Transcript Slide 1

CS246: Mining Massive Datasets
Jure Leskovec, Stanford University
http://cs246.stanford.edu
Add pictures of TAs
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TAs:
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Bahman Bahmani
Juthika Dabholkar
Pierre Kreitmann
Lu Li
Aditya Ramesh
Office hours:
 Jure: Tuesdays 9-10am, Gates 418
 See course website for TA office hours
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Course website:
http://cs246.stanford.edu
 Lecture slides (at least 6h before the lecture)
 Announcements, homeworks, solutions
 Readings!
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Readings: Book Mining of Massive Datasets
by Anand Rajaraman and Jeffrey D. Ullman
Free online:
http://i.stanford.edu/~ullman/mmds.html
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4 longer homeworks: 40%
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Theoretical and programming questions
All homeworks (even if empty) must be handed in
Assignments take time. Start early!
How to submit?
 Paper: Box outside the class and in the Gates east wing
 We will grade on paper!
 You should also submit electronic copy:
 1 PDF/ZIP file (writeups, experimental results, code)
 Submission website: http://cs246.stanford.edu/submit/
 SCPD: Only submit electronic copy & send us email
 7 late days for the quarter:
 Max 5 late days per assignment
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Short weekly quizzes: 20%
 Short e-quizzes on Gradiance (see course website!)
 First quiz is already online
 You have 7 days to complete it. No late days!
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Final exam: 40%
 March 19 at 8:30am
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It’s going to be fun and hard work 
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Homework schedule:
Date
1/11
1/25
2/8
2/22
3/7
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Out
HW1
HW2
HW3
HW4
In
HW1
HW2
HW3
HW4
No class: 1/16: Martin Luther King Jr.
2/20: President’s day
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Algorithms (CS161)
 Dynamic programming, basic data structures
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Basic probability (CS109 or Stat116)
 Moments, typical distributions, MLE, …
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Programming (CS107 or CS145)
 Your choice, but C++/Java will be very useful
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We provide some background, but
the class will be fast paced
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Recitation sessions:
 Review of probability and statistics
 Installing and working with Hadoop
 We prepared a virtual machine with Hadoop preinstalled
 HW0 helps you write your first Hadoop program
 See course website!
 We will announce the dates later
 Sessions will be recorded
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Can skip CS345a
and just say that
there is a follow up
class in Spring that
is project oriented
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CS345a: Data mining got split into 2 courses
 CS246: Mining massive datasets:
 Methods/algorithms oriented course
 Homeworks (theory & programming)
 No class project
 CS341: Project in mining massive datasets:
 Project oriented class
 Lectures/readings related to the project
 Unlimited access to Amazon EC2 cluster
 We intend to keep the class small
 Taking CS246 is basically prerequisite
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For questions/clarifications use Piazza!
 If you don’t have @stanford.edu email address
email us and we will register you
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To communicate with the course staff use
 [email protected]
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We will post announcements to
 [email protected]
 If you are not registered or auditing send us email
and we will subscribe you!
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You are welcome to sit-in & audit the class
 Send us email saying that you will be auditing
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Chould skip!
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Much of the course will be devoted to
ways to data mining on the Web:
 Mining to discover things about the Web
 E.g., PageRank, finding spam sites
 Mining data from the Web itself
 E.g., analysis of click streams, similar products at
Amazon, making recommendations
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Much of the course will be devoted to
large scale computing for data mining
Challenges:
 How to distribute computation?
 Distributed/parallel programming is hard
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Map-reduce addresses all of the above
 Google’s computational/data manipulation model
 Elegant way to work with big data
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High-dimensional data:
 Locality Sensitive Hashing
 Dimensionality reduction
 Clustering
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The data is a graph:
 Link Analysis: PageRank, Hubs & Authorities
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Machine Learning:
 k-NN, Perceptron, SVM, Decision Trees
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Data is infinite:
 Mining data streams
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Applications:
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Association Rules
Recommender systems
Advertising on the Web
Web spam detection
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Discovery of patterns and models that are:
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Valid: hold on new data with some certainty
Useful: should be possible to act on the item
Unexpected: non-obvious to the system
Understandable: humans should be able to
interpret the pattern
Subsidiary issues:
 Data cleansing: detection of bogus data
 Visualization: something better than MBs of output
 Warehousing of data (for retrieval)
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Predictive Methods
 Use some variables to predict unknown
or future values of other variables
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Descriptive Methods
 Find human-interpretable patterns that
describe the data
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Skip
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Scalability
Dimensionality
Complex and Heterogeneous Data
Data Quality
Data Ownership and Distribution
Privacy Preservation
Streaming Data
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Overlaps with:
 Databases: Large-scale (non-main-memory) data
 Machine learning: Complex methods, small data
 Statistics: Models
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Different cultures:
 To a DB person, data mining
is an extreme form of
analytic processing –
queries that examine large
amounts of data
Statistics/
AI
Machine Learning/
Pattern
Recognition
Data Mining
 Result is the query answer
 To a statistician, data-mining is
the inference of models
 Result is the parameters of the model
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Database
systems
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A big data-mining risk is that you will
“discover” patterns that are meaningless.
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Bonferroni’s principle: (roughly) if you look in
more places for interesting patterns than your
amount of data will support, you are bound to
find crap
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Joseph Rhine was a parapsychologist in the
1950’s who hypothesized that some people
had Extra-Sensory Perception
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He devised an experiment where subjects
were asked to guess 10 hidden cards – red or
blue
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He discovered that almost 1 in 1000 had ESP –
they were able to get all 10 right!
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The point is that the
patterns should be
real and significant
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He told these people they had ESP and called
them in for another test of the same type
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Alas, he discovered that almost all of them
had lost their ESP
What did he conclude?
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He concluded that you shouldn’t tell people
they have ESP; it causes them to lose it 
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CPU
Machine Learning, Statistics
Memory
“Classical” Data Mining
Disk
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20+ billion web pages x 20KB = 400+ TB
1 computer reads 30-35 MB/sec from disk
 ~4 months to read the web
