Heatmap

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Information about Heatmap

Published on March 20, 2014

Author: tikalknowledge

Source: slideshare.net

Description

A presentation Tikal Fullstack Israel - http://www.tikalk.com/

1 ProcessingProcessing “BIG-DATA”“BIG-DATA” InIn Real TimeReal Time Yanai Franchi , TikalYanai Franchi , Tikal

2

3 Vacation to BarcelonaVacation to Barcelona

4 After a Long Travel DayAfter a Long Travel Day

5 Going to a Salsa Club

6 Best Salsa Club NOW ● Good Music ● Crowded – Now!

7 Same Problem in “gogobot”

8

9 gogobot checkin Heat Map Service Lets' Develop “Gogobot Checkins Heat-Map”

10 Key Notes ● Collector Service - Collects checkins as text addresses – We need to use GeoLocation ServiceWe need to use GeoLocation Service ● Upon elapsed interval, the last locations list will be displayed as Heat-Map in GUI. ● Web Scale service – 10Ks checkins/seconds all over the world (imaginary, but lets do it for the exercise). ● Accuracy – Sample data, NOT critical data. – Proportionately representative – Data volume is large enough tois large enough to compensate for data loss.compensate for data loss.

11 Heat-Map Context Text-Address Checkins Heat-Map Service Gogobot System Gogobot Micro Service Gogobot Micro Service Gogobot Micro Service Geo Location Service Get-GeoCode(Address) Heat-Map Last Interval Locations

12 Database Persist Checkin Intervals Processing Checkins Read Text Address Check-in #1 Check-in #2 Check-in #3 Check-in #4 Check-in #5 Check-in #6 Check-in #7 Check-in #8 Check-in #9 ... Simulate Checkins with a File Plan A GET Geo Location Geo Location Service

13 Tons of Addresses Arriving Every Second

14 Architect - First Reaction...

15 Second Reaction...

16 Developer First Reaction

17 Second Reaction

18 Problems ? ● Tedious: Spend time confi guring where to send messages, deploying workers, and deploying intermediate queues. ● Brittle: There's little fault-tolerance. ● Painful to scale: Partition of running worker/s is complicated.

19 What We Want ? ● Horizontal scalability ● Fault-tolerance ● No intermediate message brokers! ● Higher level abstraction than message passing ● “Just works” ● Guaranteed data processing (not in this case)

20 Apache Storm ✔Horizontal scalability ✔Fault-tolerance ✔No intermediate message brokers! ✔Higher level abstraction than message passing ✔“Just works” ✔Guaranteed data processing

21 Anatomy of Storm

22 What is Storm ? ● CEP - Open source and distributed realtime computation system. – Makes it easy toMakes it easy to reliably process unboundedreliably process unbounded streamsstreams ofof tuplestuples – Doing for realtime processing what Hadoop did for batchDoing for realtime processing what Hadoop did for batch processing.processing. ● Fast - 1M Tuples/sec per node. – It is scalable,fault-tolerant, guarantees your data will beIt is scalable,fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.processed, and is easy to set up and operate.

23 Streams Tuple Tuple Tuple Tuple Tuple Tuple Unbounded sequence of tuples

24 Spouts Tuple Tuple Sources of Streams Tuple Tuple

25 Bolts Tuple TupleTuple Processes input streams and produces new streams Tuple TupleTupleTuple Tuple TupleTuple

26 Storm Topology Network of spouts and bolts Tuple TupleTuple TupleTuple TupleTuple Tuple TupleTupleTuple Tuple Tuple Tuple Tuple TupleTupleTuple

27 Guarantee for Processing ● Storm guarantees the full processing of a tuple by tracking its state ● In case of failure, Storm can re-process it. ● Source tuples with full “acked” trees are removed from the system

28 Tasks (Bolt/Spout Instance) Spouts and bolts execute as many tasks across the cluster

29 Stream Grouping When a tuple is emitted, which task (instance) does it go to?

30 Stream Grouping ● Shuffl e grouping: pick a random task ● Fields grouping: consistent hashing on a subset of tuple fi elds ● All grouping: send to all tasks ● Global grouping: pick task with lowest id

31 Tasks , Executors , Workers Task Task Task Worker Process Sput / Bolt Sput / Bolt Sput / Bolt = Executor Thread JVM Executor Thread

32 Bolt B Bolt B Worker Process Executor Spout A Executor Node Supervisor Bolt C Bolt C Executor Bolt B Bolt B Worker Process Executor Spout A Executor Node Supervisor Bolt C Bolt C Executor

33 Nimbus Supervisor Supervisor Supervisor Supervisor Supervisor Supervisor Upload/Rebalance Heat-Map Topology Zoo Keeper Nodes Storm Architecture Master Node (similar to Hadoop JobTracker) NOT critical for running topology

