1. A Hadoop Overview

2. Outline Progress Report MapReduce Programming Hadoop Cluster Overview HBase Overview Q & A

3. Outline Progress Report MapReduce Programming Hadoop Cluster Overview HBase Overview Q & A

4. Progress Hadoop buildup has been completed.  Version 0.19.0, running under Standalone mode. HBase buildup has been completed.  Version 0.19.3, with no assists of HDFS. Simple demonstration over MapReduce.  Simple word count program.

5. Testing Platform Fedora 10 JDK1.6.0_18 Hadoop-0.19.0 Hbase-0.19.3 One can connect to the machine using pietty or putty.  Host: 140.112.90.180  Account: labuser  Password: robot3233  Port: 3385 (using ssh connection)

6. Outline Progress Report MapReduce Programming Hadoop Cluster Overview HBase Overview Q & A

7. MapReduce A computing framework including map phase, shuffling phase and reduce phase. Map function and Reduce function are provided by the user. Key-Value Pair(KVP)  map is initiated with each KVP ingested, and output any number of KVPs.  reduce is initiated with each key and its corresponding values, and output any number of KVPs.

8. MapReduce(cont.)

9. What user has to do? 1. Specify the input/output format 2. Specify the output key/value type 3. Specify the input/output location 4. Specify the mapper/reducer class 5. Specify the number of reduce tasks 6. Specify the partitioner class(dicussed later)

10. What user has to do?(cont.) Specify the input/output format  “Input/Output format” is class that translate raw data and KVPs.  Has to inherit class InputFormat / OutputFormat.  Input format is required.  The most common choice is KeyValueTextInputFormat class and SequenceFileInputFormat class.  Output format is selective, the default is TextOutputFormat class.

11. What user has to do?(cont.) Specify the output key/value type  The KVP type output by reducer.  The Key type has to implements WritableComparable interface.  The Value type has to implements Writable interface. Specify the input/output location  The directory or for input files/output files.  The input directory should exist and contain at least one file.  The output directory should not exist or be empty.

12. What user has to do?(cont.) Specify the mapper/reducer class  The two classes should extend MapReduceBase class.  The map/reduce class should implement Mapper /Reducer interface Specify the number of reduce tasks  Usually approximate the number of computing nodes.  1 if we want a single output file.  0 if we don’t need the reduce phase.  Note that we will not have our result sorted.  The reducer class is not required in this case.

13. Map Phase Configuration ElementRequired?Default Input path(s)Yes Class to convert the input path elements to KVPs Yes Map output key classNoJob output key class Map output value classNoJob output value class Class supplying the map functionYes Suggested minimum number of map tasksNoCluster default Number of threads to run each map taskNo1

14. Reduce Phase Configuration ElementRequired?Default Output pathYes Class to convert the KVPs to output filesNoTextOutputFormat Job input key classNoJob output key class Job input value classNoJob output value class Job output key classYes Job output value classYes Class supplying the reduce functionYes The number of reduce tasksNoCluster default

15. MapReduceIntro.java public class MapReduceIntro { protected static Logger logger = Logger.getLogger(MapReduceIntro.class); public static void main(final String[] args) { try { final JobConf conf = new JobConf(MapReduceIntro.class); conf.set("hadoop.tmp.dir","/tmp"); conf.setInputFormat(KeyValueTextInputFormat.class); FileInputFormat.setInputPaths(conf, MapReduceIntroConfig.getInputDirectory()); conf.setMapperClass(IdentityMapper.class); FileOutputFormat.setOutputPath(conf, MapReduceIntroConfig.getOutputDirectory()); conf.setOutputKeyClass(Text.class); conf.setOutputValueClass(Text.class); conf.setNumReduceTasks(1); conf.setReducerClass(IdentityReducer.class); final RunningJob job = JobClient.runJob(conf); if (!job.isSuccessful()) { logger.error("The job failed."); System.exit(1); } System.exit(0); } public class MapReduceIntro { protected static Logger logger = Logger.getLogger(MapReduceIntro.class); public static void main(final String[] args) { try { final JobConf conf = new JobConf(MapReduceIntro.class); conf.set("hadoop.tmp.dir","/tmp"); conf.setInputFormat(KeyValueTextInputFormat.class); FileInputFormat.setInputPaths(conf, MapReduceIntroConfig.getInputDirectory()); conf.setMapperClass(IdentityMapper.class); FileOutputFormat.setOutputPath(conf, MapReduceIntroConfig.getOutputDirectory()); conf.setOutputKeyClass(Text.class); conf.setOutputValueClass(Text.class); conf.setNumReduceTasks(1); conf.setReducerClass(IdentityReducer.class); final RunningJob job = JobClient.runJob(conf); if (!job.isSuccessful()) { logger.error("The job failed."); System.exit(1); } System.exit(0); } Initial Configuration Map Phase Configuration Reduce Phase Configuration Job Running

