Manage and Test Hadoop MapReduce Applications


About this Course

This course covers how to write MapReduce applications in Java. Students will learn how to use the MapReduce API, work with counters and the Hadoop CLI, and to debug, manage jobs, improve performance, work with different data sources, manage workflows, and use other programming languages for MapReduce. This is the second course in the MapReduce Series from MapR.

What’s Covered in the Course

4: Use the MapReduce API
  • API Overview
  • Mapper Input Processing and Reducer Output Processing Data Flow
  • Explore the Mapper, Reducer, and Job Class API
Lab Activities
    • Write a MapReduce Program
5: Managing, Monitoring, and Testing MapReduce Jobs
  • Work with Counters
  • Use the MCS to Monitor Jobs
  • Use the Hadoop CLI to Manage Jobs
  • Display Job History and Logs
  • Write Unit Tests for MapReduce Programs
Lab Activities
    • Examine Default Job Output
    • Use Custom Counters
    • Use Standard Output, Error, and Logging
    • Use the Hadoop CLI to Manage Jobs
    • Use MRUnit to Test a MapReduce Applications
6: Managing Performance
  • Review Components of MapReduce Performance
  • Enhance Performance in MapReduce Jobs
  • Overview of MapR Performance Enhancements
Lab Activities
    • De-tune a Job and Measure Performance Impact