34 Nimbus Supervisor Supervisor Supervisor Supervisor Supervisor Supervisor Upload/Rebalance Heat-Map Topology Zoo Keeper Storm Architecture Used For Cluster Coordination A few nodes

35 Nimbus Supervisor Supervisor Supervisor Supervisor Supervisor Supervisor Upload/Rebalance Heat-Map Topology Zoo Keeper Storm Architecture Run Worker Processes

36 Assembling Heatmap Topology

37 HeatMap Input/Output Tuples ● Input Tuples: Timestamp and Text Address : – (9:00:07 PM , “287 Hudson St New York NY 10013”)(9:00:07 PM , “287 Hudson St New York NY 10013”) ● Output Tuple: Time interval, and a list of points for it: – (9:00:00 PM to 9:00:15 PM,(9:00:00 PM to 9:00:15 PM, ListList((((40.719,-73.98740.719,-73.987),(40.726,-74.001),(),(40.726,-74.001),(40.719,-73.98740.719,-73.987))))

38 Checkins Spout Geocode Lookup Bolt Heatmap Builder Bolt Persistor Bolt (9:01 PM @ 287 Hudson st) (9:01 PM , (40.736, -74,354))) Heat Map Storm Topology (9:00 PM – 9:15 PM , List((40.73, -74,34), (51.36, -83,33),(69.73, -34,24)) Upon Elapsed Interval

39 Checkins Spout public class CheckinsSpout extends BaseRichSpout { private List<String> sampleLocations; private int nextEmitIndex; private SpoutOutputCollector outputCollector; @Override public void open(Map map, TopologyContext topologyContext, SpoutOutputCollector spoutOutputCollector) { this.outputCollector = spoutOutputCollector; this.nextEmitIndex = 0; sampleLocations = IOUtils.readLines( ClassLoader.getSystemResourceAsStream("sanple-locations.txt")); } @Override public void nextTuple() { String address = checkins.get(nextEmitIndex); String checkin = new Date().getTime()+"@ADDRESS:"+address; outputCollector.emit(new Values(checkin)); nextEmitIndex = (nextEmitIndex + 1) % sampleLocations.size(); } @Override public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("str")); } We hold state No need for thread safety Declare output fields Been called iteratively by Storm

40 Geocode Lookup Bolt public class GeocodeLookupBolt extends BaseBasicBolt { private LocatorService locatorService; @Override public void prepare(Map stormConf, TopologyContext context) { locatorService = new GoogleLocatorService(); } @Override public void execute(Tuple tuple, BasicOutputCollector outputCollector) { String str = tuple.getStringByField("str"); String[] parts = str.split("@"); Long time = Long.valueOf(parts[0]); String address = parts[1]; LocationDTO locationDTO = locatorService.getLocation(address); if(checkinDTO!=null) outputCollector.emit(new Values(time,locationDTO) ); } @Override public void declareOutputFields(OutputFieldsDeclarer fieldsDeclarer) { fieldsDeclarer.declare(new Fields("time", "location")); } } Get Geocode, Create DTO

41 Tick Tuple – Repeating Mantra

42 Two Streams to Heat-Map Builder On tick tuple, we fl ush our Heat-Map Checkin 1 Checkin 4 Checkin 5 Checkin 6 HeatMap- Builder Bolt

43 Tick Tuple in Action public class HeatMapBuilderBolt extends BaseBasicBolt { private Map<String, List<LocationDTO>> heatmaps; @Override public Map<String, Object> getComponentConfiguration() { Config conf = new Config(); conf.put(Config.TOPOLOGY_TICK_TUPLE_FREQ_SECS, 60 ); return conf; } @Override public void execute(Tuple tuple, BasicOutputCollector outputCollector) { if (isTickTuple(tuple)) { // Emit accumulated intervals } else { // Add check-in info to the current interval in the Map } } private boolean isTickTuple(Tuple tuple) { return tuple.getSourceComponent().equals(Constants.SYSTEM_COMPONENT_ID) && tuple.getSourceStreamId().equals(Constants.SYSTEM_TICK_STREAM_ID); } Tick interval Hold latest intervals

44 Persister Bolt public class PersistorBolt extends BaseBasicBolt { private Jedis jedis; @Override public void execute(Tuple tuple, BasicOutputCollector outputCollector) { Long timeInterval = tuple.getLongByField("time-interval"); String city = tuple.getStringByField("city"); String locationsList = objectMapper.writeValueAsString ( tuple.getValueByField("locationsList")); String dbKey = "checkins-" + timeInterval+"@"+city; jedis.setex(dbKey, 3600*24 ,locationsList); jedis.publish("location-key", dbKey); } } Publish in Redis channel for debugging Persist in Redis for 24h

