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The test code is already quite outdated and uses a lot of deprecated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>): Table
At 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+API
The 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
