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HELLO WORLD! IN HADOOP Chandra Yarlagadda randommuses chandrayarlagadda twitter.com/ linkedin.com/in/ Email : [email protected]

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Titanium Sponsors Platinum Sponsors Gold Sponsors

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Location / People / Events SMB Analytics Infrastructure Analytics Applications Data Sources Storage Security Crowdsourcing Hadoop Related NoSQL Databases Data Visualization Cluster Services Personal Data MPP Databases Industry Applications Social Media Sentiment Analysis Analytics Solutions Crowdsourced Analytics IT Analytics Data Sources Data Marketplaces Publisher Tools Marketing Management / Monitoring Real-Time Ad Optimization Statistical Computing Cross Infrastructure / Analytics Application Service Providers Big Data Search Analytics Services Collection / Transport Open Source Projects Framework Query / Data Flow Data Access Coordination / Workflow Machine Learning Real - Time Statistical Tools Cloud Deployment NewSQL Databases Source : www.bigdatalandscape.com

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Location / People / Events SMB Analytics Infrastructure Analytics Applications Data Sources Storage Security Crowdsourcing Hadoop Related NoSQL Databases Data Visualization Cluster Services Personal Data MPP Databases Industry Applications Social Media Sentiment Analysis Analytics Solutions Crowdsourced Analytics IT Analytics Data Sources Data Marketplaces Publisher Tools Marketing Management / Monitoring Real-Time Ad Optimization Statistical Computing Cross Infrastructure / Analytics Application Service Providers Big Data Search Analytics Services Collection / Transport Open Source Projects Framework Query / Data Flow Data Access Coordination / Workflow Machine Learning Real - Time Statistical Tools Cloud Deployment NewSQL Databases Source : www.bigdatalandscape.com

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HDFS

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HDFS MAP REDUCE

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What is Big Data?

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What is Big Data?

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What is Big Data? Volume Variety Velocity

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Before We Jump In!

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Before We Jump In! • Hadoop 1.2.1

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Before We Jump In! • Hadoop 1.2.1 • Daemon

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Before We Jump In! • Hadoop 1.2.1 • Daemon • Pseudo - Distributed Mode

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Before We Jump In! • Hadoop 1.2.1 • Daemon • Pseudo - Distributed Mode • Cloudera VM

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Before We Jump In! • Hadoop 1.2.1 • Daemon • Pseudo - Distributed Mode • Cloudera VM • Do Not Start the Cloudera Manager

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Before We Jump In! • Hadoop 1.2.1 • Daemon • Pseudo - Distributed Mode • Cloudera VM • Do Not Start the Cloudera Manager • Word Count

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HDFS

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HDFS • Hadoop Distributed File System

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HDFS • Hadoop Distributed File System • Designed for Storing Very large files with

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HDFS • Hadoop Distributed File System • Designed for Storing Very large files with • Streaming data access

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HDFS • Hadoop Distributed File System • Designed for Storing Very large files with • Streaming data access • running on clusters of Commodity hardware

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`

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` Data Nodes

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` Data Nodes

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` Name Node Data Nodes

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` Name Node Data Nodes

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` Name Node Block1 Block2 Block3 Data Nodes

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` Name Node Block1 Block2 Block3 Block1 Block2 Block3 Data Nodes

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` Name Node Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes

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` Name Node Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes

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` Name Node Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Name Node fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Name Node fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Name Node Secondary Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Name Node Secondary Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Name Node Secondary Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Secondary Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Secondary Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Secondary Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Secondary Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Secondary Name Node Edit Log fsImage Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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` Secondary Name Node Edit Log fsImage New Name Node Block1 Block2 Block3 Block1 Block1 Block2 Block2 Block3 Block3 Data Nodes Block1

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HDFS - ATTRIBUTES

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HDFS - ATTRIBUTES • Distributed

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HDFS - ATTRIBUTES • Distributed • Write Once - Read Many Times Pattern

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HDFS - ATTRIBUTES • Distributed • Write Once - Read Many Times Pattern • High Throughput - High Latency

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HDFS - ATTRIBUTES • Distributed • Write Once - Read Many Times Pattern • High Throughput - High Latency • CAP ( Consitency - Availability - Parition Tolerance)

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HDFS - ATTRIBUTES • Distributed • Write Once - Read Many Times Pattern • High Throughput - High Latency • CAP ( Consitency - Availability - Parition Tolerance) • Not Suitable for a lot of Small Files

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HDFS - ATTRIBUTES • Distributed • Write Once - Read Many Times Pattern • High Throughput - High Latency • CAP ( Consitency - Availability - Parition Tolerance) • Not Suitable for a lot of Small Files • High Network Utilization and Disk I/O

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MAP REDUCE

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MAP REDUCE It is a programming model and an associated implementation for

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MAP REDUCE It is a programming model and an associated implementation for • Processing and Generating large data sets with a

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MAP REDUCE It is a programming model and an associated implementation for • Processing and Generating large data sets with a • Parallel,

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MAP REDUCE It is a programming model and an associated implementation for • Processing and Generating large data sets with a • Parallel, • Distributed Algorithm on a

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MAP REDUCE It is a programming model and an associated implementation for • Processing and Generating large data sets with a • Parallel, • Distributed Algorithm on a • Cluster ( HDFS)

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MAP REDUCE - KEY POINTS

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MAP REDUCE - KEY POINTS • Map and Reduce Phase

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MAP REDUCE - KEY POINTS • Map and Reduce Phase • User defines the Map and Reduce Functions

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MAP REDUCE - KEY POINTS • Map and Reduce Phase • User defines the Map and Reduce Functions • Both Phases have a set of Key - Value Pairs

