我正在使用spark-streaming整合Kafka和Spark。我创建了一个作为kafka制作人的主题：
bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test
我正在kafka发布消息并尝试使用spark-streaming java代码读取它们并在屏幕上显示它们
守护进程全都出现了：Spark-master，worker;动物园管理员;卡夫卡。
我正在编写一个使用KafkaUtils.createStream的java代码
代码如下：
public class SparkStream {
public static void main(String args[])
{
if(args.length != 3)
{
System.out.println("SparkStream <zookeeper_ip> <group_nm> <topic1,topic2,...>");
System.exit(1);
}
Map<String,Integer> topicMap = new HashMap<String,Integer>();
String[] topic = args[2].split(",");
for(String t: topic)
{
topicMap.put(t, new Integer(1));
}
JavaStreamingContext jssc = new JavaStreamingContext("spark://192.168.88.130:7077", "SparkStream", new Duration(3000));
JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(jssc, args[0], args[1], topicMap );
System.out.println("Connection done++++++++++++++");
JavaDStream<String> data = messages.map(new Function<Tuple2<String, String>, String>()
{
public String call(Tuple2<String, String> message)
{
System.out.println("NewMessage: "+message._2()+"++++++++++++++++++");
return message._2();
}
}
);
data.print();
jssc.start();
jssc.awaitTermination();
}
}
我正在运行这个工作，而在其他终端我正在运行kafka-producer来发布消息：
Hi kafka
second message
another message
但是，火花流控制台的输出日志不显示消息，但显示收到零块：
-------------------------------------------
Time: 1417438988000 ms
-------------------------------------------
2014-12-01 08:03:08,008 INFO [sparkDriver-akka.actor.default-dispatcher-4] scheduler.JobScheduler (Logging.scala:logInfo(59)) - Starting job streaming job 1417438988000 ms.0 from job set of time 1417438988000 ms
2014-12-01 08:03:08,008 INFO [sparkDriver-akka.actor.default-dispatcher-4] scheduler.JobScheduler (Logging.scala:logInfo(59)) - Finished job streaming job 1417438988000 ms.0 from job set of time 1417438988000 ms
2014-12-01 08:03:08,009 INFO [sparkDriver-akka.actor.default-dispatcher-4] scheduler.JobScheduler (Logging.scala:logInfo(59)) - Total delay: 0.008 s for time 1417438988000 ms (execution: 0.000 s)
2014-12-01 08:03:08,010 INFO [sparkDriver-akka.actor.default-dispatcher-15] scheduler.JobScheduler (Logging.scala:logInfo(59)) - Added jobs for time 1417438988000 ms
2014-12-01 08:03:08,015 INFO [sparkDriver-akka.actor.default-dispatcher-15] rdd.MappedRDD (Logging.scala:logInfo(59)) - Removing RDD 39 from persistence list
2014-12-01 08:03:08,024 INFO [sparkDriver-akka.actor.default-dispatcher-4] storage.BlockManager (Logging.scala:logInfo(59)) - Removing RDD 39
2014-12-01 08:03:08,027 INFO [sparkDriver-akka.actor.default-dispatcher-15] rdd.BlockRDD (Logging.scala:logInfo(59)) - Removing RDD 38 from persistence list
2014-12-01 08:03:08,031 INFO [sparkDriver-akka.actor.default-dispatcher-2] storage.BlockManager (Logging.scala:logInfo(59)) - Removing RDD 38
2014-12-01 08:03:08,033 INFO [sparkDriver-akka.actor.default-dispatcher-15] kafka.KafkaInputDStream (Logging.scala:logInfo(59)) - Removing blocks of RDD BlockRDD[38] at BlockRDD at ReceiverInputDStream.scala:69 of time 1417438988000 ms
2014-12-01 08:03:09,002 INFO [sparkDriver-akka.actor.default-dispatcher-2] scheduler.ReceiverTracker (Logging.scala:logInfo(59)) - Stream 0 received 0 blocks
为什么数据块没有收到？我已经尝试在控制台bin/kafka-console-producer....上使用kafka producer-consumer和bin/kafka-console-consumer...它的工作完美，但为什么我的代码没有...任何想法？
答案 0 :(得分：7)
问题解决了。
上面的代码是正确的。 我们将再添加两行来抑制生成的[INFO]和[WARN]。所以最终的代码是：
package com.spark;
import scala.Tuple2;
import org.apache.log4j.Logger;
import org.apache.log4j.Level;
import kafka.serializer.Decoder;
import kafka.serializer.Encoder;
import org.apache.spark.streaming.Duration;
import org.apache.spark.*;
import org.apache.spark.api.java.function.*;
import org.apache.spark.api.java.*;
import org.apache.spark.streaming.kafka.KafkaUtils;
import org.apache.spark.streaming.kafka.*;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream;
import java.util.Map;
import java.util.HashMap;
public class SparkStream {
public static void main(String args[])
{
if(args.length != 3)
{
System.out.println("SparkStream <zookeeper_ip> <group_nm> <topic1,topic2,...>");
System.exit(1);
}
Logger.getLogger("org").setLevel(Level.OFF);
Logger.getLogger("akka").setLevel(Level.OFF);
Map<String,Integer> topicMap = new HashMap<String,Integer>();
String[] topic = args[2].split(",");
for(String t: topic)
{
topicMap.put(t, new Integer(3));
}
JavaStreamingContext jssc = new JavaStreamingContext("local[4]", "SparkStream", new Duration(1000));
JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(jssc, args[0], args[1], topicMap );
System.out.println("Connection done++++++++++++++");
JavaDStream<String> data = messages.map(new Function<Tuple2<String, String>, String>()
{
public String call(Tuple2<String, String> message)
{
return message._2();
}
}
);
data.print();
jssc.start();
jssc.awaitTermination();
}
}
我们还需要在POM.xml中添加依赖项：
<dependency>
<groupId>com.msiops.footing</groupId>
<artifactId>footing-tuple</artifactId>
<version>0.2</version>
</dependency>
此依赖关系用于使用scala.Tuple2
Stream 0 received 0 block的错误是由于火花工人不可用而且spark-worker-core设置为1.对于火花流，我们需要核心> = 2。
所以我们需要在spark-config文件中进行更改。请参阅安装手册。添加行export SPARK_WORKER_CORE=5同时将SPARK_MASTER='hostname'更改为SPARK_MASTER=<your local IP>。当您访问Spark UI Web控制台时，您会在BOLD中看到此本地IP ...类似于：spark://192.168..:<port>。我们这里不需要这个端口。只需要知识产权。
现在重新启动你的spark-master和spark-worker并开始流式传输：）
输出：
-------------------------------------------
Time: 1417443060000 ms
-------------------------------------------
message 1
-------------------------------------------
Time: 1417443061000 ms
-------------------------------------------
message 2
-------------------------------------------
Time: 1417443063000 ms
-------------------------------------------
message 3
message 4
-------------------------------------------
Time: 1417443064000 ms
-------------------------------------------
message 5
message 6
messag 7
-------------------------------------------
Time: 1417443065000 ms
-------------------------------------------
message 8
答案 1 :(得分：2)
是的，您需要从DStream访问内容。
messages.foreachRDD(<<processing for the input received in the interval>>);