ALL COVERED TOPICS

NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter

NAVIGATE MAIN CATEGORIES

Close

The Future of Hadoop: YARN Explained

You’ve probably read about the central goals of YARN and seen the architecture of YARN, but it’s worth having as many details about it as possible:

Hadoop YARN Architecture

A key paragraph in Arun Murthy’s post about Apache Hadoop YARN:

MapReduce is great for many applications, but not everything; other programming models better serve requirements such graph processing (Google Pregel / Apache Giraph) and iterative modeling (MPI). When all the data in the enterprise is already available in Hadoop HDFS, multiple paths for processing data is critical.

That’s for all the critiques Hadoop is getting.

Original title and link: The Future of Hadoop: YARN Explained (NoSQL database©myNoSQL)

via: http://hortonworks.com/blog/apache-hadoop-yarn-background-and-an-overview/