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Hadoop Technical Introduction

Hadoop Technical Introduction 31
Lecture 3 – Hadoop Technical Introduction CSE 490HAnnouncements  My office hours: M 2:30—3:30 in CSE 212  Cluster is operational; instructions in assignment 1 heavily rewritten  Eclipse plugin is “deprecated”  Students who already created accounts: let me know if you have troubleBreaking news  Hadoop tested on 4,000 node cluster 32K cores (8 / node) 16 PB raw storage (4 x 1 TB disk / node) (about 5 PB usable storage) scalinghadoopto4000nodesa.htmlYou Say, “tomato…” Google calls it: Hadoop equivalent: MapReduce Hadoop GFS HDFS Bigtable HBase Chubby ZookeeperSome 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 a.k.a. TaskInProgress (TIP)  Task Attempt – A particular instance of an attempt to execute a task on a machineTerminology Example  Running “Word Count” across 20 files is one job  20 files to be mapped imply 20 map tasks + some number of reduce tasks  At least 20 map task attempts will be performed… more if a machine crashes, etc.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  Task ID from TaskInProgress is not a unique identifier; don’t use it that wayMapReduce: High Level                             NodetoNode Communication  Hadoop uses its own RPC protocol  All communication begins in slave nodes Prevents circularwait deadlock Slaves periodically poll for “status” message  Classes must provide explicit serialization 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 instancesJob 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 distributionData 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 HDFSConfiguring With JobConf  MR Programs have many configurable options  JobConf objects hold (key, value) components mapping String  ’a  e.g., “” 20  JobConf is serialized and distributed before running the job  Objects implementing JobConfigurable can retrieve elements from a JobConfWhat Happens In MapReduce Depth FirstJob Launch Process: Client  Client program creates a JobConf Identify classes implementing Mapper and Reducer interfaces  JobConf.setMapperClass(), setReducerClass() Specify inputs, outputs  FileInputFormat.addInputPath(),  FileOutputFormat.setOutputPath() Optionally, other options too:  JobConf.setNumReduceTasks(), JobConf.setOutputFormat()…Job Launch Process: JobClient  Pass JobConf to JobClient.runJob() or submitJob() runJob() blocks, submitJob() does not  JobClient: Determines proper division of input into InputSplits Sends job data to master JobTracker serverJob Launch Process: JobTracker  JobTracker: Inserts jar and JobConf (serialized to XML) in shared location Posts a JobInProgress to its run queueJob Launch Process: TaskTracker  TaskTrackers running on slave nodes periodically query JobTracker for work  Retrieve jobspecific jar and config  Launch task in separate instance of Java main() is provided by HadoopJob Launch Process: Task  TaskTracker.Child.main(): Sets up the child TaskInProgress attempt Reads XML configuration Connects back to necessary MapReduce components via RPC Uses TaskRunner to launch user processJob Launch Process: TaskRunner  TaskRunner, MapTaskRunner, MapRunner work in a daisychain to launch 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 sameCreating the Mapper  You provide the instance of Mapper Should extend MapReduceBase  One instance of your Mapper is initialized by the MapTaskRunner for a TaskInProgress Exists in separate process from all other instances of Mapper – no data sharingMapper  void map(K1 key, V1 value, OutputCollectorK2, V2 output, Reporter reporter)  K types implement WritableComparable  V types implement WritableWhat is Writable  Hadoop defines its own “box” classes for strings (Text), integers (IntWritable), etc.  All values are instances of Writable  All keys are instances of WritableComparableGetting Data To The Mapper  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 sourceFileInputFormat 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())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 listRecord Readers  Each InputFormat provides its own RecordReader implementation Provides (unused) capability multiplexing  LineRecordReader – Reads a line from a text file  KeyValueRecordReader – Used by KeyValueTextInputFormatInput Split Size  FileInputFormat will divide large files into chunks Exact size controlled by mapred.min.split.size  RecordReaders receive file, offset, and length of chunk  Custom InputFormat implementations may override split size – e.g., “NeverChunkFile”Sending Data To Reducers  Map function receives OutputCollector object OutputCollector.collect() takes (k, v) elements  Any (WritableComparable, Writable) can be used  By default, mapper output type assumed to be same as reducer output typeWritableComparator  Compares WritableComparable data Will call Can provide fast path for serialized data  JobConf.setOutputValueGroupingComparator()Sending Data To The Client  Reporter object sent to Mapper allows simple asynchronous feedback incrCounter(Enum key, long amount) setStatus(String msg)  Allows selfidentification of input InputSplit getInputSplit()Partition And Shuffle                                                    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  JobConf sets Partitioner implementationReduction  reduce( K2 key, IteratorV2 values, OutputCollectorK3, V3 output, Reporter reporter)  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” keysFinally: Writing The OutputOutputFormat  Analogous to InputFormat  TextOutputFormat – Writes “key val\n” strings to output file  SequenceFileOutputFormat – Uses a binary format to pack (k, v) pairs  NullOutputFormat – Discards outputQuestions
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