We’re very happy to announce the 2.3 release of Hue, the open source Web UI that makes Apache Hadoop easier to use.

Hue 2.3 comes only two months after 2.2 but contains more than 100 improvements and fixes. In particular, two new apps were added (including an Apache Pig editor) and the query editors are now easier to use.

Here’s a video demoing the major changes:

In the first installment of the demo series about Hue — the open source Web UI that makes Apache Hadoop easier to use — you learned how file operations are simplified via the File Browser application. In this installment, we’ll focus on analyzing data with Hue, using Apache Hive via Hue’s Beeswax and Catalog applications (based on Hue 2.3 and later).

The Yelp Dataset Challenge provides a good use case. This post explains, through a video and tutorial, how you can get started doing some analysis and exploration of Yelp data with Hue. The goal is to find the coolest restaurants in Phoenix!

Dataset Challenge with Hue

The demo below demonstrates how the “business” and “review” datasets are cleaned and then converted to a Hive table before being queried with SQL.

Managing and viewing data in HDFS is an important part of Big Data analytics. Hue, the open source web-based interface that makes Apache Hadoop easier to use, helps you do that through a GUI in your browser —  instead of logging into a Hadoop gateway host with a terminal program and using the command line.

The first episode in a new series of Hue demos, the video below demonstrates how to get up and running quickly with HDFS file operations via Hue’s File Browser application.

Other Features

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

The first step is to create the “flume” user and his home on the HDFS where the data will be stored. This can be done via the User Admin application.

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

Importing a workflow is pretty straightforward. All it requires is the workflow definition file and access to the Oozie application in Hue. Follow these steps to import a workflow:

1. Go to Oozie Editor/Dashboard > Workflows and click the “Import” button.

This post is about the new release of Hue, an open source web-based interface that makes Apache Hadoop easier to use, that’s included in CDH4.2.

Hue lets you interact with Hadoop services from within your browser without having to go to a command-line interface. It features a file browser for HDFS, an Apache Oozie Application for creating workflows of data processing jobs, a job designer/browser for MapReduce, Apache Hive and Cloudera Impala query editors, a Shell, and a collection of Hadoop APIs.

The goal of this release was to add a set of new features and improve the user experience. Read on for a list of the major changes (from 304 commits).

Oozie Application

For several good reasons, 2013 is a Happy New Year for Apache Hadoop enthusiasts.

In 2012, we saw continued progress on developing the next generation of the MapReduce processing framework (MRv2), work that will bear fruit this year. HDFS experienced major progress toward becoming a lights-out, fully enterprise-ready distributed filesystem with the addition of high availability features and increased performance. And a hint of the future of the Hadoop platform was provided with the Beta release of Cloudera Impala, a real-time query engine for analytics across HDFS and Apache HBase data.

Let’s look at the highlights of the 2012 developments around projects supported by Cloudera.

Apache Hadoop Releases

Hue is a web interface for Apache Hadoop that makes common Hadoop tasks such as running MapReduce jobs, browsing HDFS, and creating Apache Oozie workflows, easier. In this post, we’re going to focus on the dynamic workflow builder that Hue provides for Oozie that will be released in Hue 2.2.0 (For a high-level description of Oozie integration in Hue, see this blog post).

The dynamic workflow editor is feature packed and emphasizes usability. The heavy lifting is delegated to the client via Javascript to provide a more “dynamic” experience for the user. This is achieved by using the MVVM (Model View View-Model) design pattern with KnockoutJS and event handling with JQuery. In effect, moving, creating, modifying, and deleting actions will be much easier. Also, the workflow editor will support Oozie’s Decision node.

Basic Operations on Actions

The experience of performing basic operations on actions has been simplified (IE: Creating, updating, and deleting a node).

Hue is a web interface for Apache Hadoop that makes common Hadoop tasks such as running MapReduce jobs, browsing HDFS, and creating Apache Oozie workflows, easier. (To learn more about the integration of Oozie and Hue, see this blog post.) In this post, we’re going to focus on how one of the fundamental components in Hue, Useradmin, has matured.

New User and Permission Features

User and permission management in Hue has changed drastically over the past year. Oozie workflows, Apache Hive queries, and MapReduce jobs can be shared with other users or kept private. Permissions exist at the app level. Access to particular apps can be restricted, as well as certain sections of the apps. For instance, access to the shell app can be restricted, as well as access to the Apache HBase, Apache Pig, and Apache Flume shells themselves. Access privileges are defined for groups and users can be members of one or more groups.

Changes to Users, Groups, and Permissions

Hue now supports authentication against PAM, Spnego, and an LDAP server. Users and groups can be imported from LDAP and be treated like their non-external counterparts. The import is manual and is on a per user/group basis. Users can authenticate using different backends such as LDAP. Using the LDAP authentication backend will allow users to login using their LDAP password. This can be configured in /etc/hue/hue.ini by changing the ‘desktop.auth.backend’ setting to ‘desktop.auth.backend.LdapBackend’. The LDAP server to authenticate against can be configured through the settings under ‘desktop.ldap’.

Hue is a Web-based interface that makes it easier to use Apache Hadoop. Hue 2.1 (included in CDH4.1) provides a new application on top of Apache Oozie (a workflow scheduler system for Apache Hadoop) for creating workflows and scheduling them repetitively. For example, Hue makes it easy to group a set of MapReduce jobs and Hive scripts and run them every day of the week.

In this post, we’re going to focus on the Workflow component of the new application.

Workflow Editor

Workflows consist of one or multiple actions that can be executed sequentially or in parallel. Each action will run a program that can be configured with parameters (e.g. output=${OUTPUT} instead of hardcoding a directory path) in order to be easily reusable.