AAAI2011 Tutorial Slides

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Large-Scale Data Processing

with MapReduce

AAAI 2011 Tutorial

Jimmy LinUniversity of Maryland

Sunday, August 7, 2011

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United StatesSee http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details

These slides are available on my homepage at http://www.umiacs.umd.edu/~jimmylin/


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First things first

About me

Course history

Audience survey


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Agenda

Setting the stage: Why large data? Why is this different?

Introduction to MapReduce

MapReduce algorithm design

Text retrieval

Managing relational data

Graph algorithms

Beyond MapReduce


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Expectations

Focus on thinking at scale

Deconstruction into design patterns

Basic intuitions, not fancy math

Mapping well-known algorithms to MapReduce

Not a tutorial on programming Hadoop

Entry point to book


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Setting the Stage:Why large data?

Setting the stageIntroduction to MapReduceMapReduce algorithm designText retrievalManaging relational dataGraph algorithmsBeyond MapReduce


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Source: Wikipedia (Everest)


7/213How much data?

6.5 PB of user data +50 TB/day (5/2009)

processes 20 PB a day (2008)

36 PB of user data +80-90 TB/day (6/2010)

Wayback Machine: 3 PB +100 TB/month (3/2009)

LHC: 15 PB a year(any day now)

LSST: 6-10 PB a year (~2015)640Kought to beenough for anybody.


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No data like more data!

(Banko and Brill, ACL 2001)(Brants et al., EMNLP 2007)

s/knowledge/data/g;

How do we get here if were not Google?


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+ simple, distributed programming models

cheap commodity clusters

= data-intensive computing for the masses!


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Setting the Stage:Why is this different?



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Parallel computing is hard!

Message Passing

P1 P2 P3 P4 P5

Shared Memory

P1 P2 P3 P4 P5

Memory

Different programming models

Different programming constructsmutexes, conditional variables, barriers,

masters/slaves, producers/consumers, work queues,

Fundamental issuesscheduling, data distribution, synchronization,

inter-process communication, robustness, faulttolerance,

Common problemslivelock, deadlock, data starvation, priority inversiondining philosophers, sleeping barbers, cigarette smokers,

Architectural issues

Flynns taxonomy (SIMD, MIMD, etc.),network typology, bisection bandwidthUMA vs. NUMA, cache coherence

The reality: programmer shoulders the burden of managing concurrency(I want my students developing new algorithms, not debugging race conditions)

master

slaves

producer consumer

producer consumer

workqueue


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Where the rubber meets the road

Concurrency is difficult to reason about

At the scale of datacenters (even across datacenters)

In the presence of failures

In terms of multiple interacting services

The reality:

Lots of one-off solutions, custom code

Write you own dedicated library, then program with it

Burden on the programmer to explicitly manage everything


13/213Source: Ricardo Guimares Herrmann


14/213Source: NY Times (6/14/2006)

The datacenter isthe computer!

I think there is a worldmarket for about fivecomputers.


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Whats the point?

Its all about the right level of abstraction

Hide system-level details from the developers

No more race conditions, lock contention, etc.

Separating the whatfrom how

Developer specifies the computation that needs to be performed Execution framework (runtime) handles actual execution



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Big Ideas

Scale out, not up

Limits of SMP and large shared-memory machines

Move processing to the data

Cluster have limited bandwidth

Process data sequentially, avoid random access Seeks are expensive, disk throughput is reasonable

Seamless scalability

From the mythical man-month to the tradable machine-hour


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Introduction to MapReduce

Setting the stageIntroduction to MapReduceMapReduce algorithm designText retrievalManaging relational dataGraph algorithms

Beyond MapReduce


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Typical Large-Data Problem

Iterate over a large number of records

Extract something of interest from each

Shuffle and sort intermediate results

Aggregate intermediate results

Generate final output

Key idea: provide a functional abstraction forthese two operations

(Dean and Ghemawat, OSDI 2004)


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g g g g g

f f f f fMap

Fold

Roots in Functional Programming


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MapReduce

Programmers specify two functions:

map (k, v) *reduce(k, v) *

All values with the same key are sent to the same reducer

The execution framework handles everything else


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mapmap map map

Shuffle and Sort: aggregate values by keys

reduce reduce reduce

k1 k2 k3 k4 k5 k6v1 v2 v3 v4 v5 v6

ba 1 2 c c3 6 a c5 2 b c7 8

a 1 5 b 2 7 c 2 3 6 8

r1 s1 r2 s2 r3 s3


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MapReduce



All values with the same key are sent to the same reducer


Whats everything else?


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MapReduce Runtime

Handles scheduling

Assigns workers to map and reduce tasks

Handles data distribution

Moves processes to data

Handles synchronization

Gathers, sorts, and shuffles intermediate data

Handles errors and faults

Detects worker failures and restarts

Everything happens on top of a distributed FS


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MapReduce



All values with the same key are reduced together


Not quiteusually, programmers also specify:partition(k, number of partitions) partition for k

Often a simple hash of the key, e.g., hash(k) mod n

Divides up key space for parallel reduce operations

combine(k, v) * Mini-reducers that run in memory after the map phase

Used as an optimization to reduce network traffic


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combinecombine combine combine

ba 1 2 c 9 a c5 2 b c7 8

partition partition partition partition

mapmap map map

k1 k2 k3 k4 k5 k6v1 v2 v3 v4 v5 v6

ba 1 2 c c3 6 a c5 2 b c7 8



a 1 5 b 2 7 c 2 9 8

r1 s1 r2 s2 r3 s3

c 2 3 6 8


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Hello World: Word Count


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MapReduce can refer to

The programming model

The execution framework (aka runtime)

The specific implementation

Usage is usually clear from context!


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MapReduce Implementations

Google has a proprietary implementation in C++

Bindings in Java, Python

Hadoop is an open-source implementation in Java

Original development led by Yahoo

Now an Apache open source project

Emerging as the de factobig data stack

Rapidly expanding software ecosystem

Lots of custom research implementations

For GPUs, cell processors, etc.

Includes variations of the basic programming model

Most of these slides are focused on Hadoop


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split 0

split 1

split 2

split 3

split 4

worker

worker

worker

worker

worker

Master

UserProgram

outputfile 0

outputfile 1

(1) submit

(2) schedule map (2) schedule reduce

(3) read(4) local write

(5) remote read(6) write

Inputfiles

Mapphase

Intermediate files(on local disk)

Reducephase

Outputfiles

Adapted from (Dean and Ghemawat, OSDI 2004)


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How do we get data to the workers?

Compute Nodes

NAS

SAN

Whats the problem here?


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Distributed File System

Dont move data to workers move workers to the data!

