Use Spring together with Spark

Question

I'm developing a Spark Application and I'm used to Spring as a Dependency Injection Framework. Now I'm stuck with the problem, that the processing part uses the @Autowired functionality of Spring, but it is serialized and deserialized by Spark.

So the following code gets me into trouble:

Processor processor = ...; // This is a Spring constructed object
                           // and makes all the trouble
JavaRDD<Txn> rdd = ...; // some data for Spark
rdd.foreachPartition(processor);

The Processor looks like that:

public class Processor implements VoidFunction<Iterator<Txn>>, Serializeable {
    private static final long serialVersionUID = 1L;

    @Autowired // This will not work if the object is deserialized
    private transient DatabaseConnection db;

    @Override
    public void call(Iterator<Txn> txns) {
        ... // do some fance stuff
        db.store(txns);
    }
}

So my question is: Is it even possible to use something like Spring in combination with Spark? If not, what is the most elegant way to do something like that? Any help is appreciated!

Answer 1

FROM THE QUESTION ASKER: Added: To interfere the deserialization part directly without modifying your own classes use the following spring-spark project by parapluplu. This projects autowires your bean when it gets deserialized by spring.

---

EDIT:

In order to use Spark, you need the following setup (also seen in this repository):

• Spring Boot + Spark:

.

<parent>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-parent</artifactId>
    <version>1.5.2.RELEASE</version>
    <relativePath/>
    <!-- lookup parent from repository -->
</parent>

...

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
        <exclusions>
            <exclusion>
                <groupId>ch.qos.logback</groupId>
                <artifactId>logback-classic</artifactId>
            </exclusion>
        </exclusions>
    </dependency>

    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-test</artifactId>
        <scope>test</scope>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core_2.11 -->
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.11</artifactId>
        <version>2.1.0</version>
        <exclusions>
            <exclusion>
                <groupId>org.slf4j</groupId>
                <artifactId>slf4j-log4j12</artifactId>
            </exclusion>
            <exclusion>
                <groupId>log4j</groupId>
                <artifactId>log4j</artifactId>
            </exclusion>
        </exclusions>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql_2.11 -->
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql_2.11</artifactId>
        <version>2.1.0</version>
    </dependency>

    <!-- fix java.lang.ClassNotFoundException: org.codehaus.commons.compiler.UncheckedCompileException -->
    <dependency>
        <groupId>org.codehaus.janino</groupId>
        <artifactId>commons-compiler</artifactId>
        <version>2.7.8</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.slf4j/log4j-over-slf4j -->
    <dependency>
        <groupId>org.slf4j</groupId>
        <artifactId>log4j-over-slf4j</artifactId>
        <version>1.7.25</version>
    </dependency>
    <dependency>
        <groupId>org.slf4j</groupId>
        <artifactId>slf4j-api</artifactId>
        <version>1.7.5</version>
    </dependency>
    <dependency>
        <groupId>org.slf4j</groupId>
        <artifactId>slf4j-simple</artifactId>
        <version>1.6.4</version>
    </dependency>

</dependencies>

Then you need the application class, as usual with Spring Boot:

@SpringBootApplication
public class SparkExperimentApplication {

    public static void main(String[] args) {
        SpringApplication.run(SparkExperimentApplication.class, args);
    }
}

And then a configuration that binds it all together

@Configuration
@PropertySource("classpath:application.properties")
public class ApplicationConfig {

    @Autowired
    private Environment env;

    @Value("${app.name:jigsaw}")
    private String appName;

    @Value("${spark.home}")
    private String sparkHome;

    @Value("${master.uri:local}")
    private String masterUri;

    @Bean
    public SparkConf sparkConf() {
        SparkConf sparkConf = new SparkConf()
                .setAppName(appName)
                .setSparkHome(sparkHome)
                .setMaster(masterUri);

        return sparkConf;
    }

    @Bean
    public JavaSparkContext javaSparkContext() {
        return new JavaSparkContext(sparkConf());
    }

    @Bean
    public SparkSession sparkSession() {
        return SparkSession
                .builder()
                .sparkContext(javaSparkContext().sc())
                .appName("Java Spark SQL basic example")
                .getOrCreate();
    }

    @Bean
    public static PropertySourcesPlaceholderConfigurer propertySourcesPlaceholderConfigurer() {
        return new PropertySourcesPlaceholderConfigurer();
    }
}

Then you can use SparkSession class to communicate with Spark SQL:

/**
 * Created by achat1 on 9/23/15.
 * Just an example to see if it works.
 */
@Component
public class WordCount {
    @Autowired
    private SparkSession sparkSession;

    public List<Count> count() {
        String input = "hello world hello hello hello";
        String[] _words = input.split(" ");
        List<Word> words = Arrays.stream(_words).map(Word::new).collect(Collectors.toList());
        Dataset<Row> dataFrame = sparkSession.createDataFrame(words, Word.class);
        dataFrame.show();
        //StructType structType = dataFrame.schema();

        RelationalGroupedDataset groupedDataset = dataFrame.groupBy(col("word"));
        groupedDataset.count().show();
        List<Row> rows = groupedDataset.count().collectAsList();//JavaConversions.asScalaBuffer(words)).count();
        return rows.stream().map(new Function<Row, Count>() {
            @Override
            public Count apply(Row row) {
                return new Count(row.getString(0), row.getLong(1));
            }
        }).collect(Collectors.toList());
    }
}

Referring to these two classes:

public class Word {
    private String word;

    public Word() {
    }

    public Word(String word) {
        this.word = word;
    }

    public void setWord(String word) {
        this.word = word;
    }

    public String getWord() {
        return word;
    }
}

public class Count {
    private String word;
    private long count;

    public Count() {
    }

    public Count(String word, long count) {
        this.word = word;
        this.count = count;
    }

    public String getWord() {
        return word;
    }

    public void setWord(String word) {
        this.word = word;
    }

    public long getCount() {
        return count;
    }

    public void setCount(long count) {
        this.count = count;
    }
}

Then you can run see it returns the right data:

@RequestMapping("api")
@Controller
public class ApiController {
    @Autowired
    WordCount wordCount;

    @RequestMapping("wordcount")
    public ResponseEntity<List<Count>> words() {
        return new ResponseEntity<>(wordCount.count(), HttpStatus.OK);
    }
}

Says

[{"word":"hello","count":4},{"word":"world","count":1}]