Apache Spark is one of the most popular big data projects, offering greatly improved performance over traditional MapReduce models. Much of Apache Spark’s power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging. Holden Karau and Joey Echeverria explore how to debug Apache Spark applications, the different options for logging in Spark’s variety of supported languages, and some common errors and how to detect them. Spark’s own internal logging can often be quite verbose. Holden and Joey demonstrate how to effectively search logs from Apache Spark to spot common problems and discuss options for logging from within your program itself. Spark’s accumulators have gotten a bad rap because of how they interact in the event of cache misses or partial recomputes, but Holden and Joey look at how to effectively use Spark’s current accumulators for debugging before gazing into the future to see the data property type accumulators that may be coming to Spark in future versions. And in addition to reading logs and instrumenting your program with accumulators, Spark’s UI can be of great help for quickly detecting certain types of problems. Holden and Joey cover how to quickly use the UI to figure out if certain types of issues are occurring in our job.

1. Debugging Apache Spark
“Professional Stack Trace Reading”
with your friends
Holden & Joey

2. Who is Holden?
● My name is Holden Karau
● Prefered pronouns are she/her
● I’m a Principal Software Engineer at IBM’s Spark Technology Center
● Apache Spark committer (as of last month!) :)
● previously Alpine, Databricks, Google, Foursquare & Amazon
● co-author of Learning Spark & Fast Data processing with Spark
○ co-author of a new book focused on Spark performance coming this year*
● @holdenkarau
● Slide share http://www.slideshare.net/hkarau
● Linkedin https://www.linkedin.com/in/holdenkarau
● Github https://github.com/holdenk
● Spark Videos http://bit.ly/holdenSparkVideos

4. Spark Technology
Center
4
IBM
Spark
Technology
Center
Founded in 2015.
Location:
Physical: 505 Howard St., San Francisco CA
Web: http://spark.tc Twitter: @apachespark_tc
Mission:
Contribute intellectual and technical capital to the Apache Spark
community.
Make the core technology enterprise- and cloud-ready.
Build data science skills to drive intelligence into business
applications — http://bigdatauniversity.com
Key statistics:
About 50 developers, co-located with 25 IBM designers.
Major contributions to Apache Spark http://jiras.spark.tc
Apache SystemML is now an Apache Incubator project.
Founding member of UC Berkeley AMPLab and RISE Lab
Member of R Consortium and Scala Center
Spark Technology
Center

5. Who is Joey?
● Preferred pronouns: he/him
● Where I work: Rocana – Platform Technical Lead
● Where I used to work: Cloudera (’11-’15), NSA
● Distributed systems, security, data processing, big
data
● @fwiffo

6. What is Rocana?
● We built a system for large scale real-time
collection, processing, and analysis of
event-oriented machine data
● On prem or in the cloud, but not SaaS
● Supportability is a big deal for us
○ Predictability of performance under load and failures
○ Ease of configuration and operation
○ Behavior in wacky environments

7. Who do we think y’all are?
● Friendly[ish] people
● Don’t mind pictures of cats or stuffed animals
● Know some Spark
● Want to debug your Spark applications
● Ok with things getting a little bit silly
Lori Erickson

8. What will be covered?
● Getting at Spark’s logs & persisting them
● What your options for logging are
● Attempting to understand common Spark error messages
● Understanding the DAG (and how pipelining can impact your life)
● Subtle attempts to get you to use spark-testing-base or similar
● Fancy Java Debugging tools & clusters - not entirely the path of sadness
● Holden’s even less subtle attempts to get you to buy her new book
● Pictures of cats & stuffed animals

9. Aka: Building our Monster Identification Guide

10. So where are the logs/errors?
(e.g. before we can identify a monster we have to find it)
● Error messages reported to the console*
● Log messages reported to the console*
● Log messages on the workers - access through the
Spark Web UI or Spark History Server :)
● Where to error: driver versus worker
(*When running in client mode)
PROAndrey

