Practice Scheduling in MapReduce: Orchestrating Parallel Execution - 1.4 | Week 8: Cloud Applications: MapReduce, Spark, and Apache Kafka | Distributed and Cloud Systems Micro Specialization
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1.4 - Scheduling in MapReduce: Orchestrating Parallel Execution

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What was the primary function of the JobTracker in Hadoop 1.x?

💡 Hint: Think about the dual role it played.

Question 2

Easy

What does YARN stand for?

💡 Hint: It's an acronym that reflects its purpose.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the main role of YARN?

  • Manage job scheduling and resource allocation.
  • Store data in HDFS.
  • Provide a user interface for job submission.

💡 Hint: Focus on how YARN changes the landscape of Hadoop architecture.

Question 2

True or False: The JobTracker in Hadoop 1.x is designed to allow multiple jobs to run simultaneously without limitations.

  • True
  • False

💡 Hint: Consider the scalability issues with JobTracker.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a scenario where multiple Map tasks are dropping packets due to excessive network load. How would you restructure the task scheduling to avoid these problems?

💡 Hint: Think about reducing travel distance for data.

Question 2

Design a fault tolerance framework for a fictional MapReduce operation that fails to handle task interruptions effectively. What modifications would you suggest?

💡 Hint: Focus on ensuring reliability and data integrity.

Challenge and get performance evaluation