MapReduce and YARN

Apache Hadoop is one of the most popular tools for big data processing. It has been successfully deployed in production by many companies for several years. Though Hadoop is considered a reliable, scalable, and cost-effective solution, it is constantly being improved by a large community of developers. As a result, the 2.0 version offers several revolutionary features, including Yet Another Resource Negotiator (YARN), HDFS Federation, and high availability, which make the Hadoop cluster much more efficient, powerful, and reliable.

The most serious limitations of classical MapReduce are primarily related to scalability, resource utilization, and the support of workloads different from MapReduce. In the MapReduce framework, the job execution is controlled by two types of processes: a single master process called JobTracker and a number of subordinate processes called TaskTrackers.

Apache Hadoop 2.0 includes YARN, which separates the resource management and processing components. The YARN-based architecture is not constrained to MapReduce. In YARN, MapReduce is simply degraded to a role of a distributed application (but still a very popular and useful one) and is now called MRv2. MRv2 is simply the re-implementation of the classic MapReduce engine, now called MRv1, which runs on top of YARN.

The course reviews MapReduce and provides insight into the design and implementation of YARN: ResourceManager instead of a cluster manager, ApplicationMaster instead of a dedicated and short-lived JobTracker, NodeManager instead of TaskTracker, a distributed application instead of a MapReduce job.

Course Syllabus
Lesson 1: Introduction to MapReduce and YARN

Describe the MapReduce model v1 — this is the “classic” version that comes with Hadoop 1
List the limitations of both Hadoop 1 and MapReduce 1
Review the Java code required to handle the Mapper class, the Reducer class, and the program driver needed to access MapReduce
Describe the YARN model, including the features of YARN and how a YARN program is run, and
Compare “YARN / Hadoop 2 / MR2” versus “Hadoop 1 with MR1”
Lesson 2:Issues with/Limitations of Hadoop v1 & MapReduce v1

List the limitations of MapReduce v1 and the need for MR v2 / YARN
Describe MR2 / YARN processing
Lesson 3: The Architecture of YARN

Understand the high level architecture of YARN
Configuring, monitoring, and running applications in the YARN environment
Recommended skills prior to taking this course
Know some basic Linux administration and commands
Grading scheme
The minimum passing mark for the course is 60%, where the review questions are worth 40% and the final exam is worth 60% of the course mark.
You have 1 attempt to take the exam with multiple attempts per question.