hi John, > As a practical matter, the reason metadata is not a good solution for me is > that it requires awareness on the part of the reader. I want (e.g.) a > researcher in Python to be able to map a file of batches in IPC format > without needing to worry about the fact that the file was built in a > streaming fashion and therefore has some unused array elements.
I don't find this argument to be persuasive. pyarrow is intended as a developer-facing library, not a user-facing one. I don't think you should be having the kinds of users you are describing using pyarrow directly, instead consuming the library through a layer above it. Specifically, we are deliberately avoiding doing anything too "smart" or "magical", instead maintaining tight developer control over what is going on. - Wes On Wed, Oct 16, 2019 at 2:18 AM Micah Kornfield <emkornfi...@gmail.com> wrote: > > Still thinking through the implications here, but to save others from > having to go search [1] is the PR. > > [1] https://github.com/apache/arrow/pull/5663/files > > On Tue, Oct 15, 2019 at 1:42 PM John Muehlhausen <j...@jgm.org> wrote: > > > A proposal with linked PR now exists in ARROW-5916 and Wes commented that > > we should kick it around some more. > > > > The high-level topic is how Apache Arrow intersects with streaming > > methodologies: > > > > If record batches are strictly immutable, a difficult trade-off is created > > for streaming data collection: either I can have low-latency presentation > > of new data by appending very small batches (often 1 row) to the IPC stream > > and lose columnar layout benefits, or I can have high-latency presentation > > of new data by waiting to append a batch until it is large enough to gain > > significant columnar layout benefits. During this waiting period the new > > data is unavailable to processing. > > > > If, on the other hand, [0,length) of a batch is immutable but length may > > increase, the trade-off is eliminated: I can pre-allocate a batch and > > populate records in it when they occur (without waiting), and also gain > > columnar benefits as each "closed" batch will be large. (A batch may be > > practically "closed" before the arrays are full when the projection of > > variable-length buffer space is wrong... a space/time tradeoff in favor of > > time.) > > > > Looking ahead to a day when the reference implementation(s) will be able to > > bump RecordBatch.length while populating pre-allocated records > > in-place, ARROW-5916 reads such batches by ignoring portions of arrays that > > are beyond RecordBatch.length. > > > > If we are not looking ahead to such a day, the discussion is about the > > alternative way that Arrow will avoid the latency/locality tradeoff > > inherent in streaming data collection. Or, if the answer is "streaming > > apps are and will always be out of scope", that idea needs to be defended > > from the observation that practitioners are moving more towards the fusion > > of batch and streaming, not away from it. > > > > As a practical matter, the reason metadata is not a good solution for me is > > that it requires awareness on the part of the reader. I want (e.g.) a > > researcher in Python to be able to map a file of batches in IPC format > > without needing to worry about the fact that the file was built in a > > streaming fashion and therefore has some unused array elements. > > > > The change itself seems relatively simple. What negative consequences do > > we anticipate, if any? > > > > Thanks, > > -John > > > > On Fri, Jul 5, 2019 at 10:42 AM John Muehlhausen <j...@jgm.org> wrote: > > > > > This seems to help... still testing it though. > > > > > > Status GetFieldMetadata(int field_index, ArrayData* out) { > > > auto nodes = metadata_->nodes(); > > > // pop off a field > > > if (field_index >= static_cast<int>(nodes->size())) { > > > return Status::Invalid("Ran out of field metadata, likely > > > malformed"); > > > } > > > const flatbuf::FieldNode* node = nodes->Get(field_index); > > > > > > * //out->length = node->length();* > > > * out->length = metadata_->length();* > > > out->null_count = node->null_count(); > > > out->offset = 0; > > > return Status::OK(); > > > } > > > > > > On Fri, Jul 5, 2019 at 10:24 AM John Muehlhausen <j...