It has been really interesting to read all the perspectives in this thread and I have even switched sides back and forth given the advantages / issues exposed here, so it means we have clear pros/cons.
One ‘not so nice‘ discovery related to this discussion for me was BEAM-10375 [1] tldr; Reads use java serialization so they don’t have a default deterministic coder and if they are used as keys they break on GbK because Java’s implementation requires keys to be deterministic [2] (is this the case in all the other languages?). We can workaround this by having an alternative Coder for Reads but somehow it does not feel natural and adds extra maintenance. I really like Kenn’s idea that we should rethink from scratch or write a proposal of how we can have designed this with the present awareness about DoFn based composition, code reuse and schema friendliness. Maybe worth to enumerate what are the essentials we want to have (or not) first. I will be OOO for the next month so I cannot actively work on this, but I will be interested on reviewing/contributing in case someone wants to take the lead on a better solution or we can in the meantime keep bringing ideas to this thread. Configuration based on functions translates hardly across languages so I wonder if we should have also a mechanism to map those. Notice that an important use case of this is the detailed configuration of clients for IOs which we have started to expose in some IOs to avoid filling IOs API with ‘knobs‘ and better let the user do their tuning by providing a client via a function. [1] https://issues.apache.org/jira/browse/BEAM-10375 [2] https://github.com/apache/beam/blob/a9d70fed9069c4f4e9a12860ef711652f5f9c21a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/GroupByKey.java#L232-L237 On Thu, Jul 9, 2020 at 5:52 AM Kenneth Knowles <k...@apache.org> wrote: > > If we are forced to create a fresh class due to a breaking change, let's > migrate to the "what we would do from scratch" approach, please. > > Kenn > > On Wed, Jul 8, 2020 at 5:15 PM Robert Bradshaw <rober...@google.com> wrote: >> >> OK, I'm +0 on this change. Using the PTransform as an element is >> probably better than duplicating the full API on another interface, >> and think it's worth getting this ublocked. This will require a Read2 >> if we have to add options in a upgrade-compatible way. >> >> On Tue, Jul 7, 2020 at 3:19 PM Luke Cwik <lc...@google.com> wrote: >> > >> > Robert, you're correct in your understanding that the Read PTransform >> > would be encoded via the schema coder. >> > >> > Kenn, different serializers are ok as long as the output coder can >> > encode/decode the output type. Different watermark fns are also ok since >> > it is about computing the watermark for each individual source and won't >> > impact the watermark computed by other sources. Watermark advancement will >> > still be held back by the source that is furthest behind and still has the >> > same problems when a user chooses a watermark fn that was incompatible >> > with the windowing strategy for producing output (e.g. global window + >> > default trigger + streaming pipeline). >> > >> > Both are pretty close so if we started from scratch then it could go >> > either way but we aren't starting from scratch (I don't think a Beam 3.0 >> > is likely to happen in the next few years as there isn't enough stuff that >> > we want to remove vs the amount of stuff we would gain). >> > >> > On Tue, Jul 7, 2020 at 2:57 PM Kenneth Knowles <k...@apache.org> wrote: >> >> >> >> On Tue, Jul 7, 2020 at 2:24 PM Robert Bradshaw <rober...@google.com> >> >> wrote: >> >>> >> >>> On Tue, Jul 7, 2020 at 2:06 PM Luke Cwik <lc...@google.com> wrote: >> >>> > >> >>> > Robert, the intent is that the Read object would use a schema coder >> >>> > and for XLang purposes would be no different then a POJO. >> >>> >> >>> Just to clarify, you're saying that the Read PTransform would be >> >>> encoded via the schema coder? That still feels a bit odd (and >> >>> specificically if we were designing IO from scratch rather than >> >>> adapting to what already exists would we choose to use PTransforms as >> >>> elements?) but would solve the cross language issue. >> >> >> >> >> >> I like this question. If we were designing from scratch, what would we >> >> do? Would we encourage users to feed Create.of(SourceDescriptor) into >> >> ReadAll? We would probably provide a friendly wrapper for reading one >> >> static thing, and call it Read. But it would probably have an API like >> >> Read.from(SourceDescriptor), thus eliminating duplicate documentation and >> >> boilerplate that Luke described while keeping the separation that Brian >> >> described and clarity around xlang environments. But I'm +0 on whatever >> >> has momentum. I think the main downside is the weirdness around >> >> serializers/watermarkFn/etc on Read. I am not sure how much this will >> >> cause users problems. It would be very ambitious of them to produce a >> >> PCollection<Read> where they had different fns per element... >> >> >> >> Kenn >> >> >> >>> >> >>> > The issue of how to deal with closures applies to both equally and >> >>> > that is why I suggested to favor using data over closures. Once there >> >>> > is an implementation for how to deal with UDFs in an XLang world, this >> >>> > guidance can change. >> >>> > >> >>> > Kenn, I did mean specifying an enum that the XLang expansion service >> >>> > would return a serialized blob of code. The XLang expansion service is >> >>> > responsible for returning an environment that contains all the >> >>> > necessary dependencies to execute the transforms and the serialized >> >>> > blob of code and hence would be a non-issue for the caller. >> >>> > >> >>> > From reviewing the SDF Kafka PR, the reduction in maintenance is >> >>> > definitely there (100s of lines of duplicated boilerplate and >> >>> > documentation). >> >>> > >> >>> > What are the next steps to get a resolution on this? >> >>> > >> >>> > On Thu, Jul 2, 2020 at 10:38 AM Robert Bradshaw <rober...@google.com> >> >>> > wrote: >> >>> >> >> >>> >> On Thu, Jul 2, 2020 at 10:26 AM Kenneth Knowles <k...@apache.org> >> >>> >> wrote: >> >>> >>> >> >>> >>> >> >>> >>> On Wed, Jul 1, 2020 at 4:17 PM Eugene Kirpichov >> >>> >>> <kirpic...@google.com> wrote: >> >>> >>>> >> >>> >>>> Kenn - I don't mean an enum of common closures, I mean expressing >> >>> >>>> closures in a restricted sub-language such as the language of SQL >> >>> >>>> expressions. >> >>> >>> >> >>> >>> >> >>> >>> My lack of clarity: enums was my phrasing of Luke's item 1). I >> >>> >>> understood what you meant. I think either a set of well-known >> >>> >>> closures or a tiny sublanguage could add value. >> >>> >>> >> >>> >>>> >> >>> >>>> That would only work if there is a portable way to interpret SQL >> >>> >>>> expressions, but if there isn't, maybe there should be - for the >> >>> >>>> sake of, well, expressing closures portably. Of course these would >> >>> >>>> be closures that only work with rows - but that seems powerful >> >>> >>>> enough for many if not most purposes. >> >>> >>> >> >>> >>> >> >>> >>> You can choose a SQL dialect or choose the tiniest subset just for >> >>> >>> this purpose and go with it. But when the data type going in or out >> >>> >>> of the lambda are e.g. some Java or Python object then what? One >> >>> >>> idea is to always require these to be rows. But if you can really >> >>> >>> get away with a dependency-free context-free lambda, then Javascript >> >>> >>> or Python is as doable as SQL in terms of having a tiny restricted >> >>> >>> language for just this purpose. I would expect once it got used, >> >>> >>> folks would start to ask to include the rest of what the language >> >>> >>> has to offer - its ecosystem. This is always the main design point I >> >>> >>> am interested in for "lightweight" embedded UDF proposals. >> >>> >> >> >>> >> >> >>> >> This is getting off the topic of ReadAll, but I think being able to >> >>> >> do arbitrary computation in preceding/succeeding transform plus a >> >>> >> (quite) restricted language in the transform itself can go a long >> >>> >> way. (For example, one could have a dynamic destinations write that >> >>> >> takes a KV<element, dest> where dest is a format string like >> >>> >> "foo-{{shard_num}}.txt" to plug in the truly dynamic pieces, but the >> >>> >> dest string itself can be computed (e.g. based on the element) using >> >>> >> arbitrary code in the caller language.) >> >>> >> >> >>> >>> >> >>> >>> >> >>> >>> Kenn >> >>> >>> >> >>> >>>> >> >>> >>>> For example, maybe the Java example: >> >>> >>>> >> >>> >>>> PCollection<BankTransaction> transactions = ...; >> >>> >>>> transactions.apply(FileIO.<TransactionType, >> >>> >>>> Transaction>writeDynamic() >> >>> >>>> .by(Transaction::getType) >> >>> >>>> .via(tx -> tx.getType().toFields(tx), // Convert the data to >> >>> >>>> be written to CSVSink >> >>> >>>> type -> new CSVSink(type.getFieldNames())) >> >>> >>>> .to(".../path/to/") >> >>> >>>> .withNaming(type -> defaultNaming(type + "-transactions", >> >>> >>>> ".csv")); >> >>> >>>> >> >>> >>>> could be written in Python as: >> >>> >>>> >> >>> >>>> transactions | fileio.write_dynamic( >> >>> >>>> by="it.type", # "it" is implicitly available in these SQL >> >>> >>>> expressions as the same thing as the Java lambda argument >> >>> >>>> format="it.fields", >> >>> >>>> sink="CSV_SINK(it.type.field_names)", # A bunch of preset sinks >> >>> >>>> supported in every language? >> >>> >>>> to=".../path/to/", >> >>> >>>> naming="DEFAULT_NAMING(CONCAT(it, '-transactions'), '.csv')") >> >>> >>>> >> >>> >>>> Again, to be clear, I'm not suggesting to block what Ismael is >> >>> >>>> proposing on getting this done - getting this done wouldn't be a >> >>> >>>> short term effort, but seems potentially really nice. >> >>> >>>> >> >>> >>>> >> >>> >>>> On Wed, Jul 1, 2020 at 3:19 PM Robert Burke <rob...@frantil.com> >> >>> >>>> wrote: >> >>> >>>>> >> >>> >>>>> From the Go side of the table, the Go language doesn't provide a >> >>> >>>>> mechanism to serialize or access closure data, which means DoFns >> >>> >>>>> can't be functional closures.This combined with the move to have >> >>> >>>>> the "Structural DoFns" be serialized using Beam Schemas, has the >> >>> >>>>> net result that if Go transforms are used for Cross Language, they >> >>> >>>>> will be configurable with a Schema of the configuration data. >> >>> >>>>> >> >>> >>>>> Of course, this just means that each language will probably >> >>> >>>>> provide whichever mechanisms it likes for use of it's cross >> >>> >>>>> language transforms. >> >>> >>>>> >> >>> >>>>> On Tue, 30 Jun 2020 at 16:07, Kenneth Knowles <k...@apache.org> >> >>> >>>>> wrote: >> >>> >>>>>> >> >>> >>>>>> I don't think an enum of most common closures will work. The >> >>> >>>>>> input types are typically generics that are made concrete by the >> >>> >>>>>> caller who also provides the closures. I think Luke's (2) is the >> >>> >>>>>> same idea as my "Java still assembles it [using opaque Python >> >>> >>>>>> closures/transforms]". It seems like an approach to (3). Passing >> >>> >>>>>> over actual code could address some cases, but libraries become >> >>> >>>>>> the issue. >> >>> >>>>>> >> >>> >>>>>> I think it is fair to say that "WriteAll" style would involve >> >>> >>>>>> entering unexplored territory. >> >>> >>>>>> >> >>> >>>>>> On the main topic, I think Brian has a pretty strong point and >> >>> >>>>>> his example of type conversion lambdas is a good example. I did a >> >>> >>>>>> quick survey and every other property I could find does seem like >> >>> >>>>>> it fits on the Read, and most IOs have a few of these closures >> >>> >>>>>> for example also extracting timestamps. So maybe just a >> >>> >>>>>> resolution convention of putting them on the ReadAll and that >> >>> >>>>>> taking precedence. Then you would be deserializing a Read >> >>> >>>>>> transform with insta-crash methods or some such? >> >>> >>>>>> >> >>> >>>>>> Kenn >> >>> >>>>>> >> >>> >>>>>> On Tue, Jun 30, 2020 at 10:24 AM Eugene Kirpichov >> >>> >>>>>> <kirpic...