[SPARK-56975][SS] Reject user-specified schema in DataStreamReader.table()#56017
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### What changes were proposed in this pull request?
Make `DataStreamReader.table()` reject user-specified schemas by calling `assertNoSpecifiedSchema("table")`, mirroring `DataStreamReader.changes()`.
### Why are the changes needed?
`DataStreamReader.table()` accepts a user-specified schema without complaint and then silently ignores it:
```scala
spark.readStream
.schema(new StructType().add("a", IntegerType))
.table("some_table") // no error; the schema has no effect
```
User-specified schema is not a meaningful input to `.table()` — catalog tables declare their own schema, and `TableCatalog.loadTable(Identifier)` has no parameter to receive a user schema, so even if Spark wanted to forward one it couldn't. The user's `.schema(...)` call is therefore always a misconfiguration.
The rest of `DataStreamReader` already surfaces this kind of misconfiguration as a clear error:
- `.load()` goes through `DataSourceV2Utils.getTableFromProvider`, which throws `_LEGACY_ERROR_TEMP_2242` ("`<provider>` source does not support user-specified schema") when the provider does not implement `supportsExternalMetadata()`.
- `.changes()` explicitly calls `assertNoSpecifiedSchema("changes")` and throws `_LEGACY_ERROR_TEMP_1189` ("User specified schema not supported with `changes`.").
`.table()` is the odd one out: same invalid configuration, no error. Users can write `readStream.schema(s).table(name)`, see a working query, and reasonably assume `s` had an effect — when in fact the resulting stream uses the catalog schema and `s` was dropped. Surfacing this as a clear error aligns `.table()` with the existing behavior of `.load()` and `.changes()`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added `DataStreamTableAPISuite` test `"read: user-specified schema is not allowed with table API"`.
### Was this patch authored or co-authored using generative AI tooling?
No
Closes #56017 from PorridgeSwim/forbidSpecifySchemaForTable.
Lead-authored-by: You Zhou <98635051+PorridgeSwim@users.noreply.github.com>
Co-authored-by: You Zhou <you.zhou@databricks.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
(cherry picked from commit 05b4d81)
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
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What changes were proposed in this pull request?
Make
DataStreamReader.table()reject user-specified schemas by callingassertNoSpecifiedSchema("table"), mirroringDataStreamReader.changes().Why are the changes needed?
DataStreamReader.table()accepts a user-specified schema without complaint and then silently ignores it:User-specified schema is not a meaningful input to
.table()— catalog tables declare their own schema, andTableCatalog.loadTable(Identifier)has no parameter to receive a user schema, so even if Spark wanted to forward one it couldn't. The user's.schema(...)call is therefore always a misconfiguration.The rest of
DataStreamReaderalready surfaces this kind of misconfiguration as a clear error:.load()goes throughDataSourceV2Utils.getTableFromProvider, which throws_LEGACY_ERROR_TEMP_2242("<provider>source does not support user-specified schema") when the provider does not implementsupportsExternalMetadata()..changes()explicitly callsassertNoSpecifiedSchema("changes")and throws_LEGACY_ERROR_TEMP_1189("User specified schema not supported withchanges.")..table()is the odd one out: same invalid configuration, no error. Users can writereadStream.schema(s).table(name), see a working query, and reasonably assumeshad an effect — when in fact the resulting stream uses the catalog schema andswas dropped. Surfacing this as a clear error aligns.table()with the existing behavior of.load()and.changes().Does this PR introduce any user-facing change?
No
How was this patch tested?
Added
DataStreamTableAPISuitetest"read: user-specified schema is not allowed with table API".Was this patch authored or co-authored using generative AI tooling?
No