If input is a DataFrame
just use agg
:
import pyspark.sql.functions as sqlf
df = sc.parallelize([
("foo", 1.0), ("foo", 2.5), ("bar", -1.0), ("bar", 99.0)
]).toDF(["k", "v"])
df.groupBy("k").agg(sqlf.min("v"), sqlf.max("v"), sqlf.sum("v")).show()
## +---+------+------+------+
## | k|min(v)|max(v)|sum(v)|
## +---+------+------+------+
## |bar| -1.0| 99.0| 98.0|
## |foo| 1.0| 2.5| 3.5|
## +---+------+------+------+
With RDDs you can use statcounter
:
from pyspark.statcounter import StatCounter
rdd = df.rdd
stats = rdd.aggregateByKey(
StatCounter(), StatCounter.merge, StatCounter.mergeStats
).mapValues(lambda s: (s.min(), s.max(), s.sum()))
stats.collect()
## [('bar', (-1.0, 99.0, 98.0)), ('foo', (1.0, 2.5, 3.5))]
Using your functions you could do something like this:
def apply(x, y, funs=[minFunc, maxFunc, sumFunc]):
return [f(x_, y_) for f, x_, y_ in zip(*(funs, x, y))]
rdd.combineByKey(lambda x: (x, x, x), apply, apply).collect()
## [('bar', [-1.0, 99.0, 98.0]), ('foo', [1.0, 2.5, 3.5])]