You Zhou created SPARK-56975:
--------------------------------
Summary: DataStreamReader.table() should reject user-specified
schema instead of silently ignoring it
Key: SPARK-56975
URL: https://issues.apache.org/jira/browse/SPARK-56975
Project: Spark
Issue Type: Improvement
Components: Structured Streaming
Affects Versions: 4.2.0
Reporter: You Zhou
Fix For: 4.2.0
`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()`.
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