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Posts Tagged ‘Yarn’

 

Is MapReduce Dead?

Tuesday, July 15th, 2014 by

With the recent announcement by Google of Cloud DataFlow (intended as the successor to MapReduce) and with Cloudera now focusing on Spark for many of its projects, it looks like the days of MapReduce may be numbered. Although the change may seem sudden, it’s been a long time coming. Google wrote the MapReduce white paper 10 years ago, and developers have been using at least one distribution of Hadoop for about 8 years. Users have had ample time to determine the strengths and weaknesses of MapReduce. However, the release of Hadoop 2.0 and YARN clearly indicated that users wanted to live in a more diverse Big Data world.

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Earlier versions of Hadoop could be described as MapReduce + HDFS (Hadoop Distributed File System) because that was the paradigm that everything Hadoop revolved around. Because users clamored for interactive access to Hadoop data, the Hive and Pig projects were started. And even though you could write SQL queries with Hive and script in Pig Latin with Pig, under the covers Hadoop was still running MapReduce jobs. That all changed in Hadoop 2.0 with the introduction of YARN. YARN became the resource manager for a Hadoop cluster that broke the dependence between MapReduce and HDFS. Although HDFS still remained as the file system, MapReduce became just another application that can interface with Hadoop through YARN. This change made it possible for other applications to now run on Hadoop through YARN.

Google is not known as a backer in the mold of Hortonworks or Cloudera with the open source Hadoop ecosystem. After all, Google was running its own versions of MapReduce and HDFS (the Google File System) on which these open-source projects are based. Because they are integral parts of Google’s internal applications, Google has the most experience with using these technologies. And although Cloud DataFlow is specifically for use on the Google cloud and appears more like a competitor to Amazon’s Kinesis product, Google is very influential in Big Data circles, so I can see other developers following Google’s lead and leveraging a similar technology in favor of MapReduce.

Although Google’s Cloud DataFlow may have a thought leadership-type impact, Cloudera’s decision to leverage Spark as the standard processing engine for its projects (in particular, Hive) will have a greater impact on open-source Big Data developers. Cloudera has one of the most popular Hadoop distributions on the market and has partnered with Databricks, Intel, MapR, and IBM to work on their Spark integration with Hive. This trend is surprising given Cloudera’s investment in Impala (its SQL query engine), but the company clearly feels that Spark is the future. As little as a year ago, Spark was mostly seen as fast in-memory computing for machine learning algorithms. However with its promotion to an Apache Top-Level Project in February 2014 and its backing company Databricks receiving $33 million in Series B funding, Spark clearly has greater ambitions. The advent of YARN made it much easier to tie Spark to the growing Hadoop ecosystem. Cloudera’s decision to leverage Spark in Hive and other projects makes it even more important to users of the CDH distribution.

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HBase Made Simple

Wednesday, April 30th, 2014 by

GoGrid has just released its 1-Button Deploy™ of HBase, available to all customers in the US-West-1 data center. This technology makes it easy to deploy either a development or production HBase cluster on GoGrid’s high-performance infrastructure. GoGrid’s 1-Button Deploy™ technology combines the capabilities of one of the leading NoSQL databases with our expertise in building high-performance Cloud Servers.

HBase is a scalable, high-performance, open-source database. HBase is often called the Hadoop distributed database – it leverages the Hadoop framework but adds several capabilities such as real-time queries and the ability to organize data into a table-like structure. GoGrid’s 1-Button Deploy™ of HBase takes advantage of our SSD and Raw Disk Cloud Servers while making it easy to deploy a fully configured cluster. GoGrid deploys the latest Hortonworks’ distribution of HBase on Hadoop 2.0. If you’ve ever tried to deploy HBase or Hadoop yourself, you know it can be challenging. GoGrid’s 1-button Deploy™ does all the heavy lifting and applies all the recommended configurations to ensure a smooth path to deployment.

Why GoGrid Cloud Servers?

SSD Cloud Servers have several high-performance characteristics. They all come with attached SSD storage and large available RAM for the high I/O uses common to HBase. The Name Nodes benefit from the large RAM options available on SSD Cloud Servers and the Data Nodes use our Raw Disk Cloud Servers, which are configured as JBOD (Just a Bunch of Disks). This is the recommended disk configuration for Data Nodes, and GoGrid is one of the first providers to offer this configuration in a Cloud Server. Both SSD and Raw Disk Cloud Servers use a redundant 10-Gbps public and private network to ensure you have the maximum bandwidth to transfer your data. Plus, the cloud makes it easy to add more Data Nodes to your cluster as needed. You can use GoGrid’s 1-Button Deploy™ to provision either a 5-server development cluster or an 11-server production cluster with Firewall Service enabled.

Development Environments

The smallest recommended size for a development cluster is 5 servers. Although it’s possible to run HBase on a single server, you won’t be able to test failover or how data is replicated across nodes. You’ll most likely have a small database so you won’t need as much RAM, but will still benefit from SSD storage and a fast network. The Data Nodes use Raw Disk Cloud Servers and are configured with a replication factor of 3.

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The 2013 Hadoop Summit

Monday, July 29th, 2013 by

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I recently attended the Hadoop Summit in San Jose. This is one of two major conferences organized around Hadoop, the other being Hadoop World. Nearly all the companies with Hadoop distributions were present along with several big users of Hadoop like Netflix, Twitter, and Linkedin.

Crossing The Chasm

If you’re not deeply involved with Hadoop, attending one of these conferences a year apart can be shocking. The advancements made in just the span of a year are amazing. The conference seemed notably larger this year, and I noticed more non-tech companies in the audience. I think it’s safe to say that Hadoop has crossed the chasm, at least for enterprise IT users.

Other than the type of attendees at the event, the other signal to me was the emergence of Hadoop 2.0. This second version of Hadoop focused on features that are important for users who want to run production-grade software for mission-critical systems. High-availability finally arrived for the name node (for the Open Source project, not the version Cloudera released for its distribution), a new version of Hive with more SQL-friendly features, and YARN which allows users to run just about anything on the Hadoop Distributed File System (HDFS). These types of stability and availability features tend to show up when there is a critical mass of users who want to use software for production.

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Quite A YARN

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