45 Shuffle Grouping Shuffle Grouping Check-in #1 Check-in #2 Check-in #3 Check-in #4 Check-in #5 Check-in #6 Check-in #7 Check-in #8 Check-in #9 ... Sample Checkins File Read Text Addresses Transforming the Tuples Checkins Spout Geocode Lookup Bolt Heatmap Builder Bolt Database Persistor Bolt Get Geo Location Geo Location Service Field Grouping(city) Group by city

46 Heat Map Topology public class LocalTopologyRunner { public static void main(String[] args) { TopologyBuilder builder = buildTopolgy(); StormSubmitter.submitTopology( "local-heatmap", new Config(), builder.createTopology()); } private static TopologyBuilder buildTopolgy() { topologyBuilder builder = new TopologyBuilder(); builder.setSpout("checkins", new CheckinsSpout()); builder.setBolt("geocode-lookup", new GeocodeLookupBolt() ) .shuffleGrouping("checkins"); builder.setBolt("heatmap-builder", new HeatMapBuilderBolt() ) .fieldsGrouping("geocode-lookup", new Fields("city")); builder.setBolt("persistor", new PersistorBolt() ) .shuffleGrouping("heatmap-builder"); return builder; } }

47 Its NOT Scaled

48

49 Scaling the Topology public class LocalTopologyRunner { conf.setNumWorkers(20); public static void main(String[] args) { TopologyBuilder builder = buildTopolgy(); Config conf = new Config(); conf.setNumWorkers(2); StormSubmitter.submitTopology( "local-heatmap", conf, builder.createTopology()); } private static TopologyBuilder buildTopolgy() { topologyBuilder builder = new TopologyBuilder(); builder.setSpout("checkins", new CheckinsSpout(), 4 ); builder.setBolt("geocode-lookup", new GeocodeLookupBolt() , 8 ) .shuffleGrouping("checkins").setNumTasks(64); builder.setBolt("heatmap-builder", new HeatMapBuilderBolt() , 4) .fieldsGrouping("geocode-lookup", new Fields("city")); builder.setBolt("persistor", new PersistorBolt() , 2 ) .shuffleGrouping("heatmap-builder").setNumTasks(4); return builder; Parallelism hint Increase Tasks For Future Set no. of workers

50 Demo

51 Database Storm Heat-Map Topology Persist Checkin Intervals GET Geo Location Check-in #1 Check-in #2 Check-in #3 Check-in #4 Check-in #5 Check-in #6 Check-in #7 Check-in #8 Check-in #9 ... Read Text Address Sample Checkins File Recap – Plan A Geo Location Service

52 We have something working

53 Add Kafka Messaging

54 Plan B - Kafka Spout&Bolt to HeatMap Geocode Lookup Bolt Heatmap Builder Bolt Kafka Checkins Spout Database Persistor Bolt Geo Location Service Read Text Addresses Checkin Kafka Topic Publish Checkins Locations Topic Kafka Locations Bolt

55

56 They all are Good But not for all use-cases

57 Kafka A little introduction

58

59 Pub-Sub Messaging System

60

61

62

63

64 Stateless Broker & Doesn't Fear the File System

65

66

67

68 Topics ● Logical collections of partitions (the physical fi les). ● A broker contains some of the partitions for a topic

69 A partition is Consumed by Exactly One Group's Consumer

70 Distributed & Fault-Tolerant

71 Broker 1 Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2

72 Broker 1 Broker 4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2

73 Broker 1 Broker 4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2

74 Broker 1 Broker 4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2

75 Broker 1 Broker 4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2

76 Broker 1 Broker 4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2

77 Broker 1 Broker 4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2

78 Broker 1 Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2

79 Broker 1 Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2

80 Broker 1 Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2

81 Broker 1 Broker 3Broker 2 Zoo Keeper Consumer 1 Producer 1 Producer 2

82 Broker 1 Broker 3Broker 2 Zoo Keeper Consumer 1 Producer 1 Producer 2

83 Broker 1 Broker 3Broker 2 Zoo Keeper Consumer 1 Producer 1 Producer 2

84 Performance Benchmark 1 Broker 1 Producer 1 Consumer

85

86

87 Add Kafka to our Topology public class LocalTopologyRunner { ... private static TopologyBuilder buildTopolgy() { ... builder.setSpout("checkins", new KafkaSpout(kafkaConfig)); ... builder.setBolt("kafkaProducer", new KafkaOutputBolt ( "localhost:9092", "kafka.serializer.StringEncoder", "locations-topic")) .shuffleGrouping("persistor"); return builder; } } Kafka Bolt Kafka Spout

88 Checkin HTTP Reactor Publish Checkins Plan C – Add Reactor Database Checkin Kafka Topic Consume Checkins Storm Heat-Map Topology Locations Kafka Topic Publish Interval Key Persist Checkin Intervals Geo Location ServiceGET Geo Location Index Interval Locations Search Server Index Text-Address

