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What is MapReduce?
- How to process large data sets and easily utilize the resources of a large distributed system.
- A programming model for parallel processing of a distributed data on a cluster
- 2 staged data processing ie, Map and Reduce
- Each stage emits key value pairs as a result of its work
- Programing MapReduc, In Java, there are three classes ie, a) Map b)Reduce c) Job configuration(with a, main' function)
- MapReduce is a model for parallel data processing on Hadoop in a batch fachion
- Logic written in Java
- Resource allocation controlled by YARN
- Programming model for expressing distributed computions at a massive scale.
- Popularized by open source Hadoop project.
- Compiled language
- Lower level of abstraction
- More lines of code
- More development effort is involved
- Code efficiency is high when compared to pig and hive
What is Hadoop?
- Open source data storage and processing API.
- Hadoop is reliable and fault tolerant with no rely on hardware for these properties.
- It is made by apache software foundation in 2011. written in JAVA
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)
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):
Difference between hapdoop mapreduce, pig and hive:
|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|
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
- 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
foreach word in line.split():
Def reducer(key, 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>