chapter 11

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What is Apache Storm?

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52 Terms

1

What is Apache Storm?

A distributed, fault-tolerant framework for real-time computation that processes data streams from sources like Kafka, Kinesis, and RabbitMQ.

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2

What are the main Storm Concepts?

Topology, Stream, Spouts, Bolts, Tasks.

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3

What is a Topology in Apache Storm?

A graph of computations that defines how data flows and is processed across the cluster.

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4

What is a Stream in Apache Storm?

An unbounded sequence of tuples (data records) that flows through the topology.

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5

What are Spouts in Apache Storm?

Components that act as sources of streams, emitting tuples into the topology.

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6

What are Bolts in Apache Storm?

Components that process incoming tuples, performing operations like filtering, aggregating, or joining data.

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7

What are Tasks in Apache Storm?

Parallel threads that execute spouts and bolts within worker processes to handle data processing.

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8

What are the different Stream Groupings in Storm?

Shuffle, Field Grouping, All/Global, Direct.

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9

What are the main components of a Storm Cluster?

Nimbus, Supervisor, Zookeeper.

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10

What is Nimbus in a Storm Cluster?

The master node that manages topologies and distributes tasks to supervisors.

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11

What is the role of the Supervisor in Storm?

Executes worker processes that run spouts and bolts as part of the topology.

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12

What is Zookeeper used for in a Storm Cluster?

Coordinates the cluster by managing configuration, synchronization, and leader election.

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13

What is Spark Streaming?

A high-throughput, fault-tolerant stream processing component of Apache Spark.

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14

What are DStreams in Spark Streaming?

Sequences of Resilient Distributed Datasets (RDDs) representing data from specific time intervals.

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15

What are the primary sources for Spark Streaming?

Kafka, HDFS, custom connectors, and other streaming data sources.

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16

How are DStreams similar to RDDs in Spark?

DStreams represent data over time intervals, allowing for batch-like processing of streaming data.

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17

What are the two types of DStream Transformations?

Stateless Transformations and Stateful Transformations.

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18

What are Window Operations in Spark Streaming?

Operations that compute over sliding data windows, allowing aggregation and analysis within specified time frames.

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19

Name some Window Operations in Spark Streaming.

Window, CountByWindow/ReduceByWindow, ReduceByKeyAndWindow, CountByValueAndWindow, UpdateStateByKey.

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20

What is Apache Flink?

A framework for real-time, stateful stream processing that supports both bounded and unbounded data streams.

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21

What are the main APIs provided by Apache Flink?

DataStream, DataSet, Table, CEP, Gelly, FlinkML.

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22

What are Streaming Dataflows in Apache Flink?

Directed Acyclic Graphs (DAGs) consisting of sources, transformations, and sinks.

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23

What deployment options does Flink Architecture support?

Local, cluster, and cloud deployments.

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24

What libraries does Apache Flink provide?

Libraries for graph processing, machine learning, and event processing.

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25

How does Apache Flink handle stateful stream processing?

By maintaining and managing state information across events.

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26

What makes Apache Flink suitable for both bounded and unbounded data streams?

Its flexible architecture supporting batch and real-time processing paradigms.

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27

Compare Apache Storm and Spark Streaming.

Storm focuses on real-time computation with a topology-based approach; Spark Streaming utilizes DStreams for micro-batch processing.

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28

Compare Apache Flink with Spark Streaming.

Flink provides true stream processing with low latency; Spark Streaming uses micro-batching.

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29

What is the primary advantage of using Apache Flink for real-time analytics?

Its ability to handle both batch and stream processing seamlessly.

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30

What is a Tuple in Apache Storm?

A data record emitted by spouts and processed by bolts.

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31

How does Shuffle Grouping work in Storm?

Distributes tuples evenly across all target bolts.

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32

What is Field Grouping in Storm?

Groups tuples based on specific fields.

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33

What is the purpose of All/Global Grouping in Storm?

Broadcasts each tuple to all bolt instances.

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34

What does Direct Grouping enable in Storm?

Allows the sender to specify the exact bolt instance for each tuple.

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35

What ensures fault tolerance in Apache Storm?

The distributed architecture with supervisors and the use of Zookeeper for coordination.

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36

What is the main use case for Spark Streaming?

High-throughput, fault-tolerant stream processing for real-time data analytics.

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37

How do DStreams achieve fault tolerance in Spark Streaming?

By using RDD lineage information and checkpointing.

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38

What is checkpointing in Spark Streaming?

A mechanism to save the state of DStreams to reliable storage.

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39

How does Apache Flink achieve high performance in stream processing?

Through advanced scheduling, efficient state management, and support for event-time processing.

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40

What is CEP in Apache Flink?

Complex Event Processing API for detecting patterns in event streams.

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41

What is Gelly in Apache Flink?

Flink’s API for graph processing.

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42

What is FlinkML?

Flink’s machine learning library for scalable algorithms.

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43

How do Flink's Streaming Dataflows differ from traditional batch processing?

They process data continuously as it arrives.

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44

What is the Driver in a Spark Cluster?

The program that creates a SparkContext to coordinate task execution.

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45

What is the role of the Cluster Manager in Spark?

Allocates resources across the cluster and manages task distribution.

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46

What are Executors in Spark?

Processes on worker nodes that run application code.

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47

What is the Driver Program in Spark?

The process that runs the main function of the application.

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48

How does Apache Flink support multi-tenancy?

By managing resources and isolating jobs from different users.

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49

What is the primary difference between Apache Storm and Apache Flink?

Storm focuses on unbounded stream processing; Flink supports both bounded and unbounded with advanced state management.

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50

What is event-time processing in Apache Flink?

Processing events based on the time they occurred.

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51

What is a DAG in the context of Apache Flink?

Directed Acyclic Graphs that represent the flow of data and transformations.

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52

What are the main advantages of using Apache Flink for real-time analytics?

True stream processing, robust state management, support for complex event processing, and flexibility to handle data streams.

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