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Introduction about Hadoop Platform?
Hadoop is a data management and distributed processing system. It contains many components, including:
HDFS is a file system that distributes data across many machines
MapReduce for batch parallel computing
Automatic parallelization and distribution
Monitoring and Status updates
How MapReduce Data programming model works
MapReduce has two main tasks: Map, Reduce
The data blocks distributed across different machines are processed by Map tasks in parallel.
Results are aggregated in Reducers
Works only with KEY/VALUE pairs
MapReduce Key/Value Pairs
The data exchanged between Map and Reduce, and more, in the entire job are pairs (key, value):
a key: it is any type of data: integer, text. . .
a value: it is any type of data
Everything is represented like this. For example :
a text file is a set of (line number, line).
a weather file is a set of (date and time, temperature)
It is this notion that makes programs quite strange to beginners: the two functions Map and Reduce receive and transmit such pairs.
MapReduce – Word count example:
Pseudo-code: word count
Map (Long input_key, String input_values) :
foreach word w in input_values:
EmitIntermediate (w, ‘1’);
Reduce (String key, Iterator intermediate_values):
foreach v in intermediate_values:
result += ParseInt( v );
Emit (key, String( result ));
Using MapReduce, calculate the sum of the odd and even numbers contained in a Text file:
Using MapReduce, calculate the sum of a list of numbers contained in a Text file:
Using MapReduce, determine the duplicate elements in a list of numbers contained in a file:
Using MapReduce, determine the maximum and minimum of a list of numbers contained in a file:
Using MapReduce, determine how many odd and even numbers in a list of numbers contained in a file:
Using MapReduce, determine the total number of words in a in a Text file:
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