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new 3e40e71246ca Revert "[SPARK-56975][SS] Reject user-specified schema in
DataStreamReader.table()"
3e40e71246ca is described below
commit 3e40e71246caddcecbb9ce1c3cdf444a8c1f30b9
Author: You Zhou <[email protected]>
AuthorDate: Fri May 29 17:01:03 2026 -0700
Revert "[SPARK-56975][SS] Reject user-specified schema in
DataStreamReader.table()"
### What changes were proposed in this pull request?
This reverts commit `05b4d81f3f938ff140886d6f66ad66d08c66d5b2`
(SPARK-56975), which made `DataStreamReader.table()` reject a user-specified
schema by calling `assertNoSpecifiedSchema("table")`. This restores the
previous behavior, where a user-specified schema passed before `.table()` is
accepted (and ignored).
### Why are the changes needed?
SPARK-56975 is a behavior-breaking change. Code that previously ran
successfully — e.g. `spark.readStream.schema(s).table(name)` — now throws an
`AnalysisException` (`_LEGACY_ERROR_TEMP_1189`). While a schema has no effect
on `.table()`, rejecting it outright breaks existing user workloads that set a
schema on the `DataStreamReader` before calling `.table()`.
A user-facing behavior change like this must go through the project's
breaking-change process, which was not followed for SPARK-56975. We are
reverting it to restore backward compatibility; a proper deprecation path can
be pursued separately if the stricter behavior is still desired.
### Does this PR introduce _any_ user-facing change?
Yes. It restores the pre-SPARK-56975 behavior: `DataStreamReader.table()`
again accepts (and silently ignores) a user-specified schema instead of
throwing `AnalysisException` (`_LEGACY_ERROR_TEMP_1189`). Since SPARK-56975
only landed in unreleased branches (`master` and `branch-4.2`), there is no
change relative to any released Spark version.
### How was this patch tested?
This is a straight `git revert`. Existing `DataStreamTableAPISuite` tests
pass; the test added by SPARK-56975 (`"read: user-specified schema is not
allowed with table API"`) is removed as part of the revert.
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes #56189 from PorridgeSwim/revert-SPARK-56975.
Lead-authored-by: You Zhou <[email protected]>
Co-authored-by: You Zhou <[email protected]>
Signed-off-by: Anish Shrigondekar <[email protected]>
(cherry picked from commit 6039af85e961aa6c3a361ed63665f3f8d3878f10)
Signed-off-by: Anish Shrigondekar <[email protected]>
---
.../org/apache/spark/sql/classic/DataStreamReader.scala | 1 -
.../sql/streaming/test/DataStreamTableAPISuite.scala | 16 ----------------
2 files changed, 17 deletions(-)
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/classic/DataStreamReader.scala
b/sql/core/src/main/scala/org/apache/spark/sql/classic/DataStreamReader.scala
index c8df93768808..eb3120cac05a 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/classic/DataStreamReader.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/classic/DataStreamReader.scala
@@ -102,7 +102,6 @@ final class DataStreamReader private[sql](sparkSession:
SparkSession)
/** @inheritdoc */
def table(tableName: String): DataFrame = {
require(tableName != null, "The table name can't be null")
- assertNoSpecifiedSchema("table")
val identifier =
sparkSession.sessionState.sqlParser.parseMultipartIdentifier(tableName)
val unresolved = UnresolvedRelation(
identifier,
diff --git
a/sql/core/src/test/scala/org/apache/spark/sql/streaming/test/DataStreamTableAPISuite.scala
b/sql/core/src/test/scala/org/apache/spark/sql/streaming/test/DataStreamTableAPISuite.scala
index 3930beec084d..e2c74533e7f3 100644
---
a/sql/core/src/test/scala/org/apache/spark/sql/streaming/test/DataStreamTableAPISuite.scala
+++
b/sql/core/src/test/scala/org/apache/spark/sql/streaming/test/DataStreamTableAPISuite.scala
@@ -84,22 +84,6 @@ class DataStreamTableAPISuite extends StreamTest with
BeforeAndAfter {
checkErrorTableNotFound(e, "`non_exist_table`")
}
- test("read: user-specified schema is not allowed with table API") {
- val tblName = "my_table"
- withTable(tblName) {
- spark.range(3).write.format("parquet").saveAsTable(tblName)
- val e = intercept[AnalysisException] {
- spark.readStream
- .schema(new StructType().add("a", IntegerType))
- .table(tblName)
- }
- checkError(
- exception = e,
- condition = "_LEGACY_ERROR_TEMP_1189",
- parameters = Map("operation" -> "table"))
- }
- }
-
test("read: stream table API with temp view") {
val tblName = "my_table"
val stream = MemoryStream[Int]
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