Top 5 Mistakes When Writing Spark Applications by Mark Grover and Ted Malaska
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The document discusses 5 common mistakes people make when writing Spark applications: 1) Not properly sizing executors for memory and cores. 2) Having shuffle blocks larger than 2GB which can cause jobs to fail. 3) Not addressing data skew which can cause joins and shuffles to be very slow. 4) Not properly managing the DAG to minimize shuffles and stages. 5) Classpath conflicts from conflicting dependencies like Guava which can cause errors.
![Don’t believe me?
• In MapStatus.scala
def apply(loc: BlockManagerId, uncompressedSizes:
Array[Long]): MapStatus = {
if (uncompressedSizes.length > 2000) {
HighlyCompressedMapStatus(loc,
uncompressedSizes)
} else {
new CompressedMapStatus(loc, uncompressedSizes)
}
}](https://image.slidesharecdn.com/4scgrovermalaska-160224033823/85/Top-5-Mistakes-When-Writing-Spark-Applications-by-Mark-Grover-and-Ted-Malaska-36-320.jpg)
Top 5 Mistakes When Writing Spark Applications by Mark Grover and Ted Malaska
- 1. Top 5 Mistakes when writing Spark applications Mark Grover | @mark_grover | Software Engineer Ted Malaska | @TedMalaska | Principal Solutions Architect tiny.cloudera.com/spark-mistakes
- 2. About the book • @hadooparchbook • hadooparchitecturebook.com • github.com/hadooparchitecturebook • slideshare.com/hadooparchbook
- 3. Mistakes people make when using Spark
- 4. Mistakes people we made when using Spark
- 5. Mistake # 1
- 6. # Executors, cores, memory !?! • 6 Nodes • 16 cores each • 64 GB of RAM each
- 7. Decisions, decisions, decisions • Number of executors (--num-executors) • Cores for each executor (--executor-cores) • Memory for each executor (--executor- memory) • 6 nodes • 16 cores each • 64 GB of RAM
- 9. Answer #1 – Most granular • Have smallest sized executors as possible • 1 core each • Total of 16 x 6 = 96 cores • 96 executors • 64/16 = 4 GB per executor (per node)
- 10. Answer #1 – Most granular • Have smallest sized executors as possible • 1 core each • Total of 16 x 6 = 96 cores • 96 executors • 64/16 = 4 GB per executor (per node)
- 11. Why? • Not using benefits of running multiple tasks in same JVM
- 12. Answer #2 – Least granular • 6 executors • 64 GB memory each • 16 cores each
- 13. Answer #2 – Least granular • 6 executors • 64 GB memory each • 16 cores each
- 14. Why? • Need to leave some memory overhead for OS/Hadoop daemons
- 15. Answer #3 – with overhead • 6 executors • 63 GB memory each • 15 cores each
- 16. Answer #3 – with overhead • 6 executors • 63 GB memory each • 15 cores each
- 17. Spark on YARN – Memory usage • --executor-memory controls the heap size • Need some overhead (controlled by spark.yarn.executor.memory.overhead)for off heap memory • Default is max(384MB, .07 * spark.executor.memory)
- 18. YARN AM needs a core: Client mode
- 19. YARN AM needs a core: Cluster mode
- 20. HDFS Throughput • 15 cores per executor can lead to bad HDFS I/O throughput. • Best is to keep under 5 cores per executor
- 21. Calculations • 5 cores per executor – For max HDFS throughput • Cluster has 6 * 15 = 90 cores in total (after taking out Hadoop/Yarn daemon cores) • 90 cores / 5 cores/executor = 18 executors • 1 executor for AM => 17 executors • Each node has 3 executors • 63 GB/3 = 21 GB, 21 x (1-0.07) ~ 19 GB (counting off heap overhead)
- 22. Correct answer • 17 executors • 19 GB memory each • 5 cores each * Not etched in stone
- 23. Read more • From a great blog post on this topic by Sandy Ryza: http://blog.cloudera.com/blog/2015/03/how- to-tune-your-apache-spark-jobs-part-2/
- 24. Mistake # 2
- 25. Application failure 15/04/16 14:13:03 WARN scheduler.TaskSetManager: Lost task 19.0 in stage 6.0 (TID 120, 10.215.149.47): java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:828) at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:123) at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:132) at org.apache.spark.storage.BlockManager.doGetLocal(BlockManager.scala:51 7) at org.apache.spark.storage.BlockManager.getLocal(BlockManager.scala:432) at org.apache.spark.storage.BlockManager.get(BlockManager.scala:618) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:146 ) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70)
- 26. Why? • No Spark shuffle block can be greater than 2 GB
- 27. Ok, what’s a shuffle block again? • In MapReduce terminology, a Mapper- Reducer pair – the file from local disk that the reducers read from local disk in MapReduce.
- 28. In other words Each yellow arrow in this diagram represents a shuffle block.
- 29. Wait! What!?! This is Big Data stuff, no? • Yeah! Nope! • Spark uses ByteBuffer as abstraction for storing blocks val buf = ByteBuffer.allocate(length.toInt) • ByteBuffer is limited by Integer.MAX_SIZE(2 GB)!
- 30. Once again • No Spark shuffle block can be greater than 2 GB
- 31. Spark SQL • Especially problematic for Spark SQL • Default number of partitions to use when doing shuffles is 200 – This low number of partitions leads to high shuffle block size
- 32. Umm, ok, so what can I do? 1. Increase the number of partitions – Thereby, reducing the average partition size 2. Get rid of skew in your data – More on that later
- 33. Umm, how exactly? • In Spark SQL, increase the value of spark.sql.shuffle.partitions • In regular Spark applications, use rdd.repartition() or rdd.coalesce()
- 34. But, how many partitions should I have? • Rule of thumb is around 128 MB per partition
- 35. But! • Spark uses a different data structure for bookkeeping during shuffles, when the number of partitions is less than 2000, vs. more than 2000.
- 36. Don’t believe me? • In MapStatus.scala def apply(loc: BlockManagerId, uncompressedSizes: Array[Long]): MapStatus = { if (uncompressedSizes.length > 2000) { HighlyCompressedMapStatus(loc, uncompressedSizes) } else { new CompressedMapStatus(loc, uncompressedSizes) } }
- 37. Ok, so what are you saying? • If your number of partitions is less than 2000, but close enough to it, bump that number up to be slightly higher than 2000.
- 38. Can you summarize, please? • Don’t have too big partitions – Your job will fail due to 2 GB limit • Don’t have too few partitions – Your job will be slow, not making using of parallelism • Rule of thumb: ~128 MB per partition • If #partitions < 2000, but close, bump to just > 2000
- 39. Mistake # 3
- 40. Slow jobs on Join/Shuffle • Your dataset takes 20 seconds to run over with a map job, but take 4 hours when joined or shuffled. What wrong?
- 42. Mistake - Skew Single Thread Single Thread Single Thread Single Thread Single Thread Single Thread Single Thread Normal Distributed The Holy Grail of Distributed Systems
- 43. Mistake - Skew Single Thread Normal Distributed What about Skew, because that is a thing
- 44. Mistake – Skew : Answers • Salting • Isolation Salting • Isolation Map Joins
- 45. Mistake – Skew : Salting • Normal Key: “Foo” • Salted Key: “Foo” + random.nextInt(saltFactor)
- 47. Mistake – Skew: Salting
- 49. Mistake – Skew : Salting • Two Stage Aggregation – Stage one to do operations on the salted keys – Stage two to do operation access unsalted key results Data Source Map Convert to Salted Key & Value Tuple Reduce By Salted Key Map Convert results to Key & Value Tuple Reduce By Key Results
- 50. Mistake – Skew : Isolated Salting • Second Stage only required for Isolated Keys Data Source Map Convert to Key & Value Isolate Key and convert to Salted Key & Value Tuple Reduce By Key & Salted Key Filter Isolated Keys From Salted Keys Map Convert results to Key & Value Tuple Reduce By Key Union to Results
- 51. Mistake – Skew : Isolated Map Join • Filter Out Isolated Keys and use Map Join/Aggregate on those • And normal reduce on the rest of the data • This can remove a large amount of data being shuffled Data Source Filter Normal Keys From Isolated Keys Reduce By Normal Key Union to Results Map Join For Isolated Keys
- 52. Managing Parallelism Cartesian Join Map Task Shuffle Tmp 1 Shuffle Tmp 2 Shuffle Tmp 3 Shuffle Tmp 4 Map Task Shuffle Tmp 1 Shuffle Tmp 2 Shuffle Tmp 3 Shuffle Tmp 4 Map Task Shuffle Tmp 1 Shuffle Tmp 2 Shuffle Tmp 3 Shuffle Tmp 4 ReduceTask ReduceTask ReduceTask ReduceTask Amount of Data Amount of Data 10x 100x 1000x 10000x 100000x 1000000x Or more
- 53. Managing Parallelism • To fight Cartesian Join – Nested Structures – Windowing – Skip Steps
- 54. Mistake # 4
- 55. Out of luck? • Do you every run out of memory? • Do you every have more then 20 stages? • Is your driver doing a lot of work?
- 56. Mistake – DAG Management • Shuffles are to be avoided • ReduceByKey over GroupByKey • TreeReduce over Reduce • Use Complex Types
- 57. Mistake – DAG Management: Shuffles • Map Side Reducing if possible • Think about partitioning/bucketing ahead of time • Do as much as possible with a single Shuffle • Only send what you have to send • Avoid Skew and Cartesians
- 58. ReduceByKey over GroupByKey • ReduceByKey can do almost anything that GroupByKey can do • Aggregations • Windowing • Use memory • But you have more control • ReduceByKey has a fixed limit of Memory requirements • GroupByKey is unbound and dependent of the data
- 59. TreeReduce over Reduce • TreeReduce & Reduce returns a result to the driver • TreeReduce does more work on the executors • Where Reduce bring everything back to the driver Partition Partition Partition Partition Driver 100% Partition Partition Partition Partition Driver 4 25% 25% 25% 25%
- 60. Complex Types • Top N List • Multiple types of Aggregations • Windowing operations • All in one pass
- 61. Complex Types • Think outside of the box use objects to reduce by • (Make something simple)
- 62. Mistake # 5
- 63. Ever seen this? Exception in thread "main" java.lang.NoSuchMethodError: com.google.common.hash.HashFunction.hashInt(I)Lcom/google/common/hash/HashCode; at org.apache.spark.util.collection.OpenHashSet.org $apache$spark$util$collection$OpenHashSet$$hashcode(OpenHashSet.scala:261) at org.apache.spark.util.collection.OpenHashSet$mcI$sp.getPos$mcI$sp(OpenHashSet.scala:165) at org.apache.spark.util.collection.OpenHashSet$mcI$sp.contains$mcI$sp(OpenHashSet.scala:102) at org.apache.spark.util.SizeEstimator$$anonfun$visitArray$2.apply$mcVI$sp(SizeEstimator.scala:214) at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141) at org.apache.spark.util.SizeEstimator$.visitArray(SizeEstimator.scala:210) at…....
- 64. But! • I already included guava in my app’s maven dependencies?
- 65. Ah! • My guava version doesn’t match with Spark’s guava version!
- 67. Summary
- 68. 5 Mistakes • Size up your executors right • 2 GB limit on Spark shuffle blocks • Evil thing about skew and cartesians • Learn to manage your DAG, yo! • Do shady stuff, don’t let classpath leaks mess you up
- 69. THANK YOU. tiny.cloudera.com/spark-mistakes Mark Grover | @mark_grover Ted Malaska | @TedMalaska