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();
}
private static JavaStreamingContext createContext(String input, String checkpointDirectory) {
System.out.println("Creating new context");
// final File outputFile = new File("/flume_recover");
// if (outputFile.exists()) {
// outputFile.delete();
// }
SparkConf conf =
new SparkConf()
.setMaster("local[2]")
.setAppName("Stream File")
.set("spark.driver.allowMultipleContexts", "true");
conf.set("spark.serializer", KryoSerializer.class.getName());
conf.set("es.index.auto.create", "true");
conf.set("es.nodes", "10.26.1.134:9200");
conf.set("es.resource", "flume/test");
conf.set("es.input.json", "true");
JavaStreamingContext jssc = new JavaStreamingContext(conf, new Duration(3000));
jssc.checkpoint(checkpointDirectory);
JavaDStream<String> textFile = jssc.textFileStream(input);
JavaDStream<String> jsonStr =
textFile.map(
new Function<String, String>() {
public String call(String arg0) throws Exception {
Matcher m = log.matcher(arg0);
if (m.find()) {
return transferJson(m);
}
return "";
}
});
jsonStr.print();
jsonStr.foreach(
new Function<JavaRDD<String>, Void>() {
public Void call(JavaRDD<String> arg0) throws Exception {
if (!arg0.isEmpty() && arg0 != null) {
JavaEsSpark.saveToEs(arg0, "flume/test");
}
return null;
}
});
return jssc;
}
public static void create(final Configuration configuration) {
final SparkConf sparkConf = new SparkConf();
configuration
.getKeys()
.forEachRemaining(key -> sparkConf.set(key, configuration.getProperty(key).toString()));
sparkConf.setAppName("Apache TinkerPop's Spark-Gremlin");
CONTEXT = SparkContext.getOrCreate(sparkConf);
}
public SparkOperatorCreater(String appName) throws IOException {
super(appName);
properties = new Properties();
properties.load(
this.getClass().getClassLoader().getResourceAsStream("spark-cluster.properties"));
SparkConf conf = new SparkConf().setMaster(this.getMaster()).setAppName(appName);
conf.set("spark.streaming.ui.retainedBatches", "2000");
jssc = new JavaStreamingContext(conf, Durations.milliseconds(this.getDurationsMilliseconds()));
}
private static JavaSparkContext createSparkContext(SparkContextOptions contextOptions) {
if (contextOptions.getUsesProvidedSparkContext()) {
LOG.info("Using a provided Spark Context");
JavaSparkContext jsc = contextOptions.getProvidedSparkContext();
if (jsc == null || jsc.sc().isStopped()) {
LOG.error("The provided Spark context " + jsc + " was not created or was stopped");
throw new RuntimeException("The provided Spark context was not created or was stopped");
}
return jsc;
} else {
LOG.info("Creating a brand new Spark Context.");
SparkConf conf = new SparkConf();
if (!conf.contains("spark.master")) {
// set master if not set.
conf.setMaster(contextOptions.getSparkMaster());
}
conf.setAppName(contextOptions.getAppName());
// register immutable collections serializers because the SDK uses them.
