diff --git a/common/utils/src/main/resources/error/error-conditions.json b/common/utils/src/main/resources/error/error-conditions.json index 889ecf9f7b08a..8536c6385f2bd 100644 --- a/common/utils/src/main/resources/error/error-conditions.json +++ b/common/utils/src/main/resources/error/error-conditions.json @@ -191,6 +191,24 @@ ], "sqlState" : "0A000" }, + "AUTOCDC_COLUMNS_NOT_FOUND_IN_SCHEMA" : { + "message" : [ + "Using column name comparison, the following columns are not present in the schema: . Available columns: ." + ], + "sqlState" : "42703" + }, + "AUTOCDC_MULTIPART_COLUMN_IDENTIFIER" : { + "message" : [ + "Expected a single column identifier; got the multi-part identifier (parts: )." + ], + "sqlState" : "42703" + }, + "AUTOCDC_RESERVED_COLUMN_NAME_CONFLICT" : { + "message" : [ + "Using column name comparison, the column `` in the schema conflicts with the reserved AutoCDC column name ``. Rename or remove the column." + ], + "sqlState" : "42710" + }, "AVRO_CANNOT_WRITE_NULL_FIELD" : { "message" : [ "Cannot write null value for field defined as non-null Avro data type .", diff --git a/sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/autocdc/ChangeArgs.scala b/sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/autocdc/ChangeArgs.scala new file mode 100644 index 0000000000000..5774781b8ab9f --- /dev/null +++ b/sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/autocdc/ChangeArgs.scala @@ -0,0 +1,159 @@ +/* + * 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.autocdc + +import org.apache.spark.sql.{AnalysisException, Column} +import org.apache.spark.sql.catalyst.parser.CatalystSqlParser +import org.apache.spark.sql.catalyst.util.QuotingUtils +import org.apache.spark.sql.types.StructType + +/** + * A single, unqualified column identifier (no nested path or table/alias qualifier). Backticks + * are consumed: "`a.b`" is stored as "a.b" in [[name]]. Use [[name]] for direct schema-fieldName + * comparison and [[quoted]] for APIs that re-parse identifier strings. + */ +case class UnqualifiedColumnName private (name: String) { + def quoted: String = QuotingUtils.quoteIdentifier(name) +} + +object UnqualifiedColumnName { + def apply(input: String): UnqualifiedColumnName = { + val nameParts = CatalystSqlParser.parseMultipartIdentifier(input) + if (nameParts.length != 1) { + throw multipartColumnIdentifierError(input, nameParts) + } + new UnqualifiedColumnName(nameParts.head) + } + + private def multipartColumnIdentifierError( + columnName: String, + nameParts: Seq[String] + ): AnalysisException = + new AnalysisException( + errorClass = "AUTOCDC_MULTIPART_COLUMN_IDENTIFIER", + messageParameters = Map( + "columnName" -> columnName, + "nameParts" -> nameParts.mkString(", ") + ) + ) +} + +sealed trait ColumnSelection +object ColumnSelection { + + case class IncludeColumns(columns: Seq[UnqualifiedColumnName]) extends ColumnSelection + case class ExcludeColumns(columns: Seq[UnqualifiedColumnName]) + extends ColumnSelection + + /** + * Applies [[ColumnSelection]] to a [[StructType]] and returns the filtered schema. Field order + * follows the original schema; only matching fields are retained in the returned schema. + * + * @param schemaName Logical name of the schema being filtered, surfaced in error messages + * when columns are not found (e.g. "microbatch", "target"). + * @param schema The schema to filter. + * @param columnSelection The user-provided selection. `None` is a no-op and returns `schema` + * unchanged. + * @param caseSensitive Whether to match column names case-sensitively against the schema. + * Callers should derive this from the session, e.g. + * `session.sessionState.conf.caseSensitiveAnalysis`, so column matching + * stays consistent with `spark.sql.caseSensitive`. + */ + def applyToSchema( + schemaName: String, + schema: StructType, + columnSelection: Option[ColumnSelection], + caseSensitive: Boolean): StructType = columnSelection match { + case None => + // A None column selection is interpreted as a no-op. + schema + case Some(IncludeColumns(cols)) => + val keepIndices = lookupFieldIndices(schemaName, schema, cols, caseSensitive) + StructType(schema.fields.zipWithIndex.collect { + case (field, idx) if keepIndices.contains(idx) => field + }) + case Some(ExcludeColumns(cols)) => + val dropIndices = lookupFieldIndices(schemaName, schema, cols, caseSensitive) + StructType(schema.fields.zipWithIndex.collect { + case (field, idx) if !dropIndices.contains(idx) => field + }) + } + + private def lookupFieldIndices( + schemaName: String, + schema: StructType, + fields: Seq[UnqualifiedColumnName], + caseSensitive: Boolean): Set[Int] = { + val caseAwareGetFieldIndex: String => Option[Int] = + if (caseSensitive) schema.getFieldIndex else schema.getFieldIndexCaseInsensitive + + val fieldIndexResolutions = fields.map(f => f -> caseAwareGetFieldIndex(f.name)) + val missingFieldNames = fieldIndexResolutions.collect { case (f, None) => f.name }.distinct + if (missingFieldNames.nonEmpty) { + throw