SPARK-1562 Fix visibility / annotation of Spark SQL APIs

project/SparkBuild.scala

@@ -367,7 +367,7 @@ object SparkBuild extends Build {
     publish := {},
 
     unidocProjectFilter in (ScalaUnidoc, unidoc) :=
-      inAnyProject -- inProjects(repl, examples, tools, yarn, yarnAlpha),
+      inAnyProject -- inProjects(repl, examples, tools, catalyst, yarn, yarnAlpha),
     unidocProjectFilter in (JavaUnidoc, unidoc) :=
       inAnyProject -- inProjects(repl, examples, bagel, graphx, catalyst, tools, yarn, yarnAlpha),
 
@@ -457,7 +457,7 @@ object SparkBuild extends Build {
   def catalystSettings = sharedSettings ++ Seq(
     name := "catalyst",
     // The mechanics of rewriting expression ids to compare trees in some test cases makes
-    // assumptions about the the expression ids being contiguious.  Running tests in parallel breaks
+    // assumptions about the the expression ids being contiguous.  Running tests in parallel breaks
     // this non-deterministically.  TODO: FIX THIS.
     parallelExecution in Test := false,
     libraryDependencies ++= Seq(

sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala

@@ -24,7 +24,7 @@ import org.apache.spark.sql.catalyst.plans.logical._
 /**
  * Contains functions that are shared between all SchemaRDD types (i.e., Scala, Java)
  */
-trait SchemaRDDLike {
+private[sql] trait SchemaRDDLike {
   @transient val sqlContext: SQLContext
   @transient protected[spark] val logicalPlan: LogicalPlan
 

sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala

@@ -19,12 +19,14 @@ package org.apache.spark.sql.execution
 
 import java.util.HashMap
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.SparkContext
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 
 /**
+ * :: DeveloperApi ::
  * Groups input data by `groupingExpressions` and computes the `aggregateExpressions` for each
  * group.
  *
@@ -34,6 +36,7 @@ import org.apache.spark.sql.catalyst.plans.physical._
  * @param aggregateExpressions expressions that are computed for each group.
  * @param child the input data source.
  */
+@DeveloperApi
 case class Aggregate(
     partial: Boolean,
     groupingExpressions: Seq[Expression],

sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.{HashPartitioner, RangePartitioner, SparkConf}
 import org.apache.spark.rdd.ShuffledRDD
 import org.apache.spark.sql.Row
@@ -26,6 +27,10 @@ import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.util.MutablePair
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode {
 
   override def outputPartitioning = newPartitioning
@@ -81,7 +86,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
  * [[catalyst.plans.physical.Distribution Distribution]] requirements for each operator by inserting
  * [[Exchange]] Operators where required.
  */
-object AddExchange extends Rule[SparkPlan] {
+private[sql] object AddExchange extends Rule[SparkPlan] {
   // TODO: Determine the number of partitions.
   val numPartitions = 150
 

sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala

@@ -17,9 +17,11 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.expressions.{Generator, JoinedRow, Literal, Projection}
 
 /**
+ * :: DeveloperApi ::
  * Applies a [[catalyst.expressions.Generator Generator]] to a stream of input rows, combining the
  * output of each into a new stream of rows.  This operation is similar to a `flatMap` in functional
  * programming with one important additional feature, which allows the input rows to be joined with
@@ -29,6 +31,7 @@ import org.apache.spark.sql.catalyst.expressions.{Generator, JoinedRow, Literal,
  * @param outer when true, each input row will be output at least once, even if the output of the
  *              given `generator` is empty. `outer` has no effect when `join` is false.
  */
+@DeveloperApi
 case class Generate(
     generator: Generator,
     join: Boolean,

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{Logging, Row}
 import org.apache.spark.sql.catalyst.trees
@@ -26,6 +27,10 @@ import org.apache.spark.sql.catalyst.plans.{QueryPlan, logical}
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.columnar.InMemoryColumnarTableScan
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging {
   self: Product =>
 
