Download as PDF, PPTX
![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](https://image.slidesharecdn.com/heatmap-140320163133-phpapp01/85/Heatmap-40-320.jpg)
![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;
}
}](https://image.slidesharecdn.com/heatmap-140320163133-phpapp01/85/Heatmap-46-320.jpg)
![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](https://image.slidesharecdn.com/heatmap-140320163133-phpapp01/85/Heatmap-49-320.jpg)
Uploaded byTikal Knowledge
Heatmap
The document discusses building a real-time heatmap service using Apache Storm. [1] It proposes using a Storm topology to process streaming check-in data in real-time, geocode the locations, build heatmaps for time intervals, and persist the results. [2] The topology would include spouts to generate simulated check-in data and read from Kafka, bolts for geocoding, building heatmaps, and persisting to a database, and would scale horizontally by increasing parallelism. [3] The document outlines the components, provides code examples for the spouts and bolts, and discusses options for integrating a messaging system like Kafka to make the topology more robust and scalable.
Heatmap
- 1. 1 ProcessingProcessing “BIG-DATA”“BIG-DATA” InIn Real TimeRealTime Yanai Franchi , TikalYanai Franchi , Tikal
- 2.
- 3. 3 Vacation to BarcelonaVacationto Barcelona
- 4. 4 After a LongTravel DayAfter a Long Travel Day
- 5. 5 Going to aSalsa Club
- 6. 6 Best Salsa Club NOW ●Good Music ● Crowded – Now!
- 7. 7 Same Problem in“gogobot”
- 8.
- 9. 9 gogobot checkin Heat MapService Lets' Develop “Gogobot Checkins Heat-Map”
- 10. 10 Key Notes ● CollectorService - 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. 11 Heat-Map Context Text-Address Checkins Heat-Map Service GogobotSystem Gogobot Micro Service Gogobot Micro Service Gogobot Micro Service Geo Location Service Get-GeoCode(Address) Heat-Map Last Interval Locations
- 12. 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. 13 Tons of Addresses ArrivingEvery Second
- 14. 14 Architect - FirstReaction...
- 15.
- 16.
- 17.
- 18. 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. 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. 20 Apache Storm ✔Horizontal scalability ✔Fault-tolerance ✔Nointermediate message brokers! ✔Higher level abstraction than message passing ✔“Just works” ✔Guaranteed data processing
- 21.
- 22. 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. 23 Streams Tuple Tuple TupleTuple Tuple Tuple Unbounded sequence of tuples
- 24.
- 25. 25 Bolts Tuple TupleTuple Processes input streamsand produces new streams Tuple TupleTupleTuple Tuple TupleTuple
- 26. 26 Storm Topology Network ofspouts and bolts Tuple TupleTuple TupleTuple TupleTuple Tuple TupleTupleTuple Tuple Tuple Tuple Tuple TupleTupleTuple
- 27. 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. 28 Tasks (Bolt/Spout Instance) Spoutsand bolts execute as many tasks across the cluster
- 29. 29 Stream Grouping When atuple is emitted, which task (instance) does it go to?
- 30. 30 Stream Grouping ● Shuffle 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. 31 Tasks , Executors, Workers Task Task Task Worker Process Sput / Bolt Sput / Bolt Sput / Bolt = Executor Thread JVM Executor Thread
- 32. 32 Bolt B BoltB 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. 33 Nimbus Supervisor Supervisor Supervisor Supervisor SupervisorSupervisor Upload/Rebalance Heat-Map Topology Zoo Keeper Nodes Storm Architecture Master Node (similar to Hadoop JobTracker) NOT critical for running topology
- 34. 34 Nimbus Supervisor Supervisor Supervisor Supervisor SupervisorSupervisor Upload/Rebalance Heat-Map Topology Zoo Keeper Storm Architecture Used For Cluster Coordination A few nodes
- 35. 35 Nimbus Supervisor Supervisor Supervisor Supervisor SupervisorSupervisor Upload/Rebalance Heat-Map Topology Zoo Keeper Storm Architecture Run Worker Processes
- 36.
