RussellSpitzer commented on code in PR #6012: URL: https://github.com/apache/iceberg/pull/6012#discussion_r1068764399
########## spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/procedures/GenerateChangesProcedure.java: ########## @@ -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.iceberg.spark.procedures; + +import java.sql.Timestamp; +import java.util.Arrays; +import java.util.List; +import java.util.Map; +import java.util.stream.Collectors; +import org.apache.iceberg.MetadataColumns; +import org.apache.iceberg.Table; +import org.apache.iceberg.relocated.com.google.common.collect.Maps; +import org.apache.iceberg.spark.ChangelogIterator; +import org.apache.iceberg.spark.SparkReadOptions; +import org.apache.iceberg.spark.source.SparkChangelogTable; +import org.apache.spark.api.java.function.MapPartitionsFunction; +import org.apache.spark.sql.Column; +import org.apache.spark.sql.DataFrameReader; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.catalyst.InternalRow; +import org.apache.spark.sql.catalyst.encoders.RowEncoder; +import org.apache.spark.sql.connector.catalog.Identifier; +import org.apache.spark.sql.connector.catalog.TableCatalog; +import org.apache.spark.sql.connector.iceberg.catalog.ProcedureParameter; +import org.apache.spark.sql.types.DataTypes; +import org.apache.spark.sql.types.Metadata; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import org.apache.spark.unsafe.types.UTF8String; +import org.jetbrains.annotations.NotNull; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; +import scala.runtime.BoxedUnit; + +/** + * A procedure that creates a view for changed rows. + * + * <p>The procedure computes update-rows and removes the carry-over rows by default. You can disable + * them through parameters to get better performance. + * + * <p>Carry-over rows are the result of a removal and insertion of the same row within an operation + * because of the copy-on-write mechanism. For example, given a file which contains row1 (id=1, + * data='a') and row2 (id=2, data='b'). A copy-on-write delete of row2 would require erasing this + * file and preserving row1 in a new file. The change-log table would report this as (id=1, + * data='a', op='DELETE') and (id=1, data='a', op='INSERT'), despite it not being an actual change + * to the table. The iterator finds the carry-over rows and removes them from the result. + * + * <p>An update-row is converted from a pair of delete row and insert row. Identifier columns are + * needed for identifying whether they refer to the same row. You can either set Identifier Field + * IDs as the table properties or input them as the procedure parameters. Here is an example of + * update-row with an identifier column(id). A pair of delete row and insert row with the same id: + * + * <ul> + * <li>(id=1, data='a', op='DELETE') + * <li>(id=1, data='b', op='INSERT') + * </ul> + * + * <p>will be marked as update-rows: + * + * <ul> + * <li>(id=1, data='a', op='UPDATE_BEFORE') + * <li>(id=1, data='b', op='UPDATE_AFTER') + * </ul> + */ +public class GenerateChangesProcedure extends BaseProcedure { + private static final Logger LOG = LoggerFactory.getLogger(GenerateChangesProcedure.class); + + private static final ProcedureParameter[] PARAMETERS = + new ProcedureParameter[] { + ProcedureParameter.required("table", DataTypes.StringType), + ProcedureParameter.optional("table_change_view", DataTypes.StringType), + ProcedureParameter.optional("options", STRING_MAP), + ProcedureParameter.optional("compute_updates", DataTypes.BooleanType), + ProcedureParameter.optional("remove_carryovers", DataTypes.BooleanType), + ProcedureParameter.optional("identifier_columns", DataTypes.StringType), + }; + + private static final StructType OUTPUT_TYPE = + new StructType( + new StructField[] { + new StructField("view_name", DataTypes.StringType, false, Metadata.empty()) + }); + + public static SparkProcedures.ProcedureBuilder builder() { + return new BaseProcedure.Builder<GenerateChangesProcedure>() { + @Override + protected GenerateChangesProcedure doBuild() { + return new GenerateChangesProcedure(tableCatalog()); + } + }; + } + + private GenerateChangesProcedure(TableCatalog tableCatalog) { + super(tableCatalog); + } + + @Override + public ProcedureParameter[] parameters() { + return PARAMETERS; + } + + @Override + public StructType outputType() { + return OUTPUT_TYPE; + } + + @Override + public InternalRow[] call(InternalRow args) { + String tableName = args.getString(0); + + // Read data from the table.changes + Dataset<Row> df = changelogRecords(tableName, args); + + // compute updated rows and remove carry-over rows by default + boolean computeUpdatedRow = args.isNullAt(3) ? true : args.getBoolean(3); + boolean removeCarryoverRow = args.isNullAt(4) ? true : args.getBoolean(4); + + if (computeUpdatedRow) { + String[] identifierColumns = identifierColumns(args, tableName); + + if (identifierColumns.length > 0) { + Column[] repartitionColumns = getRepartitionExpr(df, identifierColumns); + df = transform(df, repartitionColumns); + } else { + LOG.warn("Cannot compute the update-rows because identifier columns are not set"); + if (removeCarryoverRow) { + df = removeCarryoverRows(df); + } + } + } else if (removeCarryoverRow) { + df = removeCarryoverRows(df); + } + + String viewName = viewName(args, tableName); + + // Create a view for users to query + df.createOrReplaceTempView(viewName); + + return toOutputRows(viewName); + } + + private Dataset<Row> removeCarryoverRows(Dataset<Row> df) { + Column[] repartitionColumns = + Arrays.stream(df.columns()) + .filter(c -> !c.equals(MetadataColumns.CHANGE_TYPE.name())) + .map(df::col) + .toArray(Column[]::new); + return transform(df, repartitionColumns); + } + + private String[] identifierColumns(InternalRow args, String tableName) { + String[] identifierColumns = new String[0]; + if (!args.isNullAt(5) && !args.getString(5).isEmpty()) { + identifierColumns = args.getString(5).split(","); + } + + if (identifierColumns.length == 0) { + Identifier tableIdent = toIdentifier(tableName, PARAMETERS[0].name()); + Table table = loadSparkTable(tableIdent).table(); + identifierColumns = table.schema().identifierFieldNames().toArray(new String[0]); + } + + return identifierColumns; + } + + private Dataset<Row> changelogRecords(String tableName, InternalRow args) { + // no need to validate the read options here since the reader will validate them + DataFrameReader reader = spark().read(); + reader.options(readOptions(args)); + return reader.table(tableName + "." + SparkChangelogTable.TABLE_NAME); + } + + private Map<String, String> readOptions(InternalRow args) { + Map<String, String> options = Maps.newHashMap(); + + if (!args.isNullAt(2)) { + args.getMap(2) + .foreach( + DataTypes.StringType, + DataTypes.StringType, + (k, v) -> { + if (k.toString().equals(SparkReadOptions.START_TIMESTAMP) + || k.toString().equals(SparkReadOptions.END_TIMESTAMP)) { + options.put(k.toString(), toMillis(v.toString())); Review Comment: I'm a little lost on our conversion here, don't we already have code to convert this read option from String => millis within the reader itself? -- This is an automated message from the Apache Git Service. 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