Hi, > I am not sure that I follow why > we are constrained to the ArrowSchema to represent them.
Ah, sorry. Using ArrowSchema isn't required. It's just one idea. We can choose another approach like we just define a schema for statistics ArrowArray as I proposed. > 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?). I think that we can use simpler one for this. For example, ADBC uses dictionary encoding like approach for statistics key. It requires additional ID and name mapping for application-specific statistics key. We can use just name for it. See also the related discussion on the issue: https://github.com/apache/arrow/issues/38837#issuecomment-2108895904 Thanks, -- kou In <cafb7qsfc-tgr-ef1ixd-slffeut+amzvyg1xh69eyc4d6r4...@mail.gmail.com> "Re: [DISCUSS] Statistics through the C data interface" on Thu, 23 May 2024 09:57:05 -0300, 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,
