MapReduce and YARN Cognitive Class Exam Answers

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Introduction to MapReduce and YARN

MapReduce and YARN are fundamental components of Apache Hadoop, designed to handle the processing and management of large datasets in a distributed computing environment. Here’s an introduction to each:

MapReduce:

MapReduce is a programming model and framework for processing and generating large datasets in parallel across a distributed cluster of compute nodes.

Key Components:

• Mapper: Executes the “map” task, which processes input data and generates key-value pairs.
• Reducer: Executes the “reduce” task, which processes the output of the mapper to produce final output.

Workflow:

1. Map Phase: Input data is divided into chunks and processed by mapper tasks in parallel.
2. Shuffle and Sort: Intermediate outputs from mappers are shuffled and sorted by keys across the cluster.
3. Reduce Phase: Reducer tasks aggregate the intermediate data based on keys and produce the final output.

Advantages:

• Scalability: Scales efficiently with more nodes added to the cluster.
• Fault-tolerance: Handles node failures by re-executing failed tasks on other nodes.
• Simplifies parallel processing: Abstracts away the complexity of managing distributed computing.

YARN (Yet Another Resource Negotiator):

YARN is the resource management layer of Hadoop that manages resources and schedules tasks across the cluster.

Key Components:

• ResourceManager: Manages resources across the cluster, allocates resources to applications.
• NodeManager: Runs on each node, manages resources (CPU, memory) on the node, and executes tasks.

Capabilities:

• Resource Allocation: Allocates resources (CPU, memory) to various applications running on the cluster.
• Application Scheduling: Handles scheduling of tasks and monitors their execution.
• Fault-tolerance: Recovers from failures and ensures continuous operation.

Advantages:

• Supports diverse workloads: Allows multiple applications to run on the same cluster simultaneously.
• Efficient resource utilization: Optimizes resource allocation based on application requirements.
• Scalability: Scales to large clusters and supports thousands of nodes.

Integration with Hadoop Ecosystem:

• MapReduce jobs are managed and executed by YARN, which allocates resources to individual tasks.
• Together, MapReduce and YARN form the core processing and resource management framework of Apache Hadoop.

In summary, MapReduce provides the programming model for distributed data processing, while YARN manages resources and schedules tasks across the Hadoop cluster, enabling efficient and scalable data processing and analysis.

MapReduce and YARN Cognitive Class Certification Answers

Module 1: Introduction to MapReduce and YARN Quiz Answers

Question 1: Which phase of MapReduce is optional?

• Shuffle
• Reduce
• Combiner
• Map

Question 2: Which node is responsible for assigning (key, value) pairs to different reducers?

• Shuffle node
• Reducer node
• Combiner node
• Mapper node

Question 3: Where are the output files of the Reducer task stored?

• A data warehouse
• Hadoop FS
• Within the Reducer node
• Linux FS

Module 2: Limitations of Hadoop v1 & MapReduce v1 Quiz Answers

Question 1: What is an issue or limitation of the original MapReduce v1 paradigm?

• It’s not scalable
• It only has one TaskTracker
• It only supports Parquet file types
• It only has one JobTracker

Question 2: How is YARN an improvement over the MapReduce v1 paradigm?

• It’s completely open source
• It splits the JobTracker into two processes: ResourceManager and ApplicationManager
• It reduces multi-tenancy to improve performance
• It splits the TaskTracker into two processes: ResourceManager and ApplicationManager

Question 3: Existing applications can run on YARN without recompilation. True or False?

• True
• False

Module 3: The Architecture of YARN Quiz Answers

Question 1: The main change from Hadoop v1 to Hadoop v2 was the consolidation of both resource management and job processing. True or False?

• True
• False

Question 2: The NodeManager is a more generic and efficient version of the TaskTracker. True or False?

• True
• False

Question 3: A new ApplicationMaster is launched for each job and ends when the job completes. True or False?

• True
• False

MapReduce and YARN Final Exam Answers

Question 1: Which of the following is the correct sequence of MapReduce flow?

• Reduce —> Combine —> Map
• Combine —> Reduce —> Map
• Map —> Reduce —> Combine
• Map —> Combine —> Reduce

Question 2: Which of the following can be used to control the number of part files in a MapReduce program’s output directory?

• Shuffle parameters
• Number of Reducers
• Counter
• Number of Mappers

Question 3: Which of the following operations will work improperly when using a Combiner?

• Average
• Maximum
• Count
• Minimum

Question 4: Which of the following is true about MapReduce?

• Compression of input files is optional.
• Output from the Map phase is replicated.
• The programmer must write the Map code, the Shuffle code, and the Reduce code.
• MapReduce programs must be written in Java.

Question 5: Input data to MapReduce is record-oriented and blocks of data contain the same number of full records. True or False?

• False.
• True.

Question 6: Which statement is true about the Reduce phase of MapReduce?

• Output results are sent to the client program.
• Data arrives from the Shuffle phase already sorted by key.
• The Reducer phase sums up the values associated with each key.
• Each Reduce task processes all the data for one key only.

Question 7: Which statement is true about the Reduce phase of MapReduce?

• Containers are used instead of slots in MRv1, and can be used with either Map or Reduce tasks in MRv2.
• There is one JobTracker in the cluster.
• MapReduce jobs written in Java for MRv1 never require recompilation.
• Each job has an ApplicationManager that obtains Container IDs from the NodeManager.

Question 8: With YARN, long-running jobs acquire and retain fixed-size containers before execution starts. True or False?

• False.
• True.

Question 9: Which of the following statements is true?

• The NameNode in Hadoop 2 is fully fault-tolerant, whereas in Hadoop 1 it was a single point of failure.
• The NodeManager in Hadoop 2 replaces the TaskTracker in Hadoop 1.
• YARN requires a minimum of two nodes, one master and one slave, to run
• Both MapReduce and YARN can scale to any cluster size

Question 10: The command athhadoop provides the CLASSPATH needed for compiling Java programs written for MapReduce or YARN. True or False?

• False.
• True.

Question 11: Which statement is true about MapReduce’s use of replication in HDFS?

• Only one copy of each replicated block is processed by MapReduce in normal operation.
• Speculative execution is normally performed on all copies of each “split.”
• Each DataNode uses RAID to store its data.
• Multiple copies of each record are kept on each node.

Question 12: On which file system (FS) is the output of a Mapper task stored?

• Linux FS, and it is replicated 3 times.
• HDFS, and it is replicated 3 times.
• Linux FS, but it is not replicated.
• HDFS, but it is not replicated.

Question 13: Which of the following statements is true?

• You can set the number of Reducers.
• The Shuffle phase is optional.
• You can set the number of Mappers and the number of Reducers.
• The number of Combiners is the same as the number of Reducers.
• You can set the number of Mappers

Question 14: What will a Hadoop job do if you try to run it with an output directory that is already present?

• It will create new files, but with a different suffix.
• It will create another directory to store the output.
• It will erase all files in that directory before running.
• It will not run.

Question 15: What are the main components of the ResourceManager in YARN? Select two.

• Scheduler
• JobTracker
• DataManager
• HDFS
• ApplicationManager