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Spark Amazon Kinesis Integration in 7 Steps Using VS Code

  1. Create a Project in VS Code:
  2. Open Visual Studio Code and create a new folder for your project.
  3. Initialize your project by creating a build.sbt file (for SBT) or pom.xml (for Maven) to manage dependencies.

  4. If you’re using SBT, your build.sbt should include the necessary configurations and dependencies for Spark and Kinesis.

  5. Add Spark Kinesis Jars:

  6. In your build.sbt or pom.xml, add dependencies for spark-streaming-kinesis-asl and any other necessary libraries.
  7. For example, in build.sbt: 
    libraryDependencies += "org.apache.spark" %% "spark-streaming-kinesis-asl" % "2.4.8"

  8. Run sbt update or mvn install to download the dependencies.

  9. Initialize Streaming Context:

  10. In your main Scala or Java file, initialize the StreamingContext.

  11. Example: 

    val conf = new SparkConf().setAppName("KinesisSparkIntegration").setMaster("local[*]")
val ssc = new StreamingContext(conf, Seconds(10))

  12. Initialize Kinesis Utils:

  13. Use KinesisUtils.createStream to create a DStream from the Kinesis stream.

  14. Example: 

    val kinesisStream = KinesisUtils.createStream(
  ssc,
  "KinesisAppName",
  "KinesisStreamName",
  "kinesis.us-east-1.amazonaws.com",
  "us-east-1",
  InitialPositionInStream.LATEST,
  Seconds(10),
  StorageLevel.MEMORY_AND_DISK_2
)

  15. Byte Array Deserialization:

  16. Convert the byte array data from Kinesis into a more usable format, such as a string or JSON.

  17. Example: 

    val stringStream = kinesisStream.map(record => new String(record))

  18. Print the Stream:

  19. Use the print() action to output the contents of the stream for testing and debugging.

  20. Example: 

    stringStream.print()

  21. Start the Stream:

  22. Start the streaming context and keep the application running.
  23. Example: 
    ssc.start()
ssc.awaitTermination()

Extra Note

  • Extensions: Install the Scala and SBT extensions for VS Code to improve code highlighting, auto-completion, and build management.
  • Terminal: Use the integrated terminal in VS Code to run sbt or mvn commands.

  • Debugging: Configure VS Code to debug Spark jobs by setting up a launch configuration in the launch.json file.

  • Some users use Eclipse or IntelliJ and not VS Code.

Some Kinesis and Kafka Concepts

1. Streaming Context

  • Definition: In Apache Spark, StreamingContext is the main entry point for all Spark Streaming functionality. It is used to define a streaming computation by specifying the sources of streaming data (like Kafka or Kinesis), the transformations to apply to this data, and the output operations.
  • Purpose: It manages the execution of the streaming job, setting the batch interval (how often the data is processed) and initiating the actual processing of data streams.

2. Topic Name / Stream Name / Kafka Topic

  • Topic Name (Kafka): A Kafka Topic is a category or feed name to which records are published. It is the basic building block of Kafka's messaging system. Topics are partitioned and replicated across Kafka brokers for scalability and fault tolerance.
  • Stream Name (Kinesis): Similar to Kafka topics, Stream Name in Amazon Kinesis refers to the name of a data stream where data records are continuously ingested. Data streams in Kinesis are sharded, which is akin to partitions in Kafka.

3. Consumption Model

  • Definition: The consumption model defines how messages are read from the streaming data source (like Kafka or Kinesis).
  • Earliest: This model instructs the consumer to start reading from the earliest available data in the stream or topic. In Kafka, this means starting from the earliest offset, and in Kinesis, from the beginning of the shard.
  • Latest: This model instructs the consumer to start reading only new messages arriving after the consumer starts, ignoring older messages. In Kafka, this means starting from the latest offset, and in Kinesis, from the end of the shard.

4. Consumption Duration

  • Definition: This refers to the period during which data is consumed from the stream. It could be defined as the length of time the consumer stays active, or it could be tied to how long the consumer is configured to keep processing data (e.g., indefinitely, until the end of a batch, or a specified time frame).

5. Storage Levels

  • Definition: In Spark, storage levels determine how RDDs (Resilient Distributed Datasets) are stored in memory or on disk. These levels help optimize performance by controlling the persistence and redundancy of data.

  • Types of Storage Levels: There are 8 primary storage levels in Spark:

    1. MEMORY_ONLY: Store RDDs in memory only. If the data doesn't fit in memory, it won't be stored.
    2. MEMORY_ONLY_SER: Store RDDs in memory in a serialized format. Useful to reduce memory usage.
    3. MEMORY_AND_DISK: Store RDDs in memory and spill to disk if memory is insufficient.
    4. MEMORY_AND_DISK_SER: Store RDDs in memory in a serialized format, and spill to disk if needed.
    5. DISK_ONLY: Store RDDs only on disk.
    6. MEMORY_ONLY_2: Same as MEMORY_ONLY but with replication for fault tolerance.
    7. MEMORY_AND_DISK_2: Same as MEMORY_AND_DISK but with replication.
    8. OFF_HEAP: Store RDDs in off-heap memory (outside the Java heap). Useful for certain memory configurations.

  • Kinesis Storage Level: When integrating Spark with Kinesis, specifying a storage level is mandatory. Typically, you'd choose a storage level like MEMORY_AND_DISK_2 to ensure data resiliency across Spark nodes, given that streaming data is often critical and needs to be preserved even if some nodes fail.

6. Kinesis Storage Level is Mandatory

  • Explanation: When using KinesisUtils.createStream in Spark Streaming, you must specify a storage level. This is to ensure that the data fetched from Kinesis is appropriately cached or stored, allowing for fault tolerance and reprocessing if necessary. The choice of storage level impacts how the data is handled, either being kept in memory, disk, or both, depending on your setup.

Summary

  • Streaming Context: Main entry point for Spark Streaming.
  • Topic Name/Stream Name: Identifiers for data streams in Kafka (Topic) or Kinesis (Stream Name).
  • Consumption Model: Determines how messages are consumed (Earliest or Latest).
  • Consumption Duration: Time frame or condition for consuming data.
  • Storage Levels: Defines how RDDs are persisted in Spark, with 8 levels available.
  • Kinesis Storage Level: Must be specified when integrating Kinesis with Spark to ensure data is stored properly.