Spark Tuning for Enterprise System Administrators

Anya T. Bida, PhD Rachel B. Warren

Don't worry about missing something...

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 !! !2

About Anya About RachelOperations Engineer !!!

Spark & Scala Enthusiast / Data Engineer

About Alpine Data!alpinenow.com

Alpine deploys Spark in Production for our Enterprise Customers

About You*

Intermittent

Reliable Optimal

Enterprise System Administrators

mySparkApp Success

*

Intermittent Reliable

Optimal

mySparkApp Success

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

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Which parameters are important? !

How do I configure them?

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Default != Recommended

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

Intermittent Reliable

Optimal

mySparkApp Success

mySparkApp memory issues

Shared Cluster

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Fair Schedulers

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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</schedulingMode> <weight>1</weight> <minShare>2</minShare> </pool> </allocations>

Fair Schedulers

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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</schedulingMode> <weight>1</weight> <minShare>2</minShare> </pool> </allocations>

Configure these parameters too!

Fair Schedulers

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YARN <allocations> <user name="sample_user"> <maxRunningApps>6</maxRunningApps> </user> <userMaxAppsDefault>5</userMaxAppsDefault> !</allocations>

What is the memory limit for mySparkApp?

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Max Memory in "pool" x 3/4 = mySparkApp_mem_limit !!!

<maxResources>8000 mb</maxResources>

Limitation

What is the memory limit for mySparkApp?

Reserve 25% for overhead.

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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?

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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: Each driver and executor must not be larger than a

single node.

Limitation: Driver and executor memory must not be larger than

a single node.

!(yarn.nodemanager.resource.memory-mb - 1Gb)

executor.memory ~ # executors per node

Limitation

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Max Memory in "pool" x 3/4 = mySparkApp_mem_limit !

mySparkApp_mem_limit = driver.memory + (executor.memory x dynamicAllocation.maxExecutors)

Limitation: maxExecutors should not exceed pool allocation.

!Yarn: <maxResources>8vcores</maxResources>

Limitation

What is the memory limit for mySparkApp?

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I want a little more information...Top 5 Mistakes When Writing Spark Applications

by Mark Grover and Ted Malaska of Cloudera http://www.slideshare.net/hadooparchbook/top-5-mistakes-when-writing-spark-applications

How-to: Tune Your Apache Spark Jobs (Part 2) by Sandy Ryza of Cloudera

http://blog.cloudera.com/blog/2015/03/how-to-tune-your-apache-spark-jobs-part-2/

I want lots more...

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Intermittent Reliable

Optimal

mySparkApp Success

mySparkApp memory issues

Shared Cluster

Reduce the memory needed for mySparkApp. How?

Gracefully handle memory limitations. How?

mySparkApp memory issues

Reduce the memory needed for mySparkApp. How?

mySparkApp memory issues

here let's talk about one scenario

Reduce the memory needed for mySparkApp. How?

mySparkApp memory issues

persist(storageLevel.[*]_SER)

Recommended: kryoserializer *

Gracefully handle memory limitations. How?

mySparkApp memory issues

Reduce the memory needed for mySparkApp. How?

Gracefully handle memory limitations. How?

mySparkApp memory issues

here let's talk about one scenario

Symptoms:

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• mySparkApp is running for several hours Container is lost.

• I notice one container fails, then the rest fail one by one

• The first container to fail was the driver • Driver is a SPOF

Investigate:

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collect unbounded data to the driver

• Driver failures are often caused by:

• I verified only bounded data is brought to the driver, but still the driver fails intermittently.

Potential Solution: RDD.checkpoint()

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Use in these cases: • high-traffic cluster • network blips • preemption • disk space nearly full !!

Function: • saves the RDD to stable

storage (eg hdfs or S3)

How-to: SparkContext.setCheckpointDir(directory: String)

RDD.checkpoint()

Intermittent Reliable

Optimal

mySparkApp Success

mySparkApp memory issues

Shared Cluster

Instead of 2.5 hours, myApp completes in 1 hour.

Cheat-sheet techsuppdiva.github.io/

Intermittent Reliable

Optimal

mySparkApp Success

mySparkApp memory issues

Shared Cluster

HighPerformanceSpark.com

Further Reading:• Learning Spark, by H. Karau, A. Konwinski, P. Wendell, M. Zaharia, 2015, O'Reilly

https://databricks.com/blog/2015/02/09/learning-spark-book-available-from-oreilly.html

• Scheduling:https://spark.apache.org/docs/latest/job-scheduling.html#scheduling-within-an-application

• Tuning the Spark Conf:Mark Grover and Ted Malaska from Cloudera http://www.slideshare.net/hadooparchbook/top-5-mistakes-when-writing-spark-applicationsSandy Ryza (Cloudera) http://blog.cloudera.com/blog/2015/03/how-to-tune-your-apache-spark-jobs-part-2/

• Checkpointing:http://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

• Troubleshooting:Miklos Christine from Databricks https://spark-summit.org/east-2016/events/operational-tips-for-deploying-spark/

• High Performance Spark by R. Warren, H. Karau, coming in 2016, O'Reilly http://highperformancespark.com/

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More Questions?

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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 !!