Store data on the local disks of nodes in the cluster

Start up the workers on the node that has the data local

A distributed file system is the answer

GFS (Google File System) for Googles MapReduce

HDFS (Hadoop Distributed File System) for Hadoop


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GFS: Assumptions

Commodity hardware over exotic hardware

Scale out, not up

High component failure rates

Inexpensive commodity components fail all the time

Modest number of huge files Multi-gigabyte files are common, if not encouraged

Files are write-once, mostly appended to

Perhaps concurrently

Large streaming reads over random access

High sustained throughput over low latency

GFS slides adapted from material by (Ghemawat et al., SOSP 2003)


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GFS: Design Decisions

Files stored as chunks

Fixed size (64MB)

Reliability through replication

Each chunk replicated across 3+ chunkservers

Single master to coordinate access, keep metadata Simple centralized management

No data caching

Little benefit due to large datasets, streaming reads

Simplify the API

Push some of the issues onto the client (e.g., data layout)

HDFS = GFS clone (same basic ideas)


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From GFS to HDFS

Terminology differences:

GFS master = Hadoop namenode

GFS chunkservers = Hadoop datanodes

Functional differences:

File appends in HDFS is relatively new

HDFS performance is (likely) slower

For the most part, well use the Hadoop terminology


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Adapted from (Ghemawat et al., SOSP 2003)

(file name, block id)

(block id, block location)

instructions to datanode

datanode state(block id, byte range)

block data

HDFS namenode

HDFS datanode

Linux file system

HDFS datanode

Linux file system

File namespace

/foo/bar

block 3df2

Application

HDFS Client

HDFS Architecture


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Namenode Responsibilities

Managing the file system namespace:

Holds file/directory structure, metadata, file-to-block mapping,access permissions, etc.

Coordinating file operations:

Directs clients to datanodes for reads and writes

No data is moved through the namenode

Maintaining overall health:

Periodic communication with the datanodes

Block re-replication and rebalancing

Garbage collection


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Putting everything together

datanode daemon

Linux file system

tasktracker

slave node

datanode daemon

Linux file system

tasktracker

slave node

datanode daemon

Linux file system

tasktracker

slave node

namenode

namenode daemon

job submission node

jobtracker

Setting the stage


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MapReduce Algorithm Design

Setting the stageIntroduction to MapReduceMapReduce algorithm designText retrievalManaging relational dataGraph algorithms

Beyond MapReduce


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MapReduce: Recap

Programmers must specify:


All values with the same key are reduced together

Optionally, also:

partition(k, number of partitions) partition for k Often a simple hash of the key, e.g., hash(k) mod n

Divides up key space for parallel reduce operations

combine(k, v) *

Mini-reducers that run in memory after the map phase

Used as an optimization to reduce network traffic



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combinecombine combine combine

ba 1 2 c 9 a c5 2 b c7 8

partition partition partition partition

mapmap map map

k1 k2 k3 k4 k5 k6v1 v2 v3 v4 v5 v6

ba 1 2 c c3 6 a c5 2 b c7 8



a 1 5 b 2 7 c 2 9 8

r1 s1 r2 s2 r3 s3


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Everything Else


Scheduling: assigns workers to map and reduce tasks

Data distribution: moves processes to data

Synchronization: gathers, sorts, and shuffles intermediate data

Errors and faults: detects worker failures and restarts

Limited control over data and execution flow All algorithms must expressed in m, r, c, p

You dont know:

Where mappers and reducers run

When a mapper or reducer begins or finishes

Which input a particular mapper is processing

Which intermediate key a particular reducer is processing


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Tools for Synchronization

Cleverly-constructed data structures

Bring partial results together

Sort order of intermediate keys

Control order in which reducers process keys

Partitioner Control which reducer processes which keys

Preserving state in mappers and reducers

Capture dependencies across multiple keys and values


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Preserving State

Mapper object

configure

map

close

state

one object per task

Reducer object

configure

reduce

close

state

one call per inputkey-value pair

one call perintermediate key

API initialization hook

API cleanup hook


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Scalable Hadoop Algorithms: Themes

Avoid object creation

Inherently costly operation

Garbage collection

Avoid buffering

Limited heap size

Works for small datasets, but wont scale!


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Importance of Local Aggregation

Ideal scaling characteristics:

Twice the data, twice the running time

Twice the resources, half the running time

Why cant we achieve this?

Synchronization requires communication

Communication kills performance

Thus avoid communication!

Reduce intermediate data via local aggregation

Combiners can help


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Shuffle and Sort

Mapper

Reducer

other mappers

other reducers

circular buffer(in memory)

spills (on disk)

merged spills(on disk)

intermediate files(on disk)

Combiner

Combiner


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Word Count: Baseline

Whats the impact of combiners?


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Word Count: Version 1

Are combiners still needed?


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Word Count: Version 2



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Design Pattern for Local Aggregation

In-mapper combining

Fold the functionality of the combiner into the mapper bypreserving state across multiple map calls

Advantages

Speed

Why is this faster than actual combiners?

Disadvantages

Explicit memory management required

Potential for order-dependent bugs


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Combiner Design

Combiners and reducers share same method signature

Sometimes, reducers can serve as combiners

Often, not

Remember: combiner are optional optimizations

Should not affect algorithm correctness

May be run 0, 1, or multiple times

Example: find average of all integers associated with thesame key


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Computing the Mean: Version 1

Why cant we use reducer as combiner?


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Why doesnt this work?


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Fixed?


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Count and Normalize

Many algorithms reduce to estimating relative frequencies:

In the case of EM, pseudo-counts instead of actual counts

For a large class of algorithms: intuition is the same, justvarying complexity in terms of bookkeeping

Lets start with the intuition

'

)',(count

),(count

)(count

),(count)|(

B

BA

BA

A

BAABf

Al ith D i R i E l


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Algorithm Design: Running Example

Term co-occurrence matrix for a text collection

M = N x N matrix (N = vocabulary size) Mij: number of times iandjco-occur in some context

(for concreteness, lets say context = sentence)

Why?

Distributional profiles as a way of measuring semantic distance Semantic distance useful for many language processing tasks

M R d L C ti P bl


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MapReduce: Large Counting Problems

Term co-occurrence matrix for a text collection

= specific instance of a large counting problem A large event space (number of terms)

A large number of observations (the collection itself)

Goal: keep track of interesting statistics about the events

Basic approach Mappers generate partial counts

Reducers aggregate partial counts

How do we aggregate partial counts efficiently?

Fi t T P i


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First Try: Pairs

Each mapper takes a sentence:

Generate all co-occurring term pairs For all pairs, emit (a, b) count

Reducers sum up counts associated with these pairs

Use combiners!

P i P d C d


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Pairs: Pseudo-Code

P i A l i


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Pairs Analysis

Advantages

Easy to implement, easy to understand

Disadvantages

Lots of pairs to sort and shuffle around (upper bound?)