16. IdentityMapper.java public class IdentityMapper extends MapReduceBase implements Mapper { public void map(K key, V val, OutputCollector output, Reporter reporter) throws IOException { output.collect(key, val); } public class IdentityMapper extends MapReduceBase implements Mapper { public void map(K key, V val, OutputCollector output, Reporter reporter) throws IOException { output.collect(key, val); } Input type Output type Discussed later Collect output KVPs

17. IdentityReducer.java public class IdentityReducer extends MapReduceBase implements Reducer { public void reduce(K key, Iterator values, OutputCollector output, Reporter reporter) throws IOException { while (values.hasNext()) { output.collect(key, values.next()); } public class IdentityReducer extends MapReduceBase implements Reducer { public void reduce(K key, Iterator values, OutputCollector output, Reporter reporter) throws IOException { while (values.hasNext()) { output.collect(key, values.next()); } The input value is an Iterator !

18. Compiling Using default java compiler  Note that we have to supply – classpath parameter so that the compiler can find the hadoop core libraries and other classes needed.  $ javac –classpath $HADOOP_HOME/hadoop-0.19.0-core.jar:. –d. Myclass.java The hadoop core libraries The location of other class files

19. Creating jar file To create an executable jar file: 1. Create a file “manifest.mf” 2. Type the command:  $ jar –cmf MyExample.jar manifest.mf  Wildcard character * is also accepted. Main-Class: myclass Class-Path: MyExample.jar A white space! A return carriage! White space separate list! The driver class

20. Run the jar file Using hadoop command.  $ hadoop jar MyExample.jar Remember that the output path should not exist.  If the path exist, use rm path –r command.

21. A simple demonstration A simple word count program.

22. Reporter

23. Outline Progress Report MapReduce Programming Hadoop Cluster Overview HBase Overview Q & A

24. Hadoop Full name Apache Hadoop project.  Open source implementation for reliable, scalable distributed computing.  An aggregation of the following projects (and its core):  Avro  Chukwa  HBase  HDFS  Hive  MapReduce  Pig  ZooKeeper

25. Virtual Machine (VM) Virtualization  All services are delivered through VMs.  Allows for dynamically configuring and managing.  There can be multiple VMs running on a single commodity machine.  VMware

26. HDFS(Hadoop Distributed File System) The highly scalable distributed file system of Hadoop.  Resembles Google File System(GFS).  Provides reliability by replication. NameNode & DataNode  NameNode  Maintains file system metadata and namespace.  Provides management and control services.  Usually one instance.  DataNode  Provides data storage and retrieval services.  Usually several instances.

27. MapReduce The sophisticate distributed computing service of Hadoop.  A computation framework.  Usually resides on HDFS. JobTracker & TaskTracker  JobTracker  Manages the distribution of tasks to the TaskTrackers.  Provides job monitoring and control, and the submission of jobs.  TaskTracker  Manages single map or reduce tasks on a compute node.

28. Cluster Makeup A Hadoop cluster is usually make up by:  Real Machines.  Not required to be homogeneous.  Homogeneity will help maintainability.  Server Process.  Multiple process can be run on a single VM. Master & Slave  The node/machine running the JobTracker or NameNode will be Master node.  The ones running the TaskTracker or DataNode will be Slave node.

29. Cluster Makeup(cont.)

30. Administrator Scripts Administrator can use the following script files to start or stop server processes.  Can be located in $HADOOP_HOME/bin  start-all.sh/stop-all.sh  start-mapred.sh/stop-mapred.sh  start-dfs.sh/stop-dfs.sh  slaves.sh  hadoop

31. Configuration By default, each Hadoop Core server will load the configuration from several files.  These file will be located in $HADOOP_HOME/conf  Usually identical copies of those files are maintained in every machine in the cluster.

32. Outline Progress Report MapReduce Programming Hadoop Cluster Overview HBase Overview Q & A

33. HBase The Hadoop scalable distributed database.  Resembles Google BigTable.  Not relational database.  Resides in HDFS. Master & RegionServer  Master  For bootstrapping and RegionServer recovering.  Assigning regions to RegionServers.  RegionServer  Hold 0 or more regions.  responsible for data transaction.

34. Hbase(cont.)

35. Row, Column, Timestamp The data cell is the intersection of an individual row key and a column.  Cells stores uninterrupted array of byte.  Cell data is versioned by timestamp.

36. Row Row (Key) is the primary key of database  Can be consisted by arbitrary byte array.  Strings, binary data.  Each row has to be distinguished.  The table is sorted by row key.  Any mutation action of a single row is atomic.

