For that case, I think we would have a property that defines whether
supports-decimal is assumed or checked with the capability.
Wouldn't we have this problem no matter what the capability API is? If we
used a trait to signal decimal support, then we would have to deal with
sources that were written before the trait was introduced. That doesn't
change the need for some way to signal support for specific capabilities
like the ones I've suggested.
On Fri, Nov 9, 2018 at 12:38 PM Reynold Xin <r...@databricks.com> wrote:
> "If there is no way to report a feature (e.g., able to read missing as
> null) then there is no way for Spark to take advantage of it in the first
> place"
>
> Consider this (just a hypothetical scenario): We added "supports-decimal"
> in the future, because we see a lot of data sources don't support decimal
> and we want a more graceful error handling. That'd break all existing data
> sources.
>
> You can say we would never add any "existing" features to the feature list
> in the future, as a requirement for the feature list. But then I'm
> wondering how much does it really give you, beyond telling data sources to
> throw exceptions when they don't support a specific operation.
>
>
> On Fri, Nov 9, 2018 at 11:54 AM Ryan Blue <rb...@netflix.com> wrote:
>
>> Do you have an example in mind where we might add a capability and break
>> old versions of data sources?
>>
>> These are really for being able to tell what features a data source has.
>> If there is no way to report a feature (e.g., able to read missing as null)
>> then there is no way for Spark to take advantage of it in the first place.
>> For the uses I've proposed, forward compatibility isn't a concern. When we
>> add a capability, we add handling for it that old versions wouldn't be able
>> to use anyway. The advantage is that we don't have to treat all sources the
>> same.
>>
>> On Fri, Nov 9, 2018 at 11:32 AM Reynold Xin <r...@databricks.com> wrote:
>>
>>> How do we deal with forward compatibility? Consider, Spark adds a new
>>> "property". In the past the data source supports that property, but since
>>> it was not explicitly defined, in the new version of Spark that data source
>>> would be considered not supporting that property, and thus throwing an
>>> exception.
>>>
>>>
>>> On Fri, Nov 9, 2018 at 9:11 AM Ryan Blue <rb...@netflix.com> wrote:
>>>
>>>> I'd have two places. First, a class that defines properties supported
>>>> and identified by Spark, like the SQLConf definitions. Second, in
>>>> documentation for the v2 table API.
>>>>
>>>> On Fri, Nov 9, 2018 at 9:00 AM Felix Cheung <felixcheun...@hotmail.com>
>>>> wrote:
>>>>
>>>>> One question is where will the list of capability strings be defined?
>>>>>
>>>>>
>>>>> ------------------------------
>>>>> *From:* Ryan Blue <rb...@netflix.com.invalid>
>>>>> *Sent:* Thursday, November 8, 2018 2:09 PM
>>>>> *To:* Reynold Xin
>>>>> *Cc:* Spark Dev List
>>>>> *Subject:* Re: DataSourceV2 capability API
>>>>>
>>>>>
>>>>> Yes, we currently use traits that have methods. Something like
>>>>> “supports reading missing columns” doesn’t need to deliver methods. The
>>>>> other example is where we don’t have an object to test for a trait (
>>>>> scan.isInstanceOf[SupportsBatch]) until we have a Scan with pushdown
>>>>> done. That could be expensive so we can use a capability to fail faster.
>>>>>
>>>>> On Thu, Nov 8, 2018 at 1:54 PM Reynold Xin <r...@databricks.com>
>>>>> wrote:
>>>>>
>>>>>> This is currently accomplished by having traits that data sources can
>>>>>> extend, as well as runtime exceptions right? It's hard to argue one way
>>>>>> vs
>>>>>> another without knowing how things will evolve (e.g. how many different
>>>>>> capabilities there will be).
>>>>>>
>>>>>>
>>>>>> On Thu, Nov 8, 2018 at 12:50 PM Ryan Blue <rb...@netflix.com.invalid>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi everyone,
>>>>>>>
>>>>>>> I’d like to propose an addition to DataSourceV2 tables, a capability
>>>>>>> API. This API would allow Spark to query a table to determine whether it
>>>>>>> supports a capability or not:
>>>>>>>
>>>>>>> val table = catalog.load(identifier)
>>>>>>> val supportsContinuous = table.isSupported("continuous-streaming")
>>>>>>>
>>>>>>> There are a couple of use cases for this. First, we want to be able
>>>>>>> to fail fast when a user tries to stream a table that doesn’t support
>>>>>>> it.
>>>>>>> The design of our read implementation doesn’t necessarily support this.
>>>>>>> If
>>>>>>> we want to share the same “scan” across streaming and batch, then we
>>>>>>> need
>>>>>>> to “branch” in the API after that point, but that is at odds with
>>>>>>> failing
>>>>>>> fast. We could use capabilities to fail fast and not worry about that
>>>>>>> concern in the read design.
>>>>>>>
>>>>>>> I also want to use capabilities to change the behavior of some
>>>>>>> validation rules. The rule that validates appends, for example, doesn’t
>>>>>>> allow a write that is missing an optional column. That’s because the
>>>>>>> current v1 sources don’t support reading when columns are missing. But
>>>>>>> Iceberg does support reading a missing column as nulls, so that users
>>>>>>> can
>>>>>>> add a column to a table without breaking a scheduled job that populates
>>>>>>> the
>>>>>>> table. To fix this problem, I would use a table capability, like
>>>>>>> read-missing-columns-as-null.
>>>>>>>
>>>>>>> Any comments on this approach?
>>>>>>>
>>>>>>> rb
>>>>>>> --
>>>>>>> Ryan Blue
>>>>>>> Software Engineer
>>>>>>> Netflix
>>>>>>>
>>>>>>
>>>>>
>>>>> --
>>>>> Ryan Blue
>>>>> Software Engineer
>>>>> Netflix
>>>>>
>>>>
>>>>
>>>> --
>>>> Ryan Blue
>>>> Software Engineer
>>>> Netflix
>>>>
>>>
>>
>> --
>> Ryan Blue
>> Software Engineer
>> Netflix
>>
>
--
Ryan Blue
Software Engineer
Netflix
