@Override
public SparkWorkloadOperator<WithTime<String>> stringStreamFromKafkaWithTime(
String zkConStr,
String kafkaServers,
String group,
String topics,
String offset,
String componentId,
int parallelism) {
HashSet<String> topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
HashMap<String, String> kafkaParams = new HashMap<>();
kafkaParams.put("metadata.broker.list", kafkaServers);
kafkaParams.put("auto.offset.reset", offset);
kafkaParams.put("zookeeper.connect", zkConStr);
kafkaParams.put("group.id", group);
// Create direct kafka stream with brokers and topics
JavaPairInputDStream<String, String> messages =
KafkaUtils.createDirectStream(
jssc,
String.class,
String.class,
StringDecoder.class,
StringDecoder.class,
kafkaParams,
topicsSet);
JavaDStream<WithTime<String>> lines = messages.map(mapFunctionWithTime);
return new SparkWorkloadOperator<>(lines, parallelism);
}
public static void main(String[] args) {
if (args.length < 4) {
System.err.println("Usage: PDCKafkaConsumer <zkQuorum> <group> <topics> <numThreads>");
System.exit(1);
}
String zkQuorum = args[0];
String kfGrp = args[1];
String[] topics = args[2].split(",");
int numThreads = Integer.valueOf(args[3]);
Map<String, Integer> topicMap = new HashMap<String, Integer>();
for (String topic : topics) {
topicMap.put(topic, numThreads);
}
SparkConf conf = new SparkConf().setAppName("PDCKafkaConsumer");
conf.set("spark.ui.port", "4040");
JavaStreamingContext ctx = new JavaStreamingContext(conf, new Duration(10000));
JavaPairReceiverInputDStream<String, String> kfStream =
KafkaUtils.createStream(ctx, zkQuorum, kfGrp, topicMap);
kfStream.saveAsHadoopFiles(
"/phasor/pmu/pdc", "in", Text.class, Text.class, TextOutputFormat.class);
ctx.start();
ctx.awaitTermination();
}
public static void main(String[] args) {
// Create a Spark Context.
SparkConf conf = new SparkConf().setAppName("Activity").set("spark.eventLog.enabled", "true");
;
JavaSparkContext sc = new JavaSparkContext(conf);
JavaStreamingContext jssc = new JavaStreamingContext(sc, STREAM_INTERVAL);
String TOPIC = "activityevent";
String zkQuorum = "localhost:2181";
String group = "1";
Map<String, Integer> topicMap = new HashMap<String, Integer>();
topicMap.put(TOPIC, 1);
JavaPairReceiverInputDStream<String, String> messages =
KafkaUtils.createStream(jssc, zkQuorum, group, topicMap);
// messages.print();
JavaDStream<String> activitydatastream =
messages.map(
new Function<Tuple2<String, String>, String>() {
@Override
public String call(Tuple2<String, String> tuple2) {
return tuple2._2();
}
});
final Long teamWindowDurationMs = Durations.minutes(1).milliseconds();
JavaDStream<Activity> ActivityEntryDStream = activitydatastream.map(Activity::parseFromLine);
JavaPairDStream<WithTimestamp<String>, Double> ActivityWindowDStream =
ActivityEntryDStream.mapToPair(
windows ->
new Tuple2<>(
WithTimestamp.create(
windows.getActivity(),
// Apply Fixed Window by rounding the timestamp down to the nearest
// multiple of the window size
(convertMillsecs(windows.getTimestamp()) / teamWindowDurationMs)
* teamWindowDurationMs),
windows.getXaxis()))
.reduceByKey(SUM_REDUCER);
ActivityWindowDStream.print();
jssc.start();
jssc.awaitTermination();
// jssc.close();
sc.stop();
sc.close();
}
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("kafka-sandbox").setMaster("local[*]");
JavaSparkContext sc = new JavaSparkContext(conf);
JavaStreamingContext ssc = new JavaStreamingContext(sc, new Duration(2000));
Set<String> topics = Collections.singleton("mytopic");
Map<String, String> kafkaParams = new HashMap<>();
kafkaParams.put("metadata.broker.list", "sandbox.hortonworks.com:6667");
