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MapReduce : Simplified Data Processing on Large Clusters
1. by Jeffrey Dean and Sanjay Ghemawat
Communication of the ACM, 2008
Presented by: Abolfazl Asudeh
MapReduce: Simplified Data Processing
on Large Clusters
2. Map Reduce
7/4/20132
Patented by Google
A parallel programming model
and an associated implementation
for processing and generating large datasets
Users specify the computation in terms of a map and
a reduce function
The system automatically parallelizes the
computation across large-scale clusters
3. 7/4/20133
Previously, users had to handle the parallelization of
the programs over hundred or thousand machines
Distribute the data
Handle Failure
Schedule inter-machine communications – to make efficient
use of resources
By Experiment:
most of their computations involved applying a map
operation to produce intermediate key/value pairs
Then applying a reduce operation to
combine/aggregate the pairs
4. Programming Model
7/4/20134
Input: a set of input key/value pairs
Output: a set of output key/value pairs
Map (written by the user):
takes an input pair and produces a set of intermediate
key/value pairs.
MapReduce library:
groups all intermediate values associated with the same
intermediate key and passes them to the reduce function.
Reduce (written by the user):
accepts an intermediate key and a set of values for that
key.
It merges these values together to form a possibly smaller
set of values
7. Basic Example: Word Counting
7/4/20137
<How,1>
<now,1>
<brown,1>
<cow,1>
<How,1>
<does,1>
<it,1>
<work,1>
<now,1>
<How,1 1>
<now,1 1>
<brown,1>
<cow,1>
<does,1>
<it,1>
<work,1>
How now
Brown cow
How does
It work now
Input
brown 1
cow 1
does 1
How 2
it 1
now 2
work 1
Output
M
M
M
M
Map
R
R
Reduce
8. Execution Overview
7/4/20138
1. The MapReduce library splits the input files into M
pieces of typically 16-64MB per piece and starts up
many copies of the program on a cluster of
machines.
2. One of the workers becomes Master. It manages
assigning M map jobs and R reduce jobs to the
Workers. It picks the Idle workers and assign the
jobs.
3. The worker that is doing a Map job: reads the
corresponding input split, parses the key/value
pairs and pass to map function (by user)
9. Execution Overview
7/4/20139
4. the buffered pairs are written to local disk,
partitioned into R regions.
The locations of buffered pairs on the local disk are
passed back to the master who is responsible for
forwarding these locations to the reduce workers
5. Reduce Worker remotely reads the buffered data
from the local disc of the corresponding mapper.
Sorts the read data by Intermediate key and group the
results together.
10. Execution Overview
7/4/201310
6. The reduce worker passes the results for each
intermediate key to the reduce function
7. When all the tasks are done, the Map-Reduce
function returns back to the user program
12. Fault Tolerance
7/4/201312
Failure: if a worker does not response the PING of
master
If a map worker Fails:
Reschedule the WHOLE map tasks (because it writes on
the local disk)
Send the results Address in the new map worker to all
corresponding reduce workers (if the did not still read
from the previous mapper, read from the new one)
If a reduce worker Fails:
Completed reduce tasks do not need to be re-executed
since their output is stored in a global file system
13. Execution Optimization
7/4/201313
Locality
Network bandwidth is a relatively scarce resource
Compute on local copies which are distributed by HFDS
Task Granularity
Ideally, M and R should be much larger than the
number of worker machines
Having each worker perform many different tasks
improves dynamic load balancing and also speeds
up recovery when a worker fails
