Archive for the ‘Oozie’ Category

Cloudera Live (beta)

Thursday, April 17th, 2014

Cloudera Live (beta)

From the webpage:

Try a live demo of Hadoop, right now.

Cloudera Live is a new way to get started with Apache Hadoop, online. No downloads, no installations, no waiting. Watch tutorial videos and work with real-world examples of the complete Hadoop stack included with CDH, Cloudera’s completely open source Hadoop platform, to:

  • Learn Hue, the Hadoop User Interface developed by Cloudera
  • Query data using popular projects like Apache Hive, Apache Pig, Impala, Apache Solr, and Apache Spark (new!)
  • Develop workflows using Apache Oozie

Great news for people interested in Hadoop!

Question: Will this become the default delivery model for test driving software and training?

Enjoy!

New Hue Demos:…

Friday, February 28th, 2014

New Hue Demos: Spark UI, Job Browser, Oozie Scheduling, and YARN Support by Justin Kestelyn.

From the post:

Hue, the open source Web UI that makes Apache Hadoop easier to use, is now a standard across the ecosystem — shipping within multiple software distributions and sandboxes. One of the reasons for its success is an agile developer community behind it that is constantly rolling out new features to its users.

Just as important, the Hue team is diligent in its documentation and demonstration of those new features via video demos. In this post, for your convenience, I bring you the most recent examples (released since December):

  • The new Spark Igniter App
  • Using YARN and Job Browser
  • Job Browser with YARN Security
  • Apache Oozie crontab scheduling

All short but all worthwhile. Nice way to start off your Saturday morning. The kids have cartoons and you have Hue. 😉

Apache Bigtop: The “Fedora of Hadoop”…

Wednesday, June 26th, 2013

Apache Bigtop: The “Fedora of Hadoop” is Now Built on Hadoop 2.x by Roman Shaposhnik.

From the post:

Just in time for Hadoop Summit 2013, the Apache Bigtop team is very pleased to announce the release of Bigtop 0.6.0: The very first release of a fully integrated Big Data management distribution built on the currently most advanced Hadoop 2.x, Hadoop 2.0.5-alpha.

Bigtop, as many of you might already know, is a project aimed at creating a 100% open source and community-driven Big Data management distribution based on Apache Hadoop. (You can learn more about it by reading one of our previous blog posts on Apache Blogs.) Bigtop also plays an important role in CDH, which utilizes its packaging code from Bigtop — Cloudera takes pride in developing open source packaging code and contributing the same back to the community.

The very astute readers of this blog will notice that given our quarterly release schedule, Bigtop 0.6.0 should have been called Bigtop 0.7.0. It is true that we skipped a quarter. Our excuse is that we spent all this extra time helping the Hadoop community stabilize the Hadoop 2.x code line and making it a robust kernel for all the applications that are now part of the Bigtop distribution.

And speaking of applications, we haven’t forgotten to grow the Bigtop family: Bigtop 0.6.0 adds Apache HCatalog and Apache Giraph to the mix. The full list of Hadoop applications available as part of the Bigtop 0.6.0 release is:

  • Apache Zookeeper 3.4.5
  • Apache Flume 1.3.1
  • Apache HBase 0.94.5
  • Apache Pig 0.11.1
  • Apache Hive 0.10.0
  • Apache Sqoop 2 (AKA 1.99.2)
  • Apache Oozie 3.3.2
  • Apache Whirr 0.8.2
  • Apache Mahout 0.7
  • Apache Solr (SolrCloud) 4.2.1
  • Apache Crunch (incubating) 0.5.0
  • Apache HCatalog 0.5.0
  • Apache Giraph 1.0.0
  • LinkedIn DataFu 0.0.6
  • Cloudera Hue 2.3.0

And we were just talking about YARN and applications weren’t we? 😉

Enjoy!

(Participate if you can but at least send a note of appreciation to Cloudera.)

Analyzing Twitter Data with Hadoop [Hiding in a Public Data Stream]

Wednesday, September 19th, 2012

Analyzing Twitter Data with Hadoop by Jon Natkins

From the post:

Social media has gained immense popularity with marketing teams, and Twitter is an effective tool for a company to get people excited about its products. Twitter makes it easy to engage users and communicate directly with them, and in turn, users can provide word-of-mouth marketing for companies by discussing the products. Given limited resources, and knowing we may not be able to talk to everyone we want to target directly, marketing departments can be more efficient by being selective about whom we reach out to.