@jgm.org> wrote: > > > > > >> So far it seems as if pyarrow is completely ignoring the > > >> RecordBatch.length field. More info to follow... > > >> > > >> On Tue, Jul 2, 2019 at 3:02 PM John Muehlhausen <j...@jgm.org> wrote: > > >> > > >>> Crikey! I'll do some testing around that and suggest some test cases to > > >>> ensure it continues to work, assuming that it does. > > >>> > > >>> -John > > >>> > > >>> On Tue, Jul 2, 2019 at 2:41 PM Wes McKinney <wesmck...@gmail.com> > > wrote: > > >>> > > >>>> Thanks for the attachment, it's helpful. > > >>>> > > >>>> On Tue, Jul 2, 2019 at 1:40 PM John Muehlhausen <j...@jgm.org> wrote: > > >>>> > > > >>>> > Attachments referred to in previous two messages: > > >>>> > > > >>>> > > https://www.dropbox.com/sh/6ycfuivrx70q2jx/AAAt-RDaZWmQ2VqlM-0s6TqWa?dl=0 > > >>>> > > > >>>> > On Tue, Jul 2, 2019 at 1:14 PM John Muehlhausen <j...@jgm.org> > > wrote: > > >>>> > > > >>>> > > Thanks, Wes, for the thoughtful reply. I really appreciate the > > >>>> > > engagement. In order to clarify things a bit, I am attaching a > > >>>> graphic of > > >>>> > > how our application will take record-wise (row-oriented) data from > > >>>> an event > > >>>> > > source and incrementally populate a pre-allocated Arrow-compatible > > >>>> buffer, > > >>>> > > including for variable-length fields. (Obviously at this stage I > > >>>> am not > > >>>> > > using the reference implementation Arrow code, although that would > > >>>> be a > > >>>> > > goal.... to contribute that back to the project.) > > >>>> > > > > >>>> > > For sake of simplicity these are non-nullable fields. As a > > result a > > >>>> > > reader of "y" that has no knowledge of the "utilized" metadata > > >>>> would get a > > >>>> > > long string (zeros, spaces, uninitialized, or whatever we decide > > >>>> for the > > >>>> > > pre-allocation model) for the record just beyond the last utilized > > >>>> record. > > >>>> > > > > >>>> > > I don't see any "big O"-analysis problems with this approach. The > > >>>> > > space/time tradeoff is that we have to guess how much room to > > >>>> allocate for > > >>>> > > variable-length fields. We will probably almost always be wrong. > > >>>> This > > >>>> > > ends up in "wasted" space. However, we can do calculations based > > >>>> on these > > >>>> > > partially filled batches that take full advantage of the columnar > > >>>> layout. > > >>>> > > (Here I've shown the case where we had too little variable-length > > >>>> buffer > > >>>> > > set aside, resulting in "wasted" rows. The flip side is that rows > > >>>> achieve > > >>>> > > full [1] utilization but there is wasted variable-length buffer if > > >>>> we guess > > >>>> > > incorrectly in the other direction.) > > >>>> > > > > >>>> > > I proposed a few things that are "nice to have" but really what > > I'm > > >>>> eyeing > > >>>> > > is the ability for a reader-- any reader (e.g. pyarrow)-- to see > > >>>> that some > > >>>> > > of the rows in a RecordBatch are not to be read, based on the new > > >>>> > > "utilized" (or whatever name) metadata. That single tweak to the > > >>>> > > metadata-- and readers honoring it-- is the core of the proposal. > > >>>> > > (Proposal 4.) This would indicate that the attached example (or > > >>>> something > > >>>> > > similar) is the blessed approach for those seeking to accumulate > > >>>> events and > > >>>> > > process them while still expecting more data, with the > > >>>> heavier-weight task > > >>>> > > of creating a new pre-allocated batch being a rare occurrence. > > >>>> > > > > >>>> > > >>>> So the "length" field in RecordBatch is already the utilized number of > > >>>> rows. The body buffers can certainly have excess unused space. So your > > >>>> application can mutate Flatbuffer "length" field