Github user pwendell commented on a diff in the pull request:
https://github.com/apache/incubator-spark/pull/180#discussion_r10020917
--- Diff:
core/src/test/scala/org/apache/spark/storage/LargeIteratorSuite.scala ---
@@ -0,0 +1,61 @@
+/*
+ * 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.storage
+
+import org.scalatest.FunSuite
+import org.apache.spark.{LocalSparkContext, SparkContext}
+import org.apache.commons.io.FileUtils
+import java.io.File
+
+class Expander(base:String, count:Int) extends Iterator[String] {
+ var i = 0;
+ def next() : String = {
+ i += 1;
+ return base + i.toString;
+ }
+ def hasNext() : Boolean = i < count;
+}
+
+object Expander {
+ def expand(s:String, i:Int) : Iterator[String] = {
+ return new Expander(s,i)
+ }
+}
+
+class LargeIteratorSuite extends FunSuite with LocalSparkContext {
+ /* Tests the ability of Spark to deal with user provided iterators that
+ * generate more data then available memory. In any memory based
persistance
+ * Spark will unroll the iterator into an ArrayBuffer for caching,
however in
+ * the case that the use defines DISK_ONLY persistance, the iterator
will be
+ * fed directly to the serializer and written to disk.
+ */
+ val clusterUrl = "local-cluster[1,1,512]"
+ test("Flatmap iterator") {
+ sc = new SparkContext(clusterUrl, "mem_test");
+ val seeds = sc.parallelize( Array(
+ "This is the first sentence that we will test:",
+ "This is the second sentence that we will test:",
+ "This is the third sentence that we will test:"
+ ) );
+ val expand_size = 10000000;
--- End diff --
It seems like this test writes a very large amount of data to disk. Spark
has hundreds of tests and each one can only take a short amount of time. So it
would be great if you could write a test that just writes a small amount of
data to disk.
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