AnishMahto commented on code in PR #52119:
URL: https://github.com/apache/spark/pull/52119#discussion_r2299913607
##########
sql/connect/server/src/test/scala/org/apache/spark/sql/connect/pipelines/PythonPipelineSuite.scala:
##########
@@ -434,6 +434,34 @@ class PythonPipelineSuite
.map(_.identifier) == Seq(graphIdentifier("a"),
graphIdentifier("something")))
}
+ test("MV/ST with partition columns works") {
+ val graph = buildGraph("""
+ |from pyspark.sql.functions import col
+ |
+ |@dp.materialized_view(partition_cols = ["id_mod"])
+ |def mv():
+ | return spark.range(5).withColumn("id_mod", col("id") % 2)
+ |
+ |@dp.table(partition_cols = ["id_mod"])
+ |def st():
+ | return spark.readStream.table("mv")
+ |""".stripMargin)
+
+ val updateContext = new PipelineUpdateContextImpl(graph, eventCallback = _
=> ())
+ updateContext.pipelineExecution.runPipeline()
+ updateContext.pipelineExecution.awaitCompletion()
Review Comment:
nit: use `PipelineTest.startPipelineAndWaitForCompletion`
##########
sql/connect/server/src/test/scala/org/apache/spark/sql/connect/pipelines/PythonPipelineSuite.scala:
##########
@@ -434,6 +434,34 @@ class PythonPipelineSuite
.map(_.identifier) == Seq(graphIdentifier("a"),
graphIdentifier("something")))
}
+ test("MV/ST with partition columns works") {
+ val graph = buildGraph("""
+ |from pyspark.sql.functions import col
+ |
+ |@dp.materialized_view(partition_cols = ["id_mod"])
+ |def mv():
+ | return spark.range(5).withColumn("id_mod", col("id") % 2)
+ |
+ |@dp.table(partition_cols = ["id_mod"])
+ |def st():
+ | return spark.readStream.table("mv")
+ |""".stripMargin)
+
+ val updateContext = new PipelineUpdateContextImpl(graph, eventCallback = _
=> ())
+ updateContext.pipelineExecution.runPipeline()
+ updateContext.pipelineExecution.awaitCompletion()
+
+ // check table is created with correct partitioning
+ Seq("mv", "st").foreach { tableName =>
+ val table =
spark.sessionState.catalog.getTableMetadata(graphIdentifier(tableName))
+ assert(table.partitionColumnNames == Seq("id_mod"))
+
+ val rows = spark.table(tableName).collect().map(r => (r.getLong(0),
r.getLong(1))).toSet
+ val expected = (0 until 5).map(id => (id.toLong, (id % 2).toLong)).toSet
+ assert(rows == expected)
Review Comment:
nit: use `checkAnswer`
##########
sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/graph/SqlPipelineSuite.scala:
##########
@@ -266,6 +266,45 @@ class SqlPipelineSuite extends PipelineTest with
SharedSparkSession {
)
}
+ test("MV/ST with partition columns works") {
+ val unresolvedDataflowGraph = unresolvedDataflowGraphFromSql(
+ sqlText = """
+ |CREATE MATERIALIZED VIEW mv
+ |PARTITIONED BY (id_mod)
+ |AS
+ |SELECT
+ | id,
+ | id % 2 AS id_mod
+ |FROM range(3);
+ |
+ |CREATE STREAMING TABLE st
+ |PARTITIONED BY (id_mod)
+ |AS
+ |SELECT * FROM STREAM(mv);
+ |""".stripMargin
+ )
+ startPipelineAndWaitForCompletion(unresolvedDataflowGraph)
+ val expected = Seq(
+ Row(0, 0),
+ Row(1, 1),
+ Row(2, 0)
+ )
+
+ Seq("mv", "st").foreach { tableName =>
+ // check table partition columns
+ val tableMeta = spark.sessionState.catalog.getTableMetadata(
+ fullyQualifiedIdentifier(tableName)
+ )
+ assert(tableMeta.partitionColumnNames == Seq("id_mod"))
Review Comment:
Same nit as above, use DSv2 if possible
##########
sql/connect/server/src/test/scala/org/apache/spark/sql/connect/pipelines/PythonPipelineSuite.scala:
##########
@@ -434,6 +434,34 @@ class PythonPipelineSuite
.map(_.identifier) == Seq(graphIdentifier("a"),
graphIdentifier("something")))
}
+ test("MV/ST with partition columns works") {
+ val graph = buildGraph("""
+ |from pyspark.sql.functions import col
+ |
+ |@dp.materialized_view(partition_cols = ["id_mod"])
+ |def mv():
+ | return spark.range(5).withColumn("id_mod", col("id") % 2)
+ |
+ |@dp.table(partition_cols = ["id_mod"])
+ |def st():
+ | return spark.readStream.table("mv")
+ |""".stripMargin)
+
+ val updateContext = new PipelineUpdateContextImpl(graph, eventCallback = _
=> ())
+ updateContext.pipelineExecution.runPipeline()
+ updateContext.pipelineExecution.awaitCompletion()
+
+ // check table is created with correct partitioning
+ Seq("mv", "st").foreach { tableName =>
+ val table =
spark.sessionState.catalog.getTableMetadata(graphIdentifier(tableName))
Review Comment:
nit: lets use DSv2 API, i.e `spark.sessionState.catalogManager`
##########
sql/connect/server/src/test/scala/org/apache/spark/sql/connect/pipelines/PythonPipelineSuite.scala:
##########
@@ -434,6 +434,34 @@ class PythonPipelineSuite
.map(_.identifier) == Seq(graphIdentifier("a"),
graphIdentifier("something")))
}
+ test("MV/ST with partition columns works") {
Review Comment:
Should we also add a test for what happens when a user attempts to change
partition columns between pipeline runs?
I'd expect that to either trigger a full refresh or throw an exception.
Either are probably acceptable, but it'd be nice for this behavior to be
tested.
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