conf.set("spark.kryo.registrator", BeamSparkRunnerRegistrator.class.getName());
conf.set("spark.serializer", KryoSerializer.class.getName());
return new JavaSparkContext(conf);
}
}
@Override
public int run(SparkConf conf, CommandLine cli) throws Exception {
long startMs = System.currentTimeMillis();
conf.set("spark.ui.enabled", "false");
JavaSparkContext jsc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(jsc);
long diffMs = (System.currentTimeMillis() - startMs);
System.out.println(">> took " + diffMs + " ms to create SQLContext");
Map<String, String> options = new HashMap<>();
options.put("zkhost", "localhost:9983");
options.put("collection", "ml20news");
options.put("query", "content_txt:[* TO *]");
options.put("fields", "content_txt");
DataFrame solrData = sqlContext.read().format("solr").options(options).load();
DataFrame sample = solrData.sample(false, 0.1d, 5150).select("content_txt");
List<Row> rows = sample.collectAsList();
System.out.println(">> loaded " + rows.size() + " docs to classify");
StructType schema = sample.schema();
CrossValidatorModel cvModel = CrossValidatorModel.load("ml-pipeline-model");
PipelineModel bestModel = (PipelineModel) cvModel.bestModel();
int r = 0;
startMs = System.currentTimeMillis();
for (Row next : rows) {
Row oneRow = RowFactory.create(next.getString(0));
DataFrame oneRowDF =
sqlContext.createDataFrame(Collections.<Row>singletonList(oneRow), schema);
DataFrame scored = bestModel.transform(oneRowDF);
Row scoredRow = scored.collect()[0];
String predictedLabel = scoredRow.getString(scoredRow.fieldIndex("predictedLabel"));
// an acutal app would save the predictedLabel
// System.out.println(">> for row["+r+"], model returned "+scoredRows.length+" rows,
// "+scoredRows[0]);
r++;
}
diffMs = (System.currentTimeMillis() - startMs);
System.out.println(">> took " + diffMs + " ms to score " + rows.size() + " docs");
return 0;
}
public static void main(String[] args) {
SparkConf conf =
new SparkConf()
.setMaster("local[1]")
.setAppName(RDDParallelizeSample.class.getSimpleName());
conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
JavaSparkContext sc = new JavaSparkContext(conf);
// create a List of Characters
List<Character> characterList = new ArrayList<Character>();
characterList.addAll(
Arrays.asList(
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q'));
// create an RDD from an internal List using parallelize method
JavaRDD<Character> characterRDD = sc.parallelize(characterList);
System.out.println("list size : " + characterList.size());
System.out.println("rdd size : " + characterRDD.count());
System.out.println("list content : " + characterList);
System.out.println("rdd content : " + characterRDD.collect());
}
public static void main(String args[]) {
SparkConf conf = new SparkConf().setAppName("esh-spark").setMaster("local[4]");
conf.set("es.index.auto.create", "true");
JavaSparkContext context = new JavaSparkContext(conf);
JavaRDD<String> textFile = context.textFile("hdfs://localhost:9000/ch07/crimes_dataset.csv");
JavaRDD<Crime> dataSplits =
textFile.map(
line -> {
CSVParser parser = CSVParser.parse(line, CSVFormat.RFC4180);
Crime c = new Crime();
CSVRecord record = parser.getRecords().get(0);
c.setId(record.get(0));
c.setCaseNumber(record.get(1));
c.setEventDate(record.get(2));
c.setBlock(record.get(3));
c.setIucr(record.get(4));
c.setPrimaryType(record.get(5));
c.setDescription(record.get(6));
c.setLocation(record.get(7));
c.setArrest(Boolean.parseBoolean(record.get(8)));
c.setDomestic(Boolean.parseBoolean(record.get(9)));
String lat = record.get(10);
String lon = record.get(11);
Map<String, Double> geoLocation = new HashMap<>();
geoLocation.put("lat", StringUtils.isEmpty(lat) ? null : Double.parseDouble(lat));
geoLocation.put("lon", StringUtils.isEmpty(lon) ? null : Double.parseDouble(lon));
c.setGeoLocation(geoLocation);
return c;
});
SQLContext sqlContext = new SQLContext(context);
DataFrame df = sqlContext.createDataFrame(dataSplits, Crime.class);
JavaEsSparkSQL.saveToEs(df, "esh_sparksql/crimes_reflection");
}
/** Main method for performing the random partition based model ensembler evaluation */
public static void main(String[] args) {
// Construction of Spark Configuration
SparkConf sContext = new SparkConf();