37. Column/Column Family Columns are grouped into families, with which shares a common prefix.  Ex: temperature:air and temperature:dew_point  The prefix has to be a printable string.  The column name can also be arbitrary byte array.  Column family member can be dynamically added or dropped.  Column families must be pre-specified as table schemas.  HBase is indeed a column-family-oriented storing.  The same column family will be stored together in any file system.

38. Region The table is automatically horizontally-partitioned into regions.  That is, a region is a subset of data rows.  Regions are stored in separated RegionServers.  A region is defined by its first row, last row, and a randomly generated identifier.  The partition will be completed by the master automatically.

39. Administrator Scripts Administrator can use the following script files to start or stop server processes.  Can be located in $HBASE_INSTALL/bin  start-hbase.sh / stop-hbase.sh  hbase hbase shell to initial a command line interface. hbase master / hbase regionserver

40. HBase shell command line Type command help to get information.  create ‘table’, ‘column family1’, ‘column family2’, …  put ‘table’, ‘row’, ‘column’, ‘value’  get ‘table’, ‘row’, {COLUMN=>…}  alter ‘table’, {NAME=>‘...’}  To modify a table schema, we have to disable it first!  scan ‘table’  disable ‘table’  drop ‘table’  To drop a table, we have to disable it first!!  list

41. A Simple Demonstration Command line operation

42. Operations Create table (and its schema)  Shell  create ‘table’, ‘cf1’, ‘cf2’,…  create ‘table’, {NAME=>‘cf1’}, {NAME=>‘cf2’},…  API HBaseAdmin admin = new HBaseAdmin(new HBaseConfiguration()); HTableDescriptor table = new HTableDescriptor(“table”); table.addFamily(new HColumnDescriptor(“cf1:”)); table.addFamily(new HColumnDescriptor(“cf2:”)); admin.createTable(table); HBaseAdmin admin = new HBaseAdmin(new HBaseConfiguration()); HTableDescriptor table = new HTableDescriptor(“table”); table.addFamily(new HColumnDescriptor(“cf1:”)); table.addFamily(new HColumnDescriptor(“cf2:”)); admin.createTable(table);

43. Operations(cont.) Modify table (and its schema)  Shell  alter ‘table’, {NAME=>’cf’, KEY=>’value’, …}  API  Note that there will be exceptions if the table is not disabled. HBaseAdmin admin = new HBaseAdmin(); Admin.modifyColumn(“table”,”cf”, new HColumnDescriptor(…)); Admin.modifyTable(new HTableDescriptor(…)); HBaseAdmin admin = new HBaseAdmin(); Admin.modifyColumn(“table”,”cf”, new HColumnDescriptor(…)); Admin.modifyTable(new HTableDescriptor(…));

44. Operations(cont.) Write data  Shell  put ‘table’, ‘row’, ‘cf:name’, ‘value’, ts  API HTable table = new Htable(“table”); BatchUpdate update = new BatchUpdate(“row”); update.put(“cf:name”,”value”); table.commit(update); HTable table = new Htable(“table”); BatchUpdate update = new BatchUpdate(“row”); update.put(“cf:name”,”value”); table.commit(update);

45. Operations(cont.) Retrieve data  Shell  get ‘table’, ‘row’, {COLUMN=>’cf:name’, …}  API  If we don’t know the row retrieved at prior, we can use Scanner object instead. Scanner scanner = table.getScanner(“cf:name”); HTable table = new HTable(“table”); RowResult row = table.getRow(“row”); Cell data = table.get(“row”,”cf:name”); HTable table = new HTable(“table”); RowResult row = table.getRow(“row”); Cell data = table.get(“row”,”cf:name”);

46. Operations Delete a cell  Shell  delete ‘table’, ‘row’, ‘cf:name’  API HTable table = new HTable(“table”); BatchUpdate update = new BatchUpdate(“row”); Udpate.delete(“cf:name”); table.commit(update); HTable table = new HTable(“table”); BatchUpdate update = new BatchUpdate(“row”); Udpate.delete(“cf:name”); table.commit(update);

47. Operations(cont.) Enable/Disable a table  Shell  enable/disable ‘table’  API HBaseAdmin admin = new HBaseAdmin(new HBaseConfiguration()); admin.disableTable(“table”); admin.enableTable(“table”); HBaseAdmin admin = new HBaseAdmin(new HBaseConfiguration()); admin.disableTable(“table”); admin.enableTable(“table”);

48. Outline Progress Report MapReduce Programming Hadoop Cluster Overview HBase Overview Q & A

49. Hadoop 0.19.0 API  http://hadoop.apache.org/common/docs/r0.19.0/api/index.h tml HBase 0.19.3 API  http://hadoop.apache.org/hbase/docs/r0.19.3/api/index.html Any question?