i'm here again, i trying to read data from kafka_0.9.0.0 topic using spark streaming_1.6.1 class written in scala -2.10.5. Its a simple program i built it in sbt_0.13.12. When i run the program i'm getting this exception
(run-main-0) org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1, localhost): java.lang.ClassCastException: [B cannot be cast to java.lang.String [error] at org.kafka.receiver.AvroCons$$anonfun$1.apply(AvroConsumer.scala:54) [error] at org.kafka.receiver.AvroCons$$anonfun$1.apply(AvroConsumer.scala:54) [error] at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) [error]
at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1597) [error] at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1157) [error] at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1157) [error] at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858) [error] at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858) [error] at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) [error] at org.apache.spark.scheduler.Task.run(Task.scala:89) [error] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) [error] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) [error] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) [error] at java.lang.Thread.run(Thread.java:745) [error] [error] Driver stacktrace: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1, localhost): java.lang.ClassCastException: [B cannot be cast to java.lang.String
Here is the scala program,
1 package org.kafka.receiver
2 case class mobileData(action: String, tenantid: Int, lat: Float, lon: Float, memberid: Int, event_name: String, productUpccd: Int, device_type: String, device_os_ver: Float, item_na me: String)
3 import java.util.HashMap
4 import org.apache.avro.SchemaBuilder
5 import org.apache.spark.SparkConf
6 import org.apache.spark.SparkContext
7 import org.apache.spark.serializer.KryoSerializer
8 import org.apache.spark.storage.StorageLevel
9 import org.apache.spark.streaming.Seconds
10 import org.apache.spark.streaming.StreamingContext
11 import org.apache.spark.streaming.StreamingContext._
12 import org.apache.spark.SparkContext._
13 import org.apache.spark.streaming.dstream.ReceiverInputDStream
14 import org.apache.spark.streaming.kafka.KafkaUtils
15 import kafka.serializer.DefaultDecoder
16 import org.apache.spark.sql.SQLContext
17 import com.sun.istack.internal.logging.Logger
18 object AvroCons {
19 val eventSchema = SchemaBuilder.record("eventRecord").fields
20 .name("action").`type`().stringType().noDefault()
21 .name("tenantid").`type`().intType().noDefault()
22 .name("lat").`type`().doubleType().noDefault()
23 .name("lon").`type`().doubleType().noDefault()
24 .name("memberid").`type`().intType().noDefault()
25 .name("event_name").`type`().stringType().noDefault()
26 .name("productUpccd").`type`().intType().noDefault()
27 .name("device_type").`type`().stringType().noDefault()
28 .name("device_os_ver").`type`().stringType().noDefault()
29 .name("item_name").`type`().stringType().noDefault().endRecord
30 def main(args: Array[String]): Unit = {
31
32 val sparkConf = new SparkConf().setAppName("Avro Consumer").
33 set("spark.driver.allowMultipleContexts", "true").setMaster("local[2]")
34 sparkConf.set("spark.cores.max", "2")
35 sparkConf.set("spark.serializer", classOf[KryoSerializer].getName)
36 sparkConf.set("spark.sql.tungsten.enabled", "true")
37 sparkConf.set("spark.eventLog.enabled", "true")
38 sparkConf.set("spark.app.id", "KafkaConsumer")
39 sparkConf.set("spark.io.compression.codec", "snappy")
40 sparkConf.set("spark.rdd.compress", "true")
41 sparkConf.set("spark.streaming.backpressure.enabled", "true")
42 sparkConf.set("spark.sql.avro.compression.codec", "snappy")
43 sparkConf.set("spark.sql.avro.mergeSchema", "true")
44 sparkConf.set("spark.sql.avro.binaryAsString", "true")
45 val sc = new SparkContext(sparkConf)
46 sc.hadoopConfiguration.set("avro.enable.summary-metadata", "false")
47 val ssc = new StreamingContext(sc, Seconds(2))
48 val kafkaConf = Map[String, String]("metadata.broker.list" -> "############:9092",
49 "zookeeper.connect" -> "#############",
50 "group.id" -> "KafkaConsumer",
51 "zookeeper.connection.timeout.ms" -> "1000000")
52 val topicMaps = Map("fishbowl" -> 1)
