{"action":"created","comment":{"url":"https://api.github.com/repos/apache/spark/pulls/comments/81456125","pull_request_review_id":2436966,"id":81456125,"diff_hunk":"@@ -0,0 +1,175 @@\n+/*\n+ * Licensed to the Apache Software Foundation (ASF) under one or more\n+ * contributor license agreements.  See the NOTICE file distributed with\n+ * this work for additional information regarding copyright ownership.\n+ * The ASF licenses this file to You under the Apache License, Version 2.0\n+ * (the \"License\"); you may not use this file except in compliance with\n+ * the License.  You may obtain a copy of the License at\n+ *\n+ *    http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+package org.apache.spark.sql.execution.command\n+\n+import scala.collection.mutable\n+\n+import org.apache.spark.sql._\n+import org.apache.spark.sql.catalyst.TableIdentifier\n+import org.apache.spark.sql.catalyst.analysis.EliminateSubqueryAliases\n+import org.apache.spark.sql.catalyst.catalog.{CatalogRelation, CatalogTable}\n+import org.apache.spark.sql.catalyst.expressions._\n+import org.apache.spark.sql.catalyst.expressions.aggregate._\n+import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, ColumnStat, LogicalPlan, Statistics}\n+import org.apache.spark.sql.execution.datasources.LogicalRelation\n+import org.apache.spark.sql.types._\n+\n+\n+/**\n+ * Analyzes the given columns of the given table to generate statistics, which will be used in\n+ * query optimizations.\n+ */\n+case class AnalyzeColumnCommand(\n+    tableIdent: TableIdentifier,\n+    columnNames: Seq[String]) extends RunnableCommand {\n+\n+  override def run(sparkSession: SparkSession): Seq[Row] = {\n+    val sessionState = sparkSession.sessionState\n+    val db = tableIdent.database.getOrElse(sessionState.catalog.getCurrentDatabase)\n+    val tableIdentWithDB = TableIdentifier(tableIdent.table, Some(db))\n+    val relation = EliminateSubqueryAliases(sessionState.catalog.lookupRelation(tableIdentWithDB))\n+\n+    relation match {\n+      case catalogRel: CatalogRelation =>\n+        updateStats(catalogRel.catalogTable,\n+          AnalyzeTableCommand.calculateTotalSize(sessionState, catalogRel.catalogTable))\n+\n+      case logicalRel: LogicalRelation if logicalRel.catalogTable.isDefined =>\n+        updateStats(logicalRel.catalogTable.get, logicalRel.relation.sizeInBytes)\n+\n+      case otherRelation =>\n+        throw new AnalysisException(\"ANALYZE TABLE is not supported for \" +\n+          s\"${otherRelation.nodeName}.\")\n+    }\n+\n+    def updateStats(catalogTable: CatalogTable, newTotalSize: Long): Unit = {\n+      val (rowCount, columnStats) = computeColStats(sparkSession, relation)\n+      val statistics = Statistics(\n+        sizeInBytes = newTotalSize,\n+        rowCount = Some(rowCount),\n+        colStats = columnStats ++ catalogTable.stats.map(_.colStats).getOrElse(Map()))\n+      sessionState.catalog.alterTable(catalogTable.copy(stats = Some(statistics)))\n+      // Refresh the cached data source table in the catalog.\n+      sessionState.catalog.refreshTable(tableIdentWithDB)\n+    }\n+\n+    Seq.empty[Row]\n+  }\n+\n+  def computeColStats(\n+      sparkSession: SparkSession,\n+      relation: LogicalPlan): (Long, Map[String, ColumnStat]) = {\n+\n+    // check correctness of column names\n+    val attributesToAnalyze = mutable.MutableList[Attribute]()\n+    val duplicatedColumns = mutable.MutableList[String]()\n+    val resolver = sparkSession.sessionState.conf.resolver\n+    columnNames.foreach { col =>\n+      val exprOption = relation.output.find(attr => resolver(attr.name, col))\n+      val expr = exprOption.getOrElse(throw new AnalysisException(s\"Invalid column name: $col.\"))\n+      // do deduplication\n+      if (!attributesToAnalyze.contains(expr)) {\n+        attributesToAnalyze += expr\n+      } else {\n+        duplicatedColumns += col\n+      }\n+    }\n+    if (duplicatedColumns.nonEmpty) {\n+      logWarning(s\"Duplicated columns ${duplicatedColumns.mkString(\"(\", \", \", \")\")} detected \" +\n+        s\"when analyzing columns ${columnNames.mkString(\"(\", \", \", \")\")}, ignoring them.\")\n+    }\n+\n+    // Collect statistics per column.\n+    // The first element in the result will be the overall row count, the following elements\n+    // will be structs containing all column stats.\n+    // The layout of each struct follows the layout of the ColumnStats.