Github user ron8hu commented on a diff in the pull request:
https://github.com/apache/spark/pull/15363#discussion_r106766281
--- 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 {
--- End diff --
} else if (conf.joinReorderEnabled) {
emptyStarJoinPlan
} else {
When both configuration parameters joinReorderEnabled and
starSchemaDetection are true, we want to avoid performing join reorder twice.
There is no added value to perform join reorder twice.
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