Hello Arrow devs, I don't understand why we would start deprecating features in the Arrow > format. Even starting this talk might already be a bad idea PR-wise. >
I agree we don't want to make breaking changes to the Arrow format. But several maintainers have already stated they have no interest in maintaining both list types with full compute functionality [1][2], so I think it's very likely one list type or the other will be implicitly preferred in the ecosystem if this data type was added. If that's the case, I'd prefer that we agreed as a community which one should be preferred. Maybe that's not the best path; it's just one way for us to balance stability, maintenance burden, and relevance. Can someone help distill down the primary rationale and usecase for > adding ArrayView to the Arrow Spec? > Looking back at that old thread, I think one of the main motivations is to try to prevent query engine implementers from feeling there is a tradeoff between having state-of-the-art performance and being Arrow-native. For some of the new array types, we had both Velox and DuckDB use them, so it was reasonable to expect they were innovations that might proliferate. I'm not sure if the ArrayView is part of that. From Wes earlier [3]: The idea is that in a world of data and query federation (for example, > consider [1] where Arrow is being used as a data federation layer with many > query engines), we want to increase the amount of data in-flight and > in-memory that is in Arrow format. So if query engines are having to depart > substantially from the Arrow format to get performance, then this creates a > potential lose-lose situation: * Depart from Arrow: get better performance > but pay serialization costs to read and write Arrow (the performance and > resource utilization benefits outweigh the serialization costs). This puts > additional pressure on query engines to build specialized components for > solving problems rather than making use of off-the-shelf components that > use Arrow. This has knock-on effects on ecosystem fragmentation. * Or use > Arrow, and accept suboptimal query processing performance > Will mentions one usecase is Velox consuming python UDF output, which seems > to be mostly about how fast Velox can consume this format, not how fast it > can be written. Are there other usecases? > To be clear, I don't know if that's the use case they want. That's just me speculating. I still have some questions myself: 1. Is this array type currently only used in Velox? (not DuckDB like some of the other new types?) What evidence do we have that it will become used outside of Velox? 2. We already have three list types: list, large list (64-bit offsets), and fixed size list. Do we think we will only want a view version of the 32-bit offset variable length list? Or are we potentially talking about view variants for all three? Best, Will Jones [1] https://lists.apache.org/thread/smn13j1rnt23mb3fwx75sw3f877k3nwx [2] https://lists.apache.org/thread/cc4w3vs3foj1fmpq9x888k51so60ftr3 [3] https://lists.apache.org/thread/mk2yn62y6l8qtngcs1vg2qtwlxzbrt8t On Mon, May 22, 2023 at 3:48 AM Andrew Lamb <al...@influxdata.com> wrote: > Can someone help distill down the primary rationale and usecase for > adding ArrayView to the Arrow Spec? > > From the above discussions, the stated rationale seems to be fast > (zero-copy) interchange with Velox. > > This thread has qualitatively enumerated the benefits of (offset+len) > encoding over the existing Arrow ListArray (offets) approach, but I haven't > seen any performance measurements that might help us to gauge the tradeoff > in additional complexity vs runtime overhead. > > Will mentions one usecase is Velox consuming python UDF output, which seems > to be mostly about how fast Velox can consume this format, not how fast it > can be written. Are there other usecases? > > Do we have numbers showing how much overhead converting to /from Velox's > internal representation and the existing ListArray adds? Has anyone in > Velox land considered adding faster support for Arrow style ListArray > encoding? > > > Andrew > > On Mon, May 22, 2023 at 4:38 AM Antoine Pitrou <anto...