In this post, we’ll learn how we can use Apache Flume, Apache HDFS, Apache Oozie, and Apache Hive to design an end-to-end data pipeline that will enable us to analyze Twitter data. This will be the first post in a series. The posts to follow to will describe, in more depth, how each component is involved and how the custom code operates. All the code and instructions necessary to reproduce this pipeline is available on the Cloudera Github.

Looking forward to more posts in this series!

Social media is a focus for marketing teams for obvious reasons.

Analysis of snaps, crackles and pops en masse.

What if you wanted to communicate securely with others using social media?

Thinking of something more robust and larger than two (or three) lovers agreeing on code words.

How would you hide in a public data stream?

Or the converse, how would you hunt for someone in a public data stream?

How would you use topic maps to manage the semantic side of such a process?

Apache Oozie (incubating) 3.2.0 release

Sunday, July 1st, 2012

Apache Oozie (incubating) 3.2.0 release

From the post:

This blog was originally posted on the Apache Blog for Oozie.

In June 2012, we released Apache Oozie (incubating) 3.2.0. Oozie is currently undergoing incubation at The Apache Software Foundation (see http://incubator.apache.org/oozie).

Oozie is a workflow scheduler system for Apache Hadoop jobs. Oozie Workflows are Directed Acyclical Graphs (DAGs), and they can be scheduled to run at a given time frequency and when data becomes available in HDFS.

Oozie 3.1.3 was the first incubating release. Oozie 3.1.3 added Bundle job capabilities to Oozie. A bundle job is a collection of coordinator jobs that can be managed as a single application. This is a key feature for power users that need to run complex data-pipeline applications.

Oozie 3.2.0 is the second incubating release, and the first one to include features and fixes done in the context of the Apache Community. The Apache Oozie Community is growing organically with more users, more contributors, and new committers. Speaking as one of the initial developers of Oozie, it is exciting and fulfilling to see the Apache Oozie project gaining traction and mindshare.

While Oozie 3.2.0 is a minor upgrade, it adds significant new features and fixes that make the upgrade worthwhile. Here are the most important new features:

  • Support for Hadoop 2 (YARN Map-Reduce)
  • Built in support for new workflow actions: Hive, Sqoop, and Shell
  • Kerberos SPNEGO authentication for Oozie HTTP REST API and Web UI
  • Support for proxy-users in the Oozie HTTP REST API (equivalent to Hadoop proxy users)
  • Job ACLs support (equivalent to Hadoop job ACLs)
  • Tool to create and upgrade Oozie database schema (works with Derby, MySQL, Oracle, and PostgreSQL databases)
  • Improved Job information over HTTP REST API
  • New Expression Language functions for Workflow and Coordinator applications
  • Share library per action (including only the JARs required for the specific action)

Oozie 3.2.0 also includes several improvements for performance and stability, as well as bug fixes. And, as with previous Oozie releases, we are ensuring 100% backwards compatibility with applications written for previous versions of Oozie.

For those of you who know Michael Sperberg-McQueen, these are Directed Acyclical Graphs (DAGs) put to a useful purpose in an information environment. (Yes, that is an “insider” joke.)

Another important part of the Hadoop ecosystem.

Hortonworks Data Platform v1.0 Download Now Available

Thursday, June 21st, 2012

Hortonworks Data Platform v1.0 Download Now Available

From the post:

If you haven’t yet noticed, we have made Hortonworks Data Platform v1.0 available for download from our website. Previously, Hortonworks Data Platform was only available for evaluation for members of the Technology Preview Program or via our Virtual Sandbox (hosted on Amazon Web Services). Moving forward and effective immediately, Hortonworks Data Platform is available to the general public.

Hortonworks Data Platform is a 100% open source data management platform, built on Apache Hadoop. As we have stated on many occasions, we are absolutely committed to the Apache Hadoop community and the Apache development process. As such, all code developed by Hortonworks has been contributed back to the respective Apache projects.

Version 1.0 of Hortonworks Data Platform includes Apache Hadoop-1.0.3, the latest stable line of Hadoop as defined by the Apache Hadoop community. In addition to the core Hadoop components (including MapReduce and HDFS), we have included the latest stable releases of essential projects including HBase 0.92.1, Hive 0.9.0, Pig 0.9.2, Sqoop 1.4.1, Oozie 3.1.3 and Zookeeper 3.3.4. All of the components have been tested and certified to work together. We have also added tools that simplify the installation and configuration steps in order to improve the experience of getting started with Apache Hadoop.

