Data Analytics for IoT - Chapter 10
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Questions and Answers

What scheduling mechanism is used within each queue in the Capacity Scheduler?

  • Random scheduling
  • Weighted round-robin scheduling
  • FIFO scheduling with priority (correct)
  • Round-robin scheduling

    • How does the Capacity Scheduler handle unused capacity among queues?

  • Each queue retains its unused capacity indefinitely.
  • Unused capacity is permanently lost.
  • Only the highest priority queue can utilize unused capacity.
  • Unused capacity is shared among the queues. (correct)

    • What is the purpose of limiting the percentage of running tasks per user in the Capacity Scheduler?

  • To ensure users share a cluster equally. (correct)
  • To reduce the overall wait time for tasks.
  • To prioritize certain users over others.
  • To increase the processing speed of individual queues.

    • What happens when a queue exceeds its configured wait time without being scheduled?

    <p>It can preempt tasks of other queues. (B)</p> Signup and view all the answers

    In the Capacity Scheduler, what is the role of guaranteed capacity for each queue?

    <p>To ensure each queue receives its capacity when it contains jobs. (A)</p> Signup and view all the answers

    What is the primary role of the TaskTracker in Hadoop 2.0?

    <p>To monitor and report the status of JVM processes for tasks (C)</p> Signup and view all the answers

    Which component is responsible for negotiating resources from the Resource Manager?

    <p>Application Master (D)</p> Signup and view all the answers

    Which of the following best describes a Container in YARN?

    <p>A conceptual entity representing allocated resources for a task (B)</p> Signup and view all the answers

    What separates the processing engine from resource management in Hadoop 2.0?

    <p>YARN (B)</p> Signup and view all the answers

    Which service is primarily responsible for enforcing resource scheduling policy in YARN?

    <p>Scheduler (B)</p> Signup and view all the answers

    In the context of YARN, what is the role of the Applications Manager?

    <p>To manage running Application Masters and handle failures (B)</p> Signup and view all the answers

    What happens when a task process fails in the TaskTracker?

    <p>The JobTracker is notified of the failure (C)</p> Signup and view all the answers

    Which component in YARN manages user processes on a single machine?

    <p>Node Manager (C)</p> Signup and view all the answers

    What is the primary function of the Map phase in a MapReduce job?

    <p>To read and partition data into key-value pairs (C)</p> Signup and view all the answers

    Which component is responsible for locating TaskTracker nodes in a MapReduce job execution?

    <p>JobTracker (A)</p> Signup and view all the answers

    What type of messages do TaskTracker nodes send to the JobTracker?

    <p>Heartbeat Messages (C)</p> Signup and view all the answers

    In the Reduce phase, what is done with the intermediate data?

    <p>It is aggregated based on the same key. (D)</p> Signup and view all the answers

    What is the purpose of the optional Combine task in a MapReduce job?

    <p>To improve the performance of the Reduce phase (D)</p> Signup and view all the answers

    What is the default scheduling algorithm used by the JobTracker?

    <p>FIFO (D)</p> Signup and view all the answers

    Which statement about the relationship between TaskTracker instances and DataNode instances is accurate?

    <p>TaskTrackers can be deployed on the same servers that host DataNodes. (B)</p> Signup and view all the answers

    How does the JobTracker keep updated with the available slots in the TaskTracker nodes?

    <p>Through the heartbeat messages sent by TaskTrackers (C)</p> Signup and view all the answers

    What is the primary role of the NameNode in a Hadoop cluster?

    <p>It keeps the directory tree of all files and tracks their locations. (C)</p> Signup and view all the answers

    Which of the following components is responsible for executing Map and Reduce tasks in Hadoop?

    <p>TaskTracker (A)</p> Signup and view all the answers

    What is a key function of the Secondary NameNode in a Hadoop cluster?

    <p>It creates checkpoints of the namespace to prevent data loss. (D)</p> Signup and view all the answers

    Which component of the Hadoop ecosystem is primarily used for processing large sets of data in a distributed manner?

    <p>MapReduce (D)</p> Signup and view all the answers

    What does the JobTracker do in a Hadoop cluster?

    <p>It schedules and distributes MapReduce tasks to nodes. (A)</p> Signup and view all the answers

    In the context of Hadoop, which of the following statements is true regarding DataNodes?

    <p>DataNodes store data and respond to requests from the NameNode. (D)</p> Signup and view all the answers

    What role does YARN play in the Hadoop ecosystem?

    <p>It performs the actual management of node resources. (A)</p> Signup and view all the answers

    Which of the following components does not belong to the Hadoop ecosystem?

    <p>Apache Spark (A)</p> Signup and view all the answers

    What is the default scheduler in Hadoop?

    <p>FIFO Scheduler (B)</p> Signup and view all the answers

    How does the Fair Scheduler allocate resources to jobs?

    <p>It ensures each job gets an equal share of resources over time. (D)</p> Signup and view all the answers

    What happens when there is a single job running in the Fair Scheduler?

