On Tue, Oct 10, 2023 at 4:02 PM Robert Bradshaw <rober...@google.com> wrote:
> On Tue, Oct 10, 2023 at 3:53 PM Chamikara Jayalath <chamik...@google.com> > wrote: > >> >> On Tue, Oct 10, 2023 at 3:41 PM Reuven Lax <re...@google.com> wrote: >> >>> I suspect some simple pattern templating would solve most use cases. We >>> probably would want to support timestamp formatting (e.g. $YYYY $M $D) as >>> well. >>> >>> On Tue, Oct 10, 2023 at 3:35 PM Robert Bradshaw <rober...@google.com> >>> wrote: >>> >>>> On Mon, Oct 9, 2023 at 3:09 PM Chamikara Jayalath <chamik...@google.com> >>>> wrote: >>>> >>>>> I would say: >>>>> >>>>> sink: >>>>> type: WriteToParquet >>>>> config: >>>>> path: /beam/filesytem/dest >>>>> prefix: <my prefix> >>>>> suffix: <my suffix> >>>>> >>>>> Underlying SDK will add the middle part of the file names to make sure >>>>> that files generated by various bundles/windows/shards do not conflict. >>>>> >>>> >>>> What's the relationship between path and prefix? Is path the >>>> directory part of the full path, or does prefix precede it? >>>> >>> >> prefix would be the first part of the file name so each shard will be >> named. >> <path>/<prefix>-<shard components added by the runner>-<suffix> >> >> This is similar to what we do in existing SDKS. For example, Java FileIO, >> >> >> https://github.com/apache/beam/blob/65eaf45026e9eeb61a9e05412488e5858faec6de/sdks/java/core/src/main/java/org/apache/beam/sdk/io/FileIO.java#L187 >> > > Yeah, although there's no distinction between path and prefix. > Ah, for FIleIO, path comes from the "to" call. https://github.com/apache/beam/blob/65eaf45026e9eeb61a9e05412488e5858faec6de/sdks/java/core/src/main/java/org/apache/beam/sdk/io/FileIO.java#L1125 > > >>>> >>>>> This will satisfy the vast majority of use-cases I believe. Fully >>>>> customizing the file pattern sounds like a more advanced use case that can >>>>> be left for "real" SDKs. >>>>> >>>> >>>> Yea, we don't have to do everything. >>>> >>>> >>>>> For dynamic destinations, I think just making the "path" component >>>>> support a lambda that is parameterized by the input should be adequate >>>>> since this allows customers to direct files written to different >>>>> destination directories. >>>>> >>>>> sink: >>>>> type: WriteToParquet >>>>> config: >>>>> path: <destination lambda> >>>>> prefix: <my prefix> >>>>> suffix: <my suffix> >>>>> >>>>> I'm not sure what would be the best way to specify a lambda here >>>>> though. Maybe a regex or the name of a Python callable ? >>>>> >>>> >>>> I'd rather not require Python for a pure Java pipeline, but some kind >>>> of a pattern template may be sufficient here. >>>> >>>> >>>>> On Mon, Oct 9, 2023 at 2:06 PM Robert Bradshaw via dev < >>>>> dev@beam.apache.org> wrote: >>>>> >>>>>> .On Mon, Oct 9, 2023 at 1:49 PM Reuven Lax <re...@google.com> wrote: >>>>>> >>>>>>> Just FYI - the reason why names (including prefixes) in >>>>>>> DynamicDestinations were parameterized via a lambda instead of just >>>>>>> having >>>>>>> the user add it via MapElements is performance. We discussed something >>>>>>> along the lines of what you are suggesting (essentially having the user >>>>>>> create a KV where the key contained the dynamic information). The >>>>>>> problem >>>>>>> was that often the size of the generated filepath was often much larger >>>>>>> (sometimes by 2 OOM) than the information in the record, and there was a >>>>>>> desire to avoid record blowup. e.g. the record might contain a single >>>>>>> integer userid, and the filepath prefix would then be >>>>>>> /long/path/to/output/users/<id>. This was especially bad in cases where >>>>>>> the >>>>>>> data had to be shuffled, and the existing dynamic destinations method >>>>>>> allowed extracting the filepath only _after_ the shuffle. >>>>>>> >>>>>> >>>>>> That is a consideration I hadn't thought much of, thanks for >>>>>> bringing this up. >>>>>> >>>>>> >>>>>>> Now there may not be any good way to keep this benefit in a >>>>>>> declarative approach such as YAML (or at least a good easy way - we >>>>>>> could >>>>>>> always allow the user to pass in a SQL expression to extract the >>>>>>> filename >>>>>>> from the record!), but we should keep in mind that this might mean that >>>>>>> YAML-generated pipelines will be less efficient for certain use cases. >>>>>>> >>>>>> >>>>>> Yep, it's not as straightforward to do in a declarative way. I would >>>>>> like to avoid mixing UDFs (with their associated languages