in-place as new > > >>>> records are filled in. > > >>>> > > >>>> > > Notice that the mutability is only in the sense of "appending." > > The > > >>>> > > current doctrine of total immutability would be revised to refer > > to > > >>>> the > > >>>> > > immutability of only the already-populated rows. > > >>>> > > > > >>>> > > It gives folks an option other than choosing the lesser of two > > >>>> evils: on > > >>>> > > the one hand, length 1 RecordBatches that don't result in a stream > > >>>> that is > > >>>> > > computationally efficient. On the other hand, adding artificial > > >>>> latency by > > >>>> > > accumulating events before "freezing" a larger batch and only then > > >>>> making > > >>>> > > it available to computation. > > >>>> > > > > >>>> > > -John > > >>>> > > > > >>>> > > On Tue, Jul 2, 2019 at 12:21 PM Wes McKinney <wesmck...@gmail.com > > > > > >>>> wrote: > > >>>> > > > > >>>> > >> hi John, > > >>>> > >> > > >>>> > >> On Tue, Jul 2, 2019 at 11:23 AM John Muehlhausen <j...@jgm.org> > > >>>> wrote: > > >>>> > >> > > > >>>> > >> > During my time building financial analytics and trading systems > > >>>> (23 > > >>>> > >> years!), both the "batch processing" and "stream processing" > > >>>> paradigms have > > >>>> > >> been extensively used by myself and by colleagues. > > >>>> > >> > > > >>>> > >> > Unfortunately, the tools used in these paradigms have not > > >>>> successfully > > >>>> > >> overlapped. For example, an analyst might use a Python notebook > > >>>> with > > >>>> > >> pandas to do some batch analysis. Then, for acceptable latency > > and > > >>>> > >> throughput, a C++ programmer must implement the same schemas and > > >>>> processing > > >>>> > >> logic in order to analyze real-time data for real-time decision > > >>>> support. > > >>>> > >> (Time horizons often being sub-second or even sub-millisecond for > > >>>> an > > >>>> > >> acceptable reaction to an event. The most aggressive > > >>>> software-based > > >>>> > >> systems, leaving custom hardware aside other than things like > > >>>> kernel-bypass > > >>>> > >> NICs, target 10s of microseconds for a full round trip from data > > >>>> ingestion > > >>>> > >> to decision.) > > >>>> > >> > > > >>>> > >> > As a result, TCO is more than doubled. A doubling can be > > >>>> accounted for > > >>>> > >> by two implementations that share little or nothing in the way of > > >>>> > >> architecture. Then additional effort is required to ensure that > > >>>> these > > >>>> > >> implementations continue to behave the same way and are upgraded > > in > > >>>> > >> lock-step. > > >>>> > >> > > > >>>> > >> > Arrow purports to be a "bridge" technology that eases one of > > the > > >>>> pain > > >>>> > >> points of working in different ecosystems by providing a common > > >>>> event > > >>>> > >> stream data structure. (Discussion of common processing > > >>>> techniques is > > >>>> > >> beyond the scope of this discussion. Suffice it to say that a > > >>>> streaming > > >>>> > >> algo can always be run in batch, but not vice versa.) > > >>>> > >> > > > >>>> > >> > Arrow seems to be growing up primarily in the batch processing > > >>>> world. > > >>>> > >> One publication notes that "the missing piece is streaming, where > > >>>> the > > >>>> > >> velocity of incoming data poses a special challenge. There are > > >>>> some early > > >>>> > >> experiments to populate Arrow nodes in microbatches..." [1] Part > > >>>> our our > > >>>> > >> discussion could be a response to this observation. In what ways > > >>>> is it > > >>>> > >> true or false? What are the plans to remedy this shortcoming, if > > >>>> it > > >>>> > >> exists? What steps can be taken now to ease the transition to > > >>>> low-latency > > >>>> > >> streaming support in the future? > > >>>> > >> > > > >>>> > >> > > >>>> > >> Arrow columnar format describes a collection of records with > > values > > >>>> > >> between records being placed adjacent to each other in memory. If > > >>>> you > > >>>> > >> break that assumption, you