HI all,
The belowing is the code of RangePartitioner.
class RangePartitioner[K : Ordering : ClassTag, V](
partitions: Int,
rdd: RDD[_ <: Product2[K, V]],
private var ascending: Boolean = true,
val samplePointsPerPartitionHint: Int = 20)
I feel puzzled ab
ure
> out how to figure out the size of the file it’s trying to write out.
>
> Second, We use to use RDDs and RangePartitioner for task partitioning.
> However, I don’t see this available in DataFrames. How does one
density we certainly could be hitting the fact
we are getting tiles too dense. I’m trying to figure out how to figure out the
size of the file it’s trying to write out.
Second, We use to use RDDs and RangePartitioner for task partitioning.
However, I don’t see this available in DataFrames. How
Hi, Sparkers:
I just happened to search in google for something related to the
RangePartitioner of spark, and found an old thread in this email list as here:
http://apache-spark-user-list.1001560.n3.nabble.com/RDD-and-Partition-td991.html
I followed the code example mentioned in that email thread
RangePartitioner does not actually provide a guarantee that all partitions
will be equal sized (that is hard), and instead uses sampling to
approximate equal buckets. Thus, it is possible that a bucket is left empty.
If you want the specified behavior, you should define your own partitioner
spark.SparkContext: Starting job: RangePartitioner
at Exchange.scala:79
A bit of background which may or may not be relevant. The program was
working fine in eclipse, however, was getting hung upon submission to the
cluster. In an attempt to debug, I changed the version in build.sbt to
match the one
proceeds.
What might be the issue and possible solution?
INFO SparkContext: Starting job: RangePartitioner at Exchange.scala:79
Table 1 has 450 columns
Table2 has 100 columns
Both tables have few million rows
val table1= myTable1.as('table1)
val table2= myTable2
I am joining two tables as below, the program stalls at below log line and
never proceeds.
What might be the issue and possible solution?
INFO SparkContext: Starting job: RangePartitioner at Exchange.scala:79
Table 1 has 450 columns
Table2 has 100 columns
Both tables have few million
MB)
15/01/17 11:44:16 INFO spark.SparkContext: Starting job: RangePartitioner
at Exchange.scala:79
A bit of background which may or may not be relevant. The program was
working fine in eclipse, however, was getting hung upon submission to the
cluster. In an attempt to debug, I changed the version
