How to compute cumulative sum using Spark - scala

Question

I have an rdd of (String,Int) which is sorted by key
val data = Array(("c1",6), ("c2",3),("c3",4))
val rdd = sc.parallelize(data).sortByKey
Now I want to start the value for the first key with zero and the subsequent keys as sum of the previous keys.
Eg: c1 = 0 , c2 = c1's value , c3 = (c1 value +c2 value) , c4 = (c1+..+c3 value)
expected output:
(c1,0), (c2,6), (c3,9)...
Is it possible to achieve this ?
I tried it with map but the sum is not preserved inside the map.
var sum = 0 ;
val t = keycount.map{ x => { val temp = sum; sum = sum + x._2 ; (x._1,temp); }}

Answer 1

Compute partial results for each partition:
val partials = rdd.mapPartitionsWithIndex((i, iter) => {
val (keys, values) = iter.toSeq.unzip
val sums = values.scanLeft(0)(_ + _)
Iterator((keys.zip(sums.tail), sums.last))
})
Collect partials sums
val partialSums = partials.values.collect
Compute cumulative sum over partitions and broadcast it:
val sumMap = sc.broadcast(
(0 until rdd.partitions.size)
.zip(partialSums.scanLeft(0)(_ + _))
.toMap
)
Compute final results:
val result = partials.keys.mapPartitionsWithIndex((i, iter) => {
val offset = sumMap.value(i)
if (iter.isEmpty) Iterator()
else iter.next.map{case (k, v) => (k, v + offset)}.toIterator
})

Answer 2

Spark has buit-in supports for hive ANALYTICS/WINDOWING functions and the cumulative sum could be achieved easily using ANALYTICS functions.
Hive wiki ANALYTICS/WINDOWING functions.
Example:
Assuming you have sqlContext object-
val datardd = sqlContext.sparkContext.parallelize(Seq(("a",1),("b",2), ("c",3),("d",4),("d",5),("d",6)))
import sqlContext.implicits._
//Register as test table
datardd.toDF("id","val").createOrReplaceTempView("test")
//Calculate Cumulative sum
sqlContext.sql("select id,val, " +
"SUM(val) over ( order by id rows between unbounded preceding and current row ) cumulative_Sum " +
"from test").show()
This approach cause to below warning. In case executor runs outOfMemory, tune job’s memory parameters accordingly to work with huge dataset.
WARN WindowExec: No Partition Defined for Window operation! Moving
all data to a single partition, this can cause serious performance
degradation
I hope this helps.

Answer 3

Here is a solution in PySpark. Internally it's essentially the same as #zero323's Scala solution, but it provides a general-purpose function with a Spark-like API.
import numpy as np
def cumsum(rdd, get_summand):
"""Given an ordered rdd of items, computes cumulative sum of
get_summand(row), where row is an item in the RDD.
"""
def cumsum_in_partition(iter_rows):
total = 0
for row in iter_rows:
total += get_summand(row)
yield (total, row)
rdd = rdd.mapPartitions(cumsum_in_partition)
def last_partition_value(iter_rows):
final = None
for cumsum, row in iter_rows:
final = cumsum
return (final,)
partition_sums = rdd.mapPartitions(last_partition_value).collect()
partition_cumsums = list(np.cumsum(partition_sums))
partition_cumsums = [0] + partition_cumsums
partition_cumsums = sc.broadcast(partition_cumsums)
def add_sums_of_previous_partitions(idx, iter_rows):
return ((cumsum + partition_cumsums.value[idx], row)
for cumsum, row in iter_rows)
rdd = rdd.mapPartitionsWithIndex(add_sums_of_previous_partitions)
return rdd
# test for correctness by summing numbers, with and without Spark
rdd = sc.range(10000,numSlices=10).sortBy(lambda x: x)
cumsums, values = zip(*cumsum(rdd,lambda x: x).collect())
assert all(cumsums == np.cumsum(values))

Answer 4

I came across a similar problem and implemented #Paul 's solution. I wanted to do cumsum on a integer frequency table sorted by key(the integer), and there was a minor problem with np.cumsum(partition_sums), error being unsupported operand type(s) for +=: 'int' and 'NoneType'.
Because if the range is big enough, the probability of each partition having something is thus big enough(no None values). However, if the range is much smaller than count, and number of partitions remains the same, some of the partitions would be empty. Here comes the modified solution:
def cumsum(rdd, get_summand):
"""Given an ordered rdd of items, computes cumulative sum of
get_summand(row), where row is an item in the RDD.
"""
def cumsum_in_partition(iter_rows):
total = 0
for row in iter_rows:
total += get_summand(row)
yield (total, row)
rdd = rdd.mapPartitions(cumsum_in_partition)
def last_partition_value(iter_rows):
final = None
for cumsum, row in iter_rows:
final = cumsum
return (final,)
partition_sums = rdd.mapPartitions(last_partition_value).collect()
# partition_cumsums = list(np.cumsum(partition_sums))
#----from here are the changed lines
partition_sums = [x if x is not None else 0 for x in partition_sums]
temp = np.cumsum(partition_sums)
partition_cumsums = list(temp)
#----
partition_cumsums = [0] + partition_cumsums
partition_cumsums = sc.broadcast(partition_cumsums)
def add_sums_of_previous_partitions(idx, iter_rows):
return ((cumsum + partition_cumsums.value[idx], row)
for cumsum, row in iter_rows)
rdd = rdd.mapPartitionsWithIndex(add_sums_of_previous_partitions)
return rdd
#test on random integer frequency
x = np.random.randint(10, size=1000)
D = sqlCtx.createDataFrame(pd.DataFrame(x.tolist(),columns=['D']))
c = D.groupBy('D').count().orderBy('D')
c_rdd = c.rdd.map(lambda x:x['count'])
cumsums, values = zip(*cumsum(c_rdd,lambda x: x).collect())

Answer 5

you can want to try out with windows over using rowsBetween. hope still helpful.
import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.Window
val data = Array(("c1",6), ("c2",3),("c3",4))
val df = sc.parallelize(data).sortByKey().toDF("c", "v")
val w = Window.orderBy("c")
val r = df.select( $"c", sum($"v").over(w.rowsBetween(-2, -1)).alias("cs"))
display(r)