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Posted to issues@spark.apache.org by "Josh Rosen (JIRA)" <ji...@apache.org> on 2016/09/21 18:52:20 UTC
[jira] [Updated] (SPARK-17618) Dataframe except returns incorrect
results when combined with coalesce
[ https://issues.apache.org/jira/browse/SPARK-17618?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Josh Rosen updated SPARK-17618:
-------------------------------
Description:
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:
{code}
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())
{code}
We should get the same result from both uses of except, but the one using coalesce returns 100 instead of 94.
was:
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.
> 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:
> {code}
> 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())
> {code}
> 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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