CODEWITHSUNDEEP
apache-sparkapache-spark-sql
WoodChopper
WoodChopper

Reputation: 4375

How to get multiple queries in single run

For example I have a dataframe like below,

df
DataFrame[columnA: int, columnB: int]

If I have to do two checks. I am going over the data two times like below,

df.where(df.columnA == 412).count()
df.where(df.columnB == 25).count()

In normal code I would be having two count variable and incrementing on True. How would I go with spark dataframe? Appreciate if some one could link to right documentation also. Happy to see python or scala.

Upvotes: 1

Views: 1149

Answers (2)

Ewan Leith
Ewan Leith

Reputation: 1665

@zero323's answer is spot on, but just to indicate the most flexible programming model is Spark, you could do your checks as if statements inside a map with a lambda function, e.g. (using the same dataframe as above)

import org.apache.spark.sql.functions._  

val r1 = df.map(x => {
  var x0 = 0
  var x1 = 0
  if (x(0) == 412) x0=1
  if (x(1) == 25) x1=1
  (x0, x1)
}).toDF("x0", "x1").select(sum("x0"), sum("x1")).show()

This model lets you do almost anything you can think of, though you're much better off sticking with the specific APIs where available.

Upvotes: 2

zero323
zero323

Reputation: 330093

For example like this:

import org.apache.spark.sql.functions.sum

val df = sc.parallelize(Seq(
  (412, 0),
  (0,   25), 
  (412, 25), 
  (0,   25)
)).toDF("columnA", "columnB")

df.agg(
  sum(($"columnA" === 412).cast("long")).alias("columnA"),
  sum(($"columnB" === 25).cast("long")).alias("columnB")
).show

// +-------+-------+
// |columnA|columnB|
// +-------+-------+
// |      2|      3|
// +-------+-------+

or like this:

import org.apache.spark.sql.functions.{count, when}

df.agg(
  count(when($"columnA" === 412, $"columnA")).alias("columnA"),
  count(when($"columnB" === 25, $"columnB")).alias("columnB")
).show

// +-------+-------+
// |columnA|columnB|
// +-------+-------+
// |      2|      3|
// +-------+-------+

I am not aware of any specific documentation but I am pretty sure you'll find this in any good SQL reference.

Upvotes: 2

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