Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/7334#discussion_r51960794
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -0,0 +1,116 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical._
+
+
+/**
+ * Take the first `limit` elements and collect them to a single partition.
+ *
+ * This operator will be used when a logical `Limit` operation is the
final operator in an
+ * logical plan, which happens when the user is collecting results back to
the driver.
+ */
+case class CollectLimit(limit: Int, child: SparkPlan) extends UnaryNode {
+ override def output: Seq[Attribute] = child.output
+ override def outputPartitioning: Partitioning = SinglePartition
+ override def executeCollect(): Array[InternalRow] =
child.executeTake(limit)
+ protected override def doExecute(): RDD[InternalRow] =
sparkContext.makeRDD(executeCollect(), 1)
--- End diff --
Similar to ReturnAnswer, we could put a CachePlan() as the root (See
CacheManager.cacheQuery), and have a special case for it.
If it's not trivial, we could just leave a TODO for this.
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