I’m a member of the general public! And you probably are too! 😉

See the rest of the post for more goodies that are included with this release.

Apache Bigtop 0.3.0 (incubating) has been released

Wednesday, April 4th, 2012

Apache Bigtop 0.3.0 (incubating) has been released by Roman Shaposhnik.

From the post:

Apache Bigtop 0.3.0 (incubating) is now available. This is the first fully integrated, community-driven, 100% Apache Big Data management distribution based on Apache Hadoop 1.0. In addition to a major change in the Hadoop version, all of the Hadoop ecosystem components have been upgraded to the latest stable versions and thoroughly tested:

  • Apache Hadoop 1.0.1
  • Apache Zookeeper 3.4.3
  • Apache HBase 0.92.0
  • Apache Hive 0.8.1
  • Apache Pig 0.9.2
  • Apache Mahout 0.6.1
  • Apache Oozie 3.1.3
  • Apache Sqoop 1.4.1
  • Apache Flume 1.0.0
  • Apache Whirr 0.7.0

Thoughts on what is missing from this ecosystem?

What if you moved from the company where you wrote the scripts? And they needed new scripts?

Re-write? On what basis?

Is your “big data” big enough to need “big documentation?”

Jeff Hammerbacher on Experiences Evolving a New Analytical Platform

Sunday, November 20th, 2011

Jeff Hammerbacher on Experiences Evolving a New Analytical Platform

Slides from Jeff’s presentation and numerous references, including to a live blogging summary by Jeff Dalton.

In terms of the new analytical platform, I would strongly suggest that you take Cloudera’s substrate:

Cloudera starts with a substrate architecture of Open Compute commodity Linux servers configured using Puppet and Chef and coordinated using ZooKeeper. Naturally this entire stack is open-source. They use HFDS and Ceph to provide distributed, schema-less storage. They offer append-only table storage and metadata using Avro, RCFile, and HCatalog; and mutable table storage and metadata using HBase. For computation, they offer YARN (inter-job scheduling, like Grid Engine, for data intensive computing) and Mesos for cluster resource management; MapReduce, Hamster (MPI), Spark, Dryad / DryadLINQ, Pregel (Giraph), and Dremel as processing frameworks; and Crunch (like Google’s FlumeJava), PigLatin, HiveQL, and Oozie as high-level interfaces. Finally, Cloudera offers tool access through FUSE, JDBC, and ODBC; and data ingest through Sqoop and Flume.

Rather than asking the usual questions, how to make this faster, more storage, etc., all of which are important, ask the more difficult questions:

  1. In or between which of these elements, would human analysis/judgment have the greatest impact?
  2. Would human analysis/judgment be best made by experts or crowds?
  3. What sort of interface would elicit the best human analysis/judgment? (visual/aural; contest/game/virtual)
  4. Performance with feedback or homeostasis mechanisms?

That is a very crude and uninformed starter set of questions.

Putting higher speed access to more data with better tools at our fingertips expands the questions we can ask of interfaces and our interaction with the data. (Before we ever ask questions of the data.)

Oozie by Example

Tuesday, July 26th, 2011

Oozie by Example

From the post:

In our previous article [Introduction to Oozie] we described Oozie workflow server and presented an example of a very simple workflow. We also described deployment and configuration of workflow for Oozie and tools for starting, stoping and monitoring Oozie workflows.

In this article we will describe a more complex Oozie example, which will allow us to discuss more Oozie features and demonstrate how to use them.

More on workflow for Hadoop!

Introduction to Oozie

Saturday, July 23rd, 2011

Introduction to Oozie

From the post:

Tasks performed in Hadoop sometimes require multiple Map/Reduce jobs to be chained together to complete its goal. [1] Within the Hadoop ecosystem, there is a relatively new component Oozie [2], which allows one to combine multiple Map/Reduce jobs into a logical unit of work, accomplishing the larger task. In this article we will introduce Oozie and some of the ways it can be used.

What is Oozie ?

Oozie is a Java Web-Application that runs in a Java servlet-container – Tomcat and uses a database to store:

  • Workflow definitions
  • Currently running workflow instances, including instance states and variables

Oozie workflow is a collection of actions (i.e. Hadoop Map/Reduce jobs, Pig jobs) arranged in a control dependency DAG (Direct Acyclic Graph), specifying a sequence of actions execution. This graph is specified in hPDL (a XML Process Definition Language).

Workflow management for Hadoop!