Not sure what that suggests On Tue, Oct 3, 2023, 6:24 PM XQ Hu via user <[email protected]> wrote:
> Looks like this is the current behaviour. If you have `t = > beam.Filter(identity_filter)`, `t.label` is defined as > `Filter(identity_filter)`. > > On Mon, Oct 2, 2023 at 9:25 AM Joey Tran <[email protected]> > wrote: > >> You don't have to specify the names if the callable you pass in is >> /different/ for two `beam.Map`s, but if the callable is the same you must >> specify a label. For example, the below will raise an exception: >> >> ``` >> | beam.Filter(identity_filter) >> | beam.Filter(identity_filter) >> ``` >> >> Here's an example on playground that shows the error message you get [1]. >> I marked every line I added with a "# ++". >> >> It's a contrived example, but using a map or filter at the same pipeline >> level probably comes up often, at least in my inexperience. For example, >> you. might have a pipeline that partitions a pcoll into three different >> pcolls, runs some transforms on them, and then runs the same type of filter >> on each of them. >> >> The case that happens most often for me is using the `assert_that` [2] >> testing transform. In this case, I think often users will really have no >> need for a disambiguating label as they're often just writing unit tests >> that test a few different properties of their workflow. >> >> [1] https://play.beam.apache.org/?sdk=python&shared=hIrm7jvCamW >> [2] >> https://beam.apache.org/releases/pydoc/2.29.0/apache_beam.testing.util.html#apache_beam.testing.util.assert_that >> >> On Mon, Oct 2, 2023 at 9:08 AM Bruno Volpato via user < >> [email protected]> wrote: >> >>> If I understand the question correctly, you don't have to specify those >>> names. >>> >>> As Reuven pointed out, it is probably a good idea so you have a stable / >>> deterministic graph. >>> But in the Python SDK, you can simply use pcollection | map_fn, instead >>> of pcollection | 'Map' >> map_fn. >>> >>> See an example here >>> https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/cookbook/group_with_coder.py#L100-L116 >>> >>> >>> On Sun, Oct 1, 2023 at 9:08 PM Joey Tran <[email protected]> >>> wrote: >>> >>>> Hmm, I'm not sure what you mean by "updating pipelines in place". Can >>>> you elaborate? >>>> >>>> I forgot to mention my question is posed from the context of a python >>>> SDK user, and afaict, there doesn't seem to be an obvious way to >>>> autogenerate names/labels. Hearing that the java SDK supports it makes me >>>> wonder if the python SDK could support it as well though... (If so, I'd be >>>> happy to do implement it). Currently, it's fairly tedious to have to name >>>> every instance of a transform that you might reuse in a pipeline, e.g. when >>>> reapplying the same Map on different pcollections. >>>> >>>> On Sun, Oct 1, 2023 at 8:12 PM Reuven Lax via user < >>>> [email protected]> wrote: >>>> >>>>> Are you talking about transform names? The main reason was because for >>>>> runners that support updating pipelines in place, the only way to do so >>>>> safely is if the runner can perfectly identify which transforms in the new >>>>> graph match the ones in the old graph. There's no good way to auto >>>>> generate >>>>> names that will stay stable across updates - even small changes to the >>>>> pipeline might change the order of nodes in the graph, which could result >>>>> in a corrupted update. >>>>> >>>>> However, if you don't care about update, Beam can auto generate these >>>>> names for you! When you call PCollection.apply (if using BeamJava), simply >>>>> omit the name parameter and Beam will auto generate a unique name for you. >>>>> >>>>> Reuven >>>>> >>>>> On Sat, Sep 30, 2023 at 11:54 AM Joey Tran <[email protected]> >>>>> wrote: >>>>> >>>>>> After writing a few pipelines now, I keep getting tripped up from >>>>>> accidentally have duplicate labels from using multiple of the same >>>>>> transforms without labeling them. I figure this must be a common >>>>>> complaint, >>>>>> so I was just curious, what the rationale behind this design was? My >>>>>> naive >>>>>> thought off the top of my head is that it'd be more user friendly to just >>>>>> auto increment duplicate transforms, but I figure I must be missing >>>>>> something >>>>>> >>>>>> Cheers, >>>>>> Joey >>>>>> >>>>>
