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Use mapPartitionsWithIndex for DStream - Spark Streaming

I want to do something very simple: to check what is the content of each partition in the first RDD of my DStream.
This is what I'm doing now:
SparkConf sparkConfiguration= new SparkConf().setAppName("DataAnalysis").setMaster("local[*]");
JavaStreamingContext sparkStrContext=new JavaStreamingContext(sparkConfiguration, Durations.seconds(1));
JavaReceiverInputDStream<String> receiveParkingData=sparkStrContext.socketTextStream("localhost",5554);
Time time=new Time(1000);
JavaRDD<String>dataRDD= receiveParkingData.compute(time);
//I get an error in this RDD
JavaRDD<String>indexDataRDD=dataRDD.mapPartitionsWithIndex(new Function2<Integer, Iterator<String>, Iterator<String>>() {
#Override
public Iterator<String> call(Integer integer, Iterator<String> stringIterator) throws Exception {
return null;
}
});
indexDataRDD.collect();
So I want to print the content of each partition and its ID. However, on the indexDataRDD I get this message in my IntelliJ IDE: mapPartitionsWithIndex (Function2<Integer, Iterator<String>, Iterator<String>>, boolean) in AbstractJavaRDDLike cannot be applied to (Function2<Integer, Iterator<String>, Iterator<String>>)
Can someone help me with this issue? Is there another, easier way to get the content in each partition? I really want to know the specific content of each partition.
Thank you so much.

Here is sample program for mapPartitionsWithIndex for your reference.
public class SparkDemo {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("SparkDemo").setMaster("local");
JavaSparkContext sc = new JavaSparkContext(conf);
List<String> data = Arrays.asList("one","two","three","four","five");
JavaRDD<String> javaRDD = sc.parallelize(data, 2);
JavaRDD<String> mapPartitionsWithIndexRDD = javaRDD
.mapPartitionsWithIndex(new Function2<Integer, Iterator<String>, Iterator<String>>() {
#Override
public Iterator<String> call(Integer index, Iterator<String> iterator) throws Exception {
LinkedList<String> linkedList = new LinkedList<String>();
while (iterator.hasNext()){
linkedList.add(Integer.toString(index) + "-" + iterator.next());
}
return linkedList.iterator();
}
}, false);
System.out.println("mapPartitionsWithIndexRDD " + mapPartitionsWithIndexRDD.collect());
sc.stop();
sc.close();
}
}

Getting Hadoop OutputFormat RunTimeException while running Apache Spark Kafka Stream

I am running a program which uses Apache Spark to get get data from Apache Kafka cluster and puts the data in a Hadoop file. My program is below:
public final class SparkKafkaConsumer {
public static void main(String[] args) {
SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount");
JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
Map<String, Integer> topicMap = new HashMap<String, Integer>();
String[] topics = "Topic1, Topic2, Topic3".split(",");
for (String topic: topics) {
topicMap.put(topic, 3);
}
JavaPairReceiverInputDStream<String, String> messages =
KafkaUtils.createStream(jssc, "kafka.test.com:2181", "NameConsumer", topicMap);
JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
public String call(Tuple2<String, String> tuple2) {
return tuple2._2();
}
});
JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
public Iterable<String> call(String x) {
return Lists.newArrayList(",".split(x));
}
});
JavaPairDStream<String, Integer> wordCounts = words.mapToPair(
new PairFunction<String, String, Integer>() {
public Tuple2<String, Integer> call(String s) {
return new Tuple2<String, Integer>(s, 1);
}
}).reduceByKey(new Function2<Integer, Integer, Integer>() {
public Integer call(Integer i1, Integer i2) {
return i1 + i2;
}
});
wordCounts.print();
wordCounts.saveAsHadoopFiles("hdfs://localhost:8020/user/spark/stream/", "txt");
jssc.start();
jssc.awaitTermination();
}
}
I am using the this command to submit the application: C:\spark-1.6.2-bin-hadoop2.6\bin\spark-submit --packages org.apache.spark:spark-streaming-kafka_2.10:1.6.2 --class "SparkKafkaConsumer" --master local[4] target\simple-project-1.0.jar
I am getting this error: java.lang.RuntimeException: class scala.runtime.Nothing$ not org.apache.hadoop.mapred.OutputFormat at org.apache.hadoop.conf.Configuration.setClass(Configuration.java:2148)
What is causing this error and how do I solve it?