@python.org> wrote: > > > > > Hi, > > > > I don't understand why we would start deprecating features in the Arrow > > format. Even starting this talk might already be a bad idea PR-wise. > > > > As for implementing conversions at the I/O boundary, it's a reasonably > > policy, but it still requires work by implementors and it's not granted > > that all consumers of the Arrow format will grow such conversions > > if/when we add non-trivial types such as ListView or StringView. > > > > Regards > > > > Antoine. > > > > > > Le 22/05/2023 à 00:39, Will Jones a écrit : > > > One more thing: Looking back on the previous discussion[1] (which > Weston > > > pointed out in his earlier message), Jorge suggested that the old list > > > types might be deprecated in favor of view variants [2]. Others were > > > worried that it might undermine the perception that the Arrow format is > > > stable. I think it might be worth thinking about "soft deprecating" the > > old > > > list type: suggesting new implementations prefer the list view, but > > > reassuring that implementations should support the old format, even if > > they > > > just convert to the new format. To be clear, this wouldn't mean we plan > > to > > > create breaking changes in the format; but if we ever did for other > > > reasons, the old list type might go. > > > > > > Arrow compute libraries could choose either format for compute support, > > and > > > plan to do conversion at the boundaries. Libraries that use the new > type > > > will have cheap conversion when reading the old type. Meanwhile those > > that > > > are still on the old type will have some incentive to move towards the > > new > > > one, since that conversion will not be as efficient. > > > > > > [1] https://lists.apache.org/thread/49qzofswg1r5z7zh39pjvd1m2ggz2kdq > > > [2] https://lists.apache.org/thread/smn13j1rnt23mb3fwx75sw3f877k3nwx > > > > > > On Sun, May 21, 2023 at 3:07 PM Will Jones <will.jones...@gmail.com> > > wrote: > > > > > >> Hello, > > >> > > >> I think Sasha brings up a good point, that the advantages of this > format > > >> seem to be primarily about query processing. Other encodings like REE > > and > > >> dictionary have space-saving advantages that justify them simply in > > terms > > >> of space efficiency (although they have query processing advantages as > > >> well). As discussed, most use cases are already well served by > existing > > >> list types and dictionary encoding. > > >> > > >> I agree that there are cases where transferring this type without > > >> conversion would be ideal. One use case I can think of is if Velox > > wants to > > >> be able to take Arrow-based UDFs (possibly written with PyArrow, for > > >> example) that operate on this column type and therefore wants > zero-copy > > >> exchange over the C Data Interface. > > >> > > >> One big question I have: we already have three list types: list, large > > >> list (64-bit offsets), and fixed size list. Do we think we will only > > want a > > >> view version of the 32-bit offset variable length list? Or are we > > >> potentially talking about view variants for all three? > > >> > > >> Best, > > >> > > >> Will Jones > > >> > > >> > > >> On Sun, May 21, 2023 at 2:19 PM Felipe Oliveira Carvalho < > > >> felipe...@gmail.com> wrote: > > >> > > >>> The benefit of having a memory format that’s friendly to > > non-deterministic > > >>> order writes is unlocked by the transport and processing of the data > > being > > >>> agnostic to the physical order as much as possible. > > >>> > > >>> Requiring a conversion could cancel out that benefit. But it can be a > > >>> provisory step for compatibility between systems that don’t > understand > > the > > >>> format yet. This is similar to the situation with compression schemes > > like > > >>> run-end encoding — the goal is processing the compressed data > directly > > >>> without an expansion step whenever possible. > > >>> > > >>> This is why having it as part of the open Arrow format is so > important: > > >>> everyone can agree on a format that’s friendly to parallel and/or > > >>> vectorized compute kernels without introducing multiple incompatible > > >>> formats to the ecosystem and without imposing a conversion step > between > > >>> the > > >>> different systems. > > >>> > > >>> — > > >>> Felipe > > >>> > > >>> On Sat, 20 May 2023 at 20:04 Aldrin <octalene....