Book Image

Mastering Hadoop 3

By : Chanchal Singh, Manish Kumar
Book Image

Mastering Hadoop 3

By: Chanchal Singh, Manish Kumar

Overview of this book

Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency. With this guide, you’ll understand advanced concepts of the Hadoop ecosystem tool. You’ll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You’ll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you’ll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals. By the end of this book, you’ll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you’ll be equipped to tackle a range of real-world problems in data pipelines.
Table of Contents (23 chapters)
Title Page
Dedication
About Packt
Foreword
Contributors
Preface
Index

Mix-workloads


This section covers the benchmarking strategy for mixed loads on clusters such as MapReduce history job profiling and other production job profiling.

Rumen

Apache Rumen is the tool that parses your MapReduce job history logs. It outputs meaningful and easily readable text. The output from this job is used in other benchmarking tools like YARN Scheduler Load Simulator or Gridmix. It has the following two parts:

  1. Tracebuilder: Converts Hadoop job history logs to an easily parsable format, JSON. The following is the command to run Tracebuilder (Ref: Hadoop3 Documentation):
      hadoop rumentrace [options] <jobtrace-output> <topology-output> <inputs>

      <jobtrace-output> - Location of the Json output file
      <topology-output> - Cluster layout file
      <inputs> - Jobhistory logs location

      Options are
      -demuxer  Used to read the jobhistory files. The default isDefaultInputDemuxer.
      -recursive Recursively traverse input paths...