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@vegarsti vegarsti commented Oct 11, 2025

Which issue does this PR close?

Rationale for this change

This PR implements casting support for RunEndEncoded arrays in Apache Arrow.

What changes are included in this PR?

  • run_end_encoded_cast in arrow-cast/src/cast/run_array.rs
  • cast_to_run_end_encoded in arrow-cast/src/cast/run_array.rs
  • Tests in arrow-cast/src/cast/mod.rs

Are these changes tested?

Yes!

Are there any user-facing changes?

No breaking changes, just new functionality

@github-actions github-actions bot added the arrow Changes to the arrow crate label Oct 11, 2025
@vegarsti vegarsti changed the title Casting to/from RunEndEncoded arrays Casting support for RunEndEncoded arrays Oct 11, 2025
@vegarsti vegarsti force-pushed the cast-run-end-encoded-arrays branch 2 times, most recently from 87e543d to f9ae6f9 Compare October 11, 2025 06:25
@vegarsti
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Raised this PR to get Richard Baah's excellent work over the line! cc @albertlockett @brancz @alamb @Rich-T-kid

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I think we're getting close to the finish line with these changes!

rich-t-kid-datadog and others added 10 commits October 12, 2025 07:49
Implement casting between REE arrays and other Arrow types. REE-to-REE casting
validates run-end upcasts only (Int16→Int32, Int16→Int64, Int32→Int64) to prevent
invalid sequences.
Implement casting between REE arrays and other Arrow types. REE-to-REE casting
validates run-end upcasts only (Int16→Int32, Int16→Int64, Int32→Int64) to prevent
invalid sequences.

rebased changes
@vegarsti vegarsti force-pushed the cast-run-end-encoded-arrays branch from 88c0d8a to 2358010 Compare October 12, 2025 05:49
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tustvold commented Oct 12, 2025

Is there some way we can avoid the quadratic codegen with code paths parameterized on both run end type and value type? Perhaps it'd be possible to identify where the transitions are, perhaps using the comparison kernels and comparing the array with a slice offset by one, and then use this to construct the indexes and a filter to construct the values array?

Have we done any empirical quantification into the impact this has on code bloat / compile times?

Edit: https://docs.rs/arrow-ord/latest/arrow_ord/partition/fn.partition.html is the function I'm thinking of.

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vegarsti commented Oct 13, 2025

Have we done any empirical quantification into the impact this has on code bloat / compile times?

I have not! Happy to do that though. Any pointers to how you'd like me to do that, from previous PRs for example? Or does a basic comparison of compile time and binary size on main and this branch suffice?

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Or does a basic comparison of compile time and binary size on main and this branch suffice?

Just this, quadratic codegen is typically severe enough to be easily measurable.

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The compile time increased by 2 seconds.

         cargo build --release
main     569.35s user 23.69s system 863% cpu 1:08.66 total
branch   567.33s user 23.96s system 891% cpu 1:06.33 total

The size of libarrow_cast.rlib increased by 279kb (3.82%)

         libarrow_cast.rlib size
main     7,316,832
branch   7,596,568

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tustvold commented Oct 13, 2025

Yeah... That's quite bad for a single kernel, especially given the relatively niche usage of RunEndEncodedArrays, I hope you can understand that we need to be careful to keep this under control.

What did you think of my suggestion about using the partition kernel to compute the run ends? It might actually be faster and would largely eliminate the additional codegen.

It would mean making arrow-cast depend on arrow-ord, which is a bit meh, but perhaps unavoidable. It could possibly be a feature flag. 🤔

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vegarsti commented Oct 13, 2025

I understand! I haven't had time to look into your suggestion, but I will.

Out of curiosity, though, the approach in this PR seems quite similar to the code for dictionaries. Does the dictionary code similarly bloat the binary, and if so, why is that acceptable but not for REE?

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tustvold commented Oct 13, 2025

Out of curiosity, though, the approach in this PR seems quite similar to the code for dictionaries. Does the dictionary code similarly bloat the binary, and if so, why is that acceptable but not for REE?