@google.com> wrote: >> >>> >>>>>>> >> >>> >>>>>>> Yeah, mainly I just feel like dynamic reads and dynamic writes >> >>> >>>>>>> (and perhaps not-yet-invented similar transforms of other kinds) >> >>> >>>>>>> are tightly related - they are either very similar, or are duals >> >>> >>>>>>> of each other - so they should use the same approach. If they >> >>> >>>>>>> are using different approaches, it is a sign that either one of >> >>> >>>>>>> them is being done wrong or that we are running into a >> >>> >>>>>>> fundamental limitation of Beam (e.g. difficulty of encoding >> >>> >>>>>>> closures compared to encoding elements). >> >>> >>>>>>> >> >>> >>>>>>> But I agree with Luke that we shouldn't give up on closures. >> >>> >>>>>>> Especially with the work that has been done on schemas and SQL, >> >>> >>>>>>> I see no reason why we couldn't express closures in a portable >> >>> >>>>>>> restricted sub-language. If we can express SQL, we can express >> >>> >>>>>>> many or most use cases of dynamic reads/writes - I don't mean >> >>> >>>>>>> that we should actually use SQL (though we could - e.g. SQL >> >>> >>>>>>> scalar expressions seem powerful enough to express the closures >> >>> >>>>>>> appearing in most use cases of FileIO.writeDynamic), I just mean >> >>> >>>>>>> that SQL is an existence proof. >> >>> >>>>>>> >> >>> >>>>>>> (I don't want to rock the boat too much, just thought I'd chime >> >>> >>>>>>> in as this topic is dear to my heart) >> >>> >>>>>>> >> >>> >>>>>>> On Tue, Jun 30, 2020 at 9:59 AM Luke Cwik <lc...@google.com> >> >>> >>>>>>> wrote: >> >>> >>>>>>>> >> >>> >>>>>>>> Kenn, I'm not too worried about closures since: >> >>> >>>>>>>> 1) the expansion service for a transform could have a well set >> >>> >>>>>>>> of defined closures by name that are returned as serialized >> >>> >>>>>>>> objects that don't need to be interpretable by the caller >> >>> >>>>>>>> 2) the language could store serialized functions of another >> >>> >>>>>>>> language as constants >> >>> >>>>>>>> 3) generic XLang function support will eventually be needed >> >>> >>>>>>>> but I do agree that closures do make things difficult to >> >>> >>>>>>>> express vs data which is why primarily why we should prefer >> >>> >>>>>>>> data over closures when possible and use closures when >> >>> >>>>>>>> expressing it with data would be too cumbersome. >> >>> >>>>>>>> >> >>> >>>>>>>> Brian, so far the cases that have been migrated have shown that >> >>> >>>>>>>> the source descriptor and the Read transform are almost the >> >>> >>>>>>>> same (some parameters that only impact pipeline construction >> >>> >>>>>>>> such as coders differ). >> >>> >>>>>>>> >> >>> >>>>>>>> On Mon, Jun 29, 2020 at 2:33 PM Brian Hulette >> >>> >>>>>>>> <bhule...@google.com> wrote: >> >>> >>>>>>>>> >> >>> >>>>>>>>> Sorry for jumping into this late and casting a vote against >> >>> >>>>>>>>> the consensus... but I think I'd prefer standardizing on a >> >>> >>>>>>>>> pattern like PCollection<KafkaSourceDescriptor> rather than >> >>> >>>>>>>>> PCollection<Read>. That approach clearly separates the >> >>> >>>>>>>>> parameters that are allowed to vary across a ReadAll (the ones >> >>> >>>>>>>>> defined in KafkaSourceDescriptor) from the parameters that >> >>> >>>>>>>>> should be constant (other parameters in the Read object, like >> >>> >>>>>>>>> SerializedFunctions for type conversions, parameters for >> >>> >>>>>>>>> different operating modes, etc...). I think it's helpful to >> >>> >>>>>>>>> think of the parameters that are allowed to vary as some >> >>> >>>>>>>>> "location descriptor", but I imagine IO authors may want other >> >>> >>>>>>>>> parameters to vary across a ReadAll as well. >> >>> >>>>>>>>> >> >>> >>>>>>>>> To me it seems safer to let an IO author "opt-in" to a >> >>> >>>>>>>>> parameter being dynamic at execution time. >> >>> >>>>>>>>> >> >>> >>>>>>>>> Brian >> >>> >>>>>>>>> >> >>> >>>>>>>>> On Mon, Jun 29, 2020 at 9:26 AM Eugene Kirpichov >> >>> >>>>>>>>> <kirpic...@google.com> wrote: >> >>> >>>>>>>>>> >> >>> >>>>>>>>>> I'd like to raise one more time the question of consistency >> >>> >>>>>>>>>> between dynamic reads and dynamic writes, per my email at the >> >>> >>>>>>>>>> beginning of the thread. >> >>> >>>>>>>>>> If the community prefers ReadAll to read from Read, then >> >>> >>>>>>>>>> should dynamicWrite's write to Write? >> >>> >>>>>>>>>> >> >>> >>>>>>>>>> On Mon, Jun 29, 2020 at 8:57 AM Boyuan Zhang >> >>> >>>>>>>>>> <boyu...@google.com> wrote: >> >>> >>>>>>>>>>> >> >>> >>>>>>>>>>> It seems like most of us agree on the idea that ReadAll >> >>> >>>>>>>>>>> should read from Read. I'm going to update the Kafka ReadAll >> >>> >>>>>>>>>>> with the same pattern. >> >>> >>>>>>>>>>> Thanks for all your help! >> >>> >>>>>>>>>>> >> >>> >>>>>>>>>>> On Fri, Jun 26, 2020 at 12:12 PM Chamikara Jayalath >> >>> >>>>>>>>>>> <chamik...@google.com> wrote: >> >>> >>>>>>>>>>>> >> >>> >>>>>>>>>>>> >> >>> >>>>>>>>>>>> >> >>> >>>>>>>>>>>> On Fri, Jun 26, 2020 at 11:49 AM Luke Cwik >> >>> >>>>>>>>>>>> <lc...@google.com> wrote: >> >>> >>>>>>>>>>>>> >> >>> >>>>>>>>>>>>> I would also like to suggest that transforms that >> >>> >>>>>>>>>>>>> implement ReadAll via Read should also provide methods >> >>> >>>>>>>>>>>>> like: >> >>> >>>>>>>>>>>>> >> >>> >>>>>>>>>>>>> // Uses the specified values if unspecified in the input >> >>> >>>>>>>>>>>>> element from the PCollection<Read>. >> >>> >>>>>>>>>>>>> withDefaults(Read read); >> >>> >>>>>>>>>>>>> // Uses the specified values regardless of what the input >> >>> >>>>>>>>>>>>> element from the PCollection<Read> specifies. >> >>> >>>>>>>>>>>>> withOverrides(Read read); >> >>> >>>>>>>>>>>>> >> >>> >>>>>>>>>>>>> and only adds methods that are required at construction >> >>> >>>>>>>>>>>>> time (e.g. coders). This way the majority of documentation >> >>> >>>>>>>>>>>>> sits on the Read transform. >> >>> >>>>>>>>>>>> >> >>> >>>>>>>>>>>> >> >>> >>>>>>>>>>>> +0 from me. Sounds like benefits outweigh the drawbacks >> >>> >>>>>>>>>>>> here and some of the drawbacks related to cross-language >> >>> >>>>>>>>>>>> can be overcome through future advancements. >> >>> >>>>>>>>>>>> Thanks for bringing this up Ismaël. >> >>> >>>>>>>>>>>> >> >>> >>>>>>>>>>>> - Cham >> >>> >>>>>>>>>>>> >> >>> >>>>>>>>>>>>> >> >>> >>>>>>>>>>>>> >> >>> >>>>>>>>>>>>> On Fri, Jun 26, 2020 at 9:58 AM Luke Cwik >> >>> >>>>>>>>>>>>> <lc...@google.com> wrote: >> >>> >>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>> Ismael, it is good to hear that using Read as the input >> >>> >>>>>>>>>>>>>> didn't have a bunch of parameters that were being >> >>> >>>>>>>>>>>>>> skipped/ignored. Also, for the polymorphism issue you >> >>> >>>>>>>>>>>>>> have to rely on the user correctly telling you the type >> >>> >>>>>>>>>>>>>> in such a way where it is a common ancestor of all the >> >>> >>>>>>>>>>>>>> runtime types that will ever be used. This usually boils >> >>> >>>>>>>>>>>>>> down to something like Serializable or DynamicMessage >> >>> >>>>>>>>>>>>>> such that the coder that is chosen works for all the >> >>> >>>>>>>>>>>>>> runtime types. Using multiple types is a valid use case >> >>> >>>>>>>>>>>>>> and would allow for a simpler graph with less flattens >> >>> >>>>>>>>>>>>>> merging the output from multiple sources. >> >>> >>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>> Boyuan, as you have mentioned we can have a coder for >> >>> >>>>>>>>>>>>>> KafkaIO.Read which uses schemas even if some of the >> >>> >>>>>>>>>>>>>> parameters can't be represented in a meaningful way >> >>> >>>>>>>>>>>>>> beyond "bytes". This would be helpful for cross language >> >>> >>>>>>>>>>>>>> as well since every parameter would become available if a >> >>> >>>>>>>>>>>>>> language could support it (e.g. it could serialize a java >> >>> >>>>>>>>>>>>>> function up front and keep it saved as raw bytes within >> >>> >>>>>>>>>>>>>> said language). Even if we figure out a better way to do >> >>> >>>>>>>>>>>>>> this in the future, we'll have to change the schema for >> >>> >>>>>>>>>>>>>> the new way anyway. This would mean that the external >> >>> >>>>>>>>>>>>>> version of the transform adopts Row to Read and we drop >> >>> >>>>>>>>>>>>>> KafkaSourceDescriptor. The conversion from Row to Read >> >>> >>>>>>>>>>>>>> could validate that the parameters make sense (e.g. the >> >>> >>>>>>>>>>>>>> bytes are valid serialized functions). The addition of an >> >>> >>>>>>>>>>>>>> endReadTime/endReadOffset would make sense for >> >>> >>>>>>>>>>>>>> KafkaIO.Read as well and this would enable having a >> >>> >>>>>>>>>>>>>> bounded version that could be used for backfills (this >> >>> >>>>>>>>>>>>>> doesn't have to be done as part of any current ongoing >> >>> >>>>>>>>>>>>>> PR). Essentially any parameter that could be added for a >> >>> >>>>>>>>>>>>>> single instance of a Kafka element+restriction would also >> >>> >>>>>>>>>>>>>> make sense to the KafkaIO.Read transform since it too is >> >>> >>>>>>>>>>>>>> a single instance. There are parameters that would apply >> >>> >>>>>>>>>>>>>> to the ReadAll that wouldn't apply to a read and these >> >>> >>>>>>>>>>>>>> would be global parameters across all element+restriction >> >>> >>>>>>>>>>>>>> pairs such as config overrides or default values. >> >>> >>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>> I am convinced that we should do as Ismael is suggesting >> >>> >>>>>>>>>>>>>> and use KafkaIO.Read as the type. >> >>> >>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>> On Thu, Jun 25, 2020 at 6:00 PM Chamikara Jayalath >> >>> >>>>>>>>>>>>>> <chamik...@google.com> wrote: >> >>> >>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>> Discussion regarding cross-language transforms is a >> >>> >>>>>>>>>>>>>>> slight tangent here. But I think, in general, it's great >> >>> >>>>>>>>>>>>>>> if we can use existing transforms (for example, IO >> >>> >>>>>>>>>>>>>>> connectors) as cross-language transforms without having >> >>> >>>>>>>>>>>>>>> to build more composites (irrespective of whether in >> >>> >>>>>>>>>>>>>>> ExternalTransformBuilders or a user pipelines) just to >> >>> >>>>>>>>>>>>>>> make them cross-language compatible. A future >> >>> >>>>>>>>>>>>>>> cross-language compatible SchemaCoder might help >> >>> >>>>>>>>>>>>>>> (assuming that works for Read transform) but I'm not >> >>> >>>>>>>>>>>>>>> sure we have a good idea when we'll get to that state. >> >>> >>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>> Thanks, >> >>> >>>>>>>>>>>>>>> Cham >> >>> >>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>> On Thu, Jun 25, 2020 at 3:13 PM Boyuan Zhang >> >>> >>>>>>>>>>>>>>> <boyu...