[GitHub] [spark] xkrogen commented on a diff in pull request #37634: [SPARK-40199][SQL] Provide useful error when projecting a non-null column encounters null value
xkrogen commented on code in PR #37634:
URL: https://github.com/apache/spark/pull/37634#discussion_r974499166
##
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala:
##
@@ -252,28 +267,44 @@ object GenerateUnsafeProjection extends
CodeGenerator[Seq[Expression], UnsafePro
""".stripMargin
}
+ /**
+ * Wrap `inputExpr` in a try-catch block that will catch any
[[NullPointerException]] that is
+ * thrown, instead throwing a (more helpful) error message as provided by
+ *
[[org.apache.spark.sql.errors.QueryExecutionErrors.valueCannotBeNullError]].
+ */
+ private def wrapWithNpeHandling(inputExpr: String, descPath: Seq[String]):
String =
+s"""
+ |try {
+ | ${inputExpr.trim}
Review Comment:
I prefer exception-catching as it handles this issue with zero overhead.
Adding a null-check here essentially falls back to the logic for a nullable
schema:
https://github.com/apache/spark/blob/0494dc90af48ce7da0625485a4dc6917a244d580/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala#L119-L133
From the benchmark results, we can see that there is nontrivial overhead for
the null-check; for the simple case of a projection of a primitive, the
overhead is almost 50%:
https://github.com/apache/spark/blob/2a1f9767213c321bd52e7714fa3b5bfc4973ba40/sql/catalyst/benchmarks/UnsafeProjectionBenchmark-jdk17-results.txt#L9-L10
You call out the situation of a null silently being replaced with a default
value; this is a good point. I'm not sure how we can handle that without
additional overhead of an explicit check. It seems that the default value
replacement logic is coming from [Scala's own unboxing
logic](https://github.com/scala/scala/blob/986dcc160aab85298f6cab0bf8dd0345497cdc01/src/library/scala/runtime/BoxesRunTime.java#L102).
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[GitHub] [spark] xkrogen commented on a diff in pull request #37634: [SPARK-40199][SQL] Provide useful error when projecting a non-null column encounters null value
xkrogen commented on code in PR #37634:
URL: https://github.com/apache/spark/pull/37634#discussion_r960070035
##
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala:
##
@@ -252,28 +266,44 @@ object GenerateUnsafeProjection extends
CodeGenerator[Seq[Expression], UnsafePro
""".stripMargin
}
+ /**
+ * Wrap `inputExpr` in a try-catch block that will catch any
[[NullPointerException]] that is
+ * thrown, instead throwing a (more helpful) error message as provided by
+ *
[[org.apache.spark.sql.errors.QueryExecutionErrors.valueCannotBeNullError]].
+ */
+ private def wrapWithNpeHandling(inputExpr: String, descPath: Seq[String]):
String =
+s"""
+ |try {
+ | ${inputExpr.trim}
+ |} catch (NullPointerException npe) {
+ | throw QueryExecutionErrors.valueCannotBeNullError(
Review Comment:
Printing the single datum won't be helpful since it's always NULL, and it
would be challenging to access the whole input row from this location. We
create the projection recursively, so at this point while recursing, we don't
even have a reference to the fully created projection to grab the other fields.
Note also that this is a failure to project the data, and we would also need to
project the data to print it, so we'd have to selectively skip this field.
Open to suggestions, but I don't see a clear path forward.
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[GitHub] [spark] xkrogen commented on a diff in pull request #37634: [SPARK-40199][SQL] Provide useful error when projecting a non-null column encounters null value
xkrogen commented on code in PR #37634:
URL: https://github.com/apache/spark/pull/37634#discussion_r955234541
##
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala:
##
@@ -252,28 +264,43 @@ object GenerateUnsafeProjection extends
CodeGenerator[Seq[Expression], UnsafePro
""".stripMargin
}
+ /**
+ * Wrap `inputExpr` in a try-catch block that will catch any
[[NullPointerException]] that is
+ * thrown, instead throwing a (more helpful) error message as provided by
+ *
[[org.apache.spark.sql.errors.QueryExecutionErrors.valueCannotBeNullError]].
+ */
+ private def wrapWithNpeHandling(inputExpr: String, descPath: Seq[String]):
String =
+s"""
+ |try {
+ | ${inputExpr.trim}
+ |} catch (NullPointerException npe) {
+ | throw
QueryExecutionErrors.valueCannotBeNullError("${descPath.mkString(".")}");
Review Comment:
Good catch! I can't believe the ridiculous stuff Spark will accept as a
valid column name. Fixed and added a test for this.
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[GitHub] [spark] xkrogen commented on a diff in pull request #37634: [SPARK-40199][SQL] Provide useful error when projecting a non-null column encounters null value
xkrogen commented on code in PR #37634:
URL: https://github.com/apache/spark/pull/37634#discussion_r955182512
##
sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala:
##
@@ -447,6 +447,13 @@ private[sql] object QueryExecutionErrors extends
QueryErrorsBase {
new RuntimeException(fieldCannotBeNullMsg(index, fieldName))
}
+ def valueCannotBeNullError(locationDesc: String): RuntimeException = {
+new RuntimeException(s"The value at $locationDesc cannot be null, but a
NULL was found. " +
+ "This is typically caused by the presence of a NULL value when the
schema indicates the " +
+ "value should be non-null. Check that the input data matches the schema
and/or that UDFs " +
Review Comment:
Yeah, it handles this situation. Marking a UDF as non-nullable just adjusts
the schema, then the output row will contain a null value -- so the situation
is identical to what is already tested in `GeneratedProjectionSuite`. But I can
add this to `DataFrameSuite` to explicitly demonstrate that it is covered.
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