Far be it from me to think that I know more than Jorge or Wes on this subject. Sorry if my post gives that perception, that is clearly not my intention. I'm just trying to defend the idea that when designing this kind of transformation, it might be interesting to have a library to test several mappings and evaluate them before doing a more direct implementation if the performance is not there.
On Thu, Jul 28, 2022 at 12:15 PM Benjamin Blodgett < benjaminblodg...@gmail.com> wrote: > He was trying to nicely say he knows way more than you, and your ideas > will result in a low performance scheme no one will use in production > ai/machine learning. > > Sent from my iPhone > > > On Jul 28, 2022, at 12:14 PM, Benjamin Blodgett < > benjaminblodg...@gmail.com> wrote: > > > > I think Jorge’s opinion has is that of an expert and him being humble > is just being tactful. Probably listen to Jorge on performance and > architecture, even over Wes as he’s contributed more than anyone else and > know the bleeding edge of low level performance stuff more than anyone. > > > > Sent from my iPhone > > > >> On Jul 28, 2022, at 12:03 PM, Laurent Quérel <laurent.que...@gmail.com> > wrote: > >> > >> Hi Jorge > >> > >> I don't think that the level of in-depth knowledge needed is the same > >> between using a row-oriented internal representation and "Arrow" which > not > >> only changes the organization of the data but also introduces a set of > >> additional mapping choices and concepts. > >> > >> For example, assuming that the initial row-oriented data source is a > stream > >> of nested assembly of structures, lists and maps. The mapping of such a > >> stream to Protobuf, JSON, YAML, ... is straightforward because on both > >> sides the logical representation is exactly the same, the schema is > >> sometimes optional, the interest of building batches is optional, ... In > >> the case of "Arrow" things are different - the schema and the batching > are > >> mandatory. The mapping is not necessarily direct and will generally be > the > >> result of the combination of several trade-offs (normalization vs > >> denormalization representation, mapping influencing the compression > rate, > >> queryability with Arrow processors like DataFusion, ...). Note that > some of > >> these complexities are not intrinsically linked to the fact that the > target > >> format is column oriented. The ZST format ( > >> https://zed.brimdata.io/docs/formats/zst/) for example does not > require an > >> explicit schema definition. > >> > >> IMHO, having a library that allows you to easily experiment with > different > >> types of mapping (without having to worry about batching, dictionaries, > >> schema definition, understanding how lists of structs are represented, > ...) > >> and to evaluate the results according to your specific goals has a value > >> (especially if your criteria are compression ratio and queryability). Of > >> course there is an overhead to such an approach. In some cases, at the > end > >> of the process, it will be necessary to manually perform this direct > >> transformation between a row-oriented XYZ format and "Arrow". However, > this > >> effort will be done after a simple experimentation phase to avoid > changes > >> in the implementation of the converter which in my opinion is not so > simple > >> to implement with the current Arrow API. > >> > >> If the Arrow developer community is not interested in integrating this > >> proposal, I plan to release two independent libraries (Go and Rust) that > >> can be used on top of the standard "Arrow" libraries. This will have the > >> advantage to evaluate if such an approach is able to raise interest > among > >> Arrow users. > >> > >> Best, > >> > >> Laurent > >> > >> > >> > >>> On Wed, Jul 27, 2022 at 9:53 PM Jorge Cardoso Leitão < > >>> jorgecarlei...@gmail.com> wrote: > >>> > >>> Hi Laurent, > >>> > >>> I agree that there is a common pattern in converting row-based formats > to > >>> Arrow. > >>> > >>> Imho the difficult part is not to map the storage format to Arrow > >>> specifically - it is to map the storage format to any in-memory (row- > or > >>> columnar- based) format, since it requires in-depth knowledge about > the 2 > >>> formats (the source format and the target format). > >>> > >>> - Understanding the Arrow API which can be challenging for complex > cases of > >>>> rows representing complex objects (list of struct, struct of struct, > >>> ...). > >>>> > >>> > >>> the developer would have the same problem - just shifted around - they > now > >>> need to convert their complex objects to the intermediary > representation. > >>> Whether it is more "difficult" or "complex" to learn than Arrow is an > open > >>> question, but we would essentially be shifting the problem from > "learning > >>> Arrow" to "learning the Intermediate in-memory". > >>> > >>> @Micah Kornfield, as described before my goal is not to define a memory > >>>> layout specification but more to define an API and a translation > >>> mechanism > >>>> able to take this intermediate representation (list of generic objects > >>>> representing the entities to translate) and to convert it into one or > >>> more > >>>> Arrow records. > >>>> > >>> > >>> imho a spec of "list of generic objects representing the entities" is > >>> specified by an in-memory format (not by an API spec). > >>> > >>> A second challenge I anticipate is that in-memory formats inneerently > "own" > >>> the memory they outline (since by definition they outline how this > memory > >>> is outlined). An Intermediate in-memory representation would be no > >>> different. Since row-based formats usually require at least one > allocation > >>> per row (and often more for variable-length types), the transformation > >>> (storage format -> row-based in-memory format -> Arrow) incurs a > >>> significant cost (~2x slower last time I played with this problem in > JSON > >>> [1]). > >>> > >>> A third challenge I anticipate is that given that we have 10+ > languages, we > >>> would eventually need to convert the intermediary representation across > >>> languages, which imo just hints that we would need to formalize an > agnostic > >>> spec for such representation (so languages agree on its > representation), > >>> and thus essentially declare a new (row-based) format. > >>> > >>> (none of this precludes efforts to invent an in-memory row format for > >>> analytics workloads) > >>> > >>> @Wes McKinney <wesmck...@gmail.com> > >>> > >>> I still think having a canonical in-memory row format (and libraries > >>>> to transform to and from Arrow columnar format) is a good idea — but > >>>> there is the risk of ending up in the tar pit of reinventing Avro. > >>>> > >>> > >>> afaik Avro does not have O(1) random access neither to its rows nor > columns > >>> - records are concatenated back to back, every record's column is > >>> concatenated back to back within a record, and there is no indexing > >>> information on how to access a particular row or column. There are > blocks > >>> of rows that reduce the cost of accessing large offsets, but imo it is > far > >>> from the O(1) offered by Arrow (and expected by analytics workloads). > >>> > >>> [1] https://github.com/jorgecarleitao/arrow2/pull/1024 > >>> > >>> Best, > >>> Jorge > >>> > >>> On Thu, Jul 28, 2022 at 5:38 AM Laurent Quérel < > laurent.que...@gmail.com> > >>> wrote: > >>> > >>>> Let me clarify the proposal a bit before replying to the various > previous > >>>> feedbacks. > >>>> > >>>> > >>>> > >>>> It seems to me that the process of converting a row-oriented data > source > >>>> (row = set of fields or something more hierarchical) into an Arrow > record > >>>> repeatedly raises the same challenges. A developer who must perform > this > >>>> kind of transformation is confronted with the following questions and > >>>> problems: > >>>> > >>>> - Understanding the Arrow API which can be challenging for complex > cases > >>> of > >>>> rows representing complex objects (list of struct, struct of struct, > >>> ...). > >>>> > >>>> - Decide which Arrow schema(s) will correspond to your data source. In > >>> some > >>>> complex cases it can be advantageous to translate the same > row-oriented > >>>> data source into several Arrow schemas (e.g. OpenTelementry data > >>> sources). > >>>> > >>>> - Decide on the encoding of the columns to make the most of the > >>>> column-oriented format and thus increase the compression rate (e.g. > >>> define > >>>> the columns that should be represent as dictionaries). > >>>> > >>>> > >>>> > >>>> By experience, I can attest that this process is usually iterative. > For > >>>> non-trivial data sources, arriving at the arrow representation that > >>> offers > >>>> the best compression ratio and is still perfectly usable and queryable > >>> is a > >>>> long and tedious process. > >>>> > >>>> > >>>> > >>>> I see two approaches to ease this process and consequently increase > the > >>>> adoption of Apache Arrow: > >>>> > >>>> - Definition of a canonical in-memory row format specification that > every > >>>> row-oriented data source provider can progressively adopt to get an > >>>> automatic translation into the Arrow format. > >>>> > >>>> - Definition