The YARN Architecture in Hadoop - dummies

The YARN Architecture in Hadoop

By Dirk deRoos

YARN, for those just arriving at this particular party, stands for Yet Another Resource Negotiator, a tool that enables other data processing frameworks to run on Hadoop. The glory of YARN is that it presents Hadoop with an elegant solution to a number of longstanding challenges.

YARN is meant to provide a more efficient and flexible workload scheduling as well as a resource management facility, both of which will ultimately enable Hadoop to run more than just MapReduce jobs.


The figure shows in general terms how YARN fits into Hadoop and also makes clear how it has enabled Hadoop to become a truly general-purpose platform for data processing. The following list gives the lyrics to the melody:

  • Distributed storage: Nothing has changed here with the shift from MapReduce to YARN — HDFS is still the storage layer for Hadoop.

  • Resource management: The key underlying concept in the shift to YARN from Hadoop 1 is decoupling resource management from data processing. This enables YARN to provide resources to any processing framework written for Hadoop, including MapReduce.

  • Processing framework: Because YARN is a general-purpose resource management facility, it can allocate cluster resources to any data processing framework written for Hadoop. The processing framework then handles application runtime issues.

    To maintain compatibility for all the code that was developed for Hadoop 1, MapReduce serves as the first framework available for use on YARN. At the time of this writing, the Apache Tez project was an incubator project in development as an alternative framework for the execution of Pig and Hive applications. Tez will likely emerge as a standard Hadoop configuration.

  • Application Programming Interface (API): With the support for additional processing frameworks, support for additional APIs will come. At the time of this writing, Hoya (for running HBase on YARN), Apache Giraph (for graph processing), Open MPI (for message passing in parallel systems), Apache Storm (for data stream processing) are in active development.