Hadoop Online training India hyderabad|Hadoop training for Low cost-price

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Hadoop Online training India hyderabad|Hadoop training for Low cost-price

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Title: Hadoop Online training India hyderabad|Hadoop training for Low cost-price


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Hadoop Technical Introduction from RVH
Technologies.
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Hadoop is a free, Java-based programming
framework that supports the processing of large
data sets in a distributed computing environment.
It is part of the Apache project sponsored by the
Apache Software Foundation.
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Terminology
Google calls it Hadoop equivalent
MapReduce Hadoop
GFS HDFS
Bigtable HBase
Chubby Zookeeper
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Some 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. Task-In-Progress (TIP)
  • Task Attempt A particular instance of an
    attempt to execute a task on a machine

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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 dont use it that way

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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

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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 cod

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Creating 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 sharing!

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Mapper
  • void map(K1 key,
  • V1 value,
  • OutputCollectorltK2, V2gt output,
  • Reporter reporter)
  • K types implement WritableComparable
  • V types implement Writable

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Getting Data To The Mapper
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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

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Sending Data To The Client
  • Reporter object sent to Mapper allows simple
    asynchronous feedback
  • incrCounter(Enum key, long amount)
  • setStatus(String msg)
  • Allows self-identification of input
  • InputSplit getInputSplit()

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Example Program - Wordcount
  • map()
  • Receives a chunk of text
  • Outputs a set of word/count pairs
  • reduce()
  • Receives a key and all its associated values
  • Outputs the key and the sum of the values
  • package org.myorg
  • import java.io.IOException
  • import java.util.
  • import org.apache.hadoop.fs.Path
  • import org.apache.hadoop.conf.
  • import org.apache.hadoop.io.
  • import org.apache.hadoop.mapred.
  • import org.apache.hadoop.util.
  • public class WordCount

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