[jira] [Created] (SPARK-38091) AvroSerializer can cause java.lang.ClassCastException at run time

["Zhenhao Li (Jira)" <ji...@apache.org>]

Zhenhao Li created SPARK-38091:
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             Summary: AvroSerializer can cause java.lang.ClassCastException at run time
                 Key: SPARK-38091
                 URL: https://issues.apache.org/jira/browse/SPARK-38091
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 3.2.1, 3.2.0, 3.1.2, 3.1.1, 3.1.0, 3.0.3, 3.0.2, 3.0.1, 3.0.0
            Reporter: Zhenhao Li


`AvroSerializer`'s implementation, at least in `newConverter`, was not 100% based on the `InternalRow` and `SpecializedGetters` interface. It assumes many implementation details of the interface. 

For example, in 

```scala
      case (TimestampType, LONG) => avroType.getLogicalType match {
          // For backward compatibility, if the Avro type is Long and it is not logical type
          // (the `null` case), output the timestamp value as with millisecond precision.
          case null | _: TimestampMillis => (getter, ordinal) =>
            DateTimeUtils.microsToMillis(timestampRebaseFunc(getter.getLong(ordinal)))
          case _: TimestampMicros => (getter, ordinal) =>
            timestampRebaseFunc(getter.getLong(ordinal))
          case other => throw new IncompatibleSchemaException(errorPrefix +
            s"SQL type ${TimestampType.sql} cannot be converted to Avro logical type $other")
        }
```

it assumes the `InternalRow` instance encodes `TimestampType` as `java.lang.Long`. That's true for `Unsaferow` but not for `GenericInternalRow`. 

Hence the above code will end up with runtime exceptions when used on an instance of `GenericInternalRow`, which is the case for Python UDF. 

I didn't get time to dig deeper than that. I got the impression that Spark's optimizer(s) will turn a row into a `UnsafeRow` and Python UDF doesn't involve the optimizer(s) and hence each row is a `GenericInternalRow`. 

It would be great if someone can correct me or offer a better explanation. 

 

To reproduce the issue, 

`git checkout master` and `git cherry-pick --no-commit` [this-commit|https://github.com/Zhen-hao/spark/commit/1ffe8e8f35273b2f3529f6c2d004822f480e4c88]

and run the test `org.apache.spark.sql.avro.AvroSerdeSuite`.

 

You will see runtime exceptions like the following one

```

- Serialize DecimalType to Avro BYTES with logical type decimal *** FAILED ***
  java.lang.ClassCastException: class java.math.BigDecimal cannot be cast to class org.apache.spark.sql.types.Decimal (java.math.BigDecimal is in module java.base of loader 'bootstrap'; org.apache.spark.sql.types.Decimal is in unnamed module of loader 'app')
  at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getDecimal(rows.scala:45)
  at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getDecimal$(rows.scala:45)
  at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getDecimal(rows.scala:195)
  at org.apache.spark.sql.avro.AvroSerializer.$anonfun$newConverter$10(AvroSerializer.scala:136)
  at org.apache.spark.sql.avro.AvroSerializer.$anonfun$newConverter$10$adapted(AvroSerializer.scala:135)
  at org.apache.spark.sql.avro.AvroSerializer.$anonfun$newStructConverter$2(AvroSerializer.scala:283)
  at org.apache.spark.sql.avro.AvroSerializer.serialize(AvroSerializer.scala:60)
  at org.apache.spark.sql.avro.AvroSerdeSuite.$anonfun$new$5(AvroSerdeSuite.scala:82)
  at org.apache.spark.sql.avro.AvroSerdeSuite.$anonfun$new$5$adapted(AvroSerdeSuite.scala:67)
  at org.apache.spark.sql.avro.AvroSerdeSuite.$anonfun$withFieldMatchType$2(AvroSerdeSuite.scala:217)
```



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