MRShare: Sharing Across Multiple Queries in MapReduce

Tomasz Nykiel (University of Toronto)

Michalis Potamias (Boston University)

Chaitanya Mishra (University of Toronto, currently Facebook)

George Kollios (Boston University)

Nick Koudas (University of Toronto)

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Data management landscape

efficiency

flex

ibili

ty

σ π

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• Time performance

• Arbitrary data• Large scale setups

MRShare – sharing framework for MR

MRShare – a sharing framework for Map Reduce

• MRShare framework:

– Inspired by sharing primitives from relational domain

– Introduces a cost model for Map Reduce jobs

– Searches for the optimal sharing strategies

– Does not change the Map Reduce computational model

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Outline

• Introduction

• Map Reduce recap.

• MRShare – Sharing primitives in Map-Reduce

• MRShare – Cost based approach to sharing

• MRShare Evaluation

• Summary

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Outline

• Map Reduce recap.

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Map Reduce recap.

I

I

I

I

Map Reduce

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Outline

• MRShare - Sharing primitives in Map-Reduce

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Sharing primitives – sharing scans

• SELECT COUNT(*) FROM user GROUP BY hometown

• SELECT AVG(age) FROM user GROUP BY hometown

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User_id Hometown Occupation Age

MRShare – sharing scans (map).

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MRShare – sharing scans (reduce)

J1 J2 J3 J4 key value

Toronto 1

Toronto 1

Toronto 1

Toronto 17

Toronto 19

Toronto 2

Toronto 5

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Outline

• MRShare - Sharing primitives in Map-Reduce

– Sharing scans

– Sharing intermediate data

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Sharing primitives - Sharing intermediate data.

• SELECT COUNT(*) FROM user WHERE occupation=‘student’ GROUP BY hometown

• SELECT COUNT(*) FROM user WHERE age > 18 GROUP BY hometown

User_id Hometown Occupation Age

Occupation ?= ‘student’ Age ?> 18

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MRShare – sharing intermediate data (map).

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MRShare – sharing intermediate data (reduce).

J1 J2 J3 J4 key value

Toronto 1

Toronto 4

Toronto 1

Toronto 1

Toronto 2

Toronto 2

Toronto 5

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Outline

• MRShare – Cost based approach to sharing

– Cost model for finding the optimal sharing strategy

– SplitJobs – cost based algorithm for sharing scans

– MultiSplitJobs – an improvement of SplitJobs

– γ-MultiSplitJobs – the algorithm for sharing intermediate data

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Cost model for Map Reduce (single job)

• Reading – f(input size)

• Sorting – f(intermediate data size)

• Copying – f(intermediate data size)

• Writing – f(output size)

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Cost of executing a group of jobs

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Finding the optimal sharing strategy

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• An optimization problem

“NoShare”

“GreedyShare”

Outline

• MRShare – Cost based approach to sharing

– Cost model for finding the optimal sharing strategy

– SplitJobs – cost based algorithm for sharing scans

– MultiSplitJobs – an improvement of SplitJobs

– γ-MultiSplitJobs – the algorithm for sharing intermediate data

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Sharing scans - cost based optimization

• Savings come from reduced number of scans• The sorting cost might change• The costs of copying and writing the output do not

change

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• We prove NP-hardness of the problem of finding the optimal sharing strategy

SplitJobs – a DP solution for sharing scans.

• We reduce the problem of grouping to the problem of splitting a sorted list of jobs – by approximating the cost of sorting.

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• Using our cost model and the approximation, we employ a DP algorithm to find the optimal split points.

Outline

• MRShare – Cost based approach to sharing

– Cost model for finding the optimal sharing strategy

– SplitJobs – cost based algorithm for sharing scans

– MultiSplitJobs – an improvement of SplitJobs

– γ-MultiSplitJobs – the algorithm for sharing intermediate data

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MultiSplitJobs – an improvement of SplitJobs

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Outline

• MRShare – Cost based approach to sharing

– Cost model for finding the optimal sharing strategy

– SplitJobs – cost based algorithm for sharing scans

– MultiSplitJobs – an improvement of SplitJobs

– γ-MultiSplitJobs – the algorithm for sharing intermediate data

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Sharing intermediate data - cost based optimization

• The sorting and copying costs change – depending on the size of the intermediate data

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We need to estimate the size of the intermediate data of all combinations of jobs.

γ-MultiSplitJobs – the solution for sharing intermediate data

• Approximate the size of the intermediate data

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• γ –MultiSplitJobs – applies MultiSplitJobs with modified cost function

• γ set heuristically

Outline

• MRShare Evaluation

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Evaluation setup

• 40 EC2 small instance virtual machines

• Modified Hadoop engine

• 30 GB text dataset consisting of blogs

• Multiple grep-wordcount queries

– Counts words matching a regular expression

– Allows for variable intermediate data sizes

– Generic aggregation Map Reduce job

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Evaluation goals

• Sharing is not always beneficial.

– ‘GreedyShare’ policy

• How much can we save on sharing scans?

– MRShare - MultiSplitJobs evaluation

• How much can we save on sharing intermediate data?

– MRShare - γ-MultiSplitJobs evaluation

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Is sharing always beneficial?- ‘GreedyShare’ policy

Group of jobs

Group size

d=|intermediate data| / |input data|

H1 16 0.3 < d <0.7

H2 16 0.7 < d

H3 16 0.9 < d

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How much we save on sharing scans –MRShare MultiSplitJobs

Group of jobs

Group size

d=|intermediate data| / |input data|

G1 16 0.7 < d

G2 16 0.2 < d < 0.7

G3 16 0.0 < d < 0.2

G4 16 0.0 < d < max

G5 64 0.0 < d < max

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How much we save on sharing intermediate data -

MRShare - γ-MultiSplitJobs

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Group of jobs

Group size

d=|intermediate data| / |input data|

G1 16 0.7 < d

G2 16 0.2 < d < 0.7

G3 16 0.0 < d < 0.2

Summary

• We introduced MRShare – a framework for automatic work sharing in Map Reduce.

• We identified sharing primitives and demonstrated the implementation thereof in a Map Reduce engine.

• We established a cost model and solved several work sharing optimization problems.

• We demonstrated vast savings when using MRShare.

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Thank you!!!

Questions?

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Ongoing work – sharing expensive computation

• Sharing across multiple Map Reduce jobs with expensive predicates.

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Ongoing work – dynamic sharing

• Dynamic sharing.

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time

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