Since I've been in GHA files lately...
I think they have a very useful pattern which we could borrow from or learn
from, where setting up the variables happens separately, like
https://github.com/apache/beam/blob/57821c191d322f9f21c01a34c55e0c40eda44f1e/.github/workflows/build_release_candidate.yml#L270
If we called the section "vars" and then the config could use the vars in
the destination. I'm making this example deliberately a little gross:
- vars:
- USER_REGION: $.user.metadata.region
- USER_GROUP: $.user.groups[0].name
- config:
- path:
gs://output-bucket-${vars.USER_REGION}/files/${vars.USER_GROUP}-${fileio.SHARD_NUM}-${fileio.WINDOW}
I think it strikes a good balance between arbitrary lambdas and just a
prefix/suffix control, giving a really easy place where we can say "the
whole value of this YAML field is a path expression into the structured
data"
Kenn
On Mon, Oct 9, 2023 at 6:09 PM Chamikara Jayalath via dev <
dev@beam.apache.org> 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.
>
> 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.
>
> 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 ?
>
> Thanks,
> Cham
>
>
>
>
>
>
>
>
>
>
> 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
>>>>
>>>>
