Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/8573#discussion_r39212898
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
---
@@ -0,0 +1,79 @@
+/*
+ * 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.local
+
+import java.util.Random
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.util.random.{BernoulliCellSampler, PoissonSampler}
+
+/**
+ * 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.
+ * @param withReplacement Whether to sample with replacement.
+ * @param seed the random seed
+ * @param child the LocalNode
+ */
+case class SampleNode(
+ lowerBound: Double,
+ upperBound: Double,
+ withReplacement: Boolean,
+ seed: Long,
+ child: LocalNode) extends UnaryLocalNode {
+
+ override def output: Seq[Attribute] = child.output
+
+ private[this] var iterator: Iterator[InternalRow] = _
+
+ private[this] var currentRow: InternalRow = _
+
+ override def open(): Unit = {
+ child.open()
+ val (sampler, _seed) = if (withReplacement) {
+ val random = new Random(seed)
+ // Disable gap sampling since the gap sampling method buffers two
rows internally,
+ // requiring us to copy the row, which is more expensive than the
random number generator.
+ (new PoissonSampler[InternalRow](upperBound - lowerBound,
useGapSamplingIfPossible = false),
+ // Use the seed for partition 0 like PartitionwiseSampledRDD to
generate the same result
+ // of DataFrame
+ random.nextLong())
--- End diff --
hm, why not just use the provided seed? It will allow us to test this more
deterministically. We can just have the seed in the constructor default to
`Utils.random.nextLong`.
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