Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/15090#discussion_r78740027
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/command/AnalyzeColumnCommand.scala
---
@@ -0,0 +1,209 @@
+/*
+ * 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
+import org.apache.spark.sql.catalyst.analysis.EliminateSubqueryAliases
+import org.apache.spark.sql.catalyst.catalog.{CatalogRelation,
CatalogTable}
+import org.apache.spark.sql.catalyst.plans.logical.{BasicColStats,
Statistics}
+import org.apache.spark.sql.execution.datasources.LogicalRelation
+import org.apache.spark.sql.functions._
+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(
+ tableName: String,
+ columnNames: Seq[String]) extends RunnableCommand {
+
+ override def run(sparkSession: SparkSession): Seq[Row] = {
+ val sessionState = sparkSession.sessionState
+ val tableIdent = sessionState.sqlParser.parseTableIdentifier(tableName)
+ val relation =
EliminateSubqueryAliases(sessionState.catalog.lookupRelation(tableIdent))
+
+ // check correctness for column names
+ val attributeNames = relation.output.map(_.name.toLowerCase)
+ val invalidColumns = columnNames.filterNot { col =>
attributeNames.contains(col.toLowerCase)}
+ if (invalidColumns.nonEmpty) {
+ throw new AnalysisException(s"Invalid columns for table $tableName:
$invalidColumns.")
+ }
+
+ relation match {
+ case catalogRel: CatalogRelation =>
+ updateStats(catalogRel.catalogTable,
+ AnalyzeTableCommand.calculateTotalSize(sparkSession,
catalogRel.catalogTable))
+
+ case logicalRel: LogicalRelation if
logicalRel.catalogTable.isDefined =>
+ updateStats(logicalRel.catalogTable.get,
logicalRel.relation.sizeInBytes)
+
+ case otherRelation =>
+ throw new AnalysisException(s"ANALYZE TABLE is not supported for "
+
+ s"${otherRelation.nodeName}.")
+ }
+
+ def updateStats(catalogTable: CatalogTable, newTotalSize: Long): Unit
= {
+ val lowerCaseNames = columnNames.map(_.toLowerCase)
+ val attributes =
+ relation.output.filter(attr =>
lowerCaseNames.contains(attr.name.toLowerCase))
+
+ // collect column statistics
+ val aggColumns = mutable.ArrayBuffer[Column](count(Column("*")))
+ attributes.foreach(entry => aggColumns ++= statsAgg(entry.name,
entry.dataType))
+ val statsRow: InternalRow = Dataset.ofRows(sparkSession,
relation).select(aggColumns: _*)
+ .queryExecution.toRdd.collect().head
+
+ // We also update table-level stats to prevent inconsistency in case
of table modification
--- End diff --
I think we can a lot more concise here. It is much easier to construct a
nested row per expressions. The schema of each row matches the schema of
`BasicColStats`, this makes translation a lot easier (you could also try to use
an encoder for this).
This would be an example of this:
```scala
// Collect statistics per column.
// The first element in the result will be the overal row count, the
following elements will be structs containing all columnstats.
// The layout of each structs follows the layout of the BasicColStats
val zero = Literal(0, LongType)
val one = Literal(1, LongType)
val nullLong = Literal(null, LongType)
val nullDouble = Literal(null, DoubleType)
val nullBoolean = Literal(null, BooleanType)
def countNull(e: Expression): Expression =
val expressions = attributes.map { a =>
val statistics = attr.dataType match {
case NumericType | TimestampType | DateType =>
Seq(Min(a), Max(a), HyperLogLogPlusPlus(a), nullDouble, nullLong,
nullLong, nullLong)
case StringType | BinaryType =>
Seq(Min(a), Max(a), HyperLogLogPlusPlus(a), Avg(Length(a)),
Max(Length(a)), nullLong, nullLong)
case BooleanType =>
Seq(nullBoolean, nullBoolean, nullLong, nullDouble, nullLong,
Sum(If(a, one, zero)), Sum(If(a, zero, one)))
case _ => sys.error("not supported?")
}
CreateStruct(Sum(If(IsNull(a), one, zero)) +: expressions)
}
val statsRow = Dataset(session, Aggregate(Nil, expressions,
relation)).queryExecution.toRdd.collect().head
val rowCount = statsRow.getLong(0)
val colStats = attributes.zipWithIndex.map { case (a, i) =>
val colInfo = statsRow.getStruct(i + 1, 8)
// .. unwrap the row
}
```
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