JavaPairInputDStream<String, byte[]> directKafkaStream =
KafkaUtils.createDirectStream(
ssc,
String.class,
byte[].class,
StringDecoder.class,
DefaultDecoder.class,
kafkaParams,
topics);
directKafkaStream.foreachRDD(
rdd -> {
rdd.foreach(
avroRecord -> {
Schema.Parser parser = new Schema.Parser();
Schema schema = parser.parse(AvroVulabProducer.USER_SCHEMA);
Injection<GenericRecord, byte[]> recordInjection =
GenericAvroCodecs.toBinary(schema);
GenericRecord record = recordInjection.invert(avroRecord._2).get();
System.out.println(
"str1= "
+ record.get("str1")
+ ", str2= "
+ record.get("str2")
+ ", int1="
+ record.get("int1"));
});
});
ssc.start();
ssc.awaitTermination();
}
public void run() {
System.setProperty("spark.hadoop.dfs.replication", "2");
Logger.getLogger("org").setLevel(Level.OFF);
Logger.getLogger("akka").setLevel(Level.OFF);
SparkConf conf = new SparkConf().setAppName("WindowingKafkaWordCountWithFaultTolerance");
conf.set("spark.master", PropertiesStack.getProperty("spark.master"));
conf.set("spark.executor.memory", PropertiesStack.getProperty("spark.executor.memory"));
conf.set("spark.driver.memory", PropertiesStack.getProperty("spark.driver.memory"));
conf.set(
"spark.driver.maxResultSize", PropertiesStack.getProperty("spark.driver.maxResultSize"));
// .setAppName("WindowingKafkaWordCountWithoutFaultTolerance");
JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(10));
HashSet<String> topicsSet = new HashSet<String>(Arrays.asList(PropertiesStack.getKafkaTopic()));
HashMap<String, String> kafkaParams = new HashMap<String, String>();
kafkaParams.put("metadata.broker.list", PropertiesStack.getKafkaBootstrapServers());
kafkaParams.put("zookeeper.connect", PropertiesStack.getZookeeperConnect());
kafkaParams.put("auto.offset.reset", "smallest");
kafkaParams.put("group.id", PropertiesStack.getKafkaGroupId());
kafkaParams.put("auto.commit.enable", "false");
Map<String, Integer> topicMap = new HashMap<String, Integer>();
topicMap.put(PropertiesStack.getKafkaTopic(), 1);
// Map<kafka.common.TopicAndPartition, java.lang.Long> fromOffsets = new HashMap<>();
// fromOffsets.put(new TopicAndPartition(PropertiesStack.getKafkaTopic(),
// 1), 1000L);
// Create direct kafka stream with brokers and topics
// JavaInputDStream<String> messages = KafkaUtils
// .createDirectStream(
// jssc,
// String.class,
// String.class,
// StringDecoder.class,
// StringDecoder.class,
// String.class,
// kafkaParams,
// fromOffsets,
// new Function<kafka.message.MessageAndMetadata<String, String>, String>() {
// @Override
// public String call(
// MessageAndMetadata<String, String> v1)
// throws Exception {
// return v1.message();
// }
// });
JavaPairInputDStream<String, String> messages =
KafkaUtils.createDirectStream(
jssc,
String.class,
String.class,
StringDecoder.class,
StringDecoder.class,
kafkaParams,
topicsSet);
messages.count().print();
// .createStream(jssc, PropertiesStack.getZookeeperConnect(),
// PropertiesStack.getKafkaGroupId(), topicMap);
// Start the computation
jssc.start();
jssc.awaitTermination();
}
public static void main(String[] args) throws Exception {
if (args.length < 2) {
System.err.println(
"Usage: JavaDirectKafkaWordCount <brokers> <topics>\n"
+ " <brokers> is a list of one or more Kafka brokers\n"
+ " <topics> is a list of one or more kafka topics to consume from\n\n");
System.exit(1);
}
String brokers = args[0];
String topics = args[1];
// Create context with a 2 seconds batch interval
SparkConf sparkConf = new SparkConf().setAppName("JavaDirectKafkaWordCount");
JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(2));
