The only functionality missing is a `system_rowtime()` or `system_proctime`. I don't know if we need to give users the chance to also select the location of the attribute or always append at the end.
If we go with the new TypeInfo -> DataType mapping as mentioned in the FLIP, users don't need to specify all fields for giving POJOs a deterministic field order. Most of that would happen automatically. I think we can definetely get rid of the column renaming functionality.
@Danny: The renaming of the API is mostly because of the slightly different semantics. In particular a different type mapping, different field order. It is a more conservative approach to introduce that in a new method rather than changing the existing one under the hood and potentially break existing pipelines silently.
"Do you mean the physical type of the record ?"Yes, if we accapt a DataStream<T> than T can be just atomic (like String), TupleX, Row, POJO. We can derive a lot of information from DataStream.getType() but should still give users the chance to influence it for `fromDataStream`. However, for `toDataStream` users need to be able to express whether they would prefer Row, POJO or atomic. And this cannot be expressed with Schema, but only with DataType.
Regards, Timo On 02.09.20 12:23, Danny Chan wrote:
Thanks Timo ~ “No this is not possible, because T records have no changeflag. Without a changeflag, a ChangelogMode makes not much sense. “ I agree, but just distinguish the different ChangelogMode with a renamed API still does not resolve the problem either, an API change compared to an additional parameter, i would choose the later. “However, with a schema you cannot specify the data type of the top-level record itself” What is a “top-level record “ ? Do you mean the physical type of the record ? From a Schema we can infer its original type though. “Is it possible to keep using `fromDataStream(DataStream, Expression…)`” From the SQL side, an Expression list usually means a computation (projection) there, while here we actually want to define the schema of the stream(which is static). Compared to "fromInsertStream(DataStream).select()”, they actually indicate the same thing from the API level, although I would not vote `fromDataStream(DataStream, Expression…)` it is still better than `fromInsertStream(DataStream).select()`. Best, Danny Chan 在 2020年9月2日 +0800 PM4:19，Timo Walther <twal...@apache.org>，写道：Hi everyone thanks for your feedback. It's a lot of content that needs to be digested. I will update the FLIP shortly to incorporate some of the feedback already. But let me respond to some topics first: "not deprecate these API", "the API of the table layer is changing too fast" I agree that deprecating API is definitely not great for users, but in this cases I think it is for the greater good it makes the API more understandable and focuses on common use cases for the future. I would rather say that the API is about to settle because there only a couple of shortcomings left and the bigger picture is clearer than ever. IMO The proposed changes are one of the last bigger API changes on the roadmap. I cannot see other bigger refactorings in the mid-term. Keeping methods just because we changed so much in the last releases should not be a reason to keep confusing API. Users are happy to upgrade if they also get more features by upgrading (e.g. fromChangelogStream). 1. "fromDataStream VS fromInsertStream" The main reason to change this API is to have the possibility to update the type mapping without breaking backwards compatibility. The name `fromInsertStream` makes it possible to have new semantics and makes concepts more explicit by naming. 2. "toAppendStream VS toInsertStream" "Append" is common in the Flink community but the outside world uses "insert". Actually, the term "append-only table" is wrong because SQL tables have bag semantics without any order. So "appending" is more of an "insertion". This is also represented in FLIP-95's `RowKind` where we call the concepts INSERT and `ChangelogKind.insertOnly`. 3. "`.rowtime()` and `.proctime()`" "API is also widely used, even in our test code" The test code is already quite outdated and uses a lot of deprecated API. We need to deal with that with a better testing infrastructure. But this can be future work. "users have already accepted it" I'm not sure if users have already accepted it. I think we get at least one question around this topic every week because users would like to call `.rowtime` on arbitrary timestamps in the middle of the pipeline. And specifying all fields just to give the StreamRecord timestamp a name should be made easier. This is necessary in 80% of all use cases. 4. "toAppendStream(Table table, Class<T>/TypeInformation)" The DataType system is way easier than the TypeInformation system because it provides a consistent look and feel with a lot of utilities. E.g. many users didn't know that they can just pass `Row.class` in the past. Actually extracting types from a `Row.class` is not supported by the TypeExtractor (we recently even printed a warning to the logs) but we hacked some logic into the method. With AbstractDataType, users can still use classes via `DataTypes.of`; for example `toInsertStream(DataTypes.of(MyPojo.class))`. 