new AnalysisException( + errorClass = "AUTOCDC_COLUMNS_NOT_FOUND_IN_SCHEMA", + messageParameters = Map( + "caseSensitivity" -> CaseSensitivityLabels.of(caseSensitive), + "schemaName" -> schemaName, + "missingColumns" -> missingFieldNames.mkString(", "), + "availableColumns" -> schema.fieldNames.mkString(", ") + ) + ) + } + fieldIndexResolutions.flatMap { case (_, idx) => idx }.toSet + } +} + +/** User-facing case-sensitivity labels surfaced in AutoCDC error messages. */ +private[autocdc] object CaseSensitivityLabels { + val CaseSensitive: String = "case-sensitive" + val CaseInsensitive: String = "case-insensitive" + + def of(caseSensitive: Boolean): String = + if (caseSensitive) CaseSensitive else CaseInsensitive +} + +/** The SCD (Slowly Changing Dimension) strategy for a CDC flow. */ +sealed trait ScdType + +object ScdType { + /** Representation for the standard SCD1 strategy. */ + case object Type1 extends ScdType + /** Representation for the standard SCD2 strategy. */ + case object Type2 extends ScdType +} + +/** + * Configuration for an AutoCDC flow. + * + * @param keys The column(s) that uniquely identify a row in the source data. + * @param sequencing Expression ordering CDC events to correctly resolve out-of-order + * arrivals. Must be a sortable type. + * @param deleteCondition Expression that marks a source row as a DELETE. When None, all + * rows are treated as upserts. + * @param storedAsScdType The SCD strategy these args should be applied to. + * @param columnSelection Which source columns to select in the target table. None means + * all columns. + */ +case class ChangeArgs( + keys: Seq[UnqualifiedColumnName], + sequencing: Column, + storedAsScdType: ScdType, + deleteCondition: Option[Column] = None, + columnSelection: Option[ColumnSelection] = None +) diff --git a/sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/autocdc/Scd1BatchProcessor.scala b/sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/autocdc/Scd1BatchProcessor.scala new file mode 100644 index 0000000000000..5554efdafd68c --- /dev/null +++ b/sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/autocdc/Scd1BatchProcessor.scala @@ -0,0 +1,175 @@ +/* + * 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.autocdc + +import org.apache.spark.SparkException +import org.apache.spark.sql.{functions => F, AnalysisException} +import org.apache.spark.sql.Column +import org.apache.spark.sql.catalyst.util.QuotingUtils +import org.apache.spark.sql.classic.DataFrame +import org.apache.spark.sql.types.{DataType, StructField, StructType} +import org.apache.spark.util.ArrayImplicits._ + +/** + * Per-microbatch processor for SCD Type 1 AutoCDC flows, complying to the specified [[changeArgs]] + * configuration. + * + * @param changeArgs The CDC flow configuration. + * @param resolvedSequencingType The post-analysis [[DataType]] of the sequencing column, derived + * from the flow's resolved DataFrame at flow setup time. + */ +case class Scd1BatchProcessor( + changeArgs: ChangeArgs, + resolvedSequencingType: DataType) { + + /** + * Deduplicate the incoming CDC microbatch by key, keeping the most recent event per key + * as ordered by [[ChangeArgs.sequencing]]. + * + * For SCD1 we only care about the most recent (by sequence value) event per key. When + * multiple events share the same key and the same sequence value, the row selected is + * non-deterministic and undefined. + * + * @param validatedMicrobatch A microbatch that has already been validated such that the + * sequencing column should not contain null values, and its data type + * should support ordering. + * + * The schema of the returned dataframe matches the schema of the microbatch exactly. + */ + def deduplicateMicrobatch(validatedMicrobatch: DataFrame): DataFrame = { + // The `max_by` API can only return a single column, so pack/unpack the entire row into a + // temporary column before and after the `max_by` operation. + val winningRowCol = Scd1BatchProcessor.winningRowColName + + val allMicrobatchColumns = + validatedMicrobatch.columns + .map(colName => F.col(QuotingUtils.quoteIdentifier(colName))) + .toImmutableArraySeq + + validatedMicrobatch + .groupBy(changeArgs.keys.map(k => F.col(k.quoted)): _*) + .agg( + F.max_by(F.struct(allMicrobatchColumns: _*), changeArgs.sequencing) + .as(winningRowCol) + ) + .select(F.col(s"$winningRowCol.