Set<String> topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
Map<String, String> kafkaParams = new HashMap<>();
kafkaParams.put("metadata.broker.list", brokers);
// Create direct kafka stream with brokers and topics
JavaPairInputDStream<String, String> messages =
KafkaUtils.createDirectStream(
jssc,
String.class,
String.class,
StringDecoder.class,
StringDecoder.class,
kafkaParams,
topicsSet);
// Get the lines, split them into words, count the words and print
JavaDStream<String> lines =
messages.map(
new Function<Tuple2<String, String>, String>() {
@Override
public String call(Tuple2<String, String> tuple2) {
return tuple2._2();
}
});
JavaDStream<String> words =
lines.flatMap(
new FlatMapFunction<String, String>() {
@Override
public Iterable<String> call(String x) {
return Arrays.asList(SPACE.split(x));
}
});
JavaPairDStream<String, Integer> wordCounts =
words
.mapToPair(
new PairFunction<String, String, Integer>() {
@Override
public Tuple2<String, Integer> call(String s) {
return new Tuple2<>(s, 1);
}
})
.reduceByKey(
new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer i1, Integer i2) {
return i1 + i2;
}
});
wordCounts.print();
// Start the computation
jssc.start();
jssc.awaitTermination();
}
public static void main(String[] args) {
if (args.length < 4) {
System.err.println("Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>");
System.exit(1);
}
StreamingExamples.setStreamingLogLevels();
// SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount");
// sparkConf.setMaster("spark://60f81dc6426c:7077");
// SparkConf sparkConf = new
// SparkConf().setAppName("JavaKafkaWordCount").setMaster("spark://60f81dc6426c:7077");
// Create the context with a 1 second batch size
JavaStreamingContext jssc =
new JavaStreamingContext("local[4]", "JavaKafkaWordCount", new Duration(2000));
int numThreads = Integer.parseInt(args[3]);
Logger.getLogger("org").setLevel(Level.OFF);
Logger.getLogger("akka").setLevel(Level.OFF);
Map<String, Integer> topicMap = new HashMap<String, Integer>();
String[] topics = args[2].split(",");
for (String topic : topics) {
topicMap.put(topic, numThreads);
}
/* for(String t: topic)
{
topicMap.put(t, new Integer(3));
}*/
// NotSerializable notSerializable = new NotSerializable();
// JavaRDD<String> rdd = sc.textFile("/tmp/myfile");
// rdd.map(s -> notSerializable.doSomething(s)).collect();
JavaPairReceiverInputDStream<String, String> messages =
KafkaUtils.createStream(jssc, args[0], args[1], topicMap);
// JavaPairReceiverInputDStream<String, String> kafkaStream =
// KafkaUtils.createStream(jssc, "localhost:2181","streamingContext",
// topicMap);
System.out.println("Connection !!!!");
/*JavaDStream<String> data = messages.map(new Function<Tuple2<String, String>, String>()
{
public String call(Tuple2<String, String> message)
{
return message._2();
}
}
);*/
JavaDStream<String> lines =
messages.map(
new Function<Tuple2<String, String>, String>() {
@Override
public String call(Tuple2<String, String> tuple2) {
return tuple2._2();
}
});
JavaDStream<String> words =
lines.flatMap(
new FlatMapFunction<String, String>() {
@Override
public Iterable<String> call(String x) {
return Lists.newArrayList(SPACE.split(x));
}
});
JavaPairDStream<String, Integer> wordCounts =
words
.mapToPair(
new PairFunction<String, String, Integer>() {
@Override
public Tuple2<String, Integer> call(String s) {
return new Tuple2<String, Integer>(s, 1);
}
})
.reduceByKey(
new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer i1, Integer i2) {
return i1 + i2;
}
});
wordCounts.print();
jssc.start();
jssc.awaitTermination();
}