@pm.me.invalid> > > wrote: > > >>> > > >>>> I don't feel like this representation is necessarily a detail of the > > >>> query > > >>>> engine, but I am also not sure why this representation would have to > > be > > >>>> converted to a non-view format when serializing. Could you clarify > > >>> that? My > > >>>> impression is that this representation could be used for persistence > > or > > >>>> data transfer, though it can be more complex to guarantee the > portion > > of > > >>>> the buffer that an index points to is also present in memory. > > >>>> > > >>>> Sent from Proton Mail for iOS > > >>>> > > >>>> > > >>>> On Sat, May 20, 2023 at 15:00, Sasha Krassovsky < > > >>> krassovskysa...@gmail.com > > >>>> <On+Sat,+May+20,+2023+at+15:00,+Sasha+Krassovsky+%3C%3Ca+href=>> > > wrote: > > >>>> > > >>>> Hi everyone, > > >>>> I understand that there are numerous benefits to this representation > > >>>> during query processing, but would it be fair to say that this is an > > >>>> implementation detail of the query engine? Query engines don’t > > >>> necessarily > > >>>> need to conform to the Arrow format internally, only at > ingest/egress > > >>>> points, and performing a conversion from the non-view to view format > > >>> seems > > >>>> like it would be very cheap (though I understand not necessarily the > > >>> other > > >>>> way around, but you’d need to do that anyway if you’re serializing). > > >>>> > > >>>> Sasha Krassovsky > > >>>> > > >>>>> 20 мая 2023 г., в 13:00, Will Jones <will.jones...@gmail.com> > > >>>> написал(а): > > >>>>> > > >>>>> Thanks for sharing these details, Pedro. The conditional branches > > >>>> argument > > >>>>> makes a lot of sense to me. > > >>>>> > > >>>>> The tensors point brings up some interesting issues. For now, we've > > >>>> defined > > >>>>> our only tensor extension type to be built on a fixed size list. > If a > > >>> use > > >>>>> case of this might be manipulating tensors with zero copy, perhaps > > >>> that > > >>>>> suggests that we want a fixed size list variant? In addition, would > > we > > >>>> have > > >>>>> to define another extension type to be a ListView variant? Or would > > we > > >>>> want > > >>>>> to think about making extension types somehow valid across various > > >>>>> encodings of the same "logical type"? > > >>>>> > > >>>>>> On Fri, May 19, 2023 at 1:59 PM Pedro Eugenio Rocha Pedreira > > >>>>>> <pedro...@meta.com.invalid> wrote: > > >>>>>> > > >>>>>> Hi all, > > >>>>>> > > >>>>>> This is Pedro from the Velox team at Meta. This is my first time > > >>> here, > > >>>> so > > >>>>>> nice to e-meet you! > > >>>>>> > > >>>>>> Adding to what Felipe said, the main reason we created “ListView” > > >>>> (though > > >>>>>> we just call them ArrayVector/MapVector in Velox) is that, along > > with > > >>>>>> StringViews for strings, they allow us to write any columnar > buffer > > >>>>>> out-or-order, regardless of their types or encodings. This is > > >>> naturally > > >>>>>> doable for all primitive types (fixed-size), but not for types > that > > >>>> don’t > > >>>>>> have fixed size and are required to be contiguous. The StringView > > and > > >>>>>> ListView formats allow us to keep this invariant in Velox. > > >>>>>> > > >>>>>> Being able to write vectors out-of-order is useful when executing > > >>>>>> conditionals like IF/SWITCH statements, which are pervasive among > > our > > >>>>>> workloads. To fully vectorize it, one first evaluates the > > expression, > > >>>> then > > >>>>>> generate a bitmap containing which rows take the THEN and which > take > > >>> the > > >>>>>> ELSE branch. Then you populate all rows that