Practice Fault Tolerance in MapReduce: Resilience to Node and Task Failures - 1.5 | Week 8: Cloud Applications: MapReduce, Spark, and Apache Kafka | Distributed and Cloud Systems Micro Specialization
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1.5 - Fault Tolerance in MapReduce: Resilience to Node and Task Failures

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does task re-execution mean in MapReduce?

πŸ’‘ Hint: Think about what happens when tasks fail.

Question 2

Easy

Why is intermediate data durability important?

πŸ’‘ Hint: Consider the impact of task failures on data.

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 happens if a Map task fails?

  • It continues on another worker.
  • It is completely discarded.
  • It is re-executed on another healthy node.

πŸ’‘ Hint: Think about the resilience of the system.

Question 2

True or False: Intermediate data is lost if the NodeManager fails.

  • True
  • False

πŸ’‘ Hint: Consider the data storage protocols.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a MapReduce fault tolerance strategy that includes redundancy plans for both task re-execution and data storage.

πŸ’‘ Hint: Think about how you would ensure continuous operation in case of an isolated failure.

Question 2

Analyze the impact of a NodeManager failure on a long-running MapReduce job and propose solutions to mitigate risks involved.

πŸ’‘ Hint: How would the loss of a single point of failure affect job throughput?

Challenge and get performance evaluation