of an integration library allowing to map any > row-oriented > >>>> source into a generic row-oriented source understood by the > converter. It > >>>> is not about defining a unique in-memory format but more about > defining a > >>>> standard API to represent row-oriented data. > >>>> > >>>> > >>>> > >>>> In my opinion these two approaches are complementary. The first option > >>> is a > >>>> long-term approach targeting directly the data providers, which will > >>>> require to agree on this generic row-oriented format and whose > adoption > >>>> will be more or less long. The second approach does not directly > require > >>>> the collaboration of data source providers but allows an "integrator" > to > >>>> perform this transformation painlessly with potentially several > >>>> representation trials to achieve the best results in his context. > >>>> > >>>> > >>>> > >>>> The current proposal is an implementation of the second approach, > i.e. an > >>>> API that maps a row-oriented source XYZ into an intermediate > row-oriented > >>>> representation understood mechanically by the translator. This > translator > >>>> also adds a series of optimizations to make the most of the Arrow > format. > >>>> > >>>> > >>>> > >>>> You can find multiple examples of a such transformation in the > following > >>>> examples: > >>>> > >>>> - > >>>> > >>>> > >>> > https://github.com/lquerel/otel-arrow-adapter/blob/main/pkg/otel/trace/otlp_to_arrow.go > >>>> this example converts OTEL trace entities into their corresponding > >>> Arrow > >>>> IR. At the end of this conversion the method returns a collection of > >>>> Arrow > >>>> Records. > >>>> - A more complex example can be found here > >>>> > >>>> > >>> > https://github.com/lquerel/otel-arrow-adapter/blob/main/pkg/otel/metrics/otlp_to_arrow.go > >>>> . > >>>> In this example a stream of OTEL univariate row-oriented metrics are > >>>> translate into multivariate row-oriented metrics and then > >>> automatically > >>>> translated into Apache Records. > >>>> > >>>> > >>>> > >>>> In these two examples, the creation of dictionaries and multi-column > >>>> sorting is automatically done by the framework and the developer > doesn’t > >>>> have to worry about the definition of Arrow schemas. > >>>> > >>>> > >>>> > >>>> Now let's get to the answers. > >>>> > >>>> > >>>> > >>>> @David Lee, I don't think Parquet and from_pylist() solve this problem > >>>> particularly well. Parquet is a column-oriented data file format and > >>>> doesn't really help to perform this transformation. The Python method > is > >>>> relatively limited and language specific. > >>>> > >>>> > >>>> > >>>> @Micah Kornfield, as described before my goal is not to define a > memory > >>>> layout specification but more to define an API and a translation > >>> mechanism > >>>> able to take this intermediate representation (list of generic objects > >>>> representing the entities to translate) and to convert it into one or > >>> more > >>>> Arrow records. > >>>> > >>>> > >>>> > >>>> @Wes McKinney, If I interpret your answer correctly, I think you are > >>>> describing the option 1 mentioned above. Like you I think it is an > >>>> interesting approach although complementary to the one I propose. > >>>> > >>>> > >>>> > >>>> Looking forward to your feedback. > >>>> > >>>> On Wed, Jul 27, 2022 at 4:19 PM Wes McKinney <wesmck...@gmail.com> > >>> wrote: > >>>> > >>>>> We had an e-mail thread about this in 2018 > >>>>> > >>>>> https://lists.apache.org/thread/35pn7s8yzxozqmgx53ympxg63vjvggvm > >>>>> > >>>>> I still think having a canonical in-memory row format (and libraries > >>>>> to transform to and from Arrow columnar format) is a good idea — but > >>>>> there is the risk of ending up in the tar pit of reinventing Avro. > >>>>> > >>>>> > >>>>> On Wed, Jul 27, 2022 at 5:11 PM Micah Kornfield < > emkornfi...@gmail.com > >>>> > >>>>> wrote: > >>>>>> > >>>>>> Are there more details on what exactly an "Arrow Intermediate > >>>>>> Representation (AIR)" is? We've talked about in the past maybe > >>> having > >>>> a > >>>>>> memory layout specification for row-based data as well as column > >>> based > >>>>>> data. There was also a recent attempt at least in C++ to try to > >>> build > >>>>>> utilities to do these pivots but it was decided that it didn't add > >>> much > >>>>>> utility (it was added a comprehensive example). > >>>>>> > >>>>>> Thanks, > >>>>>> Micah > >>>>>> > >>>>>> On Tue, Jul 26, 2022 at 2:26 PM Laurent Quérel < > >>>> laurent.que...