Hi Becket,
Generally I don't think connector developers should bother with
understanding any of the SQL concepts.
I am not sure if we understand "connector developer" the same way. Let me
describe how I see the process of writing a new source (that can be used in
both Table & DataStream API)
1. Connector developer writes a Source that deals with the actual reading
and deserializing (preferably with a pluggable format/deserializer). The
result of that step should be something like:
FilesystemSource
.path(...)
.format(ParquetFormat
.filterPredicate(/* parquet specific filter */)
.project(/* parquet specific projection */)
.map(...))
.watermarkAssigner(...)
This is useful for DataStream and we can and want to use this in the Table
API. Those interface shouldn't accept any *Translators though. It does
make no sense cause internally they are not dealing e.g. with the
Expression. They should accept already created predicates.
We are not designing anything at that level. This we expect from FLIP-27
2. Then we need to have a DynamicTableSource with different abilities that
can create e.g. the parquet filter or projection from expressions. I think
this is what you also describe in your second point. And this is what we
are designing in the FLIP. Bear in mind that e.g. Deserializer will be
created out of multiple SQL concepts: regular schema/computed
columns/possibly projections etc., each applied at different planning
stages.
All of those interfaces serve the purpose of configuring the
DynamicTableSource so that it is able to instantiate the Source with proper
configuration. In other words it is a factory for the source that you can
configure with SQL concepts. In turn this Factory will call another factory
from point 1.
I don't see a potential for unifying factories across different high level
APIs. Taking your example with Spatial Database that operates on
Coordinates and Area (even though those would rather be modeled as SQL
types and we would still operate on Rows, but just for the sake of the
example). In that respect there is no point in having a
PushDownComputedColumns interface in the factory for the spatial database.
Best,
Dawid
On 26/03/2020 11:47, Becket Qin wrote:
Hi Timo,
Regarding "connector developers just need to know how to write an
ExpressionToParquetFilter":
This is the entire purpose of the DynamicTableSource/DynamicTableSink.
The bridging between SQL concepts and connector specific concepts.
Because this is the tricky part. How to get from a SQL concept to a
connctor concept.
Maybe it is just a naming issue depending on whether one is looking upward
from the Connectors perspective, or looking downward from the SQL
perspective. If we agree that the connectors should provide semantic free
API to the high level use cases, it seems we should follow the former path.
And if there are one or two APIs that the connector developers have to
understand in order to support Table / SQL, I think we can just address
them case by case, instead of wrapping the entire low level source API
with a set of new concepts.
Correct me if I am wrong, can we tell the following story to a connector
developer and get a all the TableSource functionality work?
To provide a TableSource from a Source, one just need to know two more
concepts: *Row* and *Expression*. The work to create a TableSource are
following:
1. A connector developer can write three classes in order to build a table
source:
- Deserializer<Row> (Must-have)
- PredicateTranslator<Expression, FilterPredicate> (optional, only
applicable if the Source is a FilterableSource)
- PredicateTranslator<Expression, ProjectionPredicate> (optional, only
applicable if the Source is a ProjectableSource)
2. In order to let the table source be discoverable, one need to provide a
Factory, and that Factory provides the following as a bundle:
- The Source itself (Must-have)
- The Deserializer<Row> (Must-have)
- PredicateTranslator<Expression, FilterPredicate> (optional, only
applicable when the Factory is a FilterFactory)
- PredicateTranslator<Expression, ProjectionPredicate> (optional, only
applicable when the Factory is a ProjectorFactory)
3. The Deserializer<Row> may implement one more decorative interfaces to
further convert the record after deserialization.
- withMapFunction<Row, Row>;
Note that the above description only require the connector developer to
understand Expression and Row. If this works, It is much easier to explain
than throwing a full set of new concepts. More importantly, it is way more
generic. For example, If we change Row to Coordinates, and Expression to
Area, we easily get a Source for a Spatial Database.
