Spark Streaming with Scala: Getting Started Guide

Spark Streaming enables scalable, fault-tolerant processing of real-time data streams such as Kafka and Kinesis. Spark Streaming is an extension of the core Spark API that provides high-throughput processing of live data streams.

Scala is a programming language that is designed to run on the Java Virtual Machine (JVM). It is a statically-typed language that supports functional programming and object-oriented programming paradigms. Scala is a popular language for building Spark applications, including Spark Streaming applications. Scala is well-suited for building Spark Streaming applications because it provides a concise syntax for expressing complex algorithms and supports functional programming constructs like map, reduce, and window functions.

Spark Streaming and Scala provide a powerful platform for building real-time data processing applications. With Spark Streaming and Scala, developers can build real-time data processing applications that can handle large volumes of data with ease.

Table of Contents

• Spark Streaming Scala Overview
• Basics of Spark Streaming with Scala
  • DStreams
  • Transformations
  • Actions
• Spark Streaming with Scala Example
  • Installing and Setting Up Spark Streaming in Scala
  • Running the NetworkWordCount example
  • Making and Running Our Own NetworkWordCount
• Spark Streaming Scala Conclusion
• Further Reference

Spark Streaming Scala Overview

The combination of Scala and Spark Streaming is a powerful tool for processing real-time data streams. Scala provides concise and expressive syntax, making it easy to write complex algorithms in a readable and maintainable way. Spark Streaming, on the other hand, provides a scalable and fault-tolerant platform for processing large volumes of data in real-time.

One of the key benefits of using Scala with Spark Streaming is the ability to write code that is both concise and expressive. Scala’s functional programming features, such as higher-order functions and pattern matching, allow developers to write code that is both readable and concise. This makes it easier to write complex algorithms that can be easily understood and maintained.

Another benefit of using Scala with Spark Streaming is the ability to take advantage of the rich ecosystem of libraries and frameworks that are available for Scala. For example, the Akka toolkit provides a powerful set of tools for building distributed systems, while the Play framework provides a powerful web framework for building scalable and responsive web applications.

Overall, Scala and Spark Streaming provide a powerful combination for processing real-time data streams. The combination of Scala’s expressive syntax and Spark Streaming’s scalable and fault-tolerant platform makes it an ideal choice for building real-time data processing applications.

Basics of Spark Streaming with Scala

Spark Streaming is an extension of the core Spark API that allows for scalable, high-throughput, fault-tolerant stream processing of live data streams. With Spark Streaming, data can be ingested from various sources like Kafka, Kinesis, or TCP sockets, and can be processed using complex algorithms expressed with high-level functions like map, reduce, join, and window.

DStreams

The fundamental abstraction of Spark Streaming is called Discretized Streams (DStreams). DStreams represent a continuous stream of data, which is divided into small batches of data. These batches are then processed by Spark Streaming as RDDs (Resilient Distributed Datasets). DStreams can be created from various input sources like Kafka, Flume, HDFS, and many more.

Transformations

Transformations are operations that can be performed on DStreams to create a new DStream. Some of the commonly used transformations include map, filter, flatMap, reduceByKey, and window. Transformations are executed lazily, which means that they are not executed until an action is performed on the DStream.

Actions

Actions are operations that trigger the execution of transformations on DStreams. Some of the commonly used actions include count, reduce, foreachRDD, and saveAsTextFiles. Actions are executed eagerly, which means that they trigger the execution of all the transformations that are dependent on them.

In summary, Spark Streaming with Scala provides a powerful framework for processing live data streams. By leveraging the DStream abstraction, developers can easily build scalable and fault-tolerant stream processing applications. Transformations and actions allow for complex data processing and analysis, while the integration with various data sources makes it easy to ingest data from a wide range of sources.

Spark Streaming with Scala Example

Spark comes with some great examples and convenient scripts for running Streaming code.  Let’s make sure you can run these examples.  In case it helps, I made a screencast of me running through these steps.  Link to the screencast below.