I agree that the error is not really evocative, but it is usually better to specify the format of the data you want to output in any of the saveAsHadoopFile methods to protect yourself from this type of exception.
Here's the prototype of your particular method in the documentation :
saveAsHadoopFiles(java.lang.String prefix, java.lang.String suffix, java.lang.Class<?> keyClass, java.lang.Class<?> valueClass, java.lang.Class<F> outputFormatClass)
In your example, that would correspond to :
wordCounts.saveAsHadoopFiles("hdfs://localhost:8020/user/spark/stream/", "txt", Text.class, IntWritable.class, TextOutputFormat.class)
Based on the format of your wordCounts PairDStream, I chose Text as the key is of type String, and IntWritable as the value associated to the key is of type Integer.
Use TextOutputFormat if you just want basic plain text files, but you can look into the subclasses of FileOutputFormat for more output options.
As this was also asked, the Text class comes from the org.apache.hadoop.io package and the TextOutputFormat comes from the org.apache.hadoop.mapred package.

Just for completeness (#Jonathan gave the right answer )
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.TextOutputFormat;
...
wordCounts.saveAsHadoopFiles("hdfs://localhost:8020/user/spark/stream/", "txt", Text.class, IntWritable.class, TextOutputFormat.class)

Getting error when invoking elasticSearch from spark

I have a use case, where I need to read messages from kafka and for each message, extract data and invoke elasticsearch Index. The response will be further used to do further processing.
I am getting below error when invoking JavaEsSpark.esJsonRDD
java.lang.ClassCastException: org.elasticsearch.spark.rdd.EsPartition incompatible with org.apache.spark.rdd.ParallelCollectionPartition
at org.apache.spark.rdd.ParallelCollectionRDD.compute(ParallelCollectionRDD.scala:102)
My code snippet is below
public static void main(String[] args) {
if (args.length < 4) {
System.err.println("Usage: JavaKafkaIntegration <zkQuorum> <group> <topics> <numThreads>");
System.exit(1);
}
SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaIntegration").setMaster("local[2]").set("spark.driver.allowMultipleContexts", "true");
//Setting when using JavaEsSpark.esJsonRDD
sparkConf.set("es.nodes",<NODE URL>);
sparkConf.set("es.nodes.wan.only","true");
context = new JavaSparkContext(sparkConf);
// Create the context with 2 seconds batch size
JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
int numThreads = Integer.parseInt(args[3]);
Map<String, Integer> topicMap = new HashMap<>();
String[] topics = args[2].split(",");
for (String topic: topics) {
topicMap.put(topic, numThreads);
}
//Receive Message From kafka
JavaPairReceiverInputDStream<String, String> messages =
KafkaUtils.createStream(jssc,args[0], args[1], topicMap);
JavaDStream<String> jsons = messages
.map(new Function<Tuple2<String, String>, String>() {
/**
*
*/
private static final long serialVersionUID = 1L;
#Override
public String call(Tuple2<String, String> tuple2){
JavaRDD<String> esRDD = JavaEsSpark.esJsonRDD(context, <index>,<search string> ).values() ;
return null;
}
});
jsons.print();
jssc.start();
jssc.awaitTermination();
}
I am getting error when invoking JavaEsSpark.esJsonRDD. Is it correct way to do it? How do I successfully invoke ES from spark?
I am running kafka and spark on windows and invoking external elastic search index.

