[SPARK-11113] [SQL] Remove DeveloperApi annotation from private classes.

o.a.s.sql.catalyst and o.a.s.sql.execution are supposed to be private.

Author: Reynold Xin <rxin@databricks.com>

Closes #9121 from rxin/SPARK-11113.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala

@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.rules
 import org.apache.spark.util.Utils
 
@@ -40,10 +39,8 @@ package object codegen {
   }
 
   /**
-   * :: DeveloperApi ::
    * Dumps the bytecode from a class to the screen using javap.
    */
-  @DeveloperApi
   object DumpByteCode {
     import scala.sys.process._
     val dumpDirectory = Utils.createTempDir()

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

@@ -19,7 +19,6 @@ package org.apache.spark.sql.execution
 
 import java.util.HashMap
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
@@ -28,7 +27,6 @@ import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
- * :: DeveloperApi ::
  * Groups input data by `groupingExpressions` and computes the `aggregateExpressions` for each
  * group.
  *
@@ -38,7 +36,6 @@ import org.apache.spark.sql.execution.metric.SQLMetrics
  * @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

@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution
 
 import java.util.Random
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.hash.HashShuffleManager
@@ -33,13 +33,10 @@ import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjectio
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.util.MutablePair
-import org.apache.spark._
 
 /**
- * :: DeveloperApi ::
  * Performs a shuffle that will result in the desired `newPartitioning`.
  */
-@DeveloperApi
 case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode {
 
   override def nodeName: String = if (tungstenMode) "TungstenExchange" else "Exchange"

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

@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters}
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
@@ -27,10 +26,7 @@ import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Row, SQLContext}
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
+
 object RDDConversions {
   def productToRowRdd[A <: Product](data: RDD[A], outputTypes: Seq[DataType]): RDD[InternalRow] = {
     data.mapPartitions { iterator =>

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

@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
@@ -32,7 +31,6 @@ import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartit
  * @param output      The output Schema
  * @param child       Child operator
  */
-@DeveloperApi
 case class Expand(
     projections: Seq[Seq[Expression]],
     output: Seq[Attribute],

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

@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
@@ -35,7 +34,6 @@ private[execution] sealed case class LazyIterator(func: () => TraversableOnce[In
 }
 
 /**
- * :: DeveloperApi ::
  * Applies a [[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
@@ -48,7 +46,6 @@ private[execution] sealed case class LazyIterator(func: () => TraversableOnce[In
  * @param output the output attributes of this node, which constructed in analysis phase,
  *               and we can not change it, as the parent node bound with it already.
  */
-@DeveloperApi
 case class Generate(
     generator: Generator,
     join: Boolean,

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

@@ -18,8 +18,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters}
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Attribute
 
 

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

@@ -17,18 +17,15 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.annotation.{Experimental, DeveloperApi}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.{InternalRow, optimizer}
-import org.apache.spark.sql.{SQLContext, Row}
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 
 /**
- * :: DeveloperApi ::
  * The primary workflow for executing relational queries using Spark.  Designed to allow easy
  * access to the intermediate phases of query execution for developers.
  */
-@DeveloperApi
 class QueryExecution(val sqlContext: SQLContext, val logical: LogicalPlan) {
   val analyzer = sqlContext.analyzer
   val optimizer = sqlContext.optimizer

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

@@ -21,7 +21,6 @@ import org.apache.spark._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.types.DataType
 
 private class ShuffledRowRDDPartition(val idx: Int) extends Partition {
   override val index: Int = idx

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

@@ -22,7 +22,6 @@ import java.util.concurrent.atomic.AtomicBoolean
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.{RDD, RDDOperationScope}
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.InternalRow
@@ -32,7 +31,7 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetric, SQLMetrics}
+import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetric}
 import org.apache.spark.sql.types.DataType
 
 object SparkPlan {
@@ -40,9 +39,8 @@ object SparkPlan {
 }
 
 /**
- * :: DeveloperApi ::
+ * The base class for physical operators.
  */
-@DeveloperApi
 abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializable {
 
