This document discusses MapReduce, a programming model and implementation developed by Google for processing large datasets across clusters of computers. It describes MapReduce as having two phases - a map phase where key-value pairs are input and output, and a reduce phase where the outputs of map are combined. An example is given of finding the highest CGPA for each year from a Makerere University dataset to illustrate how MapReduce works by breaking the processing into parallelized map and reduce steps.

1. MCS 7101: Cloud Technologies and Architectures
A. Simon Alex
MapReduce
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2. Overview
1 MapReduce
What is MapReduce?
MapReduce Programming Model
How MapReduce Works?
MapReduce Example
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3. MapReduce What is MapReduce?
What is MapReduce?
Programming model and implementation developed at Google for pro-
cessing and generating large datasets across a cluster of computers.
MapReduce is used in Nutch (open source web search) and Apache
Hadoop (developed to index rich textual and structural information).
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4. MapReduce MapReduce Programming Model
MapReduce Programming Model
This programming model is inspired∗ by the map and reduce primitives
of functional programming languages such as Lisp.
map: takes as input a procedure and a sequence of values and applies
the procedure to each value in the sequence.
reduce: takes as input a sequence of values and combines all values
using binary operator.
∗
but not equivalent!
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5. MapReduce How MapReduce Works?
How MapReduce Works?
MapReduce works by breaking the processing into two phases: the map
phase and the reduce phase.
Each phase has key-value pairs as input and output, the types of which
may be chosen by the programmer.The programmer also specifies two
functions: the map function and the reduce function.
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6. MapReduce MapReduce Example
MapReduce Example
Challenge
What’s the highest ever recorded Makerere’s CGPA for each year in
their dataset?
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7. MapReduce MapReduce Example
MapReduce Example
Figure: MapReduce logical data flow
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8. MapReduce Conclusion
Questions?
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