Github user ioana-delaney commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15363#discussion_r106788630
  
    --- Diff: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/joins.scala 
---
    @@ -20,19 +20,347 @@ package org.apache.spark.sql.catalyst.optimizer
     import scala.annotation.tailrec
     
     import org.apache.spark.sql.catalyst.expressions._
    -import org.apache.spark.sql.catalyst.planning.ExtractFiltersAndInnerJoins
    +import 
org.apache.spark.sql.catalyst.planning.{ExtractFiltersAndInnerJoins, 
PhysicalOperation}
     import org.apache.spark.sql.catalyst.plans._
     import org.apache.spark.sql.catalyst.plans.logical._
     import org.apache.spark.sql.catalyst.rules._
    +import org.apache.spark.sql.catalyst.CatalystConf
    +
    +/**
    + * Encapsulates star-schema join detection.
    + */
    +case class StarSchemaDetection(conf: CatalystConf) extends PredicateHelper 
{
    +
    +  /**
    +   * Star schema consists of one or more fact tables referencing a number 
of dimension
    +   * tables. In general, star-schema joins are detected using the 
following conditions:
    +   *  1. Informational RI constraints (reliable detection)
    +   *    + Dimension contains a primary key that is being joined to the 
fact table.
    +   *    + Fact table contains foreign keys referencing multiple dimension 
tables.
    +   *  2. Cardinality based heuristics
    +   *    + Usually, the table with the highest cardinality is the fact 
table.
    +   *    + Table being joined with the most number of tables is the fact 
table.
    +   *
    +   * To detect star joins, the algorithm uses a combination of the above 
two conditions.
    +   * The fact table is chosen based on the cardinality heuristics, and the 
dimension
    +   * tables are chosen based on the RI constraints. A star join will 
consist of the largest
    +   * fact table joined with the dimension tables on their primary keys. To 
detect that a
    +   * column is a primary key, the algorithm uses table and column 
statistics.
    +   *
    +   * Since Catalyst only supports left-deep tree plans, the algorithm 
currently returns only
    +   * the star join with the largest fact table. Choosing the largest fact 
table on the
    +   * driving arm to avoid large inners is in general a good heuristic. 
This restriction can
    +   * be lifted with support for bushy tree plans.
    +   *
    +   * The highlights of the algorithm are the following:
    +   *
    +   * Given a set of joined tables/plans, the algorithm first verifies if 
they are eligible
    +   * for star join detection. An eligible plan is a base table access with 
valid statistics.
    +   * A base table access represents Project or Filter operators above a 
LeafNode. Conservatively,
    +   * the algorithm only considers base table access as part of a star join 
since they provide
    +   * reliable statistics.
    +   *
    +   * If some of the plans are not base table access, or statistics are not 
available, the algorithm
    +   * returns an empty star join plan since, in the absence of statistics, 
it cannot make
    +   * good planning decisions. Otherwise, the algorithm finds the table 
with the largest cardinality
    +   * (number of rows), which is assumed to be a fact table.
    +   *
    +   * Next, it computes the set of dimension tables for the current fact 
table. A dimension table
    +   * is assumed to be in a RI relationship with a fact table. To infer 
column uniqueness,
    +   * the algorithm compares the number of distinct values with the total 
number of rows in the
    +   * table. If their relative difference is within certain limits (i.e. 
ndvMaxError * 2, adjusted
    +   * based on 1TB TPC-DS data), the column is assumed to be unique.
    +   */
    +  def findStarJoins(
    +      input: Seq[LogicalPlan],
    +      conditions: Seq[Expression]): Seq[Seq[LogicalPlan]] = {
    +
    +    val emptyStarJoinPlan = Seq.empty[Seq[LogicalPlan]]
    +
    +    if (!conf.starSchemaDetection || input.size < 2) {
    +      emptyStarJoinPlan
    +    } else {
    +      // Find if the input plans are eligible for star join detection.
    +      // An eligible plan is a base table access with valid statistics.
    +      val foundEligibleJoin = input.forall {
    --- End diff --
    
    @cloud-fan Star-join detection works with and without CBO. Project/Filter 
cardinality is computed only if cbo is enabled. Here, I am doing a simple 
validation to ensure statistics were run on the base relations. 



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