sContext.setMaster("local[4]");
sContext.setAppName("JavaLR");
sContext.set("spark.executor.memory", "4G");
// Creates the spark context
sc = new JavaSparkContext(sContext); // "local[4]", "JavaLR");
// Load train and test data
JavaRDD<String> trainingData =
readData("/Users/erangap/Documents/ML_Project/datasets/trainImputedNormalized.csv", "Id")
.sample(false, 0.1, 11L);
JavaRDD<String> testdata =
readData("/Users/erangap/Documents/ML_Project/datasets/testImputedNormalized.csv", "Id")
.sample(false, 0.1, 11L);
// trainingData.saveAsTextFile("/Users/erangap/Documents/ML_Project/datasets/reduced.csv");
JavaRDD<LabeledPoint> points = trainingData.map(new ParsePoint());
// points.persist(StorageLevel.MEMORY_AND_DISK());
// System.out.println(points.first().features());
JavaRDD<LabeledPoint> testPoints = testdata.map(new ParsePoint());
// testPoints.persist(StorageLevel.MEMORY_AND_DISK());
System.out.println("Total number of records -> " + points.count());
RandomPartitionedEnSembler ensembler = new RandomPartitionedEnSembler();
ensembler.setNoofModels(32);
ensembler.setThreshold(0.499999);
// Perform the training
DateFormat dateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss");
Date trainStartTime = Calendar.getInstance().getTime();
String trainStart = dateFormat.format(trainStartTime);
ensembler.train(points);
Date trainEndTime = Calendar.getInstance().getTime();
String trainEnd = dateFormat.format(trainEndTime);
// Training time calculations and console print
long trainElapsed = (trainEndTime.getTime() - trainStartTime.getTime()) / 1000;
System.out.println("Training Started at -> " + trainStart);
System.out.println("Training Ended at -> " + trainEnd);
System.out.println("Time Taken to Train -> " + trainElapsed + " Sec.");
// Prepare data for testing
JavaRDD<Double> testingLabels =
testPoints
.map(
new Function<LabeledPoint, Double>() {
private static final long serialVersionUID = -6597374940461185814L;
public Double call(LabeledPoint dataPoint) throws Exception {
return dataPoint.label();
}
})
.cache();
List<Double> classLabels = testingLabels.toArray();
// Perform the predictions
Date predictStartTime = Calendar.getInstance().getTime();
String predictStart = dateFormat.format(predictStartTime);
List<Double> predictedLabels = ensembler.voteAndPredit(testPoints).toArray();
Date predictEndTime = Calendar.getInstance().getTime();
String predictEnd = dateFormat.format(predictEndTime);
// Predict time calculations and console print
long preditElapsed = (predictEndTime.getTime() - predictStartTime.getTime()) / 1000;
System.out.println("Prediction Started at -> " + predictStart);
System.out.println("Prediction Ended at -> " + predictEnd);
System.out.println("Time Taken to Predit -> " + preditElapsed + " Sec.");
// Calculate and Display the accuracy
System.out.println("Testing accuracy (%): " + Metrics.accuracy(classLabels, predictedLabels));
BinaryClassificationMetrics binaryClassificationMetrics =
getBinaryClassificationMatrix(ensembler, testPoints);
System.out.println("Area under the curve -> " + binaryClassificationMetrics.areaUnderROC());
}
public static void main(String[] args) throws IOException {
Parameters param = new Parameters();
long initTime = System.currentTimeMillis();
SparkConf conf = new SparkConf().setAppName("StarJoin");
JavaSparkContext sc = new JavaSparkContext(conf);
if (param.useKryo) {
conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
conf.set("spark.kryo.registrator", MyBloomFilter.BloomFilterRegistrator.class.getName());
conf.set("spark.kryoserializer.buffer.mb", param.buffer);
}
MyBloomFilter.BloomFilter<String> BFS =
new MyBloomFilter.BloomFilter(1.0, param.bitsS, param.hashes);
MyBloomFilter.BloomFilter<String> BFD =
new MyBloomFilter.BloomFilter(1.0, param.bitsD, param.hashes);
MyBloomFilter.BloomFilter<String> BFC =
new MyBloomFilter.BloomFilter(1.0, param.bitsC, param.hashes);
JavaPairRDD<String, String> supps =
sc.textFile(param.suppPath)
.map(
new Function<String, String[]>() {
public String[] call(String line) {
return line.split("\\|");
}
})
.filter(
new Function<String[], Boolean>() {
public Boolean call(String[] s) {
return s[3].equals("UNITED KI1") | s[3].equals("UNITED KI5");
}
})
.mapToPair(
new PairFunction<String[], String, String>() {