   /**

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

@@ -17,19 +17,14 @@
 
 package org.apache.spark.sql.execution
 
-import java.util
-
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.types.IntegerType
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.collection.CompactBuffer
-import scala.collection.mutable
 
 /**
- * :: DeveloperApi ::
  * This class calculates and outputs (windowed) aggregates over the rows in a single (sorted)
  * partition. The aggregates are calculated for each row in the group. Special processing
  * instructions, frames, are used to calculate these aggregates. Frames are processed in the order
@@ -76,7 +71,6 @@ import scala.collection.mutable
  * Entire Partition, Sliding, Growing & Shrinking. Boundary evaluation is also delegated to a pair
  * of specialized classes: [[RowBoundOrdering]] & [[RangeBoundOrdering]].
  */
-@DeveloperApi
 case class Window(
     projectList: Seq[Attribute],
     windowExpression: Seq[NamedExpression],
@@ -229,7 +223,7 @@ case class Window(
     // function result buffer.
     val framedWindowExprs = windowExprs.groupBy(_.windowSpec.frameSpecification)
     val factories = Array.ofDim[() => WindowFunctionFrame](framedWindowExprs.size)
-    val unboundExpressions = mutable.Buffer.empty[Expression]
+    val unboundExpressions = scala.collection.mutable.Buffer.empty[Expression]
     framedWindowExprs.zipWithIndex.foreach {
       case ((frame, unboundFrameExpressions), index) =>
         // Track the ordinal.
@@ -529,7 +523,7 @@ private[execution] final class SlidingWindowFunctionFrame(
   private[this] var inputLowIndex = 0
 
   /** Buffer used for storing prepared input for the window functions. */
-  private[this] val buffer = new util.ArrayDeque[Array[AnyRef]]
+  private[this] val buffer = new java.util.ArrayDeque[Array[AnyRef]]
 
   /** Index of the row we are currently writing. */
   private[this] var outputIndex = 0

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

@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.{PartitionwiseSampledRDD, RDD, ShuffledRDD}
 import org.apache.spark.shuffle.sort.SortShuffleManager
 import org.apache.spark.sql.catalyst.InternalRow
@@ -28,10 +27,7 @@ import org.apache.spark.util.MutablePair
 import org.apache.spark.util.random.PoissonSampler
 import org.apache.spark.{HashPartitioner, SparkEnv}
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
+
 case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = projectList.map(_.toAttribute)
 
@@ -90,10 +86,6 @@ case class TungstenProject(projectList: Seq[NamedExpression], child: SparkPlan)
 }
 
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
 case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 
@@ -125,8 +117,8 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
 }
 
 /**
- * :: DeveloperApi ::
  * Sample the dataset.
+ *
  * @param lowerBound Lower-bound of the sampling probability (usually 0.0)
  * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled
  *                   will be ub - lb.
@@ -134,7 +126,6 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
  * @param seed the random seed
  * @param child the SparkPlan
  */
-@DeveloperApi
 case class Sample(
     lowerBound: Double,
     upperBound: Double,
@@ -165,9 +156,8 @@ case class Sample(
 }
 
 /**
- * :: DeveloperApi ::
+ * Union two plans, without a distinct. This is UNION ALL in SQL.
  */
-@DeveloperApi
 case class Union(children: Seq[SparkPlan]) extends SparkPlan {
   // TODO: attributes output by union should be distinct for nullability purposes
   override def output: Seq[Attribute] = children.head.output
@@ -179,14 +169,12 @@ case class Union(children: Seq[SparkPlan]) extends SparkPlan {
 }
 
 /**
- * :: 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 something similar to 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)
   extends UnaryNode {
   // TODO: Implement a partition local limit, and use a strategy to generate the proper limit plan:
@@ -219,14 +207,12 @@ case class Limit(limit: Int, child: SparkPlan)
 }
 
 /**
- * :: DeveloperApi ::
  * Take the first limit elements as defined by the sortOrder, and do projection if needed.
  * This is logically equivalent to having a [[Limit]] operator after a [[Sort]] operator,
  * or having a [[Project]] operator between them.
  * 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 TakeOrderedAndProject(
     limit: Int,
     sortOrder: Seq[SortOrder],
@@ -271,13 +257,11 @@ case class TakeOrderedAndProject(
 }
 
