Apache Spark CombineByKey with list of elements in Python

Apache Spark CombineByKey with list of elements in Python

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Apache Spark CombineByKey with list of elements in Python
Tag : python , By : micate
Date : November 23 2020, 09:01 AM

will help you If you want to keep all values as a list there is no reason to use combineByKey at all. It is more efficient to simply groupBy:
aggregated = data.groupByKey().mapValues(lambda vs: (list(vs), len(vs)))
## [('a', (['u', 'v'], 2)), ('b', (['w', 'x', 'x'], 3))]
aggregated_counts = (data
    .map(lambda kv: (kv, 1))
    .map(lambda kv: (kv[0][0], (kv[0][1], kv[1])))
    .mapValues(lambda xs: (list(xs), sum(x[1] for x in xs))))

## [('a', ([('v', 1), ('u', 1)], 2)), ('b', ([('w', 1), ('x', 2)], 3))]
from collections import Counter

def merge_value(acc, x):
    return acc

def merge_combiners(acc1, acc2):
    return acc1

aggregated_counts_ = (data
    .combineByKey(Counter, merge_value, merge_combiners)
    .mapValues(lambda cnt: (cnt, sum(cnt.values()))))

## [('a', (Counter({'u': 1, 'v': 1}), 2)), ('b', (Counter({'w': 1, 'x': 2}), 3))]

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Apache Spark Python GroupByKey or reduceByKey or combineByKey

Tag : python , By : Ohad Barzilay
Date : March 29 2020, 07:55 AM
This might help you Just replace combineByKey() with groupByKey() and then you should be fine.
Example code
data = sc.parallelize(['abc123Key1asdas','abc123Key1asdas','abc123Key1asdas', 'abcw23Key2asdad', 'abcw23Key2asdad', 'abcasdKeynasdas', 'asfssdKeynasda', 'asdaasKeynsdfa'])
data.map(lambda line: (line[6:10],line)).groupByKey().mapValues(list).collect()

Python Spark combineByKey Average

Tag : python-3.x , By : user90210
Date : November 20 2020, 11:01 PM
This might help you Step by step:
lambda (key, (totalSum, count)): ... is so-called Tuple Parameter Unpacking which has been removed in Python.
lambda key, vals: ...
lambda key_vals: (key_vals[0], key_vals[1][0] / key_vals[1][1])
def get_mean(key_vals):
    key, (total, cnt) = key_vals
    return key, total / cnt

cbk.mapValues(lambda x: x[0] / x[1])
from pyspark.statcounter import StatCounter

        lambda x: StatCounter([x]),

Using Spark CombineByKey with set of values

Tag : scala , By : Grace Jones
Date : March 29 2020, 07:55 AM
wish helps you I have the following dataset: , You don't combineByKey here. reduceByKey will do just fine:
data.map((_, 1))
  .reduceByKey(_ + _)
  .map { case ((k1, k2), v) => (k1, k2, v) }

// Array[(String, String, Int)] = Array((group3,value3,1), (group1,value1,3), (group1,value2,1), (group2,value1,1)
(value) => {
  println(s"Create combiner -> ${value}")
 (value, 1)
val reduced = data.combineByKey(
  (value) => {
    (Array(value), 1)
  (acc: (Array[String], Int), v) => {
    (acc._1 :+ v, acc._2 + 1)
  (acc1: (Array[String], Int), acc2: (Array[String], Int)) => {
    (acc1._1 ++ acc2._1, acc1._2 + acc2._2)
// Array[(String, (Array[String], Int))]  = Array((group3,(Array(value3),1)), (group1,(Array(value1, value2, value1, value1),4)), (group2,(Array(value1),1)))

Apache Spark Streaming - reduceByKey, groupByKey, aggregateByKey or combineByKey?

Tag : apache-spark , By : lifchicker
Date : March 29 2020, 07:55 AM
this will help @phillip for the details. Let's go in the details of each keys:
(1). groupByKey - It can help to rank, sort and even aggregate using any key. Performance wise it is slower because does not use combiner. groupByKey() is just to group your dataset based on a key

Spark CombineByKey

Tag : scala , By : delphiace
Date : March 29 2020, 07:55 AM
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