don't have a columnar format anymore. > > >>>> So I > > >>>> > >> don't where the "shortcoming" is. We don't have any software in > > the > > >>>> > >> project for managing the creation of record batches in a > > streaming > > >>>> > >> application, but this seems like an interesting development > > >>>> expansion > > >>>> > >> area for the project. > > >>>> > >> > > >>>> > >> Note that many contributors have already expanded the surface > > area > > >>>> of > > >>>> > >> what's in the Arrow libraries in many directions. > > >>>> > >> > > >>>> > >> Streaming data collection is yet another area of expansion, but > > >>>> > >> _personally_ it is not on the short list of projects that I will > > >>>> > >> personally be working on (or asking my direct or indirect > > >>>> colleagues > > >>>> > >> to work on). Since this is a project made up of volunteers, it's > > >>>> up to > > >>>> > >> contributors to drive new directions for the project by writing > > >>>> design > > >>>> > >> documents and pull requests. > > >>>> > >> > > >>>> > >> > In my own experience, a successful strategy for stream > > >>>> processing where > > >>>> > >> context (i.e. recent past events) must be considered by > > >>>> calculations is to > > >>>> > >> pre-allocate memory for event collection, to organize this memory > > >>>> in a > > >>>> > >> columnar layout, and to run incremental calculations at each > > event > > >>>> ingress > > >>>> > >> into the partially populated memory. [Fig 1] When the > > >>>> pre-allocated > > >>>> > >> memory has been exhausted, allocate a new batch of column-wise > > >>>> memory and > > >>>> > >> continue. When a batch is no longer pertinent to the calculation > > >>>> look-back > > >>>> > >> window, free the memory back to the heap or pool. > > >>>> > >> > > > >>>> > >> > Here we run into the first philosophical barrier with Arrow, > > >>>> where > > >>>> > >> "Arrow data is immutable." [2] There is currently little or no > > >>>> > >> consideration for reading a partially constructed RecordBatch, > > >>>> e.g. one > > >>>> > >> with only some of the rows containing event data at the present > > >>>> moment in > > >>>> > >> time. > > >>>> > >> > > > >>>> > >> > > >>>> > >> It seems like the use case you have heavily revolves around > > >>>> mutating > > >>>> > >> pre-allocated, memory-mapped datasets that are being consumed by > > >>>> other > > >>>> > >> processes on the same host. So you want to incrementally fill > > some > > >>>> > >> memory-mapped data that you've already exposed to another > > process. > > >>>> > >> > > >>>> > >> Because of the memory layout for variable-size and nested cells, > > >>>> it is > > >>>> > >> impossible in general to mutate Arrow record batches. This is > > not a > > >>>> > >> philosophical position: this was a deliberate technical decision > > to > > >>>> > >> guarantee data locality for scans and predictable O(1) random > > >>>> access > > >>>> > >> on variable-length and nested data. > > >>>> > >> > > >>>> > >> Technically speaking, you can mutate memory in-place for > > fixed-size > > >>>> > >> types in-RAM or on-disk, if you want to. It's an "off-label" use > > >>>> case > > >>>> > >> but no one is saying you can't do this. > > >>>> > >> > > >>>> > >> > Proposal 1: Shift the Arrow "immutability" doctrine to apply to > > >>>> > >> populated records of a RecordBatch instead of to all records? > > >>>> > >> > > > >>>> > >> > > >>>> > >> Per above, this is impossible in generality. You can't alter > > >>>> > >> variable-length or nested records without rewriting the record > > >>>> batch. > > >>>> > >> > > >>>> > >> > As an alternative approach, RecordBatch can be used as a single > > >>>> Record > > >>>> > >> (batch length of one). [Fig 2] In this approach the benefit of > > >>>> the > > >>>> > >> columnar layout is lost for look-back window processing. > > >>>> > >> > > > >>>> > >> > Another alternative approach is to collect an entire > > RecordBatch > > >>>> before > > >>>> > >> stepping through it