@@ -51,6 +56,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging {
 }
 
 /**
+ * :: DeveloperApi ::
  * Allows already planned SparkQueries to be linked into logical query plans.
  *
  * Note that in general it is not valid to use this class to link multiple copies of the same
@@ -59,6 +65,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging {
  * replace the output attributes with new copies of themselves without breaking any attribute
  * linking.
  */
+@DeveloperApi
 case class SparkLogicalPlan(alreadyPlanned: SparkPlan)
   extends logical.LogicalPlan with MultiInstanceRelation {
 
@@ -77,15 +84,15 @@ case class SparkLogicalPlan(alreadyPlanned: SparkPlan)
   }
 }
 
-trait LeafNode extends SparkPlan with trees.LeafNode[SparkPlan] {
+private[sql] trait LeafNode extends SparkPlan with trees.LeafNode[SparkPlan] {
   self: Product =>
 }
 
-trait UnaryNode extends SparkPlan with trees.UnaryNode[SparkPlan] {
+private[sql] trait UnaryNode extends SparkPlan with trees.UnaryNode[SparkPlan] {
   self: Product =>
   override def outputPartitioning: Partitioning = child.outputPartitioning
 }
 
-trait BinaryNode extends SparkPlan with trees.BinaryNode[SparkPlan] {
+private[sql] trait BinaryNode extends SparkPlan with trees.BinaryNode[SparkPlan] {
   self: Product =>
 }

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala

@@ -27,7 +27,7 @@ import org.apache.spark.serializer.KryoSerializer
 import org.apache.spark.util.MutablePair
 import org.apache.spark.util.Utils
 
-class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(conf) {
+private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(conf) {
   override def newKryo(): Kryo = {
     val kryo = new Kryo()
     kryo.setRegistrationRequired(false)
@@ -50,7 +50,7 @@ class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(conf) {
   }
 }
 
-object SparkSqlSerializer {
+private[sql] object SparkSqlSerializer {
   // TODO (lian) Using KryoSerializer here is workaround, needs further investigation
   // Using SparkSqlSerializer here makes BasicQuerySuite to fail because of Kryo serialization
   // related error.
@@ -68,7 +68,7 @@ object SparkSqlSerializer {
   }
 }
 
-class BigDecimalSerializer extends Serializer[BigDecimal] {
+private[sql] class BigDecimalSerializer extends Serializer[BigDecimal] {
   def write(kryo: Kryo, output: Output, bd: math.BigDecimal) {
     // TODO: There are probably more efficient representations than strings...
     output.writeString(bd.toString())
@@ -83,7 +83,7 @@ class BigDecimalSerializer extends Serializer[BigDecimal] {
  * Maps do not have a no arg constructor and so cannot be serialized by default. So, we serialize
  * them as `Array[(k,v)]`.
  */
-class MapSerializer extends Serializer[Map[_,_]] {
+private[sql] class MapSerializer extends Serializer[Map[_,_]] {
   def write(kryo: Kryo, output: Output, map: Map[_,_]) {
     kryo.writeObject(output, map.flatMap(e => Seq(e._1, e._2)).toArray)
   }

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala

@@ -25,7 +25,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.parquet._
 
-abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
+private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   self: SQLContext#SparkPlanner =>
 
   object HashJoin extends Strategy {

sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala

@@ -19,6 +19,7 @@ package org.apache.spark.sql.execution
 
 import scala.reflect.runtime.universe.TypeTag
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.{HashPartitioner, SparkConf, SparkContext}
 import org.apache.spark.rdd.{RDD, ShuffledRDD}
 import org.apache.spark.sql.catalyst.ScalaReflection
@@ -27,6 +28,10 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{OrderedDistribution, UnspecifiedDistribution}
 import org.apache.spark.util.MutablePair
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode {
   override def output = projectList.map(_.toAttribute)
 
@@ -36,6 +41,10 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends
   }
 }
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
   override def output = child.output
 
@@ -44,6 +53,10 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
   }
 }
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class Sample(fraction: Double, withReplacement: Boolean, seed: Int, child: SparkPlan)
     extends UnaryNode {
 
@@ -53,6 +66,10 @@ case class Sample(fraction: Double, withReplacement: Boolean, seed: Int, child:
   override def execute() = child.execute().sample(withReplacement, fraction, seed)
 }
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class Union(children: Seq[SparkPlan])(@transient sc: SparkContext) extends SparkPlan {
   // TODO: attributes output by union should be distinct for nullability purposes
   override def output = children.head.output
@@ -62,12 +79,14 @@ case class Union(children: Seq[SparkPlan])(@transient sc: SparkContext) extends
 }
 
