Hi Shoumyo,
The problem with communicating data statistics through schema metadata is that it's not compatible with use cases where you want to know the schema *before* the data is produced. Regards Antoine. On Thu, 23 May 2024 14:28:43 -0000 "Shoumyo Chakravorti (BLOOMBERG/ 120 PARK)" <schakravo...@bloomberg.net> wrote: > This is a really exciting development, thank you for putting together this > proposal! > > It looks like this thread and the linked GitHub issue has lots of input from > folks who work with Arrow at a low level and have better familiarity with the > Arrow specifications than I do, so I'll refrain from commenting on the > technicalities of the proposal. I would, however, like to share my > perspective as an application developer that heavily uses Arrow at higher > levels for composing data systems. > > My main concern with the direction of this proposal is that it seems too > narrowly focused on what the integration with DuckDB will look like (how the > statistics can be fed into DuckDB). In many applications, executing the query > is often the "last mile", and it's important to consider where the statistics > will actually come from. To start, data might be sourced in various manners: > > - Arrow IPC files may be mapped from shared memory > - Arrow IPC streams may be received via some RPC framework (à la Flight) > - The Arrow libraries may be used to read from file formats like Parquet or > CSV > - ADBC drivers may be used to read from databases > > Note that in at least the first two cases, the system _executing the query_ > will not be able to provide statistics simply because it is not actually the > data producer. As an example, if Process A writes an Arrow IPC file to shared > memory, and Process B wants to run a query on it -- how is Process B supposed > to get the statistics for query planning? There are a few approaches that I > anticipate application developers might consider: > > 1. Design an out-of-band mechanism for Process B to fetch statistics from > Process A. > 2. Design an encoding that is a superset of Arrow IPC and includes statistics > information, allowing statistics to be communicated in-band. > 3. Use custom schema metadata to communicate statistics in-band. > > Options 1 and 2 require considerably more effort than Option 3. Also, Option > 3 feels somewhat natural because it makes sense for the statistics to come > with the data (similar to how statistics are embedded in Parquet files). In > some sense, the statistics actually *are* a property of the stream. > > In systems that I work on, we already use schema metadata to communicate > information that is unrelated to the structure of the data. From my reading > of the documentation [1], this sounds like a reasonable (and perhaps > intended?) use of metadata, and nowhere is it mentioned that metadata must be > used to determine schema equivalence. Unless there are other ways of > producing stream-level application metadata outside of the schema/field > metadata, the lack of purity was not a concern for me to begin with. > > I would appreciate an approach that communicates statistics via schema > metadata, or at least in some in-band fashion that is consistent across the > IPC and C data specifications. This would make it much easier to uniformly > and transparently plumb statistics through applications, regardless of where > they source Arrow data from. As developers are likely to create bespoke > conventions for this anyways, it seems reasonable to standardize it as > canonical metadata. > > I say this all as a happy user of DuckDB's Arrow scan functionality that is > excited to see better query optimization capabilities. It's just that, in its > current form, the changes in this proposal are not something I could > foreseeably integrate with. > > Best, > Shoumyo > > [1]: > https://arrow.apache.org/docs/format/Columnar.html#custom-application-metadata > > From: dev@arrow.apache.org At: 05/23/24 10:10:51 UTC-4:00To: > dev@arrow.apache.org > Subject: Re: [DISCUSS] Statistics through the C data interface > > I want to +1 on what Dewey is saying here and some comments. > > Sutou Kouhei wrote: > > ADBC may be a bit larger to use only for transmitting statistics. ADBC has > > > statistics related APIs but it has more other APIs. > > It's impossible to keep the responsibility of communication protocols > cleanly separated, but IMO, we should strive to keep the C Data > Interface more of a Transport Protocol than an Application Protocol. > > Statistics are application dependent and can complicate the > implementation