 /**
- * :: DeveloperApi ::
  * Return a new RDD that has exactly `numPartitions` partitions.
  * Similar to coalesce defined on an [[RDD]], this operation results in a narrow dependency, e.g.
  * if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of
  * the 100 new partitions will claim 10 of the current partitions.
  */
-@DeveloperApi
 case class Coalesce(numPartitions: Int, child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 
@@ -294,11 +278,9 @@ case class Coalesce(numPartitions: Int, child: SparkPlan) extends UnaryNode {
 }
 
 /**
- * :: DeveloperApi ::
  * Returns a table with the elements from left that are not in right using
  * the built-in spark subtract function.
  */
-@DeveloperApi
 case class Except(left: SparkPlan, right: SparkPlan) extends BinaryNode {
   override def output: Seq[Attribute] = left.output
 
@@ -308,11 +290,9 @@ case class Except(left: SparkPlan, right: SparkPlan) extends BinaryNode {
 }
 
 /**
- * :: DeveloperApi ::
  * Returns the rows in left that also appear in right using the built in spark
  * intersection function.
  */
-@DeveloperApi
 case class Intersect(left: SparkPlan, right: SparkPlan) extends BinaryNode {
   override def output: Seq[Attribute] = children.head.output
 
@@ -322,12 +302,10 @@ case class Intersect(left: SparkPlan, right: SparkPlan) extends BinaryNode {
 }
 
 /**
- * :: DeveloperApi ::
  * A plan node that does nothing but lie about the output of its child.  Used to spice a
  * (hopefully structurally equivalent) tree from a different optimization sequence into an already
  * resolved tree.
  */
-@DeveloperApi
 case class OutputFaker(output: Seq[Attribute], child: SparkPlan) extends SparkPlan {
   def children: Seq[SparkPlan] = child :: Nil
 

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

@@ -20,11 +20,10 @@ package org.apache.spark.sql.execution
 import java.util.NoSuchElementException
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters}
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
-import org.apache.spark.sql.catalyst.expressions.{ExpressionDescription, Expression, Attribute, AttributeReference}
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.types._
@@ -74,10 +73,7 @@ private[sql] case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan
   override def argString: String = cmd.toString
 }
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
+
 case class SetCommand(kv: Option[(String, Option[String])]) extends RunnableCommand with Logging {
 
   private def keyValueOutput: Seq[Attribute] = {
@@ -180,10 +176,7 @@ case class SetCommand(kv: Option[(String, Option[String])]) extends RunnableComm
  *
  * Note that this command takes in a logical plan, runs the optimizer on the logical plan
  * (but do NOT actually execute it).
- *
- * :: DeveloperApi ::
  */
-@DeveloperApi
 case class ExplainCommand(
     logicalPlan: LogicalPlan,
     override val output: Seq[Attribute] =
@@ -203,10 +196,7 @@ case class ExplainCommand(
   }
 }
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
+
 case class CacheTableCommand(
     tableName: String,
     plan: Option[LogicalPlan],
@@ -231,10 +221,6 @@ case class CacheTableCommand(
 }
 
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
 case class UncacheTableCommand(tableName: String) extends RunnableCommand {
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
@@ -246,10 +232,8 @@ case class UncacheTableCommand(tableName: String) extends RunnableCommand {
 }
 
 /**
- * :: DeveloperApi ::
  * Clear all cached data from the in-memory cache.
  */
-@DeveloperApi
 case object ClearCacheCommand extends RunnableCommand {
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
@@ -260,10 +244,7 @@ case object ClearCacheCommand extends RunnableCommand {
   override def output: Seq[Attribute] = Seq.empty
 }
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
+
 case class DescribeCommand(
     child: SparkPlan,
     override val output: Seq[Attribute],
@@ -286,9 +267,7 @@ case class DescribeCommand(
  * {{{
  *    SHOW TABLES [IN databaseName]
  * }}}
- * :: DeveloperApi ::
  */
-@DeveloperApi
 case class ShowTablesCommand(databaseName: Option[String]) extends RunnableCommand {
 
   // The result of SHOW TABLES has two columns, tableName and isTemporary.

