Hi,
I have run into a weird caching problem (Using Spark 1.3.1 + Java 1.8.0) that I
can only explain as a bug.
In summary, I source the RDD from an Avro file, I apply a mapToPair Function,
count & cache. However, the RDD is not cached nor it appears in Spark UI
Storage. (This is not cached at all, not even partially)
JavaSparkContext ctx = …;
JavaRDD a = ….;
JavaPairRDD b = a.mapToPaiR(..).cache();
b.count(); //RDD is not cached.
I looked around but could not find any known bugs around this.
I debugged the b RDD and it is set as cached:
(80) MapPartitionsRDD[31] at mapToPair at ABC.java:684 [Memory Deserialized 1x
Replicated]
| RDD1 MapPartitionsRDD[22] at map at XXXAvroDao.java:xx [Memory Deserialized
1x Replicated]
| MapPartitionsRDD[21] at keys at XXXAvroDao.java:xx [Memory Deserialized 1x
Replicated]
| maprfs:/mapr/XXX NewHadoopRDD[20] at newAPIHadoopFile at XXXAvroDao.java:xx
[Memory Deserialized 1x Replicated]
I also checked the b RDD storage level using a debugger and it seems correctly
set as well.
StorageLevel(false, true, false, true, 1)
Now thing get more interesting as the following does result in cached rdd:
a.cache().count();
Also the following works:
ctx.parallelise(b.take(1000)).cache().count();
However, any attempts to “fool” b.cache() fail as well(action completes but
data are not cached at all). E.g.
b.repartition(150).cache().count();
b.values().cache().count();
b.keys().cache().count();
b.persist(StorageLevel.DISK_ONLY()).count();
b.persist(StorageLevel.MEMORY_ONLY()).count();
b.persist(StorageLevel.MEMORY_ONLY_SER()).count();
b.unpersist().cache().count();
I haven’t managed to replicate the issue without the exact data, to be able to
provide a reproducible example as it works just fine in any other data types I
have or any example I tried.
Any ideas on where I should look?
Thanks.
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