of importers/exporters which would hinder the adoption > of the C Data Interface. Statistics also bring in security concerns > that are application-specific. e.g. can an algorithm trust min/max > stats and risk producing incorrect results if the statistics are > incorrect? A question that can't really be answered at the C Data > Interface level. > > The need for more sophisticated statistics only grows with time, so > there is no such thing as a "simple statistics schema". > > Protocols that produce/consume statistics might want to use the C Data > Interface as a primitive for passing Arrow arrays of statistics. > > ADBC might be too big of a leap in complexity now, but "we just need C > Data Interface + statistics" is unlikely to remain true for very long > as projects grow in complexity. > > -- > Felipe > > On Thu, May 23, 2024 at 9:57 AM Dewey Dunnington > <de...@voltrondata.com.invalid> wrote: > > > > Thank you for the background! I understand that these statistics are > > important for query planning; however, I am not sure that I follow why > > we are constrained to the ArrowSchema to represent them. The examples > > given seem to going through Python...would it be easier to request > > statistics at a higher level of abstraction? There would already need > > to be a separate mechanism to request an ArrowArrayStream with > > statistics (unless the PyCapsule `requested_schema` argument would > > suffice). > > > > > ADBC may be a bit larger to use only for transmitting > > > statistics. ADBC has statistics related APIs but it has more > > > other APIs. > > > > Some examples of producers given in the linked threads (Delta Lake, > > Arrow Dataset) are well-suited to being wrapped by an ADBC driver. One > > can implement an ADBC driver without defining all the methods (where > > the producer could call AdbcConnectionGetStatistics(), although > > AdbcStatementGetStatistics() might be more relevant here and doesn't > > exist). One example listed (using an Arrow Table as a source) seems a > > bit light to wrap in an ADBC driver; however, it would not take much > > code to do so and the overhead of getting the reader via ADBC it is > > something like 100 microseconds (tested via the ADBC R package's > > "monkey driver" which wraps an existing stream as a statement). In any > > case, the bulk of the code is building the statistics array. > > > > > How about the following schema for the > > > statistics ArrowArray? It's based on ADBC. > > > > Whatever format for statistics is decided on, I imagine it should be > > exactly the same as the ADBC standard? (Perhaps pushing changes > > upstream if needed?). > > > > On Thu, May 23, 2024 at 3:21 AM Sutou Kouhei <k...@clear-code.com> wrote: > > > > > > Hi, > > > > > > > Why not simply pass the statistics ArrowArray separately in your > > > > producer API of choice > > > > > > It seems that we should use the approach because all > > > feedback said so. How about the following schema for the > > > statistics ArrowArray? It's based on ADBC. > > > > > > | Field Name | Field Type | Comments | > > > |--------------------------|-----------------------| -------- | > > > | column_name | utf8 | (1) | > > > | statistic_key | utf8 not null | (2) | > > > | statistic_value | VALUE_SCHEMA not null | | > > > | statistic_is_approximate | bool not null | (3) | > > > > > > 1. If null, then the statistic applies to the entire table. > > > It's for "row_count". > > > 2. We'll provide pre-defined keys such as "max", "min", > > > "byte_width" and "distinct_count" but users can also use > > > application specific keys. > > > 3. If true, then the value is approximate or best-effort. > > > > > > VALUE_SCHEMA is a dense union with members: > > > > > > | Field Name | Field Type | > > > |------------|------------| > > > | int64 | int64 | > > > | uint64 | uint64 | > > > | float64 | float64 | > > > | binary | binary | > > > > > > If a column is an int32 column, it uses int64 for > > > "max"/"min". We don't provide all types here. Users should > > > use a compatible type (int64 for a int32 column) instead. > > > > > > > > > Thanks, > > > -- > > > kou > > > > > > In <a3ce5e96-176c-4226-9d74-6a458317a...@python.org> > > > "Re: [DISCUSS] Statistics through the C data interface" on Wed, 22 May > > > > 2024 17:04:57 +0200, > > > Antoine Pitrou <anto...