about pig-to-json in JSONize anything in Pig with ToJson. Once we convert our data to json, we can use the pig-redis project to load redis.

Build the pig to json project:

git clone git@github.com:rjurney/pig-to-json.git ant

Then run our Pig code:

/* Load Avro jars and define shortcut */ register /me/Software/pig/build/ivy/lib/Pig/avro-1.5.3.jar register /me/Software/pig/build/ivy/lib/Pig/json-simple-1.1.jar register /me/Software/pig/contrib/piggybank/java/piggybank.jar define AvroStorage org.apache.pig.piggybank.storage.avro.AvroStorage(); register /me/Software/pig-to-json/dist/lib/pig-to-json.jar register /me/Software/pig-redis/dist/pig-redis.jar -- Enron emails are available at https://s3.amazonaws.com/rjurney_public_web/hadoop/enron.avro emails = load '/me/Data/enron.avro' using AvroStorage(); json_test = foreach emails generate message_id, com.hortonworks.pig.udf.ToJson(tos) as bag_json; store json_test into 'dummy-name' using com.hackdiary.pig.RedisStorer('kv', 'localhost');

Now run our Flask web server:

python server.py

Code for this post is available here: https://github.com/rjurney/enron-pig-tojson-redis-node.…

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