The three generations of Big Data processing by RUBÉN CASADO at Big Data Spain 2013

Big Data is often characterized by the 3 “Vs”: variety, volume and velocity. While variety refers to the nature of the information (multiple sources, schema-less data, etc), both volume and velocity refer to processing issues that have to be addressed by different processing paradigms.
Session presented at Big Data Spain 2013 Conference
7th Nov 2013
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by http://www.paradigmatecnologico.com
Abstract: http://www.bigdataspain.org/2013/conference/the-three-generations-of-big-data-processing

Transcript

1. The three generations of Big Data processing Rubén Casado
2. The three generations of Big Data processing Rubén Casado ruben.casado@treelogic.com
3. 1. Big Data processing 2. Batch processing 3. Real-time processing 4. Hybrid computation model 5. Conclusions Agenda
4. About me :-)
5.  PhD in Software Engineering  MSc in Computer Science  BSc in Computer Science Academics Work Experience
6. About Treelogic
7. Treelogic is an R&D intensive company with the mission of creating, boosting, developing and adapting scientific and technological knowledge to improve quality standards in our daily life
8. TREELOGIC – Distributor and Sales
9.  International Projects  National Projects  Regional Projects  R&D Manag. System  Internal Projects Research Lines Computer Vision Big Data Teraherzt technology Data science Social Media Analysis Semantics Security & Safety Justice Health Transport Financial services ICT tailored solutions Solutions R&D
10. 7 ongoing FP7 projects ICT, SEC, OCEAN Coordinating 5 of them 3 ongoing Eurostars projects Coordinating all of them
11. Research INNOVATIO N & 7 years’ experience in R&D projects Project coordinator in 7 European projects
12. www.datadopter.com
13. 1. Big Data processing 2. Batch processing 3. Real-time processing 4. Hybrid computation model 5. Conclusions Agenda
14. A massive volume of both structured and unstructured data that is so large to process with traditional database and software techniques What is Big Data?
15. Big Data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization How is Big Data? - Gartner IT Glossary -
16. 3 problems Volume Variety Velocity
17. 3 solutions Batch processing NoSQL Real-time processing
18. 3 solutions Batch processing NoSQL Real-time processing
19. • Scalable • Large amount of static data • Distributed • Parallel • Fault tolerant • High latency Batch processing Volume
20. • Low latency • Continuous unbounded streams of data • Distributed • Parallel • Fault-tolerant Real-time processing Velocity
21. • Low latency • Massive data + Streaming data • Scalable • Combine batch and real-time results Hybrid computation model Volume Velocity
22. All data New data Batch processing Real-time processing Batch results Stream results Combination Final results Hybrid computation model
23.  Batch processing  Large amount of statics data  Scalable solution  Volume  Real-time processing  Computing streaming data  Low latency  Velocity  Hybrid computation  Lambda Architecture  Volume + Velocity 2006 2010 2014 1ª Generation 2ª Generation 3ª Generation Inception 2003 Processing Paradigms
24. Batch 10 years of Big Data processing technologies 2003 2004 2005 2013 2011 2010 2008 The Google File System MapReduce: Simplified Data Processing on Large Clusters Doug Cutting starts developing Hadoop 2006 Yahoo! starts working on Hadoop Apache Hadoop is in production Nathan Marz creates Storm Yahoo! creates S4 2009 Facebook creates Hive Yahoo! creates Pig Google publishes MillWheel: Fault-Tolerant Stream Processing at Internet Scale LinkedIn presents Samza LinkedIn! presents KafkA Cloudera presents Flume 2012 Nathan Marz defines the Lambda Architecture Real-Time Hybrid
25. Processing Pipeline DATA ACQUISITION DATA STORAGE DATA ANALYSIS RESULTS
26.  Static stations and mobile sensors in Asturias sending streaming data  Historical data of > 10 years  Monitoring, trends identification, predictions Air Quality case study
27. 1. Big Data processing overview 2. Batch processing 3. Real-time processing 4. Hybrid computation model 5. Conclusions Agenda
