Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/15363#discussion_r105030111
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/joins.scala
---
@@ -20,19 +20,340 @@ 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.{BaseTableAccess,
ExtractFiltersAndInnerJoins}
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 DetectStarSchemaJoin(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
+ * falls back to the positional join reordering, 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 tpcds data), the column is assumed to be unique.
+ *
+ * Given a star join, i.e. fact and dimension tables, the algorithm
considers three cases:
+ *
+ * 1) The star join is an expanding join i.e. the fact table is joined
using inequality
+ * predicates or Cartesian product. In this case, the algorithm
conservatively falls back
+ * to the default join reordering since it cannot make good planning
decisions in the absence
+ * of the cost model.
+ *
+ * 2) The star join is a selective join. This case is detected by
observing local predicates
+ * on the dimension tables. In a star schema relationship, the join
between the fact and the
+ * dimension table is a FK-PK join. Heuristically, a selective dimension
may reduce
+ * the result of a join.
+ *
+ * 3) The star join is not a selective join (i.e. doesn't reduce the
number of rows). In this
+ * case, the algorithm conservatively falls back to the default join
reordering.
+ *
+ * If an eligible star join was found in step 2 above, the algorithm
reorders the tables based
+ * on the following heuristics:
+ * 1) Place the largest fact table on the driving arm to avoid large
tables on the inner of a
+ * join and thus favor hash joins.
+ * 2) Apply the most selective dimensions early in the plan to reduce
data flow.
+ *
+ * Other assumptions made by the algorithm, mainly to prevent
regressions in the absence of a
+ * cost model, are the following:
+ * 1) Only considers star joins with more than one dimensions, which is
a typical
+ * star join scenario.
+ * 2) If the top largest tables have comparable number of rows, fall
back to the default
+ * join reordering. This will prevent changing the position of the
large tables in the join.
+ */
+ def findStarJoinPlan(
+ input: Seq[(LogicalPlan, InnerLike)],
+ conditions: Seq[Expression]): Seq[(LogicalPlan, InnerLike)] = {
+ assert(input.size >= 2)
+
+ val emptyStarJoinPlan = Seq.empty[(LogicalPlan, InnerLike)]
+
+ // 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 { case (plan, _) =>
--- End diff --
NIT: we can be shorter:
```scala
val foundEligibleJoin = input.forall {
case (BaseTableAccess(t, _), _) => t.stats(conf).rowCount.isDefined
case _ => false
}
```
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