Data streams processing with STORM Mariusz Gil 

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data expire fast. very fast 

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realtime processing? 

Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. 

Storm is fast, a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. 

concept architecture 

Stream (val1, val2) (val3, val4) (val5, val6) unbounded sequence of tuples tuple tuple tuple tuple tuple tuple tuple 

Spouts source of streams tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple 

Reliable and unreliable Spouts replay or forget about touple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple 

Spouts source of streams tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple Storm-Kafka 

Spouts source of streams tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple Storm-Kestrel 

Spouts source of streams tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple Storm-AMQP-Spout 

Spouts source of streams tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple Storm-JMS 

Spouts source of streams tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple Storm-PubSub* 

Spouts source of streams tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple Storm-Beanstalkd-Spout 

Bolts process input streams and produce new streams tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple 

Bolts process input streams and produce new streams tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple tuple 

Topologies network of spouts and bolts TextSpout SplitSentenceBolt WordCountBolt [sentence] [word] [word, count] 

Topologies network of spouts and bolts TextSpout SplitSentenceBolt WordCountBolt [sentence] [word] [word, count] TextSpout SplitSentenceBolt [sentence] xyzBolt 

servers architecture 

Nimbus process responsible for distributing processing across the cluster 

Supervisors worker process responsible for executing subset of topology 

zookeepers coordination layer between Nimbus and Supervisors 

fast CLUSTER STATE IS STORED LOCALLY OR IN ZOOKEEPERS fail 

sample code 

Spouts public class RandomSentenceSpout extends BaseRichSpout { SpoutOutputCollector _collector; Random _rand; @Override public void open(Map conf, TopologyContext context, SpoutOutputCollector collector) { _collector = collector; _rand = new Random(); } @Override public void nextTuple() { Utils.sleep(100); String[] sentences = new String[] { "the cow jumped over the moon", "an apple a day keeps the doctor away", "four score and seven years ago", "snow white and the seven dwarfs", "i am at two with nature"}; String sentence = sentences[_rand.nextInt(sentences.length)]; _collector.emit(new Values(sentence)); } @Override public void ack(Object id) { } @Override public void fail(Object id) { } @Override public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("word")); } } 

Bolts public static class WordCount extends BaseBasicBolt { Map counts = new HashMap(); @Override public void execute(Tuple tuple, BasicOutputCollector collector) { String word = tuple.getString(0); Integer count = counts.get(word); if (count == null) count = 0; count++; counts.put(word, count); collector.emit(new Values(word, count)); } @Override public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("word", "count")); } } 

Bolts public static class ExclamationBolt implements IRichBolt { OutputCollector _collector; public void prepare(Map conf, TopologyContext context, OutputCollector collector) { _collector = collector; } public void execute(Tuple tuple) { _collector.emit(tuple, new Values(tuple.getString(0) + "!!!")); _collector.ack(tuple); } public void cleanup() { } public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("word")); } public Map getComponentConfiguration() { return null; } } 

Topology public class WordCountTopology { public static void main(String[] args) throws Exception { TopologyBuilder builder = new TopologyBuilder(); builder.setSpout("spout", new RandomSentenceSpout(), 5); builder.setBolt("split", new SplitSentence(), 8) .shuffleGrouping("spout"); builder.setBolt("count", new WordCount(), 12) .fieldsGrouping("split", new Fields("word")); Config conf = new Config(); conf.setDebug(true); if (args != null && args.length > 0) { conf.setNumWorkers(3); StormSubmitter.submitTopology(args[0], conf, builder.createTopology()); } else { conf.setMaxTaskParallelism(3); LocalCluster cluster = new LocalCluster(); cluster.submitTopology("word-count", conf, builder.createTopology()); Thread.sleep(10000); cluster.shutdown(); } } } 

Bolts public static class SplitSentence extends ShellBolt implements IRichBolt { public SplitSentence() { super("python", "splitsentence.py"); } public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("word")); } } import storm class SplitSentenceBolt(storm.BasicBolt): def process(self, tup): words = tup.values[0].split(" ") for word in words: storm.emit([word]) SplitSentenceBolt().run() 

github.com/nathanmarz/storm-starter 

streams groupping 

Topology public class WordCountTopology { public static void main(String[] args) throws Exception { TopologyBuilder builder = new TopologyBuilder(); builder.setSpout("spout", new RandomSentenceSpout(), 5); builder.setBolt("split", new SplitSentence(), 8) .shuffleGrouping("spout"); builder.setBolt("count", new WordCount(), 12) .fieldsGrouping("split", new Fields("word")); Config conf = new Config(); conf.setDebug(true); if (args != null && args.length > 0) { conf.setNumWorkers(3); StormSubmitter.submitTopology(args[0], conf, builder.createTopology()); } else { conf.setMaxTaskParallelism(3); LocalCluster cluster = new LocalCluster(); cluster.submitTopology("word-count", conf, builder.createTopology()); Thread.sleep(10000); cluster.shutdown(); } } } 

Groupping shuffle, fields, all, global, none, direct, local or shuffle 

distributed rpc 

RPC distributed arguments results [request-id, arguments] [request-id, results] 

RPC distributed arguments results [request-id, arguments] [request-id, results] public static class ExclaimBolt extends BaseBasicBolt { public void execute(Tuple tuple, BasicOutputCollector collector) { String input = tuple.getString(1); collector.emit(new Values(tuple.getValue(0), input + "!")); } public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("id", "result")); } } public static void main(String[] args) throws Exception { LinearDRPCTopologyBuilder builder = new LinearDRPCTopologyBuilder("exclamation"); builder.addBolt(new ExclaimBolt(), 3); LocalDRPC drpc = new LocalDRPC(); LocalCluster cluster = new LocalCluster(); cluster.submitTopology("drpc-demo", conf, builder.createLocalTopology(drpc)); System.out.println("Results for 'hello':" + drpc.execute("exclamation", "hello")); cluster.shutdown(); drpc.shutdown(); } 

realtime analytics personalization search revenue optimization monitoring 

content search realtime analytics generating feeds integrated with elastic search, Hbase,hadoop and hdfs 

realtime scoring moments generation integrated with kafka queues and hdfs storage 

Storm-YARN enables Storm applications to utilize the computational resources in a Hadoop cluster along with accessing Hadoop storage resources such As HBase and HDFS 

thanks! mail: [email protected] twitter: @mariuszgil