Cloud Application Development (SE808, School of Software, Sun Yat-Sen University) Yabo (Arber) Xu

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1 Lecture 5 Programming Hadoop I Cloud Application Development (SE808, School of Software, Sun Yat-Sen University) Yabo (Arber) Xu

2 Outline MapReduce basics A closer look at WordCount MR Anatomy of MapReduce job run on Hadoop Workflow Dataflow 2 / 47

3 Checking Your Hadoop Status jps a tool for checking hadoop process running Linux netstat util 3 / 47

4 Checking Your Hadoop Status Hadoop Web UI Assume your master node IP = master.icloudlab.net HDFS Status: MapReduce Administration: TaskTracker Status on Slave: 4 / 47

5 Checking Your Hadoop Status LOG is the ultimate tool for diagnosing problems Log location: <hadoop>/logs/ hadoop-<username>-namenode-<nodename>.log hadoop-<username>-datanode-<nodename>.log hadoop-<username>-jobtracker-<nodename>.log hadoop-<username>-tasktracker-<nodename>.log 5 / 47

6 Checking Your Hadoop Status JPS command tool Linux netstat util 6 / 47

7 MapReduce Terminology Job a full program -an execution of a Mapper and Reducer across a data set Task an execution of a Mapper or a Reducer on a slice of data Task Attempt a particular instance of an attempt to execute a task on a machine 7 / 47

8 MapReduce Job in High Level $bin/hadoop jar hadoop examples.jar wordcount input output 8 / 47

9 Task Attempts A particular task will be attempted at least once, possibly more times if it crashes If the same input causes crashes over and over, that input will eventually be abandoned Multiple attempts at one task may occur in parallel with speculative execution turned on 9 / 47

10 Nodes, Trackers, Tasks Master node runs JobTracker instance, which accepts Job requests from clients TaskTracker instances run on slave nodes TaskTracker forks separate Java process for task instances 10 / 47

11 Job Distribution MapReduce programs are contained in a Java jar file + an XML file containing serialized program configuration options Running a MapReduce job places these files into the HDFS and notifies TaskTrackers where to retrieve the relevant program code Where s the data distribution? 11 / 47

12 Data Distribution Implicit in design of MapReduce! All mappers are equivalent; so map whatever data is local to a particular node in HDFS If lots of data does happen to pile up on the same node, nearby nodes will map instead Data transfer is handled implicitly by HDFS 12 / 47

13 Configuring With Job MR Programs have many configurable options Job objects hold (key, value) mapping e.g., mapred.map.tasks 20 Job is serialized and distributed before running the job 13 / 47

14 A Closer Look at Word Count MapReduce Program

15 New interface for Hadoop 0.20.*; job.setjarbyclass(wordcount.class) ; Code from

16 WordCount Map Class 16 / 47

17 WordCount Reduce Class 17 / 47

18 Anatomy of a MapReduce Job Run with Hadoop 18 / 47

19

20 Job Launch Process: Client Client program creates a Job Identify classes implementing Mapper and Reducer interfaces job.setmapperclass(map.class); job.setreducerclass(reduce.class); Specify inputs, outputs job.setinputformatclass(textinputformat.class); job.setoutputformatclass(textoutputformat.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); Optionally, other options too: job.setnumreducetasks(), 20 / 47

21 Job Launch Process: Submit Job Call Job.waitForCompletion(boolean verbose) to submit the job Get new job ID from JobTracker Validate job output Determines proper division of input into InputSplits Sends job data ( jar/conf/input splits ) to the jobtracker s filesystem in a directory named after the job ID. job jar with a high replication factor (mapred.submit.replication=10), easy for tasktrackers to access. Call submitjob( JobID ) on jobtracker 21 / 47

22 Job Launch Process: JobTracker JobTracker: Put Job in the queue, and have Scheduler handle it. Different schedulers available Inserts jar and JobConf (serialized to XML) in shared location Posts a JobInProgress to its run queue 22 / 47

23 Job Launch Process: TaskTracker TaskTrackers running on slave nodes periodically query JobTracker for work ( also as a heart-beat) Retrieve job-specific jar and config Launch task in separate instance of Java main() is provided by Hadoop TaskTracker close to data selected in priority Each TaskTracker has a fixed number of map/reduce task slots ( resource-bounded ), with map tasks in priority. 23 / 47

24 Job Launch Process: Task TaskTracker.Child.main(): Sets up the child TaskInProgress attempt Reads XML configuration Connects back to necessary MapReduce Independent task instance Uses TaskRunner to launch user process crashes do not affect TaskTracker User process talks to TaskerTracker via umbilical interface 24 / 47