One thing I want to call out is that while the old SourceFunction and
InputFormat are concrete implementations that does the actual IO work. The
Source API in FLIP-27 itself is kind of a Factory by itself already. So if
we can push the decorative interfaces from the TableFactory layer to the
Source layer, it will help unify the experience for DataStream and Table
Source. This will also align with our goal of letting the DataStream Source
provide a semantic free API that can be used by different high level API.
BTW, Jark suggested that we can probably have an offline call to accelerate
the discussion. I think it is a good idea. Can we do that?
Thanks,
Jiangjie (Becket) Qin
On Thu, Mar 26, 2020 at 5:28 PM Timo Walther <twal...@apache.org>
<twal...@apache.org> wrote:
Hi Becket,
Regarding "PushDown/NestedPushDown which is internal to optimizer":
Those concepts cannot be entirely internal to the optimizer, at some
point the optimizer needs to pass them into the connector specific code.
This code will then convert it to e.g. Parque expressions. So there must
be some interface that takes SQL Expression and converts to connector
specific code. This interface between planner and connector is modelled
by the SupportsXXX interfaces. And you are right, if developers don't
care, they don't need to implement those optional interfaces but will
not get performant connectors.
Regarding "Table connector can work with the above two mechanism":
A table connector needs three mechanisms that are represented in the
current design.
1. a stateless discovery interface (Factory) that can convert
ConfigOptions to a stateful factory interface
(DynamicTableSource/DynamicTableSink)
2. a stateful factory interface (DynamicTableSource/DynamicTableSink)
that receives concepts from the optimizer (watermarks, filters,
projections) and produces runtime classes such as your
`ExpressionToParquetFilter`
3. runtime interfaces that are generated from the stateful factory; all
the factories that you mentioned can be used in `getScanRuntimeProvider`.
Regarding "connector developers just need to know how to write an
ExpressionToParquetFilter":
This is the entire purpose of the DynamicTableSource/DynamicTableSink.
The bridging between SQL concepts and connector specific concepts.
Because this is the tricky part. How to get from a SQL concept to a
connctor concept.
Regards,
Timo
On 26.03.20 04:46, Becket Qin wrote:
Hi Timo,
Thanks for the reply. I totally agree that there must be something new
added to the connector in order to make it work for SQL / Table. My
concern
is mostly over what they should be, and how to add them. To be honest, I
was kind of lost when looking at the interfaces such as
DataStructureConverter, RuntimeConverter and their internal context.
Also I
believe most connector developers do not care about the concept of
"PushDown" / "NestedPushDown" which is internal to optimizer and not even
exposed to SQL writers.
Therefore I am trying to see if we can:
A) Keep those additions minimum to the connector developers if they don't
have to know the details.
B) Expose as less high level concept as possible. More specifically, try
to
speak the connector language and expose the general mechanism instead of
binding them with use case semantic.
If we can achieve the above two goals, we could avoid adding unnecessary
burden to the connector developers, and also make the connectors more
generic.
It might worth thinking about what additional work is necessary for the
connector developers, here are what I am thinking of, please correct me
if
I miss something.
1. A Factory interface that allows high level use case, in this case
SQL, to find a matching source using service provider mechanism.
2. Allows the high level use case to specify the plugins that are
supported by the underneath DataStream Source.
If Table connector can work with the above two mechanism, maybe we can
make
some slight modifications to the interfaces in the current FLIP.
- A *SourceFactory* which extends the Factory interface in the FLIP,
with one more method:
- *Source getSource();*
- Some decorative interfaces to the SourceFactory such as:
- *FilterFactory<PREDICATE, T extends Supplier<PREDICATE>>*, with
the
following method
- T getFilter();
- *ProjectorFactory<PREDICATE, T extends Supplier<PREDICATE>>*,
with
the following method.
- T getProjector();
- *DeserializerFactory<INPUT, OUTPUT>*
With this set of API, a ParquetTableSourceFactory may become:
class ParqeutTableSourceFactory implements
SourceFactory,
DeserializerFactory<ParquetRecords, Row>,
FilterFactory<ParquetFilter, ExressionToParquetFilter> {
@Override
ParquetSource getSource() { ... }
@Override
ExressionToParquetFilter getFilterSupplier() { ... };
}
The ExressionToParquetFilter will have an *applyPredicate(Expression)*
method.