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~1,000 hard drives to store the web
Takes even more to do something useful
with the data!
Standard architecture is emerging:
 Cluster of commodity Linux nodes
 Gigabit ethernet interconnect
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Can skip this slide
2-10 Gbps backbone between racks
1 Gbps between
any pair of nodes
in a rack
Switch
Switch
CPU
Mem
Disk
…
Switch
CPU
CPU
Mem
Mem
Disk
Disk
CPU
…
Mem
Disk
Each rack contains 16-64 nodes
In Aug 2006 Google had ~450,000 machines
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Large-scale computing for data mining
problems on commodity hardware
Challenges:
 How do you distribute computation?
 How can we make it easy to write distributed
programs?
 Machines fail:
 One server may stay up 3 years (1,000 days)
 If you have 1,0000 servers, expect to loose 1/day
 In Aug 2006 Google had ~450,000 machines
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Idea:
 Bring computation close to the data
 Store files multiple times for reliability
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Map-reduce addresses these problems
 Google’s computational/data manipulation model
 Elegant way to work with big data
 Storage Infrastructure – File system
 Google: GFS
 Hadoop: HDFS
 Programming model
 Map-Reduce
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Problem
 If nodes fail, how to store data persistently?
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Answer
 Distributed File System:
 Provides global file namespace
 Google GFS; Hadoop HDFS;
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Typical usage pattern
 Huge files (100s of GB to TB)
 Data is rarely updated in place
 Reads and appends are common
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Chunk Servers
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File is split into contiguous chunks
Typically each chunk is 16-64MB
Each chunk replicated (usually 2x or 3x)
Try to keep replicas in different racks
Master node
 a.k.a. Name Nodes in Hadoop’s HDFS
 Stores metadata
 Might be replicated
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Client library for file access
 Talks to master to find chunk servers
 Connects directly to chunk servers to access data
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Reliable distributed file system
Data kept in “chunks” spread across machines
Each chunk replicated on different machines
 Seamless recovery from disk or machine failure
C0
C1
D0
C1
C2
C5
C5
C2
C5
C3
D0
D1
Chunk server 1
Chunk server 2
…
Chunk server 3
C0
C5
D0
C2
Chunk server N
Bring computation directly to the data!
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Warm-up task:
 We have a huge text document
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Count the number of times each
distinct word appears in the file
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Sample application:
 Analyze web server logs to find popular URLs
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Case 1:
 File too large for memory, but all <word, count>
pairs fit in memory
Case 2:
 Count occurrences of words:
 words(doc.txt) | sort | uniq -c
 where words takes a file and outputs the words in it,
one per a line
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Captures the essence of MapReduce
 Great thing is it is naturally parallelizable
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Sequentially read a lot of data
Map:
 Extract something you care about
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Group by key: Sort and Shuffle
Reduce:
 Aggregate, summarize, filter or transform
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Write the result
Outline stays the same, map and reduce
change to fit the problem
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Input
key-value pairs
Intermediate
key-value pairs
k
v
k
v
k
v
map
k
v
k
v
…
k
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map
…
v
k
v
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Intermediate
key-value pairs
Output
key-value pairs
Key-value groups
reduce
k
v
k
v
v
v
k
v
k
v
reduce
k
v
k
v
group
k
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v
…
…
k
v
v
k
…
v
Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
k
v
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Input: a set of key/value pairs
Programmer specifies two methods:
 Map(k, v)  <k’, v’>*
 Takes a key value pair and outputs a set of key value pairs
 E.g., key is the filename, value is a single line in the file
 There is one Map call for every (k,v) pair
 Reduce(k’, <v’>*)  <k’, v’’>*
 All values v’ with same key k’ are reduced
together and processed in v’ order
 There is one Reduce function call per unique key k’
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Bif\gger document
text. So that
people can read the
example – know
the answer.
MAP:
reads input and
produces a set of
key value pairs
Provided by the
programmer
Group by key:
Reduce:
Collect all pairs
with same key
Collect all values
belonging to the
key and output
The crew of the space shuttle
Endeavor recently returned to
Earth as ambassadors,
harbingers of a new era of
space exploration. Scientists
at NASA are saying that the
recent assembly of the Dextre
bot is the first step in a longterm space-based
man/machine partnership.
'"The work we're doing now -the robotics we're doing -- is
what we're going to need to
do to build any work station
or habitat structure on the
moon or Mars," said Allard
Beutel.
(the, 1)
(crew, 1)
(of, 1)
(the, 1)
(space, 1)
(shuttle, 1)
(Endeavor, 1)
(recently, 1)
….
(crew, 1)
(crew, 1)
(space, 1)
(the, 1)
(the, 1)
(the, 1)
(shuttle, 1)
(recently, 1)
…
(crew, 2)
(space, 1)
(the, 3)
(shuttle, 1)
(recently, 1)
…
Big document
(key, value)
(key, value)
(key, value)
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reads
Only
sequential
data
read the
Sequentially
Provided by the
programmer
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map(key, value):
// key: document name; value: text of the document
for each word w in value:
emit(w, 1)
reduce(key, values):
// key: a word; value: an iterator over counts
result = 0
for each count v in values:
result += v
emit(key, result)
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Map-Reduce environment takes care of:
 Partitioning the input data
 Scheduling the program’s execution across a
set of machines
 Performing the group by key step
 Handling machine failures
 Managing required inter-machine
communication
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The sort is the
magical part. The
map reduce does it
by itself.
Big document
MAP:
reads input and
produces a set of
key value pairs
Group by key:
Call it hash merge
Group by key
Sort
Call it Partition or
Hash Merge
Collect all pairs
with same key
Reduce:
Collect all values
belonging to the
key and output
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Input and final output are stored on a
distributed file system:
 Scheduler tries to schedule map tasks “close” to
physical storage location of input data
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Intermediate results are stored on local FS
of map and reduce workers
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Output is often input to another map
reduce task
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Skip
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Master data structures:
 Task status: (idle, in-progress, completed)
 Idle tasks get scheduled as workers become
available
 When a map task completes, it sends the master
the location and sizes of its R intermediate files,
one for each reducer
 Master pushes this info to reducers
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Master pings workers periodically
to detect failures
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How exactly is
reducer failure
recovered?
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Map worker failure
 Map tasks completed or in-progress at worker are
reset to idle
 Reduce workers are notified when task is
rescheduled on another worker
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Reduce worker failure
 Only in-progress tasks are reset to idle
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Master failure
 MapReduce task is aborted and client is notified
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M map tasks, R reduce tasks
Rule of a thumb:
Debugging have 1
mapper1 reducer
M and R are
independent of
chungs, system
diced which
mapper gets what
part of the input file.
Similarly for
reducers.
 Make M and R much larger than the number of
nodes in cluster
 One DFS chunk per map is common
 Improves dynamic load balancing and speeds
recovery from worker failure

