Transcript Performance Modeling and Optimization for Pig program

Meeting Service Level Objectives
of Pig Programs
Zhuoyao Zhang, Ludmila Cherkasova,
Abhishek Verma, Boon Thau Loo
University of Pennsylvania
Hewlett-Packard Labs
Cloud Environment
• Advantages
▫ Large amount of resources
▫ Elasticity
▫ Pay-as-you-go pricing model
• Challenges
▫ Distributed resources
▫ Error-prone
MapReduce and Pig
• MapReduce: Simple and fault tolerant
framework for data processing in the cloud
• Pig
▫ Advanced MapReduce based platform
▫ Widely used: Yahoo!, Twitter, LinkedIn
▫ PigLatin: A high-level declaratice language for
expressing data analysis tasks as Pig programs
j2
j4
j6
j1
j3
j5
j7
Motivation
• Latency-sensitive applications
▫ Personalized advertising
▫ Spam and fraud detection
▫ Real-time log analysis
• How much resource does an application need
to meet their deadlines?
Contributions
• Performance modeling for Pig programs
▫ Given a Pig grogram, estimates its completion
time as a function of assigned resource
• Deadline driven resource allocation
estimates for Pig programs
▫ Given a completion time target, determine the
amount of resources for a Pig program to
achieve it
Outline
• Introduction
• Building block
▫ Performance model for single MapReduce jobs
• Resource allocation for Pig programs
• Evaluation
• Conclusion and ongoing work
Theoretical Makespan Bounds
• Bounds- based makespan estimates
▫ n tasks, k servers
▫ avg: average duration of the n tasks
▫ max: maximum duration of the n tasks
• Lower bound
Tlow
• Upper bound
n
 avg 
k
(n  1)
Tup  avg 
 max
k
Illustration
Schedule 1: 1 4 3 2 3 1 2
1
2
3
Makespan = 4
Lower bound = 4
4
Schedule 2: 3 1 2 3 2 1 4
1
2
3
4
Makespan = 7
Upper bound = 8
Estimate Completion Time for Single MR Job
• Estimate the bounds of the job completion
time based on job profile
▫ Most production jobs are executed routinely on
new data sets
▫ Job profile based on previous running
 Map stage: Mavg, Mmax, AvgInputSize, Selectivity
 Reduce stage: Shavg, Shmax, Ravg, Rmax, Selectivity
▫ Predict the completion time for future running with
the profile
Estimate Completion Time for Single MR Job
• Estimating bounds on the duration of map and
reduce stages
• Map stage duration depends on:
▫ NM -- the number of map tasks
▫ SM -- the number of map slots
NM
SM
( N 1)
TM up  M avg  M
 M max
SM
TM low  M avg 
• Reduce stage duration depends on:
▫ NR -- the number of reduce tasks
▫ SR -- the number of reduce slots
• Job duration TJlow , TJup , Tjavg
▫ Sum of the map and reduce stage duration
10
Resource Allocation for Single MR Job
• Given a deadline D and the job profile, find the
minimal resource to complete the job within D
Statistics from
job profile
Given number of
map/reduce tasks
Find the value of SMJ, SRJ with minimum value of
SMJ+ SRJ using Lagrange's multipliers
Outline
• Introduction
• Building block
▫ Performance model for single MapReduce jobs
• Resource allocation for Pig programs
• Evaluation
• Conclusion and ongoing work
Performance Model for Pig Programs
• Let P = {J1, J2,….JN } , extract the job profile of
each job contained in P
▫ Assign unique name for each job within a program
• The program completion time  sum of the
completion time of all the jobs contained in P
TP  1i  N Ti
Resource Allocation for Pig Programs
• Possible strategy: find out an appropriate pair of
map and reduce slots for each job in the program














A
S
A
S
1
1
M
2
2
M
A
S
N
N
M
B

S
B

S

B

S
1
1
C

1
R
2
2
C
2

R
N
N
R
C
N


d1 


d 2 




dN 


with

1i  N
di  D
• Problem: difficult to implement and manage by
the scheduler
Resource Allocation for Pig Programs
• A simpler and more elegant solution
▫ Allocate the same set of resource to the entire
program instead of to each job
• Rewrite the previous equations into
TP 
 1i N Ai
S
P
M

 1i N Bi
S
P
R
  1iN Ci  D
Find the minimum set of map and reduce slots
( SMP , SRP ) for the entire Pig program
Experiment Setup
• 66 nodes cluster in 2 racks
▫ 4 AMD 2.39GHz cores
▫ 8 GB RAM,
▫ two 160GB hard disks
• Configuration
▫ 1 jobtracker, 1 namenode, 64 worker nodes
▫ 2 map slots and 1 reduce slot for each node
Benchmark
• Pigmix benchmark
▫ 17 programs
▫ 8 tables as the input data
• Dataset
▫ Test dataset
 Generated with the Pig mix data generator
 Total size around 1TB.
▫ Experimental dataset
 Same layout as the test dataset
 20% larger in size
Model Accuracy
• How well of our performance model captures Pig
program completion time?
Normalized results for predicted and measured completion time
Meeting Deadlines
• Are we meeting deadlines with our resource
allocation mode?
Pigmix executed on experimental data set : do we meet deadlines?
Conclusion
• Conclusion
▫ The performance model can accurately estimate the
completion time of MapReduce workflow
▫ Enables automatic resource provisioning for
MapReduce workflow with deadlines
• Ongoing work
▫ Refine the performance model for workflow with
concurrent jobs
▫ Incorporating failure scenarios in the current model
Thank you