Cloudera Developer Blog · How-to Posts

How-to: Analyze Twitter Data with Hue

Hue 2.2 , the open source web-based interface that makes Apache Hadoop easier to use, lets you interact with Hadoop services from within your browser without having to go to a command-line interface. It features different applications like an Apache Hive editor and Apache Oozie dashboard and workflow builder.

This post is based on our “Analyzing Twitter Data with Hadoop” sample app and details how the same results can be achieved through Hue in a simpler way. Moreover, all the code and examples of the previous series have been updated to the recent CDH4.2 release.

Collecting Data

How-to: Import a Pre-existing Oozie Workflow into Hue

Hue is an open-source web interface for Apache Hadoop packaged with CDH that focuses on improving the overall experience for the average user. The Apache Oozie application in Hue provides an easy-to-use interface to build workflows and coordinators. Basic management of workflows and coordinators is available through the dashboards with operations such as killing, suspending, or resuming a job.

Prior to Hue 2.2 (included in CDH 4.2), there was no way to manage workflows within Hue that were created outside of Hue. As of Hue 2.2, importing a pre-existing Oozie workflow by its XML definition is now possible.

How to import a workflow

How To: Use Oozie Shell and Java Actions

Apache Oozie, the workflow coordinator for Apache Hadoop, has actions for running MapReduce, Apache Hive, Apache Pig, Apache Sqoop, and Distcp jobs; it also has a Shell action and a Java action. These last two actions allow us to execute any arbitrary shell command or Java code, respectively.

In this blog post, we’ll look at an example use case and see how to use both the Shell and Java actions in more detail. Please follow along below; you can get a copy of the full project at Cloudera’s GitHub as well. This how-to assumes some basic familiarity with Oozie.

Example Use Case

How-to: Use the Apache HBase REST Interface, Part 1

There are various ways to access and interact with Apache HBase. The Java API provides the most functionality, but many people want to use HBase without Java.

There are two main approaches for doing that: One is the Thrift interface, which is the faster and more lightweight of the two options. The other way to access HBase is using the REST interface, which uses HTTP verbs to perform an action, giving developers a wide choice of languages and programs to use.

How-to: Set Up Cloudera Manager 4.5 for Apache Hive

Last week Cloudera released the 4.5 release of Cloudera Manager, the leading framework for end-to-end management of Apache Hadoop clusters. (Download Cloudera Manager here, and see install instructions here.) Among many other features, Cloudera Manager 4.5 adds support for Apache Hive. In this post, I’ll explain how to set up a Hive server for use with Cloudera Manager 4.5 (and later).

For details about other new features in this release, please see the full release notes:

How-to: Set Up a Hadoop Cluster with Network Encryption

Hadoop network encryption is a feature introduced in Apache Hadoop 2.0.2-alpha and in CDH4.1.

In this blog post, we’ll first cover Hadoop’s pre-existing security capabilities. Then, we’ll explain why network encryption may be required. We’ll also provide some details on how it has been implemented. At the end of this blog post, you’ll get step-by-step instructions to help you set up a Hadoop cluster with network encryption.

A Bit of History on Hadoop Security

How-to: Resample from a Large Data Set in Parallel (with R on Hadoop)

UPDATED 20130424: The new RHadoop treats output to Streaming a bit differently, so do.trace=FALSE must be set in the randomForest call.

UPDATED 20130408: Antonio Piccolboni, the author of RHadoop, has improved the code somewhat using his substantially greater experience with R. The most material change is that the latest version of RHadoop can bind multiple calls to keyval correctly.

How-To: Run a MapReduce Job in CDH4 using Advanced Features

In my previous post, you learned how to write a basic MapReduce job and run it on Apache Hadoop. In this post, we’ll delve deeper into MapReduce programming and cover some of the framework’s more advanced features. In particular, we’ll explore:

How-to: Use Apache ZooKeeper to Build Distributed Apps (and Why)

It’s widely accepted that you should never design or implement your own cryptographic algorithms but rather use well-tested, peer-reviewed libraries instead. The same can be said of distributed systems: Making up your own protocols for coordinating a cluster will almost certainly result in frustration and failure.

Architecting a distributed system is not a trivial problem; it is very prone to race conditions, deadlocks, and inconsistency. Making cluster coordination fast and scalable is just as hard as making it reliable. That’s where Apache ZooKeeper, a coordination service that gives you the tools you need to write correct distributed applications, comes in handy.

From Zero to Impala in Minutes

This was post was originally published by U.C. Berkeley AMPLab developer (and former Clouderan) Matt Massie, on his personal blog. Matt has graciously permitted us to re-publish here for your convenience.

Note: The post below is valid for Impala version 0.6 only and is not being maintained for subsequent releases. To deploy Impala 0.7 and later using a much easier (and also free) method, use this how-to.

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