Spark: Two SparkContexts in a single Application Best Practice

I think I have an interesting question for all of you today. In the code below you will notice I have two SparkContexts one for SparkStreaming and the other one which is a normal SparkContext. According to best practices you should only have one SparkContext in a Spark application even though its possible to circumvent this via allowMultipleContexts in the configuration.
Problem is, I need to retrieve data from hive and from a Kafka topic to do some logic, and whenever I submit my application it obviously returns "Cannot have 2 Spark Contexts Running on JVM".
My question is, is there a correct way to do this than how I am doing it right now?
public class MainApp {
private final String logFile= Properties.getString("SparkLogFileDir");
private static final String KAFKA_GROUPID = Properties.getString("KafkaGroupId");
private static final String ZOOKEEPER_URL = Properties.getString("ZookeeperURL");
private static final String KAFKA_BROKER = Properties.getString("KafkaBroker");
private static final String KAFKA_TOPIC = Properties.getString("KafkaTopic");
private static final String Database = Properties.getString("HiveDatabase");
private static final Integer KAFKA_PARA = Properties.getInt("KafkaParrallel");
public static void main(String[] args){
//set settings
String sql="";
//START APP
System.out.println("Starting NPI_TWITTERAPP...." + new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
System.out.println("Configuring Settings...."+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
SparkConf conf = new SparkConf()
.setAppName(Properties.getString("SparkAppName"))
.setMaster(Properties.getString("SparkMasterUrl"));
//Set Spark/hive/sql Context
JavaSparkContext sc = new JavaSparkContext(conf);
JavaStreamingContext jssc = new JavaStreamingContext(conf, new Duration(5000));
JavaHiveContext HiveSqlContext = new JavaHiveContext(sc);
//Check if Twitter Hive Table Exists
try {
HiveSqlContext.sql("DROP TABLE IF EXISTS "+Database+"TWITTERSTORE");
HiveSqlContext.sql("CREATE TABLE IF NOT EXISTS "+Database+".TWITTERSTORE "
+" (created_at String, id String, id_str String, text String, source String, truncated String, in_reply_to_user_id String, processed_at String, lon String, lat String)"
+" STORED AS TEXTFILE");
}catch(Exception e){
System.out.println(e);
}
//Check if Ivapp Table Exists
sql ="CREATE TABLE IF NOT EXISTS "+Database+".IVAPPGEO AS SELECT DISTINCT a.LATITUDE, a.LONGITUDE, b.ODNCIRCUIT_OLT_CLLI, b.ODNCIRCUIT_OLT_TID, a.CITY, a.STATE, a.ZIP FROM "
+Database+".T_PONNMS_SERVICE B, "
+Database+".CLLI_LATLON_MSTR A WHERE a.BID_CLLI = substr(b.ODNCIRCUIT_OLT_CLLI,0,8)";
try {
System.out.println(sql + new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
HiveSqlContext.sql(sql);
sql = "SELECT LATITUDE, LONGITUDE, ODNCIRCUIT_OLT_CLLI, ODNCIRCUIT_OLT_TID, CITY, STATE, ZIP FROM "+Database+".IVAPPGEO";
JavaSchemaRDD RDD_IVAPPGEO = HiveSqlContext.sql(sql).cache();
}catch(Exception e){
System.out.println(sql + new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
}
//JavaHiveContext hc = new JavaHiveContext();
System.out.println("Retrieve Data from Kafka Topic: "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
Map<String, Integer> topicMap = new HashMap<String, Integer>();
topicMap.put(KAFKA_TOPIC,KAFKA_PARA);
JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(
jssc, KAFKA_GROUPID, ZOOKEEPER_URL, topicMap);
JavaDStream<String> json = messages.map(
new Function<Tuple2<String, String>, String>() {
private static final long serialVersionUID = 42l;
#Override
public String call(Tuple2<String, String> message) {
return message._2();
}
}
);
System.out.println("Completed Kafka Messages... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
System.out.println("Filtering Resultset... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
JavaPairDStream<Long, String> tweets = json.mapToPair(
new TwitterFilterFunction());
JavaPairDStream<Long, String> filtered = tweets.filter(
new Function<Tuple2<Long, String>, Boolean>() {
private static final long serialVersionUID = 42l;
#Override
public Boolean call(Tuple2<Long, String> tweet) {
return tweet != null;
}
}
);
JavaDStream<Tuple2<Long, String>> tweetsFiltered = filtered.map(
new TextFilterFunction());
tweetsFiltered = tweetsFiltered.map(
new StemmingFunction());
System.out.println("Finished Filtering Resultset... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
System.out.println("Processing Sentiment Data... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
//calculate postive tweets
JavaPairDStream<Tuple2<Long, String>, Float> positiveTweets =
tweetsFiltered.mapToPair(new PositiveScoreFunction());
//calculate negative tweets
JavaPairDStream<Tuple2<Long, String>, Float> negativeTweets =
tweetsFiltered.mapToPair(new NegativeScoreFunction());
JavaPairDStream<Tuple2<Long, String>, Tuple2<Float, Float>> joined =
positiveTweets.join(negativeTweets);
//Score tweets
JavaDStream<Tuple4<Long, String, Float, Float>> scoredTweets =
joined.map(new Function<Tuple2<Tuple2<Long, String>,
Tuple2<Float, Float>>,
Tuple4<Long, String, Float, Float>>() {
private static final long serialVersionUID = 42l;
#Override
public Tuple4<Long, String, Float, Float> call(
Tuple2<Tuple2<Long, String>, Tuple2<Float, Float>> tweet)
{
return new Tuple4<Long, String, Float, Float>(
tweet._1()._1(),
tweet._1()._2(),
tweet._2()._1(),
tweet._2()._2());
}
});
System.out.println("Finished Processing Sentiment Data... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
System.out.println("Outputting Tweets Data to flat file "+Properties.getString("HdfsOutput")+" ... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
JavaDStream<Tuple5<Long, String, Float, Float, String>> result =
scoredTweets.map(new ScoreTweetsFunction());
result.foreachRDD(new FileWriter());
System.out.println("Outputting Sentiment Data to Hive... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
jssc.start();
jssc.awaitTermination();
}
}