    <p>All resources are assigned to that job. (B)</p> Signup and view all the answers

    What is a primary feature of the Capacity Scheduler?

    <p>It allows for capacity guarantees between jobs. (A)</p> Signup and view all the answers

    Which statement is true regarding the FIFO Scheduler?

    <p>It maintains a work queue in which jobs are processed FIFO. (C)</p> Signup and view all the answers

    How does the Fair Scheduler compute which job to schedule next?

    <p>It determines which job has the highest deficit of computing time. (A)</p> Signup and view all the answers

    In the context of Hadoop, what is a 'job pool'?

    <p>A set of pools into which jobs are placed for resource allocation. (D)</p> Signup and view all the answers

    What distinguishes the Capacity Scheduler from the Fair Scheduler?

    <p>It has a different underlying philosophy for scheduling. (C)</p> Signup and view all the answers

    Flashcards

    Apache Hadoop

    An open-source framework for distributed batch processing of big data. It includes HDFS, YARN, and MapReduce, among others.

    HDFS (Hadoop Distributed File System)

    A file system designed for distributed storage of large datasets across multiple nodes in a Hadoop cluster.

    Hadoop MapReduce

    The core component of Hadoop responsible for running MapReduce jobs.

    NameNode

    The central node in a Hadoop cluster that manages HDFS and keeps track of file locations.

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    DataNode

    A node that stores data in HDFS and responds to requests from the NameNode.

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    JobTracker

    The service that manages the execution of MapReduce jobs in Hadoop, assigning tasks to different nodes.

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    TaskTracker

    A node that runs tasks assigned by the JobTracker, such as Map, Reduce, and Shuffle tasks.

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    Secondary NameNode

    An optional node in a Hadoop cluster that creates checkpoints of the HDFS namespace, providing a backup for the NameNode.

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    Hadoop Scheduler

    A pluggable component in Hadoop that manages job execution.

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    FIFO Scheduler

    The default scheduler in Hadoop, which processes jobs in the order they are submitted.

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    Fair Scheduler

    An advanced scheduler that aims to distribute resources fairly among multiple jobs.

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    Job Pools

    Groups of jobs within the Fair Scheduler, each with a guaranteed amount of resources.

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    Pool Capacity

    The amount of resources a Job Pool is guaranteed to have.

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    Fairness in Fair Scheduler

    The Fair Scheduler calculates how much each job should have received ideally compared to what it actually received. The job with the greatest deficit is scheduled next.

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    Capacity Scheduler

    An advanced scheduler that focuses on providing specific resource allocations for different user groups.

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    Capacity Guarantees

    The Capacity Scheduler offers guaranteed resource allocations for various user and application groups.

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    Map Phase

    The first phase of a MapReduce job that splits data into key-value pairs and processes them independently.

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    Reduce Phase

    The second phase of a MapReduce job that aggregates the intermediate results from the Map phase based on keys.

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    Combine Task

    An optional step in a MapReduce job that can be used to perform data aggregation before the Reduce phase, reducing the amount of data transferred between nodes.

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    Heartbeat

    A process that sends regular messages from TaskTrackers to the JobTracker to report their status and availability.

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    Capacity Scheduler in Hadoop

    Capacity Scheduler in Hadoop assigns resources to jobs based on defined queues. Each queue has specific map/reduce slots and guaranteed capacity. Unused capacity is shared fairly among queues.

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    FIFO Scheduling within a Queue

    Within each queue, jobs are processed in a First-In, First-Out (FIFO) order, but priority can be assigned for faster processing. This means jobs submitted earlier are processed first.

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    Fairness in Capacity Scheduler

    Fairness in Hadoop's Capacity Scheduler is achieved by limiting the percentage of tasks allowed for each user. This ensures no single user monopolizes cluster resources.

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    Preemption in Capacity Scheduler

    If a queue doesn't get its fair share of resources for a specified time (wait time), it can preempt (interrupt) tasks in other queues to get its allocated capacity.

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    Wait Time and Preemption in Capacity Scheduler

    Each queue has a configurable wait time. If a queue doesn't receive its allocated resources for longer than the wait time, it can preempt tasks from other queues.

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    TaskTracker Process

    A separate Java Virtual Machine (JVM) process that is spawned by the TaskTracker to execute each individual task in a MapReduce job. This isolation ensures that a task failure doesn't impact the entire TaskTracker and allows for better fault tolerance in the system.

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    YARN (Yet Another Resource Negotiator)

    A powerful resource management system in Hadoop 2.0 that serves as an operating system for various Hadoop processing engines, including MapReduce for batch processing, Apache Tez for interactive queries, and Apache Storm for stream processing.

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    ResourceManager (RM)

    The component in YARN responsible for managing the global allocation of compute resources to applications running on the Hadoop cluster. It ensures efficient utilization of resources across different jobs and users.

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    Scheduler

    A pluggable service within the ResourceManager that is responsible for implementing the scheduling policy of the cluster, deciding which applications get resources and when based on various factors like priority, resource needs, and fairness.

