Cloudera Developer Blog · CDH Posts
As announced last Sunday (Aug. 25) on the project mailing list, Apache Hadoop 2.1.0 is the first beta release for Hadoop 2. (See the Release Notes for full list of new features and fixes.) Our congratulations to the Hadoop community for reaching this important milestone in the ongoing adoption of the core Hadoop platform!
With the release of this new beta, and the follow-on GA release on the horizon, we expect to see more customers exploring Hadoop 2 for production use cases. In fact, the upcoming CDH5 beta will be based on the Hadoop 2 GA release, delivering features that we’ve thoroughly tested against enterprise requirements, including (but not limited to):
Apache HBase supports three primary client APIs that developers can use to bind applications with HBase: the Java API, the REST API, and the Thrift API. Therefore, as developers build apps against HBase, it’s very important for them to be aware of the compatibility guidelines with respect to CDH.
This blog post will describe the efforts that go into protecting the experience of a developer using the Java API. Through its testing work, Cloudera allows developers to write code and sleep well at night, knowing that their code will remain compatible through supported upgrade paths.
First, we’ll explore the compatibility guidelines themselves. From there, we will discuss some of the testing that ensures compatibility across CDH versions, as well as some of the interesting incompatibilities we’ve detected and fixed along the way.
Earlier this week, our partners NetApp and Cisco announced the Flexpod Select Family with support for Cloudera’s Distribution including Apache Hadoop (CDH).
We’re looking forward to the expansion of Flexpod Select to include Hadoop, as it provides additional options for customers to consume the benefits of the Cloudera Enterprise platform.
FlexPod Select with Hadoop is an extension of the FlexPod initiative built on the Cisco Common Platform Architecture (CPA) for Big Data. Designed for deployments that need enterprise class external storage array features, this solution offers a comprehensive analytic stack for big data that includes compute, storage, connectivity, and CDH with a full range of services such as Cloudera Manager to manage heavy workloads.
This is a great day for technical end-users – developers, admins, analysts, and data scientists alike. Starting now, Cloudera complements its traditional mailing lists with a new, feature-rich community forums intended for users of Cloudera’s Platform for Big Data! (Login using your existing credentials or click the link to register.)
Although mailing lists have long been a standard for user interaction, and will undoubtedly continue to be, they have flaws. For example, they lack structure or taxonomy, which makes consumption difficult. Search functionality is often less than stellar and users are unable to build reputations that span an appreciable period of time. For these reasons, although they’re easy to create and manage, mailing lists inherently limit access to knowledge and hence limit adoption.
The new service brings key additions to the conversation: functionality, search, structure and scalability. It is now considerably easier to ask questions, find answers (or questions to answer), follow and share threads, and create a visible and sustainable reputation in the community. And for Cloudera customers, there’s a bonus: your questions will be escalated as bonafide support cases under certain circumstances (see below).
Users of CDH, Cloudera’s Big Data platform, are solving big problems and building amazing solutions with Apache Hadoop. We at Cloudera are very proud of our customers’ accomplishments, and it’s time to showcase them. This year we’re thrilled to present the first annual Data Impact Awards, an awards program designed to recognize Hadoop innovators for their achievements in five categories:
The Data Warehousing Institute (TDWI) runs an annual Best Practices Awards program to recognize organizations for their achievements in business intelligence and data warehousing. A few months ago, I was introduced to Motorola Mobility’s VP of cloud platforms and services, Balaji Thiagarajan. After learning about its interesting Apache Hadoop use case and the success it has delivered, Balaji and I worked together to nominate Motorola Mobility for the TDWI Best Practices Award for Emerging Technologies and Methods. And to my delight, it won!
Chances are, you’ve heard of Motorola Mobility. It released the first commercial portable cell phone back in 1984, later dominated the mobile phone market with the super-thin RAZR, and today a large portion of the massive smartphone market runs on its Android operating system.
In recent years, Motorola Mobility implemented a Hadoop system based on Cloudera Enterprise to help create a “unified data repository” that is positively impacting business KPIs. For instance, CSAT scores have risen based on call center insights and product returns (a metric of quality) are decreasing based on service center insights.
At Cloudera, we believe that Cloudera Manager is the best way to install, configure, manage, and monitor your Apache Hadoop stack. Of course, most users prefer not to take our word for it — they want to know how Cloudera Manager works under the covers, first.
In this post, I’ll explain some of its inner workings.
The Vocabulary of Cloudera Manager
The image below illustrates the basic nouns and relationships of Cloudera Manager:
For those who are unfamiliar with it, Hue is a very popular, end-user focused, fully open source Web UI designed for interaction with Apache Hadoop and its ecosystem components. Founded by Cloudera employees, Hue has been around for quite some time, but only in the last 12 months has it evolved into the great ramp-up and interaction tool it is today. It’s fair to say that Hue is the most popular open source GUI for the Hadoop ecosystem among beginners — as well as a valuable tool for seasoned Hadoop users (and users generally in an enterprise environment) – and it is the only end-user tool that ships with Hadoop distributions today. In fact, Hue is even redistributed and marketed as part of other user-experience and ramp-up-on-Hadoop VMs in the market.
We have reached where we are today – 1,000+ commits later – thanks to the talented Cloudera Hue team (special kudos needed to Romain, Enrico, and Abe) and our customers and users in the community. Therefore it is time to celebrate with a classy new logo and community website at gethue.com!
Hue is the most popular open source GUI for Hadoop among beginners — an is a valuable tool for seasoned Hadoop users as well.
Just in time for Hadoop Summit 2013, the Apache Bigtop team is very pleased to announce the release of Bigtop 0.6.0: The very first release of a fully integrated Big Data management distribution built on the currently most advanced Hadoop 2.x, Hadoop 2.0.5-alpha.
Bigtop, as many of you might already know, is a project aimed at creating a 100% open source and community-driven Big Data management distribution based on Apache Hadoop. (You can learn more about it by reading one of our previous blog posts on Apache Blogs.) Bigtop also plays an important role in CDH, which utilizes its packaging code from Bigtop — Cloudera takes pride in developing open source packaging code and contributing the same back to the community.
The very astute readers of this blog will notice that given our quarterly release schedule, Bigtop 0.6.0 should have been called Bigtop 0.7.0. It is true that we skipped a quarter. Our excuse is that we spent all this extra time helping the Hadoop community stabilize the Hadoop 2.x code line and making it a robust kernel for all the applications that are now part of the Bigtop distribution.
Cloudera Impala has many exciting features, but one of the most impressive is the ability to analyze data in multiple formats, with no ETL needed, in HDFS and Apache HBase. Furthermore, you can use multiple frameworks, such as MapReduce and Impala, to analyze that same data. Consequently, Impala will often run side-by-side with MapReduce on the same physical hardware, with both supporting business-critical workloads. For such multi-tenant clusters, Impala and MapReduce both need to perform well despite potentially conflicting demands for cluster resources.
In this post, we’ll share our experiences configuring Impala and MapReduce for optimal multi-tenant performance. Our goal is to help users understand how to tune their multi-tenant clusters to meet production service level objectives (SLOs), and to contribute to the community some test methods and performance models that can be helpful beyond Cloudera.
Defining Realistic Test Scenarios
Cloudera’s broad and diverse customer base makes it a top concern to do testing for real-world scenarios. Realistic tests based on common use cases offer meaningful guidance, whereas guidance based on contrived, unrealistic testing often fails to translate to real-life deployments.