I meant "or sub-class it and define fields with type annotations" not "with attributes". I believe that version doesn't work in python 2 since it doesn't support the `name: type` syntax.
On Fri, Aug 2, 2019 at 5:55 PM Brian Hulette <bhule...@google.com> wrote: > > Do we need to support python 2? If supporting python 2 will complicate > things, we could make this a python3 only feature. > I don't think supporting python 2 complicates things. It's just that there > are two different ways to use typing.NamedTuple in python 3 - you can > either instantiate it and provide a list of (name, type) pairs, or > sub-class it and define fields with attributes. But in python 2 only the > former works. > > > Why are we mapping to numpy types? Design document suggests mapping to > python native types as the plan. > We did discuss using numpy types in a comment [1], but you're right we > never resolved it and the doc still lists native types. My biggest concern > with just using native int/float types is I think we definitely need *some* > way to distinguish between the schema proto's various int/float sizes in > the python representation. If we don't we would need to either a) reject > schemas that contain any size other than the one that we support, or b) no > longer have a bijective mapping between proto and python (i.e. any integer > type that passes through the Python SDK would get converted to an int64). > And if we do need some way to distinguish between the integer types, I > thought a de facto standard was better than creating our own - as Robert > noted in that comment thread "The only strong opinion I have is that we > shouldn't invent our own." > > As I was experimenting with different approaches I also discovered the > numpy numeric types are very nice because you can instantiate them and they > look just like ints, for example `np.int16(107) == 107` evaluates to true > even though `type(np.int16(107)) == type(107)` does not. > > Another concern with python's int type is that it supports unlimited > precision [2], so it's really not a good type to use for any of the schema > ints. My PR as it is right now actually doesn't even support int. I > probably should at least make a change to accept int as a type > specification for iint64 but throw an error when encoding if an int is too > big. > > [1] > https://docs.google.com/a/google.com/document/d/1uu9pJktzT_O3DxGd1-Q2op4nRk4HekIZbzi-0oTAips/edit?disco=AAAACtLItNA > [2] https://docs.python.org/3/library/stdtypes.html#typesnumeric > > On Fri, Aug 2, 2019 at 4:12 PM Ahmet Altay <al...@google.com> wrote: > > > > Thank you Brian. > > > > I did not spend enough time yet to review. Some early questions, I > apologize if I missed an earlier discussion. > > - Do we need to support python 2? If supporting python 2 will complicate > things, we could make this a python3 only feature. > > - Why are we mapping to numpy types? Design document suggests mapping to > python native types as the plan. > > > > On Wed, Jul 31, 2019 at 2:51 PM Brian Hulette <bhule...@google.com> > wrote: > >> > >> tl;dr: I have a PR at [1] that defines an initial Schema API in python > based on the typing module, and uses typing.NamedTuple to represent a > Schema. There are some risks with that approach but I propose we move > forward with it as a first draft and iterate. > >> > >> > >> I've opened up a PR [1] that implements RowCoder in the Python SDK and > verifies it's compatibility with the Java implementation via tests in > standard_coders.yaml. A lot of miscellaneous changes are required to get > that point, including a pretty significant one: providing some native > python representation for schemas. > >> > >> As discussed in the PR description I opted to fully embrace the typing > module for the native representation of schema types: > >> - Primitive types all map to numpy types (e.g. np.int16, np.unicode). > >> - Arrays map to typing.List. In https://s.apache.org/beam-schemas we > settled on typing.Collection, but unfortunately this doesn't seem to be > supported in python 2, I'm open to other suggestions here. > >> - Map maps to typing.Mapping. > >> - Rows map to typing.NamedTuple. > >> - nullability is indicated with typing.Optional. Note there's no > distinction between Optional[Optional[T]] and Optional[T] in typing, both > map to Union[T, None] - so this is actually a good analog for the nullable > flag on FieldType in schema.proto. > >> > >> With this approach a schema in Python might look like: > >> ``` > >> class Movie(NamedTuple): > >> name: np.unicode > >> year: Optional[np.int16] > >> > >> # The class/type annotation syntax doesn't work in Python 2. Instead > you can use: > >> # Movie = NamedTuple('Movie', [('name', np.unicode), ('year', > Optional[np.int16])] > >> > >> # DoFns annotated with_output_types(Movie) will use RowCoder > >> coders.registry.register_coder(Movie, coders.RowCoder) > >> ``` > >> > >> I think the choice to use typing.NamedTuple as a row type is > potentially controversial - Udi, Robert Bradshaw and I were already > discussing it a bit in a comment on the portable schemas doc [2], but I > wanted to bring that discussion to the ML. > >> > >> On the pro side: > >> + NamedTuple is a pretty great analog for Java's Row type [3]. Both > store attributes internally as an ordered collection (List<Object> in Row, > a tuple in NamedTuple) and provide shortcuts for accessing those attributes > by field name based on the schema. > >> + NamedTuple is a native type, and we're trying to get out of the > business of defining our own type hints (I think). > >> > >> On the con side: > >> - When using the class-based version of NamedTuple in python 3 a user > might be tempted to add more functionality to their class (for example, > define a method) rather than just defining a schema - but I'm not sure > we're prepared to guarantee that we will always produce an instance of > their class, just something that has the defined attributes. This concern > can potentially be alleviated once we have support for logical types. > >> > >> Unless there are any objections I think it would make sense to start > with this implementation (documenting the limitations), and then iterate on > it. Please take a look at the PR [1] and let me know what you think about > this proposal. > >> > >> Thanks, > >> Brian > >> > >> [1] https://github.com/apache/beam/pull/9188 > >> [2] > https://docs.google.com/a/google.com/document/d/1uu9pJktzT_O3DxGd1-Q2op4nRk4HekIZbzi-0oTAips/edit?disco=AAAADSP8gx8 > >> [3] > https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/values/Row.java >
