[GitHub] [iceberg] rdblue commented on a diff in pull request #6012: Spark 3.3: Add a procedure to generate table changes

[GitBox <gi...@apache.org>]

rdblue commented on code in PR #6012:
URL: https://github.com/apache/iceberg/pull/6012#discussion_r1024278647


##########
spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/procedures/GenerateChangesProcedure.java:
##########
@@ -0,0 +1,210 @@
+/*
+ * 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.util.Arrays;
+import org.apache.iceberg.ChangelogOperation;
+import org.apache.iceberg.MetadataColumns;
+import org.apache.iceberg.spark.SparkReadOptions;
+import org.apache.iceberg.spark.source.SparkChangelogTable;
+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.connector.catalog.TableCatalog;
+import org.apache.spark.sql.connector.iceberg.catalog.ProcedureParameter;
+import org.apache.spark.sql.expressions.Window;
+import org.apache.spark.sql.functions;
+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;
+
+public class GenerateChangesProcedure extends BaseProcedure {
+
+  public static SparkProcedures.ProcedureBuilder builder() {
+    return new BaseProcedure.Builder<GenerateChangesProcedure>() {
+      @Override
+      protected GenerateChangesProcedure doBuild() {
+        return new GenerateChangesProcedure(tableCatalog());
+      }
+    };
+  }
+
+  private static final ProcedureParameter[] PARAMETERS =
+      new ProcedureParameter[] {
+        ProcedureParameter.required("table", DataTypes.StringType),
+        ProcedureParameter.optional("start_snapshot_id_exclusive", DataTypes.LongType),
+        ProcedureParameter.optional("end_snapshot_id_inclusive", DataTypes.LongType),
+        ProcedureParameter.optional("table_change_view", DataTypes.StringType),
+        ProcedureParameter.optional("identifier_columns", DataTypes.StringType),
+      };
+
+  private static final StructType OUTPUT_TYPE =
+      new StructType(
+          new StructField[] {
+            new StructField("orphan_file_location", DataTypes.StringType, false, Metadata.empty())
+          });
+
+  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 the pre-image and post-images if the identifier columns are provided.
+    if (!args.isNullAt(4)) {
+      String[] identifierColumns = args.getString(4).split(",");
+      if (identifierColumns == null || identifierColumns.length > 0) {
+        df = withUpdate(df, identifierColumns);
+      }
+    }
+
+    String viewName = viewName(args, tableName);
+
+    // Create a view for users to query
+    df.createOrReplaceTempView(viewName);
+
+    return toOutputRows(viewName);
+  }
+
+  private Dataset<Row> changelogRecords(String tableName, InternalRow args) {
+    DataFrameReader reader = spark().read();
+
+    // we don't have to validate the snapshot ids here because the reader will do it for us.
+    if (!args.isNullAt(1)) {
+      long startSnapshotId = args.getLong(1);
+      reader = reader.option(SparkReadOptions.START_SNAPSHOT_ID, startSnapshotId);
+    }
+
+    if (!args.isNullAt(2)) {
+      long endSnapshotId = args.getLong(2);
+      reader = reader.option(SparkReadOptions.END_SNAPSHOT_ID, endSnapshotId);
+    }
+
+    return reader.table(tableName + "." + SparkChangelogTable.TABLE_NAME);
+  }
+
+  @NotNull
+  private static String viewName(InternalRow args, String tableName) {
+    String viewName = args.isNullAt(3) ? null : args.getString(3);
+    if (viewName == null) {
+      String shortTableName =
+          tableName.contains(".") ? tableName.substring(tableName.lastIndexOf(".") + 1) : tableName;
+      viewName = shortTableName + "_changes";
+    }
+    return viewName;
+  }
+
+  private Dataset<Row> withUpdate(Dataset<Row> df, String[] identifiers) {
+    Column[] partitionSpec = getPartitionSpec(df, identifiers);
+
+    Dataset<Row> dfWithUpdate =
+        df.withColumn("count", functions.count("*").over(Window.partitionBy(partitionSpec)))
+            .withColumn(
+                "rank",
+                functions
+                    .rank()
+                    .over(
+                        Window.partitionBy(partitionSpec)
+                            .orderBy(MetadataColumns.CHANGE_TYPE.name())));
+
+    Dataset<Row> preImageDf =
+        dfWithUpdate
+            .filter("rank = 1")
+            .filter("count = 2")
+            .drop("rank", "count")
+            .withColumn(
+                MetadataColumns.CHANGE_TYPE.name(),
+                functions.lit(ChangelogOperation.UPDATE_PREIMAGE.name()));
+
+    Dataset<Row> postImageDf =
+        dfWithUpdate
+            .filter("rank = 2")
+            .filter("count = 2")
+            .drop("rank", "count")
+            .withColumn(
+                MetadataColumns.CHANGE_TYPE.name(),
+                functions.lit(ChangelogOperation.UPDATE_POSTIMAGE.name()));
+
+    // remove the carry-over rows
+    Dataset<Row> dfWithoutCarryOver = removeCarryOvers(preImageDf.union(postImageDf));

Review Comment:
   I don't think I understand why we would need the previous row and the next row. If we are iterating over rows, then the current will become the previous, so we should only look forward or backward right?



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