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>> For unbounded SDF in Kafka, we also consider the >> >>> >>>>>>>>>>>>>>>> upgrading/downgrading compatibility in the pipeline >> >>> >>>>>>>>>>>>>>>> update scenario(For detailed discussion, please refer >> >>> >>>>>>>>>>>>>>>> to >> >>> >>>>>>>>>>>>>>>> https://lists.apache.org/thread.html/raf073b8741317244339eb5b2bce844c0f9e0d700c3e4de392fc648d6%40%3Cdev.beam.apache.org%3E). >> >>> >>>>>>>>>>>>>>>> In order to obtain the compatibility, it requires the >> >>> >>>>>>>>>>>>>>>> input of the read SDF is schema-aware. >> >>> >>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>> Thus the major constraint of mapping >> >>> >>>>>>>>>>>>>>>> KafkaSourceDescriptor to PCollection<Read> is, the >> >>> >>>>>>>>>>>>>>>> KafkaIO.Read also needs to be schema-aware, otherwise >> >>> >>>>>>>>>>>>>>>> pipeline updates might fail unnecessarily. If looking >> >>> >>>>>>>>>>>>>>>> into KafkaIO.Read, not all necessary fields are >> >>> >>>>>>>>>>>>>>>> compatible with schema, for example, SerializedFunction. >> >>> >>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>> I'm kind of confused by why ReadAll<Read, OutputT> is a >> >>> >>>>>>>>>>>>>>>> common pattern for SDF based IO. The Read can be a >> >>> >>>>>>>>>>>>>>>> common pattern because the input is always a PBegin. >> >>> >>>>>>>>>>>>>>>> But for an SDF based IO, the input can be anything. By >> >>> >>>>>>>>>>>>>>>> using Read as input, we will still have the maintenance >> >>> >>>>>>>>>>>>>>>> cost when SDF IO supports a new field but Read doesn't >> >>> >>>>>>>>>>>>>>>> consume it. For example, we are discussing adding >> >>> >>>>>>>>>>>>>>>> endOffset and endReadTime to KafkaSourceDescriptior, >> >>> >>>>>>>>>>>>>>>> which is not used in KafkaIO.Read. >> >>> >>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>> On Thu, Jun 25, 2020 at 2:19 PM Ismaël Mejía >> >>> >>>>>>>>>>>>>>>> <ieme...@gmail.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> We forgot to mention (5) External.Config used in >> >>> >>>>>>>>>>>>>>>>> cross-lang, see KafkaIO >> >>> >>>>>>>>>>>>>>>>> ExternalTransformBuilder. This approach is the >> >>> >>>>>>>>>>>>>>>>> predecessor of (4) and probably a >> >>> >>>>>>>>>>>>>>>>> really good candidate to be replaced by the Row based >> >>> >>>>>>>>>>>>>>>>> Configuration Boyuan is >> >>> >>>>>>>>>>>>>>>>> envisioning (so good to be aware of this). >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> Thanks for the clear explanation Luke you mention the >> >>> >>>>>>>>>>>>>>>>> real issue(s). All the >> >>> >>>>>>>>>>>>>>>>> approaches discussed so far in the end could be easily >> >>> >>>>>>>>>>>>>>>>> transformed to produce a >> >>> >>>>>>>>>>>>>>>>> PCollection<Read> and those Read Elements could be >> >>> >>>>>>>>>>>>>>>>> read by the generic ReadAll >> >>> >>>>>>>>>>>>>>>>> transform. Notice that this can be internal in some >> >>> >>>>>>>>>>>>>>>>> IOs e.g. KafkaIO if they >> >>> >>>>>>>>>>>>>>>>> decide not to expose it. I am not saying that we >> >>> >>>>>>>>>>>>>>>>> should force every IO to >> >>> >>>>>>>>>>>>>>>>> support ReadAll in its public API but if we do it is >> >>> >>>>>>>>>>>>>>>>> probably a good idea to be >> >>> >>>>>>>>>>>>>>>>> consistent with naming the transform that expects an >> >>> >>>>>>>>>>>>>>>>> input PCollection<Read> in >> >>> >>>>>>>>>>>>>>>>> the same way. Also notice that using it will save us >> >>> >>>>>>>>>>>>>>>>> of the maintenance issues >> >>> >>>>>>>>>>>>>>>>> discussed in my previous email. >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> Back to the main concern: the consequences of >> >>> >>>>>>>>>>>>>>>>> expansion based on Read: So far I >> >>> >>>>>>>>>>>>>>>>> have not seen consequences for the Splitting part >> >>> >>>>>>>>>>>>>>>>> which maps really nice >> >>> >>>>>>>>>>>>>>>>> assuming the Partition info / Restriction is available >> >>> >>>>>>>>>>>>>>>>> as part of Read. So far >> >>> >>>>>>>>>>>>>>>>> there are not Serialization because Beam is already >> >>> >>>>>>>>>>>>>>>>> enforcing this. Notice that >> >>> >>>>>>>>>>>>>>>>> ReadAll expansion is almost ‘equivalent’ to a poor man >> >>> >>>>>>>>>>>>>>>>> SDF at least for the >> >>> >>>>>>>>>>>>>>>>> Bounded case (see the code in my previous email). For >> >>> >>>>>>>>>>>>>>>>> the other points: >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> > a) Do all properties set on a Read apply to the >> >>> >>>>>>>>>>>>>>>>> > ReadAll? For example, the >> >>> >>>>>>>>>>>>>>>>> > Kafka Read implementation allows you to set the key >> >>> >>>>>>>>>>>>>>>>> > and value deserializers >> >>> >>>>>>>>>>>>>>>>> > which are also used to dictate the output >> >>> >>>>>>>>>>>>>>>>> > PCollection type. It also allows you >> >>> >>>>>>>>>>>>>>>>> > to set how the watermark should be computed. >> >>> >>>>>>>>>>>>>>>>> > Technically a user may want the >> >>> >>>>>>>>>>>>>>>>> > watermark computation to be configurable per Read >> >>> >>>>>>>>>>>>>>>>> > and they may also want an >> >>> >>>>>>>>>>>>>>>>> > output type which is polymorphic (e.g. >> >>> >>>>>>>>>>>>>>>>> > Pcollection<Serializable>). >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> Most of the times they do but for parametric types we >> >>> >>>>>>>>>>>>>>>>> cannot support different >> >>> >>>>>>>>>>>>>>>>> types in the outputs of the Read or at least I did not >> >>> >>>>>>>>>>>>>>>>> find how to do so (is >> >>> >>>>>>>>>>>>>>>>> there a way to use multiple output Coders on Beam?), >> >>> >>>>>>>>>>>>>>>>> we saw this in CassandraIO >> >>> >>>>>>>>>>>>>>>>> and we were discussing adding explicitly these Coders >> >>> >>>>>>>>>>>>>>>>> or Serializer >> >>> >>>>>>>>>>>>>>>>> specific methods to the ReadAll transform. This is >> >>> >>>>>>>>>>>>>>>>> less nice because it will >> >>> >>>>>>>>>>>>>>>>> imply some repeated methods, but it is still a >> >>> >>>>>>>>>>>>>>>>> compromise to gain the other >> >>> >>>>>>>>>>>>>>>>> advantages. I suppose the watermark case you mention >> >>> >>>>>>>>>>>>>>>>> is similar because you may >> >>> >>>>>>>>>>>>>>>>> want the watermark to behave differently in each Read >> >>> >>>>>>>>>>>>>>>>> and we probably don’t >> >>> >>>>>>>>>>>>>>>>> support this, so it corresponds to the polymorphic >> >>> >>>>>>>>>>>>>>>>> category. >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> > b) Read extends PTransform which brings its own >> >>> >>>>>>>>>>>>>>>>> > object modelling concerns. >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> > During the implementations of >> >>> >>>>>>>>>>>>>>>>> > ReadAll(PCollection<Read>), was it discovered >> >>> >>>>>>>>>>>>>>>>> > that some properties became runtime errors or were >> >>> >>>>>>>>>>>>>>>>> > ignored if they were set? >> >>> >>>>>>>>>>>>>>>>> > If no, then the code deduplication is likely worth >> >>> >>>>>>>>>>>>>>>>> > it because we also get a >> >>> >>>>>>>>>>>>>>>>> > lot of javadoc deduplication, but if yes is this an >> >>> >>>>>>>>>>>>>>>>> > acceptable user >> >>> >>>>>>>>>>>>>>>>> > experience? >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> No, not so far. This is an interesting part, notice >> >>> >>>>>>>>>>>>>>>>> that the Read translation >> >>> >>>>>>>>>>>>>>>>> ends up delegating the read bits to the ReadFn part of >> >>> >>>>>>>>>>>>>>>>> ReadAll so the ReadFn is >> >>> >>>>>>>>>>>>>>>>> the real read and must be aware and use all the >> >>> >>>>>>>>>>>>>>>>> parameters. >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> @Override >> >>> >>>>>>>>>>>>>>>>> public PCollection<SolrDocument> expand(PBegin >> >>> >>>>>>>>>>>>>>>>> input) { >> >>> >>>>>>>>>>>>>>>>> return input.apply("Create", >> >>> >>>>>>>>>>>>>>>>> Create.of(this)).apply("ReadAll", readAll()); >> >>> >>>>>>>>>>>>>>>>> } >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> I might be missing something for the Unbounded SDF >> >>> >>>>>>>>>>>>>>>>> case which is the only case >> >>> >>>>>>>>>>>>>>>>> we have not explored so far. I think one easy way to >> >>> >>>>>>>>>>>>>>>>> see the limitations would >> >>> >>>>>>>>>>>>>>>>> be in the ongoing KafkaIO SDF based implementation to >> >>> >>>>>>>>>>>>>>>>> try to map >> >>> >>>>>>>>>>>>>>>>> KafkaSourceDescriptor to do the extra >> >>> >>>>>>>>>>>>>>>>> PCollection<Read> and the Read logic on >> >>> >>>>>>>>>>>>>>>>> the ReadAll with the SDF to see which constraints we >> >>> >>>>>>>>>>>>>>>>> hit, the polymorphic ones >> >>> >>>>>>>>>>>>>>>>> will be there for sure, maybe others will appear (not >> >>> >>>>>>>>>>>>>>>>> sure). However it would be >> >>> >>>>>>>>>>>>>>>>> interesting to see if we have a real gain in the >> >>> >>>>>>>>>>>>>>>>> maintenance points, but well >> >>> >>>>>>>>>>>>>>>>> let’s not forget also that KafkaIO has a LOT of knobs >> >>> >>>>>>>>>>>>>>>>> so probably the generic >> >>> >>>>>>>>>>>>>>>>> implementation could be relatively complex. >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> On Thu, Jun 25, 2020 at 6:30 PM Luke Cwik >> >>> >>>>>>>>>>>>>>>>> <lc...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > I had mentioned that approach 1 and approach 2 work >> >>> >>>>>>>>>>>>>>>>> > for cross language. The difference being that the >> >>> >>>>>>>>>>>>>>>>> > cross language transform would take a well known >> >>> >>>>>>>>>>>>>>>>> > definition and convert it to the Read transform. A >> >>> >>>>>>>>>>>>>>>>> > normal user would have a pipeline that would look >> >>> >>>>>>>>>>>>>>>>> > like: >> >>> >>>>>>>>>>>>>>>>> > 1: PCollection<Read> -> PTransform(ReadAll) -> >> >>> >>>>>>>>>>>>>>>>> > PCollection<Output> >> >>> >>>>>>>>>>>>>>>>> > 2: PCollection<SourceDescriptor> -> >> >>> >>>>>>>>>>>>>>>>> > PTransform(ReadAll) -> PCollection<Output> >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > And in the cross language case this would look like: >> >>> >>>>>>>>>>>>>>>>> > 1: PCollection<Row of SourceDescriptor> -> >> >>> >>>>>>>>>>>>>>>>> > PTransform(Convert Row to Read) -> PCollection<Read> >> >>> >>>>>>>>>>>>>>>>> > -> PTransform(ReadAll) -> PCollection<Output> >> >>> >>>>>>>>>>>>>>>>> > 2: PCollection<Row of SourceDescriptor> -> >> >>> >>>>>>>>>>>>>>>>> > PTransform(Convert Row to SourceDescriptor) -> >> >>> >>>>>>>>>>>>>>>>> > PCollection<SourceDescriptor> -> PTransform(ReadAll) >> >>> >>>>>>>>>>>>>>>>> > -> PCollection<Output>* >> >>> >>>>>>>>>>>>>>>>> > * note that PTransform(Convert Row to >> >>> >>>>>>>>>>>>>>>>> > SourceDescriptor) only exists since we haven't >> >>> >>>>>>>>>>>>>>>>> > solved how to use schemas with language bound types >> >>> >>>>>>>>>>>>>>>>> > in a cross language way. SchemaCoder isn't portable >> >>> >>>>>>>>>>>>>>>>> > but RowCoder is which is why the conversion step >> >>> >>>>>>>>>>>>>>>>> > exists. We could have a solution for this at some >> >>> >>>>>>>>>>>>>>>>> > point in time. >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > My concern with using Read was around: >> >>> >>>>>>>>>>>>>>>>> > a) Do all properties set on a Read apply to the >> >>> >>>>>>>>>>>>>>>>> > ReadAll? For example, the Kafka Read implementation >> >>> >>>>>>>>>>>>>>>>> > allows you to set the key and value deserializers >> >>> >>>>>>>>>>>>>>>>> > which are also used to dictate the output >> >>> >>>>>>>>>>>>>>>>> > PCollection type. It also allows you to set how the >> >>> >>>>>>>>>>>>>>>>> > watermark should be computed. Technically a user may >> >>> >>>>>>>>>>>>>>>>> > want the watermark computation to be configurable >> >>> >>>>>>>>>>>>>>>>> > per Read and they may also want an output type which >> >>> >>>>>>>>>>>>>>>>> > is polymorphic (e.g. PCollection<Serializable>). >> >>> >>>>>>>>>>>>>>>>> > b) Read extends PTransform which brings its own >> >>> >>>>>>>>>>>>>>>>> > object modelling concerns. >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > During the implementations of >> >>> >>>>>>>>>>>>>>>>> > ReadAll(PCollection<Read>), was it discovered that >> >>> >>>>>>>>>>>>>>>>> > some properties became runtime errors or were >> >>> >>>>>>>>>>>>>>>>> > ignored if they were set? If no, then the code >> >>> >>>>>>>>>>>>>>>>> > deduplication is likely worth it because we also get >> >>> >>>>>>>>>>>>>>>>> > a lot of javadoc deduplication, but if yes is this >> >>> >>>>>>>>>>>>>>>>> > an acceptable user experience? >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > On Thu, Jun 25, 2020 at 7:55 AM Alexey Romanenko >> >>> >>>>>>>>>>>>>>>>> > <aromanenko....