stevedlawrence commented on code in PR #1515:
URL: https://github.com/apache/daffodil/pull/1515#discussion_r2251991004
##########
daffodil-core/src/main/scala/org/apache/daffodil/runtime1/processors/DataProcessor.scala:
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@@ -155,8 +116,7 @@ class DataProcessor(
* @return the serializable object
*/
@throws(classOf[java.io.ObjectStreamException])
- private def writeReplace(): Object =
- new SerializableDataProcessor(ssrd, tunables, variableMap.copy())
+ private def writeReplace(): Object = new DataProcessor(ssrd, tunables,
variableMap.copy())
Review Comment:
We could change the `save` method to also disable debugging, similar to how
we disable validation, e.g.
```scala
val dpToSave = this.copy(
// reset to original variables defined in schema
variableMap = ssrd.originalVariables,
validator = NoValidator,
// don't save any warnings that were generated
diagnostics = Seq.empty,
// disable debugger if provided
areDebugging = false,
optDebugger = None
)
```
So using `save` will clear the debugger, but manually serializing will not.
And if you want to debug using a save/reload parser, you just need to call
withDebugger after reloading it, similar to what you have to do with validators.
I don't think we need to support our own debuggers being serializable, but I
could imagine a use case where Spark or some other framework that directly
serializes the DataProcessor could create a custom debugger that is
serializable, so we don't necessarily need to exclude that capability.
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