jose-torres commented on code in PR #57584:
URL: https://github.com/apache/spark/pull/57584#discussion_r3663325069
##########
sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/graph/AutoCdcOutOfOrderConvergenceSuite.scala:
##########
@@ -197,11 +219,18 @@ class AutoCdcScd1OutOfOrderConvergenceSuite
runPipeline(outOfOrderCtx)
}
+ // Only the user-visible target must converge. The auxiliary tables
legitimately differ by
Review Comment:
This is fine under the assumption, which I think is correct but want to
confirm, that any auxiliary row generated at version N is guaranteed to stop
affecting results if the ingestion durably advances to some version M not too
much higher than N. (It would be a problem, to pick an exaggerated example, if
some category of auxiliary row caused different results starting at N + 1000;
then just checking consistency betwen the targets would not be enough to
confirm that the behavior is meaningfully the same.)
##########
sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/graph/AutoCdcScd2SinglePipelineSuite.scala:
##########
@@ -0,0 +1,191 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.pipelines.graph
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.execution.streaming.runtime.MemoryStream
+import org.apache.spark.sql.functions
+import org.apache.spark.sql.pipelines.autocdc.{
+ AutoCdcReservedNames,
+ ColumnSelection,
+ ScdType,
+ UnqualifiedColumnName
+}
+import org.apache.spark.sql.pipelines.utils.{ExecutionTest,
TestGraphRegistrationContext}
+import org.apache.spark.sql.test.SharedSparkSession
+
+/**
+ * End-to-end smoke tests for AutoCDC SCD Type 2 flows running within a single
pipeline: one
+ * [[DataflowGraph]] / [[TestPipelineUpdateContext]] executes an SCD2 AutoCDC
flow through the
+ * [[Scd2MergeStreamingWrite]] streaming write, and both the target table and
the auxiliary
+ * table contents are asserted at the end.
+ *
+ * This exercises the full wiring landed for SCD2: the flow planner routing an
SCD2
+ * [[AutoCdcMergeFlow]] to [[Scd2MergeStreamingWrite]], the auxiliary-table
materialization, and
+ * the [[org.apache.spark.sql.pipelines.autocdc.Scd2ForeachBatchHandler]]
reconciliation.
+ */
+class AutoCdcScd2SinglePipelineSuite
+ extends ExecutionTest
+ with SharedSparkSession
+ with AutoCdcGraphExecutionTestMixin {
+
+ /** The SCD2 target's `_cdc_metadata` struct value for a given
recordStartAt. */
+ private def scd2Meta(recordStartAt: Long): Row = Row(recordStartAt)
+
+ /**
+ * DDL for an SCD2 target table with user columns `(id, name, version)` plus
the framework
+ * columns `__START_AT` / `__END_AT` (sequencing type BIGINT) and the SCD2
`_cdc_metadata`
+ * struct. `version` is the sequencing column and, unless excluded via a
column selection, is
+ * retained as an ordinary user column in the target.
+ */
+ private def createScd2Target(table: String): Unit = {
+ val meta = AutoCdcReservedNames.cdcMetadataColName
+ spark.sql(
+ s"CREATE TABLE $table (" +
+ "id INT NOT NULL, name STRING, version BIGINT NOT NULL, " +
+ "__START_AT BIGINT, __END_AT BIGINT, " +
+ s"$meta STRUCT<__RECORD_START_AT:BIGINT> NOT NULL)"
+ )
+ }
+
+ test("SCD2: an upsert lands an open current record in an empty target
table") {
Review Comment:
I agree with this analysis. I trust the principle that the results shouldn't
be ingestion order dependent more than I trust my ability to read through a big
pile of specific rows and intuit the correct result.
##########
sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/graph/AutoCdcOutOfOrderConvergenceSuite.scala:
##########
@@ -140,20 +146,33 @@ class AutoCdcScd1OutOfOrderConvergenceSuite
deleteCondition = Some(functions.col(isDeleteColumn) === true),
columnSelection = Some(ColumnSelection.ExcludeColumns(
Seq(UnqualifiedColumnName(isDeleteColumn))
- ))
+ )),
+ scdType = scdType
))
}
}
- private def createTargetTable(targetTable: String): Unit = {
+ /**
+ * DDL fragment for the SCD-type-specific reserved columns a target table
carries after the
+ * user-selected data columns: just the CDC metadata column for SCD1, plus
the __START_AT /
+ * __END_AT interval bounds for SCD2. The sequencing type is BIGINT here.
+ */
+ private def reservedColumnsDdl(scdType: ScdType): String = scdType match {
Review Comment:
I ultimately decided I don't care that SCD1 and SCD2 disagree on the
encapsulation boundary for the DDL, but I'm leaving this comment as a record of
the train of thought in case it jumps out at anyone.
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