@gmail.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> I believe that the initial goal of unifying ReadAll >> >>> >>>>>>>>>>>>>>>>> >> as a general "PTransform<PCollection<Read>, >> >>> >>>>>>>>>>>>>>>>> >> PCollection<OutputType>>” was to reduce the amount >> >>> >>>>>>>>>>>>>>>>> >> of code duplication and error-prone approach >> >>> >>>>>>>>>>>>>>>>> >> related to this. It makes much sense since usually >> >>> >>>>>>>>>>>>>>>>> >> we have all needed configuration set in Read >> >>> >>>>>>>>>>>>>>>>> >> objects and, as Ismaeil mentioned, ReadAll will >> >>> >>>>>>>>>>>>>>>>> >> consist mostly of only Split-Shuffle-Read stages. >> >>> >>>>>>>>>>>>>>>>> >> So this case usually can be unified by using >> >>> >>>>>>>>>>>>>>>>> >> PCollection<Read> as input. >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> On the other hand, we have another need to use Java >> >>> >>>>>>>>>>>>>>>>> >> IOs as cross-language transforms (as Luke >> >>> >>>>>>>>>>>>>>>>> >> described) which seems only partly in common with >> >>> >>>>>>>>>>>>>>>>> >> previous pattern of ReadAll using. >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> I’d be more in favour to have only one concept of >> >>> >>>>>>>>>>>>>>>>> >> read configuration for all needs but seems it’s not >> >>> >>>>>>>>>>>>>>>>> >> easy and I’d be more in favour with Luke and Boyuan >> >>> >>>>>>>>>>>>>>>>> >> approach with schema. Though, maybe ReadAll is not >> >>> >>>>>>>>>>>>>>>>> >> a very suitable name in this case because it will >> >>> >>>>>>>>>>>>>>>>> >> can bring some confusions related to previous >> >>> >>>>>>>>>>>>>>>>> >> pattern of ReadAll uses. >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> On 25 Jun 2020, at 05:00, Boyuan Zhang >> >>> >>>>>>>>>>>>>>>>> >> <boyu...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> Sorry for the typo. I mean I think we can go with >> >>> >>>>>>>>>>>>>>>>> >> (3) and (4): use the data type that is schema-aware >> >>> >>>>>>>>>>>>>>>>> >> as the input of ReadAll. >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> On Wed, Jun 24, 2020 at 7:42 PM Boyuan Zhang >> >>> >>>>>>>>>>>>>>>>> >> <boyu...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>> >> >>> >>>>>>>>>>>>>>>>> >>> Thanks for the summary, Cham! >> >>> >>>>>>>>>>>>>>>>> >>> >> >>> >>>>>>>>>>>>>>>>> >>> I think we can go with (2) and (4): use the data >> >>> >>>>>>>>>>>>>>>>> >>> type that is schema-aware as the input of ReadAll. >> >>> >>>>>>>>>>>>>>>>> >>> >> >>> >>>>>>>>>>>>>>>>> >>> Converting Read into ReadAll helps us to stick >> >>> >>>>>>>>>>>>>>>>> >>> with SDF-like IO. But only having (3) is not >> >>> >>>>>>>>>>>>>>>>> >>> enough to solve the problem of using ReadAll in >> >>> >>>>>>>>>>>>>>>>> >>> x-lang case. >> >>> >>>>>>>>>>>>>>>>> >>> >> >>> >>>>>>>>>>>>>>>>> >>> The key point of ReadAll is that the input type of >> >>> >>>>>>>>>>>>>>>>> >>> ReadAll should be able to cross language >> >>> >>>>>>>>>>>>>>>>> >>> boundaries and have compatibilities of >> >>> >>>>>>>>>>>>>>>>> >>> updating/downgrading. After investigating some >> >>> >>>>>>>>>>>>>>>>> >>> possibilities(pure java pojo with custom coder, >> >>> >>>>>>>>>>>>>>>>> >>> protobuf, row/schema) in Kafka usage, we find that >> >>> >>>>>>>>>>>>>>>>> >>> row/schema fits our needs most. Here comes (4). I >> >>> >>>>>>>>>>>>>>>>> >>> believe that using Read as input of ReadAll makes >> >>> >>>>>>>>>>>>>>>>> >>> sense in some cases, but I also think not all IOs >> >>> >>>>>>>>>>>>>>>>> >>> have the same need. I would treat Read as a >> >>> >>>>>>>>>>>>>>>>> >>> special type as long as the Read is schema-aware. >> >>> >>>>>>>>>>>>>>>>> >>> >> >>> >>>>>>>>>>>>>>>>> >>> On Wed, Jun 24, 2020 at 6:34 PM Chamikara Jayalath >> >>> >>>>>>>>>>>>>>>>> >>> <chamik...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> I see. So it seems like there are three options >> >>> >>>>>>>>>>>>>>>>> >>>> discussed so far when it comes to defining source >> >>> >>>>>>>>>>>>>>>>> >>>> descriptors for ReadAll type transforms >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> (1) Use Read PTransform as the element type of >> >>> >>>>>>>>>>>>>>>>> >>>> the input PCollection >> >>> >>>>>>>>>>>>>>>>> >>>> (2) Use a POJO that describes the source as the >> >>> >>>>>>>>>>>>>>>>> >>>> data element of the input PCollection >> >>> >>>>>>>>>>>>>>>>> >>>> (3) Provide a converter as a function to the Read >> >>> >>>>>>>>>>>>>>>>> >>>> transform which essentially will convert it to a >> >>> >>>>>>>>>>>>>>>>> >>>> ReadAll (what Eugene mentioned) >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> I feel like (3) is more suitable for a related >> >>> >>>>>>>>>>>>>>>>> >>>> set of source descriptions such as files. >> >>> >>>>>>>>>>>>>>>>> >>>> (1) will allow most code-reuse but seems like >> >>> >>>>>>>>>>>>>>>>> >>>> will make it hard to use the ReadAll transform as >> >>> >>>>>>>>>>>>>>>>> >>>> a cross-language transform and will break the >> >>> >>>>>>>>>>>>>>>>> >>>> separation of construction time and runtime >> >>> >>>>>>>>>>>>>>>>> >>>> constructs >> >>> >>>>>>>>>>>>>>>>> >>>> (2) could result to less code reuse if not >> >>> >>>>>>>>>>>>>>>>> >>>> careful but will make the transform easier to be >> >>> >>>>>>>>>>>>>>>>> >>>> used as a cross-language transform without >> >>> >>>>>>>>>>>>>>>>> >>>> additional modifications >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> Also, with SDF, we can create ReadAll-like >> >>> >>>>>>>>>>>>>>>>> >>>> transforms that are more efficient. So we might >> >>> >>>>>>>>>>>>>>>>> >>>> be able to just define all sources in that format >> >>> >>>>>>>>>>>>>>>>> >>>> and make Read transforms just an easy to use >> >>> >>>>>>>>>>>>>>>>> >>>> composite built on top of that (by adding a >> >>> >>>>>>>>>>>>>>>>> >>>> preceding Create transform). >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> Thanks, >> >>> >>>>>>>>>>>>>>>>> >>>> Cham >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> On Wed, Jun 24, 2020 at 11:10 AM Luke Cwik >> >>> >>>>>>>>>>>>>>>>> >>>> <lc...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>> I believe we do require PTransforms to be >> >>> >>>>>>>>>>>>>>>>> >>>>> serializable since anonymous DoFns typically >> >>> >>>>>>>>>>>>>>>>> >>>>> capture the enclosing PTransform. >> >>> >>>>>>>>>>>>>>>>> >>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>> On Wed, Jun 24, 2020 at 10:52 AM Chamikara >> >>> >>>>>>>>>>>>>>>>> >>>>> Jayalath <chamik...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> Seems like Read in PCollection<Read> refers to >> >>> >>>>>>>>>>>>>>>>> >>>>>> a transform, at least here: >> >>> >>>>>>>>>>>>>>>>> >>>>>> https://github.com/apache/beam/blob/master/sdks/java/io/hbase/src/main/java/org/apache/beam/sdk/io/hbase/HBaseIO.java#L353 >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> I'm in favour of separating construction time >> >>> >>>>>>>>>>>>>>>>> >>>>>> transforms from execution time data objects >> >>> >>>>>>>>>>>>>>>>> >>>>>> that we store in PCollections as Luke >> >>> >>>>>>>>>>>>>>>>> >>>>>> mentioned. Also, we don't guarantee that >> >>> >>>>>>>>>>>>>>>>> >>>>>> PTransform is serializable so users have the >> >>> >>>>>>>>>>>>>>>>> >>>>>> additional complexity of providing a corder >> >>> >>>>>>>>>>>>>>>>> >>>>>> whenever a PTransform is used as a data object. >> >>> >>>>>>>>>>>>>>>>> >>>>>> Also, agree with Boyuan that using simple Java >> >>> >>>>>>>>>>>>>>>>> >>>>>> objects that are convertible to Beam Rows allow >> >>> >>>>>>>>>>>>>>>>> >>>>>> us to make these transforms available to other >> >>> >>>>>>>>>>>>>>>>> >>>>>> SDKs through the cross-language transforms. >> >>> >>>>>>>>>>>>>>>>> >>>>>> Using transforms or complex sources as data >> >>> >>>>>>>>>>>>>>>>> >>>>>> objects will probably make this difficult. >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> Thanks, >> >>> >>>>>>>>>>>>>>>>> >>>>>> Cham >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> On Wed, Jun 24, 2020 at 10:32 AM Boyuan Zhang >> >>> >>>>>>>>>>>>>>>>> >>>>>> <boyu...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>> Hi Ismael, >> >>> >>>>>>>>>>>>>>>>> >>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>> I think the ReadAll in the IO connector refers >> >>> >>>>>>>>>>>>>>>>> >>>>>>> to the IO with SDF implementation despite the >> >>> >>>>>>>>>>>>>>>>> >>>>>>> type of input, where Read refers to >> >>> >>>>>>>>>>>>>>>>> >>>>>>> UnboundedSource. One major pushback of using >> >>> >>>>>>>>>>>>>>>>> >>>>>>> KafkaIO.Read as source description is that not >> >>> >>>>>>>>>>>>>>>>> >>>>>>> all configurations of KafkaIO.Read are >> >>> >>>>>>>>>>>>>>>>> >>>>>>> meaningful to populate during execution time. >> >>> >>>>>>>>>>>>>>>>> >>>>>>> Also when thinking about x-lang useage, making >> >>> >>>>>>>>>>>>>>>>> >>>>>>> source description across language boundaries >> >>> >>>>>>>>>>>>>>>>> >>>>>>> is also necessary. As Luke mentioned, it's >> >>> >>>>>>>>>>>>>>>>> >>>>>>> quite easy to infer a Schema from an AutoValue >> >>> >>>>>>>>>>>>>>>>> >>>>>>> object: KafkaSourceDescription.java. Then the >> >>> >>>>>>>>>>>>>>>>> >>>>>>> coder of this schema-aware object will be a >> >>> >>>>>>>>>>>>>>>>> >>>>>>> SchemaCoder. When crossing language >> >>> >>>>>>>>>>>>>>>>> >>>>>>> boundaries, it's also easy to convert a Row >> >>> >>>>>>>>>>>>>>>>> >>>>>>> into the source description: Convert.fromRows. >> >>> >>>>>>>>>>>>>>>>> >>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>> On Wed, Jun 24, 2020 at 9:51 AM Luke Cwik >> >>> >>>>>>>>>>>>>>>>> >>>>>>> <lc...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> To provide additional context, the KafkaIO >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> ReadAll transform takes a >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> PCollection<KafkaSourceDescriptor>. This >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> KafkaSourceDescriptor is a POJO that contains >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> the configurable parameters for reading from >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> Kafka. This is different from the pattern >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> that Ismael listed because they take >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> PCollection<Read> as input and the Read is >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> the same as the Read PTransform class used >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> for the non read all case. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> The KafkaSourceDescriptor does lead to >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> duplication since parameters used to >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> configure the transform have to be copied >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> over to the source descriptor but decouples >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> how a transform is specified from the object >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> that describes what needs to be done. I >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> believe Ismael's point is that we wouldn't >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> need such a decoupling. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> Another area that hasn't been discussed and I >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> believe is a non-issue is that the Beam Java >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> SDK has the most IO connectors and we would >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> want to use the IO implementations within >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> Beam Go and Beam Python. This brings in its >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> own set of issues related to versioning and >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> compatibility for the wire format and how one >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> parameterizes such transforms. The wire >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> format issue can be solved with either >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> approach by making sure that the cross >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> language expansion always takes the well >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> known format (whatever it may be) and >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> converts it into >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> Read/KafkaSourceDescriptor/... object that is >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> then passed to the ReadAll transform. Boyuan >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> has been looking to make the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> KafkaSourceDescriptor have a schema so it can >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> be represented as a row and this can be done >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> easily using the AutoValue integration (I >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> don't believe there is anything preventing >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> someone from writing a schema row -> Read -> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> row adapter or also using the AutoValue >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> configuration if the transform is also an >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> AutoValue). >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> I would be more for the code duplication and >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> separation of concerns provided by using a >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> different object to represent the contents of >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> the PCollection from the pipeline >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> construction time PTransform. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> On Wed, Jun 24, 2020 at 9:09 AM Eugene >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> Kirpichov <kirpic...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> Hi Ismael, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> Thanks for taking this on. Have you >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> considered an approach similar (or dual) to >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> FileIO.write(), where we in a sense also >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> have to configure a dynamic number different >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> IO transforms of the same type (file writes)? >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> E.g. how in this example we configure many >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> aspects of many file writes: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> transactions.apply(FileIO.<TransactionType, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> Transaction>writeDynamic() >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .by(Transaction::getType) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .via(tx -> tx.getType().toFields(tx), >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> // Convert the data to be written to CSVSink >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> type -> new >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> CSVSink(type.getFieldNames())) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .to(".../path/to/") >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .withNaming(type -> defaultNaming(type >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> + "-transactions", ".csv")); >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> we could do something similar for many >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> JdbcIO reads: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> PCollection<Bar> bars; // user-specific >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> type from which all the read parameters can >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> be inferred >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> PCollection<Moo> moos = >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> bars.apply(JdbcIO.<Bar, Moo>readAll() >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .fromQuery(bar -> ...compute query for >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> this bar...) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .withMapper((bar, resultSet) -> new >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> Moo(...)) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .withBatchSize(bar -> ...compute batch >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> size for this bar...) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> ...etc); >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> On Wed, Jun 24, 2020 at 6:53 AM Ismaël Mejía >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> <ieme...@gmail.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Hello, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> (my excuses for the long email but this >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> requires context) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> As part of the move from Source based IOs >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to DoFn based ones. One pattern >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> emerged due to the composable nature of >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> DoFn. The idea is to have a different >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> kind of composable reads where we take a >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection of different sorts of >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> intermediate specifications e.g. tables, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> queries, etc, for example: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> JdbcIO: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll<ParameterT, OutputT> extends >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PTransform<PCollection<ParameterT>, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection<OutputT>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> RedisIO: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll extends >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PTransform<PCollection<String>, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection<KV<String, String>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> HBaseIO: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll extends >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PTransform<PCollection<HBaseQuery>, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection<Result>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> These patterns enabled richer use cases >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> like doing multiple queries in the same >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Pipeline, querying based on key patterns or >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> querying from multiple tables at the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> same time but came with some maintenance >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> issues: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> - We ended up needing to add to the ReadAll >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> transforms the parameters for >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> missing information so we ended up with >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> lots of duplicated with methods and >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> error-prone code from the Read transforms >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> into the ReadAll transforms. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> - When you require new parameters you have >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to expand the input parameters of the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> intermediary specification into something >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> that resembles the full `Read` >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> definition for example imagine you want >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to read from multiple tables or >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> servers as part of the same pipeline but >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> this was not in the intermediate >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> specification you end up adding those >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> extra methods (duplicating more code) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> just o get close to the be like the Read >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> full spec. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> - If new parameters are added to the Read >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> method we end up adding them >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> systematically to the ReadAll transform >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> too so they are taken into account. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Due to these issues I recently did a change >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to test a new approach that is >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> simpler, more complete and maintainable. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> The code became: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> HBaseIO: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll extends >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PTransform<PCollection<Read>, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection<Result>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> With this approach users gain benefits of >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> improvements on parameters of normal >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Read because they count with the full Read >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> parameters. But of course there are >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> some minor caveats: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> 1. You need to push some information into >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> normal Reads for example >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> partition boundaries information or >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Restriction information (in the SDF >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> case). Notice that this consistent >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> approach of ReadAll produces a simple >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> pattern that ends up being almost >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> reusable between IOs (e.g. the non-SDF >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> case): >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> public static class ReadAll extends >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PTransform<PCollection<Read>, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection<SolrDocument>> { >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> @Override >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> public PCollection<SolrDocument> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> expand(PCollection<Read> input) { >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> return input >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> .apply("Split", ParDo.of(new >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> SplitFn())) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> .apply("Reshuffle", >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Reshuffle.viaRandomKey()) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> .apply("Read", ParDo.of(new >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadFn())); >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> } >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> } >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> 2. If you are using Generic types for the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> results ReadAll you must have the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Coders used in its definition and >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> require consistent types from the data >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> sources, in practice this means we need >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to add extra withCoder method(s) on >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll but not the full specs. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> At the moment HBaseIO and SolrIO already >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> follow this ReadAll pattern. RedisIO >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> and CassandraIO have already WIP PRs to do >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> so. So I wanted to bring this subject >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to the mailing list to see your opinions, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> and if you see any sort of issues that >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> we might be missing with this idea. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Also I would like to see if we have >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> consensus to start using consistently the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> terminology of ReadAll transforms based on >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Read and the readAll() method for new >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> IOs (at this point probably outdoing this >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> in the only remaining inconsistent >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> place in JdbcIO might not be a good idea >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> but apart of this we should be ok). >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> I mention this because the recent PR on >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> KafkaIO based on SDF is doing something >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> similar to the old pattern but being called >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll and maybe it is worth to be >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> consistent for the benefit of users. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Regards, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Ismaël >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> On Thu, Jun 25, 2020 at 6:30 PM Luke Cwik >> >>> >>>>>>>>>>>>>>>>> <lc...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > I had mentioned that approach 1 and approach 2 work >> >>> >>>>>>>>>>>>>>>>> > for cross language. The difference being that the >> >>> >>>>>>>>>>>>>>>>> > cross language transform would take a well known >> >>> >>>>>>>>>>>>>>>>> > definition and convert it to the Read transform. A >> >>> >>>>>>>>>>>>>>>>> > normal user would have a pipeline that would look >> >>> >>>>>>>>>>>>>>>>> > like: >> >>> >>>>>>>>>>>>>>>>> > 1: PCollection<Read> -> PTransform(ReadAll) -> >> >>> >>>>>>>>>>>>>>>>> > PCollection<Output> >> >>> >>>>>>>>>>>>>>>>> > 2: PCollection<SourceDescriptor> -> >> >>> >>>>>>>>>>>>>>>>> > PTransform(ReadAll) -> PCollection<Output> >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > And in the cross language case this would look like: >> >>> >>>>>>>>>>>>>>>>> > 1: PCollection<Row of SourceDescriptor> -> >> >>> >>>>>>>>>>>>>>>>> > PTransform(Convert Row to Read) -> PCollection<Read> >> >>> >>>>>>>>>>>>>>>>> > -> PTransform(ReadAll) -> PCollection<Output> >> >>> >>>>>>>>>>>>>>>>> > 2: PCollection<Row of SourceDescriptor> -> >> >>> >>>>>>>>>>>>>>>>> > PTransform(Convert Row to SourceDescriptor) -> >> >>> >>>>>>>>>>>>>>>>> > PCollection<SourceDescriptor> -> PTransform(ReadAll) >> >>> >>>>>>>>>>>>>>>>> > -> PCollection<Output>* >> >>> >>>>>>>>>>>>>>>>> > * note that PTransform(Convert Row to >> >>> >>>>>>>>>>>>>>>>> > SourceDescriptor) only exists since we haven't >> >>> >>>>>>>>>>>>>>>>> > solved how to use schemas with language bound types >> >>> >>>>>>>>>>>>>>>>> > in a cross language way. SchemaCoder isn't portable >> >>> >>>>>>>>>>>>>>>>> > but RowCoder is which is why the conversion step >> >>> >>>>>>>>>>>>>>>>> > exists. We could have a solution for this at some >> >>> >>>>>>>>>>>>>>>>> > point in time. >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > My concern with using Read was around: >> >>> >>>>>>>>>>>>>>>>> > a) Do all properties set on a Read apply to the >> >>> >>>>>>>>>>>>>>>>> > ReadAll? For example, the Kafka Read implementation >> >>> >>>>>>>>>>>>>>>>> > allows you to set the key and value deserializers >> >>> >>>>>>>>>>>>>>>>> > which are also used to dictate the output >> >>> >>>>>>>>>>>>>>>>> > PCollection type. It also allows you to set how the >> >>> >>>>>>>>>>>>>>>>> > watermark should be computed. Technically a user may >> >>> >>>>>>>>>>>>>>>>> > want the watermark computation to be configurable >> >>> >>>>>>>>>>>>>>>>> > per Read and they may also want an output type which >> >>> >>>>>>>>>>>>>>>>> > is polymorphic (e.g. PCollection<Serializable>). >> >>> >>>>>>>>>>>>>>>>> > b) Read extends PTransform which brings its own >> >>> >>>>>>>>>>>>>>>>> > object modelling concerns. >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > During the implementations of >> >>> >>>>>>>>>>>>>>>>> > ReadAll(PCollection<Read>), was it discovered that >> >>> >>>>>>>>>>>>>>>>> > some properties became runtime errors or were >> >>> >>>>>>>>>>>>>>>>> > ignored if they were set? If no, then the code >> >>> >>>>>>>>>>>>>>>>> > deduplication is likely worth it because we also get >> >>> >>>>>>>>>>>>>>>>> > a lot of javadoc deduplication, but if yes is this >> >>> >>>>>>>>>>>>>>>>> > an acceptable user experience? >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > >> >>> >>>>>>>>>>>>>>>>> > On Thu, Jun 25, 2020 at 7:55 AM Alexey Romanenko >> >>> >>>>>>>>>>>>>>>>> > <aromanenko....@gmail.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> I believe that the initial goal of unifying ReadAll >> >>> >>>>>>>>>>>>>>>>> >> as a general "PTransform<PCollection<Read>, >> >>> >>>>>>>>>>>>>>>>> >> PCollection<OutputType>>” was to reduce the amount >> >>> >>>>>>>>>>>>>>>>> >> of code duplication and error-prone approach >> >>> >>>>>>>>>>>>>>>>> >> related to this. It makes much sense since usually >> >>> >>>>>>>>>>>>>>>>> >> we have all needed configuration set in Read >> >>> >>>>>>>>>>>>>>>>> >> objects and, as Ismaeil mentioned, ReadAll will >> >>> >>>>>>>>>>>>>>>>> >> consist mostly of only Split-Shuffle-Read stages. >> >>> >>>>>>>>>>>>>>>>> >> So this case usually can be unified by using >> >>> >>>>>>>>>>>>>>>>> >> PCollection<Read> as input. >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> On the other hand, we have another need to use Java >> >>> >>>>>>>>>>>>>>>>> >> IOs as cross-language transforms (as Luke >> >>> >>>>>>>>>>>>>>>>> >> described) which seems only partly in common with >> >>> >>>>>>>>>>>>>>>>> >> previous pattern of ReadAll using. >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> I’d be more in favour to have only one concept of >> >>> >>>>>>>>>>>>>>>>> >> read configuration for all needs but seems it’s not >> >>> >>>>>>>>>>>>>>>>> >> easy and I’d be more in favour with Luke and Boyuan >> >>> >>>>>>>>>>>>>>>>> >> approach with schema. Though, maybe ReadAll is not >> >>> >>>>>>>>>>>>>>>>> >> a very suitable name in this case because it will >> >>> >>>>>>>>>>>>>>>>> >> can bring some confusions related to previous >> >>> >>>>>>>>>>>>>>>>> >> pattern of ReadAll uses. >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> On 25 Jun 2020, at 05:00, Boyuan Zhang >> >>> >>>>>>>>>>>>>>>>> >> <boyu...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> Sorry for the typo. I mean I think we can go with >> >>> >>>>>>>>>>>>>>>>> >> (3) and (4): use the data type that is schema-aware >> >>> >>>>>>>>>>>>>>>>> >> as the input of ReadAll. >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> On Wed, Jun 24, 2020 at 7:42 PM Boyuan Zhang >> >>> >>>>>>>>>>>>>>>>> >> <boyu...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>> >> >>> >>>>>>>>>>>>>>>>> >>> Thanks for the summary, Cham! >> >>> >>>>>>>>>>>>>>>>> >>> >> >>> >>>>>>>>>>>>>>>>> >>> I think we can go with (2) and (4): use the data >> >>> >>>>>>>>>>>>>>>>> >>> type that is schema-aware as the input of ReadAll. >> >>> >>>>>>>>>>>>>>>>> >>> >> >>> >>>>>>>>>>>>>>>>> >>> Converting Read into ReadAll helps us to stick >> >>> >>>>>>>>>>>>>>>>> >>> with SDF-like IO. But only having (3) is not >> >>> >>>>>>>>>>>>>>>>> >>> enough to solve the problem of using ReadAll in >> >>> >>>>>>>>>>>>>>>>> >>> x-lang case. >> >>> >>>>>>>>>>>>>>>>> >>> >> >>> >>>>>>>>>>>>>>>>> >>> The key point of ReadAll is that the input type of >> >>> >>>>>>>>>>>>>>>>> >>> ReadAll should be able to cross language >> >>> >>>>>>>>>>>>>>>>> >>> boundaries and have compatibilities of >> >>> >>>>>>>>>>>>>>>>> >>> updating/downgrading. After investigating some >> >>> >>>>>>>>>>>>>>>>> >>> possibilities(pure java pojo with custom coder, >> >>> >>>>>>>>>>>>>>>>> >>> protobuf, row/schema) in Kafka usage, we find that >> >>> >>>>>>>>>>>>>>>>> >>> row/schema fits our needs most. Here comes (4). I >> >>> >>>>>>>>>>>>>>>>> >>> believe that using Read as input of ReadAll makes >> >>> >>>>>>>>>>>>>>>>> >>> sense in some cases, but I also think not all IOs >> >>> >>>>>>>>>>>>>>>>> >>> have the same need. I would treat Read as a >> >>> >>>>>>>>>>>>>>>>> >>> special type as long as the Read is schema-aware. >> >>> >>>>>>>>>>>>>>>>> >>> >> >>> >>>>>>>>>>>>>>>>> >>> On Wed, Jun 24, 2020 at 6:34 PM Chamikara Jayalath >> >>> >>>>>>>>>>>>>>>>> >>> <chamik...