Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/15090#discussion_r79163397
--- 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 --
I think that we should document the policy for (partial) statistics updates
very very well. It is important that an end user understands what is going on.
The current policy is to drop them as soon as we touch the table stats.
In this case I think it it important to consider a few elements:
1. Relative column statistics typically change less than table statistics.
So they do not need to changed as often.
2. When do we consider column statistics to be stale (less trustworthy)? If
the size of the table has changed by x%? If the statistics are older than x
time?
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