28. Batch processing technologies DATA ACQUISITION DATA STORAGE DATA ANALYSIS RESULTS o HDFS commands o Sqoop o Flume o Scribe o HDFS o HBase o MapReduce o Hive o Pig o Cascading o Spark o
29. • Import to HDFS hadoop dfs -copyFromLocal <path-to-local> <path-to-remote> hadoop dfs –copyFromLocal /home/hduser/AirQuality/ /hdfs/AirQuality/ HDFS commands DATA ACQUISITION B A T C H
30. • Tool designed for transferring data between HDFS/HBase and structural datastores • Based in MapReduce • Includes connectors for multiple databases o MySQL, o PostgreSQL, o Oracle, o SQL Server and o DB2 o Generic JDBC connector • Java API Sqoop DATA ACQUISITION B A T C H
31. import -all-tables --connect jdbc:mysql://localhost/testDatabase --target-dir hdfs://rootHDFS/testDatabase --username user1 --password pass1 -m 1 1) Import data from database to HDFS export --connect jdbc:mysql://localhost/testDatabase --export-dir hdfs://rootHDFS/testDatabase --username user1 --password pass1 -m 1 3) Export results to database 2) Analyze data (HADOOP) Sqoop DATA ACQUISITION B A T C H
32. • Service for collecting, aggregating, and moving large amounts of log data • Simple and flexible architecture based on streaming data flows • Reliability, scalability, extensibility, manageability • Support log stream types • Avro • Syslog • Netcast Flume DATA ACQUISITION B A T C H
33.  Sources  Channel s  Sinks  Avro  Memory  HDFS  Thrift  JDBC  Logger  Exec  File  Avro  JMS   Thrift  NetCat   IRC  Syslog TCP/UDP   File Roll  HTTP   Null    HBase  Custom   Custom • Architecture o Source o Waiting for events . o Sink o Sends the information towards another agent or system. o Channel o Stores the information until it is consumed by the sink. Flume DATA ACQUISITION B A T C H
34. Stations send the information to the servers. Flume collects this information and move it into the HDFS for further analsys  Air quality syslogs Flume DATA ACQUISITION B A T C H Station; Tittle; latitude; longitude; Date ; SO2; NO; CO; PM10; O3; dd; vv; TMP; HR; PRB; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "7"; "8"; "0.35"; "13"; "67"; "158"; "3.87"; "18.8"; "34"; "982"; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "7"; "7"; "0.32"; "16"; "66"; "158"; "4.03"; "19"; "35"; "981"; "23"; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "7"; "6"; "0.26"; "24"; "68"; "158"; "3.76"; "19.1"; "36"; "980"; "23"; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "6"; "6"; "0.31"; "7"; "67"; "135"; "2.41"; "19.2"; "36"; "981"; "23"; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "6"; "9"; "0.24"; "24"; "63"; "44"; "1.7"; "15.9"; "62"; "983"; "23";
35. • Server for aggregating log data streamed in real time from a large number of servers • There is a scribe server running on every node in the system, configured to aggregate messages and send them to a central scribe server (or servers) in larger groups. • The central scribe server(s) can write the messages to the files that are their final destination Scribe DATA ACQUISITION B A T C H
36. category=‘mobile‘; // '1; 43.5298; -5.6734; 2000-01-01; 23; 89; 1.97; …' message= sensor_log.readLine(); log_entry = scribe.LogEntry(category, message) // Create a Scribe Client client = scribe.Client(iprot=protocol, oprot=protocol) transport.open() result = client.Log(messages=[log_entry]) transport.close() • Sending a sensor message to a Scribe Server Scribe DATA ACQUISITION B A T C H
37. • Distributed FileSystem for Hadoop • Master-Slaves Architecture (NameNode – DataNodes) • NameNode: Manage the directory tree and regulates access to files by clients • DataNodes: Store the data • Files are split into blocks of the same size and these blocks are stored and replicated in a set of DataNodes HDFS DATA STORAGE B A T C H
38. • Open-source non-relational distributed column-oriented database modeled after Google’s BigTable. • Random, realtime read/write access to the data. • Not a relational database. • Very light «schema» • Rows are stored in sorted order. DATA STORAGE B A T C H HBase
