Cloudera Developer Blog · Oozie Posts
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).
Basic Operations on Actions
The experience of performing basic operations on actions has been simplified (IE: Creating, updating, and deleting a node).
As Apache Oozie, the workflow engine for Apache Hadoop, continues to receive wider adoption from our customers and the community, we’re seeing patterns with respect to the biggest challenges for users. One such point of difficulty is setting up and using Oozie’s ShareLib for allowing JARs to be shared by different workflows. This blog post is intended to help you with those tasks.
A missing or improperly installed ShareLib will cause some action types (DistCp, Streaming, Pig, Sqoop, and Hive) to fail. In this case, you’ll typically see any of the following exceptions in the Oozie and JobTracker logs:
java.lang.ClassNotFoundException: org.apache.hadoop.tools.DistCp java.lang.NoClassDefFoundError: org/apache/pig/Main java.lang.ClassNotFoundException: org.apache.pig.Main java.lang.NoClassDefFoundError: org/apache/sqoop/Sqoop java.lang.ClassNotFoundException: org.apache.sqoop.Sqoop java.lang.NoClassDefFoundError: org/apache/hadoop/hive/cli/CliDriver java.lang.ClassNotFoundException: org.apache.hadoop.hive.cli.CliDriver java.lang.ClassNotFoundException: Class org.apache.hadoop.streaming.PipeMapRunner not found
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’.
This is the second article in a series about analyzing Twitter data using some of the components of the Hadoop ecosystem available in CDH, Cloudera’s open-source distribution of Apache Hadoop and related projects. In the first article, you learned how to pull CDH components together into a single cohesive application, but to really appreciate the flexibility of each of these components, we need to dive deeper.
Every story has a beginning, and every data pipeline has a source. So, to build Hadoop applications, we need to get data from a source into HDFS.
Apache Flume is one way to bring data into HDFS using CDH. The Apache Flume website describes Flume as “a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.” At the most basic level, Flume enables applications to collect data from its origin and send it to a resting location, such as HDFS. At a slightly more detailed level, Flume achieves this goal by defining dataflows consisting of three primary structures: sources, channels and sinks. The pieces of data that flow through Flume are called events, and the processes that run the dataflow are called agents.
Update time! As a reminder, Cloudera releases major versions of CDH, our 100% open source distribution of Apache Hadoop and related projects, annually and then updates to CDH every three months. Updates primarily comprise bug fixes but we will also add enhancements. We only include fixes or enhancements in updates that maintain compatibility, improve system stability and still allow customers and users to skip updates as they see fit.
We’re pleased to announce the availability of CDH4.1. We’ve seen excellent adoption of CDH4.0 since it went GA at the end of June and a number of exciting use cases have moved to production. CDH4.1 is an update that has a number of fixes but also a number of useful enhancements. Among them:
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.
Who is Influential?
To understand whom we should target, let’s take a step back and try to understand the mechanics of Twitter. A user – let’s call him Joe – follows a set of people, and has a set of followers. When Joe sends an update out, that update is seen by all of his followers. Joe can also retweet other users’ updates. A retweet is a repost of an update, much like you might forward an email. If Joe sees a tweet from Sue, and retweets it, all of Joe’s followers see Sue’s tweet, even if they don’t follow Sue. Through retweets, messages can get passed much further than just the followers of the person who sent the original tweet. Knowing that, we can try to engage users whose updates tend to generate lots of retweets. Since Twitter tracks retweet counts for all tweets, we can find the users we’re looking for by analyzing Twitter data.
Organizations in diverse industries have adopted Apache Hadoop-based systems for large-scale data processing. As a leading force in Hadoop development with customers in half of the Fortune 50 companies, Cloudera is in a unique position to characterize and compare real-life Hadoop workloads. Such insights are essential as developers, data scientists, and decision makers reflect on current use cases to anticipate technology trends.
Recently we collaborated with researchers at UC Berkeley to collect and analyze a set of Hadoop traces. These traces come from Cloudera customers in e-commerce, telecommunications, media, and retail (Table 1). Here I will explain a subset of the observations, and the thoughts they triggered about challenges and opportunities in the Hadoop ecosystem, both present and in the future.
Table 1. Summary of Hadoop workloads analyzed
We are happy to announce the general availability of CDH3 update 5. This update is a maintenance release of CDH3 platform and provides a considerable amount of bug-fixes and stability enhancements. Alongside these fixes, we have also included a few new features, most notable of which are the following:
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.
The Development track at Hadoop World is a technical deep dive dedicated to discussion about Apache Hadoop and application development for Apache Hadoop. You will hear committers, contributors and expert users from various Hadoop projects discuss the finer points of building applications with Hadoop and the related ecosystem. The sessions will touch on foundational topics such as HDFS, HBase, Pig, Hive, Flume and other related technologies. In addition, speakers will address key development areas including tools, performance, bringing the stack together and testing the stack. Sessions in this track are for developers of all levels who want to learn more about upcoming features and enhancements, new tools, advanced techniques and best practices.
Building Web Analytics Processing on Hadoop at CBS Interactive
Michael Sun, CBS Interactive