match the first > branch > > >>> by > > >>>>>> evaluating the THEN expression in a vectorized (branch-less and > > cache > > >>>>>> friendly) way, and subsequently the ELSE branch. If you can’t > write > > >>> them > > >>>>>> out-of-order, you would either have a big branch per row > dispatching > > >>> to > > >>>> the > > >>>>>> right expression (slow), or populate two distinct vectors then > > >>> merging > > >>>> them > > >>>>>> at the end (potentially even slower). How much faster our approach > > is > > >>>>>> highly depends on the buffer sizes and expressions, but we found > it > > >>> to > > >>>> be > > >>>>>> faster enough on average to justify us extending the underlying > > >>> layout. > > >>>>>> > > >>>>>> With that said, this is all within a single thread of execution. > > >>>>>> Parallelization is done by feeding each thread its own > vector/data. > > >>> As > > >>>>>> pointed out in a previous message, this also gives you the > > >>> flexibility > > >>>> to > > >>>>>> implement cardinality increasing/reducing operations, but we don’t > > >>> use > > >>>> it > > >>>>>> for that purpose. Operations like filtering, joining, unnesting > and > > >>>> similar > > >>>>>> are done by wrapping the internal vector in a dictionary, as these > > >>> need > > >>>> to > > >>>>>> work not only on “ListViews” but on any data types with any > > encoding. > > >>>> There > > >>>>>> are more details on Section 4.2.1 in [1] > > >>>>>> > > >>>>>> Beyond this, it also gives function/kernel developers more > > >>> flexibility > > >>>> to > > >>>>>> implement operations that manipulate Arrays/Maps. For example, > > >>>> operations > > >>>>>> that slice these containers can be implemented in a zero-copy > manner > > >>> by > > >>>>>> just rearranging the lengths/offsets indices, without ever > touching > > >>> the > > >>>>>> larger internal buffers. This is a similar motivation as for > > >>> StringView > > >>>>>> (think of substr(), trim(), and similar). One nice last property > is > > >>> that > > >>>>>> this layout allows for overlapping ranges. This is something > > >>> discussed > > >>>> with > > >>>>>> our ML people to allow deduping feature values in a tensor (which > is > > >>>> fairly > > >>>>>> common), but not something we have leveraged just yet. > > >>>>>> > > >>>>>> [1] - https://vldb.org/pvldb/vol15/p3372-pedreira.pdf > > >>>>>> > > >>>>>> Best, > > >>>>>> -- > > >>>>>> Pedro Pedreira > > >>>>>> ________________________________ > > >>>>>> From: Felipe Oliveira Carvalho <felipe...@gmail.com> > > >>>>>> Sent: Friday, May 19, 2023 10:01 AM > > >>>>>> To: dev@arrow.apache.org <dev@arrow.apache.org> > > >>>>>> Cc: Pedro Eugenio Rocha Pedreira <pedro...@meta.com> > > >>>>>> Subject: Re: [DISCUSS][Format] Starting the draft implementation > of > > >>> the > > >>>>>> ArrayView array format > > >>>>>> > > >>>>>> +pedroerp On Thu, 11 May 2023 at 17: 51 Raphael Taylor-Davies <r. > > >>>>>> taylordavies@ googlemail. com. invalid> wrote: Hi All, > if we > > added > > >>>>>> this, do we think many Arrow and query > engine implementations > (for > > >>>>>> example, DataFusion) will be > > >>>>>> ZjQcmQRYFpfptBannerStart > > >>>>>> This Message Is From an External Sender > > >>>>>> > > >>>>>> ZjQcmQRYFpfptBannerEnd > > >>>>>> +pedroerp > > >>>>>> > > >>>>>> On Thu, 11 May 2023 at 17:51 Raphael Taylor-Davies > > >>>>>> <r.taylordav...@googlemail.com.invalid> wrote: > > >>>>>> Hi All, > > >>>>>> > > >>>>>>> if we added this, do we think many Arrow and query > > >>>>>>> engine implementations (for example, DataFusion) will be eager to > > >>> add > > >>>>>> full > > >>>>>>> support for the type, including compute kernels? Or are they > likely > > >>> to > > >>>>>> just > > >>>>>>> convert this type to ListArray at import boundaries? > > >>>>>> I can't speak for query engines in general, but at least for > > arrow-rs > > >>>>>> and by extension DataFusion, and based on my current understanding > > of > > >>>>>> the use-cases I would be rather hesitant to add support to the > > >>> kernels > > >>>>>> for this