39. • Framework for processing large amount of data in parallel across a distributed cluster • Slightly inspired in the Divide and Conquer (D&C) classic strategy • Developer has to implement Map and Reduce functions: • Map: It takes the input, partitions it up into smaller sub-problems, and distributes them to worker nodes parsed to the format <K, V> • Reduce: It collects the <K, List(V)> and generates the results MapReduce DATA ANALYTICS B A T C H
40. • Design Patterns • Joins o Reduce side Join o Replicated join o Semi join • Sorting: o Secondary sort o Total Order Sort • Filtering MapReduce • Statistics o AVG o VAR o Count o … • Top-K • Binning • … DATA ANALYTICS B A T C H
41. • Obtain the S02 average of each station MapReduce Station; Tittle; latitude; longitude; Date ; SO2; NO; CO; PM10; O3; dd; vv; TMP; HR; PRB; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "7"; "8"; "0.35"; "13"; "67"; "158"; "3.87"; "18.8"; "34"; "982"; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "7"; "7"; "0.32"; "16"; "66"; "158"; "4.03"; "19"; "35"; "981"; "23"; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "7"; "6"; "0.26"; "24"; "68"; "158"; "3.76"; "19.1"; "36"; "980"; "23"; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "6"; "6"; "0.31"; "7"; "67"; "135"; "2.41"; "19.2"; "36"; "981"; "23"; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "6"; "9"; "0.24"; "24"; "63"; "44"; "1.7"; "15.9"; "62"; "983"; "23"; DATA ANALYTICS B A T C H
42. Input Data Mapper Mapper Mapper <1, 6> … … … Shuffling <1, 2> <3, 1> <1, 9> <3, 9> <2, 6> <2, 6> <1, 6> <2, 0> <2, 8> <1, 2> <3,9> <Station_ID, S02_VALUE> MapReduce DATA ANALYTICS B A T C H • Maps get records and produce the SO2 value in <Station_Id, SO2_value>
43. Station_ID, AVG_SO2 1, 2,013 2, 2,695 3, 3,562 Reducer Sum Divide <2, [2, 3, 0, …]> <1, [1, 0, 4, …]> Shuffling Reducer Sum Divide … … <Station_ID, [SO1, SO2,…,SOn> • Reducer receives <Station_Id, List<SO2_value> > and computes the average for the station MapReduce DATA ANALYTICS B A T C H
44. Hive • Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets • Abstraction layer on top of MapReduce • SQL-like language called HiveQL. • Metastore: Central repository of Hive metadata. DATA ANALYTICS B A T C H
45. CREATE TABLE air_quality(Estacion int, Titulo string, latitud double, longitud double, Fecha string, SO2 int, NO int, CO float, …) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘;' LINES TERMINATED BY '\n' STORED AS TEXTFILE; LOAD DATA INPATH '/CalidadAire_Gijon' OVERWRITE INTO TABLE calidad_aire; Hive • Obtain the S02 average of each station • SELECT Titulo, avg(SO2) • FROM air_quality • GROUP BY Estacion DATA ANALYTICS B A T C H
46. • Platform for analyzing large data sets • High-level language for expressing data analysis programs. Pig Latin. Data flow programming language. • Abstraction layer on top of MapReduce • Procedural language Pig DATA ANALYTICS B A T C H
47. Pig DATA ANALYTICS B A T C H • Obtain the S02 average of each station calidad_aire = load '/CalidadAire_Gijon' using PigStorage(';') AS (estacion:chararray, titulo:chararray, latitud:chararray, longitud:chararray, fecha:chararray, so2:chararray, no:chararray, co:chararray, pm10:chararray, o3:chararray, dd:chararray, vv:chararray, tmp:chararray, hr:chararray, prb:chararray, rs:chararray, ll:chararray, ben:chararray, tol:chararray, mxil:chararray, pm25:chararray); grouped = GROUP air_quality BY estacion; avg = FOREACH grouped GENERATE group, AVG(so2); dump avg;
48. • Cascading is a data processing API and processing query planner used for defining, sharing, and executing data-processing workflows • Makes development of complex Hadoop MapReduce workflows easy • In the same way that Pig DATA ANALYTICS B A T C H Cascading
49. // define source and sink Taps. Tap source = new Hfs( sourceScheme, inputPath ); Scheme sinkScheme = new TextLine( new Fields( “Estacion", “SO2" ) ); Tap sink = new Hfs( sinkScheme, outputPath, SinkMode.REPLACE ); Pipe assembly = new Pipe( “avgSO2" ); assembly = new GroupBy( assembly, new Fields( “Estacion" ) ); // For every Tuple group Aggregator avg = new Average( new Fields( “SO2" ) ); assembly = new Every( assembly, avg ); // Tell Hadoop which jar file to use Flow flow = flowConnector.connect( “avg-SO2", source, sink, assembly ); // execute the flow, block until complete flow.complete(); DATA ANALYTICS B A T C H • Obtain the S02 average of each station Cascading