\n+    val ndvMaxErr = sparkSession.sessionState.conf.ndvMaxError\n+    val expressions = Count(Literal(1)).toAggregateExpression() +:\n+      attributesToAnalyze.map(ColumnStatStruct(_, ndvMaxErr))\n+    val namedExpressions = expressions.map(e => Alias(e, e.toString)())\n+    val statsRow = Dataset.ofRows(sparkSession, Aggregate(Nil, namedExpressions, relation))\n+      .queryExecution.toRdd.collect().head\n+\n+    // unwrap the result\n+    val rowCount = statsRow.getLong(0)\n+    val columnStats = attributesToAnalyze.zipWithIndex.map { case (expr, i) =>\n+      val numFields = ColumnStatStruct.numStatFields(expr.dataType)\n+      (expr.name, ColumnStat(statsRow.getStruct(i + 1, numFields)))\n+    }.toMap\n+    (rowCount, columnStats)\n+  }\n+}\n+\n+object ColumnStatStruct {\n+  val zero = Literal(0, LongType)\n+  val one = Literal(1, LongType)\n+\n+  def numNulls(e: Expression): Expression = if (e.nullable) Sum(If(IsNull(e), one, zero)) else zero\n+  def max(e: Expression): Expression = Max(e)\n+  def min(e: Expression): Expression = Min(e)\n+  def ndv(e: Expression, relativeSD: Double): Expression = {\n+    // the approximate ndv should never be larger than the number of rows\n+    Least(Seq(HyperLogLogPlusPlus(e, relativeSD), Count(one)))\n+  }\n+  def avgLength(e: Expression): Expression = Average(Length(e))\n+  def maxLength(e: Expression): Expression = Max(Length(e))\n+  def numTrues(e: Expression): Expression = Sum(If(e, one, zero))\n+  def numFalses(e: Expression): Expression = Sum(If(Not(e), one, zero))\n+\n+  def getStruct(exprs: Seq[Expression]): CreateStruct = {\n+    CreateStruct(exprs.map { expr: Expression =>\n+      expr.transformUp {\n+        case af: AggregateFunction => af.toAggregateExpression()\n+      }\n+    })\n+  }\n+\n+  def numericColumnStat(e: Expression, relativeSD: Double): Seq[Expression] = {\n+    Seq(numNulls(e), max(e), min(e), ndv(e, relativeSD))\n+  }\n+\n+  def stringColumnStat(e: Expression, relativeSD: Double): Seq[Expression] = {\n+    Seq(numNulls(e), avgLength(e), maxLength(e), ndv(e, relativeSD))\n+  }\n+\n+  def binaryColumnStat(e: Expression): Seq[Expression] = {\n+    Seq(numNulls(e), avgLength(e), maxLength(e))\n+  }\n+\n+  def booleanColumnStat(e: Expression): Seq[Expression] = {","path":"sql/core/src/main/scala/org/apache/spark/sql/execution/command/AnalyzeColumnCommand.scala","position":154,"original_position":154,"commit_id":"734abad045a5378d14489a4e956b7a8e1c95a811","original_commit_id":"734abad045a5378d14489a4e956b7a8e1c95a811","user":{"login":"gatorsmile","id":11567269,"avatar_url":"https://avatars.githubusercontent.com/u/11567269?v=3","gravatar_id":"","url":"https://api.github.com/users/gatorsmile","html_url":"https://github.com/gatorsmile","followers_url":"https://api.github.com/users/gatorsmile/followers","following_url":"https://api.github.com/users/gatorsmile/following{/other_user}","gists_url":"https://api.github.com/users/gatorsmile/gists{/gist_id}","starred_url":"https://api.github.com/users/gatorsmile/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gatorsmile/subscriptions","organizations_url":"https://api.github.com/users/gatorsmile/orgs","repos_url":"https://api.github.com/users/gatorsmile/repos","events_url":"https://api.github.com/users/gatorsmile/events{/privacy}","received_events_url":"https://api.github.com/users/gatorsmile/received_events","type":"User","site_admin":false},"body":"All the above 13 functions should be private, right?","created_at":"2016-10-01T18:52:35Z","updated_at":"2016-10-01T18:52:36Z","html_url":"https://github.com/apache/spark/pull/15090#discussion_r81456125","pull_request_url":"https://api.github.com/repos/apache/spark/pulls/15090","_links":{"self":{"href":"https://api.github.com/repos/apache/spark/pulls/comments/81456125"},"html":{"href":"https://github.com/apache/spark/pull/15090#discussion_r81456125"},"pull_request":{"href":"https://api.github.com/repos/apache/spark/pulls/15090"}}},"pull_request":{"url":"https://api.github.com/repos/apache/spark/pulls/15090","id":85163180,"html_url":"https://github.com/apache/spark/pull/15090","diff_url":"https://github.com/apache/spark/pull/15090.diff","patch_url":"https://github.com/apache/spark/pull/15090.patch","issue_url":"https://api.github.com/repos/apache/spark/issues/15090","number":15090,"state":"open","locked":false,"title":"[SPARK-17073] [SQL] generate column-level