Not many opportunities for combiners to work

A th T St i


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Another Try: Stripes

Idea: group together pairs into an associative array

Each mapper takes a sentence: Generate all co-occurring term pairs

For each term, emit a { b: countb, c: countc, d: countd }

Reducers perform element-wise sum of associative arrays

(a, b) 1(a, c) 2

(a, d) 5

(a, e) 3

(a, f) 2

a { b: 1, c: 2, d: 5, e: 3, f: 2 }

a { b: 1, d: 5, e: 3 }a { b: 1, c: 2, d: 2, f: 2 }a { b: 2, c: 2, d: 7, e: 3, f: 2 }

+

St i P d C d


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Stripes: Pseudo-Code

St i A l i


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Stripes Analysis

Advantages

Far less sorting and shuffling of key-value pairs Can make better use of combiners

Disadvantages

More difficult to implement

Underlying object more heavyweight

Fundamental limitation in terms of size of event space


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Cluster size: 38 cores

Data Source: Associated Press Worldstream (APW) of the English Gigaword Corpus (v3),which contains 2.27 million documents (1.8 GB compressed, 5.7 GB uncompressed)


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Relative Frequencies


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Relative Frequencies

How do we estimate relative frequencies from counts?

Why do we want to do this?

How do we do this with MapReduce?

'

)',(count

),(count

)(count

),(count)|(

B

BA

BA

A

BAABf

f(B|A): Stripes


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f(B|A): Stripes

Easy!

One pass to compute (a, *)

Another pass to directly compute f(B|A)

a {b1:3, b

2:12, b

3:7, b

4:1, }

f(B|A): Pairs


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f(B|A): Pairs

For this to work:

Must emit extra (a, *) for every bn in mapper

Must make sure all as get sent to same reducer (use partitioner) Must make sure (a, *) comes first (define sort order)

Must hold state in reducer across different key-value pairs

(a, b1) 3(a, b2) 12(a, b3) 7(a, b4) 1

(a, *) 32

(a, b1) 3 / 32(a, b2) 12 / 32(a, b3) 7 / 32(a, b4) 1 / 32

Reducer holds this value in memory

Order Inversion


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Order Inversion

Common design pattern

Computing relative frequencies requires marginal counts But marginal cannot be computed until you see all counts

Buffering is a bad idea!

Trick: getting the marginal counts to arrive at the reducer beforethe joint counts

Optimizations

Apply in-memory combining pattern to accumulate marginal counts

Should we apply combiners?

Synchronization: Pairs vs Stripes


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Synchronization: Pairs vs. Stripes

Approach 1: turn synchronization into an ordering problem

Sort keys into correct order of computation Partition key space so that each reducer gets the appropriate set

of partial results

Hold state in reducer across multiple key-value pairs to performcomputation

Illustrated by the pairs approach

Approach 2: construct data structures that bring partialresults together

Each reducer receives all the data it needs to complete the

computation

Illustrated by the stripes approach

Secondary Sorting


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Secondary Sorting

MapReduce sorts input to reducers by key

Values may be arbitrarily ordered

What if want to sort value also?

E.g., k (v1, r), (v3, r), (v4, r), (v8, r)

Secondary Sorting: Solutions


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Secondary Sorting: Solutions

Solution 1:

Buffer values in memory, then sort Why is this a bad idea?

Solution 2:

Value-to-key conversion design pattern: form composite

intermediate key, (k, v1) Let execution framework do the sorting

Preserve state across multiple key-value pairs to handleprocessing

Anything else we need to do?

Recap: Tools for Synchronization


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Recap: Tools for Synchronization

Cleverly-constructed data structures

Bring data together

Sort order of intermediate keys

Control order in which reducers process keys

Partitioner

Control which reducer processes which keys

Preserving state in mappers and reducers

Capture dependencies across multiple keys and values

Issues and Tradeoffs


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Issues and Tradeoffs

Number of key-value pairs

Object creation overhead Time for sorting and shuffling pairs across the network

Size of each key-value pair

De/serialization overhead

Local aggregation

Opportunities to perform local aggregation varies

Combiners make a big difference

Combiners vs. in-mapper combining

RAM vs. disk vs. network

Setting the stageIntroduction to MapReduce


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Text Retrieval

MapReduce algorithm designText retrievalManaging relational dataGraph algorithms

Beyond MapReduce

Abstract IR Architecture


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Abstract IR Architecture

DocumentsQuery

Hits

RepresentationFunction

RepresentationFunction

Query Representation Document Representation

ComparisonFunction Index

offlineonline

Bag of Words


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Bag of Words

Terms weights computed as functions of:

Term frequency Collection frequency

Document frequency

Average document length

Well-known weighting functions

TF.IDF

BM25

Dirichlet scores (LM framework) Similarity boils down to inner products of feature vectors:

n

i kijikjkjwwddddsim

1 ,,),(

Inverted Index


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2

1

1

2

1

1

1

1

1

1

1

Inverted Index

2

1

2

1

1

1

1 2 3

1

1

1

4

1

1

1

1

1

1

2

1

tf

df

blue

cat

egg

fish

green

ham

hat

one

1

1

1

1

1

1

2

1

blue

cat

egg

fish

green

ham

hat

one

1 1red

1 1two

1red

1two

one fish, two fishDoc 1

red fish, blue fishDoc 2

cat in the hatDoc 3

green eggs and hamDoc 4

3

4

1

4

4

3

2

1

2

2

1

Inverted Index: Positional Information


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[2,4]

[3]

[2,4]

[2]

[1]

[1]

[3]

[2]

[1]

[1]

[3]

2

1

1

2

1

1

1

1

1

1

1

Inverted Index: Positional Information

2

1

2

1

1

1

1 2 3

1

1

1

4

1

1

1

1

1

1

2

1

tf

df

blue

cat

egg

fish

green

ham

hat

one

1

1

1

1

1

1

2

1

blue

cat

egg

fish

green

ham

hat

one

1 1red

1 1two

1red

1two



cat in the hatDoc 3

green eggs and hamDoc 4

3

4

1

4

4

3

2

1

2

2

1

Retrieval in a Nutshell


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Retrieval in a Nutshell

Look up postings lists corresponding to query terms

Traverse postings for each query term

Store partial query-document scores in accumulators

Select top kresults to return

Retrieval: Document-at-a-Time


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Retrieval: Document at a Time

Evaluate documents one at a time (score all query terms)

Tradeoffs

Small memory footprint (good)

Must read through all postings (bad), but skipping possible

More disk seeks (bad), but blocking possible

fish 2 1 3 1 2 31 9 21 34 35 80

blue 2 1 19 21 35

Accumulators(e.g. priority queue)

Document score in top k?

