Cloudera Engineering Blog · HDFS Posts
A common question on the Apache Hadoop mailing lists is what’s going on with availability? This post takes a look at availability in the context of Hadoop, gives an overview of the work in progress and where things are headed.
When discussing Hadoop availability people often start with the NameNode since it is a single point of failure (SPOF) in HDFS, and most components in the Hadoop ecosystem (MapReduce, Apache HBase, Apache Pig, Apache Hive etc) rely on HDFS directly, and are therefore limited by its availability. However, Hadoop availability is a larger, more general issue, so it’s helpful to establish some context before diving in.
Cloudera is happy to announce the availability of the third update to version 2 of our distribution for Apache Hadoop (CDH2). CDH2 Update 3 contains a number of important fixes like HADOOP-5203, HDFS-1377, MAPREDUCE-1699, MAPREDUCE-1853, and MAPREDUCE-270. Check out the release notes and change log for more details on what’s in this release. You can find the packages and tarballs on our website, or simply update your systems if you are already using our repositories. More instructions can be found in our CDH documentation.
Fraud has multiple meanings and the term can be easily abused. The definition of fraud has undergone multiple changes throughout the years and is elusive as well as fraud itself. The modern legal definition of fraud usually contains a few elements that have to be proven in court and depends on the state/country. For example, in California, the elements of fraud, which give rise to the fraud cause of action in the California Courts, are: (a) misrepresentation (false representation, concealment, or nondisclosure); (b) knowledge of falsity (or scienter); (c) intent to defraud, i.e., to induce reliance; (d) justifiable reliance; and (e) resulting damage. A more general definition may contain up to 9 elements.
From the statistical or technical perspective, fraud is a rare event that results in a significant financial impact to the organization.
Cloudera’s Apache Hadoop Training and Certification for System Administrators has made it across the Atlantic to London for the first time! This two-day course covers planning, deploying, maintaining, monitoring, and troubleshooting your Hadoop cluster. We’ll talk about HDFS, MapReduce, Apache Hive, Apache Pig, Apache HBase, Flume and more, from the System Administrator’s point of view. Take the certification exam at the end of your training and go home with a valuable validation of your Hadoop knowledge.
Enter the code “london_10pct” when registering and receive a 10% discount!
Apache Hadoop and Apache HBase are gaining popularity due to their flexibility and tremendous work that has been done to simplify their installation and use. This blog is to provide guidance in sizing your first Hadoop/HBase cluster. First, there are significant differences in Hadoop and HBase usage. Hadoop MapReduce is primarily an analytic tool to run analytic and data extraction queries over all of your data, or at least a significant portion of them (data is a plural of datum). HBase is much better for real-time read/write/modify access to tabular data. Both applications are designed for high concurrency and large data sizes. For a general discussions about Hadoop/HBase architecture and differences please refer to Cloudera, Inc. [https://wiki.cloudera.com/display/DOC/Hadoop+Installation+Documentation+for+Cloudera+Enterprise, http://blog.cloudera.com/blog/2010/07/whats-new-in-cdh3-b2-hbase], or Lars George blogs [http://www.larsgeorge.com/2009/10/hbase-architecture-101-storage.html]. We expect a new edition of the Tom White’s Hadoop book [http://www.hadoopbook.com] and a new HBase book in the near future as well.
With the recent release of CDH3b2, many users are more interested than ever to try out Cloudera’s Distribution for Hadoop (CDH). One of the questions we often hear is, “what does it take to migrate?”.
If you’re not familiar with CDH3b2, here’s what you need to know.
Hadoop has emerged as an indispensable component of any data-intensive enterprise infrastructure. In many ways, working with large datasets on a distributed computing platform (powered by commodity hardware or cloud infrastructure) has never been easier. But because customers are running clusters consisting of hundreds or thousands of nodes, and are processing massive quantities of data from production systems every hour, the logistics of efficient platform utilization can quickly become overwhelming.
To deal with this challenge, the Yahoo! engineering team created Oozie – the Hadoop workflow engine. We are pleased to provide Oozie with Cloudera’s distribution for Hadoop starting with the beta-2 release.
Why create a new workflow system?
Cloudera is happy to announce the availability of the first update to version 2 of our distribution for Hadoop. While major new features are planned for our release of version 3 we will regularly update version 2 with improvements and bug fixes. Check out the change log and release notes for details. You can find the packages and tarballs on our website, or simply update if you are already using our yum and apt repositories.
A notable addition in update 1 is a FUSE package for HDFS. This package allows you to easily mount HDFS as a standard file system for use with traditional Unix utilities. Check out the Mountable HDFS section in the CDH docs and the hadoop-fuse-dfs manpage for details.
While the vast majority of the Hadoop development discussion takes place on the Apache Jira and various project mailing lists, it’s often useful to meet face to face for high bandwidth discussion. To that end, Facebook hosted the first Apache Hadoop contributors meeting yesterday at their campus in Palo Alto. Cloudera, Facebook, Yahoo! and the Apache HBase team were well-represented. It was great to see a broad cross section of Hadoop developers in one room. Contributor meetings will be held on a monthly basis, at a rotating location. While any Hadoop project contributor is welcome to attend, the current focus of the meetings is HDFS and MapReduce. The goal of the discussion is to surface and flesh out ideas rather than make decisions, which happens on the development lists. If you’ve got ideas to add check out the meeting notes and continue the discussion.
Sanjay Radia kicked off the meeting with a discussion of development priorities. Hadoop has become a platform and industry standard for data storage and analytics. What advances are most important to users? How do we continue to innovate without disrupting the installed base? Development must maintain and improve the quality that has allowed companies to adopt Hadoop in their production environments. Fortunately there is broad agreement among contributors on development priorities: availability, compatibility, security, scalability and performance.