Spark Streaming 1 A Simple Example

    Spark streaming是Spark 核心API的一个扩展，它对实时流数据的处理具有可扩展性、高吞吐量、可容错性等特点。我们可以从Kafa、flume、Twitter、ZeroMQ、Kinesis等源获取数据，也可以通过由高阶函数map、reduce、join、window等组成的复杂算法计算出数据。最后，处理后的数据可以推送到文件系统、数据库、实时仪表盘中。事实上，你可以将处理后的数据应用到Spark的机器学习算法、图处理算法中去。

    使用Idea做开发，导入Maven的pom文件，需要考虑Spark/Scala/Hadoop的版本；这里的Hadoop 2.6.5，Spark 2.2.0，Scala 2.11.8。

 <properties>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <encoding>UTF-8</encoding>
        <hadoop.version>2.6.5</hadoop.version>
        <spark.version>2.2.0</spark.version>
        <scala.version>2.11.8</scala.version>
    </properties>


    <dependencies>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>


        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${spark.version}</version>
            <scope>provided</scope>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>

    </dependencies>


    <build>
        <pluginManagement>
            <plugins>
                <plugin>
                    <groupId>net.alchim31.maven</groupId>
                    <artifactId>scala-maven-plugin</artifactId>
                    <version>3.2.2</version>
                </plugin>
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-compiler-plugin</artifactId>
                    <version>3.5.1</version>
                </plugin>
            </plugins>
        </pluginManagement>

        <plugins>
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <executions>
                    <execution>
                        <id>scala-compile-first</id>
                        <phase>process-resources</phase>
                        <goals>
                            <goal>add-source</goal>
                            <goal>compile</goal>
                        </goals>
                    </execution>
                    <execution>
                        <id>scala-test-compile</id>
                        <phase>process-test-resources</phase>
                        <goals>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>

            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <executions>
                    <execution>
                        <phase>compile</phase>
                        <goals>
                            <goal>compile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>

            <!--打包-->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.4.3</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <filters>
                                <filter>
                                    <artifact>*:*</artifact>
                                    <exclude>META-INF/*.SF</exclude>
                                    <exclude>META-INF/*.DSA</exclude>
                                    <exclude>META-INF/*.RSA</exclude>
                                </filter>
                            </filters>
                        </configuration>
                    </execution>
                </executions>
            </plugin>

        </plugins>
    </build>

    一个简单的word count java spark实例，采用单机版的spark。

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import scala.Int;
import scala.Tuple2;

import java.util.Arrays;
import java.util.Iterator;

public class JavaWordCount {

    public static void main(String[] args) {

        //create sparkconf
        SparkConf conf = new SparkConf();
        conf.setAppName("JavaWordCount");
        conf.setMaster("local");

        //create java spark context
        JavaSparkContext jsc = new JavaSparkContext(conf);

        //read data
        JavaRDD<String> lines = jsc.textFile(args[0]);

        //split
        JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {

            @Override
            public Iterator<String> call(String line) throws Exception {
                String[] words = line.split(" ");
                return Arrays.asList(words).iterator();
            }
        });

        //put words and one together
        JavaPairRDD<String, Integer> wordAndOne = words.mapToPair(new PairFunction<String, String, Integer>() {
            @Override
            public Tuple2<String, Integer> call(String word) throws Exception{
                return new Tuple2<>(word, 1);
            }
        });

        //aggregate
        JavaPairRDD<String, Integer> reduced = wordAndOne.reduceByKey(new Function2<Integer, Integer, Integer>() {
            @Override
            public Integer call(Integer integer, Integer integer2) throws Exception {
                return integer + integer2;
            }
        });

        //switch (hello,7) => (7, hello)
        JavaPairRDD<Integer, String> swaped = reduced.mapToPair(new PairFunction<Tuple2<String, Integer>, Integer, String>() {
            @Override
            public Tuple2<Integer, String> call(Tuple2<String, Integer> tp) throws Exception{
                return tp.swap();
            }
        });

        //sort
        JavaPairRDD<Integer, String> sorted = swaped.sortByKey(false);

        //swap
        JavaPairRDD<String, Integer> result = sorted.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
            @Override
            public Tuple2<String, Integer> call(Tuple2<Integer, String> tp) throws Exception {
                return tp.swap();
            }
        });

        //save
        result.saveAsTextFile(args[1]);


        //release resource
        jsc.stop();
    }
}