Github user wzhfy commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15090#discussion_r79926080
  
    --- Diff: 
sql/core/src/main/scala/org/apache/spark/sql/execution/command/AnalyzeColumnCommand.scala
 ---
    @@ -0,0 +1,182 @@
    +/*
    + * 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.spark.sql.execution.command
    +
    +import scala.collection.mutable
    +
    +import org.apache.spark.sql._
    +import org.apache.spark.sql.catalyst.{InternalRow, TableIdentifier}
    +import org.apache.spark.sql.catalyst.analysis.EliminateSubqueryAliases
    +import org.apache.spark.sql.catalyst.catalog.{CatalogRelation, 
CatalogTable}
    +import org.apache.spark.sql.catalyst.expressions._
    +import org.apache.spark.sql.catalyst.expressions.aggregate._
    +import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, 
ColumnStats, Statistics}
    +import org.apache.spark.sql.execution.datasources.LogicalRelation
    +import org.apache.spark.sql.types._
    +
    +
    +/**
    + * Analyzes the given columns of the given table in the current database 
to generate statistics,
    + * which will be used in query optimizations.
    + */
    +case class AnalyzeColumnCommand(
    +    tableIdent: TableIdentifier,
    +    columnNames: Seq[String]) extends RunnableCommand {
    +
    +  override def run(sparkSession: SparkSession): Seq[Row] = {
    +    val sessionState = sparkSession.sessionState
    +    val db = 
tableIdent.database.getOrElse(sessionState.catalog.getCurrentDatabase)
    +    val tableIdentWithDB = TableIdentifier(tableIdent.table, Some(db))
    +    val relation = 
EliminateSubqueryAliases(sessionState.catalog.lookupRelation(tableIdentWithDB))
    +
    +    // check correctness of column names
    +    val attributesToAnalyze = mutable.MutableList[Attribute]()
    +    val caseSensitive = sessionState.conf.caseSensitiveAnalysis
    +    columnNames.foreach { col =>
    +      val exprOption = relation.output.find { attr =>
    +        if (caseSensitive) attr.name == col else 
attr.name.equalsIgnoreCase(col)
    +      }
    +      val expr = exprOption.getOrElse(throw new 
AnalysisException(s"Invalid column name: $col."))
    +      // do deduplication
    +      if (!attributesToAnalyze.contains(expr)) {
    +        attributesToAnalyze += expr
    +      }
    +    }
    +
    +    relation match {
    +      case catalogRel: CatalogRelation =>
    +        updateStats(catalogRel.catalogTable,
    +          AnalyzeTableCommand.calculateTotalSize(sessionState, 
catalogRel.catalogTable))
    +
    +      case logicalRel: LogicalRelation if 
logicalRel.catalogTable.isDefined =>
    +        updateStats(logicalRel.catalogTable.get, 
logicalRel.relation.sizeInBytes)
    +
    +      case otherRelation =>
    +        throw new AnalysisException("ANALYZE TABLE is not supported for " +
    +          s"${otherRelation.nodeName}.")
    +    }
    +
    +    def updateStats(catalogTable: CatalogTable, newTotalSize: Long): Unit 
= {
    +      // Collect statistics per column.
    +      // The first element in the result will be the overall row count, 
the following elements
    +      // will be structs containing all column stats.
    +      // The layout of each struct follows the layout of the ColumnStats.
    +      val ndvMaxErr = sessionState.conf.ndvMaxError
    +      val expressions = Count(Literal(1)).toAggregateExpression() +:
    +        attributesToAnalyze.map(ColumnStatsStruct(_, ndvMaxErr))
    +      val namedExpressions = expressions.map(e => Alias(e, e.toString)())
    +      val statsRow = Dataset.ofRows(sparkSession, Aggregate(Nil, 
namedExpressions, relation))
    +        .queryExecution.toRdd.collect().head
    +
    +      // unwrap the result
    +      val rowCount = statsRow.getLong(0)
    +      val columnStats = attributesToAnalyze.zipWithIndex.map { case (expr, 
i) =>
    +        (expr.name, ColumnStatsStruct.unwrapStruct(statsRow, i + 1, expr))
    +      }.toMap
    +
    +      val statistics = Statistics(
    +        sizeInBytes = newTotalSize,
    +        rowCount = Some(rowCount),
    +        colStats = columnStats ++ 
catalogTable.stats.map(_.colStats).getOrElse(Map()))
    +      sessionState.catalog.alterTable(catalogTable.copy(stats = 
Some(statistics)))
    +      // Refresh the cached data source table in the catalog.
    +      sessionState.catalog.refreshTable(tableIdentWithDB)
    +    }
    +
    +    Seq.empty[Row]
    +  }
    +}
    +
    +object ColumnStatsStruct {
    +  val zero = Literal(0, LongType)
    +  val one = Literal(1, LongType)
    +  val nullLong = Literal(null, LongType)
    +  val nullDouble = Literal(null, DoubleType)
    +  val nullString = Literal(null, StringType)
    +  val nullBinary = Literal(null, BinaryType)
    +  val nullBoolean = Literal(null, BooleanType)
    +  val statsNumber = 8
    +
    +  def apply(e: NamedExpression, relativeSD: Double): CreateStruct = {
    +    // Use aggregate functions to compute statistics we need:
    +    // - number of nulls: Sum(If(IsNull(e), one, zero));
    +    // - maximum value: Max(e);
    +    // - minimum value: Min(e);
    +    // - ndv (number of distinct values): HyperLogLogPlusPlus(e, 
relativeSD);
    +    // - average length of values: Average(Length(e));
    +    // - maximum length of values: Max(Length(e));
    +    // - number of true values: Sum(If(e, one, zero));
    +    // - number of false values: Sum(If(Not(e), one, zero));
    +    // - If we don't need some statistic for the data type, use null 
literal.
    +    // Note that: the order of each sequence must be as follows:
    +    // numNulls, max, min, ndv, avgColLen, maxColLen, numTrues, numFalses
    +    var statistics = e.dataType match {
    +      case _: NumericType | TimestampType | DateType =>
    +        Seq(Max(e), Min(e), HyperLogLogPlusPlus(e, relativeSD), 
nullDouble, nullLong, nullLong,
    --- End diff --
    
    Yeah, I think we should do that, thanks.


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