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Python Spark combineByKey Average


Python Spark combineByKey Average

By : Anuj Shah
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.
code :
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.map(get_mean)
cbk.mapValues(lambda x: x[0] / x[1])
from pyspark.statcounter import StatCounter

(pRDD
    .combineByKey(
        lambda x: StatCounter([x]),
        StatCounter.merge,
        StatCounter.mergeStats)
    .mapValues(StatCounter.mean))


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Apache Spark Python GroupByKey or reduceByKey or combineByKey

Apache Spark Python GroupByKey or reduceByKey or combineByKey


By : Black Lotus
Date : March 29 2020, 07:55 AM
This might help you Just replace combineByKey() with groupByKey() and then you should be fine.
Example code
code :
data = sc.parallelize(['abc123Key1asdas','abc123Key1asdas','abc123Key1asdas', 'abcw23Key2asdad', 'abcw23Key2asdad', 'abcasdKeynasdas', 'asfssdKeynasda', 'asdaasKeynsdfa'])
data.map(lambda line: (line[6:10],line)).groupByKey().mapValues(list).collect()
Apache Spark CombineByKey with list of elements in Python

Apache Spark CombineByKey with list of elements in Python


By : Svetlana
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:
code :
aggregated = data.groupByKey().mapValues(lambda vs: (list(vs), len(vs)))
aggregated.collect()
## [('a', (['u', 'v'], 2)), ('b', (['w', 'x', 'x'], 3))]
aggregated_counts = (data
    .map(lambda kv: (kv, 1))
    .reduceByKey(add)
    .map(lambda kv: (kv[0][0], (kv[0][1], kv[1])))
    .groupByKey()
    .mapValues(lambda xs: (list(xs), sum(x[1] for x in xs))))

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

def merge_value(acc, x):
    acc.update(x)
    return acc

def merge_combiners(acc1, acc2):
    acc1.update(acc2)
    return acc1

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

aggregated_counts_.collect()
## [('a', (Counter({'u': 1, 'v': 1}), 2)), ('b', (Counter({'w': 1, 'x': 2}), 3))]
Using Spark CombineByKey with set of values

Using Spark CombineByKey with set of values


By : shraddha
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:
code :
data.map((_, 1))
  .reduceByKey(_ + _)
  .map { case ((k1, k2), v) => (k1, k2, v) }
  .collect

// 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)
  }
)
result.collect
// Array[(String, (Array[String], Int))]  = Array((group3,(Array(value3),1)), (group1,(Array(value1, value2, value1, value1),4)), (group2,(Array(value1),1)))
Optimizing Spark combineByKey

Optimizing Spark combineByKey


By : Dave
Date : March 29 2020, 07:55 AM
wish helps you
I am not sure if this is the correct way to partition a dataframe
Spark CombineByKey

Spark CombineByKey


By : Dzoł
Date : March 29 2020, 07:55 AM
should help you out The types of the functions you're passing do not match the expected types. Let's look at the signature of combineByKey:
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