szehon-ho commented on code in PR #6582: URL: https://github.com/apache/iceberg/pull/6582#discussion_r1070150005
########## spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/procedures/DistinctCountProcedure.java: ########## @@ -0,0 +1,188 @@ +/* + * 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.io.IOException; +import java.io.UncheckedIOException; +import java.nio.ByteBuffer; +import java.util.UUID; +import org.apache.iceberg.GenericBlobMetadata; +import org.apache.iceberg.GenericStatisticsFile; +import org.apache.iceberg.HasTableOperations; +import org.apache.iceberg.Table; +import org.apache.iceberg.TableOperations; +import org.apache.iceberg.io.FileIO; +import org.apache.iceberg.io.OutputFile; +import org.apache.iceberg.puffin.Blob; +import org.apache.iceberg.puffin.Puffin; +import org.apache.iceberg.puffin.PuffinWriter; +import org.apache.iceberg.puffin.StandardBlobTypes; +import org.apache.iceberg.relocated.com.google.common.collect.ImmutableList; +import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap; +import org.apache.iceberg.spark.source.SparkTable; +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.util.ArrayData; +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; + +/** + * A procedure that gets approximate NDV (number of distinct value) for the requested columns and + * sets this to the table's StatisticsFile. + */ +public class DistinctCountProcedure extends BaseProcedure { + private static final Logger LOG = LoggerFactory.getLogger(DistinctCountProcedure.class); + + private static final ProcedureParameter[] PARAMETERS = + new ProcedureParameter[] { + ProcedureParameter.required("table", DataTypes.StringType), + ProcedureParameter.optional("distinct_count_view", DataTypes.StringType), + ProcedureParameter.optional("columns", STRING_ARRAY), + }; + + 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 Builder<DistinctCountProcedure>() { + @Override + protected DistinctCountProcedure doBuild() { + return new DistinctCountProcedure(tableCatalog()); + } + }; + } + + private DistinctCountProcedure(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); + Identifier tableIdent = toIdentifier(tableName, PARAMETERS[0].name()); + SparkTable sparkTable = loadSparkTable(tableIdent); + StructType schema = sparkTable.schema(); + Table table = sparkTable.table(); + ArrayData columns = args.getArray(2); + int columnSizes = columns.numElements(); + + long[] ndvs = new long[columnSizes]; + int[] fieldId = new int[columnSizes]; + String query = "SELECT "; + for (int i = 0; i < columnSizes; i++) { + String colName = columns.getUTF8String(i).toString(); + query += "APPROX_COUNT_DISTINCT(" + colName + "), "; + fieldId[i] = schema.fieldIndex(colName); + } + + query = query.substring(0, query.length() - 2) + " FROM " + tableName; + Dataset<Row> df = spark().sql(query); + + for (int i = 0; i < columnSizes; i++) { + ndvs[i] = df.head().getLong(i); + } + + TableOperations operations = ((HasTableOperations) table).operations(); + FileIO fileIO = ((HasTableOperations) table).operations().io(); + String path = operations.metadataFileLocation(String.format("%s.stats", UUID.randomUUID())); Review Comment: If it exists, we throw FileNotFoundException? Should we just check and throw better exception? ########## spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/procedures/DistinctCountProcedure.java: ########## @@ -0,0 +1,188 @@ +/* + * 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.io.IOException; +import java.io.UncheckedIOException; +import java.nio.ByteBuffer; +import java.util.UUID; +import org.apache.iceberg.GenericBlobMetadata; +import org.apache.iceberg.GenericStatisticsFile; +import org.apache.iceberg.HasTableOperations; +import org.apache.iceberg.Table; +import org.apache.iceberg.TableOperations; +import org.apache.iceberg.io.FileIO; +import org.apache.iceberg.io.OutputFile; +import org.apache.iceberg.puffin.Blob; +import org.apache.iceberg.puffin.Puffin; +import org.apache.iceberg.puffin.PuffinWriter; +import org.apache.iceberg.puffin.StandardBlobTypes; +import org.apache.iceberg.relocated.com.google.common.collect.ImmutableList; +import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap; +import org.apache.iceberg.spark.source.SparkTable; +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.util.ArrayData; +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; + +/** + * A procedure that gets approximate NDV (number of distinct value) for the requested columns and + * sets this to the table's StatisticsFile. + */ +public class DistinctCountProcedure extends BaseProcedure { + private static final Logger LOG = LoggerFactory.getLogger(DistinctCountProcedure.class); + + private static final ProcedureParameter[] PARAMETERS = + new ProcedureParameter[] { + ProcedureParameter.required("table", DataTypes.StringType), + ProcedureParameter.optional("distinct_count_view", DataTypes.StringType), + ProcedureParameter.optional("columns", STRING_ARRAY), + }; + + 