*")) + } + + /** + * Project the CDC metadata column onto the microbatch. + * + * This must run before any column selection is applied to the microbatch. The + * [[ChangeArgs.deleteCondition]] and [[ChangeArgs.sequencing]] expressions are evaluated against + * the current microbatch schema, and column selection may drop inputs required by those + * expressions. + * + * Rows are classified as deletes only when [[ChangeArgs.deleteCondition]] evaluates to true. A + * false or null delete condition classifies the row as an upsert. + * + * The returned dataframe has all of the columns in the input microbatch + the CDC metadata + * column. + */ + def extendMicrobatchRowsWithCdcMetadata(microbatchDf: DataFrame): DataFrame = { + // Proactively validate the reserved CDC metadata column does not exist in the microbatch. + validateCdcMetadataColumnNotPresent(microbatchDf) + + val rowDeleteSequence: Column = changeArgs.deleteCondition match { + case Some(deleteCondition) => + F.when(deleteCondition, changeArgs.sequencing).otherwise(F.lit(null)) + case None => + F.lit(null) + } + + val rowUpsertSequence: Column = + // A row that is not a delete must be an upsert, these are mutually exclusive and a complete + // set of CDC event types. + F.when(rowDeleteSequence.isNull, changeArgs.sequencing).otherwise(F.lit(null)) + + microbatchDf.withColumn( + Scd1BatchProcessor.cdcMetadataColName, + Scd1BatchProcessor.constructCdcMetadataCol( + deleteSequence = rowDeleteSequence, + upsertSequence = rowUpsertSequence, + sequencingType = resolvedSequencingType + ) + ) + } + + private def validateCdcMetadataColumnNotPresent(microbatchDf: DataFrame): Unit = { + val microbatchSqlConf = microbatchDf.sparkSession.sessionState.conf + val resolver = microbatchSqlConf.resolver + + microbatchDf.schema.fieldNames + .find(resolver(_, Scd1BatchProcessor.cdcMetadataColName)) + .foreach { conflictingColumnName => + throw new AnalysisException( + errorClass = "AUTOCDC_RESERVED_COLUMN_NAME_CONFLICT", + messageParameters = Map( + "caseSensitivity" -> CaseSensitivityLabels.of(microbatchSqlConf.caseSensitiveAnalysis), + "columnName" -> conflictingColumnName, + "schemaName" -> "microbatch", + "reservedColumnName" -> Scd1BatchProcessor.cdcMetadataColName + ) + ) + } + } +} + +object Scd1BatchProcessor { + // Columns prefixed with `__spark_autocdc_` are reserved for internal SDP AutoCDC processing. + private[autocdc] val winningRowColName: String = "__spark_autocdc_winning_row" + private[autocdc] val cdcMetadataColName: String = "__spark_autocdc_metadata" + + private[autocdc] val cdcDeleteSequenceFieldName: String = "deleteSequence" + private[autocdc] val cdcUpsertSequenceFieldName: String = "upsertSequence" + + /** + * Schema of the CDC metadata struct column for SCD1. + */ + private def cdcMetadataColSchema(sequencingType: DataType): StructType = + StructType( + Seq( + // The sequencing of the event if it represents a delete, null otherwise. + StructField(cdcDeleteSequenceFieldName, sequencingType, nullable = true), + // The sequencing of the event if it represents an upsert, null otherwise. + StructField(cdcUpsertSequenceFieldName, sequencingType, nullable = true) + ) + ) + + /** + * Construct the CDC metadata struct column for SCD1, following the exact schema and field + * ordering defined by [[cdcMetadataColSchema]]. + */ + private[autocdc] def constructCdcMetadataCol( + deleteSequence: Column, + upsertSequence: Column, + sequencingType: DataType): Column = { + val cdcMetadataFieldsInOrder = cdcMetadataColSchema(sequencingType).fields.map { field => + val value = field.name match { + case `cdcDeleteSequenceFieldName` => deleteSequence + case `cdcUpsertSequenceFieldName` => upsertSequence + case other => + throw SparkException.internalError( + s"Unable to construct SCD1 CDC metadata column due to unknown `${other}` field." + ) + } + value.cast(field.dataType).as(field.name) + } + F.struct(cdcMetadataFieldsInOrder.toImmutableArraySeq: _*) + } +} diff --git a/sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/autocdc/ChangeArgsSuite.scala b/sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/autocdc/ChangeArgsSuite.scala new file mode 100644 index 0000000000000..816338cb677e8 --- /dev/null +++ b/sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/autocdc/ChangeArgsSuite.scala @@ -0,0 +1,374 @@ +/* + * 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.autocdc + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.{functions => F, AnalysisException, Row} +import org.apache.spark.sql.catalyst.parser.ParseException +import org.apache.spark.sql.test.SharedSparkSession +import org.apache.spark.sql.types.{IntegerType, StringType, StructType} + +class ChangeArgsSuite extends SparkFunSuite with SharedSparkSession { + + private val sourceSchema = new StructType() + .add("id", IntegerType, nullable = false) + .add("Name", StringType) + .add("age", IntegerType) + + test("ColumnSelection None leaves schema unchanged") { + assert( + ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + columnSelection = None, + caseSensitive = true + ) == sourceSchema) + } + + test("ColumnSelection IncludeColumns(Seq()) returns an empty schema") { + // An explicit empty include-list is semantically distinct from None: it means "select + // no columns" and produces an empty StructType, not the original schema. + assert( + ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + columnSelection = Some(ColumnSelection.IncludeColumns(Seq.empty)), + caseSensitive = true + ) == new StructType()) + } + + test("ColumnSelection ExcludeColumns(Seq()) leaves schema