The document discusses an automated tuning system for Apache Spark applications using machine learning and domain-based models, aiming to optimize performance and resource efficiency. It covers motivation, the approach including Gaussian processes for iterative configuration prediction, and insights from previous research on performance tuning. A demo of the auto-tuning system, 'Uchit', is presented, illustrating significant reductions in configuration space and runtime evaluation time.

1. Amogh Margoor, Qubole Inc
Mayur Bhosale, Qubole Inc
Auto-Pilot for Apache
Spark using Machine
Learning
#UnifiedDataAnalytics #SparkAISummit

2. Agenda
• Motivation
• Approach
• Scope
• Previous Work
• Gaussian Process
• Domain based Model
• Uchit - Spark Auto Tuner
• Demo
• Experimental Evaluation
• Open Source
2#UnifiedDataAnalytics #SparkAISummit

3. Motivation
3

4. Tuning a Spark Application
Benefits
• Performance
• Resource Efficiency
4#UnifiedDataAnalytics #SparkAISummit
On Public Cloud
translates to
$$ saved.

5. Tuning is a Hard Problem !!
● Manual
● Requires Domain Knowledge
● Too many Knobs to configure
5

6. 6
Optimize
TPC-DS q2
● Analyze query plan
○ 3 Joins in Red
circle are
SortMerge Join
○ All 3 can be
converted to
Broadcast Join.

7. 7
Optimize
TPC-DS q2
● Analyze query plan
○ 3 Joins in Red
circle are
SortMerge Join
○ All 3 can be
converted to
Broadcast Join.
● Manual
● Requires Domain Knowledge
● Too many Knobs

8. Approach
8

9. Scope
9
– Goals: Improve Runtime or Cloud Cost.
– Insights through SparkLens are quite helpful (demo). Can we also
Auto Tune the Spark Configuration for above goals ?
– Target Repetitive Queries - ETL, Reporting etc.

10. Previous Work
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• “Standing of the shoulder of Giants”
– S. Kumar, S. Padakandla, C. Lakshminarayanan, P. Parihar, K. Gopinath, S. Bhatnagar, Performance tuning of hadoop mapreduce: A noisy
gradient approach, vol. abs/1611.10052, 2016.
– H. Herodotou, S. Babu, "Profiling what-if analysis and cost-based optimization of mapreduce programs", Proceedings of the VLDB
Endowment, vol. 4, no. 11, pp. 1111-1122, 2011.
– H. Herodotou, H. Lim, G. Luo, N. Borisov, L. Dong, F. B. Cetin, S. Babu, "Starfish: A self-tuning system for big data analytics", Cidr, no.
2011, pp. 261-272, 2011.
– A. J. Storm, C. Garcia-Arellano, S. Lightstone, Y. Diao, and M. Surendra. Adaptive Self-tuning Memory in DB2. In VLDB, 2006.
– D. G. Sullivan, M. I. Seltzer, and A. Pfeffer. Using probabilistic reasoning to automate software tuning. In SIGMETRICS, 2004.
– D. N. Tran, P. C. Huynh, Y. C. Tay, and A. K. H. Tung. A new approach to dynamic self-tuning of database buffers. ACM Transactions
on Storage, 4(1), 2008.
– B. Zhang, D. Van Aken, J. Wang, T. Dai, S. Jiang, et al. A Demonstration of the OtterTune Automatic Database Management System Tuning
Service. PVLDB
– S. Duan, V. Thummala, and S. Babu. Tuning Database Configuration Parameters with iTuned, VLDB, August 2009
– Prasad M. Deshpande, Amogh Margoor, Rajat Venkatesh, Automatic Tuning of SQL-on-Hadoop Engines on Cloud Platforms. IEEE
CLOUD 2018

11. Tuning a Spark Application
11#UnifiedDataAnalytics #SparkAISummit
• Machine Learning Based:
– B. Zhang, D. Van Aken, J. Wang, T. Dai, S. Jiang, et al. A
Demonstration of the OtterTune Automatic Database
Management System Tuning Service. PVLDB
– S. Duan, V. Thummala, and S. Babu. Tuning Database
Configuration Parameters with iTuned, VLDB, August 2009
• Domain Knowledge Based:
– Prasad M. Deshpande, Amogh Margoor, Rajat Venkatesh,
Automatic Tuning of SQL-on-Hadoop Engines on Cloud
Platforms. IEEE CLOUD 2018

12. Machine Learning Approach
12

13. Machine Learning Approaches
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• Based on previous works, our approach is also:
– Iterative approaches:
• Step 1: Predict good config based on Previous runs
• Step 2: Run with predicted config and add the result to Previous
runs.
• Repeat Step1 and Step 2 for `n` iterations
– Gaussian Process based approaches.

