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/*
* 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.rdd
import org.apache.spark.{SharedSparkContext, SparkFunSuite}
import org.apache.spark.util.random.{BernoulliSampler, PoissonSampler, RandomSampler}
/** a sampler that outputs its seed */
class MockSampler extends RandomSampler[Long, Long] {
private var s: Long = _
override def setSeed(seed: Long) {
s = seed
}
override def sample(items: Iterator[Long]): Iterator[Long] = {
Iterator(s)
}
override def clone: MockSampler = new MockSampler
}
class PartitionwiseSampledRDDSuite extends SparkFunSuite with SharedSparkContext {
test("seed distribution") {
val rdd = sc.makeRDD(Array(1L, 2L, 3L, 4L), 2)
val sampler = new MockSampler
val sample = new PartitionwiseSampledRDD[Long, Long](rdd, sampler, false, 0L)
assert(sample.distinct().count == 2, "Seeds must be different.")
}
test("concurrency") {
// SPARK-2251: zip with self computes each partition twice.
// We want to make sure there are no concurrency issues.
val rdd = sc.parallelize(0 until 111, 10)
for (sampler <- Seq(new BernoulliSampler[Int](0.5), new PoissonSampler[Int](0.5))) {
val sampled = new PartitionwiseSampledRDD[Int, Int](rdd, sampler, true)
sampled.zip(sampled).count()
}
}
}
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