This document summarizes a presentation on Spark tuning for enterprise system administrators. It discusses how to initially configure Spark applications to be reliable and optimal. Specifically, it recommends setting the executor memory and storage level parameters and using Kryo serialization to reduce memory usage. It also provides guidance on gracefully handling memory limitations such as by increasing the storage fraction. The overall goal is to help applications complete faster and within the cluster memory limits.

1. Spark Tuning for Enterprise
System Administrators
Anya T. Bida, PhD
Rachel B. Warren

2. Don't worry about missing something...
Video: https://www.youtube.com/watch?v=DNWaMR8uKDc&feature=youtu.be
Presentation: http://www.slideshare.net/anyabida
Cheat-sheet: http://techsuppdiva.github.io/
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!
Anya: https://www.linkedin.com/in/anyabida
Rachel: https://www.linkedin.com/in/rachelbwarren
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!
!2

3. About Anya About Rachel
Operations Engineer
!
!
!
Spark & Scala Enthusiast /
Data Engineer
Alpine Data
!
alpinenow.com

4. About You*
Intermittent
Reliable
Optimal
Spark practitioners
mySparkApp Success
*

5. Intermittent
Reliable
Optimal
mySparkApp Success

6. Default != Recommended
Example: By default, spark.executor.memory = 1g
1g allows small jobs to finish out of the box.
Spark assumes you'll increase this parameter.
!6

7. Which parameters are important?
!
How do I configure them?
!7
Default != Recommended

8. Filter* data
before an
expensive reduce
or aggregation
consider*
coalesce(
Use* data
structures that
require less
memory
Serialize*
PySpark
serializing
is built-in
Scala/
Java?
persist(storageLevel.[*]_SER)
Recommended:
kryoserializer *
tuning.html#tuning-
data-structures
See "Optimize partitions."
*
See "GC investigation." *
See "Checkpointing." *
The Spark Tuning Cheat-Sheet

9. Intermittent
Reliable
Optimal
mySparkApp Success
Memory trouble
Initial config

10. Intermittent
Reliable
Optimal
mySparkApp Success
Memory trouble
Initial config

11. !11
How many in the
audience have their own
cluster?

12. !12

13. Fair Schedulers
!13
YARN
<allocations>
<queue name="sample_queue">
<minResources>4000 mb,0vcores</minResources>
<maxResources>8000 mb,8vcores</maxResources>
<maxRunningApps>10</maxRunningApps>
<weight>2.0</weight>
<schedulingPolicy>fair</schedulingPolicy>
</queue>
</allocations>
SPARK
<allocations>
<pool name="sample_queue">
<schedulingMode>FAIR</sch
<weight>1</weight>
<minShare>2</minShare>
</pool>
</allocations>

14. Fair Schedulers
!14
YARN
<allocations>
<queue name="sample_queue">
<minResources>4000 mb,0vcores</minResources>
<maxResources>8000 mb,8vcores</maxResources>
<maxRunningApps>10</maxRunningApps>
<weight>2.0</weight>
<schedulingPolicy>fair</schedulingPolicy>
</queue>
</allocations>
SPARK
<allocations>
<pool name="sample_queue">
<schedulingMode>FAIR</sch
<weight>1</weight>
<minShare>2</minShare>
</pool>
</allocations>

15. Fair Schedulers
!15
YARN
<allocations>
<queue name="sample_queue">
<minResources>4000 mb,0vcores</minResources>
<maxResources>8000 mb,8vcores</maxResources>
<maxRunningApps>10</maxRunningApps>
<weight>2.0</weight>
<schedulingPolicy>fair</schedulingPolicy>
</queue>
</allocations>
SPARK
<allocations>
<pool name="sample_queue">
<schedulingMode>FAIR</sch
<weight>1</weight>
<minShare>2</minShare>
</pool>
</allocations>