- 37. 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. 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. 39 Checkins Spout public classCheckinsSpout 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. 40 Geocode Lookup Bolt publicclass 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. 41 Tick Tuple –Repeating Mantra
- 42. 42 Two Streams toHeat-Map Builder On tick tuple, we fl ush our Heat-Map Checkin 1 Checkin 4 Checkin 5 Checkin 6 HeatMap- Builder Bolt
- 43. 43 Tick Tuple inAction 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. 44 Persister Bolt public classPersistorBolt 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. 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. 46 Heat Map Topology publicclass 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.
- 48.
- 49. 49 Scaling the Topology publicclass 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.
- 51. 51 Database Storm Heat-Map Topology Persist Checkin Intervals GETGeo 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.
- 53.
- 54. 54 Plan B - KafkaSpout&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. 56 They all areGood But not for all use-cases
- 57.
- 58.
- 59.
- 60.
- 61.
- 62.
- 63.
- 64. 64 Stateless Broker & Doesn'tFear the File System
- 65.
- 66.
- 67.
- 68. 68 Topics ● Logical collectionsof partitions (the physical fi les). ● A broker contains some of the partitions for a topic
- 69. 69 A partition isConsumed by Exactly One Group's Consumer
- 70.
- 71. 71 Broker 1 Broker3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2
- 72. 72 Broker 1 Broker4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2
- 73. 73 Broker 1 Broker4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2
- 74. 74 Broker 1 Broker4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2
- 75. 75 Broker 1 Broker4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2
- 76. 76 Broker 1 Broker4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2
- 77. 77 Broker 1 Broker4Broker 3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2
- 78. 78 Broker 1 Broker3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2
- 79. 79 Broker 1 Broker3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2
- 80. 80 Broker 1 Broker3Broker 2 Zoo Keeper Consumer 1 Consumer 2 Producer 1 Producer 2
- 81. 81 Broker 1 Broker3Broker 2 Zoo Keeper Consumer 1 Producer 1 Producer 2
- 82. 82 Broker 1 Broker3Broker 2 Zoo Keeper Consumer 1 Producer 1 Producer 2
- 83. 83 Broker 1 Broker3Broker 2 Zoo Keeper Consumer 1 Producer 1 Producer 2
- 84. 84 Performance Benchmark 1 Broker 1Producer 1 Consumer
- 85.
- 86.
- 87. 87 Add Kafka toour 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. 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.
- 90.
- 91.
- 92.
- 93. 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. 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. 96 ● Vertciles areExecution unit of Vert.x ● Single threaded ● Verticles communicate by message passing Verticles For Blocking IO Run in Thread-PoolRun in Event Loop
- 97. 97 Vert.X Architecture Event Bus Vert.XArchitecture
- 98. 98 Vert.X Goodies ● GrowingModule ● 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.
- 100. 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. 101 Node.js vs Vert.XBenchmark 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. 102 Event Bus HTTP Server Verticle Kafka module Kafka Topic Storm Topology HeatMapReactor Architecture Vert.X Instance Automatically sends EventBus Msg → KafkaTopic Vert.X Instance
- 103. 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. 104 Database Checkin HTTP Reactor Checkin KafkaTopic 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.
- 106.
- 107.
- 108. 108 Doesn't Answer ManyAnswers... ● 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.
- 110.
- 111. 111 Complementary Views Batch ViewsReal Time Views Just a few hours of data Time Now
- 112. 112 Lambda Architecture New Data 01101001101... real-time view real-time view real-time view SpeedLayer master dataset batch view batch view batch view Serving Layer Batch Layer Query How Many ?
- 113. 113 Lambda Advantages ● Recoverfrom Human mistakes ● No need for random writes on Batch DB ● Processed with high precision, and involve algorithms without losing short-term information
- 114.
- 115. 115 When You goout to Salsa Club ● Good Music ● Crowded
- 116. 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.