Usually R is smaller than M
 because output is spread across R files
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Fine granularity tasks: map tasks >> machines
 Minimizes time for fault recovery
 Can pipeline shuffling with map execution
 Better dynamic load balancing
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Picture with chunk
servers
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Problem
 Slow workers significantly lengthen the job
completion time:
 Other jobs on the machine
 Bad disks
 Weird things
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Solution
 Near end of phase, spawn backup copies of tasks
 Whichever one finishes first “wins”
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Effect
 Dramatically shortens job completion time
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Example back to
the word count
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Often a map task will produce many pairs of
the form (k,v1), (k,v2), … for the same key k
 E.g., popular words in the Word Count example
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Can save network time by
pre-aggregating values at
the mapper:
 combine(k, list(v1))  v2
 Combiner is usually same
as the reduce function
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Works only if reduce
function is commutative and associative
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Inputs to map tasks are created by contiguous
splits of input file
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Reduce needs to ensure that records with the
same intermediate key end up at the same
worker
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System uses a default partition function:
 hash(key) mod R
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Sometimes useful to override:
 E.g., hash(hostname(URL)) mod R ensures URLs
from a host end up in the same output file
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Suppose we have a large web corpus
Look at the metadata file
 Lines of the form (URL, size, date, …)
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For each host, find the total number of bytes
 i.e., the sum of the page sizes for all URLs from
that host
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Other examples:
 Link analysis and graph processing
 Machine Learning algorithms
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Statistical machine translation:
 Need to count number of times every 5-word
sequence occurs in a large corpus of documents
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Very easy with MapReduce:
 Map:
 Extract (5-word sequence, count) from document
 Reduce:
 Combine counts
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Join was bad
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Compute the natural join R(A,B) ⋈ S(B,C)
R and S each are stored in files
Tuples are pairs (a,b) or (b,c)
A
B
a1
b1
a2
b1
a3
b2
a4
b3
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⋈
B
C
A
C
b2
c1
a3
c1
b2
c2
a3
c2
b3
c3
a4
c3
=
Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
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Explain better
what’s going on
What are tuples?
Give animation, …
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Use a hash function h from B-values to 1...k
A Map process turns:
 Each input tuple R(a,b) into key-value pair (b,(a,R))
 Each input tuple S(b,c) into (b,(c,S))

