AnishMahto commented on code in PR #56016: URL: https://github.com/apache/spark/pull/56016#discussion_r3291856720
########## sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/autocdc/Scd1ForeachBatchExec.scala: ########## @@ -0,0 +1,72 @@ +/* + * 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.catalyst.TableIdentifier +import org.apache.spark.sql.classic.DataFrame + +/** + * Exposes an API to execute one SCD Type 1 AutoCDC microbatch reconciliation on a + * foreachBatch streaming query. + */ +case class Scd1ForeachBatchExec( + batchProcessor: Scd1BatchProcessor, + auxiliaryTableIdentifier: TableIdentifier, + targetTableIdentifier: TableIdentifier) { + + /** + * Process a single CDC microbatch and merge it into the auxiliary and target tables. + */ + def execute(batchDf: DataFrame, batchId: Long): Unit = { + ScdBatchValidator( + destinationIdentifier = targetTableIdentifier, + changeArgs = batchProcessor.changeArgs, + batchDf = batchDf, + batchId = batchId + ).validateMicrobatch() + + val deduplicatedMicrobatch = batchProcessor.deduplicateMicrobatch( + validatedMicrobatch = batchDf + ) + + val microbatchWithCdcMetadata = batchProcessor.extendMicrobatchRowsWithCdcMetadata( + validatedMicrobatch = deduplicatedMicrobatch + ) + + val projectedMicrobatch = batchProcessor.projectTargetColumnsOntoMicrobatch( + microbatchWithCdcMetadataDf = microbatchWithCdcMetadata + ) + + val reconciledMicrobatch = batchProcessor.applyTombstonesToMicrobatch( + microbatchDf = projectedMicrobatch, + auxiliaryTableDf = batchDf.sparkSession.read.table( + auxiliaryTableIdentifier.quotedString + ) + ) + + batchProcessor.mergeMicrobatchOntoAuxiliaryTable( + reconciledMicrobatchDf = reconciledMicrobatch, + auxiliaryTableIdentifier = auxiliaryTableIdentifier + ) + + batchProcessor.mergeMicrobatchOntoTarget( + reconciledMicrobatchDf = reconciledMicrobatch, Review Comment: Yeah this is still idempotent even if we fail in between the auxiliary table merge and the target merge. In the auxiliary table merge, we only delete previous tombstones iff they are either be replaced by a newer tombstone or an upsert - in either case the implication is the microbatch must contain a newer event that renders the previous tombstone stale. Hence on microbatch replay, it doesn't matter whether those (now stale) tombstones are still present in the auxiliary table or not. I'll leave a comment about this in the code. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