Yes: Insert document score, extract-min if queue too large

No: Do nothing

Retrieval: Query-at-a-Time


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Retrieval: Query at a Time

Evaluate documents one query term at a time

Usually, starting from most rare term (often with tf-sorted postings)

Tradeoffs Early termination heuristics (good)

Large memory footprint (bad), but filtering heuristics possible

fish 2 1 3 1 2 31 9 21 34 35 80

blue 2 1 19 21 35

Accumulators(e.g., hash)

Score{q=x}(doc n) = s

MapReduce it?


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MapReduce it?

The indexing problem

Scalability is critical Must be relatively fast, but need not be real time

Fundamentally a batch operation

Incremental updates may or may not be important

For the web, crawling is a challenge in itself

The retrieval problem

Must have sub-second response time

For the web, only need relatively few results

Indexing: Performance Analysis


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Indexing: Performance Analysis

Fundamentally, a large sorting problem

Terms usually fit in memory Postings usually dont

How is it done on a single machine?

How can it be done with MapReduce?

First, lets characterize the problem size:

Size of vocabulary

Size of postings

Vocabulary Size: Heaps Law


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Vocabulary Size: Heaps Law

Heaps Law: linear in log-log space

Vocabulary size grows unbounded!

bkTM Mis vocabulary sizeTis collection size (number of documents)kand bare constants

Typically, kis between 30 and 100, bis between 0.4 and 0.6

Heaps Law for RCV1


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Heaps Law for RCV1

Reuters-RCV1 collection: 806,791 newswire documents (Aug 20, 1996-August 19, 1997)

k = 44

b = 0.49

First 1,000,020 terms:Predicted = 38,323Actual = 38,365

Manning, Raghavan, Schtze, Introduction to Information Retrieval (2008)

Postings Size: Zipfs Law


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Postings Size: Zipf s Law

Zipfs Law: (also) linear in log-log space

Specific case of Power Law distributions

In other words: A few elements occur very frequently

Many elements occur very infrequently

i

c

icf cfis the collection frequency of i-th common term

cis a constant

Zipfs Law for RCV1


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Zipf s Law for RCV1

Reuters-RCV1 collection: 806,791 newswire documents (Aug 20, 1996-August 19, 1997)

Fit isnt that good

but good enough!

Manning, Raghavan, Schtze, Introduction to Information Retrieval (2008)

MapReduce: Index Construction


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MapReduce: Index Construction

Map over all documents

Emit termas key, (docno, tf) as value Emit other information as necessary (e.g., term position)

Sort/shuffle: group postings by term

Reduce

Gather and sort the postings (e.g., by docnoor tf)

Write postings to disk

MapReduce does all the heavy lifting!

Inverted Indexing with MapReduce


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1

1

2

1

1

2 2

11

1

1

1

1

1

1

2

g p

1one

1two

1fish


2red

2blue

2fish


3cat

3hat

cat in the hatDoc 3

1fish 2

1one1two

2red

3cat

2blue

3hat


Map

Reduce

Inverted Indexing: Pseudo-Code


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g

Positional Indexes


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[2,4]

[1]

[3]

[1]

[2]

[1]

[1]

[3]

[2]

[3]

[2,4]

[1]

[2,4]

[2,4]

[1]

[3]

1

1

2

1

1

2

1

1

2 2

11

1

1

1

1

1one

1two

1fish

2red

2blue

2fish

3cat

3hat

1fish 2

1one1two

2red

3cat

2blue

3hat


Map

Reduce



cat in the hatDoc 3

Inverted Indexing: Pseudo-Code


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g

Scalability Bottleneck


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y

Initial implementation: terms as keys, postings as values

Reducers must buffer all postings associated with key (to sort) What if we run out of memory to buffer postings?

Uh oh!

Another Try


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[2,4]

[9]

[1,8,22]

[23]

[8,41]

[2,9,76]

[2,4]

[9]

[1,8,22]

[23]

[8,41]

[2,9,76]

2

1

3

1

2

3

y

1fish

9

21

(values)(key)

34

35

80

1fish

9

21

(values)(keys)

34

35

80

fish

fish

fish

fish

fish

How is this different? Let the framework do the sorting Term frequency implicitly stored Directly write compressed postings

Where have we seen this before?

Postings Encoding


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2 1 3 1 2 3

2 1 3 1 2 3

g g

1fish 9 21 34 35 80

1fish 8 12 13 1 45

Conceptually:

In Practice:

Dont encode docnos, encode gaps (or d-gaps)But its not obvious that this save space

Overview of Index Compression


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p

Byte-aligned vs. bit-aligned

Non-parameterized bit-aligned

Unary codes

codes

codes

Parameterized bit-aligned

Golomb codes (local Bernoulli model)

Block-based methods

Simple-9

PForDelta

Want more detail? Start with Managing Gigabytesby Witten, Moffat, and Bell!

Index Compression: Performance


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p

Witten, Moffat, Bell, Managing Gigabytes (1999)

Unary 262 1918

Binary 15 20

6.51 6.63

6.23 6.38

Golomb 6.09 5.84

Bible TREC

Bible: King James version of the Bible; 31,101 verses (4.3 MB)TREC: TREC disks 1+2; 741,856 docs (2070 MB)

One common approach

Comparison of Index Size (bits per pointer)

Issue: For Golomb compression, optimal b~ 0.69 (N/df)Which means different bfor every term!

Chicken and Egg?


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gg

1fish

9

[2,4]

[9]

21 [1,8,22]

(value)(key)

34 [23]

35 [8,41]

80 [2,9,76]

fish

fish

fish

fish

fish

Write directly to disk

But wait! How do we set theGolomb parameter b?

We need the dfto set b

But we dont know the dfuntil weveseen all postings!

Optimal b~ 0.69 (N/df)

Sound familiar?

Getting the df


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In the mapper:

Emit special key-value pairs to keep track of df

In the reducer:

Make sure special key-value pairs come first: process them todetermine df

Remember: proper partitioning!

Getting the df: Modified Mapper


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1fish [2,4]

(value)(key)

1one [1]

1two [3]

fish [1]

one [1]

two [1]

Input document

Emit normal key-value pairs

Emit special key-value pairs to keep track of df

Getting the df: Modified Reducer


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1fish

9

[2,4]

[9]

21 [1,8,22]

(value)(key)

34 [23]

35 [8,41]

80 [2,9,76]

fish

fish

fish

fish

fish

Write compressed postings

fish [63] [82] [27]

First, compute the dfby summing contributionsfrom all special key-value pair

Compute Golomb parameter b

Important: properly define sort order to makesure special key-value pairs come first!

Where have we seen this before?

MapReduce it?


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The indexing problem

Scalability is paramount Must be relatively fast, but need not be real time

Fundamentally a batch operation

Incremental updates may or may not be important

For the web, crawling is a challenge in itself

The retrieval problem

Must have sub-second response time

For the web, only need relatively few results

Retrieval with MapReduce?