public Tuple2<String, String> call(String[] s) {
return new Tuple2<String, String>(s[0], s[3]);
}
});
List<Tuple2<String, String>> s = supps.collect();
for (int i = 0; i < s.size(); i++) {
BFS.add(s.get(i)._1);
}
final Broadcast<MyBloomFilter.BloomFilter<String>> varS = sc.broadcast(BFS);
JavaPairRDD<String, String> custs =
sc.textFile(param.custPath)
.map(
new Function<String, String[]>() {
public String[] call(String line) {
return line.split("\\|");
}
})
.filter(
new Function<String[], Boolean>() {
public Boolean call(String[] s) {
return s[3].equals("UNITED KI1") | s[3].equals("UNITED KI5");
}
})
.mapToPair(
new PairFunction<String[], String, String>() {
public Tuple2<String, String> call(String[] s) {
return new Tuple2<String, String>(s[0], s[3]);
}
});
List<Tuple2<String, String>> c = custs.collect();
for (int i = 0; i < c.size(); i++) {
BFC.add(c.get(i)._1);
}
final Broadcast<MyBloomFilter.BloomFilter<String>> varC = sc.broadcast(BFC);
JavaPairRDD<String, String> dates =
sc.textFile(param.datePath)
.map(
new Function<String, String[]>() {
public String[] call(String line) {
return line.split("\\|");
}
})
.filter(
new Function<String[], Boolean>() {
public Boolean call(String[] s) {
return s[6].equals("Dec1997");
}
})
.mapToPair(
new PairFunction<String[], String, String>() {
public Tuple2<String, String> call(String[] s) {
return new Tuple2<String, String>(s[0], s[4]);
}
});
List<Tuple2<String, String>> d = dates.collect();
for (int i = 0; i < d.size(); i++) {
BFD.add(d.get(i)._1);
}
final Broadcast<MyBloomFilter.BloomFilter<String>> varD = sc.broadcast(BFD);
JavaPairRDD<String, String[]> lines =
sc.textFile(param.linePath)
.map(
new Function<String, String[]>() {
public String[] call(String line) {
return line.split("\\|");
}
})
.filter(
new Function<String[], Boolean>() {
public Boolean call(String[] s) {
return varC.value().contains(s[2].getBytes())
& varS.value().contains(s[4].getBytes())
& varD.value().contains(s[5].getBytes());
}
})
.mapToPair(
new PairFunction<String[], String, String[]>() {
public Tuple2<String, String[]> call(String[] s) {
String[] v = {s[2], s[5], s[12]};
return new Tuple2<String, String[]>(s[4], v);
}
});
JavaPairRDD<String, String[]> result =
lines
.join(supps)
.mapToPair(
new PairFunction<Tuple2<String, Tuple2<String[], String>>, String, String[]>() {
public Tuple2<String, String[]> call(Tuple2<String, Tuple2<String[], String>> s) {
String[] v = {s._2._1[1], s._2._1[2], s._2._2};
return new Tuple2<String, String[]>(s._2._1[0], v);
}
});
result =
result
.join(custs)
.mapToPair(
new PairFunction<Tuple2<String, Tuple2<String[], String>>, String, String[]>() {
public Tuple2<String, String[]> call(Tuple2<String, Tuple2<String[], String>> s) {
String[] v = {s._2._1[1], s._2._1[2], s._2._2};
return new Tuple2<String, String[]>(s._2._1[0], v);
}
});
JavaPairRDD<String, Long> final_result =
result
.join(dates)
.mapToPair(
new PairFunction<Tuple2<String, Tuple2<String[], String>>, String, Long>() {
public Tuple2<String, Long> call(Tuple2<String, Tuple2<String[], String>> s) {
return new Tuple2<String, Long>(
s._2._1[2] + "," + s._2._1[1] + "," + s._2._2, Long.parseLong(s._2._1[0]));
}
})
.reduceByKey(
new Function2<Long, Long, Long>() {
public Long call(Long i1, Long i2) {
return i1 + i2;
}
});
JavaPairRDD<String, String> sub_result =
final_result.mapToPair(
new PairFunction<Tuple2<String, Long>, String, String>() {
public Tuple2<String, String> call(Tuple2<String, Long> line) {
return new Tuple2(line._1 + "," + line._2.toString(), null);
}
});
final_result =
sub_result
.sortByKey(new Q3Comparator())
.mapToPair(
new PairFunction<Tuple2<String, String>, String, Long>() {
public Tuple2<String, Long> call(Tuple2<String, String> line) {
String[] s = line._1.split(",");
return new Tuple2<String, Long>(
s[0] + "," + s[1] + "," + s[2], Long.parseLong(s[3]));
}
});
Configuration HDFSconf = new Configuration();
FileSystem fs = FileSystem.get(HDFSconf);
fs.delete(new Path(param.output), true);
final_result.saveAsTextFile(param.output);
long finalTime = System.currentTimeMillis();
System.out.print("Tempo total(ms): ");
System.out.println(finalTime - initTime);
sc.close();
}
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();
}
