[ 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org