Installing and Setting Up Spark Streaming in Scala

To start using Spark Streaming in Scala, the first step is to install and set up the Spark environment. Here are the steps to follow:

1. Download and Install Spark: Download and install Spark. Go to the official Apache Spark website and download the latest version of Spark. Once downloaded, extract the contents of the zip file to a directory of your choice.
2. Set up the Environment: After extracting the zip file, the next optional step is to set up the environment. You can set the SPARK_HOME environment variable to the directory where you extracted Spark. For example, if you extracted Spark to the /opt/spark directory, set SPARK_HOME to /opt/spark. You can also add the $SPARK_HOME/bin directory of Spark to your PATH environment variable.

Running the NetworkWordCount example

1. Open a shell or command prompt on Windows and go to your Spark root directory. (This is where you may have set SPARK_HOME variable described previously)
2. Start Spark Master:  sbin/start-master.sh  **
3. Start a Worker: sbin/start-slave.sh spark://todd-mcgraths-macbook-pro.local:7077
4. Start netcat on port 9999: nc -lk 9999  (*** Windows users: https://nmap.org/ncat/  Let me know in page comments if this works well on Windows)
5. Run network word count using handy run-example script: bin/run-example streaming.NetworkWordCount localhost 9999

** Windows users, please adjust accordingly; i.e. sbin/start-master.cmd instead of sbin/start-master.sh

Here’s a screencast of me running these steps

Making and Running Our Own NetworkWordCount

Ok, that’s good.  We’ve succeeded in running the Scala Spark Streaming NetworkWordCount example, but what about running our own Spark Streaming program in Scala?  Let’s take another step towards that goal.  In this step, we’re going to setup our own Scala/SBT project, compile, package and deploy a modified NetworkWordCount.  Again, I made a screencast of the following steps with a link to the screencast below.

1. Choose or create a new directory for a new Spark Streaming Scala project.
2. Make dirs to make things convenient for SBT: src/main/scala
3. Create Scala object code file called NetworkWordCount.scala in src/main/scala directory
4. Copy-and-paste NetworkWordCount.scala code from Spark examples directory to your version created in the previous step
5. Remove or comment out package and StreamingExamples references
6. Change AppName to “MyNetworkWordCount”
7. Create a build.sbt file (source code below)
8. sbt compile to smoke test
9. Deploy: ~/Development/spark-1.5.1-bin-hadoop2.4/bin/spark-submit –class “NetworkWordCount” –master spark://todd-mcgraths-macbook-pro.local:7077 target/scala-2.11/streaming-example_2.11-1.0.jar localhost 9999
10. Start netcat on port 9999: nc -lk 9999  and start typing
11. Check things out in the Spark UI

build.sbt source

name := "streaming-example"

version := "1.0"

scalaVersion := "2.11.4"

libraryDependencies ++= Seq(
    "org.apache.spark" %% "spark-core" % "1.5.1",
    "org.apache.spark" %% "spark-streaming" % "1.5.1"
)

If you watched the video, notice this has been corrected to “streaming-example” and not “steaming-example” 🙂

Spark Streaming Scala Conclusion

In conclusion, Spark Streaming with Scala is a powerful tool for real-time data processing and analytics. By leveraging the power of Spark, users can easily process and analyze large amounts of streaming data with ease.

One of the key advantages of Spark Streaming with Scala is its fault-tolerance and scalability. It can handle large volumes of data and can recover from failures without any loss of data. This makes it an ideal solution for mission-critical applications.

Another advantage of Spark Streaming with Scala is its ease of use. With a simple API, users can easily create streaming applications that can process data from a variety of sources, including Kafka, Kinesis, and TCP sockets.

Overall, Spark Streaming with Scala is a valuable tool for any organization that needs to process and analyze large amounts of streaming data in real-time. With its powerful features and ease of use, it is a must-have tool for any data engineer or data scientist working in the field of real-time data processing and analytics.

Further Reference

• Spark Tutorials
• Spark Streaming Tutorials