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MapReduce is fundamentally batch-oriented

Optimized for throughput, not latency Startup of mappers and reducers is expensive

MapReduce is not suitable for real-time queries!

Use separate infrastructure for retrieval

Important Ideas


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Partitioning (for scalability)

Replication (for redundancy)

Caching (for speed)

Routing (for load balancing)

The rest is just details!

Term vs. Document Partitioning


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T

D

T1

T2

T3

D

T

D1 D2 D3

TermPartitioning

DocumentPartitioning

Typical Search Architecture


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partitions

replica

s

brokers

Setting the stageIntroduction to MapReduceMapReduce algorithm designT t t i l


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Managing Relational Data

Text retrievalManaging relational dataGraph algorithms

Beyond MapReduce

Managing Relational Data


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In the good old days, organizations used relational

databases to manage big data Then along came Hadoop

Where does MapReduce fit in?

Relational Databases vs. MapReduce


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Relational databases:

Multipurpose: analysis and transactions; batch and interactive Data integrity via ACID transactions

Lots of tools in software ecosystem (for ingesting, reporting, etc.)

Supports SQL (and SQL integration, e.g., JDBC)

Automatic SQL query optimization

MapReduce (Hadoop):

Designed for large clusters, fault tolerant

Data is accessed in native format

Supports many query languages

Programmers retain control over performance

Open source

Source: OReilly Blog post by Joseph Hellerstein (11/19/2008)

Database Workloads


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OLTP (online transaction processing)

Typical applications: e-commerce, banking, airline reservations User facing: real-time, low latency, highly-concurrent

Tasks: relatively small set of standard transactional queries

Data access pattern: random reads, updates, writes (involvingrelatively small amounts of data)

OLAP (online analytical processing)

Typical applications: business intelligence, data mining

Back-end processing: batch workloads, less concurrency

Tasks: complex analytical queries, often ad hoc

Data access pattern: table scans, large amounts of data involvedper query

One Database or Two?


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Downsides of co-existing OLTP and OLAP workloads

Poor memory management Conflicting data access patterns

Variable latency

Solution: separate databases

User-facing OLTP database for high-volume transactions Data warehouse for OLAP workloads

How do we connect the two?

OLTP/OLAP Architecture


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OLTP OLAP

ETL(Extract, Transform, and Load)

OLTP/OLAP Integration


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OLTP database for user-facing transactions

Retain records of all activity Periodic ETL (e.g., nightly)

Extract-Transform-Load (ETL)

Extract records from source

Transform: clean data, check integrity, aggregate, etc. Load into OLAP database

OLAP database for data warehousing

Business intelligence: reporting, ad hoc queries, data mining, etc.

Feedback to improve OLTP services

Business Intelligence


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Premise: more data leads to better business decisions

Periodic reporting as well as ad hoc queries Analysts, not programmers (importance of tools and dashboards)

Examples:

Slicing-and-dicing activity by different dimensions to better

understand the marketplace Analyzing log data to improve OLTP experience

Analyzing log data to better optimize ad placement

Analyzing purchasing trends for better supply-chain management

Mining for correlations between otherwise unrelated activities

OLTP/OLAP Architecture: Hadoop?


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OLTP OLAP


OLTP/OLAP/Hadoop Architecture


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OLTP OLAP


Hadoop

ETL Bottleneck


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Reporting is often a nightly task:

ETL is often slow: why? What happens if processing 24 hours of data takes longer than 24

hours?

Hadoop is perfect:

Most likely, you already have some data warehousing solution Ingest is limited by speed of HDFS

Scales out with more nodes

Massively parallel

Ability to use any processing tool

Much cheaper than parallel databases

ETL is a batch process anyway!

Working Scenario


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Two tables:

User demographics (gender, age, income, etc.) User page visits (URL, time spent, etc.)

Analyses we might want to perform:

Statistics on demographic characteristics

Statistics on page visits Statistics on page visits by URL

Statistics on page visits by demographic characteristic

How to perform common relationaloperations in MapReduce

Except, dont! (later)

Relational Algebra


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Primitives

Projection () Selection ()

Cartesian product ()

Set union ()

Set difference ()

Rename ()

Other operations

Join ()

Group by aggregation

Projection


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R1

R2

R3

R4

R5

R1

R2

R3

R4

R5

Projection in MapReduce


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Easy!

Map over tuples, emit new tuples with appropriate attributes No reducers, unless for regrouping or resorting tuples

Alternatively: perform in reducer, after some other processing

Basically limited by HDFS streaming speeds

Speed of encoding/decoding tuples becomes important Relational databases take advantage of compression

Semistructured data? No problem!

Selection


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R1

R2

R3

R4

R5

R1

R3

Selection in MapReduce


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Easy!

Map over tuples, emit only tuples that meet criteria No reducers, unless for regrouping or resorting tuples

Alternatively: perform in reducer, after some other processing

Basically limited by HDFS streaming speeds

Speed of encoding/decoding tuples becomes important Relational databases take advantage of compression

Semistructured data? No problem!

Group by Aggregation


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Example: What is the average time spent per URL?

In SQL: SELECT url, AVG(time) FROM visits GROUP BY url

In MapReduce:

Map over tuples, emit time, keyed by url

Framework automatically groups values by keys

Compute average in reducer

Optimize with combiners

Relational Joins


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R1

R2

R3

R4

S1

S2

S3

S4

R1 S2

R2 S4

R3 S1

R4 S3

Types of Relationships


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One-to-OneOne-to-ManyMany-to-Many

Join Algorithms in MapReduce


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Reduce-side join

Map-side join

In-memory join

Striped variant

Memcached variant

Reduce-side Join


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Basic idea: group by join key

Map over both sets of tuples Emit tuple as value with join key as the intermediate key

Execution framework brings together tuples sharing the same key

Perform actual join in reducer

Similar to a sort-merge join in database terminology

Two variants

1-to-1 joins

1-to-many and many-to-many joins

Reduce-side Join: 1-to-1


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R1

R4

S2

S3

R1

R4

S2

S3

keys values

Map

R1

R4

S2

S3

keys values

Reduce

Note: no guarantee if R is going to come first or S

Reduce-side Join: 1-to-many


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R1

S2

S3

R1

S2

S3

S9

keys values

Map

R1 S2

keys values

Reduce

S9

S3

Reduce-side Join: V-to-K Conversion


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R1

keys values

In reducer

S2

S3

S9

R4

S3

S7

New key encountered: hold in memory

Cross with records from other set

New key encountered: hold in memory


Reduce-side Join: many-to-many


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R1

keys values

In reducer

S2

S3

S9

Hold in memory


R5

R8

Map-side Join: Basic Idea


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Assume two datasets are sorted by the join key:

R1

R2

R3

R4

S1

S2

S3

S4

A sequential scan through both datasets to join(called a merge join in database terminology)

Map-side Join: Parallel Scans


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If datasets are sorted by join key, join can beaccomplished by a scan over both datasets

How can we accomplish this in parallel?