unchanged") { + // An empty exclude-list is a no-op: nothing to remove, so the original schema is + // returned unchanged (same observable behavior as None for this case). + assert( + ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + columnSelection = Some(ColumnSelection.ExcludeColumns(Seq.empty)), + caseSensitive = true + ) == sourceSchema) + } + + test("ColumnSelection IncludeColumns filters by exact name in schema order") { + val filteredSchema = ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + columnSelection = Some( + ColumnSelection.IncludeColumns( + Seq(UnqualifiedColumnName("age"), UnqualifiedColumnName("Name")) + ) + ), + caseSensitive = true + ) + + assert(filteredSchema == new StructType() + .add("Name", StringType) + .add("age", IntegerType)) + } + + test("ColumnSelection ExcludeColumns filters by exact name") { + val filteredSchema = ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + columnSelection = Some( + ColumnSelection.ExcludeColumns(Seq(UnqualifiedColumnName("id"))) + ), + caseSensitive = true + ) + + assert(filteredSchema == new StructType() + .add("Name", StringType) + .add("age", IntegerType)) + } + + test("ColumnSelection IncludeColumns fails for columns not present in schema") { + checkError( + exception = intercept[AnalysisException] { + ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + // Under caseSensitive = true, "name" will not match the schema field "Name". + columnSelection = Some( + ColumnSelection.IncludeColumns( + Seq(UnqualifiedColumnName("name"), UnqualifiedColumnName("missing")) + ) + ), + caseSensitive = true + ) + }, + condition = "AUTOCDC_COLUMNS_NOT_FOUND_IN_SCHEMA", + sqlState = "42703", + parameters = Map( + "caseSensitivity" -> CaseSensitivityLabels.CaseSensitive, + "schemaName" -> "test", + "missingColumns" -> "name, missing", + "availableColumns" -> "id, Name, age" + ) + ) + } + + test("ColumnSelection ExcludeColumns fails for columns not present in schema") { + checkError( + exception = intercept[AnalysisException] { + ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + // Under caseSensitive = true, "NAME" will not match the schema field "Name". + columnSelection = Some( + ColumnSelection.ExcludeColumns( + Seq(UnqualifiedColumnName("NAME"), UnqualifiedColumnName("missing")) + ) + ), + caseSensitive = true + ) + }, + condition = "AUTOCDC_COLUMNS_NOT_FOUND_IN_SCHEMA", + sqlState = "42703", + parameters = Map( + "caseSensitivity" -> CaseSensitivityLabels.CaseSensitive, + "schemaName" -> "test", + "missingColumns" -> "NAME, missing", + "availableColumns" -> "id, Name, age" + ) + ) + } + + test("ColumnSelection IncludeColumns matches case-insensitively under caseSensitive=false") { + // "NAME" and "AGE" do not exactly match the schema fields "Name" and "age", but + // caseSensitive = false folds both sides to lowercase before comparing. + val filteredSchema = ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + columnSelection = Some( + ColumnSelection.IncludeColumns( + Seq(UnqualifiedColumnName("AGE"), UnqualifiedColumnName("NAME")) + ) + ), + caseSensitive = false + ) + + // The retained fields keep their original casing from the schema, not the user's input. + assert(filteredSchema == new StructType() + .add("Name", StringType) + .add("age", IntegerType)) + } + + test("ColumnSelection deduplicates user-provided columns that normalize to the same name") { + // Under caseSensitive = false, "name" and "NAME" both fold to "name" and refer to the same + // schema field. The returned schema must include "Name" once, not twice. Output ordering + // and casing follow the schema, not the user's input. + val filteredSchema = ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + columnSelection = Some( + ColumnSelection.IncludeColumns( + Seq(UnqualifiedColumnName("name"), UnqualifiedColumnName("NAME")) + ) + ), + caseSensitive = false + ) + + assert(filteredSchema == new StructType().add("Name", StringType)) + } + + test("ColumnSelection ExcludeColumns matches case-insensitively under caseSensitive=false") { + val filteredSchema = ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + columnSelection = Some( + ColumnSelection.ExcludeColumns(Seq(UnqualifiedColumnName("name"))) + ), + caseSensitive = false + ) + + assert(filteredSchema == new StructType() + .add("id", IntegerType, nullable = false) + .add("age", IntegerType)) + } + + test("ColumnSelection missing-column error under caseSensitive=false preserves user casing") { + checkError( + exception = intercept[AnalysisException] { + ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + // "NAME" matches "Name" under caseSensitive=false, but "Missing" has no schema match. + // The error message reports the user's original casing for the missing column and + // the schema's original casing for the available columns. + columnSelection = Some( + ColumnSelection.IncludeColumns( + Seq(UnqualifiedColumnName("NAME"), UnqualifiedColumnName("Missing")) + ) + ), + caseSensitive = false + ) + }, + condition = "AUTOCDC_COLUMNS_NOT_FOUND_IN_SCHEMA", + sqlState = "42703", + parameters = Map( + "caseSensitivity" -> CaseSensitivityLabels.CaseInsensitive, + "schemaName" -> "test", + "missingColumns" -> "Missing", + "availableColumns" -> "id, Name, age" + ) + ) + } + + test("UnqualifiedColumnName accepts a simple single-part identifier") { + assert(UnqualifiedColumnName("col").name == "col") + // .quoted always wraps in back-ticks, even when the input had none. + assert(UnqualifiedColumnName("col").quoted == "`col`") + } + + test("UnqualifiedColumnName accepts a backtick-quoted name containing a literal dot") { + // Backticks make the dot part of a single name part, so this passes validation. The + // stored name is the parsed (unquoted) form so it matches the actual schema field name. + assert(UnqualifiedColumnName("`a.b`").name == "a.b") + // .quoted re-wraps the parsed name in back-ticks, round-tripping back to the input form. + assert(UnqualifiedColumnName("`a.b`").quoted == "`a.b`") + } + + test("UnqualifiedColumnName accepts redundant backticks around a single-part name") { + // Backticks around an already-single-part identifier are decorative; the parser strips them + // so the stored name has no surrounding back-ticks. + assert(UnqualifiedColumnName("`col`").name == "col") + // .quoted re-wraps the parsed name in back-ticks, round-tripping back to the input form. + assert(UnqualifiedColumnName("`col`").quoted == "`col`") + } + + test("UnqualifiedColumnName.quoted is safe to pass to functions.col for literal-dot names") { + val schema = new StructType() + .add("a.b", IntegerType) + .add("c", IntegerType) + + val df = spark.createDataFrame( + spark.sparkContext.parallelize(Seq(Row(1, 2), Row(3, 4))), + schema + ) + + val key = UnqualifiedColumnName("`a.b`") + + // Sanity-check: the unquoted `name` is not safe to pass to `functions.col`. The string is + // re-parsed and the literal dot is interpreted as a nested-field path separator, so the + // analyzer fails to resolve `a`.`b` against the available top-level columns. + checkError( + exception = intercept[AnalysisException] { + df.select(F.col(key.name)).collect() + }, + condition = "UNRESOLVED_COLUMN.WITH_SUGGESTION", + sqlState = "42703", + parameters = Map( + "objectName" -> "`a`.`b`", + "proposal" -> "`a.b`, `c`" + ), + context = ExpectedContext( + fragment = "col", + callSitePattern = "" + ) + ) + + // The `quoted` form wraps the name in back-ticks so the re-parser treats the whole thing + // as a single identifier, resolving to the top-level "a.b" column. + assert(df.select(F.col(key.quoted)).collect().toSeq == Seq(Row(1), Row(3))) + } + + test("IncludeColumns correctly matches a backtick-quoted literal-dot column") { + val schema = new StructType() + .add("a.b", IntegerType) + .add("c", StringType) + + // The user writes `a.b` to refer to the literal-dot column "a.b" in the schema. After + // construction, the [[UnqualifiedColumnName]] holds "a.b", which matches the field name + // exactly and the column is included in the filtered schema. + val filteredSchema = ColumnSelection.applyToSchema( + schemaName = "test", + schema = schema, + columnSelection = Some( + ColumnSelection.IncludeColumns(Seq(UnqualifiedColumnName("`a.b`"))) + ), + caseSensitive = true + ) + + assert(filteredSchema == new StructType().add("a.b", IntegerType)) + } + + test("IncludeColumns correctly matches a backtick-quoted mixed-case column") { + val filteredSchema = ColumnSelection.applyToSchema( + schemaName = "test", + schema = sourceSchema, + columnSelection = Some( + ColumnSelection.IncludeColumns(Seq(UnqualifiedColumnName("`Name`"))) + ), + caseSensitive = true + ) + + assert(filteredSchema == new StructType().add("Name", StringType)) + } + + test("UnqualifiedColumnName rejects a dotted (multi-part) identifier") { + checkError( + exception = intercept[AnalysisException] { + UnqualifiedColumnName("a.b") + }, + condition = "AUTOCDC_MULTIPART_COLUMN_IDENTIFIER", + sqlState = "42703", + parameters = Map( + "columnName" -> "a.b", + "nameParts" -> "a, b" + ) + ) + } + + test("UnqualifiedColumnName rejects a qualified column reference") { + checkError( + exception = intercept[AnalysisException] { + UnqualifiedColumnName("src.x") + }, + condition = "AUTOCDC_MULTIPART_COLUMN_IDENTIFIER", + sqlState = "42703", + parameters = Map( + "columnName" -> "src.x", + "nameParts" -> "src, x" + ) + ) + } + + test("UnqualifiedColumnName rejects an identifier with three or more parts") { + checkError( + exception = intercept[AnalysisException] { + UnqualifiedColumnName("a.b.c") + }, + condition = "AUTOCDC_MULTIPART_COLUMN_IDENTIFIER", + sqlState = "42703", + parameters = Map( + "columnName" -> "a.b.c", + "nameParts" -> "a, b, c" + ) + ) + } + + test("UnqualifiedColumnName lets a ParseException from the SQL parser propagate") { + checkError( + exception = intercept[ParseException] { + UnqualifiedColumnName("") + }, + condition = "PARSE_EMPTY_STATEMENT", + sqlState = Some("42617") + ) + } +} diff --git a/sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/autocdc/Scd1BatchProcessorSuite.scala b/sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/autocdc/Scd1BatchProcessorSuite.scala new file mode 100644 index 0000000000000..01d0fcc9c7107 --- /dev/null +++ b/sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/autocdc/Scd1BatchProcessorSuite.scala @@ -0,0 +1,645 @@ +/* + * 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.autocdc + +import org.apache.spark.sql.QueryTest +import org.apache.spark.sql.