szehon-ho commented on code in PR #10288: URL: https://github.com/apache/iceberg/pull/10288#discussion_r1637317765
########## spark/v3.5/spark/src/main/java/org/apache/iceberg/spark/actions/AnalyzeTableSparkAction.java: ########## @@ -0,0 +1,159 @@ +/* + * 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.actions; + +import java.io.IOException; +import java.util.Arrays; +import java.util.List; +import java.util.Set; +import java.util.stream.Collectors; +import org.apache.iceberg.StatisticsFile; +import org.apache.iceberg.Table; +import org.apache.iceberg.actions.AnalyzeTable; +import org.apache.iceberg.actions.ImmutableAnalyzeTable; +import org.apache.iceberg.exceptions.ValidationException; +import org.apache.iceberg.puffin.StandardBlobTypes; +import org.apache.iceberg.relocated.com.google.common.base.Preconditions; +import org.apache.iceberg.relocated.com.google.common.collect.ImmutableSet; +import org.apache.iceberg.relocated.com.google.common.collect.Lists; +import org.apache.iceberg.relocated.com.google.common.collect.Sets; +import org.apache.iceberg.spark.JobGroupInfo; +import org.apache.iceberg.types.Type; +import org.apache.iceberg.types.Types; +import org.apache.spark.sql.SparkSession; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/** Computes the statistic of the given columns and stores it as Puffin files. */ +public class AnalyzeTableSparkAction extends BaseSparkAction<AnalyzeTableSparkAction> + implements AnalyzeTable { + + private static final Logger LOG = LoggerFactory.getLogger(AnalyzeTableSparkAction.class); + + private final Table table; + private Set<String> columns = ImmutableSet.of(); + private Set<String> types = StandardBlobTypes.blobTypes(); + private Long snapshotId; + + AnalyzeTableSparkAction(SparkSession spark, Table table) { + super(spark); + this.table = table; + } + + @Override + protected AnalyzeTableSparkAction self() { + return this; + } + + @Override + public Result execute() { + if (snapshotId == null) { + snapshotId = table.currentSnapshot().snapshotId(); + } + String desc = String.format("Analyzing table %s for snapshot id %s", table.name(), snapshotId); + JobGroupInfo info = newJobGroupInfo("ANALYZE-TABLE", desc); + return withJobGroupInfo(info, this::doExecute); + } + + private Result doExecute() { + LOG.info("Starting the analysis of {} for snapshot {}", table.name(), snapshotId); + List<AnalysisResult> analysisResults = + types.stream() + .map( + statsName -> { + switch (statsName) { + case StandardBlobTypes.APACHE_DATASKETCHES_THETA_V1: + return generateNDVAndCommit(); + default: + return ImmutableAnalyzeTable.AnalysisResult.builder() + .type(statsName) + .addAllErrors(Lists.newArrayList("Stats type not supported")) + .build(); + } + }) + .collect(Collectors.toList()); + return ImmutableAnalyzeTable.Result.builder().analysisResults(analysisResults).build(); + } + + private boolean analyzableTypes(Set<String> columnNames) { + return columnNames.stream() + .anyMatch( + columnName -> { + Types.NestedField field = table.schema().findField(columnName); + if (field == null) { + throw new ValidationException("No column with %s name in the table", columnName); + } + Type.TypeID type = field.type().typeId(); + return type == Type.TypeID.INTEGER + || type == Type.TypeID.LONG + || type == Type.TypeID.STRING + || type == Type.TypeID.DOUBLE; + }); + } + + private AnalysisResult generateNDVAndCommit() { + try { + if (snapshotId == null) { + snapshotId = table.currentSnapshot().snapshotId(); + } + + StatisticsFile statisticsFile = + NDVSketchGenerator.generateNDV( + spark(), table, snapshotId, columns.toArray(new String[0])); + table.updateStatistics().setStatistics(snapshotId, statisticsFile).commit(); + return ImmutableAnalyzeTable.AnalysisResult.builder() + .type(StandardBlobTypes.APACHE_DATASKETCHES_THETA_V1) + .build(); + } catch (IOException ioe) { + List<String> errors = Lists.newArrayList(); Review Comment: Are we only reporting error if we have IOException? (looks like from writing puffin file). It seems a bit strange just to catch this specific case, and not other exceptions. It seems more natural to either catch all exceptions and report error, or else just throw all exceptions, what do you think? ########## spark/v3.5/spark/src/main/java/org/apache/iceberg/spark/actions/AnalyzeTableSparkAction.java: ########## @@ -0,0 +1,159 @@ +/* + * 