本文介绍了Spark中如何将JavaPairInputDStream转换为DataSet/DataFrame的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在尝试从 kafka 接收流数据.在这个过程中，我能够接收流数据并将其存储到 JavaPairInputDStream 中.现在我需要分析这些数据而不将其存储到任何数据库中.所以我想将此 JavaPairInputDStream 转换为 DataSet 或 DataFrame
I am trying to receive streaming data from kafka. In this process I am able to receive and store the streaming data into JavaPairInputDStream. Now I need to analyze this data with out storing it into any database.So I want to convert this JavaPairInputDStream to DataSet or DataFrame
到目前为止我尝试的是:
What I tried so far is:
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Set;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.catalog.Function;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.api.java.AbstractJavaDStreamLike;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaPairInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;
import kafka.serializer.StringDecoder;
import scala.Tuple2;
//Streaming Working Code
public class KafkaToSparkStreaming
{
public static void main(String arr[]) throws InterruptedException
{
SparkConf conf = new SparkConf();
conf.set("spark.app.name", "SparkReceiver"); //The name of application. This will appear in the UI and in log data.
//conf.set("spark.ui.port", "7077"); //Port for application's dashboard, which shows memory and workload data.
conf.set("dynamicAllocation.enabled","false"); //Which scales the number of executors registered with this application up and down based on the workload
//conf.set("spark.cassandra.connection.host", "localhost"); //Cassandra Host Adddress/IP
conf.set("spark.serializer","org.apache.spark.serializer.KryoSerializer"); //For serializing objects that will be sent over the network or need to be cached in serialized form.
//conf.setMaster("local");
conf.set("spark.streaming.stopGracefullyOnShutdown", "true");
JavaSparkContext sc = new JavaSparkContext(conf);
// Create the context with 2 seconds batch size
JavaStreamingContext ssc = new JavaStreamingContext(sc, new Duration(2000));
Map<String, String> kafkaParams = new HashMap<String, String>();
kafkaParams.put("zookeeper.connect", "localhost:2181"); //Make all kafka data for this cluster appear under a particular path.
kafkaParams.put("group.id", "testgroup"); //String that uniquely identifies the group of consumer processes to which this consumer belongs
kafkaParams.put("metadata.broker.list", "localhost:9092"); //Producer can find a one or more Brokers to determine the Leader for each topic.
kafkaParams.put("serializer.class", "kafka.serializer.StringEncoder"); //Serializer to use when preparing the message for transmission to the Broker.
kafkaParams.put("request.required.acks", "1"); //Producer to require an acknowledgement from the Broker that the message was received.
Set<String> topics = Collections.singleton("ny-2008.csv");
//Create an input DStream for Receiving data from socket
JavaPairInputDStream<String, String> directKafkaStream = KafkaUtils.createDirectStream(ssc,
String.class,
String.class,
StringDecoder.class,
StringDecoder.class,
kafkaParams, topics);
//System.out.println(directKafkaStream);
directKafkaStream.print();
}
}
推荐答案
这是使用 Spark 2.0 的完整工作代码.
Here is the complete working code using Spark 2.0.
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.VoidFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;
import kafka.serializer.StringDecoder;
import scala.Tuple2;
public class KafkaToSparkStreaming {
public static void main(String arr[]) throws InterruptedException
{
SparkConf conf = new SparkConf();
conf.set("spark.app.name", "SparkReceiver"); //The name of application. This will appear in the UI and in log data.
//conf.set("spark.ui.port", "7077"); //Port for application's dashboard, which shows memory and workload data.
conf.set("dynamicAllocation.enabled","false"); //Which scales the number of executors registered with this application up and down based on the workload
//conf.set("spark.cassandra.connection.host", "localhost"); //Cassandra Host Adddress/IP
conf.set("spark.serializer","org.apache.spark.serializer.KryoSerializer"); //For serializing objects that will be sent over the network or need to be cached in serialized form.
conf.setMaster("local");
conf.set("spark.streaming.stopGracefullyOnShutdown", "true");
JavaSparkContext sc = new JavaSparkContext(conf);
// Create the context with 2 seconds batch size
JavaStreamingContext ssc = new JavaStreamingContext(sc, new Duration(2000));
Map<String, String> kafkaParams = new HashMap<String, String>();
kafkaParams.put("zookeeper.connect", "localhost:2181"); //Make all kafka data for this cluster appear under a particular path.
kafkaParams.put("group.id", "testgroup"); //String that uniquely identifies the group of consumer processes to which this consumer belongs
kafkaParams.put("metadata.broker.list", "localhost:9092"); //Producer can find a one or more Brokers to determine the Leader for each topic.
kafkaParams.put("serializer.class", "kafka.serializer.StringEncoder"); //Serializer to use when preparing the message for transmission to the Broker.
kafkaParams.put("request.required.acks", "1"); //Producer to require an acknowledgement from the Broker that the message was received.
Set<String> topics = Collections.singleton("ny-2008.csv");
//Create an input DStream for Receiving data from socket
JavaPairInputDStream<String, String> directKafkaStream = KafkaUtils.createDirectStream(ssc,
String.class,
String.class,
StringDecoder.class,
StringDecoder.class,
kafkaParams, topics);
//Create JavaDStream<String>
JavaDStream<String> msgDataStream = directKafkaStream.map(new Function<Tuple2<String, String>, String>() {
@Override
public String call(Tuple2<String, String> tuple2) {
return tuple2._2();
}
});
//Create JavaRDD<Row>
msgDataStream.foreachRDD(new VoidFunction<JavaRDD<String>>() {
@Override
public void call(JavaRDD<String> rdd) {
JavaRDD<Row> rowRDD = rdd.map(new Function<String, Row>() {
@Override
public Row call(String msg) {
Row row = RowFactory.create(msg);
return row;
}
});
//Create Schema
StructType schema = DataTypes.createStructType(new StructField[] {DataTypes.createStructField("Message", DataTypes.StringType, true)});
//Get Spark 2.0 session
SparkSession spark = JavaSparkSessionSingleton.getInstance(rdd.context().getConf());
Dataset<Row> msgDataFrame = spark.createDataFrame(rowRDD, schema);
msgDataFrame.show();
}
});
ssc.start();
ssc.awaitTermination();
}
}
class JavaSparkSessionSingleton {
private static transient SparkSession instance = null;
public static SparkSession getInstance(SparkConf sparkConf) {
if (instance == null) {
instance = SparkSession
.builder()
.config(sparkConf)
.getOrCreate();
}
return instance;
}
}
这篇关于Spark中如何将JavaPairInputDStream转换为DataSet/DataFrame的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！