{functions => F, AnalysisException, Row} +import org.apache.spark.sql.classic.DataFrame +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.SharedSparkSession +import org.apache.spark.sql.types._ + +class Scd1BatchProcessorSuite extends QueryTest with SharedSparkSession { + + /** Build a microbatch [[DataFrame]] from explicit rows and an explicit schema. */ + private def microbatchOf(schema: StructType)(rows: Row*): DataFrame = + spark.createDataFrame(spark.sparkContext.parallelize(rows), schema) + + /** + * Returns the `(name, dataType)` pairs of `schema`'s fields. Used to compare two schemas for + * structural equivalence while deliberately ignoring nullability and metadata, which can shift + * benignly when columns are unpacked from a struct. + */ + private def columnNamesAndDataTypes(schema: StructType): Seq[(String, DataType)] = + schema.fields.map(f => (f.name, f.dataType)).toSeq + + test("deduplicateMicrobatch keeps only the row with the largest sequence value per key") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("value", StringType) + + val batch = microbatchOf(schema)( + Row(1, 10L, "first"), + Row(1, 30L, "winner"), + Row(1, 20L, "middle") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + checkAnswer( + df = processor.deduplicateMicrobatch(batch), + expectedAnswer = Row(1, 30L, "winner") + ) + } + + test("deduplicateMicrobatch is no-op if there's a single event for a key") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("value", StringType) + + val batch = microbatchOf(schema)( + Row(1, 10L, "only-row") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + checkAnswer( + df = processor.deduplicateMicrobatch(batch), + expectedAnswer = Row(1, 10L, "only-row") + ) + } + + test("deduplicateMicrobatch handles equal sequencing values for the same key") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("value", StringType) + + val batch = microbatchOf(schema)( + Row(1, 10L, "first-tied-row"), + Row(1, 10L, "second-tied-row") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + // On equal sequence number events for the same key we provide no guarantee on which event will + // survive, but the contract is _one_ event will survive - assert that below. + val result = processor.deduplicateMicrobatch(batch).collect() + assert(result.length == 1) + assert(result.head.getInt(0) == 1) + assert(result.head.getLong(1) == 10L) + assert(Set("first-tied-row", "second-tied-row").contains(result.head.getString(2))) + } + + test("deduplicateMicrobatch ignores rows with null sequencing when a non-null value exists") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("value", StringType) + + val batch = microbatchOf(schema)( + // In production the expectation is the microbatch will have been validated to not contain + // any null sequence values, but demonstrate that null sequence rows are de-prioritized in + // deduplication. + Row(1, null, "null-sequence"), + Row(1, 10L, "non-null-sequence") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + checkAnswer( + df = processor.deduplicateMicrobatch(batch), + expectedAnswer = Row(1, 10L, "non-null-sequence") + ) + } + + test( + "deduplicateMicrobatch returns a null row when all sequencing values for a key are null" + ) { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("value", StringType) + val batch = microbatchOf(schema)( + // In production the expectation is the microbatch will have been validated to not contain + // any null sequence values, but demonstrate that a null row will be returned by + // deduplication if all rows contain a null sequence in the microbatch. + Row(1, null, "null-sequence") + ) + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + checkAnswer( + df = processor.deduplicateMicrobatch(batch), + expectedAnswer = Row(null, null, null) + ) + } + + test("deduplicateMicrobatch processes multiple keys independently") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("value", StringType) + + val batch = microbatchOf(schema)( + Row(1, 10L, "a1"), + Row(2, 50L, "b1-winner"), + Row(1, 20L, "a2-winner"), + Row(2, 40L, "b2-loser"), + Row(3, 1L, "c1-only") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + checkAnswer( + df = processor.deduplicateMicrobatch(batch), + expectedAnswer = Seq( + Row(1, 20L, "a2-winner"), + Row(2, 50L, "b1-winner"), + Row(3, 1L, "c1-only") + ) + ) + } + + test("deduplicateMicrobatch carries non-key, non-sequence columns from the winning row") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("name", StringType) + .add("amount", IntegerType) + + val batch = microbatchOf(schema)( + Row(1, 10L, "old-name", 100), + Row(1, 20L, "winning-name", 200) + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + // All non-key columns must come from the row with the largest sequence value, never + // a mix of values from multiple rows. + checkAnswer( + df = processor.deduplicateMicrobatch(batch), + expectedAnswer = Row(1, 20L, "winning-name", 200) + ) + } + + test("deduplicateMicrobatch carries nested columns correctly from the winning row") { + val payloadType = new StructType() + .add("name", StringType) + .add("amount", IntegerType) + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("payload", payloadType) + + val batch = microbatchOf(schema)( + Row(1, 10L, Row("old", 100)), + Row(1, 20L, Row("new", 200)) + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ) + ) + + checkAnswer( + df = processor.deduplicateMicrobatch(batch), + expectedAnswer = Row(1, 20L, Row("new", 200)) + ) + } + + test("deduplicateMicrobatch supports composite (multi-column) keys") { + val schema = new StructType() + .add("region", StringType) + .add("customer_id", IntegerType) + .add("seq", LongType) + .add("value", StringType) + + val batch = microbatchOf(schema)( + Row("US", 1, 10L, "us1-old"), + Row("US", 1, 20L, "us1-new"), + // Same customer_id as above but different region: independent group. + Row("EU", 1, 5L, "eu1-only"), + // Same region as above but different customer_id: independent group. + Row("US", 2, 99L, "us2-only") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("region"), UnqualifiedColumnName("customer_id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + checkAnswer( + df = processor.deduplicateMicrobatch(batch), + expectedAnswer = Seq( + Row("US", 1, 20L, "us1-new"), + Row("EU", 1, 5L, "eu1-only"), + Row("US", 2, 99L, "us2-only") + ) + ) + } + + test("deduplicateMicrobatch supports literal-dot column names") { + val schema = new StructType() + .add("user.id", IntegerType) + .add("seq", LongType) + .add("event.value", StringType) + + val batch = microbatchOf(schema)( + Row(1, 10L, "old"), + Row(1, 20L, "new") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("`user.id`")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + checkAnswer( + df = processor.deduplicateMicrobatch(batch), + expectedAnswer = Row(1, 20L, "new") + ) + } + + test("deduplicateMicrobatch preserves the input column names, types, and ordering") { + val schema = new StructType() + .add("a", StringType) + .add("id", IntegerType) + .add("z", DoubleType) + .add("seq", LongType) + .add("flag", BooleanType) + + val batch = microbatchOf(schema)( + Row("a1", 1, 1.5, 10L, true), + Row("a2", 1, 2.5, 20L, false) + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + // Field names and dataTypes must match the input exactly, in the original order. + assert( + columnNamesAndDataTypes(processor.deduplicateMicrobatch(batch).schema) == + columnNamesAndDataTypes(schema)) + } + + test("deduplicateMicrobatch returns an empty DataFrame with preserved schema") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("value", StringType) + + val batch = microbatchOf(schema)() + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + val result = processor.deduplicateMicrobatch(batch) + assert(result.collect().isEmpty) + assert(columnNamesAndDataTypes(result.schema) == columnNamesAndDataTypes(schema)) + } + + test("extendMicrobatchRowsWithCdcMetadata classifies each row as a delete or an upsert " + + "per deleteCondition") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("is_delete", BooleanType) + + val batch = microbatchOf(schema)( + Row(1, 10L, false), + Row(2, 20L, true), + Row(3, 30L, false), + Row(4, 40L, true) + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1, + deleteCondition = Some(F.col("is_delete") === true) + ), + resolvedSequencingType = LongType + ) + + // Mutual-exclusivity invariant: each row's _cdc_metadata struct has exactly one of + // (deleteSequence, upsertSequence) non-null, and the non-null side carries the row's + // sequence value. + checkAnswer( + df = processor.extendMicrobatchRowsWithCdcMetadata(batch), + expectedAnswer = Seq( + Row(1, 10L, false, Row(null, 10L)), + Row(2, 20L, true, Row(20L, null)), + Row(3, 30L, false, Row(null, 30L)), + Row(4, 40L, true, Row(40L, null)) + ) + ) + } + + test("extendMicrobatchRowsWithCdcMetadata treats null deleteCondition results as upserts") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("is_delete", BooleanType) + + val batch = microbatchOf(schema)( + Row(1, 10L, null) + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1, + deleteCondition = Some(F.col("is_delete")) + ), + resolvedSequencingType = LongType + ) + + checkAnswer( + df = processor.extendMicrobatchRowsWithCdcMetadata(batch), + expectedAnswer = Row(1, 10L, null, Row(null, 10L)) + ) + } + + test("extendMicrobatchRowsWithCdcMetadata treats every