MapReduce Assignment Help

Are you worried about sharing your assigned task within the stated frame of time? Are you looking for a professional company to help with MapReduce Assignment Help services? 

Do not worry! ABC Assignment Help is a prominent company to help a number of scholars with one of the best online MapReduce assignment help services. Our team of programming professionals can help you with expert programming assignment help and resolve all your queries. We strive to provide you eminent support in all academic programming assignments. Our team of specialized programming tutors offers expert help in various programming assignments.

you can contact our programming assignment experts any time to get your queries resolved or make some addition of thoughts to your work. We also offer unlimited revisions on all programming orders and work until you are satisfied with the content and formatting of your programming paper. All these premium features comes at heavily discounted prices that comes within your budget and a guarantee of refund when you find the quality of content mediocre and have a rational argument to justify your objection. 

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
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
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))

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