You need to call getNumPartitions() on the DataFrame's underlying RDD, e.g., df.rdd.getNumPartitions(). In the case of Scala, this is a parameterless method: df.rdd.getNumPartitions.

convert to RDD then get the partitions length

DF.rdd.partitions.length

 val df = Seq(
("A", 1), ("B", 2), ("A", 3), ("C", 1)
).toDF("k", "v")


df.rdd.getNumPartitions

dataframe.rdd.partitions.size is another alternative apart from df.rdd.getNumPartitions() or df.rdd.length.

let me explain you this with full example...

val x = (1 to 10).toList
val numberDF = x.toDF(“number”)
numberDF.rdd.partitions.size // => 4

To prove that how many number of partitions we got with above... save that dataframe as csv

numberDF.write.csv(“/Users/Ram.Ghadiyaram/output/numbers”)

Here is how the data is separated on the different partitions.

Partition 00000: 1, 2
Partition 00001: 3, 4, 5
Partition 00002: 6, 7
Partition 00003: 8, 9, 10

Update :

@Hemanth asked a good question in the comment... basically why number of partitions are 4 in above case

Short answer : Depends on cases where you are executing. since local[4] I used, I got 4 partitions.

Long answer :

I was running above program in my local machine and used master as local[4] based on that it was taking as 4 partitions.

val spark = SparkSession.builder()
.appName(this.getClass.getName)
.config("spark.master", "local[4]").getOrCreate()

If its spark-shell in master yarn I got the number of partitions as 2

example : spark-shell --master yarn and typed same commands again

scala> val x = (1 to 10).toList
x: List[Int] = List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)




scala> val numberDF = x.toDF("number")
numberDF: org.apache.spark.sql.DataFrame = [number: int]


scala> numberDF.rdd.partitions.size
res0: Int = 2

• here 2 is default parllelism of spark
• Based on hashpartitioner spark will decide how many number of partitions to distribute. if you are running in --master local and based on your Runtime.getRuntime.availableProcessors() i.e. local[Runtime.getRuntime.availableProcessors()] it will try to allocate those number of partitions. if your available number of processors are 12 (i.e. local[Runtime.getRuntime.availableProcessors()]) and you have list of 1 to 10 then only 10 partitions will be created.

NOTE:

If you are on a 12-core laptop where I am executing spark program and by default the number of partitions/tasks is the number of all available cores i.e. 12. that means local[*] or s"local[${Runtime.getRuntime.availableProcessors()}]") but in this case only 10 numbers are there so it will limit to 10

keeping all these pointers in mind I would suggest you to try on your own

One more Interesting way to get number of partitions is 'using mapPartitions' transformation. Sample Code -

val x = (1 to 10).toList
val numberDF = x.toDF()
numberDF.rdd.mapPartitions(x => Iterator[Int](1)).sum()

Spark experts are welcome to comment on its performance.