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    Applications Manager (AsM)

    A component that manages the running Application Masters in the YARN cluster. It is responsible for launching, monitoring, and restarting them on different nodes in case of failures.

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    Application Master (AM)

    A per-application component in YARN that oversees the entire lifecycle of the application. It negotiates resources from the ResourceManager, communicates with Node Managers to execute tasks, and monitors their progress.

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    Study Notes

    Chapter 10: Data Analytics for IoT

    • This chapter focuses on data analytics for the Internet of Things (IoT).
    • It outlines the Hadoop ecosystem, MapReduce architecture, job execution flow, and schedulers.
    • Hadoop is an open-source framework for distributed batch processing of large datasets.

    Hadoop Ecosystem

    • Apache Hadoop is a framework for distributed batch processing of big data.
    • Hadoop MapReduce, HDFS, YARN, HBase, Zookeeper, Pig, Hive, Mahout, Chukwa, Cassandra, Avro, Oozie, Flume, and Sqoop are components of the Hadoop ecosystem.
    • These components provide various functionalities for data storage, processing, and analysis.
    • The illustration provides a visual representation of these components and their relationships.

    Hadoop Cluster Components

    • A Hadoop cluster consists of a master node, a backup node, and multiple slave nodes.
    • The master node runs the NameNode and JobTracker processes.
    • The slave nodes run the DataNode and TaskTracker components.
    • The backup node runs the Secondary NameNode process.
    • The NameNode maintains the directory structure and tracks file locations across the cluster.
    • Clients interact with the NameNode to locate, add, copy, move, or delete files.
    • The Secondary NameNode creates checkpoints of the NameSpace.
    • The JobTracker distributes tasks to specific nodes in the cluster.

    Apache Hadoop Components

    • TaskTracker accepts Map, Reduce, and Shuffle tasks from the JobTracker.
    • TaskTrackers have slots specifying the number of tasks they can handle.
    • DataNode stores data in HDFS.
    • Data is replicated across multiple DataNodes to enhance fault tolerance.
    • Clients can interact directly with DataNodes after the NameNode provides data location.
    • TaskTracker instances are often deployed with DataNode instances for efficient MapReduce operations.

    MapReduce

    • MapReduce jobs are composed of two phases (Map and Reduce).
    • Map phase reads data, partitions it across nodes, and produces intermediate results as key-value pairs.
    • Reduce phase aggregates intermediate data with the same key.
    • The Combine task, an optional phase, aggregates intermediate data locally before the Reduce phase.

    MapReduce Job Execution Workflow

    • The client submits a job to the JobTracker.
    • JobTracker determines the data location and allocates tasks to TaskTracker instances.
    • TaskTrackers periodically send heartbeat signals to the JobTracker.
    • TaskTrackers spawn separate processes for different tasks to prevent a failure of one task bringing down the system.
    • After task completion, it sends status information to the JobTracker.

    MapReduce 2.0 – YARN

    • Hadoop 2.0 separated the MapReduce processing engine from resource management.
    • YARN effectively operates as an OS for Hadoop.
    • It supports varied processing engines such as MapReduce, Apache Tez, and interactive queries (e.g., Apache Storm).
    • YARN incorporates Resource Manager and Application Master components.

    YARN Components

    • Resource Manager (RM) manages global resource allocation.
    • Scheduler assigns resources based on policies in the cluster.
    • Applications Manager (AsM) manages running applications.
    • Application Master (AM) manages the application life cycle.
    • Node Manager (NM) manages processes on each node.
    • Containers bundle resources (memory, CPU, network) for processes.

    Hadoop Schedulers

    • Hadoop schedulers are configurable components that support various scheduling algorithms.
    • The default scheduler in Hadoop is FIFO (First-In-First-Out).
    • Advanced schedulers like Fair Scheduler and Capacity Scheduler are also available, providing workload flexibility and performance constraints, and enabling multitasking.

    FIFO Scheduler

    • FIFO is the default Hadoop scheduler that works with a queue.
    • The jobs are processed in the order they enter the queue.
    • Priorities and job sizes are not considered in FIFO.

    Fair Scheduler

    • The Fair Scheduler distributes resources equally among jobs.
    • It ensures each job gets an equal share of resources over time.
    • Jobs are placed into pools, with each pool ensuring that a certain capacity is guaranteed.

    Capacity Scheduler

    • Capacity Scheduler resembles the Fair Scheduler but uses a different philosophy for allocation.
    • It creates queues with configurable numbers of Map and Reduce slots.
    • It assigns guaranteed capacity to these queues.
    • It shares unused capacity among queues to maintain fairness.

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    Chapter 10 - Data Analytics for IoT (PDF)

    Description

    Explore the key concepts of data analytics in the context of the Internet of Things (IoT) through Chapter 10. This chapter covers the Hadoop ecosystem, including MapReduce architecture, job execution flow, and the various components that make up a Hadoop cluster. Understand the roles of master and slave nodes and how they contribute to big data processing.

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