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What is MapReduce?


  1. How to process large data sets and easily utilize the resources of a large distributed system.
  2. A programming model for parallel processing of a distributed data on a cluster
  3. 2 staged data processing ie, Map and Reduce
  4. Each stage emits key value pairs as a result of its work
  5. Programing MapReduc, In Java, there are three classes ie, a) Map b)Reduce c) Job configuration(with a, main' function)
  6. MapReduce is a model for parallel data processing on Hadoop in a batch fachion
  7. Logic written in Java
  8. Resource allocation controlled by YARN
  9. Programming model for expressing distributed computions at a massive scale.
  10. Popularized by open source Hadoop project.
  11. Compiled language
  12. Lower level of abstraction
  13. More lines of code
  14. More development effort is involved
  15. Code efficiency is high when compared to pig and hive


What is Hadoop?


  1. Open source data storage and processing API.
  2. Hadoop is reliable and fault tolerant with no rely on hardware for these properties.
  3. It is made by apache software foundation in 2011. written in JAVA


Sample application:


  1. Search:
Input: (lineNumber, line) records
Output: line matching a given pattern
Map:
if(line matches pattern): output(line)
Reduce: identify function
-alternative: no reducer(map only job)


2. Sort:

Input: (key, value) records
Output: Same records, sorted by key
map: identify function
Reduce: Identify function
Trick: Pick partitioning function h such that k1<k2 => h(k1)   <(k2)


3. Inverted Index

Input: (filename, text) records
Output: list of files containing each word
Map: foreach word in text.split(): output(word, filename)
Combine: uniqify filenames for each word
Reduce: def reduce(word, filenames):
output(word, sort(filenames))



Difference between hapdoop mapreduce, pig and hive:


Hadoop Mapreduce
Hive
Pig
Compiled language
SQL like query language
Scripting language
Lower level of abstraction
Highter level of abstruction
Highter level of abstraction
More lines of code
Comparatively less line of code than mapreduce and apache pig
Comparatively less lines of code than mapreduce
Code efficiency is high when compared to pig and hive.
Code efficiency is relatively less
Code efficiency is relatively less



Why MapReduce:


1) You have a huge amount of data.

2) Hide system level details from the developers

     - No more race conditions, lock contention, etc

3) Two strong merits for big data analytics

     - Scalability

     - Fault tolerance

4) Move computing to data

     - Cluster have limited bandwidth

5) Hadoop is the most widely used implementation of MapReduce


MapReduce Programming Model:

1) Data type: key value records

2) Map function:

      (Kin, Vin) -> list(Kinter, Vinter)

3) Reduce function:

      (Kinter, list(Vinter)) -> list(Kout, Vout)


Example: counting words


def mapper(line):
foreach word in line.split():
output(word, 1)

Def reducer(key, values):
output(key, sum(values))


map()
  input <fileneme, file text>
  parses file and emits <word, count> pairs. eg.   <"hello", 1>
Reduce()
  sums values for the some key and emits <word, totalcount>.   eg, <"hello", (3 5 2 7)> = <"hello", 17>



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