Github user JoshRosen commented on the issue:
https://github.com/apache/spark/pull/16989
I think that the current use of `MemoryMode.OFF_HEAP` allocation will cause
problems in out-of-the-box deployments using the default configurations. In
Spark's current memory manager implementation the total amount of Spark-managed
off-heap memory that we will use is controlled by `spark.memory.offHeap.size`
and the default value is 0. In this PR, the comment on
`spark.reducer.maxReqSizeShuffleToMem` says that it should be smaller than
`spark.memory.offHeap.size` and yet the default is 200 megabytes so the default
configuration is invalid.
Because `preferDirectBufs()` is `true` by default it looks like the code
here will always try to reserve memory using `MemoryMode.OFF_HEAP` and these
reservations will always fail in the default configuration because the off-heap
size will be zero, so I think the net effect of this patch will be to always
spill to disk.
One way to address this problem is to configure the default value of
`spark.memory.offHeap.size` to match the JVM's internal limit on the amount of
direct buffers that it can allocate minus some percentage or fixed overhead.
Basically the problem is that Spark's off-heap memory manager was originally
designed to only manage off-heap memory explicitly allocated by Spark itself
when creating its own buffers / pages or caching blocks, not to account for
off-heap memory used by lower-level code or third-party libraries. I'll see if
I can think of a clean way to fix this, which I think will need to be done
before the defaults used here can work as intended.
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