16. Fair Schedulers
!16
YARN
<allocations>
<queue name="sample_queue">
<minResources>4000 mb,0vcores</minResources>
<maxResources>8000 mb,8vcores</maxResources>
<maxRunningApps>10</maxRunningApps>
<weight>2.0</weight>
<schedulingPolicy>fair</schedulingPolicy>
</queue>
</allocations>
SPARK
<allocations>
<pool name="sample_queue">
<schedulingMode>FAIR</sch
<weight>1</weight>
<minShare>2</minShare>
</pool>
</allocations>

17. Fair Schedulers
!17
YARN
<allocations>
<queue name="sample_queue">
<minResources>4000 mb,0vcores</minResources>
<maxResources>8000 mb,8vcores</maxResources>
<maxRunningApps>10</maxRunningApps>
<weight>2.0</weight>
<schedulingPolicy>fair</schedulingPolicy>
</queue>
</allocations>
SPARK
<allocations>
<pool name="sample_queue">
<schedulingMode>FAIR</sch
<weight>1</weight>
<minShare>2</minShare>
</pool>
</allocations>
Use these parameters!

18. Fair Schedulers
!18
YARN
<allocations>
<user name="sample_user">
<maxRunningApps>6</maxRunningApps>
</user>
<userMaxAppsDefault>5</userMaxAppsDefault>
!
</allocations>

19. Fair Schedulers
!19
YARN
<allocations>
<user name="sample_user">
<maxRunningApps>6</maxRunningApps>
</user>
<userMaxAppsDefault>5</userMaxAppsDefault>
!
</allocations>

20. What is the memory limit for
mySparkApp?
!20

21. !21
Driver
Executor
Cluster Manager
Sidebar: Spark Architecture
Mark Grover:
http://www.slideshare.net/SparkSummit/top-5-mistakes-when-writing-spark-applications-by-mark-grover-and-ted-
malaska
Executor

22. !22
Max Memory in "pool" x 3/4 = mySparkApp_mem_limit
!
!
!
What is the memory limit for
mySparkApp?

23. !23
Max Memory in "pool" x 3/4 = mySparkApp_mem_limit
!
!
!
What is the memory limit for
mySparkApp?

24. !24
Max Memory in "pool" x 3/4 = mySparkApp_mem_limit
!
!
!
<maxResources>___mb</maxResources>
Limitation
What is the memory limit for
mySparkApp?

25. What is the memory limit for
mySparkApp?
!25
Max Memory in "pool" x 3/4 = mySparkApp_mem_limit
!
!
!
Reserve 25% for overhead

26. !26
Max Memory in "pool" x 3/4 = mySparkApp_mem_limit
!
!
!
What is the memory limit for
mySparkApp?

27. !27

28. !28
Max Memory in "pool" x 3/4 = mySparkApp_mem_limit
!
mySparkApp_mem_limit > driver.memory + (executor.memory
x dynamicAllocation.maxExecutors)
What is the memory limit for
mySparkApp?

29. !29
Max Memory in "pool" x 3/4 = mySparkApp_mem_limit
!
mySparkApp_mem_limit > driver.memory + (executor.memory
x dynamicAllocation.maxExecutors)
What is the memory limit for
mySparkApp?

30. !30
Max Memory in "pool" x 3/4 = mySparkApp_mem_limit
!
mySparkApp_mem_limit > driver.memory + (executor.memory
x dynamicAllocation.maxExecutors)
What is the memory limit for
mySparkApp?
Limitation: Driver must not be
larger than a single node.

31. !31
yarn.nodemanager.resource.memory-mb
Driver Container
spark.driver.memory

32. !32
Max Memory in "pool" x 3/4 = mySparkApp_mem_limit
!
mySparkApp_mem_limit > driver.memory + (executor.memory
x dynamicAllocation.maxExecutors)
What is the memory limit for
mySparkApp?

33. !33
Driver
Executor
Cluster Manager
Sidebar: Spark Architecture
Mark Grover:
http://www.slideshare.net/SparkSummit/top-5-mistakes-when-writing-spark-applications-by-mark-grover-and-ted-
malaska
Executor

34. !34
Max Memory in "pool" x 3/4 = mySparkApp_mem_limit
!
mySparkApp_mem_limit > driver.memory + (executor.memory
x dynamicAllocation.maxExecutors)
What is the memory limit for
mySparkApp?
Verify my calculations respect this
limitation.

