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https://issues.apache.org/jira/browse/SPARK-22446?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16239897#comment-16239897
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Liang-Chi Hsieh commented on SPARK-22446:
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For this special case, the simplest workaround is to set {{handleInvalid}} as
keep. Actually the another predicate {{isnotnull(_3#5)}} can filter the row out
if the UDF doesn't cause error with {{handleInvalid}} as keep.
The problem is happened at the optimizer when pushing predicates down through
projection. For the catalyst expressions, applying on the supposedly filtered
out data is not a problem because other predicates should filter it out.
UDFs are special case because they can possibly cause runtime exception when
applying on unexcepted data. It is not always safe to push down such predicates.
However, because not all UDFs are not safe to push down, we may not want to
disable all pushdown UDF predicates. Currently I think we should let such UDFs
as non-deterministic and disable pushdown for it.
> Optimizer causing StringIndexerModel's indexer UDF to throw "Unseen label"
> exception incorrectly for filtered data.
> -------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-22446
> URL: https://issues.apache.org/jira/browse/SPARK-22446
> Project: Spark
> Issue Type: Bug
> Components: ML, Optimizer
> Affects Versions: 2.0.0, 2.2.0
> Environment: spark-shell, local mode, macOS Sierra 10.12.6
> Reporter: Greg Bellchambers
>
> In the following, the `indexer` UDF defined inside the
> `org.apache.spark.ml.feature.StringIndexerModel.transform` method throws an
> "Unseen label" error, despite the label not being present in the transformed
> DataFrame.
> Here is the definition of the indexer UDF in the transform method:
> {code:java}
> val indexer = udf { label: String =>
> if (labelToIndex.contains(label)) {
> labelToIndex(label)
> } else {
> throw new SparkException(s"Unseen label: $label.")
> }
> }
> {code}
> We can demonstrate the error with a very simple example DataFrame.
> {code:java}
> scala> import org.apache.spark.ml.feature.StringIndexer
> import org.apache.spark.ml.feature.StringIndexer
> scala> // first we create a DataFrame with three cities
> scala> val df = List(
> | ("A", "London", "StrA"),
> | ("B", "Bristol", null),
> | ("C", "New York", "StrC")
> | ).toDF("ID", "CITY", "CONTENT")
> df: org.apache.spark.sql.DataFrame = [ID: string, CITY: string ... 1 more
> field]
> scala> df.show
> +---+--------+-------+
> | ID| CITY|CONTENT|
> +---+--------+-------+
> | A| London| StrA|
> | B| Bristol| null|
> | C|New York| StrC|
> +---+--------+-------+
> scala> // then we remove the row with null in CONTENT column, which removes
> Bristol
> scala> val dfNoBristol = finalStatic.filter($"CONTENT".isNotNull)
> dfNoBristol: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [ID:
> string, CITY: string ... 1 more field]
> scala> dfNoBristol.show
> +---+--------+-------+
> | ID| CITY|CONTENT|
> +---+--------+-------+
> | A| London| StrA|
> | C|New York| StrC|
> +---+--------+-------+
> scala> // now create a StringIndexer for the CITY column and fit to
> dfNoBristol
> scala> val model = {
> | new StringIndexer()
> | .setInputCol("CITY")
> | .setOutputCol("CITYIndexed")
> | .fit(dfNoBristol)
> | }
> model: org.apache.spark.ml.feature.StringIndexerModel = strIdx_f5afa23333fb
> scala> // the StringIndexerModel has only two labels: "London" and "New York"
> scala> str.labels foreach println
> London
> New York
> scala> // transform our DataFrame to add an index column
> scala> val dfWithIndex = model.transform(dfNoBristol)
> dfWithIndex: org.apache.spark.sql.DataFrame = [ID: string, CITY: string ... 2
> more fields]
> scala> dfWithIndex.show
> +---+--------+-------+-----------+
> | ID| CITY|CONTENT|CITYIndexed|
> +---+--------+-------+-----------+
> | A| London| StrA| 0.0|
> | C|New York| StrC| 1.0|
> +---+--------+-------+-----------+
> {code}
> The unexpected behaviour comes when we filter `dfWithIndex` for `CITYIndexed`
> equal to 1.0 and perform an action. The `indexer` UDF in `transform` method
> throws an exception reporting unseen label "Bristol". This is irrational
> behaviour as far as the user of the API is concerned, because there is no
> such value as "Bristol" when do show all rows of `dfWithIndex`:
> {code:java}
> scala> dfWithIndex.filter($"CITYIndexed" === 1.0).count
> 17/11/04 00:33:41 ERROR Executor: Exception in task 1.0 in stage 20.0 (TID 40)
> org.apache.spark.SparkException: Failed to execute user defined
> function($anonfun$5: (string) => double)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
> at org.apache.spark.scheduler.Task.run(Task.scala:108)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: org.apache.spark.SparkException: Unseen label: Bristol. To handle
> unseen labels, set Param handleInvalid to keep.
> at
> org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:222)
> at
> org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:208)
> ... 13 more
> {code}
> To understand what is happening here, note that an action is triggered when
> we call `StringIndexer.fit()`, before the `CITYIndexed === 1` filter is
> applied, so the StringIndexerModel sees only London and New York, as
> expected. Now compare the query plans for `dfWithIndex` and
> `dfWithIndex.filter($"CITYIndexed" === 1.0)`:
> {noformat}
> scala> dfWithIndex.explain
> == Physical Plan ==
> *Project [_1#3 AS ID#7, _2#4 AS CITY#8, _3#5 AS CONTENT#9, UDF(_2#4) AS
> CITYIndexed#159]
> +- *Filter isnotnull(_3#5)
> +- LocalTableScan [_1#3, _2#4, _3#5]
> scala> dfWithIndex.filter($"CITYIndexed" === 1.0).explain
> == Physical Plan ==
> *Project [_1#3 AS ID#7, _2#4 AS CITY#8, _3#5 AS CONTENT#9, UDF(_2#4) AS
> CITYIndexed#159]
> +- *Filter (isnotnull(_3#5) && (UDF(_2#4) = 1.0))
> +- LocalTableScan [_1#3, _2#4, _3#5]
> {noformat}
> Note that in the latter, the query plan has pushed the filter `$"CITYIndexed"
> === 1.0` back to be performed at the same stage as our null filter (`Filter
> (isnotnull(_3#5) && (UDF(_2#4) = 1.0))`).
> With a debugger I have seen that both operands of `&&` are executed on each
> row of `df`: `isnotnull(_3#5)` and `UDF(_2#4) = 1.0`. Therefore, the UDF is
> passed the label `Bristol` despite isnotnull returning false for that row.
> If we cache the DataFrame `dfNoBristol` immediately after creating it, then
> there is no longer an error because the optimizer does not attempt to call
> the UDF on unseen data. The fact that we get different results depending on
> whether or not we call cache is a cause for concern.
> I have seen similar issues with pure SparkSql DataFrame operations when the
> DAG gets complicated (many joins, and aggregations). These are harder to
> isolate to such a simple example, but I plan to report them in the near
> future.
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