25 Job Launch Process: TaskRunner TaskRunner launches your Mapper Task knows ahead of time which InputSplits it should be mapping Calls Mapper once for each record retrieved from the InputSplit Running the Reducer is much the same 25 / 47

26 Job Launch Process: Status/Progress Report Reports every 5s Reports every 3s 26 / 47

27 MapReduce Data Flow 27 / 47

28 Creating the Mapper You provide the instance of Mapper Should extend Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT> One instance of your Mapper is initialized per task Exists in separate process from all other Instances of Mapper no data sharing! 28 / 47

29 Mapper 0.19 API void map(writablecomparable key, Writable value, OutputCollector output, Reporter reporter) 0.20 API ( 0.19 API also supported ) void map( KEYIN key, VALUEIN value, Context context ) { Context.write( (KEYOUT) key, (VALUEOUT) value ); } 29 / 47

30 What Is Writable? Hadoop defines its own box classes for strings (Text), integers (IntWritable), etc. All values are instances of Writable Supports serialization RPC/Data Persistence All keys are instances of WritableComparable Supports comparison Shuffle and sorting 30 / 47

31 Getting Data To The Mapper 31 / 47

32 Reading Data Data sets are specified by InputFormats Defines input data (e.g., a directory) Identifies partitions of the data that form an InputSplit Factory for RecordReader objects to extract (k, v) records from the input source 32 / 47

33 FileInputFormat And Friends TextInputFormat Treats each \n -terminated line of a file as a value KeyValueTextInputFormat Maps \n - terminated text lines of k SEP v SequenceFileInputFormat Binary file of (k, v) pairs with some add l metadata SequenceFileAsTextInputFormat Same, but maps (k.tostring(), v.tostring()) 33 / 47

34 Filtering File Inputs FileInputFormat will read all files out of a specified directory and send them to the mapper Delegates filtering this file list to a method subclasses may override e.g., Create your own xyzfileinputformat to read *.xyz from directory list 34 / 47

35 Record Readers Each InputFormat provides its own RecordReader implementation Provides (unused?) capability multiplexing LineRecordReader Reads a line from a text file KeyValueRecordReader Used by KeyValueTextInputFormat 35 / 47

36 Input Split Size FileInputFormat will divide large files into chunks Exact size controlled by mapred.min.split.size A bit more complex in practice, see getsplit() for details RecordReaders receive file, offset, and length of chunk Custom InputFormat implementations may override split size e.g., NeverChunkFile 36 / 47

37 Sending Data To Reducers Map function receives Context object Context.write() takes (k, v) elements Any (WritableComparable, Writable) can be used 37 / 47

38 Partition And Shuffle 38 / 47

39 Partitioner int getpartition(key, val, numpartitions) Outputs the partition number for a given key One partition == values sent to one Reduce task HashPartitioner used by default Uses key.hashcode() to return partition num Job sets Partitioner implementation Job.setPartitionerClass() 39 / 47

40 Map Output in Details When you call collect() in mapper Write <partition-id, key-value> in buffer //parition-id is obtained by call partitioner.getpartition(); When the buffer is full, call sortandspill() to create spill file sorted by partition-id Call combiner.reduce() if combiner is not null Write partitioned key-values one by one When map finishes, call mergeparts() to merge all the spills by partition. 40 / 47

41 Reduction Reduce Phase Copy: call ReduceCopier.fetchOutput() to get map output. Note, copy may start at the same as mapper Sort: Merge the map output, and creates a Iterator for access keyvalue pairs Reduce: caller reducer.reduce(); 41 / 47

42 Reduction reduce(keyin key, Iterable<VALUEIN> values, Context context) Keys & values sent to one partition all go to the same reduce task Calls are sorted by key earlier keys are reduced and output before later keys Remember values.next() always returns the same object, different data! 42 / 47

43 Finally: Writing The Output 43 / 47

44 OutputFormat Analogous to InputFormat TextOutputFormat Writes key val\n strings to output file SequenceFileOutputFormat Uses a binary format to pack (k, v) pairs NullOutputFormat Discards output 44 / 47

45 Conclusions That s the Hadoop work/data flow! Lots of flexibility to override components, customize inputs and outputs Using custom-built binary formats allows high-speed data movement 45 / 47

46 A Side Question Why reducer seems to start before map finishes? Some of operations in reduce phase, i.e. sort, starts at the same time with mapper, and they cause the updates of reduce progress. 46 / 47

47 No secrets in front of the source code.

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