row as an upsert " + + "when deleteCondition is None") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("value", StringType) + + val batch = microbatchOf(schema)( + Row(1, 10L, "a"), + Row(2, 20L, "b") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1, + deleteCondition = None + ), + resolvedSequencingType = LongType + ) + + checkAnswer( + df = processor.extendMicrobatchRowsWithCdcMetadata(batch), + expectedAnswer = Seq( + Row(1, 10L, "a", Row(null, 10L)), + Row(2, 20L, "b", Row(null, 20L)) + ) + ) + } + + test("extendMicrobatchRowsWithCdcMetadata appends CDC metadata as the last column") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add("value", StringType) + + val batch = microbatchOf(schema)( + Row(1, 10L, "a") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + val result = processor.extendMicrobatchRowsWithCdcMetadata(batch) + + // Original columns are preserved in their original order, with CDC metadata appended at + // the very end. + assert(result.schema.fieldNames.toSeq == + schema.fieldNames.toSeq :+ Scd1BatchProcessor.cdcMetadataColName) + } + + test("extendMicrobatchRowsWithCdcMetadata casts delete / upsert sequence fields to " + + "resolvedSequencingType") { + val schema = new StructType() + .add("id", IntegerType) + // Microbatch's sequencing column is IntegerType, but the flow's resolved sequencing type + // will be LongType. This should be upcasted in the projected CDC metadata column. + .add("seq", IntegerType) + .add("value", StringType) + + val batch = microbatchOf(schema)( + Row(1, 10, "a") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + val resultDf = processor.extendMicrobatchRowsWithCdcMetadata(batch) + + val cdcMetadataDataType = + resultDf.schema(Scd1BatchProcessor.cdcMetadataColName).dataType.asInstanceOf[StructType] + assert(columnNamesAndDataTypes(cdcMetadataDataType) == Seq( + Scd1BatchProcessor.cdcDeleteSequenceFieldName -> LongType, + Scd1BatchProcessor.cdcUpsertSequenceFieldName -> LongType)) + + // The cast must also succeed at runtime: upsertSequence is materialized as a Long value, not + // an Int. + checkAnswer( + df = resultDf, + expectedAnswer = Row(1, 10, "a", Row(null, 10L)) + ) + } + + test("extendMicrobatchRowsWithCdcMetadata fails fast when the microbatch's sequencing column " + + "is incompatible with resolvedSequencingType") { + val schema = new StructType() + .add("id", IntegerType) + // Microbatch's sequencing column is a struct, whereas the flow's resolved sequencing type + // will be LongType. These are incompatible and should throw. + .add( + "seq", + new StructType() + .add("major", LongType) + .add("minor", LongType)) + + val batch = microbatchOf(schema)( + Row(1, Row(1L, 0L)) + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + val ex = intercept[AnalysisException] { + // .schema forces analysis of the underlying logical plan, surfacing the invalid cast. + processor.extendMicrobatchRowsWithCdcMetadata(batch).schema + } + assert(ex.getCondition == "DATATYPE_MISMATCH.CAST_WITHOUT_SUGGESTION") + } + + test("extendMicrobatchRowsWithCdcMetadata rejects a microbatch that already contains the " + + "reserved CDC metadata column") { + withSQLConf(SQLConf.CASE_SENSITIVE.key -> "true") { + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add(Scd1BatchProcessor.cdcMetadataColName, StringType) + + val batch = microbatchOf(schema)( + Row(1, 10L, "user-supplied") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + checkError( + exception = intercept[AnalysisException] { + processor.extendMicrobatchRowsWithCdcMetadata(batch) + }, + condition = "AUTOCDC_RESERVED_COLUMN_NAME_CONFLICT", + sqlState = "42710", + parameters = Map( + "caseSensitivity" -> CaseSensitivityLabels.CaseSensitive, + "columnName" -> Scd1BatchProcessor.cdcMetadataColName, + "schemaName" -> "microbatch", + "reservedColumnName" -> Scd1BatchProcessor.cdcMetadataColName + ) + ) + } + } + + test("extendMicrobatchRowsWithCdcMetadata rejects reserved CDC metadata column " + + "case-insensitively") { + withSQLConf(SQLConf.CASE_SENSITIVE.key -> "false") { + val conflictingColumnName = Scd1BatchProcessor.cdcMetadataColName.toUpperCase + val schema = new StructType() + .add("id", IntegerType) + .add("seq", LongType) + .add(conflictingColumnName, StringType) + + val batch = microbatchOf(schema)( + Row(1, 10L, "user-supplied") + ) + + val processor = Scd1BatchProcessor( + changeArgs = ChangeArgs( + keys = Seq(UnqualifiedColumnName("id")), + sequencing = F.col("seq"), + storedAsScdType = ScdType.Type1 + ), + resolvedSequencingType = LongType + ) + + checkError( + exception = intercept[AnalysisException] { + processor.extendMicrobatchRowsWithCdcMetadata(batch) + }, + condition = "AUTOCDC_RESERVED_COLUMN_NAME_CONFLICT", + sqlState = "42710", + parameters = Map( + "caseSensitivity" -> CaseSensitivityLabels.CaseInsensitive, + "columnName" -> conflictingColumnName, + "schemaName" -> "microbatch", + "reservedColumnName" -> Scd1BatchProcessor.cdcMetadataColName + ) + ) + } + } +}