Dictionaries run into similar challenges, and a lot of effort has been expended trying to mitigate the bloat they cause. For example #3616 #4705 #4701 to name a few. Ultimately it's a compromise, there isn't a way to avoid this bloat and support dictionaries so we pay the tax, with run-end encoded arrays the tax isn't necessary and so it is better we don't pay it.

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Out of curiosity, though, the approach in this PR seems quite similar to the code for dictionaries. Does the dictionary code similarly bloat the binary, and if so, why is that acceptable but not for REE?

Dictionaries run into similar challenges, and a lot of effort has been expended trying to mitigate the bloat they cause. For example #3616 #4705 #4701 to name a few. Ultimately it's a compromise, there isn't a way to avoid this bloat and support dictionaries so we pay the tax, with run-end encoded arrays the tax isn't necessary and so it is better we don't pay it.

Thanks for the context!

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brancz commented Oct 13, 2025

We're talking about the pack_runs macro right? I realize it's nice as a macro, but it also seems fine to just write out by hand.

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@tustvold I've addressed your comments now, let me know what you think. Thanks for the helpful and quick review!

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Will you be able to have a look at it soon @tustvold? Sounded like you were almost ready to approve it 😄 (Sorry for nagging!)

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Love this approach with ord, also reads far simpler than before. Good suggestion @tustvold and nice work @vegarsti!

(I can't actually approve, but symbolically I'm approving this)

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Thanks for the symbolic approve! 😄

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Thank you @vegarsti @brancz and @tustvold

I went through this PR carefully and while I have a few suggestions for comments and additional tests I think they could be done as follow on PRs too if you prefer

What I suggest as a flow is:

  1. @vegarsti addresses any comments they would like
  2. File follow on tickets: benchmark for REE casting, and some ideas for performance optimizations.
  3. Merge this PR

Thanks again 🚀

arrow-array = { workspace = true }
arrow-buffer = { workspace = true }
arrow-data = { workspace = true }
arrow-ord = { workspace = true }
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I think we are trying to keep the number of dependencies to a minimum

I see this is used to call partition which is clever but overly general. I think you can also partition a (single) column using eq and an offset to look for consecutive rows which are different.

Something like

let arr = ...;
let arr_shift1 = arr.slice(1, arr.len()-1);
let transitions = eq(arr, arr_shift_1);

However, the eq kernels is also in arrow-ord so I am not sure there is a way around it

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Oh, that's nice!

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Does it suffice here to file an issue for potentially getting rid of this dependency?

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The way I'm reading this is eq might be faster but won't get rid of the dependency, but getting rid of the dependency would be the best option. Is that correct?

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Filed #8708 for getting rid of arrow-ord and #8707 for performance improvement

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alamb commented Oct 23, 2025

looks like there are some minor CI issues to address too

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vegarsti commented Oct 23, 2025

Looks like someone triggered CI - thank you! There are failures, will address tomorrow.

Edit: Oops, posted this before seeing the review.

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Thanks so much @alamb, this is excellent! Will ping you again after addressing.

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vegarsti commented Oct 25, 2025

@alamb I've addressed your very helpful review, thank you so much. I think this is ready to merge now - if you agree, that is. I've filed issues

@vegarsti vegarsti changed the title Casting support for RunEndEncoded arrays Cast support for RunEndEncoded arrays Oct 25, 2025
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@alamb I've addressed your very helpful review, thank you so much. I think this is ready to merge now - if you agree, that is. I've filed issues

PR for #8709: #8710

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alamb commented Oct 26, 2025

I pushed a commit to solve the RAT ci test, and merged up from main

https://github.com/apache/arrow-rs/actions/runs/18813730483/job/53685630690?pr=8589

@alamb alamb merged commit c149027 into apache:main Oct 26, 2025
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alamb commented Oct 26, 2025

Thank you @vegarsti

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5 participants