with the stream processing calculation. [Fig > > >>>> 3] With > > >>>> > >> this approach some columnar processing benefit can be recovered, > > >>>> however > > >>>> > >> artificial latency is introduced. As tolerance for delays in > > >>>> decision > > >>>> > >> support dwindles, this model will be of increasingly limited > > >>>> value. It is > > >>>> > >> already unworkable in many areas of finance. > > >>>> > >> > > > >>>> > >> > When considering the Arrow format and variable length values > > >>>> such as > > >>>> > >> strings, the pre-allocation approach (and subsequent processing > > of > > >>>> a > > >>>> > >> partially populated batch) encounters a hiccup. How do we know > > >>>> the amount > > >>>> > >> of buffer space to pre-allocate? If we allocate too much buffer > > >>>> for > > >>>> > >> variable-length data, some of it will be unused. If we allocate > > >>>> too little > > >>>> > >> buffer for variable-length data, some row entities will be > > >>>> unusable. > > >>>> > >> (Additional "rows" remain but when populating string fields there > > >>>> is no > > >>>> > >> longer string storage space to point them to.) > > >>>> > >> > > > >>>> > >> > As with many optimization space/time tradeoff problems, the > > >>>> solution > > >>>> > >> seems to be to guess. Pre-allocation sets aside variable length > > >>>> buffer > > >>>> > >> storage based on the typical "expected size" of the variable > > >>>> length data. > > >>>> > >> This can result in some unused rows, as discussed above. [Fig 4] > > >>>> In fact > > >>>> > >> it will necessarily result in one unused row unless the last of > > >>>> each > > >>>> > >> variable length field in the last row exactly fits into the > > >>>> remaining space > > >>>> > >> in the variable length data buffer. Consider the case where > > there > > >>>> is more > > >>>> > >> variable length buffer space than data: > > >>>> > >> > > > >>>> > >> > Given variable-length field x, last row index of y, variable > > >>>> length > > >>>> > >> buffer v, beginning offset into v of o: > > >>>> > >> > x[y] begins at o > > >>>> > >> > x[y] ends at the offset of the next record, there is no > > next > > >>>> > >> record, so x[y] ends after the total remaining area in variable > > >>>> length > > >>>> > >> buffer... however, this is too much! > > >>>> > >> > > > >>>> > >> > > >>>> > >> It isn't clear to me what you're proposing. It sounds like you > > >>>> want a > > >>>> > >> major redesign of the columnar format to permit in-place mutation > > >>>> of > > >>>> > >> strings. I doubt that would be possible at this point. > > >>>> > >> > > >>>> > >> > Proposal 2: [low priority] Create an "expected length" > > statistic > > >>>> in the > > >>>> > >> Schema for variable length fields? > > >>>> > >> > > > >>>> > >> > Proposal 3: [low priority] Create metadata to store the index > > >>>> into > > >>>> > >> variable-length data that represents the end of the value for the > > >>>> last > > >>>> > >> record? Alternatively: a row is "wasted," however pre-allocation > > >>>> is > > >>>> > >> inexact to begin with. > > >>>> > >> > > > >>>> > >> > Proposal 4: Add metadata to indicate to a RecordBatch reader > > >>>> that only > > >>>> > >> some of the rows are to be utilized. [Fig 5] This is useful not > > >>>> only when > > >>>> > >> processing a batch that is still under construction, but also for > > >>>> "closed" > > >>>> > >> batches that were not able to be fully populated due to an > > >>>> imperfect > > >>>> > >> projection of variable length storage. > > >>>> > >> > > > >>>> > >> > On this last proposal, Wes has weighed in: > > >>>> > >> > > > >>>> > >> > "I believe your use case can be addressed by pre-allocating > > >>>> record > > >>>> > >> batches and maintaining application level metadata about what > > >>>> portion of > > >>>> > >> the record batches has been 'filled' (so the unfilled records can > > >>>> be > > >>>> > >> dropped by slicing). I don't think any change to the binary > > >>>> protocol is > > >>>> > >> warranted." [3] > > >>>> > >> > > > >>>> > >> > > >>>> > >> My personal opinion is that a solution to the problem you have > > can > > >>>> be > > >>>> > >> composed from the components (combined with some new pieces of > > >>>> code) > > >>>> > >> that we have developed in the project already. > > >>>> > >> > > >>>> > >> So the "application level" could be an add-on C++ component in > > the > > >>>> > >> Apache Arrow project. Call it a "memory-mapped streaming data > > >>>> > >> collector" that pre-allocates on-disk record batches (of only > > >>>> > >> fixed-size or even possibly dictionary-encoded types) and then > > >>>> fills > > >>>> > >> them incrementally as bits of data come in, updating some > > auxiliary > > >>>> > >> metadata that other processes can use to determine what portion > > of > > >>>> the > > >>>> > >> Arrow IPC messages to "slice off". > > >>>> > >> > > >>>> > >> > Concerns with positioning this at the app level: > > >>>> > >> > > > >>>> > >> > 1- Do we need to address or begin to address the overall > > concern > > >>>> of how > > >>>> > >> Arrow data structures are to be used in "true" (non-microbatch) > > >>>> streaming > > >>>> > >> environments, cf [1] in the last paragraph, as a *first-class* > > >>>> usage > > >>>> > >> pattern? If so, is now the time? > > >>>> > >> >if you break that design invariant you don't have a columnar > > >>>> format > > >>>> > >> anymore. > > >>>> > >> > > >>>> > >> Arrow provides a binary protocol for describing a payload data on > > >>>> the > > >>>> > >> wire (or on-disk, or in-memory, all the same). I don't see how it > > >>>> is > > >>>> > >> in conflict with streaming environments, unless the streaming > > >>>> > >> application has difficulty collecting multiple records into an > > >>>> Arrow > > >>>> > >> record batches. In that case, it's a system trade-off. Currently > > >>>> > >> people are using Avro with Kafka and sending one record at a > > time, > > >>>> but > > >>>> > >> then they're also spending a lot of CPU cycles in serialization. > > >>>> > >> > > >>>> > >> > 2- If we can even make broad-stroke attempts at data structure > > >>>> features > > >>>> > >> that are likely to be useful when streaming becomes a first class > > >>>> citizen, > > >>>> > >> it reduces the chances of "breaking" format changes in the > > >>>> future. I do > > >>>> > >> not believe the proposals place an undue hardship on batch > > >>>> processing > > >>>> > >> paradigms. We are currently discussing making a breaking change > > >>>> to the IPC > > >>>> > >> format [4], so there is a window of opportunity to consider > > >>>> features useful > > >>>> > >> for streaming? (Current clients can feel free to ignore the > > >>>> proposed > > >>>> > >> "utilized" metadata of RecordBatch.) > > >>>> > >> > > > >>>> > >> > > >>>> > >> I think the perception that streaming is not a first class > > citizen > > >>>> is > > >>>> > >> an editorialization (e.g. the article you cited was an editorial > > >>>> > >> written by an industry analyst based on an interview with Jacques > > >>>> and > > >>>> > >> me). Columnar data formats in general are designed to work with > > >>>> more > > >>>> > >> than one value at a time (which we are calling a "batch" but I > > >>>> think > > >>>> > >> that's conflating terminology with the "batch processing" > > paradigm > > >>>> of > > >>>> > >> Hadoop, etc.), > > >>>> > >> > > >>>> > >> > 3- Part of the promise of Arrow is that applications are not a > > >>>> world > > >>>> > >> unto themselves, but interoperate with other Arrow-compliant > > >>>> systems. In > > >>>> > >> my case, I would like users to be able to examine RecordBatchs in > > >>>> tools > > >>>> > >> such as pyarrow without needing to be aware of any streaming > > >>>> app-specific > > >>>> > >> metadata. For example, a researcher may pull in an IPC "File" > > >>>> containing N > > >>>> > >> RecordBatch messages corresponding to those in Fig 4. I would > > >>>> very much > > >>>> > >> like for this casual user to not have