Partition and sort both datasets in the same manner

In MapReduce:

Map over one dataset, read from other corresponding partition

No reducers necessary (unless to repartition or resort)

Consistently partitioned datasets: realistic to expect?

In-Memory Join


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Basic idea: load one dataset into memory, stream overother dataset

Works if R


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Striped variant:

R too big to fit into memory? Divide R into R1, R2, R3, s.t. each Rn fits into memory

Perform in-memory join: n, Rn S

Take the union of all join results

Memcached join: Load R into memcached

Replace in-memory hash lookup with memcached lookup

Which join to use?

I j i id j i d id j i


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In-memory join > map-side join > reduce-side join

Why? Limitations of each?

In-memory join: memory

Map-side join: sort order and partitioning

Reduce-side join: general purpose

Key Features in Databases

C i i i i l i l d b


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Common optimizations in relational databases

Reducing the amount of data to read Reducing the amount of tuples to decode

Data placement

Query planning and cost estimation

Same ideas can be applied to MapReduce

For example, column stores in Google Dremel

A few commercialized products

Many research prototypes


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Setting the stageIntroduction to MapReduceMapReduce algorithm designText retrievalManaging relational data


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Graph Algorithms

Managing relational dataGraph algorithmsBeyond MapReduce

Whats a graph?

G (V E) h


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G = (V,E), where

V represents the set of vertices (nodes) E represents the set of edges (links)

Both vertices and edges may contain additional information

Different types of graphs:

Directed vs. undirected edges Presence or absence of cycles

Graphs are everywhere:

Hyperlink structure of the Web

Physical structure of computers on the Internet Interstate highway system

Social networks


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Source: Wikipedia (Knigsberg)

Some Graph Problems

Fi di h t t th


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Finding shortest paths

Routing Internet traffic and UPS trucks Finding minimum spanning trees

Telco laying down fiber

Finding Max Flow

Airline scheduling

Identify special nodes and communities

Breaking up terrorist cells, spread of avian flu

Bipartite matching Monster.com, Match.com

And of course... PageRank

Graphs and MapReduce

Graph algorithms typically involve:


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Performing computations at each node: based on node features,edge features, and local link structure

Propagating computations: traversing the graph

Key questions:

How do you represent graph data in MapReduce?

How do you traverse a graph in MapReduce?

Representing Graphs

G (V E)


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G = (V, E)

Two common representations Adjacency matrix

Adjacency list

Adjacency Matrices

Represent a graph as an n x n square matrix M


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Represent a graph as an nx nsquare matrix M

n= |V|

Mij= 1 means a link from node itoj

1 2 3 4

1 0 1 0 1

2 1 0 1 1

3 1 0 0 0

4 1 0 1 0

1

2

3

4

Adjacency Matrices: Critique

Advantages:


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Advantages:

Amenable to mathematical manipulation

Iteration over rows and columns corresponds to computations onoutlinks and inlinks

Disadvantages:

Lots of zeros for sparse matrices

Lots of wasted space

Adjacency Lists

Take adjacency matrices and throw away all the zeros


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Take adjacency matrices and throw away all the zeros

1: 2, 42: 1, 3, 43: 1

4: 1, 3

1 2 3 4

1 0 1 0 1

2 1 0 1 1

3 1 0 0 0

4 1 0 1 0

Adjacency Lists: Critique

Advantages:


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Advantages:

Much more compact representation

Easy to compute over outlinks

Disadvantages:

Much more difficult to compute over inlinks

Single Source Shortest Path

Problem: find shortest path from a source node to one or


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Problem: find shortest path from a source node to one ormore target nodes

Shortest might also mean lowest weight or cost

First, a refresher: Dijkstras Algorithm

Dijkstras Algorithm Example


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0

10

5

2 3

2

1

9

7

4 6

Example from CLR



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0

10

5

Example from CLR

10

5

2 3

2

1

9

7

4 6



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0

8

5

14

7

Example from CLR

10

5

2 3

2

1

9

7

4 6



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0

8

5

13

7

Example from CLR

10

5

2 3

2

1

9

7

4 6



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0

8

5

9

7

1

Example from CLR

10

5

2 3

2

1

9

7

4 6



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0

8

5

9

7

Example from CLR

10

5

2 3

2

1

9

7

4 6

Single Source Shortest Path

Problem: find shortest path from a source node to one or


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Problem: find shortest path from a source node to one ormore target nodes

Shortest might also mean lowest weight or cost

Single processor machine: Dijkstras Algorithm

MapReduce: parallel Breadth-First Search (BFS)

Finding the Shortest Path

Consider simple case of equal edge weights


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Consider simple case of equal edge weights

Solution to the problem can be defined inductively Heres the intuition:

Define: bis reachable from aif bis on adjacency list of a

DISTANCETO(s) = 0

For all nodes preachable from s,DISTANCETO(p) = 1

For all nodes nreachable from some other set of nodes M,DISTANCETO(n) = 1 + min(DISTANCETO(m), mM)

s

m3

m2

m1

n

d1

d2

d3


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Source: Wikipedia (Wave)

Visualizing Parallel BFS


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n0

n3n2

n1

n7

n6

n5

n4

n9

n8

From Intuition to Algorithm

Data representation:


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Data representation:

Key: node n

Value: d(distance from start), adjacency list (list of nodesreachable from n)

Initialization: for all nodes except for start node, d=

Mapper:

m adjacency list: emit (m, d+ 1)

Sort/Shuffle

Groups distances by reachable nodes

Reducer: Selects minimum distance path for each reachable node

Additional bookkeeping needed to keep track of actual path

Multiple Iterations Needed

Each MapReduce iteration advances the known frontier


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Each MapReduce iteration advances the known frontier

by one hop

Subsequent iterations include more and more reachable nodes asfrontier expands

Multiple iterations are needed to explore entire graph

Preserving graph structure:

Problem: Where did the adjacency list go?

Solution: mapper emits (n, adjacency list) as well

BFS Pseudo-Code


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Stopping Criterion

How many iterations are needed in parallel BFS (equal


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y p ( qedge weight case)?

When a node is first discovered, were guaranteed to

have found the shortest path

Comparison to Dijkstra

Dijkstras algorithm is more efficient


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j g

At any step it only pursues edges from the minimum-cost pathinside the frontier

MapReduce explores all paths in parallel

Lots of waste

Useful work is only done at the frontier

Why cant we do better using MapReduce?

Weighted Edges

Now add positive weights to the edges


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p g g

Simple change: adjacency list now includes a weight wforeach edge

In mapper, emit (m, d+ wp) instead of (m, d+ 1) for each node m

Thats it?

Stopping Criterion

How many iterations are needed in parallel BFS (positive


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y p (pedge weight case)?