array type, definitely instead favouring conversion at > the > > >>>>>> edges. We already have issues with the amount of code generation > > >>>>>> resulting in binary bloat and long compile times, and I worry this > > >>> would > > >>>>>> worsen this situation whilst not really providing compelling > > >>> advantages > > >>>>>> for the vast majority of workloads that don't interact with Velox. > > >>>>>> Whilst I can definitely see that the ListView representation is > > >>> probably > > >>>>>> a better way to represent variable length lists than what arrow > > >>> settled > > >>>>>> upon, I'm not yet convinced it is sufficiently better to > incentivise > > >>>>>> broad ecosystem adoption. > > >>>>>> > > >>>>>> Kind Regards, > > >>>>>> > > >>>>>> Raphael Taylor-Davies > > >>>>>> > > >>>>>>> On 11/05/2023 21:20, Will Jones wrote: > > >>>>>>> Hi Felipe, > > >>>>>>> > > >>>>>>> Thanks for the additional details. > > >>>>>>> > > >>>>>>> > > >>>>>>>> Velox kernels benefit from being able to append data to the > array > > >>> from > > >>>>>>>> different threads without care for strict ordering. Only the > > >>> offsets > > >>>>>> array > > >>>>>>>> has to be written according to logical order but that is > > >>> potentially a > > >>>>>> much > > >>>>>>>> smaller buffer than the values buffer. > > >>>>>>>> > > >>>>>>> It still seems to me like applications are still pretty niche, > as I > > >>>>>> suspect > > >>>>>>> in most cases the benefits are outweighed by the costs. The > benefit > > >>>> here > > >>>>>>> seems pretty limited: if you are trying to split work between > > >>> threads, > > >>>>>>> usually you will have other levels such as array chunks to > > >>> parallelize. > > >>>>>> And > > >>>>>>> if you have an incoming stream of row data, you'll want to append > > in > > >>>>>>> predictable order to match the order of the other arrays. Am I > > >>> missing > > >>>>>>> something? > > >>>>>>> > > >>>>>>> And, IIUC, the cost of using ListView with out-of-order values > over > > >>>>>>> ListArray is you lose memory locality; the values of element 2 > are > > >>> no > > >>>>>>> longer adjacent to the values of element 1. What do you think > about > > >>>> that > > >>>>>>> tradeoff? > > >>>>>>> > > >>>>>>> I don't mean to be difficult about this. I'm excited for both the > > >>> REE > > >>>> and > > >>>>>>> StringView arrays, but this one I'm not so sure about yet. I > > suppose > > >>>>>> what I > > >>>>>>> am trying to ask is, if we added this, do we think many Arrow and > > >>> query > > >>>>>>> engine implementations (for example, DataFusion) will be eager to > > >>> add > > >>>>>> full > > >>>>>>> support for the type, including compute kernels? Or are they > likely > > >>> to > > >>>>>> just > > >>>>>>> convert this type to ListArray at import boundaries? > > >>>>>>> > > >>>>>>> Because if it turns out to be the latter, then we might as well > ask > > >>>> Velox > > >>>>>>> to export this type as ListArray and save the rest of the > ecosystem > > >>>> some > > >>>>>>> work. > > >>>>>>> > > >>>>>>> Best, > > >>>>>>> > > >>>>>>> Will Jones > > >>>>>>> > > >>>>>>> On Thu, May 11, 2023 at 12:32 PM Felipe Oliveira Carvalho < > > >>>>>>> felipe...@gmail.com<mailto:felipe...@gmail.com>> wrote: > > >>>>>>> > > >>>>>>>> Initial reason for ListView arrays in Arrow is zero-copy > > >>> compatibility > > >>>>>> with > > >>>>>>>> Velox which uses this format. > > >>>>>>>> > > >>>>>>>> Velox kernels benefit from being able to append data to the > array > > >>> from > > >>>>>>>> different threads without care for strict ordering. Only the > > >>> offsets > > >>>>>> array > > >>>>>>>> has to be written according to logical order but that is > > >>> potentially a > > >>>>>> much > > >>>>>>>> smaller buffer than the values buffer. > > >>>>>>>> > > >>>>>>>> Acero kernels could take advantage of that in the future. > > >>>>>>>> > > >>>>>>>> In implementing ListViewArray/Type I was able to reuse some C++ > > >>>>>> templates > > >>>>>>>> used for ListArray which can reduce some of the burden on kernel > > >>>>>>>> implementations that aim to work with all the types. > > >>>>>>>> > > >>>>>>>> I’m can fix Acero kernels for working with ListView. This is > > >>> similar > > >>>> to > > >>>>>> the > > >>>>>>>> work I’ve doing in kernels dealing with run-end encoded arrays. > > >>>>>>>> > > >>>>>>>> — > > >>>>>>>> Felipe > > >>>>>>>> > > >>>>>>>> > > >>>>>>>> On Wed, 26 Apr 2023 at 01:03 Will Jones < > will.jones...