Yin Huai created SPARK-12662:
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Summary: Add document to randomSplit to explain the sampling
depends on the ordering of the rows in a partition
Key: SPARK-12662
URL: https://issues.apache.org/jira/browse/SPARK-12662
Project: Spark
Issue Type: Bug
Components: Documentation, SQL
Reporter: Yin Huai
With {{./bin/spark-shell --master=local-cluster[2,1,2014]}}, the following code
will provide overlapped rows for two DFs returned by the randomSplit.
{code}
sqlContext.sql("drop table if exists test")
val x = sc.parallelize(1 to 210)
case class R(ID : Int)
sqlContext.createDataFrame(x.map
{R(_)}).write.format("json").saveAsTable("bugsc1597")
var df = sql("select distinct ID from test")
var Array(a, b) = df.randomSplit(Array(0.333, 0.667), 1234L)
a.registerTempTable("a")
b.registerTempTable("b")
val intersectDF = a.intersect(b)
intersectDF.show
{code}
The reason is that {{sql("select distinct ID from test")} does not guarantee
the ordering rows in a partition. It will be good to add more document to the
api doc to explain it. To make intersectDF contain 0 row, the df needs to have
fixed row ordering within a partition.
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