@gmail.com > >>>>>> > >>>>>> wrote: > >>>>>> > >>>>>>> In the context of this OTEP > >>>>>>> < > >>>>> > >>>> > >>> > https://github.com/lquerel/oteps/blob/main/text/0156-columnar-encoding.md > >>>>>>>> > >>>>>>> (OpenTelemetry > >>>>>>> Enhancement Proposal) I developed an integration layer on top of > >>>> Apache > >>>>>>> Arrow (Go an Rust) to *facilitate the translation of row-oriented > >>>> data > >>>>>>> stream into an arrow-based columnar representation*. In this > >>>> particular > >>>>>>> case the goal was to translate all OpenTelemetry entities (metrics, > >>>>> logs, > >>>>>>> or traces) into Apache Arrow records. These entities can be quite > >>>>> complex > >>>>>>> and their corresponding Arrow schema must be defined on the fly. > >>> IMO, > >>>>> this > >>>>>>> approach is not specific to my specific needs but could be used in > >>>> many > >>>>>>> other contexts where there is a need to simplify the integration > >>>>> between a > >>>>>>> row-oriented source of data and Apache Arrow. The trade-off is to > >>>> have > >>>>> to > >>>>>>> perform the additional step of conversion to the intermediate > >>>>>>> representation, but this transformation does not require to > >>>> understand > >>>>> the > >>>>>>> arcana of the Arrow format and allows to potentially benefit from > >>>>>>> functionalities such as the encoding of the dictionary "for free", > >>>> the > >>>>>>> automatic generation of Arrow schemas, the batching, the > >>> multi-column > >>>>>>> sorting, etc. > >>>>>>> > >>>>>>> > >>>>>>> I know that JSON can be used as a kind of intermediate > >>> representation > >>>>> in > >>>>>>> the context of Arrow with some language specific implementation. > >>>>> Current > >>>>>>> JSON integrations are insufficient to cover the most complex > >>>> scenarios > >>>>> and > >>>>>>> are not standardized; e.g. support for most of the Arrow data type, > >>>>> various > >>>>>>> optimizations (string|binary dictionaries, multi-column sorting), > >>>>> batching, > >>>>>>> integration with Arrow IPC, compression ratio optimization, ... The > >>>>> object > >>>>>>> of this proposal is to progressively cover these gaps. > >>>>>>> > >>>>>>> I am looking to see if the community would be interested in such a > >>>>>>> contribution. Above are some additional details on the current > >>>>>>> implementation. All feedback is welcome. > >>>>>>> > >>>>>>> 10K ft overview of the current implementation: > >>>>>>> > >>>>>>> 1. Developers convert their row oriented stream into records > >>> based > >>>>> on > >>>>>>> the Arrow Intermediate Representation (AIR). At this stage the > >>>>>>> translation > >>>>>>> can be quite mechanical but if needed developers can decide for > >>>>> example > >>>>>>> to > >>>>>>> translate a map into a struct if that makes sense for them. The > >>>>> current > >>>>>>> implementation support the following arrow data types: bool, all > >>>>> uints, > >>>>>>> all > >>>>>>> ints, all floats, string, binary, list of any supported types, > >>> and > >>>>>>> struct > >>>>>>> of any supported types. Additional Arrow types could be added > >>>>>>> progressively. > >>>>>>> 2. The row oriented record (i.e. AIR record) is then added to a > >>>>>>> RecordRepository. This repository will first compute a schema > >>>>> signature > >>>>>>> and > >>>>>>> will route the record to a RecordBatcher based on this > >>> signature. > >>>>>>> 3. The RecordBatcher is responsible for collecting all the > >>>>> compatible > >>>>>>> AIR records and, upon request, the "batcher" is able to build an > >>>>> Arrow > >>>>>>> Record representing a batch of compatible inputs. In the current > >>>>>>> implementation, the batcher is able to convert string columns to > >>>>>>> dictionary > >>>>>>> based on a configuration. Another configuration allows to > >>> evaluate > >>>>> which > >>>>>>> columns should be sorted to optimize the compression ratio. The > >>>> same > >>>>>>> optimization process could be applied to binary columns. > >>>>>>> 4. Steps 1 through 3 can be repeated on the same > >>> RecordRepository > >>>>>>> instance to build new sets of arrow record batches. Subsequent > >>>>>>> iterations > >>>>>>> will be slightly faster due to different techniques used (e.g. > >>>>> object > >>>>>>> reuse, dictionary reuse and sorting, ...) > >>>>>>> > >>>>>>> > >>>>>>> The current Go implementation > >>>>>>> <https://github.com/lquerel/otel-arrow-adapter> (WIP) is currently > >>>>> part of > >>>>>>> this repo (see pkg/air package). If the community is interested, I > >>>>> could do > >>>>>>> a PR in the Arrow Go and Rust sub-projects. > >>>>>>> > >>>>> > >>>> > >>>> > >>>> -- > >>>> Laurent Quérel > >>>> > >>> > >> > >> > >> -- > >> Laurent Quérel > -- Laurent Quérel