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 Builder<DistinctCountProcedure>() { + @Override + protected DistinctCountProcedure doBuild() { + return new DistinctCountProcedure(tableCatalog()); + } + }; + } + + private DistinctCountProcedure(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); + Identifier tableIdent = toIdentifier(tableName, PARAMETERS[0].name()); + SparkTable sparkTable = loadSparkTable(tableIdent); + StructType schema = sparkTable.schema(); + Table table = sparkTable.table(); + ArrayData columns = args.getArray(2); + int columnSizes = columns.numElements(); + + long[] ndvs = new long[columnSizes]; + int[] fieldId = new int[columnSizes]; + String query = "SELECT "; + for (int i = 0; i < columnSizes; i++) { + String colName = columns.getUTF8String(i).toString(); + query += "APPROX_COUNT_DISTINCT(" + colName + "), "; + fieldId[i] = schema.fieldIndex(colName); + } + + query = query.substring(0, query.length() - 2) + " FROM " + tableName; + Dataset<Row> df = spark().sql(query); + + for (int i = 0; i < columnSizes; i++) { + ndvs[i] = df.head().getLong(i); + } + + TableOperations operations = ((HasTableOperations) table).operations(); + FileIO fileIO = ((HasTableOperations) table).operations().io(); + String path = operations.metadataFileLocation(String.format("%s.stats", UUID.randomUUID())); + OutputFile outputFile = fileIO.newOutputFile(path); + + try (PuffinWriter writer = + Puffin.write(outputFile).createdBy("Spark DistinctCountProcedure").build()) { + for (int i = 0; i < columnSizes; i++) { + writer.add( + new Blob( + StandardBlobTypes.NDV_BLOB, + ImmutableList.of(fieldId[i]), + table.currentSnapshot().snapshotId(), + table.currentSnapshot().sequenceNumber(), + ByteBuffer.allocate(0), + null, + ImmutableMap.of("ndv", Long.toString(ndvs[i])))); + } + writer.finish(); + + GenericStatisticsFile statisticsFile = + new GenericStatisticsFile( + table.currentSnapshot().snapshotId(), + path, + writer.fileSize(), + writer.footerSize(), + writer.writtenBlobsMetadata().stream() + .map(GenericBlobMetadata::from) + .collect(ImmutableList.toImmutableList())); + + table + .updateStatistics() + .setStatistics(table.currentSnapshot().snapshotId(), statisticsFile) + .commit(); + } catch (IOException exception) { + throw new UncheckedIOException(exception); + } + + String viewName = viewName(args, tableName); + // Create a view for users to query + df.createOrReplaceTempView(viewName); Review Comment: Not sure if I missed something, is there a point to keeping it as view, if its already returned by the procedure? ########## core/src/main/java/org/apache/iceberg/puffin/StandardBlobTypes.java: ########## @@ -26,4 +26,6 @@ private StandardBlobTypes() {} * href="https://datasketches.apache.org/";>Apache DataSketches</a> library */ public static final String APACHE_DATASKETCHES_THETA_V1 = "apache-datasketches-theta-v1"; + + public static final String NDV_BLOB = "ndv-blob"; Review Comment: 'blob' seems a bit redundant as they are all blobs? And also looking at the code, it's an approx ndv, which I didnt get from this name. ########## spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/procedures/DistinctCountProcedure.java: ########## @@ -0,0 +1,191 @@ +/* + * 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 static java.lang.String.format; +import static java.util.UUID.randomUUID; +import static org.apache.hadoop.shaded.com.google.common.collect.ImmutableList.toImmutableList; + +import java.io.IOException; +import java.io.UncheckedIOException; +import java.nio.ByteBuffer; +import org.apache.iceberg.GenericBlobMetadata; +import org.apache.iceberg.GenericStatisticsFile; +import org.apache.iceberg.HasTableOperations; +import org.apache.iceberg.Table; +import org.apache.iceberg.TableOperations; +import org.apache.iceberg.io.FileIO; +import org.apache.iceberg.io.OutputFile; +import org.apache.iceberg.puffin.Blob; +import org.apache.iceberg.puffin.Puffin; +import org.apache.iceberg.puffin.PuffinWriter; +import org.apache.iceberg.puffin.StandardBlobTypes; +import org.apache.iceberg.relocated.com.google.common.collect.ImmutableList; +import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap; +import org.apache.iceberg.spark.source.SparkTable; +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.util.ArrayData; +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; + +/** + * A procedure that gets approximate NDV (number of distinct value) for the requested columns + * and sets this to the table's StatisticsFile. + */ Review Comment: Yea we have all those in the Iceberg file level metadata already, wonder if its necessary as we could combine those to have an aggregate? -- This is an automated message from the Apache Git Service. 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