Creating SparkContext
You can create a SparkContext instance with or without creating a SparkConf object first.
Getting Existing or Creating New SparkContext (getOrCreate methods)
getOrCreate(): SparkContext
getOrCreate(conf: SparkConf): SparkContext
SparkContext.getOrCreate methods allow you to get the existing SparkContext or create a new one.
import org.apache.spark.SparkContext
val sc = SparkContext.getOrCreate()
// Using an explicit SparkConf object
import org.apache.spark.SparkConf
val conf = new SparkConf()
.setMaster("local[*]")
.setAppName("SparkMe App")
val sc = SparkContext.getOrCreate(conf)
Refer Here - https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-sparkcontext.html

Apparently if I use sc.close() to close the original SparkContext before executing JavaStreaming Context it works perfectly, no errors or issues.

you can use a singleton object ContextManager which would handle which context to provide.
public class ContextManager {
private static JavaSparkContext context;
private static String currentType;
private ContextManager() {}
public static JavaSparkContext getContext(String type) {
if(type == currentType && context != null) {
return context;
}
else if (type == "streaming"){
.. clean up the current context ..
.. initialize the context to streaming context ..
currentType = type;
}
else {
..clean up the current context..
... initialize the context to normal context ..
currentType = type;
}
return context;
}
}
There are some issues like in projects where you switch context quite rapidly the overhead would be quite large.