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.actions; + +import java.io.IOException; +import java.util.Arrays; +import java.util.List; +import java.util.Set; +import java.util.stream.Collectors; +import org.apache.iceberg.StatisticsFile; +import org.apache.iceberg.Table; +import org.apache.iceberg.actions.AnalyzeTable; +import org.apache.iceberg.actions.ImmutableAnalyzeTable; +import org.apache.iceberg.exceptions.ValidationException; +import org.apache.iceberg.puffin.StandardBlobTypes; +import org.apache.iceberg.relocated.com.google.common.base.Preconditions; +import org.apache.iceberg.relocated.com.google.common.collect.ImmutableSet; +import org.apache.iceberg.relocated.com.google.common.collect.Lists; +import org.apache.iceberg.relocated.com.google.common.collect.Sets; +import org.apache.iceberg.spark.JobGroupInfo; +import org.apache.iceberg.types.Type; +import org.apache.iceberg.types.Types; +import org.apache.spark.sql.SparkSession; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/** Computes the statistic of the given columns and stores it as Puffin files. */ +public class AnalyzeTableSparkAction extends BaseSparkAction<AnalyzeTableSparkAction> + implements AnalyzeTable { + + private static final Logger LOG = LoggerFactory.getLogger(AnalyzeTableSparkAction.class); + + private final Table table; + private Set<String> columns = ImmutableSet.of(); + private Set<String> types = StandardBlobTypes.blobTypes(); + private Long snapshotId; + + AnalyzeTableSparkAction(SparkSession spark, Table table) { + super(spark); + this.table = table; + } + + @Override + protected AnalyzeTableSparkAction self() { + return this; + } + + @Override + public Result execute() { + if (snapshotId == null) { + snapshotId = table.currentSnapshot().snapshotId(); + } + String desc = String.format("Analyzing table %s for snapshot id %s", table.name(), snapshotId); + JobGroupInfo info = newJobGroupInfo("ANALYZE-TABLE", desc); + return withJobGroupInfo(info, this::doExecute); + } + + private Result doExecute() { + LOG.info("Starting the analysis of {} for snapshot {}", table.name(), snapshotId); + List<AnalysisResult> analysisResults = + types.stream() + .map( + statsName -> { + switch (statsName) { + case StandardBlobTypes.APACHE_DATASKETCHES_THETA_V1: + return generateNDVAndCommit(); + default: + return ImmutableAnalyzeTable.AnalysisResult.builder() + .type(statsName) + .addAllErrors(Lists.newArrayList("Stats type not supported")) + .build(); + } + }) + .collect(Collectors.toList()); + return ImmutableAnalyzeTable.Result.builder().analysisResults(analysisResults).build(); + } + + private boolean analyzableTypes(Set<String> columnNames) { + return columnNames.stream() + .anyMatch( + columnName -> { + Types.NestedField field = table.schema().findField(columnName); + if (field == null) { + throw new ValidationException("No column with %s name in the table", columnName); + } + Type.TypeID type = field.type().typeId(); + return type == Type.TypeID.INTEGER + || type == Type.TypeID.LONG + || type == Type.TypeID.STRING + || type == Type.TypeID.DOUBLE; + }); + } + + private AnalysisResult generateNDVAndCommit() { + try { + if (snapshotId == null) { + snapshotId = table.currentSnapshot().snapshotId(); + } + + StatisticsFile statisticsFile = + NDVSketchGenerator.generateNDV( + spark(), table, snapshotId, columns.toArray(new String[0])); + table.updateStatistics().setStatistics(snapshotId, statisticsFile).commit(); + return ImmutableAnalyzeTable.AnalysisResult.builder() + .type(StandardBlobTypes.APACHE_DATASKETCHES_THETA_V1) + .build(); + } catch (IOException ioe) { + List<String> errors = Lists.newArrayList(); + errors.add(ioe.getMessage()); + return ImmutableAnalyzeTable.AnalysisResult.builder() + .type(StandardBlobTypes.APACHE_DATASKETCHES_THETA_V1) + .addAllErrors(errors) + .build(); + } + } + + @Override + public AnalyzeTable columns(String... columnNames) { + Preconditions.checkArgument( + columnNames != null && columnNames.length > 0, "Columns cannot be null/empty"); + Set<String> columnsSet = Sets.newHashSet(Arrays.asList(columnNames)); + Preconditions.checkArgument( + analyzableTypes(columnsSet), + "Cannot be applied to the given columns, since the column's type is not supported"); + this.columns = columnsSet; + return this; + } + + @Override + public AnalyzeTable types(Set<String> statisticTypes) { + Preconditions.checkArgument( + Sets.newHashSet(StandardBlobTypes.blobTypes()).containsAll(statisticTypes), + "type