50. Spark • Cluster computing systems for faster data analytics • Not a modified version of Hadoop • Compatible with HDFS • In-memory data storage for very fast iterative processing • MapReduce-like engine • API in Scala, Java and Python DATA ANALYTICS B A T C H
51. Spark DATA ANALYTICS B A T C H • Hadoop is slow due to replication, serialization and IO tasks
52. Spark DATA ANALYTICS B A T C H • 10x-100x faster
53. Shark • Large-scale data warehouse system for Spark • SQL on top of Spark • Actually Hive QL over Spark • Up to 100 x faster than Hive DATA ANALYTICS B A T C H
54. Pros • Faster than Hadoop ecosystem • Easier to develop new applications • (Scala, Java and Python API) Cons • Not tested in extremely large clusters yet • Problems when Reducer’s data does not fit in memory DATA ANALYTICS B A T C H Spark / Shark
55. 1. Big Data processing 2. Batch processing 3. Real-time processing 4. Hybrid computation model 5. Conclusions Agenda
56. Real-time processing technologies DATA ACQUISITION DATA STORAGE DATA ANALYSIS RESULTS o Flume o Kafka o Kestrel o Flume o Storm o Trident o S4 o Spark Streaming
57. Flume DATA ACQUISITION R E A L
58. • Kafka is a distributed, partitioned, replicated commit log service o Producer/Consumer model o Kafka maintains feeds of messages in categories called topics o Kafka is run as a cluster Kafka DATA STORAGE R E A L
59. Insert AirQuality sensor log file into Kafka cluster and consume the info. // new Producter Producer<String, String> producer = new Producer<String, String>(config); //Open sensor log file BufferedReader br… String line; while(true) { line = br.readLine(); if(line ==null) … //wait; else producer.send(new KeyedMessage<String, String>(topic, line)); } Kafka DATA STORAGE R E A L
60. AirQuality Consumer ConsumerConnector consumer = Consumer.createJavaConsumerConnector(config); Map<String, Integer> topicCountMap = new HashMap<String, Integer>(); topicCountMap.put(topic, new Integer(1)); Map<String, List<KafkaMessageStream>> consumerMap = consumer.createMessageStreams(topicCountMap); KafkaMessageStream stream = consumerMap.get(topic).get(0); ConsumerIterator it = stream.iterator(); while(it.hasNext()){ // consume it.next() Kafka DATA STORAGE R E A L
61. • Simple distributed message queue • A single Kestrel server has a set of queues (strictly-ordered FIFO) • On a cluster of Kestrel servers, they don’t know about each other and don’t do any cross communication • Kestrel vs Kafka o Kafka consumers cheaper (basically just the bandwidth usage) o Kestrel does not depend on Zookeeper which means it is operationally less complex if you don't already have a zookeeper installation. o Kafka has significantly better throughput. o Kestrel does not support ordered consumption Kestrel DATA STORAGE R E A L
62. Interceptor • Interface org.apache.flume.interceptor.Interceptor • Can modify or even drop events based on any criteria • Flume supports chaining of interceptors. • Types: o Timestamp interceptor o Host interceptor o Static interceptor o UUID interceptor o Morphline interceptor o Regex Filtering interceptor o Regex Extractor interceptor DATA ANALYTICS R E A L Flume
63. • The sensors’ information must be filtered by "Station 2" o An interceptor will filter information between Source and Channel. Station; Tittle; latitude; longitude; Date ; SO2; NO; CO; PM10; O3; dd; vv; TMP; HR; PRB; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "7"; "8"; "0.35"; "13"; "67"; "158"; "3.87"; "18.8"; "34"; "982"; "2";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "7"; "7"; "0.32"; "16"; "66"; "158"; "4.03"; "19"; "35"; "981"; "23"; "3";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "7"; "6"; "0.26"; "24"; "68"; "158"; "3.76"; "19.1"; "36"; "980"; "23"; "2";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "6"; "6"; "0.31"; "7"; "67"; "135"; "2.41"; "19.2"; "36"; "981"; "23"; "1";"Estación Avenida Constitución";"43.529806";"-5.673428";"2001-01-01"; "6"; "9"; "0.24"; "24"; "63"; "44"; "1.7"; "15.9"; "62"; "983"; "23"; DATA ANALYTICS R E A L Flume