statistics","user":{"login":"wzhfy","id":10878553,"avatar_url":"https://avatars.githubusercontent.com/u/10878553?v=3","gravatar_id":"","url":"https://api.github.com/users/wzhfy","html_url":"https://github.com/wzhfy","followers_url":"https://api.github.com/users/wzhfy/followers","following_url":"https://api.github.com/users/wzhfy/following{/other_user}","gists_url":"https://api.github.com/users/wzhfy/gists{/gist_id}","starred_url":"https://api.github.com/users/wzhfy/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wzhfy/subscriptions","organizations_url":"https://api.github.com/users/wzhfy/orgs","repos_url":"https://api.github.com/users/wzhfy/repos","events_url":"https://api.github.com/users/wzhfy/events{/privacy}","received_events_url":"https://api.github.com/users/wzhfy/received_events","type":"User","site_admin":false},"body":"## What changes were proposed in this pull request?\r\n\r\nGenerate basic column statistics for all the atomic types:\r\n- numeric types: max, min, num of nulls, ndv (number of distinct values)\r\n- date/timestamp types: they are also represented as numbers internally, so they have the same stats as above.\r\n- string: avg length, max length, num of nulls, ndv\r\n- binary: avg length, max length, num of nulls\r\n- boolean: num of nulls, num of trues, num of falsies\r\n\r\nAlso support storing and loading these statistics.\r\n\r\nOne thing to notice:\r\nWe support analyzing columns independently, e.g.:\r\nsql1: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key;`\r\nsql2: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS value;`\r\nwhen running sql2 to collect column stats for `value`, we don’t remove stats of columns `key` which are analyzed in sql1 and not in sql2. As a result, **users need to guarantee consistency** between sql1 and sql2. If the table has been changed before sql2, users should re-analyze column `key` when they want to analyze column `value`:\r\n`ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key, value;`\r\n\r\n## How was this patch tested?\r\n\r\nadd unit tests","created_at":"2016-09-14T03:56:14Z","updated_at":"2016-10-01T18:52:36Z","closed_at":null,"merged_at":null,"merge_commit_sha":"9f23937b19f7ecee528284cc8797c57393089263","assignee":null,"assignees":[],"milestone":null,"commits_url":"https://api.github.com/repos/apache/spark/pulls/15090/commits","review_comments_url":"https://api.github.com/repos/apache/spark/pulls/15090/comments","review_comment_url":"https://api.github.com/repos/apache/spark/pulls/comments{/number}","comments_url":"https://api.github.com/repos/apache/spark/issues/15090/comments","statuses_url":"https://api.github.com/repos/apache/spark/statuses/734abad045a5378d14489a4e956b7a8e1c95a811","head":{"label":"wzhfy:colStats","ref":"colStats","sha":"734abad045a5378d14489a4e956b7a8e1c95a811","user":{"login":"wzhfy","id":10878553,"avatar_url":"https://avatars.githubusercontent.com/u/10878553?v=3","gravatar_id":"","url":"https://api.github.com/users/wzhfy","html_url":"https://github.com/wzhfy","followers_url":"https://api.github.com/users/wzhfy/followers","following_url":"https://api.github.com/users/wzhfy/following{/other_user}","gists_url":"https://api.github.com/users/wzhfy/gists{/gist_id}","starred_url":"https://api.github.com/users/wzhfy/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wzhfy/subscriptions","organizations_url":"https://api.github.com/users/wzhfy/orgs","repos_url":"https://api.github.com/users/wzhfy/repos","events_url":"https://api.github.com/users/wzhfy/events{/privacy}","received_events_url":"https://api.github.com/users/wzhfy/received_events","type":"User","site_admin":false},"repo":{"id":66051854,"name":"spark","full_name":"wzhfy/spark","owner":{"login":"wzhfy","id":10878553,"avatar_url":"https://avatars.githubusercontent.com/u/10878553?v=3","gravatar_id":"","url":"https://api.github.com/users/wzhfy","html_url":"https://github.com/wzhfy","followers_url":"https://api.github.com/users/wzhfy/followers","following_url":"https://api.github.com/users/wzhfy/following{/other_user}","gists_url":"https://api.github.com/users/wzhfy/gists{/gist_id}","starred_url":"https://api.github.com/users/wzhfy/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wzhfy/subscriptions","organizations_url":"https://api.github.com/users/wzhfy/orgs","repos_url":"https://api.github.com/users/wzhfy/repos","events_url":"https://api.github.com/users/wzhfy/events{/privacy}","received_events_url":"https://api.github.com/users/wzhfy/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/wzhfy/spark","description":"Mirror 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