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 <[email protected]> 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 <[email protected]> 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 <[email protected]> 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 <[email protected]> >> 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 <[email protected]> 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 < >>>> [email protected]> 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 <[email protected]> >>>>> 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 >>>>>> <https://github.com/boyuanzz/beam/blob/kafka/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaSourceDescription.java#L41>. >>>>>> Then the coder of this schema-aware object will be a SchemaCoder >>>>>> <https://github.com/boyuanzz/beam/blob/kafka/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaSourceDescription.java#L84>. >>>>>> When crossing language boundaries, it's also easy to convert a Row into >>>>>> the >>>>>> source description: Convert.fromRows >>>>>> <https://github.com/boyuanzz/beam/blob/kafka/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaIO.java#L1480> >>>>>> . >>>>>> >>>>>> >>>>>> On Wed, Jun 24, 2020 at 9:51 AM Luke Cwik <[email protected]> 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 < >>>>>>> [email protected]> 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 <[email protected]> >>>>>>>> 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 >>>>>>>>> >>>>>>>> >
