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From Jacques Nadeau <>
Subject Re: memory mapped record batches in Java
Date Sat, 25 Jul 2020 23:25:41 GMT
The current code doesn't preclude this path, it just doesn't have it
implemented. In many cases, a more intelligent algorithm can page data into
or out of main memory more efficiently (albeit with more work). This should
be fairly straightforward to do. The easiest way to get started would
probably be to implement a new allocation manager that uses MMap memory as
backing instead of the current ones (Netty [1] and Unsafe [2]). From there,
you could then enhance the reading to use that allocator to map the right
offsets into the existing vectors.


On Sat, Jul 25, 2020 at 5:46 AM Chris Nuernberger <>

> Hey, I am the author to a Clojure dataframe library,
> <> and we are looking to
> upgrade our ability to handle out-of-memory datasets.
> I was hoping to use Arrow for this purpose specifically to have a
> conversion mechanism where I could stream data into a single Arrow file
> with multiple record batches and then load that file and mmap each record
> batch.
> The current loading mechanism appears quite poor for this use case; it
> assumes batch-at-a-time loading by mutating member variables of the root
> schema and file loading mechanism and it copies each batch into process
> memory.
> It seems to me that, assuming each batch is less than 2 GB,
> could be used for each record batch and this would allow
> one to access data in those batches in a random-access order as opposed to
> a single in-order traverse and it may allow larger-than-memory files to be
> operated on.
> Is there any interest in this pathway? It seems like Arrow is quite close
> to realizing this possibility or that it is already possible from nearly
> all the other languages but the current Java design, unless I am misreading
> the code, precludes this pathway.
> Thanks for any thoughts, feedback,
> Chris

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