When a node is first discovered, were guaranteed to

have found the shortest path

Additional Complexities


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s

pq

r

search frontier

10

n1

n2

n3

n4

n5

n6 n7n8

n9

1

11

1

1

1

1

1

Stopping Criterion

How many iterations are needed in parallel BFS (positive


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y p (pedge weight case)?

Practicalities of implementation in MapReduce

Graphs and MapReduce



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Performing computations at each node: based on node features,edge features, and local link structure

Propagating computations: traversing the graph

Generic recipe:

Represent graphs as adjacency lists

Perform local computations in mapper

Pass along partial results via outlinks, keyed by destination node

Perform aggregation in reducer on inlinks to a node

Iterate until convergence: controlled by external driver

Dont forget to pass the graph structure between iterations

Random Walks Over the Web

Random surfer model:


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User starts at a random Web page

User randomly clicks on links, surfing from page to page

PageRank

Characterizes the amount of time spent on any given page

Mathematically, a probability distribution over pages

PageRank captures notions of page importance

Correspondence to human intuition?

One of thousands of features used in web search

Note: query-independent


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Computing PageRank

Properties of PageRank


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Can be computed iteratively

Effects at each iteration are local

Sketch of algorithm:

Start with seed PRivalues

Each page distributes PRicredit to all pages it links to

Each target page adds up credit from multiple in-bound links tocompute PRi+1

Iterate until values converge

Simplified PageRank

First, tackle the simple case:


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No random jump factor

No dangling links

Then, factor in these complexities

Why do we need the random jump?

Where do dangling links come from?

Sample PageRank Iteration (1)


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n1 (0.2)

n4(0.2)

n3 (0.2)n5 (0.2)

n2 (0.2)

0.1

0.1

0.2 0.2

0.10.1

0.066 0.0660.066

n1 (0.066)

n4 (0.3)

n3 (0.166)n5 (0.3)

n2 (0.166)Iteration 1

Sample PageRank Iteration (2)


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n1 (0.066)

n4(0.3)

n3 (0.166)n5(0.3)

n2 (0.166)

0.033

0.033

0.3 0.166

0.0830.083

0.1 0.10.1

n1 (0.1)

n4(0.2)

n3 (0.183)n5(0.383)

n2 (0.133)Iteration 2

PageRank in MapReduce


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n5 [n1, n2, n3]n1 [n2, n4] n2 [n3, n5] n3 [n4] n4 [n5]

n2 n4 n3 n5 n1 n2 n3n4 n5

n2 n4n3 n5n1 n2 n3 n4 n5

n5 [n1, n2, n3]n1 [n2, n4] n2 [n3, n5] n3 [n4] n4 [n5]

Map

Reduce

PageRank Pseudo-Code


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Complete PageRank

Two additional complexities


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What is the proper treatment of dangling nodes?

How do we factor in the random jump factor?

Solution:

Second pass to redistribute missing PageRank mass andaccount for random jumps

pis PageRank value from before, p'is updated PageRank value

|G| is the number of nodes in the graph mis the missing PageRank mass

p

G

m

Gp )1(

1'

PageRank Convergence

Alternative convergence criteria


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Iterate until PageRank values dont change

Iterate until PageRank rankings dont change

Fixed number of iterations

Convergence for web graphs?

Beyond PageRank

Link structure is important for web search


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PageRank is one of many link-based features: HITS, SALSA, etc.

One of many thousands of features used in ranking

Adversarial nature of web search

Link spamming

Spider traps

Keyword stuffing

Efficient Graph Algorithms: Tricks

In-mapper combining: efficient local aggregation


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Smarter partitioning: create more opportunities for localaggregation

Schimmy: avoid shuffling the graph

Jimmy Lin and Michael Schatz. Design Patterns for Efficient Graph Algorithms in MapReduce. Proceedings of the EighthWorkshop on Mining and Learning with Graphs Workshop (MLG-2010), pages 78-85, July 2010, Washington, D.C.

In-Mapper Combining

Use combiners


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Perform local aggregation on map output

Downside: intermediate data is still materialized

Better: in-mapper combining

Preserve state across multiple map calls, aggregate messages inbuffer, emit buffer contents at end

Downside: requires memory management

configure

map

close

buffer

Better Partitioning

Default: hash partitioning


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Randomly assign nodes to partitions

Observation: many graphs exhibit local structure

E.g., communities in social networks

Better partitioning creates more opportunities for local aggregation

Unfortunately, partitioning is hard! Sometimes, chick-and-egg

But cheap heuristics sometimes available

For webgraphs: range partition on domain-sorted URLs

Schimmy Design Pattern

Basic implementation contains two dataflows:


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Messages (actual computations)

Graph structure (bookkeeping)

Schimmy: separate the two data flows, shuffle only themessages

Basic idea: merge join between graph structure and messages

S T

both relations sorted by join key

S1 T1 S2 T2 S3 T3

both relations consistently partitioned and sorted by join key

Do the Schimmy!

Schimmy = reduce side parallel merge join between graphstr ct re and messages


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S1 T1

structure and messages

Consistent partitioning between input and intermediate data

Mappers emit only messages (actual computation)

Reducers read graph structure directly from HDFS

S2 T2 S3 T3

ReducerReducerReducer

intermediate data(messages)



from HDFS(graph structure)



Experiments

Cluster setup:


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10 workers, each 2 cores (3.2 GHz Xeon), 4GB RAM, 367 GB disk

Hadoop 0.20.0 on RHELS 5.3

Dataset:

First English segment of ClueWeb09 collection

50.2m web pages (1.53 TB uncompressed, 247 GB compressed)

Extracted webgraph: 1.4 billion links, 7.0 GB

Dataset arranged in crawl order

Setup:

Measured per-iteration running time (5 iterations)

100 partitions

Results


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Best Practices

Results


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+18%1.4b

674m

Results


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+18%

-15%

1.4b

674m

Results


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+18%

-15%

-60%

1.4b

674m

86m

Results


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+18%

-15%

-60%-69%

1.4b

674m

86m



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Beyond MapReduce

Beyond MapReduce

From GFS to Bigtable

Googles GFS is a distributed file system


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Bigtable is a storage system for structured data Built on top of GFS

Solves many GFS issues: real-time access, short files, short reads

Serves as a source and a sink for MapReduce jobs

Bigtable: Data Model

A table is a sparse, distributed, persistentmultidimensional sorted map


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multidimensional sorted map

Map indexed by a row key, column key, and a timestamp

(row:string, column:string, time:int64) uninterpreted byte array

Supports lookups, inserts, deletes

Single row transactions only

Image Source: Chang et al., OSDI 2006

HBase


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Image Source: http://www.larsgeorge.com/2009/10/hbase-architecture-101-storage.html


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Source: flickr (60in3/2338247189)


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Source: NY Times (6/14/2006)


Its all about the right level of abstraction

Need for High-Level Languages

Hadoop is great for large-data processing!

