anew commented on code in PR #57669: URL: https://github.com/apache/spark/pull/57669#discussion_r3692483628
########## sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/graph/AutoCdcScd2ColumnEvolutionSuite.scala: ########## @@ -0,0 +1,278 @@ +/* + * 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.classic.DataFrame +import org.apache.spark.sql.execution.streaming.runtime.MemoryStream +import org.apache.spark.sql.functions +import org.apache.spark.sql.pipelines.autocdc.{ + ChangeArgs, + ColumnSelection, + ScdType, + UnqualifiedColumnName +} +import org.apache.spark.sql.pipelines.utils.{ExecutionTest, TestGraphRegistrationContext} +import org.apache.spark.sql.test.SharedSparkSession + +/** + * End-to-end tests for SCD Type 2 AutoCDC column-schema evolution across runs: a microbatch that + * is narrower than the already-evolved target (a source column dropped, a nested struct/array field + * dropped, or the `COLUMNS` selection narrowed) must reconcile correctly instead of failing the + * internal union. + * + * These exercise the fix for SPARK-58418. Before it, `Scd2ForeachBatchHandler.reconcileMicrobatch` + * unioned the microbatch with the affected target/aux rows without `allowMissingColumns`, so a + * narrower microbatch failed with NUM_COLUMNS_MISMATCH (top-level) or INCOMPATIBLE_COLUMN_TYPE + * (nested). The contract asserted here is additive-tolerant, matching SCD1: records already written + * keep their values for the no-longer-emitted column, and only records opened by the narrower + * microbatch carry null for it. + * + * Changing the effective *tracked-history* column set is a distinct, separately-scoped concern + * (SPARK-58452 / SPARK-58391) and is deliberately not exercised here: every scenario keeps the + * effective tracked set unchanged across runs, so the only thing evolving is the set of user + * columns the flow emits. The column-selection test therefore drops a column that is *not* in the + * tracked set (an explicit `TRACK HISTORY ON (name)` flow dropping the non-tracked `email`), so it + * stays valid once the track-history drift guard (SPARK-58391) lands. + */ +class AutoCdcScd2ColumnEvolutionSuite + 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) + + /** + * Build a single-flow SCD2 pipeline that tracks history on exactly `trackColumns` (an explicit + * `TRACK HISTORY ON (...)`), so a column outside that set can be dropped without changing the + * tracked set. The mixin's `singleAutoCdcFlowPipeline` does not expose a track-history knob, so + * the flow is built inline here. + */ + private def scd2FlowTracking( + sourceDf: DataFrame, + keys: Seq[String], + trackColumns: Seq[String]): TestGraphRegistrationContext = + new TestGraphRegistrationContext(spark) { + registerTable("target", catalog = Some(catalog), database = Some(namespace)) + registerFlow(AutoCdcFlow( + identifier = fullyQualifiedIdentifier("auto_cdc_flow", Some(catalog), Some(namespace)), + destinationIdentifier = + fullyQualifiedIdentifier("target", Some(catalog), Some(namespace)), + func = dfFlowFunc(sourceDf), + queryContext = + QueryContext(currentCatalog = Some(catalog), currentDatabase = Some(namespace)), + origin = QueryOrigin.empty, + changeArgs = ChangeArgs( + keys = keys.map(UnqualifiedColumnName(_)), + sequencing = functions.col("version"), + storedAsScdType = ScdType.Type2, + trackHistorySelection = Some(ColumnSelection.IncludeColumns( + trackColumns.map(UnqualifiedColumnName(_))))))) + } + + test("a source column dropped between runs is preserved on existing records and null on new " + + "ones") { + val session = spark + import session.implicits._ + + spark.sql( + s"CREATE TABLE $catalog.$namespace.target " + + s"(id INT NOT NULL, name STRING, email STRING, version BIGINT NOT NULL, $scd2MetadataDdl)" + ) + + // Shared stream; run #2 projects `email` away so the microbatch is narrower than the target. + // Track history on `name` only, so the dropped `email` is a non-tracked column and dropping it + // is column-schema narrowing rather than a tracked-set change (robust once SPARK-58391 lands). + val stream = MemoryStream[(Int, String, String, Long)] + def buildCtx(includeEmail: Boolean): TestGraphRegistrationContext = { + val df = stream.toDF().toDF("id", "name", "email", "version") + scd2FlowTracking( + sourceDf = if (includeEmail) df else df.drop("email"), + keys = Seq("id"), + trackColumns = Seq("name")) + } + + // Run #1 (wide): key=1 opens a record carrying email=a@x. + stream.addData((1, "alice", "a@x", 1L)) + runPipeline(buildCtx(includeEmail = true)) + checkAnswer( + spark.table(s"$catalog.$namespace.target"), + Seq(Row(1, "alice", "a@x", 1L, 1L, null, scd2Meta(1L))) + ) + + // Run #2 (narrow): update key=1 (closes its record) + insert key=2. The dropped `email` is + // preserved on key=1's now-closed record and is null on the newly-opened records. + stream.addData((1, "alice2", "ignored", 2L), (2, "bob", "ignored", 1L)) + runPipeline(buildCtx(includeEmail = false)) + checkAnswer( + spark.table(s"$catalog.$namespace.target"), + Seq( + Row(1, "alice", "a@x", 1L, 1L, 2L, scd2Meta(1L)), + Row(1, "alice2", null, 2L, 2L, null, scd2Meta(2L)), + Row(2, "bob", null, 1L, 1L, null, scd2Meta(1L)) + ) + ) + } + + test("dropping a non-tracked column from the COLUMNS selection preserves it on existing " + + "records and leaves it null on new ones") { + val session = spark + import session.implicits._ + + spark.sql( + s"CREATE TABLE $catalog.$namespace.target " + + s"(id INT NOT NULL, name STRING, email STRING, version BIGINT NOT NULL, $scd2MetadataDdl)" + ) + + // The flow tracks history on `name` only, so `email` is a selected-but-not-tracked column. + // Dropping `email` from the selection is therefore pure column-schema narrowing: the effective + // tracked set ({name}) is unchanged, so this stays column evolution rather than a tracked-set + // change even once the track-history drift guard (SPARK-58391) lands. + val stream = MemoryStream[(Int, String, String, Long)] + def buildCtx(includeEmail: Boolean): TestGraphRegistrationContext = { + val df = stream.toDF().toDF("id", "name", "email", "version") + scd2FlowTracking( + sourceDf = if (includeEmail) df else df.drop("email"), Review Comment: this was addressed already based on a previous comment. 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