11. One weird trick to debug anything
● Don’t read the logs (yet)
● Draw (possibly in your head) a model of how you think a
working app would behave
● Then predict where in that model things are broken
● Now read logs to prove or disprove your theory
● Repeat
Krzysztof Belczyński

12. Working in YARN?
(e.g. before we can identify a monster we have to find it)
● Use yarn logs to get logs after log collection
● Or set up the Spark history server
● Or yarn.nodemanager.delete.debug-delay-sec :)
Lauren Mitchell

13. Spark is pretty verbose by default
● Most of the time it tells you things you already know
● Or don’t need to know
● You can dynamically control the log level with
sc.setLogLevel
● This is especially useful to increase logging near the
point of error in your code

14. But what about when we get an error?
● Python Spark errors come in two-ish-parts often
● JVM Stack Trace (Friend Monster - comes most errors)
● Python Stack Trace (Boo - has information)
● Buddy - Often used to report the information from Friend
Monster and Boo

15. So what is that JVM stack trace?
● Java/Scala
○ Normal stack trace
○ Sometimes can come from worker or driver, if from worker may be
repeated several times for each partition & attempt with the error
○ Driver stack trace wraps worker stack trace
● R/Python
○ Same as above but...
○ Doesn’t want your actual error message to get lonely
○ Wraps any exception on the workers (& some exceptions on the
drivers)
○ Not always super useful

16. Let’s make a simple mistake & debug :)
● Error in transformation (divide by zero)
Image by: Tomomi

17. Bad outer transformation (Scala):
val transform1 = data.map(x => x + 1)
val transform2 = transform1.map(x => x/0) // Will throw an
exception when forced to evaluate
transform2.count() // Forces evaluation
David Martyn
Hunt

18. Let’s look at the error messages for it:
17/01/23 12:41:36 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
java.lang.ArithmeticException: / by zero
at com.highperformancespark.examples.errors.Throws$$anonfun$1.apply$mcII$sp(throws.scala:9)
at com.highperformancespark.examples.errors.Throws$$anonfun$1.apply(throws.scala:9)
at com.highperformancespark.examples.errors.Throws$$anonfun$1.apply(throws.scala:9)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
at scala.collection.Iterator$class.foreach(Iterator.scala:750)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1202)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:295)
at scala.collection.AbstractIterator.to(Iterator.scala:1202)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:287)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1202)
Continued for ~100 lines
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:274)

19. Bad outer transformation (Python):
data = sc.parallelize(range(10))
transform1 = data.map(lambda x: x + 1)
transform2 = transform1.map(lambda x: x / 0)
transform2.count()
David Martyn
Hunt

20. Let’s look at the error messages for it:
[Stage 0:> (0 + 0) / 4]17/02/01 09:52:07 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/home/holden/repos/spark/python/lib/pyspark.zip/pyspark/worker.py", line 180, in main
process()
File "/home/holden/repos/spark/python/lib/pyspark.zip/pyspark/worker.py", line 175, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 2406, in pipeline_func
return func(split, prev_func(split, iterator))
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 2406, in pipeline_func
return func(split, prev_func(split, iterator))
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 2406, in pipeline_func
return func(split, prev_func(split, iterator))
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 345, in func
return f(iterator)
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 1040, in <lambda>
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
Continued for ~400 lines
File "high_performance_pyspark/bad_pyspark.py", line 32, in <lambda>

21. Working in Jupyter?
“The error messages were so useless -
I looked up how to disable error reporting in Jupyter”
(paraphrased from PyData DC)

22. Working in Jupyter - try your terminal for help
Possibly fix by https://issues.apache.org/jira/browse/SPARK-19094 but may not get in
tonynetone
AttributeError: unicode object has no attribute
endsWith