and execution >>>>>> environments) if possible. Though I'd like the performance of a >>>>>> "straightforward" YAML pipeline to be that which one can get writing >>>>>> straight-line Java (and possibly better, if we can leverage the structure >>>>>> of schemas everywhere) this is not an absolute requirement for all >>>>>> features. >>>>>> >>>>>> I wonder if separating out a constant prefix vs. the dynamic stuff >>>>>> could be sufficient to mitigate the blow-up of pre-computing this in most >>>>>> cases (especially in the context of a larger pipeline). Alternatively, >>>>>> rather than just a sharding pattern, one could have a full filepattern >>>>>> that >>>>>> includes format parameters for dynamically computed bits as well as the >>>>>> shard number, windowing info, etc. (There are pros and cons to this.) >>>>>> >>>>>> >>>>>>> On Mon, Oct 9, 2023 at 12:37 PM Robert Bradshaw via dev < >>>>>>> dev@beam.apache.org> wrote: >>>>>>> >>>>>>>> Currently the various file writing configurations take a single >>>>>>>> parameter, path, which indicates where the (sharded) output should be >>>>>>>> placed. In other words, one can write something like >>>>>>>> >>>>>>>> pipeline: >>>>>>>> ... >>>>>>>> sink: >>>>>>>> type: WriteToParquet >>>>>>>> config: >>>>>>>> path: /beam/filesytem/dest >>>>>>>> >>>>>>>> and one gets files like "/beam/filesystem/dest-X-of-N" >>>>>>>> >>>>>>>> Of course, in practice file writing is often much more complicated >>>>>>>> than this (especially when it comes to Streaming). For reference, I've >>>>>>>> included links to our existing offerings in the various SDKs below. I'd >>>>>>>> like to start a discussion about what else should go in the "config" >>>>>>>> parameter and how it should be expressed in YAML. >>>>>>>> >>>>>>>> The primary concern is around naming. This can generally be split >>>>>>>> into (1) the prefix, which must be provided by the users (2) the >>>>>>>> sharing >>>>>>>> information, includes both shard counts (e.g. (the -X-of-N suffix) but >>>>>>>> also >>>>>>>> windowing information (for streaming pipelines) which we may want to >>>>>>>> allow >>>>>>>> the user to customize the formatting of, and (3) a suffix like .json or >>>>>>>> .avro that is useful for both humans and tooling and can often be >>>>>>>> inferred >>>>>>>> but should allow customization as well. >>>>>>>> >>>>>>>> An interesting case is that of dynamic destinations, where the >>>>>>>> prefix (or other parameters) may themselves be functions of the records >>>>>>>> themselves. (I am excluding the case where the format itself is >>>>>>>> variable--such cases are probably better handled by explicitly >>>>>>>> partitioning >>>>>>>> the data and doing multiple writes, as this introduces significant >>>>>>>> complexities and the set of possible formats is generally finite and >>>>>>>> known >>>>>>>> ahead of time.) I propose that we leverage the fact that we have >>>>>>>> structured >>>>>>>> data to be able to pull out these dynamic parameters. For example, if >>>>>>>> we >>>>>>>> have an input data set with a string column my_col we could allow >>>>>>>> something >>>>>>>> like >>>>>>>> >>>>>>>> config: >>>>>>>> path: {dynamic: my_col} >>>>>>>> >>>>>>>> which would pull this information out at runtime. (With the >>>>>>>> MapToFields transform, it is very easy to compute/append additional >>>>>>>> fields >>>>>>>> to existing records.) Generally this field would then be stripped from >>>>>>>> the >>>>>>>> written data, which would only see the subset of non-dynamically >>>>>>>> referenced >>>>>>>> columns (though this could be configurable: we could add an attribute >>>>>>>> like >>>>>>>> {dynamic: my_col, Keep: true} or require the set of columns to be >>>>>>>> actually >>>>>>>> written (or elided) to be enumerated in the config or allow/require the >>>>>>>> actual data to be written to be in a designated field of the "full" >>>>>>>> input >>>>>>>> records as arranged by a preceding transform). It'd be great to get >>>>>>>> input/impressions from a wide range of people here on what would be the >>>>>>>> most natural. Often just writing out snippets of various alternatives >>>>>>>> can >>>>>>>> be quite informative (though I'm avoiding putting them here for the >>>>>>>> moment >>>>>>>> to avoid biasing ideas right off the bat). >>>>>>>> >>>>>>>> For streaming pipelines it is often essential to write data out in >>>>>>>> a time-partitioned manner. The typical way to do this is to add the >>>>>>>> windowing