64. # Write format can be text or writable … #Defining channel – Memory type …1 … #Defining source – Syslog … … # Defining sink – HDFS … … #Defining interceptor agent.sources.source.interceptors = i1 class StationFilter implements Interceptor … if(!"Station".equals("2")) discard data; else save data; DATA ANALYTICS R E A L Flume
65.  Hadoop  Storm  JobTracker  Nimbus  TaskTracker  Supervisor  Job  Topology • Distributed and scalable realtime computation system • Doing for real-time processing what Hadoop did for batch processing • Topology: processing graph. Each node contains processing logic (spouts and bolts). Links between nodes are streams of data o Spout: Source of streams. Read a data source and emit the data into the topology as a stream o Bolts: Processing unit. Read data from several streams, does some processing and possibly emits new streams o Stream: Unbounded sequence of tuples. Tuples can contain any serializable object Storm DATA ANALYTICS R E A L
66. CAReader LineProcessor AvgValues • AirQuality average values o Step 1: build the topology Storm DATA ANALYTICS R E A L Spout Bolt Bolt
67. • AirQuality average values o Step 1: build the topology TopologyBuilder AirAVG= new TopologyBuilder(); builder.setSpout("ca-reader", new CAReader(), 1); //shuffleGrouping -> even distribution AirAVG.setBolt("ca-line-processor", new LineProcessor(), 3) .shuffleGrouping("ca-reader"); //fieldsGrouping -> fields with the same value goes to the same task AirAVG.setBolt("ca-avg-values", new AvgValues(), 2) .fieldsGrouping("ca-line-processor", new Fields("id")); Storm DATA ANALYTICS R E A L
68. public void open(Map conf, TopologyContext context, SpoutOutputCollector collector) { //Initialize file BufferedReader br = new … … } public void nextTuple() { • String line = br.readLine(); • if (line == null) { return; • } else collector.emit(new Values(line)); } Storm • AirQuality average values o Step 2: CAReader implementation (IRichSpout interface) DATA ANALYTICS R E A L
69. public void declareOutputFields (OutputFieldsDeclarer declarer) { declarer.declare(new Fields("id", "stationName", "lat", … } public void execute (Tuple input, BasicOutputCollector collector) { collector.emit(new Values(input.getString(0).split(";"); } Storm • AirQuality average values o Step 3: LineProcessor implementation (IBasicBolt interface) DATA ANALYTICS R E A L
70. 70 public void execute (Tuple input, BasicOutputCollector collector) { //totals and count are hashmaps with each station accumulated values if (totals.containsKey(id)) { item = totals.get(id); count = counts.get(id); } else { //Create new item } //update values item.setSo2(item.getSo2()+Integer.parseInt(input.getStringByField("so2"))); item.setNo(item.getNo()+Integer.parseInt(input.getStringByField("no"))); … } Storm • AirQuality average values o Step 4: AvgValues implementation (IBasicBolt interface) DATA ANALYTICS R E A L
71. • High level abstraction on top of Storm o Provides high level operations (joins, filters, projections, aggregations, functions…) Pros o Easy, powerful and flexible o Incremental topology development o Exactly-once semantics Cons o Very few built-in functions o Lower performance and higher latency than Storm Trident DATA ANALYTICS R E A L
72.  Simple Scalable Streaming System  Distributed, Scalable, Fault-tolerant platform for processing continuous unbounded streams of data  Inspired by MapReduce and Actor models of computation o Data processing is based on Processing Elements (PE) o Messages are transmitted between PEs in the form of events (Key, Attributes) o Processing Nodes are the logical hosts to PEs DATA ANALYTICS R E A L S4
73. … <bean id="split" class="SplitPE"> <property name="dispatcher" ref="dispatcher"/> <property name="keys"> <!-- Listen for both words and sentences --> <list> <value>LogLines *</value> </list> </property> </bean> <bean id="average" class="AveragePE"> <property name="keys"> <list> <value>CAItem stationId</value> </list> </property> </bean> • AirQuality average values S4 DATA ANALYTICS R E A L