 /**
+ * :: DeveloperApi ::
  * Take the first limit elements. Note that the implementation is different depending on whether
  * this is a terminal operator or not. If it is terminal and is invoked using executeCollect,
  * this operator uses Spark's take method on the Spark driver. If it is not terminal or is
  * invoked using execute, we first take the limit on each partition, and then repartition all the
  * data to a single partition to compute the global limit.
  */
+@DeveloperApi
 case class Limit(limit: Int, child: SparkPlan)(@transient sc: SparkContext) extends UnaryNode {
   // TODO: Implement a partition local limit, and use a strategy to generate the proper limit plan:
   // partition local limit -> exchange into one partition -> partition local limit again
@@ -91,10 +110,12 @@ case class Limit(limit: Int, child: SparkPlan)(@transient sc: SparkContext) exte
 }
 
 /**
+ * :: DeveloperApi ::
  * Take the first limit elements as defined by the sortOrder. This is logically equivalent to
  * having a [[Limit]] operator after a [[Sort]] operator. This could have been named TopK, but
  * Spark's top operator does the opposite in ordering so we name it TakeOrdered to avoid confusion.
  */
+@DeveloperApi
 case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan)
                       (@transient sc: SparkContext) extends UnaryNode {
   override def otherCopyArgs = sc :: Nil
@@ -111,7 +132,10 @@ case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan)
   override def execute() = sc.makeRDD(executeCollect(), 1)
 }
 
-
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class Sort(
     sortOrder: Seq[SortOrder],
     global: Boolean,
@@ -134,6 +158,10 @@ case class Sort(
   override def output = child.output
 }
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 object ExistingRdd {
   def convertToCatalyst(a: Any): Any = a match {
     case s: Seq[Any] => s.map(convertToCatalyst)
@@ -167,6 +195,10 @@ object ExistingRdd {
   }
 }
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class ExistingRdd(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode {
   override def execute() = rdd
 }

sql/core/src/main/scala/org/apache/spark/sql/execution/debug.scala

@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution
 
-object DebugQuery {
+private[sql] object DebugQuery {
   def apply(plan: SparkPlan): SparkPlan = {
     val visited = new collection.mutable.HashSet[Long]()
     plan transform {
@@ -28,7 +28,7 @@ object DebugQuery {
   }
 }
 
-case class DebugNode(child: SparkPlan) extends UnaryNode {
+private[sql] case class DebugNode(child: SparkPlan) extends UnaryNode {
   def references = Set.empty
   def output = child.output
   def execute() = {

sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala

@@ -21,14 +21,24 @@ import scala.collection.mutable.{ArrayBuffer, BitSet}
 
 import org.apache.spark.SparkContext
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Partitioning}
 
+@DeveloperApi
 sealed abstract class BuildSide
+
+@DeveloperApi
 case object BuildLeft extends BuildSide
+
+@DeveloperApi
 case object BuildRight extends BuildSide
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class HashJoin(
     leftKeys: Seq[Expression],
     rightKeys: Seq[Expression],
@@ -130,6 +140,10 @@ case class HashJoin(
   }
 }
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNode {
   def output = left.output ++ right.output
 
@@ -138,6 +152,10 @@ case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNod
   }
 }
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class BroadcastNestedLoopJoin(
     streamed: SparkPlan, broadcast: SparkPlan, joinType: JoinType, condition: Option[Expression])
     (@transient sc: SparkContext)

sql/core/src/main/scala/org/apache/spark/sql/execution/package.scala

@@ -18,6 +18,7 @@
 package org.apache.spark.sql
 
 /**
+ * :: DeveloperApi ::
  * An execution engine for relational query plans that runs on top Spark and returns RDDs.
  *
  * Note that the operators in this package are created automatically by a query planner using a

sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala

@@ -28,7 +28,7 @@ import parquet.schema.{MessageType, MessageTypeParser}
 import org.apache.spark.sql.catalyst.expressions.GenericRow
 import org.apache.spark.util.Utils
 
-object ParquetTestData {
+private[sql] object ParquetTestData {
 
   val testSchema =
     """message myrecord {