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 >>>>>> >>>>>
