Github user ron8hu commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15090#discussion_r79508043
  
    --- Diff: 
sql/core/src/main/scala/org/apache/spark/sql/execution/command/AnalyzeColumnCommand.scala
 ---
    @@ -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.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, 
BasicColStats, 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 relation = 
EliminateSubqueryAliases(sessionState.catalog.lookupRelation(tableIdent))
    +
    +    // check correctness of column names
    +    val validColumns = mutable.MutableList[NamedExpression]()
    +    val resolver = sessionState.conf.resolver
    +    columnNames.foreach { col =>
    +      val exprOption = relation.resolve(col.split("\\."), resolver)
    +      if (exprOption.isEmpty) {
    +        throw new AnalysisException(s"Invalid column name: $col")
    +      }
    +      if (validColumns.map(_.exprId).contains(exprOption.get.exprId)) {
    +        throw new AnalysisException(s"Duplicate column name: $col")
    +      }
    +      validColumns += exprOption.get
    +    }
    +
    +    relation match {
    +      case catalogRel: CatalogRelation =>
    +        updateStats(catalogRel.catalogTable,
    +          AnalyzeTableCommand.calculateTotalSize(sparkSession, 
catalogRel.catalogTable))
    +
    +      case logicalRel: LogicalRelation if 
logicalRel.catalogTable.isDefined =>
    +        updateStats(logicalRel.catalogTable.get, 
logicalRel.relation.sizeInBytes)
    +
    +      case otherRelation =>
    +        throw new AnalysisException("ANALYZE TABLE is not supported for " +
    +          s"${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 BasicColStats.
    +      val ndvMaxErr = sessionState.conf.ndvMaxError
    +      val expressions = Count(Literal(1)).toAggregateExpression() +:
    +        validColumns.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 colStats = validColumns.zipWithIndex.map { case (expr, i) =>
    +        val colInfo = statsRow.getStruct(i + 1, 
ColumnStatsStruct.statsNumber)
    +        val colStats = ColumnStatsStruct.unwrapRow(expr, colInfo)
    +        (expr.name, colStats)
    +      }.toMap
    +
    +      val statistics =
    +        Statistics(sizeInBytes = newTotalSize, rowCount = Some(rowCount), 
basicColStats = colStats)
    --- End diff --
    
    For the "all or nothing" statistics approach, we can better maintain 
statistics consistency.  However, from the viewpoint of usability, a user may 
want to collect column statistics for column A and column B of a given table 
first.  After a while, he may want to collect column statistics for column C 
and column D because of the need for new queries.  A user may intuitively 
specify only columns C and D in the new ANALZYE command because he expects the 
statistics of column A and B to be there as well.  Hence, refreshing column 
stats independently can better match a user's expectation. 


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