14. Gaussian Process
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● Image Source: https://katbailey.github.io/post/gaussian-processes-for-dummies/
● Gaussian is non-parametric approach.
● Other parametric regression techniques
start with fixed assumption of
parameters. Problems:
○ y = 𝛳0
+ 𝛳1
x Linear Equation with
2 parameters is not enough for
data.
○ y = 𝛳0
+ 𝛳1
x + 𝛳2
x2
Quadratic
equation with 3 parameters will
be more appropriate.
● Gaussian Process is non-parametric
i.e., it assumes all the possibilities.

15. Gaussian Process
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● Image Source: https://towardsdatascience.com/an-intuitive-guide-to-gaussian-processes-ec2f0b45c71d
Prior Posterior

16. Gaussian Process
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● Image Source: https://towardsdatascience.com/an-intuitive-guide-to-gaussian-processes-ec2f0b45c71d

17. Gaussian Process - Advantage
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● Image Source: https://towardsdatascience.com/an-intuitive-guide-to-gaussian-processes-ec2f0b45c71d
How does Gaussian Process help in finding good configs iteratively ?
GP tells degree of certainty of it’s prediction: low and high. Results in balancing
Exploitation and Exploration.
Exploration: Explore configs with low
degree certainty i.e., configs different
from training data.
Exploitation: Predict configs from
high degree certainty of
improvement.

18. ML Model Issues
• Training data is actual run of a job. Learning over
multiple runs might be required:
– Correlation between configs.
– Sensitiveness of individual config for a particular job.
– Explore large config space for global optimal.
– Domain specific insights like cloud insights etc.
• Too many runs can be expensive.
18

19. ML Model Issues
• Model searches for the optimal config using
historical data.
– Problem: Might need multiple iterations to prune out
obvious non-optimal configs.
– Solution: To converge sooner, Domain based
knowledge can be used to prune non-optimal
configs.
19

20. Domain based model
20

21. Insight 1 Spills are expensive and should be avoided at all
cost.
Spill increases Disk I/O significantly
Avoided by:
○ Increasing memory of task/containers.
○ More fine grained tasks i.e., increased parallelism. For e.g.,
decreasing split sizes or increasing shuffle partitions.
Evaluation
Time reduces for TPC DS q46 by almost 30% on increasing
shuffle partitions from 100 to 200.

22. Insight 2 For Spark, use single fat executor which uses all
cores in node.
Reasons for improvements are:
● Improved memory consumption between cores
● Reduced replicas of broadcast tables
● Reduced overheads.
Evaluation
● Figure besides show effect of increasing cores per executor.
● Increased spark.executor.cores from 1 to 8 and correspondingly
varying spark.executor.memory from 1152MB to 11094MB, thus
keeping memory per core constant.
● Saw performance benefit up to 25% with fatter executor.

23. Insight 3 Memory/vCPU ratio
Yarn allocates containers on two dimensions - memory and vcpu
Each container is given 1 vcpu and some memory
The memory/vcpu of the containers should match the memory/vcpu
ratio of the machine type
Otherwise resources are wasted!

24. Machine
Family
Different machine families have different memory/cpu
characteristics
Recommended memory profile for a query container should
match the family ratio
Otherwise, recommend change of machine family in the
cluster

25. Insight 4 Generate better SQL plans
● Collect statistics for Catalyst Optimizer.
● Tune configurations for better plans: e.g., more
broadcast joins in TPC-DS q2

26. Uchit – Spark Auto Tuner
26

27. Uchit – Spark Auto Tuner
27

28. Config Sampling
• Discretize configuration.
For e.g., if spark.executor.memory for r3.xlarge can vary
between 2GB and 24 GB
Discretized values = {2, 4, 6, 8, … 24}
• Possible configs for 5 configs ≃ 29 million.
• With sampling we could reduce it to 2000 config space:
Latin Hypercube Sampling.

29. Sampling- Latin HyperCube

30. Combined Model
ML Model
Historical runs
Best Conf
Latin
HyperCube
Sampler
Normalizer
DeNormalizer
Math Model
Combiner
Math Model and Combiner:
● Novel technique to combine domain
based Math Model and ML model.
● Combiner combines the model and
main functions are:
○ Prune non-optimal spaces
○ Guide towards optimal
settings.

31. Demo:
https://github.com/qubole/uchit/blob/master/Uchit%20Tutorial.ipynb
31

32. Experimental Evaluation
32

33. Experimental Evaluation - I
33

34. Experimental Evaluation - q2
34

35. Experimental Evaluation
35
Config 1 vs Config 2
More Joins converted to
Broadcast Join from
SortMerge Join
Config 1 Config 2

36. Correct configs
36

37. Correct configs
37

38. Combined Model vs ML Model
38
● Config Space Reduction by 400X (i.e, 2000 configs to 55 configs)
● Reduce iterations by around 3X

39. Uchit OS
39
https://github.com/qubole/uchit
● Pluggable `Bring your own model`
Framework.
● Clearly defined interfaces for combining
models.
● Scope for tuning other engines like Tez
etc.

40. 40
estions ?

41. DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
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