Map processes send each key-value pair with
key b to Reduce process h(b).
 Hadoop does this automatically; just tell it what k is.
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Each Reduce process matches all the pairs
(b,(a,R)) with all (b,(c,S)) and outputs (a,b,c).
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-- Intro is too birocratic. Maybe it would be better to create a printout
and hand it out to the students or tell them to read the class website
-- What is data mining is too abstract? Maybe say an application or
two. Check how Manning and Ng do intro lectures
-- MapReduce was good
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1.
2.
3.
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Communication cost = total I/O of all
processes.
Elapsed communication cost = max of I/O
along any path.
(Elapsed ) computation costs analogous, but
count only running time of processes.
Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
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
For a map-reduce algorithm:
 Communication cost = input file size + 2  (sum of
the sizes of all files passed from Map processes to
Reduce processes) + the sum of the output sizes of
the Reduce processes.
 Elapsed communication cost is the sum of the
largest input + output for any map process, plus
the same for any reduce process
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Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
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
Either the I/O (communication) or processing
(computation) cost dominates
 Ignore one or the other


Total costs tell what you pay in rent from your
friendly neighborhood cloud
Elapsed costs are wall-clock time using
parallelism
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Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
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

Total communication
cost = O(|R|+|S|+|R ⋈ S|)
Elapsed communication cost = O(s)
 We’re going to pick k and the number of Map
processes so I/O limit s is respected
 We put a limit s on the amount of input or output that
any one process can have. s could be:
 What fits in main memory
 What fits on local disk

With proper indexes, computation cost is linear
in the input + output size
 So computation costs are like comm. costs
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Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
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Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
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
Google
 Not available outside Google

Hadoop
 An open-source implementation in Java
 Uses HDFS for stable storage
 Download: http://lucene.apache.org/hadoop/
 Aster Data
 Cluster-optimized SQL Database that also
implements MapReduce
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Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
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
Ability to rent computing by the hour
 Additional services e.g., persistent storage

Amazon’s “Elastic Compute Cloud” (EC2)

Aster Data and Hadoop can both be run on
EC2

For CS341 (offered next quarter) Amazon will
provide free access for the class
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Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
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
Jeffrey Dean and Sanjay Ghemawat:
MapReduce: Simplified Data Processing on
Large Clusters
 http://labs.google.com/papers/mapreduce.html

Sanjay Ghemawat, Howard Gobioff, and ShunTak Leung: The Google File System
 http://labs.google.com/papers/gfs.html
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Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
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
Hadoop Wiki
 Introduction
 http://wiki.apache.org/lucene-hadoop/
 Getting Started
 http://wiki.apache.org/lucenehadoop/GettingStartedWithHadoop
 Map/Reduce Overview
 http://wiki.apache.org/lucene-hadoop/HadoopMapReduce
 http://wiki.apache.org/lucenehadoop/HadoopMapRedClasses
 Eclipse Environment
 http://wiki.apache.org/lucene-hadoop/EclipseEnvironment

Javadoc
 http://lucene.apache.org/hadoop/docs/api/
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Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
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
Releases from Apache download mirrors
 http://www.apache.org/dyn/closer.cgi/lucene/had
oop/

Nightly builds of source
 http://people.apache.org/dist/lucene/hadoop/nig
htly/

Source code from subversion
 http://lucene.apache.org/hadoop/version_control
.html
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Programming model inspired by functional language
primitives
 Partitioning/shuffling similar to many large-scale sorting
systems

 NOW-Sort ['97]

Re-execution for fault tolerance
 BAD-FS ['04] and TACC ['97]

Locality optimization has parallels with Active
Disks/Diamond work
 Active Disks ['01], Diamond ['04]

Backup tasks similar to Eager Scheduling in Charlotte
system
 Charlotte ['96]

Dynamic load balancing solves similar problem as River's
distributed queues
 River ['99]
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