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> I see. So it seems like there are three options >> >>> >>>>>>>>>>>>>>>>> >>>> discussed so far when it comes to defining source >> >>> >>>>>>>>>>>>>>>>> >>>> descriptors for ReadAll type transforms >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> (1) Use Read PTransform as the element type of >> >>> >>>>>>>>>>>>>>>>> >>>> the input PCollection >> >>> >>>>>>>>>>>>>>>>> >>>> (2) Use a POJO that describes the source as the >> >>> >>>>>>>>>>>>>>>>> >>>> data element of the input PCollection >> >>> >>>>>>>>>>>>>>>>> >>>> (3) Provide a converter as a function to the Read >> >>> >>>>>>>>>>>>>>>>> >>>> transform which essentially will convert it to a >> >>> >>>>>>>>>>>>>>>>> >>>> ReadAll (what Eugene mentioned) >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> I feel like (3) is more suitable for a related >> >>> >>>>>>>>>>>>>>>>> >>>> set of source descriptions such as files. >> >>> >>>>>>>>>>>>>>>>> >>>> (1) will allow most code-reuse but seems like >> >>> >>>>>>>>>>>>>>>>> >>>> will make it hard to use the ReadAll transform as >> >>> >>>>>>>>>>>>>>>>> >>>> a cross-language transform and will break the >> >>> >>>>>>>>>>>>>>>>> >>>> separation of construction time and runtime >> >>> >>>>>>>>>>>>>>>>> >>>> constructs >> >>> >>>>>>>>>>>>>>>>> >>>> (2) could result to less code reuse if not >> >>> >>>>>>>>>>>>>>>>> >>>> careful but will make the transform easier to be >> >>> >>>>>>>>>>>>>>>>> >>>> used as a cross-language transform without >> >>> >>>>>>>>>>>>>>>>> >>>> additional modifications >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> Also, with SDF, we can create ReadAll-like >> >>> >>>>>>>>>>>>>>>>> >>>> transforms that are more efficient. So we might >> >>> >>>>>>>>>>>>>>>>> >>>> be able to just define all sources in that format >> >>> >>>>>>>>>>>>>>>>> >>>> and make Read transforms just an easy to use >> >>> >>>>>>>>>>>>>>>>> >>>> composite built on top of that (by adding a >> >>> >>>>>>>>>>>>>>>>> >>>> preceding Create transform). >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> Thanks, >> >>> >>>>>>>>>>>>>>>>> >>>> Cham >> >>> >>>>>>>>>>>>>>>>> >>>> >> >>> >>>>>>>>>>>>>>>>> >>>> On Wed, Jun 24, 2020 at 11:10 AM Luke Cwik >> >>> >>>>>>>>>>>>>>>>> >>>> <lc...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>> I believe we do require PTransforms to be >> >>> >>>>>>>>>>>>>>>>> >>>>> serializable since anonymous DoFns typically >> >>> >>>>>>>>>>>>>>>>> >>>>> capture the enclosing PTransform. >> >>> >>>>>>>>>>>>>>>>> >>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>> On Wed, Jun 24, 2020 at 10:52 AM Chamikara >> >>> >>>>>>>>>>>>>>>>> >>>>> Jayalath <chamik...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> Seems like Read in PCollection<Read> refers to >> >>> >>>>>>>>>>>>>>>>> >>>>>> a transform, at least here: >> >>> >>>>>>>>>>>>>>>>> >>>>>> https://github.com/apache/beam/blob/master/sdks/java/io/hbase/src/main/java/org/apache/beam/sdk/io/hbase/HBaseIO.java#L353 >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> I'm in favour of separating construction time >> >>> >>>>>>>>>>>>>>>>> >>>>>> transforms from execution time data objects >> >>> >>>>>>>>>>>>>>>>> >>>>>> that we store in PCollections as Luke >> >>> >>>>>>>>>>>>>>>>> >>>>>> mentioned. Also, we don't guarantee that >> >>> >>>>>>>>>>>>>>>>> >>>>>> PTransform is serializable so users have the >> >>> >>>>>>>>>>>>>>>>> >>>>>> additional complexity of providing a corder >> >>> >>>>>>>>>>>>>>>>> >>>>>> whenever a PTransform is used as a data object. >> >>> >>>>>>>>>>>>>>>>> >>>>>> Also, agree with Boyuan that using simple Java >> >>> >>>>>>>>>>>>>>>>> >>>>>> objects that are convertible to Beam Rows allow >> >>> >>>>>>>>>>>>>>>>> >>>>>> us to make these transforms available to other >> >>> >>>>>>>>>>>>>>>>> >>>>>> SDKs through the cross-language transforms. >> >>> >>>>>>>>>>>>>>>>> >>>>>> Using transforms or complex sources as data >> >>> >>>>>>>>>>>>>>>>> >>>>>> objects will probably make this difficult. >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> Thanks, >> >>> >>>>>>>>>>>>>>>>> >>>>>> Cham >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>> On Wed, Jun 24, 2020 at 10:32 AM Boyuan Zhang >> >>> >>>>>>>>>>>>>>>>> >>>>>> <boyu...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>> Hi Ismael, >> >>> >>>>>>>>>>>>>>>>> >>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>> I think the ReadAll in the IO connector refers >> >>> >>>>>>>>>>>>>>>>> >>>>>>> to the IO with SDF implementation despite the >> >>> >>>>>>>>>>>>>>>>> >>>>>>> type of input, where Read refers to >> >>> >>>>>>>>>>>>>>>>> >>>>>>> UnboundedSource. One major pushback of using >> >>> >>>>>>>>>>>>>>>>> >>>>>>> KafkaIO.Read as source description is that not >> >>> >>>>>>>>>>>>>>>>> >>>>>>> all configurations of KafkaIO.Read are >> >>> >>>>>>>>>>>>>>>>> >>>>>>> meaningful to populate during execution time. >> >>> >>>>>>>>>>>>>>>>> >>>>>>> Also when thinking about x-lang useage, making >> >>> >>>>>>>>>>>>>>>>> >>>>>>> source description across language boundaries >> >>> >>>>>>>>>>>>>>>>> >>>>>>> is also necessary. As Luke mentioned, it's >> >>> >>>>>>>>>>>>>>>>> >>>>>>> quite easy to infer a Schema from an AutoValue >> >>> >>>>>>>>>>>>>>>>> >>>>>>> object: KafkaSourceDescription.java. Then the >> >>> >>>>>>>>>>>>>>>>> >>>>>>> coder of this schema-aware object will be a >> >>> >>>>>>>>>>>>>>>>> >>>>>>> SchemaCoder. When crossing language >> >>> >>>>>>>>>>>>>>>>> >>>>>>> boundaries, it's also easy to convert a Row >> >>> >>>>>>>>>>>>>>>>> >>>>>>> into the source description: Convert.fromRows. >> >>> >>>>>>>>>>>>>>>>> >>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>> On Wed, Jun 24, 2020 at 9:51 AM Luke Cwik >> >>> >>>>>>>>>>>>>>>>> >>>>>>> <lc...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> To provide additional context, the KafkaIO >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> ReadAll transform takes a >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> PCollection<KafkaSourceDescriptor>. This >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> KafkaSourceDescriptor is a POJO that contains >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> the configurable parameters for reading from >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> Kafka. This is different from the pattern >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> that Ismael listed because they take >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> PCollection<Read> as input and the Read is >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> the same as the Read PTransform class used >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> for the non read all case. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> The KafkaSourceDescriptor does lead to >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> duplication since parameters used to >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> configure the transform have to be copied >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> over to the source descriptor but decouples >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> how a transform is specified from the object >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> that describes what needs to be done. I >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> believe Ismael's point is that we wouldn't >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> need such a decoupling. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> Another area that hasn't been discussed and I >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> believe is a non-issue is that the Beam Java >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> SDK has the most IO connectors and we would >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> want to use the IO implementations within >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> Beam Go and Beam Python. This brings in its >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> own set of issues related to versioning and >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> compatibility for the wire format and how one >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> parameterizes such transforms. The wire >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> format issue can be solved with either >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> approach by making sure that the cross >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> language expansion always takes the well >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> known format (whatever it may be) and >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> converts it into >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> Read/KafkaSourceDescriptor/... object that is >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> then passed to the ReadAll transform. Boyuan >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> has been looking to make the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> KafkaSourceDescriptor have a schema so it can >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> be represented as a row and this can be done >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> easily using the AutoValue integration (I >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> don't believe there is anything preventing >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> someone from writing a schema row -> Read -> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> row adapter or also using the AutoValue >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> configuration if the transform is also an >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> AutoValue). >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> I would be more for the code duplication and >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> separation of concerns provided by using a >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> different object to represent the contents of >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> the PCollection from the pipeline >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> construction time PTransform. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> On Wed, Jun 24, 2020 at 9:09 AM Eugene >> >>> >>>>>>>>>>>>>>>>> >>>>>>>> Kirpichov <kirpic...@google.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> Hi Ismael, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> Thanks for taking this on. Have you >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> considered an approach similar (or dual) to >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> FileIO.write(), where we in a sense also >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> have to configure a dynamic number different >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> IO transforms of the same type (file writes)? >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> E.g. how in this example we configure many >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> aspects of many file writes: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> transactions.apply(FileIO.