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 <[email protected]> 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 <[email protected]> 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 <[email protected]> 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 <[email protected]> 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 < >>> [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. 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 <[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 >>> >> >>> >> >>> >>> On Thu, Jun 25, 2020 at 6:30 PM Luke Cwik <[email protected]> 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 < >>> [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. 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 <[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 >>> >> >>> >> >>> >>>