@gmail.com > > >>>>>> <mailto:will.jones...@gmail.com>> wrote: > > >>>>>>>> > > >>>>>>>>> I suppose one common use case is materializing list columns > after > > >>>> some > > >>>>>>>>> expanding operation like a join or unnest. That's a case where > I > > >>>> could > > >>>>>>>>> imagine a lot of repetition of values. Haven't yet thought of > > >>> common > > >>>>>>>> cases > > >>>>>>>>> where there is overlap but not full duplication, but am eager > to > > >>> hear > > >>>>>>>> any. > > >>>>>>>>> The dictionary encoding point Raphael makes is interesting, > > >>>> especially > > >>>>>>>>> given the existence of LargeList and FixedSizeList. For many > > >>>>>> operations, > > >>>>>>>> it > > >>>>>>>>> might make more sense to just compose those existing types. > > >>>>>>>>> > > >>>>>>>>> IIUC the operations that would be unique to the ArrayView are > > ones > > >>>>>>>> altering > > >>>>>>>>> the shape. One could truncate each array to a certain length > > >>> cheaply > > >>>>>>>> simply > > >>>>>>>>> by replacing the sizes buffer. Or perhaps there are interesting > > >>>>>>>> operations > > >>>>>>>>> on tensors that would benefit. > > >>>>>>>>> > > >>>>>>>>> On Tue, Apr 25, 2023 at 7:47 PM Raphael Taylor-Davies > > >>>>>>>>> <r.taylordav...@googlemail.com.invalid> wrote: > > >>>>>>>>> > > >>>>>>>>>> Unless I am missing something, I think the selection use-case > > >>> could > > >>>> be > > >>>>>>>>>> equally well served by a dictionary-encoded > > BinarArray/ListArray, > > >>>> and > > >>>>>>>>> would > > >>>>>>>>>> have the benefit of not requiring any modifications to the > > >>> existing > > >>>>>>>>> format > > >>>>>>>>>> or kernels. > > >>>>>>>>>> > > >>>>>>>>>> The major additional flexibility of the proposed encoding > would > > >>> be > > >>>>>>>>>> permitting disjoint or overlapping ranges, are these common > > >>> enough > > >>>> in > > >>>>>>>>>> practice to represent a meaningful bottleneck? > > >>>>>>>>>> > > >>>>>>>>>> > > >>>>>>>>>> On 26 April 2023 01:40:14 BST, David Li <lidav...@apache.org > > >>>> <mailto: > > >>>>>> lidav...@apache.org>> wrote: > > >>>>>>>>>>> Is there a need for a 64-bit offsets version the same way we > > >>> have > > >>>>>> List > > >>>>>>>>>> and LargeList? > > >>>>>>>>>>> And just to be clear, the difference with List is that the > > lists > > >>>>>> don't > > >>>>>>>>>> have to be stored in their logical order (or in other words, > > >>> offsets > > >>>>>> do > > >>>>>>>>> not > > >>>>>>>>>> have to be nondecreasing and so we also need sizes)? > > >>>>>>>>>>> On Wed, Apr 26, 2023, at 09:37, Weston Pace wrote: > > >>>>>>>>>>>> For context, there was some discussion on this back in [1]. > At > > >>>> that > > >>>>>>>>>> time > > >>>>>>>>>>>> this was called "sequence view" but I do not like that name. > > >>>>>>>> However, > > >>>>>>>>>>>> array-view array is a little confusing. Given this is > similar > > >>> to > > >>>>>>>> list > > >>>>>>>>>> can > > >>>>>>>>>>>> we go with list-view array? > > >>>>>>>>>>>> > > >>>>>>>>>>>>> Thanks for the introduction. I'd be interested to hear > about > > >>> the > > >>>>>>>>>>>>> applications Velox has found for these vectors, and in what > > >>>>>>>>> situations > > >>>>>>>>>>>> they > > >>>>>>>>>>>>> are useful. This could be contrasted with the current > > >>> ListArray > > >>>>>>>>>>>>> implementations. > > >>>>>>>>>>>> I believe one significant benefit is that take (and by > proxy, > > >>>>>>>> filter) > > >>>>>>>>>> and > > >>>>>>>>>>>> sort are O(# of items) with the proposed format and O(# of > > >>> bytes) > > >>>>>>>> with > > >>>>>>>>>> the > > >>>>>>>>>>>> current format. Jorge did some profiling to this effect in > > [1]. > > >>>>>>>>>>>> > > >>>>>>>>>>>> [1] > > >>>>>>>> > https://lists.apache.org/thread/49qzofswg1r5z7zh39pjvd1m2ggz2kdq< > > >>>>>> https://lists.apache.org/thread/49qzofswg1r5z7zh39pjvd1m2ggz2kdq> > > >>>>>>>>>>>> On Tue, Apr 25, 2023 at 3:13 PM Will Jones < > > >>>> will.jones...