74. Spark Streaming • Spark for real-time processing • Streaming computation as a series of very short batch jobs (windows) • Keep state in memory • API similar to Spark DATA ANALYTICS R E A L
75. 1. Big Data processing 2. Batch processing 3. Real-time processing 4. Hybrid computation model 5. Conclusions Agenda
76. • We are in the beginning of this generation • Short-term Big Data processing goal • Abstraction layer over the Lambda Architecture • Promising technologies o SummingBird o Lambdoop Hybrid Computation Model
77. SummingBird • Library to write MapReduce-like process that can be executed on Hadoop, Storm or hybrid model • Scala syntaxis • Same logic can be executed in batch, real-time and hybrid bath/real mode HYBRID COMPUTATION MODEL
78. SummingBird HYBRID COMPUTATION MODEL
79. Pros • Hybrid computation model • Same programing model for all proccesing paradigms • Extensible Cons • MapReduce-like programing • Scala • Not as abstract as some users would like SummingBird HYBRID COMPUTATION MODEL
80.  Software abstraction layer over Open Source technologies o Hadoop, HBase, Sqoop, Flume, Kafka, Storm, Trident  Common patterns and operations (aggregation, filtering, statistics…) already implemented. No MapReduce-like process  Same single API for the three processing paradigms o Batch processing similar to Pig / Cascading o Real time processing using built-in functions easier than Trident o Hybrid computation model transparent for the developer Lambdoop HYBRID COMPUTATION MODEL
81. Lambdoop Data Operation Data Workflow Streaming data Static data HYBRID COMPUTATION MODEL
82. DataInput db_historical = new StaticCSVInput(URI_db); Data historical = new Data (db_historical); Workflow batch = new Workflow (historical); Operation filter = new Filter (“Station", “=", 2); Operation select = new Select (“Titulo“, “SO2"); Operation group = new Group(“Titulo"); Operation average = new Average (“SO2"); batch.add(filter); batch.add(select); batch.add(group); batch.add(variance); batch.run(); Data results = batch.getResults(); … Lambdoop HYBRID COMPUTATION MODEL
83. DataInput stream_sensor = new StreamXMLInput(URI_sensor); Data sensor = new Data(stream_sensor) Workflow streaming = new Workflow (sensor, new WindowsTime(100) ); Operation filter = new Filter ("Station", "=", 2); Operation select = new Select ("Titulo", "S02"); Operation group = new Group("Titulo"); Operation average = new Average ("S02"); streaming.add(filter); streaming.add(select); streaming.add(group); streaming.add(average); streaming.run(); While (true) { Data live_results = streaming.getResults(); … } Lambdoop HYBRID COMPUTATION MODEL
84. DataInput historical= new StaticCSVInput(URI_folder); DataInput stream_sensor= new StreamXMLInput(URI_sensor); Data all_info = new Data (historical, stream_sensor); Workflow hybrid = new Workflow (all_info, new WindowsTime(1000) ); Operation filter = new Filter ("Station", "=", 2); Operation select = new Select ("Titulo", "SO2"); Operation group = new Group("Titulo"); Operation average = new Average ("SO2"); hybrid.add(filter); hybrid.add(select); hybrid.add(group); hybrid.add(variance); hybrid.run(); Data updated_results = hybrid.getResults(); Lambdoop HYBRID COMPUTATION MODEL
85. Pros • High abstraction layer for all processing model • All steps in the data processing pipeline • Same Java API for all programing paradigms • Extensible Cons • Ongoing project • Not open-source yet • Not tested in larger cluster yet Lambdoop HYBRID COMPUTATION MODEL
86. 1. Big Data processing 2. Batch processing 3. Real-time processing 4. Hybrid computation model 5. Conclusions Agenda
87. Conclusions • Big Data is not only Hadoop • Identify the processing requirements of your project • Analyze the alternatives for all steps in the data pipeline • The battle for real-time processing is open • Stay tuned for the hybrid computation model
88. Thanks for your attention! www.datadopter.com www.treelogic.com Contact us: ruben.casado@treelogic.com info@datadopter.com MADRID Avda. de Manoteras, 38 Oficina D507 28050 Madrid · España ASTURIAS Parque Tecnológico de Asturias Parcela 30 33428 Llanera - Asturias · España 902 286 386