5. "tEnv#createTemporaryView was introduced in release-1.10" Similar to `TableEnvironment.execute()` we did some mistakes during the big refactorings. IMHO tEnv#createTemporaryView was one mistake because we introduced it too quickly. In general this method is correct, but now we cannot change the underlying semantics again without breaking existing pipelines. We could keep this method and just change the type system under the hood, in most of the cases the pipeline should still work but we cannot guarantee this due to slight differences. 6. "could it be "StreamTableEnvironment.fromDataStream(DataStream<T>, ChangelogMode)" No this is not possible, because T records have no changeflag. Without a changeflag, a ChangelogMode makes not much sense. That's why `from/toChangelogStream` supports only `Row` whereas the `from/toInsertStream` accepts arbitrary type classes. 7. "i must say I prefer tEnv.fromDataStream(dataStream, Schema)" I also thought about this method and using `Schema` there. However, with a schema you cannot specify the data type of the top-level record itself. We would need to offer fromDataStream(dataStream, Schema, DataType) or integrate the DataType into the Schema class itself which would mix up the concepts. 8. "name-based setters should always be based on fieldNames" I'm fine with throwing an exception. If my mentioned semantics, are too confusing. Regards, Timo On 02.09.20 07:25, Jingsong Li wrote:a Row has two modes represented by an internal boolean flag`hasFieldOrder` +1 confusion with Dawid that what's the result when index-based setters and name-based setters are mixed used. And name-based setters look like append instead of set. It reminds me of Avro's `GenericRecord`, We should support real random name-based setters instead of append. So, what I think is, name-based setters should always be based on fieldNames just like name-based getters. Otherwise, throw an exception. Best, Jingsong On Wed, Sep 2, 2020 at 12:43 PM Danny Chan <yuzhao....@gmail.com> wrote:Timo, Thanks for the discussion I have only read the "Conversion of DataStream to Table" part so i would only put some objections there ~StreamTableEnvironment.fromInsertStream(DataStream<T>): TableAt first glance, from the perspective of a user, i'm confused by why we must dintinguish on the API level what a data stream is, e.g. an insert stream or whatever other kind of stream. As a user, he does not expect to must distinguish between several datastream options. The framework should have the ability to infer the ChangelogMode of the stream, but sadly we can not at the moment, becase we do not have a metadata to describe the ChangelogMode what actually the framework need. And could it be: StreamTableEnvironment.fromDataStream(DataStream<T>, ChangelogMode) where the ChanglogMode is optional because 90% of the datastream are insert for now. or: DataStream.withChangelogMode(ChangelogMode) so that DataStream can be self-describing what kind of stream it is (again, if not specified, the default is INSERT).tEnv .fromInsertStream(DataStream<T>) .select('*, system_rowtime().as("rowtime"),system_proctime().as(“proctime”)) In order to declare the time-attributes on datastream, i must say I prefer tEnv.fromDataStream(dataStream, Schema) for these reasons: - Schema is the uniform interface to declare the metadata for a table in the Table/SQL API, with an imperative coding style, in Descriptor API we also use it for the time-attributes purpose - Use a projection for time-attributes is not a good idea, because from the SQL side, we declare it as a metadata of part of the table schema when we define the DDL. Although we may explain the DDL internally using computed column, that does not mean we must do that in the DataStream API explicitly. In the SQL world, no projection function outputs type of time-attribute, we better still put the time-attributes in the scope of the table metadata. Best, Danny Chan 在 2020年8月19日 +0800 PM4:22，Timo Walther <twal...@apache.org>，写道：Hi everyone, I would like to propose a FLIP that aims to resolve the remaining shortcomings in the Table API:https://cwiki.apache.org/confluence/display/FLINK/FLIP-136%3A++Improve+interoperability+between+DataStream+and+Table+APIThe Table API has received many new features over the last year. It supports a new type system (FLIP-37), connectors support changelogs (FLIP-95), we have well defined internal data structures (FLIP-95), support for result retrieval in an interactive fashion (FLIP-84), and soon new TableDescriptors (FLIP-129). However, the interfaces from and to DataStream API have not been touched during the introduction of these new features and are kind of outdated. The interfaces lack important functionality that is available in Table API but not exposed to DataStream API users. DataStream API is still our most important API which is why a good interoperability is crucial. This FLIP is a mixture of different topics that improve the interoperability between DataStream and Table API in terms of: - DataStream <-> Table conversion - translation of type systems TypeInformation <-> DataType - schema definition (incl. rowtime, watermarks, primary key) - changelog handling - row handling in DataStream API I'm looking forward to your feedback. Regards, Timo