<TransactionType, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> Transaction>writeDynamic() >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .by(Transaction::getType) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .via(tx -> tx.getType().toFields(tx), >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> // Convert the data to be written to CSVSink >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> type -> new >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> CSVSink(type.getFieldNames())) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .to(".../path/to/") >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .withNaming(type -> defaultNaming(type >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> + "-transactions", ".csv")); >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> we could do something similar for many >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> JdbcIO reads: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> PCollection<Bar> bars; // user-specific >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> type from which all the read parameters can >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> be inferred >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> PCollection<Moo> moos = >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> bars.apply(JdbcIO.<Bar, Moo>readAll() >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .fromQuery(bar -> ...compute query for >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> this bar...) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .withMapper((bar, resultSet) -> new >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> Moo(...)) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> .withBatchSize(bar -> ...compute batch >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> size for this bar...) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> ...etc); >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> On Wed, Jun 24, 2020 at 6:53 AM Ismaël Mejía >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>> <ieme...@gmail.com> wrote: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Hello, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> (my excuses for the long email but this >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> requires context) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> As part of the move from Source based IOs >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to DoFn based ones. One pattern >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> emerged due to the composable nature of >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> DoFn. The idea is to have a different >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> kind of composable reads where we take a >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection of different sorts of >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> intermediate specifications e.g. tables, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> queries, etc, for example: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> JdbcIO: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll<ParameterT, OutputT> extends >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PTransform<PCollection<ParameterT>, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection<OutputT>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> RedisIO: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll extends >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PTransform<PCollection<String>, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection<KV<String, String>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> HBaseIO: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll extends >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PTransform<PCollection<HBaseQuery>, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection<Result>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> These patterns enabled richer use cases >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> like doing multiple queries in the same >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Pipeline, querying based on key patterns or >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> querying from multiple tables at the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> same time but came with some maintenance >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> issues: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> - We ended up needing to add to the ReadAll >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> transforms the parameters for >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> missing information so we ended up with >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> lots of duplicated with methods and >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> error-prone code from the Read transforms >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> into the ReadAll transforms. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> - When you require new parameters you have >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to expand the input parameters of the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> intermediary specification into something >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> that resembles the full `Read` >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> definition for example imagine you want >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to read from multiple tables or >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> servers as part of the same pipeline but >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> this was not in the intermediate >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> specification you end up adding those >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> extra methods (duplicating more code) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> just o get close to the be like the Read >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> full spec. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> - If new parameters are added to the Read >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> method we end up adding them >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> systematically to the ReadAll transform >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> too so they are taken into account. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Due to these issues I recently did a change >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to test a new approach that is >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> simpler, more complete and maintainable. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> The code became: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> HBaseIO: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll extends >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PTransform<PCollection<Read>, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection<Result>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> With this approach users gain benefits of >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> improvements on parameters of normal >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Read because they count with the full Read >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> parameters. But of course there are >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> some minor caveats: >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> 1. You need to push some information into >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> normal Reads for example >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> partition boundaries information or >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Restriction information (in the SDF >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> case). Notice that this consistent >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> approach of ReadAll produces a simple >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> pattern that ends up being almost >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> reusable between IOs (e.g. the non-SDF >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> case): >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> public static class ReadAll extends >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PTransform<PCollection<Read>, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> PCollection<SolrDocument>> { >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> @Override >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> public PCollection<SolrDocument> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> expand(PCollection<Read> input) { >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> return input >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> .apply("Split", ParDo.of(new >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> SplitFn())) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> .apply("Reshuffle", >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Reshuffle.viaRandomKey()) >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> .apply("Read", ParDo.of(new >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadFn())); >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> } >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> } >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> 2. If you are using Generic types for the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> results ReadAll you must have the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Coders used in its definition and >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> require consistent types from the data >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> sources, in practice this means we need >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to add extra withCoder method(s) on >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll but not the full specs. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> At the moment HBaseIO and SolrIO already >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> follow this ReadAll pattern. RedisIO >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> and CassandraIO have already WIP PRs to do >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> so. So I wanted to bring this subject >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> to the mailing list to see your opinions, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> and if you see any sort of issues that >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> we might be missing with this idea. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Also I would like to see if we have >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> consensus to start using consistently the >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> terminology of ReadAll transforms based on >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Read and the readAll() method for new >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> IOs (at this point probably outdoing this >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> in the only remaining inconsistent >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> place in JdbcIO might not be a good idea >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> but apart of this we should be ok). >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> I mention this because the recent PR on >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> KafkaIO based on SDF is doing something >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> similar to the old pattern but being called >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> ReadAll and maybe it is worth to be >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> consistent for the benefit of users. >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Regards, >> >>> >>>>>>>>>>>>>>>>> >>>>>>>>>> Ismaël >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>> >> >> >>> >>>>>>>>>>>>>>>>>
