Get current number of partitions of a DataFrame

Question

Is there any way to get the current number of partitions of a DataFrame? I checked the DataFrame javadoc (spark 1.6) and didn't found a method for that, or am I just missed it? (In case of JavaRDD there's a getNumPartitions() method.)

Answer 1

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.

Answer 2

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