Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/15090#discussion_r81455841
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/command/AnalyzeColumnCommand.scala
---
@@ -0,0 +1,175 @@
+/*
+ * 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.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, ColumnStat,
LogicalPlan, Statistics}
+import org.apache.spark.sql.execution.datasources.LogicalRelation
+import org.apache.spark.sql.types._
+
+
+/**
+ * Analyzes the given columns of the given table 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))
+
+ 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
= {
+ val (rowCount, columnStats) = computeColStats(sparkSession, relation)
+ 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]
+ }
+
+ def computeColStats(
+ sparkSession: SparkSession,
+ relation: LogicalPlan): (Long, Map[String, ColumnStat]) = {
+
+ // check correctness of column names
+ val attributesToAnalyze = mutable.MutableList[Attribute]()
+ val duplicatedColumns = mutable.MutableList[String]()
+ val resolver = sparkSession.sessionState.conf.resolver
+ columnNames.foreach { col =>
+ val exprOption = relation.output.find(attr => resolver(attr.name,
col))
+ val expr = exprOption.getOrElse(throw new
AnalysisException(s"Invalid column name: $col."))
+ // do deduplication
+ if (!attributesToAnalyze.contains(expr)) {
+ attributesToAnalyze += expr
+ } else {
+ duplicatedColumns += col
+ }
+ }
+ if (duplicatedColumns.nonEmpty) {
+ logWarning(s"Duplicated columns ${duplicatedColumns.mkString("(", ",
", ")")} detected " +
+ s"when analyzing columns ${columnNames.mkString("(", ", ", ")")},
ignoring them.")
+ }
+
+ // 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 = sparkSession.sessionState.conf.ndvMaxError
+ val expressions = Count(Literal(1)).toAggregateExpression() +:
+ attributesToAnalyze.map(ColumnStatStruct(_, 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) =>
+ val numFields = ColumnStatStruct.numStatFields(expr.dataType)
+ (expr.name, ColumnStat(statsRow.getStruct(i + 1, numFields)))
+ }.toMap
+ (rowCount, columnStats)
+ }
+}
+
+object ColumnStatStruct {
+ val zero = Literal(0, LongType)
+ val one = Literal(1, LongType)
+
+ def numNulls(e: Expression): Expression = if (e.nullable)
Sum(If(IsNull(e), one, zero)) else zero
+ def max(e: Expression): Expression = Max(e)
+ def min(e: Expression): Expression = Min(e)
+ def ndv(e: Expression, relativeSD: Double): Expression = {
+ // the approximate ndv should never be larger than the number of rows
+ Least(Seq(HyperLogLogPlusPlus(e, relativeSD), Count(one)))
+ }
+ def avgLength(e: Expression): Expression = Average(Length(e))
+ def maxLength(e: Expression): Expression = Max(Length(e))
+ def numTrues(e: Expression): Expression = Sum(If(e, one, zero))
+ def numFalses(e: Expression): Expression = Sum(If(Not(e), one, zero))
+
+ def getStruct(exprs: Seq[Expression]): CreateStruct = {
--- End diff --
All the above functions should be private, right?
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