Apache Impala (incubating) includes several features that allow you to restrict or allocate resources so as to maximize stability and performance for your Impala workloads. You can limit both CPU and memory resources used by Impala to manage and prioritize jobs on CDH clusters. This blog post describes the techniques a typical Impala deployment can use to manage its resources.
Static Service Pools
Static service pools isolate services from one another, so that a high load on one service has limited impact on other services.
In Parts 1 and 2, we covered the basics of YARN resource allocation. In this installment, we’ll provide an overview of cluster scheduling and introduce the Fair Scheduler, one of the scheduler choices available in YARN.
A standalone computer can have several CPU cores, each running a single process, but there can be as many as a few hundred processes running simultaneously. The scheduler is a part of the desktop’s operating system that assigns a process to a CPU core to run for a short period of time.
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.
Thanks to Michal Malohlava, Amy Wang, and Avni Wadhwa of H20.ai for providing the following guest post about building ML apps using Sparkling Water and Apache Spark on CDH.
The Sparkling Water project is nearing its one-year anniversary, which means Michal Malohlava, our main contributor, has been very busy for the better part of this past year. The Sparkling Water project combines H2O machine-learning algorithms with the execution power of Apache Spark.
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.