Get current number of partitions of a DataFrame
PythonScalaDataframeApache SparkApache Spark-SqlPython Problem Overview
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.)
Python Solutions
Solution 1 - Python
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.
Solution 2 - Python
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 localand based on yourRuntime.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
Solution 3 - Python
convert to RDD then get the partitions length
DF.rdd.partitions.length
Solution 4 - Python
val df = Seq(
("A", 1), ("B", 2), ("A", 3), ("C", 1)
).toDF("k", "v")
df.rdd.getNumPartitions
Solution 5 - Python
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.