53 val messages = KafkaUtils.createStream[String, String,DefaultDecoder, DefaultDecoder](ssc, kafkaConf, topicMaps, StorageLevel.MEMORY_ONLY_SER)
54 messages.print()
55 val lines = messages.map(x=>x._2); lines.foreachRDD((rdd,time)=>{
56 val count = rdd.count()
57 if(count>0)
58 rdd.foreach(record=>{println(record)})})
59
60 ssc.start()
61 ssc.awaitTermination()
62 }
63
64 }
And here is my build.sbt
name := "AvroConsumer"
version := "1.0"
scalaVersion := "2.10.6"
jarName in assembly := "AvroConsumer.jar"
libraryDependencies += "org.apache.spark" % "spark-core_2.10" % "1.6.1" % "provided"
libraryDependencies += "org.apache.spark" % "spark-sql_2.10" % "1.6.1" % "provided"
libraryDependencies += "org.apache.spark" % "spark-streaming_2.10" % "1.6.1"
libraryDependencies += "org.apache.spark" % "spark-streaming-kafka-assembly_2.10" % "1.6.1"
libraryDependencies += "org.apache.spark" % "spark-streaming-kafka_2.10" % "1.6.1"
libraryDependencies += "org.codehaus.jackson" % "jackson-mapper-asl" % "1.9.13"
libraryDependencies += "org.openrdf.sesame" % "sesame-rio-api" % "2.7.2"
libraryDependencies += "com.databricks" % "spark-csv_2.10" % "0.1"
libraryDependencies += "org.apache.avro" % "avro" % "1.8.1"
libraryDependencies += "log4j" % "log4j" % "1.2.17"
libraryDependencies += "org.apache.avro" % "avro-tools" % "1.7.4"
assemblyMergeStrategy in assembly := { case PathList("META-INF", xs @
_*) => MergeStrategy.discard case x => MergeStrategy.first }
I'm preparing this code to create a DF from the kafka topic, so i had to set all those properties in sparkConf(). Here is the schema of my incoming data,
{
"action": "AppEvent",
"tenantid": 299,
"lat": 0.0,
"lon": 0.0,
"memberid": 16445,
"event_name": "CATEGORY_CLICK",
"productUpccd": 0,
"device_type": "iPhone",
"device_os_ver": "10.1",
"item_name": "CHICKEN"
}
And here is my kafka producer class.
public class KafkaAvroProducer {
/* case class
TopicData("action":"AppEvent","tenantid":1173,"lat":0.0,"lon":0.0,"memberid":55,
"event_name":"CATEGORY_CLICK",
"productUpccd":0,"device_type":"iPhone","device_os_ver":"10.1","item_name":"CHICKEN",*/
public static final String EVENT_SCHEMA = "{" + "\"type\":\"record\","
+ "\"name\":\"eventrecord\"," + "\"fields\":["
+ " { \"name\":\"action\", \"type\":\"string\" },"
+ " { \"name\":\"tenantid\", \"type\":\"int\" },"
+ " { \"name\":\"lat\", \"type\":\"double\" },"
+ " { \"name\":\"lon\", \"type\":\"double\" },"
+ " { \"name\":\"memberid\", \"type\":\"int\" },"
+ " { \"name\":\"event_name\", \"type\":\"string\" },"
+ " { \"name\":\"productUpccd\", \"type\":\"int\" },"
+ " { \"name\":\"device_type\", \"type\":\"string\" },"
+ " { \"name\":\"device_os_ver\", \"type\":\"string\" },"
+ "{ \"name\":\"item_name\", \"type\":\"string\" }" + "]}";
public static void main(String[] args) throws InterruptedException {
Properties props = new Properties();
props.put("bootstrap.servers", "##########:9092");
props.put("key.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
props.put("producer.type", "async");
Schema.Parser parser = new Schema.Parser();
Schema schema = parser.parse(EVENT_SCHEMA);
Injection<GenericRecord, String> avroRecords = GenericAvroCodecs.toJson(schema);
KafkaProducer<String, String> producer = new KafkaProducer<>(props);
for(int i = 0; i<300;i++){
GenericData.Record avroRecord = new GenericData.Record(schema);
setEventValues(i, avroRecord);
String messages = avroRecords.apply(avroRecord);
ProducerRecord<String, String> producerRecord = new ProducerRecord<String, String>("fishbowl",String.valueOf(i),messages);
System.out.println(producerRecord);
producer.send(producerRecord);
}
producer.close();
}
private static void setEventValues(int i, Record avroRecord) {
avroRecord.put("action", "AppEvent");
avroRecord.put("tenantid", i);
avroRecord.put("lat", i*0.0);
avroRecord.put("lon", 0.0);
avroRecord.put("memberid", i*55);
avroRecord.put("event_name", "CATEGORY_CLICK");
avroRecord.put("productUpccd", 0);
avroRecord.put("device_type", "iPhone");
avroRecord.put("device_os_ver", "10.1");
avroRecord.put("item_name", "CHICKEN");
}
}
Byte[]as aStringbut the Kafka deserializers look well configured as well as the producer. Try pruning kafka of all messages before testing. – maasgKafkaProducer<String, String> producerand consumer =KafkaUtils.createStream[String, String,DefaultDecoder, DefaultDecoder]which are matching correctly with each other. – maasg