23. Ok maybe the web UI is easier? Mr Thinktank

24. And click through... afu007

25. A scroll down (not quite to the bottom)
File "high_performance_pyspark/bad_pyspark.py",
line 32, in <lambda>
transform2 = transform1.map(lambda x: x / 0)
ZeroDivisionError: integer division or modulo by zero

26. Or look at the bottom of console logs:
File "/home/holden/repos/spark/python/lib/pyspark.zip/pyspark/worker.py", line
180, in main
process()
File "/home/holden/repos/spark/python/lib/pyspark.zip/pyspark/worker.py", line
175, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 2406, in
pipeline_func
return func(split, prev_func(split, iterator))
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 2406, in
pipeline_func
return func(split, prev_func(split, iterator))
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 2406, in
pipeline_func
return func(split, prev_func(split, iterator))

27. Or look at the bottom of console logs:
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 345, in func
return f(iterator)
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 1040, in <lambda>
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 1040, in <genexpr>
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
File "high_performance_pyspark/bad_pyspark.py", line 32, in <lambda>
transform2 = transform1.map(lambda x: x / 0)
ZeroDivisionError: integer division or modulo by zero

28. And in scala….
Caused by: java.lang.ArithmeticException: / by zero
at
com.highperformancespark.examples.errors.Throws$$anonfun$4.apply$mcII$sp(throws.sc
ala:17)
at
com.highperformancespark.examples.errors.Throws$$anonfun$4.apply(throws.scala:17)
at
com.highperformancespark.examples.errors.Throws$$anonfun$4.apply(throws.scala:17)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
at scala.collection.Iterator$class.foreach(Iterator.scala:750)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1202)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:295)

29. (Aside): DAG differences illustrated Melissa Wilkins

30. Pipelines (& Python)
● Some pipelining happens inside of Python
○ For performance (less copies from Python to Scala)
● DAG visualization is generated inside of Scala
○ Misses Python pipelines :(
Regardless of language
● Can be difficult to determine which element failed
● Stack trace _sometimes_ helps (it did this time)
● take(1) + count() are your friends - but a lot of work :(
● persist can help a bit too.
Arnaud Roberti

31. Side note: Lambdas aren’t always your friend
● Lambda’s can make finding the error more challenging
● I love lambda x, y: x / y as much as the next human but
when y is zero :(
● A small bit of refactoring for your debugging never hurt
anyone*
● If your inner functions are causing errors it’s a good time
to have tests for them!
● Difficult to put logs inside of them
*A blatant lie, but…. it hurts less often than it helps
Zoli Juhasz

32. Testing - you should do it!
● spark-testing-base provides simple classes to build your
Spark tests with
○ It’s available on pip & maven central
● That’s a talk unto itself though (and it's on YouTube)

33. Adding your own logging:
● Java users use Log4J & friends
● Python users: use logging library (or even print!)
● Accumulators
○ Behave a bit weirdly, don’t put large amounts of data in them

34. Also not all errors are “hard” errors
● Parsing input? Going to reject some malformed records
● flatMap or filter + map can make this simpler
● Still want to track number of rejected records (see
accumulators)
● Invest in dead letter queues
○ e.g. write malformed records to an Apache Kafka topic
Mustafasari

35. So using names & logging & accs could be:
data = sc.parallelize(range(10))
rejectedCount = sc.accumulator(0)
def loggedDivZero(x):
import logging
try:
return [x / 0]
except Exception as e:
rejectedCount.add(1)
logging.warning("Error found " + repr(e))
return []
transform1 = data.flatMap(loggedDivZero)
transform2 = transform1.map(add1)
transform2.count()
print("Reject " + str(rejectedCount.value))