@python.org> wrote: > > > > > > > > > > > Hi Kou, > > > > > > > > I agree that Dewey that this is overstretching the capabilities of the > > > > C Data Interface. In particular, stuffing a pointer as metadata value > > > > and decreeing it immortal doesn't sound like a good design decision. > > > > > > > > Why not simply pass the statistics ArrowArray separately in your > > > > producer API of choice (Dewey mentioned ADBC but it is of course just > > > > a possible API among others)? > > > > > > > > Regards > > > > > > > > Antoine. > > > > > > > > > > > > Le 22/05/2024 à 04:37, Sutou Kouhei a écrit : > > > >> Hi, > > > >> We're discussing how to provide statistics through the C > > > >> data interface at: > > > >> https://github.com/apache/arrow/issues/38837 > > > >> If you're interested in this feature, could you share your > > > >> comments? > > > >> Motivation: > > > >> We can interchange Apache Arrow data by the C data interface > > > >> in the same process. For example, we can pass Apache Arrow > > > >> data read by Apache Arrow C++ (provider) to DuckDB > > > >> (consumer) through the C data interface. > > > >> A provider may know Apache Arrow data statistics. For > > > >> example, a provider can know statistics when it reads Apache > > > >> Parquet data because Apache Parquet may provide statistics. > > > >> But a consumer can't know statistics that are known by a > > > >> producer. Because there isn't a standard way to provide > > > >> statistics through the C data interface. If a consumer can > > > >> know statistics, it can process Apache Arrow data faster > > > >> based on statistics. > > > >> Proposal: > > > >> https://github.com/apache/arrow/issues/38837#issuecomment-2123728784 > > > >> How about providing statistics as a metadata in ArrowSchema? > > > >> We reserve "ARROW" namespace for internal Apache Arrow use: > > > >> > https://arrow.apache.org/docs/format/Columnar.html#custom-application-metadata > > > >> > > > >>> The ARROW pattern is a reserved namespace for internal > > > >>> Arrow use in the custom_metadata fields. For example, > > > >>> ARROW:extension:name. > > > >> So we can use "ARROW:statistics" for the metadata key. > > > >> We can represent statistics as a ArrowArray like ADBC does. > > > >> Here is an example ArrowSchema that is for a record batch > > > >> that has "int32 column1" and "string column2": > > > >> ArrowSchema { > > > >> .format = "+siu", > > > >> .metadata = { > > > >> "ARROW:statistics" => ArrowArray*, /* table-level statistics such > > > >> as > > > >> row count */ > > > >> }, > > > >> .children = { > > > >> ArrowSchema { > > > >> .name = "column1", > > > >> .format = "i", > > > >> .metadata = { > > > >> "ARROW:statistics" => ArrowArray*, /* column-level statistics > > > >> > such as > > > >> count distinct */ > > > >> }, > > > >> }, > > > >> ArrowSchema { > > > >> .name = "column2", > > > >> .format = "u", > > > >> .metadata = { > > > >> "ARROW:statistics" => ArrowArray*, /* column-level statistics > > > >> > such as > > > >> count distinct */ > > > >> }, > > > >> }, > > > >> }, > > > >> } > > > >> The metadata value (ArrowArray* part) of '"ARROW:statistics" > > > >> => ArrowArray*' is a base 10 string of the address of the > > > >> ArrowArray. Because we can use only string for metadata > > > >> value. You can't release the statistics ArrowArray*. (Its > > > >> release is a no-op function.) It follows > > > >> > https://arrow.apache.org/docs/format/CDataInterface.html#member-allocation > > > >> semantics. (The base ArrowSchema owns statistics > > > >> ArrowArray*.) > > > >> ArrowArray* for statistics use the following schema: > > > >> | Field Name | Field Type | Comments | > > > >> |----------------|----------------------------------| -------- | > > > >> | key | string not null | (1) | > > > >> | value | `VALUE_SCHEMA` not null | | > > > >> | is_approximate | bool not null | (2) | > > > >> 1. We'll provide pre-defined keys such as "max", "min", > > > >> "byte_width" and "distinct_count" but users can also use > > > >> application specific keys. > > > >> 2. If true, then the value is approximate or best-effort. > > > >> VALUE_SCHEMA is a dense union with members: > > > >> | Field Name | Field Type | Comments | > > > >> |------------|----------------------------------| -------- | > > > >> | int64 | int64 | | > > > >> | uint64 | uint64 | | > > > >> | float64 | float64 | | > > > >> | value | The same type of the ArrowSchema | (3) | > > > >> | | that is belonged to. | | > > > >> 3. If the ArrowSchema's type is string, this type is also string. > > > >> TODO: Is "value" good name? If we refer it from the > > > >> top-level statistics schema, we need to use > > > >> "value.value". It's a bit strange... > > > >> What do you think about this proposal? Could you share your > > > >> comments? > > > >> Thanks, > >
