java.lang.StackOverflowError when calling count()

最新推荐文章于 2025-12-11 19:13:56 发布

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本文探讨了在Spark中通过缓存和检查点来处理长线程导致的Stack Overflow错误的方法，包括如何在特定迭代中进行缓存和检查点操作，以及在内存数据丢失时使用复制和新Block RDD避免写入HDFS和防止错误。

Just to add some more clarity in the discussion, there is a difference between caching to memory and checkpointing, when considered from the lineage point of view.

When an RDD in checkpointed, the data of the RDD is saved to HDFS (or any Hadoop API compatible fault-tolerant storage) and the lineage of the RDD is truncated. This is okay because in case of the worker failure, the RDD data can be read back from the fault-tolerant storage.

When an RDD is cached, the data of the RDD is cached in memory, but the lineage is not truncated. This is because if the in-memory data is lost, the lineage is required to recompute the data.

So to deal with stackoverflow errors due to long lineage, just caching is not going to be useful. You have to checkpoint the RDD, and as far as I think, its correct way to do this is to do the following

1. Mark RDD of every Nth iteration for caching and checkpointing (both).

2. And before generating N+1 th iteration RDD, force the materialization of this RDD by doing a rdd.count(). This will persist the RDD in memory as well as save to HDFS and truncate the lineage. If you just mark all Nth iteration RDD for checkpointing, but only force the materialization after ALL the iterations (not after every N+1 th iteration as I suggested) that will still lead to stackoverflow errors.

Yes this checkpointing and materialization is definitely decrease performance, but that is the limitation of the current implementation.

If you are brave enough, you can try the following. Instead of relying on checkpointing to HDFS for truncating lineage, you can do the following.

1. Persist Nth RDD with replication (see different StorageLevels), this would replicated the in-memory RDD between workers within Spark. Lets call this RDD as R.

2. Force it materialize in the memory.

3. Create a modified RDD R` which has the same data as RDD R but does not have the lineage. This is done by creating a new BlockRDD using the ids of blocks of data representing the in-memory R (can elaborate on that if you want).

This will avoid writing to HDFS (replication in the Spark memory), but truncate the lineage (by creating new BlockRDDs), and avoid stackoverflow error.

Hope this helps

The long lineage causes a long/deep Java object tree (DAG of RDD objects), which needs to be serialized as part of the task creation. When serializing, the whole object DAG needs to be traversed leading to the stackoverflow error.