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

@@ -20,7 +20,6 @@ package org.apache.spark.sql.execution.joins
 import scala.concurrent._
 import scala.concurrent.duration._
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Expression
@@ -31,13 +30,11 @@ import org.apache.spark.util.ThreadUtils
 import org.apache.spark.{InternalAccumulator, TaskContext}
 
 /**
- * :: DeveloperApi ::
  * Performs an inner hash join of two child relations.  When the output RDD of this operator is
  * being constructed, a Spark job is asynchronously started to calculate the values for the
  * broadcasted relation.  This data is then placed in a Spark broadcast variable.  The streamed
  * relation is not shuffled.
  */
-@DeveloperApi
 case class BroadcastHashJoin(
     leftKeys: Seq[Expression],
     rightKeys: Seq[Expression],

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

@@ -20,7 +20,6 @@ package org.apache.spark.sql.execution.joins
 import scala.concurrent._
 import scala.concurrent.duration._
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
@@ -31,13 +30,11 @@ import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.{InternalAccumulator, TaskContext}
 
 /**
- * :: DeveloperApi ::
  * Performs a outer hash join for two child relations.  When the output RDD of this operator is
  * being constructed, a Spark job is asynchronously started to calculate the values for the
  * broadcasted relation.  This data is then placed in a Spark broadcast variable.  The streamed
  * relation is not shuffled.
  */
-@DeveloperApi
 case class BroadcastHashOuterJoin(
     leftKeys: Seq[Expression],
     rightKeys: Seq[Expression],

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

@@ -18,7 +18,6 @@
 package org.apache.spark.sql.execution.joins
 
 import org.apache.spark.{InternalAccumulator, TaskContext}
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
@@ -26,11 +25,9 @@ import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
- * :: DeveloperApi ::
  * Build the right table's join keys into a HashSet, and iteratively go through the left
  * table, to find the if join keys are in the Hash set.
  */
-@DeveloperApi
 case class BroadcastLeftSemiJoinHash(
     leftKeys: Seq[Expression],
     rightKeys: Seq[Expression],

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

@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.execution.joins
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
@@ -27,10 +26,7 @@ import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.util.collection.CompactBuffer
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
+
 case class BroadcastNestedLoopJoin(
     left: SparkPlan,
     right: SparkPlan,

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

@@ -17,17 +17,13 @@
 
 package org.apache.spark.sql.execution.joins
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Attribute, JoinedRow}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 import org.apache.spark.sql.execution.metric.SQLMetrics
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
+
 case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNode {
   override def output: Seq[Attribute] = left.output ++ right.output
 

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

@@ -17,9 +17,6 @@
 
 package org.apache.spark.sql.execution.joins
 
-import java.util.{HashMap => JavaHashMap}
-
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
@@ -27,7 +24,7 @@ import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.util.collection.CompactBuffer
 
-@DeveloperApi
+
 trait HashOuterJoin {
   self: SparkPlan =>
 
@@ -230,8 +227,8 @@ trait HashOuterJoin {
   protected[this] def buildHashTable(
       iter: Iterator[InternalRow],
       numIterRows: LongSQLMetric,
-      keyGenerator: Projection): JavaHashMap[InternalRow, CompactBuffer[InternalRow]] = {
-    val hashTable = new JavaHashMap[InternalRow, CompactBuffer[InternalRow]]()
+      keyGenerator: Projection): java.util.HashMap[InternalRow, CompactBuffer[InternalRow]] = {
+    val hashTable = new java.util.HashMap[InternalRow, CompactBuffer[InternalRow]]()
     while (iter.hasNext) {
       val currentRow = iter.next()
       numIterRows += 1

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

@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.execution.joins
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
@@ -26,11 +25,9 @@ import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
- * :: DeveloperApi ::
  * Using BroadcastNestedLoopJoin to calculate left semi join result when there's no join keys
  * for hash join.
  */
-@DeveloperApi
 case class LeftSemiJoinBNL(
     streamed: SparkPlan, broadcast: SparkPlan, condition: Option[Expression])
   extends BinaryNode {