not supported"); + this.types = statisticTypes; + return this; + } + + @Override + public AnalyzeTable snapshot(String snapshotIdStr) { + this.snapshotId = Long.parseLong(snapshotIdStr); Review Comment: I feel we should just make this take long ########## spark/v3.5/spark/src/main/java/org/apache/iceberg/spark/actions/AnalyzeTableSparkAction.java: ########## @@ -0,0 +1,159 @@ +/* + * 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.actions; + +import java.io.IOException; +import java.util.Arrays; +import java.util.List; +import java.util.Set; +import java.util.stream.Collectors; +import org.apache.iceberg.StatisticsFile; +import org.apache.iceberg.Table; +import org.apache.iceberg.actions.AnalyzeTable; +import org.apache.iceberg.actions.ImmutableAnalyzeTable; +import org.apache.iceberg.exceptions.ValidationException; +import org.apache.iceberg.puffin.StandardBlobTypes; +import org.apache.iceberg.relocated.com.google.common.base.Preconditions; +import org.apache.iceberg.relocated.com.google.common.collect.ImmutableSet; +import org.apache.iceberg.relocated.com.google.common.collect.Lists; +import org.apache.iceberg.relocated.com.google.common.collect.Sets; +import org.apache.iceberg.spark.JobGroupInfo; +import org.apache.iceberg.types.Type; +import org.apache.iceberg.types.Types; +import org.apache.spark.sql.SparkSession; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/** Computes the statistic of the given columns and stores it as Puffin files. */ +public class AnalyzeTableSparkAction extends BaseSparkAction<AnalyzeTableSparkAction> + implements AnalyzeTable { + + private static final Logger LOG = LoggerFactory.getLogger(AnalyzeTableSparkAction.class); + + private final Table table; + private Set<String> columns = ImmutableSet.of(); + private Set<String> types = StandardBlobTypes.blobTypes(); + private Long snapshotId; + + AnalyzeTableSparkAction(SparkSession spark, Table table) { + super(spark); + this.table = table; + } + + @Override + protected AnalyzeTableSparkAction self() { + return this; + } + + @Override + public Result execute() { + if (snapshotId == null) { + snapshotId = table.currentSnapshot().snapshotId(); + } + String desc = String.format("Analyzing table %s for snapshot id %s", table.name(), snapshotId); + JobGroupInfo info = newJobGroupInfo("ANALYZE-TABLE", desc); + return withJobGroupInfo(info, this::doExecute); + } + + private Result doExecute() { + LOG.info("Starting the analysis of {} for snapshot {}", table.name(), snapshotId); + List<AnalysisResult> analysisResults = + types.stream() + .map( + statsName -> { + switch (statsName) { + case StandardBlobTypes.APACHE_DATASKETCHES_THETA_V1: + return generateNDVAndCommit(); + default: + return ImmutableAnalyzeTable.AnalysisResult.builder() + .type(statsName) + .addAllErrors(Lists.newArrayList("Stats type not supported")) + .build(); + } + }) + .collect(Collectors.toList()); + return ImmutableAnalyzeTable.Result.builder().analysisResults(analysisResults).build(); + } + + private boolean analyzableTypes(Set<String> columnNames) { + return columnNames.stream() + .anyMatch( + columnName -> { + Types.NestedField field = table.schema().findField(columnName); + if (field == null) { + throw new ValidationException("No column with %s name in the table", columnName); + } + Type.TypeID type = field.type().typeId(); + return type == Type.TypeID.INTEGER + || type == Type.TypeID.LONG + || type == Type.TypeID.STRING + || type == Type.TypeID.DOUBLE; + }); + } + + private AnalysisResult generateNDVAndCommit() { + try { + if (snapshotId == null) { + snapshotId = table.currentSnapshot().snapshotId(); + } + + StatisticsFile statisticsFile = + NDVSketchGenerator.generateNDV( + spark(), table, snapshotId, columns.toArray(new String[0])); Review Comment: Can we do a similar thing with a columns() method as I suggested with snapshots, that checks if the user list is null/empty and sets it to all the table columns ``` private Set<String> columns() { return (columns != null) && (columns.size() > 0) ? table.schema().columns().stream() .map(Types.NestedField::name) .collect(Collectors.toSet()) : columns; } ``` Then the logic is centralized here if we have more stats, rather than in NDV class. -- This is an automated message from the Apache Git Service. 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