But writing Java programs for everything is verbose and slow


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But writing Java programs for everything is verbose and slow

Analysts dont want to (or cant) write Java

Solution: develop higher-level data processing languages

Hive: HQL is like SQL

Pig: Pig Latin is a dataflow language

Hive and Pig

Hive: data warehousing application in Hadoop

Query language is HQL variant of SQL


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Query language is HQL, variant of SQL

Tables stored on HDFS as flat files

Developed by Facebook, now open source

Pig: large-scale data processing system

Scripts are written in Pig Latin, a dataflow language

Developed by Yahoo!, now open source

Roughly 1/3 of all Yahoo! internal jobs

Common idea:

Provide higher-level language to facilitate large-data processing

Higher-level language compiles down to Hadoop jobs

Hive: Example

Hive looks similar to an SQL database


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Relational join on two tables: Table of word counts from Shakespeare collection

Table of word counts from the bible

Source: Material drawn from Cloudera training VM

SELECT s.word, s.freq, k.freq FROM shakespeare sJOIN bible k ON (s.word = k.word) WHERE s.freq >= 1 AND k.freq >= 1

ORDER BY s.freq DESC LIMIT 10;

the 25848 62394I 23031 8854and 19671 38985to 18038 13526of 16700 34654a 14170 8057you 12702 2720my 11297 4135in 10797 12445is 8882 6884

Hive: Behind the Scenes

SELECT s.word, s.freq, k.freq FROM shakespeare sJOIN bible k ON (s word = k word) WHERE s freq >= 1 AND k freq >= 1


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JOIN bible k ON (s.word = k.word) WHERE s.freq >= 1 AND k.freq >= 1

ORDER BY s.freq DESC LIMIT 10;

(TOK_QUERY (TOK_FROM (TOK_JOIN (TOK_TABREF shakespeare s) (TOK_TABREF bible k) (= (. (TOK_TABLE_OR_COL s)word) (. (TOK_TABLE_OR_COL k) word)))) (TOK_INSERT (TOK_DESTINATION (TOK_DIR TOK_TMP_FILE)) (TOK_SELECT(TOK_SELEXPR (. (TOK_TABLE_OR_COL s) word)) (TOK_SELEXPR (. (TOK_TABLE_OR_COL s) freq)) (TOK_SELEXPR (.(TOK_TABLE_OR_COL k) freq))) (TOK_WHERE (AND (>= (. (TOK_TABLE_OR_COL s) freq) 1) (>= (. (TOK_TABLE_OR_COL k)freq) 1))) (TOK_ORDERBY (TOK_TABSORTCOLNAMEDESC (. (TOK_TABLE_OR_COL s) freq))) (TOK_LIMIT 10)))

(one or more of MapReduce jobs)

(Abstract Syntax Tree)

Hive: Behind the ScenesSTAGE DEPENDENCIES:

Stage-1 is a root stage

Stage-2 depends on stages: Stage-1

Stage-0 is a root stage

STAGE PLANS:

Stage: Stage-2

Map Reduce

Alias -> Map Operator Tree:hdfs://localhost:8022/tmp/hive-training/364214370/10002


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Stage: Stage-1

Map Reduce

Alias -> Map Operator Tree:s

TableScan

alias: sFilter Operator

predicate:

expr: (freq >= 1)type: boolean

Reduce Output Operatorkey expressions:

expr: word

type: stringsort order: +

Map-reduce partition columns:

expr: word

type: stringtag: 0value expressions:

expr: freq

type: intexpr: word

type: string

kTableScan

alias: kFilter Operator

predicate:

expr: (freq >= 1)type: boolean

Reduce Output Operator

key expressions:expr: word

type: string

sort order: +Map-reduce partition columns:

expr: wordtype: string

tag: 1

value expressions:expr: freq

type: int

Reduce Operator Tree:Join Operator

condition map:Inner Join 0 to 1

condition expressions:

0 {VALUE._col0} {VALUE._col1}1 {VALUE._col0}

outputColumnNames: _col0, _col1, _col2

Filter Operatorpredicate:

expr: ((_col0 >= 1) and (_col2 >= 1))

type: booleanSelect Operator

expressions:expr: _col1

type: string

expr: _col0

type: intexpr: _col2

type: intoutputColumnNames: _col0, _col1, _col2

File Output Operatorcompressed: false

GlobalTableId: 0

table:input format: org.apache.hadoop.mapred.SequenceFileInputFormat

output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat

hdfs://localhost:8022/tmp/hive-training/364214370/10002

Reduce Output Operator

key expressions:expr: _col1

type: intsort order: -

tag: -1value expressions:

expr: _col0

type: stringexpr: _col1

type: int

expr: _col2type: int

Reduce Operator Tree:

ExtractLimit

File Output Operatorcompressed: false

GlobalTableId: 0

table:input format: org.apache.hadoop.mapred.TextInputFormat

output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat

Stage: Stage-0Fetch Operator

limit: 10

Pig: Example

Task: Find the top 10 most visited pages in each category


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User Url Time

Amy cnn.com 8:00

Amy bbc.com 10:00

Amy flickr.com 10:05

Fred cnn.com 12:00

Url Category PageRank

cnn.com News 0.9

bbc.com News 0.8

flickr.com Photos 0.7

espn.com Sports 0.9

Visits Url Info

Pig Slides adapted from Olston et al. (SIGMOD 2008)

Pig Query Plan

Load Visits


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Group by url

Foreach urlgenerate count Load Url Info

Join on url

Group by category

Foreach categorygenerate top10(urls)


Pig Script

visits = load/data/visits as (user, url, time);


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gVisits = group visits by url;

visitCounts =foreachgVisitsgenerate url, count(visits);

urlInfo = load/data/urlInfo as (url, category, pRank);

visitCounts =join visitCounts by url, urlInfo by url;

gCategories = group visitCounts by category;

topUrls = foreach gCategories generate top(visitCounts,10);

store topUrls into /data/topUrls;


Load Visits

Pig Script in Hadoop

Map1


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Group by url

Foreach urlgenerate count Load Url Info

Join on url

Group by category

Foreach categorygenerate top10(urls)

Reduce1Map2

Reduce2

Map3

Reduce3


Different Programming Models

Multitude of MapReduce hybrids, variants, etc.

Mostly research prototypes


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Mostly research prototypes

A few commercial companies

Dryad/DryadLINQ (Microsoft)

Emerging Themes

Continuing quest for alternative programming models

Batch vs real-time data processing


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Batch vs. real time data processing

Continuing quest for better implementations

MapReduce as yet another tool

Growth of the Hadoop ecosystem

Evolving role of MapReduce and parallel databases


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Date post:	07-Apr-2018
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