Graeme Edwards created SPARK-17618:
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             Summary: Dataframe except returns incorrect results when combined 
with coalesce
                 Key: SPARK-17618
                 URL: https://issues.apache.org/jira/browse/SPARK-17618
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 1.6.1
            Reporter: Graeme Edwards
            Priority: Minor


We were getting incorrect results from the DataFrame except method - all rows 
were being returned instead of the ones that intersected. Calling subtract on 
the underlying RDD returned the correct result.

We tracked it down to the use of coalesce - the following is the simplest 
example case we created that reproduces the issue:

val schema = new StructType().add("test", types.IntegerType )
val t1 = sql.createDataFrame(sql.sparkContext.parallelize(1 to 100).map(i=> 
Row(i)), schema)
val t2 = sql.createDataFrame(sql.sparkContext.parallelize(5 to 10).map(i=> 
Row(i)), schema)
val t3 = t1.join(t2, t1.col("test").equalTo(t2.col("test")), "leftsemi")
println("Count using normal except = " + t1.except(t3).count())
println("Count using coalesce = " + 
t1.coalesce(8).except(t3.coalesce(8)).count())

We should get the same result from both uses of except, but the one using 
coalesce returns 100 instead of 94.



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