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MAP REDUCE - SINGLE REDUCER

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MAP REDUCE - SINGLE REDUCER

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MAP REDUCE - SINGLE REDUCER Mapper Mapper Mapper Reducer

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MAP REDUCE - SINGLE REDUCER Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer

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MAP REDUCE - SINGLE REDUCER Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer

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MAP REDUCE - SINGLE REDUCER Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer

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MAP REDUCE - SINGLE REDUCER Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer

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MAP REDUCE - SINGLE REDUCER Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer Name Node

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MAP REDUCE - SINGLE REDUCER Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer HDFS Name Node

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MAP REDUCE - SINGLE REDUCER Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer HDFS Job Tracker Task Tracker Name Node

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MAP REDUCE - SINGLE REDUCER Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer HDFS Job Tracker Task Tracker Name Node

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MAP REDUCE - MULTIPLE REDUCERS Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer HDFS Job Tracker Task Tracker Name Node Reducer

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MAP REDUCE - MULTIPLE REDUCERS Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer HDFS Job Tracker Task Tracker Name Node Reducer

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MAP REDUCE - MULTIPLE REDUCERS Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer HDFS Job Tracker Task Tracker Name Node Reducer Sort and Shuffle

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MAP REDUCE - MULTIPLE REDUCERS Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer HDFS Job Tracker Task Tracker Name Node Reducer Sort and Shuffle

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MAP REDUCE - MULTIPLE REDUCERS Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 Reducer HDFS Job Tracker Task Tracker Name Node Reducer Sort and Shuffle

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MAP REDUCE - ZERO REDUCERS Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 HDFS Job Tracker Name Node

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MAP REDUCE - ZERO REDUCERS Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 HDFS Job Tracker Name Node

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MAP REDUCE - ZERO REDUCERS Mapper Mapper Mapper Input Split 1 Input Split 2 Input Split 3 HDFS Job Tracker Task Tracker Name Node

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Key - Value

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Key - Value map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue)

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Key - Value 0 Twinkle Twinkle Little Star Little 1 Star 1 Twinkle 1,1 => map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue)

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Key - Value 0 Twinkle Twinkle Little Star Little 1 Star 1 Twinkle 1,1 => map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue) reduce(IntermediateKeys,IntermediateValues) => Set of (OutputKey,OutputValue)

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Key - Value 0 Twinkle Twinkle Little Star Little 1 Star 1 Twinkle 1,1 => Little 1 Star 1 Twinkle 1,1 => Little 1 Star 1 Twinkle 2 map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue) reduce(IntermediateKeys,IntermediateValues) => Set of (OutputKey,OutputValue)

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MAP CODE

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MAP CODE map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue)

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MAP CODE public static class TokenizerMapper extends Mapper{ map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue)

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MAP CODE public static class extends map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue) private Text word = new Text(); private final static IntWritable one = new IntWritable(1);

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MAP CODE public static class extends map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue) public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); private private final static

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MAP CODE public static class extends map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue) public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); private private final static 0 Twinkle Twinkle Little Star

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MAP CODE public static class extends map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue) public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); private private final static 0 Twinkle Twinkle Little Star Little 1 Star 1 Twinkle 1,1

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MAP CODE public static class TokenizerMapper extends Mapper{ map ( InputKey, InputValue) => Set of (IntermediateKey,IntermediateValue) public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); private Text word = new Text(); private final static IntWritable one = new IntWritable(1); } } }

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REDUCE CODE

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REDUCE CODE reduce(IntermediateKeys,IntermediateValues) => Set of (OutputKey,OutputValue)

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REDUCE CODE public static class IntSumReducer extends Reducer{ reduce(IntermediateKeys,IntermediateValues) => Set of (OutputKey,OutputValue)

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REDUCE CODE public static class extends reduce(IntermediateKeys,IntermediateValues) => Set of (OutputKey,OutputValue) private IntWritable result = new IntWritable();

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REDUCE CODE public static class extends reduce(IntermediateKeys,IntermediateValues) => Set of (OutputKey,OutputValue) public void reduce(Text key, Iterable values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val:values) { sum += val.get(); } result.set(sum); context.write(key, result); private

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REDUCE CODE public static class extends reduce(IntermediateKeys,IntermediateValues) => Set of (OutputKey,OutputValue) public void reduce(Text key, Iterable values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val:values) { sum += val.get(); } result.set(sum); context.write(key, result); private Little 1 Star 1 Twinkle 1,1 Little 1 Star 1 Twinkle 2

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REDUCE CODE public static class IntSumReducer extends Reducer{ reduce(IntermediateKeys,IntermediateValues) => Set of (OutputKey,OutputValue) public void reduce(Text key, Iterable values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val:values) { sum += val.get(); } result.set(sum); context.write(key, result); private IntWritable result = new IntWritable(); } }

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MAIN() public static void main(String[] args) throws Exception { Job job = Job.getInstance(conf, "word count"); Configuration conf = new Configuration(); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } job.setInputFormatClass(TextInputFormat.class);

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DEMO TIME

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RESOUCES

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RESOUCES • https://hadoop.apache.org/

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RESOUCES • https://hadoop.apache.org/ • Hadoop the Definitive Guide - Tom White

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RESOUCES • https://hadoop.apache.org/ • Hadoop the Definitive Guide - Tom White • http://hortonworks.com/tutorials/

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RESOUCES • https://hadoop.apache.org/ • Hadoop the Definitive Guide - Tom White • http://hortonworks.com/tutorials/ • http://www.cloudera.com/content/cloudera/en/documentation/core/ v5-3-x/topics/cloudera_quickstart_vm.html

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Questions?