April 2015
Beginner to intermediate
754 pages
21h 39m
English
Content preview from Hadoop: The Definitive Guide, 4th Edition
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Chapter 4. YARN
Apache YARN (Yet Another Resource Negotiator) is Hadoop’s cluster resource management system. YARN was introduced in Hadoop 2 to improve the MapReduce implementation, but it is general enough to support other distributed computing paradigms as well.
YARN provides APIs for requesting and working with cluster resources, but these APIs are not typically used directly by user code. Instead, users write to higher-level APIs provided by distributed computing frameworks, which themselves are built on YARN and hide the resource management details from the user. The situation is illustrated in Figure 4-1, which shows some distributed computing frameworks (MapReduce, Spark, and so on) running as YARN applications on the cluster compute layer (YARN) and the cluster storage layer (HDFS and HBase).
Figure 4-1. YARN applications
There is also a layer of applications that build on the frameworks shown in Figure 4-1. Pig, Hive, and Crunch are all examples of processing frameworks that run on MapReduce, Spark, or Tez (or on all three), and don’t interact with YARN directly.
This chapter walks through the features in YARN and provides a basis for understanding later chapters in Part IV that cover Hadoop’s distributed processing frameworks.
Anatomy of a YARN Application Run
YARN provides its core services via two types of long-running daemon: a resource manager (one per cluster) to manage ...
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