89 Why Reactor ?

90 C10K Problem

91 2008: Thread Per Request/Response

92 ...events trigger handlers Application registers handlers Reactor Pattern Paradigm

93 Reactor Pattern – Key Points ● Single thread / single event loop ● EVERYTHING runs on it ● You MUST NOT block the event loop ● Many Implementations (partial list): – Node.js (JavaScrip), EventMachine (Ruby), TwistedNode.js (JavaScrip), EventMachine (Ruby), Twisted (Python)... and Vert.X(Python)... and Vert.X

94 Reactor Pattern Problems ● Some work is naturally blocking: – Intensive data crunchingIntensive data crunching – 3rd-party blocking API’s (e.g. JDBC)3rd-party blocking API’s (e.g. JDBC) ● Pure reactor (e.g. Node.js) is not a good fi t for this kind of work!

95

96 ● Vertciles are Execution unit of Vert.x ● Single threaded ● Verticles communicate by message passing Verticles For Blocking IO Run in Thread-PoolRun in Event Loop

97 Vert.X Architecture Event Bus Vert.X Architecture

98 Vert.X Goodies ● Growing Module ● Repository ● web server ● Persistors (Mongo, JDBC, ...) ● Work queue ● Authentication ● Manager ● Session manager ● Socket.IO ● TCP/SSL servers/clients ● HTTP/HTTPS servers/ clients ● WebSockets support ● SockJS support ● Timers ● Buffers ● Streams and Pumps ● Routing ● Asynchronous File I/O

99 Node.JS vs Vert.X

100 Node.js vs Vert.X ● Node.js – JavaScript OnlyJavaScript Only – Inherently SingleInherently Single ThreadedThreaded – No help much with IPCNo help much with IPC – All code MUST be inAll code MUST be in Event loopEvent loop ● Vert.X – Polyglot (JavaScript,Polyglot (JavaScript, Java, Ruby, Python...)Java, Ruby, Python...) – Leverages JVM multi-Leverages JVM multi- threadingthreading – Nervous Event BusNervous Event Bus – Blocking work can beBlocking work can be done off the event loopdone off the event loop

101 Node.js vs Vert.X Benchmark AMD Phenom II X6 (6 core), 8GB RAM, Ubuntu 11.04 http://vertxproject.wordpress.com/2012/05/09/vert-x-vs-node-js-simple-http-benchmarks/

102 Event Bus HTTP Server Verticle Kafka module Kafka Topic Storm Topology HeatMap Reactor Architecture Vert.X Instance Automatically sends EventBus Msg → KafkaTopic Vert.X Instance

103 Heat-Map Server – Only 6 LOC ! var vertx = require('vertx'); var container = require('vertx/container'); var console = require('vertx/console'); var config = container.config; vertx.createHttpServer().requestHandler(function(request) { request.dataHandler(function(buffer) { vertx.eventBus.send(config.address, {payload:buffer.toString()}); }); request.response.end(); }).listen(config.httpServerPort, config.httpServerHost); console.log("HTTP CheckinsReactor started on port "+config.httpServerPort); Send checkin to Vert.X EventBus

104 Database Checkin HTTP Reactor Checkin Kafka Topic Consume Checkins Storm Heat-Map Topology Hotzones Kafka Topic Publish Interval Key Persist Checkin Intervals Web App Geo Location ServiceGET Geo Location Get Interval Locations Consume Intervals Keys Push via WebSocket Publish Checkins Search Server Index Index Interval Locations Search Checkin HTTP Firehose

105 Demo

106 Lambda Architecture

107 Until Now...

108 Doesn't Answer Many Answers... ● What are the most popular Salsa club in last month? ● How many unique visitors this year , per Salsa club? ● Show histogram of “bouncing” checkins for the last year?

109 Batch Processing

110 Sing in Concert ?

111 Complementary Views Batch Views Real Time Views Just a few hours of data Time Now

112 Lambda Architecture New Data 01101001101... real-time view real-time view real-time view Speed Layer master dataset batch view batch view batch view Serving Layer Batch Layer Query How Many ?

113 Lambda Advantages ● Recover from Human mistakes ● No need for random writes on Batch DB ● Processed with high precision, and involve algorithms without losing short-term information

114 Summary

115 When You go out to Salsa Club ● Good Music ● Crowded

116 More Conclusions.. ● Storm – Great for real-time BigData processing. Complementary for Hadoop batch jobs. ● Kafka – Great messaging for logs/events data, been served as a good “source” for Storm spout ● Vert.X – Worth trial and check as an alternative for reactor. ● Lambda Architecture – Bring Real-Time and Batch- Processing concert for Big Data.

117 Thanks

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