A new installment in the series about the tangled ball of thread that is YARN
In Part 1 of this series, we covered the fundamentals of clusters of YARN. In Part 2, you’ll learn about other components than can run on a cluster and how they affect YARN cluster configuration.
Ideal YARN Allocation
As shown in the previous post, a YARN cluster can be configured to use up all the resources on the cluster.
In this multipart series, fully explore the tangled ball of thread that is YARN.
YARN (Yet Another Resource Negotiator) is the resource management layer for the Apache Hadoop ecosystem. YARN has been available for several releases, but many users still have fundamental questions about what YARN is, what it’s for, and how it works. This new series of blog posts is designed with the following goals in mind:
- Provide a basic understanding of the components that make up YARN
- Illustrate how a MapReduce job fits into the YARN model of computation.
Learn how Cloudera Navigator Encrypt bring data security to YARN containers.
With the introduction of transparent data encryption in HDFS, we are now a big step closer toward a secure platform in the Apache Hadoop world. However, there are still gaps in the platform, including how YARN and its applications manage their cache. In this post, I’ll explain how Cloudera Navigator Encrypt fills that particular gap.
When a YARN application runs in a cluster it can sometimes spill data to the hard disk,
A concise look at the differences between how Spark and MapReduce manage cluster resources under YARN
The most popular Apache YARN application after MapReduce itself is Apache Spark. At Cloudera, we have worked hard to stabilize Spark-on-YARN (SPARK-1101), and CDH 5.0.0 added support for Spark on YARN clusters.
In this post, you’ll learn about the differences between the Spark and MapReduce architectures, why you should care,
Thanks to recent work upstream, YARN is now a highly available service. This post explains its architecture and configuration details.
YARN, the next-generation compute and resource management framework in Apache Hadoop, until recently had a single point of failure: the ResourceManager, which coordinates work in a YARN cluster. With planned (upgrades) or unplanned (node crashes) events, this central service, and YARN itself, could become unavailable.
This post details Cloudera’s recent work in the Hadoop community (YARN-149) to make the ResourceManager (and thus YARN) highly available.