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Posted to issues@iceberg.apache.org by "aokolnychyi (via GitHub)" <gi...@apache.org> on 2023/01/23 21:58:12 UTC
[GitHub] [iceberg] aokolnychyi commented on a diff in pull request #6012: Spark 3.3: Add a procedure to generate table changes
aokolnychyi commented on code in PR #6012:
URL: https://github.com/apache/iceberg/pull/6012#discussion_r1084590470
##########
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),
Review Comment:
Defaulting the name will only work in the session catalog but I am not sure we have to do anything about it. Other catalogs will not support views.
##########
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),
Review Comment:
What about `changelog_view`, `changelog_view_name` or similar?
##########
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())
Review Comment:
This name should probably match the input arg name.
##########
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 {
Review Comment:
I am not sure about the name. I don't have a great alternative but it does not seem to me like we generate changes in this procedure. It seems more like we register a changelog view. Any alternatives?
##########
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) {
Review Comment:
nit: To me, it would be better to pull the needed arguments before calling this method rather than passing the row here and doing the extraction within the method itself.
##########
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");
Review Comment:
I don't think we should proceed with the execution if the user asked to compute pre/post images but that's not possible.
##########
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()) {
Review Comment:
What if the provided identifier columns don't match with identifier columns defined on one of the scan snapshots?
##########
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:
We shouldn't do any conversion here. If we want to support timestamps, we should add support to the reader. It is possible by adding new functionality to `SparkReadConf`. For now, I'd avoid any transformations to options in this PR.
##########
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]);
Review Comment:
What if some older snapshots have another set of identifier fields? We don't have to support it but I wonder whether we can validate in the reader that all snapshots that are being scanned have the expected identifier columns or those are undefined. Cause if we use a set of identifier columns and it is different from the real ones, it will become a problem.
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