35. !35

36. Intermittent
Reliable
Optimal
mySparkApp Success
Memory trouble
Initial config

37. Intermittent
Reliable
Optimal
mySparkApp Success
Memory trouble
Initial config

38. mySparkApp memory issues

39. Reduce the memory needed for
mySparkApp. How?
Gracefully handle memory
limitations. How?
mySparkApp memory issues

40. Reduce the memory needed for
mySparkApp. How?
Gracefully handle memory
limitations. How?
mySparkApp memory issues

41. Reduce the memory needed for
mySparkApp. How?
Gracefully handle memory
limitations. How?
mySparkApp memory issues
here let's talk about one scenario

43. Reduce the memory needed for
mySparkApp. How?
Gracefully handle memory
limitations. How?
mySparkApp memory issues
persist(storageLevel.[*]_SER)

44. Reduce the memory needed for
mySparkApp. How?
Gracefully handle memory
limitations. How?
mySparkApp memory issues
persist(storageLevel.[*]_SER)

45. Reduce the memory needed for
mySparkApp. How?
Gracefully handle memory
limitations. How?
mySparkApp memory issues
persist(storageLevel.[*]_SER)
Recommended: kryoserializer *

46. Reduce the memory needed for
mySparkApp. How?
Gracefully handle memory
limitations. How?
mySparkApp memory issues
persist(storageLevel.[*]_SER)
Recommended: kryoserializer *

47. Reduce the memory needed for
mySparkApp. How?
Gracefully handle memory
limitations. How?
mySparkApp memory issues

48. Reduce the memory needed for
mySparkApp. How?
Gracefully handle memory
limitations. How?
mySparkApp memory issues
here let's talk about one scenario

50. Spark 1.1-1.5,
Recommendation: Increase
spark.memory.storageFraction

51. !51
Alexey Grishchenko:
https://0x0fff.com/spark-memory-management/
Spark 1.1-1.5,
Recommendation: Increase
spark.memory.storageFraction
!
Spark 1.6, Recommendation:
UnifiedMemoryManager

52. Alexey Grishchenko:
https://0x0fff.com/spark-memory-management/
Sandy Ryza:
http://blog.cloudera.com/blog/2015/03/how-to-tune-your-apache-spark-jobs-part-2/
yarn.nodemanager.resource.memory-mb
spark.yarn.executor.memoryOverhead
Executor Container
spark.executor.memory

53. !53
Driver
Cluster Manager
Sidebar: Spark Architecture
yarn.nodema
spark.yarn.e
Exec
spark.e
yarn.nodema
spark.yarn.e
Exec
spark.e
yarn.nodema
spark.yarn.e
Exec
spark.e
Executor
Executor

54. Intermittent
Reliable
Optimal
mySparkApp Success
Memory trouble
Initial config

55. Intermittent
Reliable
Optimal
mySparkApp Success
Memory trouble
Initial config
Instead of 2.5 hours, myApp
completes in 1 hour.

56. Cheat-sheet
techsuppdiva.github.io/

57. Intermittent
Reliable
Optimal
mySparkApp Success
Memory trouble
Initial config
HighPerformanceSpark.com

58. Further Reading:
• Spark Tuning Cheat-sheet
techsuppdiva.github.io
• Apache Spark Documentation
https://spark.apache.org/docs/latest
• Checkpointing
http://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
https://github.com/jaceklaskowski/mastering-apache-spark-book/blob/master/spark-rdd-checkpointing.adoc
• Learning Spark, by H. Karau, A. Konwinski, P. Wendell, M. Zaharia, 2015
!58

59. More Questions?
!59
Video: https://www.youtube.com/watch?v=DNWaMR8uKDc&feature=youtu.be
Presentation: http://www.slideshare.net/anyabida
Cheat-sheet: http://techsuppdiva.github.io/
!
!
Anya: https://www.linkedin.com/in/anyabida
Rachel: https://www.linkedin.com/in/rachelbwarren
!
!
Thanks!