You can access the SparkContext from your JavaStreamingSparkContext, and use that reference when creating additional contexts.
SparkConf sparkConfig = new SparkConf().setAppName("foo");
JavaStreamingContext jssc = new JavaStreamingContext(sparkConfig, Duration.seconds(30));
SqlContext sqlContext = new SqlContext(jssc.sparkContext());

Kafka ->Spark streaming -> Hbase. Task not serializable Error Caused by: java.lang.IllegalStateException: Job in state DEFINE instead of RUNNING

I am trying to write data that is produced from Kafka commandline producer for some topic.
I am facing problem and unable to proceed. Below is my code which I am creating it as a jar and running through spark-submit on spark-shell.
Am I doing wrong inside foreachRDD() ? What is wrong with SparkKafkaDemo$2.call(SparkKafkaDemo.java:63) line in below error message?
SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaDemo").setMaster("local").setSparkHome("/Users/kvk/softwares/spark-1.3.1-bin-hadoop2.4");
JavaStreamingContext jsc = new JavaStreamingContext(sparkConf, Duration.seconds(1));
int numThreads = 2;
Map<String, Integer> topicMap = new HashMap<String, Integer>();
topicMap.put("nonview", numThreads);
JavaPairReceiverInputDStream<String, String> messages =
KafkaUtils.createStream(jsc, "localhost", "ViewConsumer", topicMap);
JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
#Override
public String call(Tuple2<String, String> tuple2) {
return tuple2._2();
}
});
lines.foreachRDD(new Function<JavaRDD<String>, Void>() {
#Override
public Void call(JavaRDD<String> stringJavaRDD) throws Exception {
JavaPairRDD<ImmutableBytesWritable, Put> hbasePuts = stringJavaRDD.mapToPair(
new PairFunction<String, ImmutableBytesWritable, Put>() {
#Override
public Tuple2<ImmutableBytesWritable, Put> call(String line) throws Exception {
Put put = new Put(Bytes.toBytes("Rowkey" + Math.random()));
put.addColumn(Bytes.toBytes("firstFamily"), Bytes.toBytes("firstColumn"), Bytes.toBytes(line+"fc"));
return new Tuple2<ImmutableBytesWritable, Put>(new ImmutableBytesWritable(), put);
}
});
// save to HBase- Spark built-in API method
hbasePuts.saveAsNewAPIHadoopDataset(newAPIJobConfiguration1.getConfiguration());
return null;
}
}
);
jsc.start();
jsc.awaitTermination();
Error :
./bin/spark-submit --class "SparkKafkaDemo" --master local /Users/kvk/IntelliJWorkspace/HbaseDemo/HbaseDemo.jar
Exception in thread "main" org.apache.spark.SparkException: Task not serializable
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
at org.apache.spark.SparkContext.clean(SparkContext.scala:1623)
at org.apache.spark.rdd.RDD.map(RDD.scala:286)
at org.apache.spark.api.java.JavaRDDLike$class.mapToPair(JavaRDDLike.scala:113)
at org.apache.spark.api.java.AbstractJavaRDDLike.mapToPair(JavaRDDLike.scala:46)
at SparkKafkaDemo$2.call(SparkKafkaDemo.java:63)
at SparkKafkaDemo$2.call(SparkKafkaDemo.java:60)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:311)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:311)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1.apply(DStream.scala:534)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1.apply(DStream.scala:534)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:42)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:32)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:176)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:176)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:176)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:175)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.IllegalStateException: Job in state DEFINE instead of RUNNING
at org.apache.hadoop.mapreduce.Job.ensureState(Job.java:283)
at org.apache.hadoop.mapreduce.Job.toString(Job.java:452)
at java.lang.String.valueOf(String.java:2847)
at java.lang.StringBuilder.append(StringBuilder.java:128)
at scala.StringContext.standardInterpolator(StringContext.scala:122)
at scala.StringContext.s(StringContext.scala:90)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:103)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visitSerializable(SerializationDebugger.scala:158)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:99)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visitSerializable(SerializationDebugger.scala:158)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:99)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visitSerializable(SerializationDebugger.scala:158)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:99)
at org.apache.spark.serializer.SerializationDebugger$.find(SerializationDebugger.scala:58)
at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:39)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:80)
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:164)
... 24 more

Please add a serialization
sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");