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https://issues.apache.org/jira/browse/SPARK-3693?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14148306#comment-14148306
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Reynold Xin commented on SPARK-3693:
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Just responded to you offline as well. This is a known issue.
The problem is that not all record types implement copy. As a matter of fact,
even some common ones like Avro don't deal with that as well. For now the
workaround is really to call copy by the user since the user knows what record
type they are using.
> Cached Hadoop RDD always return rows with the same value
> --------------------------------------------------------
>
> Key: SPARK-3693
> URL: https://issues.apache.org/jira/browse/SPARK-3693
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.2.0
> Reporter: Xuefu Zhang
>
> While trying RDD caching, it's found that caching a Hadoop RDD causes data
> correctness issues. The following code snippet demonstrates the usage:
> {code}
> public final class Test {
> public static void main(String[] args) throws Exception {
> SparkConf sparkConf = new SparkConf().setAppName("Test");
> JavaSparkContext ctx = new JavaSparkContext(sparkConf);
> ...
> JavaPairRDD<BytesWritable, BytesWritable> input =
> ctx.hadoopRDD(jobConf, CombineHiveInputClass.class,
> WritableComparable.class, Writable.class);
> input = input.cache();
> input.foreach(new VoidFunction<Tuple2<BytesWritable,
> BytesWritable>>() {
> @Override
> public void call(Tuple2<BytesWritable, BytesWritable> row) throws
> Exception {
> if (row._1() != null) {
> System.out.println("Key: " + row._1());
> }
> if (row._2() != null) {
> System.out.println("Value: " + row._2());
> }
> }
> });
> ctx.stop();
> }
> }
> {code}
> In this case, row._2() always gives the same value. If we disable caching by
> removing input.cache(), the program gives the expected rows.
> Further analysis shows that MemoryStore (see
> https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala#L236)
> is storing the references to (key, value) pairs returned by
> HadoopRDD.getNext() (See
> https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala#L220),
> but this method always returns the same (key, value) object references,
> except each getNext() call updates values inside these objects. When there
> are no more records (key, value) objects are filled with empty strings (no
> values) in CombineFileRecordReader. As all pairs in MemoryStore.vector refer
> to the same key, value object pairs, all values become NULL.
> Probably MemoryStore should instead store a copy of <key, value> pair rather
> than keeping a reference to it.
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