to apply N slice operations > > >>>> based on > > >>>> > >> out-of-band data to get to the data that is relevant. > > >>>> > >> > > > >>>> > >> > > >>>> > >> Per above, should this become a standard enough use case, I think > > >>>> that > > >>>> > >> code can be developed in the Apache project to address it. > > >>>> > >> > > >>>> > >> > Devil's advocate: > > >>>> > >> > > > >>>> > >> > 1- Concurrent access to a mutable (growing) RecordBatch will > > >>>> require > > >>>> > >> synchronization of some sort to get consistent metadata reads. > > >>>> Since the > > >>>> > >> above proposals do not specify how this synchronization will > > occur > > >>>> for > > >>>> > >> tools such as pyarrow (we can imagine a Python user getting > > >>>> synchronized > > >>>> > >> access to File metadata and mapping a read-only area before the > > >>>> writer is > > >>>> > >> allowed to continue "appending" to this batch, or batches to this > > >>>> File), > > >>>> > >> some "unusual" code will be required anyway, so what is the harm > > of > > >>>> > >> consulting side-band data for slicing all the batches as part of > > >>>> this > > >>>> > >> "unusual" code? [Potential response: Yes, but it is still one > > >>>> less thing > > >>>> > >> to worry about, and perhaps first-class support for common > > >>>> synchronization > > >>>> > >> patterns can be forthcoming? These patterns may not require > > >>>> further format > > >>>> > >> changes?] > > >>>> > >> > > > >>>> > >> > My overall concern is that I see a lot of wasted effort dealing > > >>>> with > > >>>> > >> the "impedance mismatch" between batch oriented and streaming > > >>>> systems. I > > >>>> > >> believe that "best practices" will begin (and continue!) to > > prefer > > >>>> tools > > >>>> > >> that help bridge the gap. Certainly this is the case in my own > > >>>> work. I > > >>>> > >> agree with the appraisal at the end of the ZDNet article. If the > > >>>> above is > > >>>> > >> not a helpful solution, what other steps can be made? Or if > > Arrow > > >>>> is > > >>>> > >> intentionally confined to batch processing for the foreseeable > > >>>> future (in > > >>>> > >> terms of first-class support), I'm interested in the rationale. > > >>>> Perhaps > > >>>> > >> the feeling is that we avoid scope creep now (which I understand > > >>>> can be > > >>>> > >> never-ending) even if it means a certain breaking change in the > > >>>> future? > > >>>> > >> > > > >>>> > >> > > >>>> > >> There's some semantic issues with what "streaming" and "batch" > > >>>> means. > > >>>> > >> When people see "streaming" nowadays they think "Kafka" (or > > >>>> > >> Kafka-like). Single events flow in and out of streaming > > computation > > >>>> > >> nodes (e.g. like https://apache.github.io/incubator-heron/ or > > >>>> others). > > >>>> > >> The "streaming" is more about computational semantics than data > > >>>> > >> representation. > > >>>> > >> > > >>>> > >> The Arrow columnar format fundamentally deals with multiple > > >>>> records at > > >>>> > >> a time (you can have a record batch with size 1, but that is not > > >>>> going > > >>>> > >> to be efficient). But I do not think Arrow is "intentially > > >>>> confined" > > >>>> > >> to batch processing. If it makes sense to use a columnar format > > to > > >>>> > >> represent data in a streaming application, then you can certainly > > >>>> use > > >>>> > >> it for that. I'm aware of people successfully using Arrow with > > >>>> Kafka, > > >>>> > >> for example. > > >>>> > >> > > >>>> > >> - Wes > > >>>> > >> > > >>>> > >> > Who else encounters the need to mix/match batch and streaming, > > >>>> and what > > >>>> > >> are your experiences? > > >>>> > >> > > > >>>> > >> > Thanks for the further consideration and discussion! > > >>>> > >> > > > >>>> > >> > [1] https://zd.net/2H0LlBY > > >>>> > >> > [2] https://arrow.apache.org/docs/python/data.html > > >>>> > >> > [3] https://bit.ly/2J5sENZ > > >>>> > >> > [4] https://bit.ly/2Yske8L > > >>>> > >> > > >>>> > > > > >>>> > > >>> > >
