Cloudera Engineering Blog · MapReduce Posts
API access was a new feature introduced in Cloudera Manager 4.0 (download free edition here.). Although not visible in the UI, this feature is very powerful, providing programmatic access to cluster operations (such as configuration and restart) and monitoring information (such as health and metrics). This article walks through an example of setting up a 4-node HDFS and MapReduce cluster via the Cloudera Manager (CM) API.
Cloudera Manager API Basics
The CM API is an HTTP REST API, using JSON serialization. The API is served on the same host and port as the CM web UI, and does not require an extra process or extra configuration. The API supports HTTP Basic Authentication, accepting the same users and credentials as the Web UI. API users have the same privileges as they do in the web UI world.
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.
The following is a guest post kindly offered by Adam Kawa, a 26-year old Hadoop developer from Warsaw, Poland. This post was originally published in a slightly different form at his blog, Hakuna MapData!
Recently I have found an interesting dataset, called Million Song Dataset (MSD), which contains detailed acoustic and contextual data about a million songs. For each song we can find information like title, hotness, tempo, duration, danceability, and loudness as well as artist name, popularity, localization (latitude and longitude pair), and many other things. There are no music files included here, but the links to MP3 song previews at 7digital.com can be easily constructed from the data.
In June 2012, Eli Collins (@elicollins), from Cloudera’s Platforms team, led a session at QCon New York 2012 on the subject “Introducing Apache Hadoop: The Modern Data Operating System.” During the conference, the QCon team had an opportunity to interview Eli about several topics, including important things to know about CDH4, main differences between MapReduce 1.0 and 2.0, Hadoop use cases, and more. It’s a great primer for people who are relatively new to Hadoop.
You can catch the full interview (video and transcript versions) here.
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:
In Building and Deploying MR2, we presented a brief introduction to MapReduce in Apache Hadoop 0.23 and focused on the steps to setup a single-node cluster. In MapReduce 2.0 in Hadoop 0.23, we discussed the new architectural aspects of the MapReduce 2.0 design. This blog post highlights the main issues to consider when migrating from MapReduce 1.0 to MapReduce 2.0. Note that both MapReduce 1.0 and MapReduce 2.0 are included in CDH4.
It is important to note that, at the time of writing this blog post, MapReduce 2.0 is still Alpha, and it is not recommended to use it in production.
This posted was originally posted to the Apache Software Foundation MRUnit blog.
The Apache MRUnit team has graduated from the Apache Incubator to an Apache TLP (Top Level Project)! MRUnit is a Java library that helps developers unit test Apache Hadoop MapReduce jobs. Unit testing is a technique for improving project quality and reducing overall costs by writing a small amount of code that can automatically verify the software you write performs as intended. This is considered a best practice in software development since it helps identify defects early, before they're deployed to a production system.
In Building and Deploying MR2 we presented a brief introduction to MapReduce in Apache Hadoop 0.23 and focused on the steps to set up a single-node cluster. This blog provides developers with architectural details of the new MapReduce design.
Apache Hadoop 0.23 has major improvements over previous releases. Here are a few highlights on the MapReduce front; note that there are also major HDFS improvements, which are out of scope of this post.
MapReduce 2.0 (a.k.a. MRv2 or YARN):
Last month at the Web 2.0 Summit in San Francisco, Cloudera CEO Mike Olson presented some work the Cloudera Data Science Team did to analyze adverse drug events. We decided to share more detail about this project because it demonstrates how to use a variety of open-source tools – R, Gephi, and Cloudera’s Distribution Including Apache Hadoop (CDH) – to solve an old problem in a new way.
Background: Adverse Drug Events
An adverse drug event (ADE) is an unwanted or unintended reaction that results from the normal use of one or more medications. The consequences of ADEs range from mild allergic reactions to death, with one study estimating that 9.7% of adverse drug events lead to permanent disability. Another study showed that each patient who experiences an ADE remains hospitalized for an additional 1-5 days and costs the hospital up to $9,000.
A number of architectural changes have been added to Hadoop MapReduce. The new MapReduce system is called MR2 (AKA MR.next). The first release version to include these changes will be Apache Hadoop 0.23.
A key change in the new architecture is the disappearance of the centralized JobTracker service. Previously, the JobTracker was responsible for provisioning the resources across the whole cluster, in addition to managing the life cycle of all submitted MapReduce applications; this typically included starting, monitoring and retrying the applications individual tasks. Throughout the years and from a practical perspective, the Hadoop community has acknowledged the problems that inherently exist in this functionally aggregated design (See MAPREDUCE-279).