information into the shard specification itself, and a (set >>>>>>>> of) >>>>>>>> file(s) is written on each window closing. Beam YAML already supports >>>>>>>> any >>>>>>>> transform being given a "windowing" configuration which will cause a >>>>>>>> WindowInto transform to be applied to its input(s) before application >>>>>>>> which >>>>>>>> can sit naturally on a sink. We may want to consider if non-windowed >>>>>>>> writes >>>>>>>> make sense as well (though how this interacts with the watermark and >>>>>>>> underlying implementations are a large open question, so this is a >>>>>>>> larger >>>>>>>> change that might make sense to defer). >>>>>>>> >>>>>>>> Note that I am explicitly excluding "coders" here. All data in YAML >>>>>>>> should be schema'd, and writers should know how to write this >>>>>>>> structured >>>>>>>> data. We may want to allow a "schema" field to allow a user to specify >>>>>>>> the >>>>>>>> desired schema in a manner compatible with the sink format itself (e.g. >>>>>>>> avro, json, whatever) that could be used both for validation and >>>>>>>> possibly >>>>>>>> resolving ambiguities (e.g. if the sink has an enum format that is not >>>>>>>> expressed in the schema of the input PCollection). >>>>>>>> >>>>>>>> Some other configuration options are that some formats (especially >>>>>>>> text-based ones) allow for specification of an external compression >>>>>>>> type >>>>>>>> (which may be inferable from the suffix), whether to write a single >>>>>>>> shard >>>>>>>> if the input collection is empty or no shards at all (an occasional >>>>>>>> user >>>>>>>> request that's supported for some Beam sinks now), whether to allow >>>>>>>> fixed >>>>>>>> sharing (generally discouraged, as it disables things like automatic >>>>>>>> shading based on input size, let alone dynamic work rebalancing, though >>>>>>>> sometimes this is useful if the input is known to be small and a single >>>>>>>> output is desired regardless of the restriction in parallelism), or >>>>>>>> other >>>>>>>> sharding parameters (e.g. limiting the number of total elements or >>>>>>>> (approximately) total number of bytes per output shard). Some of these >>>>>>>> options may not be available/implemented for all formats--consideration >>>>>>>> should be given as to how to handle this inconsistency (runtime errors >>>>>>>> for >>>>>>>> unsupported combinations or simply not allowing them on any until all >>>>>>>> are >>>>>>>> supported). >>>>>>>> >>>>>>>> A final consideration: we do not anticipate exposing the full >>>>>>>> complexity of Beam in the YAML offering. For advanced users using a >>>>>>>> "real" >>>>>>>> SDK will often be preferable, and we intend to provide a migration path >>>>>>>> from YAML to a language of your choice (codegen) as a migration path. >>>>>>>> So we >>>>>>>> should balance simplicity with completeness and utility here. >>>>>>>> >>>>>>>> Sure, we could just pick something, but given that the main point >>>>>>>> of YAML is not capability, but expressibility and ease-of-use, I think >>>>>>>> it's >>>>>>>> worth trying to get the expression of these concepts right. I'm sure >>>>>>>> many >>>>>>>> of you have written a pipeline to files at some point in time; I'd >>>>>>>> welcome >>>>>>>> any thoughts anyone has on the matter. >>>>>>>> >>>>>>>> - Robert >>>>>>>> >>>>>>>> >>>>>>>> P.S. A related consideration: how should we consider the plain Read >>>>>>>> (where that file pattern is given at pipeline construction) from the >>>>>>>> ReadAll variants? Should they be separate transforms, or should we >>>>>>>> instead >>>>>>>> allow the same named transform (e.g. ReadFromParquet) support both >>>>>>>> modes, >>>>>>>> depending on whether an input PCollection or explicit file path is >>>>>>>> given >>>>>>>> (the two being mutually exclusive, with exactly one required, and good >>>>>>>> error messaging of course)? >>>>>>>> >>>>>>>> >>>>>>>> Java: >>>>>>>> https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/TextIO.Write.html >>>>>>>> Python: >>>>>>>> https://beam.apache.org/releases/pydoc/current/apache_beam.io.textio.html#apache_beam.io.textio.WriteToText >>>>>>>> Go: >>>>>>>> https://pkg.go.dev/github.com/apache/beam/sdks/go/pkg/beam/io/textio#Write >>>>>>>> Typescript: >>>>>>>> https://beam.apache.org/releases/typedoc/current/functions/io_textio.writeToText.html >>>>>>>> Scio: >>>>>>>> https://spotify.github.io/scio/api/com/spotify/scio/io/TextIO$$WriteParam.html >>>>>>>> >>>>>>>>