36. Ok what about if we run out of memory?
In the middle of some Java stack traces:
File "/home/holden/repos/spark/python/lib/pyspark.zip/pyspark/worker.py", line 180, in main
process()
File "/home/holden/repos/spark/python/lib/pyspark.zip/pyspark/worker.py", line 175, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 2406, in pipeline_func
return func(split, prev_func(split, iterator))
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 2406, in pipeline_func
return func(split, prev_func(split, iterator))
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 2406, in pipeline_func
return func(split, prev_func(split, iterator))
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 345, in func
return f(iterator)
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 1040, in <lambda>
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
File "/home/holden/repos/spark/python/pyspark/rdd.py", line 1040, in <genexpr>
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
File "high_performance_pyspark/bad_pyspark.py", line 132, in generate_too_much
return range(10000000000000)
MemoryError

37. Tubbs doesn’t always look the same
● Out of memory can be pure JVM (worker)
○ OOM exception during join
○ GC timelimit exceeded
● OutOfMemory error, Executors being killed by kernel,
etc.
● Running in YARN? “Application overhead exceeded”
● JVM out of memory on the driver side from Py4J

38. Reasons for JVM worker OOMs
(w/PySpark)
● Unbalanced shuffles
● Buffering of Rows with PySpark + UDFs
○ If you have a down stream select move it up stream
● Individual jumbo records (after pickling)
● Off-heap storage
● Native code memory leak

39. Reasons for Python worker OOMs
(w/PySpark)
● Insufficient memory reserved for Python worker
● Jumbo records
● Eager entire partition evaluation (e.g. sort +
mapPartitions)
● Too large partitions (unbalanced or not enough
partitions)

40. And loading invalid paths:
org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/doesnotexist
at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:251)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:270)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)

41. Connecting Java Debuggers
● Add the JDWP incantation to your JVM launch:
-agentlib:jdwp=transport=dt_socket,server=y,address=[
debugport]
○ spark.executor.extraJavaOptions to attach debugger on the executors
○ --driver-java-options to attach on the driver process
○ Add “suspend=y” if only debugging a single worker & exiting too
quickly
● JDWP debugger is IDE specific - Eclipse & IntelliJ have
docs
shadow planet

42. Connecting Python Debuggers
● You’re going to have to change your code a bit :(
● You can use broadcast + singleton “hack” to start pydev
or desired remote debugging lib on all of the interpreters
● See https://wiki.python.org/moin/PythonDebuggingTools
for your remote debugging options and pick the one that
works with your toolchain
shadow planet

43. Alternative approaches:
● Move take(1) up the dependency chain
● DAG in the WebUI -- less useful for Python :(
● toDebugString -- also less useful in Python :(
● Sample data and run locally
● Running in cluster mode? Consider debugging in client
mode
Melissa Wilkins

44. Learning Spark
Fast Data
Processing with
Spark
(Out of Date)
Fast Data
Processing with
Spark
(2nd edition)
Advanced
Analytics with
Spark
Coming soon:
Spark in Action
Coming soon:
High Performance Spark
Coming Soon:
Learning PySpark

45. High Performance Spark (soon!)
First seven chapters are available in “Early Release”*:
● Buy from O’Reilly - http://bit.ly/highPerfSpark
● Python is in Chapter 7 & Debugging in Appendix
Get notified when updated & finished:
● http://www.highperformancespark.com
● https://twitter.com/highperfspark
* Early Release means extra mistakes, but also a chance to help us make a more awesome
book.

46. And some upcoming talks:
● April
○ Meetup of some type in Madrid (TBD)
○ PyData Amsterdam
○ Philly ETE
○ Scala Days Chicago
● May
○ Scala LX?
○ Strata London
○ 3rd Data Science Summit Europe in Israel
● June
○ Scala Days CPH

47. k thnx bye :)
If you care about Spark testing and
don’t hate surveys:
http://bit.ly/holdenTestingSpark
Will tweet results
“eventually” @holdenkarau
Any PySpark Users: Have some
simple UDFs you wish ran faster
you are willing to share?:
http://bit.ly/pySparkUDF
Pssst: Have feedback on the presentation? Give me a
shout (holden@pigscanfly.ca) if you feel comfortable doing
so :)