@gmail.com > > >>>>>> <mailto:will.jones...@gmail.com> > > >>>>>>>>>> wrote: > > >>>>>>>>>>>>> Hi Felipe, > > >>>>>>>>>>>>> > > >>>>>>>>>>>>> Thanks for the introduction. I'd be interested to hear > about > > >>> the > > >>>>>>>>>>>>> applications Velox has found for these vectors, and in what > > >>>>>>>>> situations > > >>>>>>>>>> they > > >>>>>>>>>>>>> are useful. This could be contrasted with the current > > >>> ListArray > > >>>>>>>>>>>>> implementations. > > >>>>>>>>>>>>> > > >>>>>>>>>>>>> IIUC it would be fairly cheap to transform a ListArray to > an > > >>>>>>>>>> ArrayView, but > > >>>>>>>>>>>>> expensive to go the other way. > > >>>>>>>>>>>>> > > >>>>>>>>>>>>> Best, > > >>>>>>>>>>>>> > > >>>>>>>>>>>>> Will Jones > > >>>>>>>>>>>>> > > >>>>>>>>>>>>> On Tue, Apr 25, 2023 at 3:00 PM Felipe Oliveira Carvalho < > > >>>>>>>>>>>>> felipe...@gmail.com<mailto:felipe...@gmail.com>> wrote: > > >>>>>>>>>>>>> > > >>>>>>>>>>>>>> Hi folks, > > >>>>>>>>>>>>>> > > >>>>>>>>>>>>>> I would like to start a public discussion on the inclusion > > >>> of a > > >>>>>>>> new > > >>>>>>>>>> array > > >>>>>>>>>>>>>> format to Arrow — array-view array. The name is also up > for > > >>>>>>>> debate. > > >>>>>>>>>>>>>> This format is inspired by Velox's ArrayVector format [1]. > > >>>>>>>>> Logically, > > >>>>>>>>>>>>> this > > >>>>>>>>>>>>>> array represents an array of arrays. Each element is an > > >>>>>>>> array-view > > >>>>>>>>>>>>> (offset > > >>>>>>>>>>>>>> and size pair) that points to a range within a nested > > >>> "values" > > >>>>>>>>> array > > >>>>>>>>>>>>>> (called "elements" in Velox docs). The nested array can be > > of > > >>>> any > > >>>>>>>>>> type, > > >>>>>>>>>>>>>> which makes this format very flexible and powerful. > > >>>>>>>>>>>>>> > > >>>>>>>>>>>>>> [image: ../_images/array-vector.png] > > >>>>>>>>>>>>>> < > > >>>>>>>>> > > >>> https://facebookincubator.github.io/velox/_images/array-vector.png< > > >>>>>> > https://facebookincubator.github.io/velox/_images/array-vector.png > > >> > > >>>>>>>>>>>>>> I'm currently working on a C++ implementation and plan to > > >>> work > > >>>>>>>> on a > > >>>>>>>>>> Go > > >>>>>>>>>>>>>> implementation to fulfill the two-implementations > > requirement > > >>>> for > > >>>>>>>>>> format > > >>>>>>>>>>>>>> changes. > > >>>>>>>>>>>>>> > > >>>>>>>>>>>>>> The draft design: > > >>>>>>>>>>>>>> > > >>>>>>>>>>>>>> - 3 buffers: [validity_bitmap, int32 offsets buffer, int32 > > >>> sizes > > >>>>>>>>>> buffer] > > >>>>>>>>>>>>>> - 1 child array: "values" as an array of the type > parameter > > >>>>>>>>>>>>>> > > >>>>>>>>>>>>>> validity_bitmap is used to differentiate between empty > array > > >>>>>>>> views > > >>>>>>>>>>>>>> (sizes[i] == 0) and NULL array views (validity_bitmap[i] > == > > >>> 0). > > >>>>>>>>>>>>>> > > >>>>>>>>>>>>>> When the validity_bitmap[i] is 0, both sizes and offsets > are > > >>>>>>>>>> undefined > > >>>>>>>>>>>>> (as > > >>>>>>>>>>>>>> usual), and when sizes[i] == 0, offsets[i] is undefined. 0 > > is > > >>>>>>>>>> recommended > > >>>>>>>>>>>>>> if setting a value is not an issue to the system producing > > >>> the > > >>>>>>>>>> arrays. > > >>>>>>>>>>>>>> offsets buffer is not required to be ordered and views > don't > > >>>> have > > >>>>>>>>> to > > >>>>>>>>>> be > > >>>>>>>>>>>>>> disjoint. > > >>>>>>>>>>>>>> > > >>>>>>>>>>>>>> [1] > > >>>>>>>>>>>>>> > > >>>>>>>> > > >>>>>> > > >>>> > > >>> > > > https://facebookincubator.github.io/velox/develop/vectors.html#arrayvector > > >>>>>> < > > >>>>>> > > >>>> > > >>> > > > https://facebookincubator.github.io/velox/develop/vectors.html#arrayvector > > >>>>>>> > > >>>>>>>>>>>>>> Thanks, > > >>>>>>>>>>>>>> Felipe O. Carvalho > > >>>>>>>>>>>>>> > > >>>>>> > > >>>> > > >>>> > > >>> > > >> > > > > > >
