Spark is a powerful open source processing engine built around speed, ease of use, and sophisticated analytics. It's no wonder that the combination of Spark and Spring Boot is a popular choice for building data-intensive applications.
In this post, we'll show you how to get started with using Spark and Spring Boot. We'll cover the basics of working with Spark, including how to create and run Spark applications. We'll also show you how to use Spring Boot to make working with Spark even easier.
Spark is a fast and general engine for large-scale data processing. It was originally developed at UC Berkeley's AMPLab, and is now an Apache project with contributions from many companies and individuals.
Spark is designed to be used in a wide range of applications, from simple data ETL to complex machine learning and graph processing. Spark is particularly well-suited to use cases that involve iterative computations, interactive queries, or streaming data.
Spark provides a rich set of features, including support for SQL, DataFrames, Datasets, and a wide variety of machine learning algorithms. In addition, Spark can be used in conjunction with other popular big data tools, such as Hadoop, Cassandra, and Kafka.
Spark can be used in a variety of ways, including standalone applications, scripts, or as a library for other programming languages. In this section, we'll show you how to create and run a simple Spark application in Scala.
The first step in creating a Spark application is to define a class that extends the SparkConf class. The SparkConf class allows you to configure Spark settings, such as the location of your Spark master and the amount of memory to use for Spark applications.
class MySparkConf extends SparkConf {
override def getAll: Map[String, String] = {
Map(
"spark.master" -> "local[*]",
"spark.executor.memory" -> "1g"
)
}
}
Next, you need to create an instance of the SparkContext class. The SparkContext class is the main entry point for Spark applications. It represents the connection to a Spark cluster, and it is used to create RDDs, DataFrames, and Datasets.
val sc = new SparkContext(new MySparkConf())
Now that you have a SparkContext, you can start creating Spark applications. In this example, we'll create a simple application that counts the words in a text file.
First, we need to read in the text file using the SparkContext.textFile method. This method returns an RDD of strings, where each string is a line from the text file.
val textFile = sc.textFile("/path/to/file.txt")
Next, we can use the RDD.flatMap method to split each line into words. The flatMap method returns a new RDD that contains the results of applying a function to each element in the original RDD. In this case, the function splits each line into words.
val words = textFile.flatMap(line => line.split(" "))
Finally, we can use the RDD.count method to count the number of words in the RDD.
val count = words.count()
Spark applications can be run using the spark-submit script. The spark-submit script takes a number of arguments, including the location of your Spark application and the amount of memory to use.
To run the example application above, you would use the following command:
spark-submit --class MySparkApp --master local[*] --executor-memory 1g /path/to/jar/file.jar
Spring Boot is a popular Java framework that makes it easy to create stand-alone, production-grade Spring-based applications. Spring Boot is particularly well-suited for creating microservices.
In addition to simplifying the process of creating stand-alone Spring applications, Spring Boot also makes it easy to work with a wide variety of data sources, including Spark.
In this section, we'll show you how to use Spring Boot to create a Spark application. We'll use the same example application from the previous section, but we'll use Spring Boot to simplify the process of creating and running the application.
The first step in creating a Spring Boot application is to create a new Maven project. You can use any Maven-compatible IDE to do this.
In the project's pom.xml file, you need to add the following dependencies:
spring-boot-starter-parent
spring-boot-starter-web
spring-boot-starter-data-spark
The spring-boot-starter-parent dependency is the parent POM for all Spring Boot dependencies. The spring-boot-starter-web dependency is used to add support for Spring MVC. The spring-boot-starter-data-spark dependency is used to add support for Spark.
Next, you need to create a new class that annotated with @SpringBootApplication. This class will contain the main method for your application.
@SpringBootApplication
public class MyApplication {
public static void main(String[] args) {
SpringApplication.run(MyApplication.class, args);
}
}
The @SpringBootApplication annotation is a convenience annotation that is used to add all of the following annotations to your application:
@Configuration: This annotation is used to indicate that a class contains configuration information for a Spring application.
@EnableAutoConfiguration: This annotation is used to enable auto-configuration of a Spring application.
@ComponentScan: This annotation is used to enable component scanning of a Spring application.
In order to create a Spark job, you need to create a new class that annotated with @Configuration and @EnableSpark. The @Configuration annotation is used to indicate that a class contains configuration information for a Spring application. The @EnableSpark annotation is used to enable Spark in a Spring application.
@Configuration
@EnableSpark
public class MySparkJob {
@Autowired
private SparkContext sparkContext;
@Autowired
private SparkConf sparkConf;
@Bean
public SparkJob sparkJob() {
// ... create and return SparkJob
}
}
The @Autowired annotation is used to inject the SparkContext and SparkConf into the Spark job. The @Bean annotation is used to indicate that a method creates a Spring bean.
In the sparkJob method, you need to create and return a SparkJob. A SparkJob is a Spring-managed component that represents a Spark application.
@Configuration
@EnableSpark
public class MySparkJob {
@Autowired
private SparkContext sparkContext;
@Autowired
private SparkConf sparkConf;
@Bean
public SparkJob sparkJob() {
return new SparkJob() {
@Override
public void run() {
// ... run Spark job
}
};
}
}
In the run method, you can add the Spark code for your application. In this example, we'll use the same code from the previous section.
@Configuration
@EnableSpark
public class MySparkJob {
@Autowired
private SparkContext sparkContext;
@Autowired
private SparkConf sparkConf;
@Bean
public SparkJob sparkJob() {
return new SparkJob() {
@Override
public void run() {
val textFile = sparkContext.textFile("/path/to/file.txt")
val words = textFile.flatMap(line => line.split(" "))
val count = words.count()
}
};
}
}
Spring Boot applications can be run using the spring-boot-run goal. This goal can be run using the Maven plugin or the Spring Boot command line tool.
To run the example application above, you would use the following command:
mvn spring-boot:run
In this post, we've shown you how to get started with using Spark and Spring Boot. We've covered the basics of working with Spark, including how to create and run Spark applications. We've also shown you how to use Spring Boot to make working with Spark even easier.
If you're interested in learning more about Spark and Spring Boot, there are a number of excellent resources available. In particular, we recommend the following: