Data processing routines with Spark and how to use them in production.

1. Think Like Spark
Some Spark Concept & A Use Case

2. Who am I?
• Software engineer, data scientist, and Spark enthusiast at
Alpine Data (SF Based Analytics Company)
• Co – Author High Performance Spark
http://shop.oreilly.com/product/0636920046967.do
Linked in: https://www.linkedin.com/in/rachelbwarren
• Slide share: http://www.slideshare.net/RachelWarren4
• Github : rachelwarren. Code for this talk
https://github.com/high-performance-spark/high-performance-
spark-examples
• Twitter: @warre_n_peace

3. Overview
• A little Spark Architecture: How are Spark Jobs
Evaluated? Why does that matter for performance?
• Execution context: driver, executors, partitions, cores
• Spark Application hierarchy: jobs/stages/tasks
• Actions vs. Transformations (lazy evaluation)
• Wide vs. Narrow Transformations (shuffles & data locality)
• Apply what we have learned with four versions of the
same algorithm to find rank statistics

4. What is Spark?
Distributed computing framework. Must run in tandem with a
data storage system
- Standalone (For Local Testing)
- Cloud (S3, EC2)
- Distributed storage, with cluster manager,
- (Hadoop Yarn, Apache Messos)
Built around and abstraction called RDDs “Resilient,
Distributed, Datasets”
- Lazily evaluated, immutable, distributed collection of
partition objects

5. What happens when I launch
a Spark Application?

6. Spark
Driver
ExecutorExecutor Executor Executor
Stable storage e.g. HDFS

7. One Spark Executor
• One JVM for in memory
computations
• Partitions care computed on
executors
• Tasks correspond to partitions
• dynamically allocated slots for
running tasks
(executor cores x executors)
• Caching takes up space on
executors Partitions / Tasks

8. Implications
Two Most common cases of failures
1. Failure during shuffle stage
• Moving data between Partitions requires communication with
the driver
 Failures often occur in the shuffle stage
2. Out of memory errors on executors and driver
The driver and each executor have a static amount of memory*
 It is easy to run out of memory on the executors or on the driver
*dynamic allocation allows changing the number of executors

9. How are Jobs Evaluated?
API Call Execution Element
Computation to evaluation
one partition (combine
narrow transforms)
Wide transformations (sort,
groupByKey)
Actions (e.g. collect,
saveAsTextFile)
Spark Context Object Spark
Application
Job
Stage
Task Task
Stage
Executed in
Sequence
Executed in
Parallel

10. Types Of Spark Operations
Actions
• RDD  Not RDD
• Force execution: Each job
ends in exactly one action
• Three Kinds
• Move data to driver: collect,
take, count
• Move data to external system
Write / Save
• Force evaluation: foreach
Transformations
• RDD  RDD
• Lazily evaluated
• Can be combined and
executed in one pass of
the data
• Computed on Spark
executors

11. Implications of Lazy Evaluation
Frustrating:
• Debugging = 
• Lineage graph is built backwards from action to reading in
data or persist/ cache/ checkpoint  if you aren’t careful you
will repeat computations *
* some times we get help from shuffle files
Awesome:
• Spark can combine some types of transformations and execute
them in a single task
• We only compute partitions that we need

12. Types of Transformations
Narrow
• Never require a shuffle
• map, mapPartitions, filter
• coalesce*
• Input partitions >= output
partitions
• & output partitions known
at design time
• A sequence of narrow
transformations are
combined and executed in
one stage as several tasks
Wide
• May require a shuffle
• sort, groupByKey,
reduceByKey, join
• Requires data movement
• Partitioning depends on data
it self (not known at design
time)
• Cause stage boundary:
Stage 2 cannot be complete
until all the partitions in
Stage 1 are computed.

13. Partition Dependencies for
input and output partitions
Narrow Wide

14. Implications of Shuffles
• Narrow transformations are faster/ more parallelizable
• Narrow transformation must be written so that they can
be computed on any subset of records
• Narrow transformations can rely on some partitioning
information, (partition remains constant in each stage)*
• Wide transformations may distribute data unevenly across
machines (for example according to the hash value of the
key)
*we can loose partitioning information with map or
mapPartitions(preservesPartitioner = false)

15. The “Goldilocks Use Case”

16. Rank Statistics on Wide Data
Design an application that would takes an arbitrary list of
longs `n1`...`nk` and return the `nth` best element in each
column of a DataFrame of doubles.
For example, if the input list is ( 8, 1000, and 20 million),
our function would need to return the 8th, 1000th and 20
millionth largest element in each column.

17. Input Data
If we were looking for 2 and 4th elements, result would be:

18. V0: Iterative solution
Loop through each column:
• map to value in the one column
• Sort the column
• Zip with index and filter for the correct rank statistic (i.e.
nth element)
• Add the result for each column to a map

19. def findRankStatistics(
dataFrame: DataFrame, ranks: List[Long]): Map[Int, Iterable[Double]] = {
val numberOfColumns = dataFrame.schema.length
var i = 0
var result = Map[Int, Iterable[Double]]()
dataFrame.persist()
while(i < numberOfColumns){
val col = dataFrame.rdd.map(row => row.getDouble(i))
val sortedCol : RDD[(Double, Long)] =
col.sortBy(v => v).zipWithIndex()
val ranksOnly = sortedCol.filter{
//rank statistics are indexed from one
case (colValue, index) => ranks.contains(index + 1)
}.keys
val list = ranksOnly.collect()
result += (i -> list)
i+=1
}
result
}
Persist prevents
multiple data reads
SortBy is Spark’s sort

20. V0 = Too Many Sorts 
• Turtle Picture
• One distributed sort
per column
(800 cols = 800 sorts)
• Each of these sorts
is executed in
sequence
• Cannot save
partitioning data
between sorts
300 Million rows
takes days!

21. V1: Parallelize by Column
• The work to sort each column can be done without
information about the other columns
• Can map the data to (column index, value) pairs
• GroupByKey on column index
• Sort each group
• Filter for desired rank statistics

22. Get Col Index, Value Pairs
private def getValueColumnPairs(dataFrame : DataFrame): RDD[(Double,
Int)] = {
dataFrame.rdd.flatMap{
row: Row => row.toSeq.zipWithIndex.map{
case (v, index) =>
(v.toString.toDouble, index)}
}
}
Flatmap is a narrow transformation
Column Index Value
1 15.0
1 2.0
.. …

23. Group By Key Solution
• def findRankStatistics(
dataFrame: DataFrame ,
ranks: List[Long]): Map[Int, Iterable[Double]] = {
require(ranks.forall(_ > 0))
//Map to column index, value pairs
val pairRDD: RDD[(Int, Double)] = mapToKeyValuePairs(dataFrame)
val groupColumns: RDD[(Int, Iterable[Double])] =
pairRDD.groupByKey()
groupColumns.mapValues(
iter => {
//convert to an array and sort
val sortedIter = iter.toArray.sorted
sortedIter.toIterable.zipWithIndex.flatMap({
case (colValue, index) =>
if (ranks.contains(index + 1))
Iterator(colValue)
else
Iterator.empty
})
}).collectAsMap()
}

24. V1. Faster on Small Data fails on Big Data
300 K rows = quick
300 M rows = fails

25. Problems with V1
• GroupByKey puts records from all the columns with the
same hash value on the same partition THEN loads them
into memory
• All columns with the same hash value have to fit in
memory on each executor
• Can’t start the sorting until after the group by key phase
has finished

26. V2 : Secondary Sort Style
1. ‘partitionAndSortWithinPartitions’: use the same hash
partitioner as GroupByKey
- Partition by key and sort all records on each partition
- Pushes some of the sorting work done on each partition
into the shuffle stage
2. Use mapPartitions to filter for the correct rank statistics
- Doesn’t force each column to be stored as an in memory
data structure (each partition stays as one iterator)
Still fails on 300 M rows

27. Iterator-Iterator-Transformation
With Map Partitions
• Iterators are not collections. They are a routine for
accessing each element
• Allows Spark to selectively spill to disk
• Don’t need to put all elements into memory
In our case: Prevents loading each column into memory
after the sorting stage

28. def findRankStatisticsV2(pairRDD: RDD[(Int, Double)],
targetRanks: List[Long],
partitions : Int ) = {
val partitioner = new HashPartitioner(partitions)
val sorted =
pairRDD
.repartitionAndSortWithinPartitions(partitioner)
}
V2: Secondary Sort
Repartition + sort using
Hash Partitioner

29. def findRankStatisticsV2(pairRDD: RDD[(Int, Double)],
sorted.mapPartitions(iter => {
var currentIndex = -1
var elementsPerIndex = 0
val filtered = iter.filter {
case (colIndex, value) =>
if (colIndex != currentIndex) {
currentIndex = 1
elementsPerIndex = 1
} else {
elementsPerIndex += 1
}
targetRanks.contains(elementsPerIndex)
}
groupSorted(filtered) //groups together ranks in same column
}, preservesPartitioning = true)
filterForTargetIndex.collectAsMap()
}
V2: Secondary Sort
Iterator-to-iterator
transformation

30. V2: Still Fails
We don’t have put each column into memory
but columns with the same hash value still have to be able
to fit on one partition

31. Back to the drawing board
• Narrow transformations are quick and easy to parallelize
• Partition locality can be retained across narrow transformations
• Wide transformations are best with many unique keys.
• Using iterator-to-iterator transforms in mapPartitions prevents
whole partitions from being loaded into memory
• We can rely on shuffle files to prevent re-computation of a
wide transformations be several subsequent actions
We can solve the problem with one sortByKey and three map
partitions

32. V3: Mo Parallel, Mo Better
1. Map to (cell value, column index) pairs
2. Do one very large sortByKey
3. Use mapPartitions to count the values per column on each
partition
4. (Locally) using the results of 3 compute location of each rank
statistic on each partition
5. Revisit each partition and find the correct rank statistics
using the information from step 4.
e.g. If the first partition has 10 elements from one column .
The13th element will be the third element on the second partition
in that column.

33. def findRankStatistics(dataFrame: DataFrame, targetRanks: List[Long]):
Map[Int, Iterable[Double]] = {
val valueColumnPairs: RDD[(Double, Int)] = getValueColumnPairs(dataFrame)
val sortedValueColumnPairs = valueColumnPairs.sortByKey()
sortedValueColumnPairs.persist(StorageLevel.MEMORY_AND_DISK)
val numOfColumns = dataFrame.schema.length
val partitionColumnsFreq =
getColumnsFreqPerPartition(sortedValueColumnPairs, numOfColumns)
val ranksLocations =
getRanksLocationsWithinEachPart(targetRanks, partitionColumnsFreq, numOfColumns)
val targetRanksValues = findTargetRanksIteratively(sortedValueColumnPairs, ranksLocations)
targetRanksValues.groupByKey().collectAsMap()
}
Complete code here: https://github.com/high-performance-spark/high-performance-spark-
examples/blob/master/src/main/scala/com/high-performance-spark-examples/GoldiLocks/GoldiLocksFirstTry.scala
1. Map to (val, col) pairs
2. Sort
3. Count per partition
4.
5. Filter for element
computed in 4

34. Complete code here:
https://github.com/high-performance-spark/high-
performance-spark-
examples/blob/master/src/main/scala/com/high-
performance-spark-
examples/GoldiLocks/GoldiLocksFirstTry.scala

35. V3: Still Blows up!
• First partitions show lots of failures and straggler tasks
• Jobs lags in the sort stage and fails in final mapPartitions
stage
More digging reveled data was not evenly distributed

36. Data skew¼ of columns are zero

37. V4: Distinct values per Partition
• Instead of mapping from (value, column index pairs),
map to ((value, column index), count) pairs on each
partition
e. g. if on a given partition, there are ten rows with 0.0 in
the 2nd column, we could save just one tuple:
(0.0, 2), 10)
• Use same sort and mapPartitions routines, but adjusted
for counts.

38. Different Key
column0 column2
2.0 3.0
0.0 3.0
0.0 1.0
0.0 0.0
(value, column Index), count)
((2.0, 0), 1)
(2.0,0), 3)
(3.0, 1), 2) ….

39. V4: Get (value, o
• Code for V4
def getAggregatedValueColumnPairs(dataFrame : DataFrame) : RDD[((Double, Int),
Long)] = {
val aggregatedValueColumnRDD = dataFrame.rdd.mapPartitions(rows => {
val valueColumnMap = new mutable.HashMap[(Double, Int), Long]()
rows.foreach(row => {
row.toSeq.zipWithIndex.foreach{
case (value, columnIndex) =>
val key = (value.toString.toDouble, columnIndex)
val count = valueColumnMap.getOrElseUpdate(key, 0)
valueColumnMap.update(key, count + 1)
}
})
valueColumnMap.toIterator
})
aggregatedValueColumnRDD
}
Map to ((value, column Index) ,count)
Using a hashmap to keep track of uniques

40. Code for V4
• Lots more code to complete the whole algorithm
https://github.com/high-performance-spark/high-
performance-spark-
examples/blob/master/src/main/scala/com/high-
performance-spark-
examples/GoldiLocks/GoldiLocksWithHashMap.scala

41. V4: Success!
• 4 times faster than previous
solution on small data
• More robust, more
parallelizable! Scaling to
billions of rows!
Happy Goldilocks!

42. Why is V4: Better
Advantages
• Sorting 75% of original records
• Most keys are distinct
• No stragglers, easy to parallelize
• Can parallelize in many different ways

43. Lessons
• Sometimes performance looks ugly
• Best unit of parallelization?  Not always the most intuitive
• Shuffle Less
• Push work into narrow transformations
• leverage data locality to prevent shuffles
• Shuffle Better
• Shuffle fewer records
• Use narrow transformations to filter or reduce when possible
• Shuffle across keys that are well distributed
• Best if records associated with one key fit in memory
• Be aware of data skew, know you data

44. Before We Part …
• Alpine Data is hiring!
Data scientists, engineers (ruby, java, as well as Hadoop/ Scala) ,
support, technical sales
“I continue to be amazed, Alpine has the nicest people ever” –
Former alpine engineer
http://alpinedata.com/careers/
• Buy my book!
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