I know it does not look like a perfect interface from the pure SQL
perspective. And I am not even sure if this would meet all the
requirements
for SQL, but the benefit is that the connector developers just need to
know
how to write an ExpressionToParquetFilter in order to make it work for
Table, without having to understand the entire SQL concept.
Thanks,
Jiangjie (Becket) Qin
On Wed, Mar 25, 2020 at 5:57 PM Timo Walther <twal...@apache.org>
<twal...@apache.org> wrote:
Hi Becket,
Let me clarify a few things first: Historically we thought of Table
API/SQL as a library on top of DataStream API. Similar to Gelly or CEP.
We used TypeInformation in Table API to integrate nicely with DataStream
API. However, the last years have shown that SQL is not just a library.
It is an entire ecosystem that defines data types, submission behavior,
execution behavior, and highly optimized SerDes. SQL is a way to declare
data processing end-to-end such that the planner has the full control
over the execution.
But I totally agree with your concerns around connectors. There is no
big difference between your concerns and the current design.
1. "native connector interface is a generic abstraction of doing IO and
Serde":
This is the case in our design. We are using SourceFunction,
DeserializationSchema, WatermarkAssigner, etc. all pluggable interfaces
that the DataStream API offers for performing runtime operations.
2. "advanced features ... could be provided in a semantic free way":
I agree here. But this is an orthogonal topic that each connector
implementer should keep in mind. If a new connector is developed, it
should *not* be developed only for SQL in mind but with good abstraction
such that also DataStream API users can use it. A connector should have
a builder pattern to plugin all capabilities like Parque filters etc.
There should be no table-specific native/runtime connectors. I think
this discussion is related to the discussion of FLIP-115.
However, as I mentioned before: This FLIP only discusses the interfaces
for communication between planner and connector factory. As Dawid said
earlier, a DynamicTableSource can be more seen as a factory that calls
pluggable interfaces of a native connextor in the end:
KafkaConnector.builder()
.watermarkAssigner(...)
.keyDeser(...)
.valueDeser(...)
....
.build()
Regards,
Timo
On 25.03.20 09:05, Becket Qin wrote:
Hi Kurt,
I do not object to promote the concepts of SQL, but I don't think we
should
do that by introducing a new dedicate set of connector public
interfaces
that is only for SQL. The same argument can be applied to Gelly, CEP,
and
Machine Learning, claiming that they need to introduce a dedicated
public
set of interfaces that fits their own concept and ask the the connector
developers to learn and follow their design. As an analogy, if we want
to
promote Chinese, we don't want to force people to learn ancient Chinese
poem while they only need to know a few words like "hello" and
"goodbye".
As some design principles, here are what I think what Flink connectors
should look like:
1. The native connector interface is a generic abstraction of doing IO
and
Serde, without semantic for high level use cases such as SQL, Gelly,
CEP,
etc.
2. Some advanced features that may help accelerate the IO and Serde
could
be provided in the native connector interfaces in a semantic free way
so
all the high level use cases can leverage.
3. Additional semantics can be built on top of the native source
interface
through providing different plugins. These plugins could be high level
use
case aware. For example, to provide a filter to the source, we can do
the
following
// An interface for all the filters that take an expression.
interface ExpressionFilter {
FilterResult applyFilterExpression();
}
// An filter plugin implementation that translate the SQL Expression
to a
ParquetFilterPredicate.
Class ParquetExpressionFilter implements
Supplier<ParquetFilterPredicate>,
ExpressionFilter {
// Called by the high level use case,
FilterResult applyFilterExpression() { ... }
// Used by the native Source interface.
ParquetFilterPredicate get() { ... }
}
In this case, the connector developer just need to write the logic of
translating an Expression to Parquet FilterPredicate. They don't have
to
understand the entire set of interfaces that we want to promote. Just
like
they only need to know how to say "Hello" without learning ancient
Chinese
poem.
Again, I am not saying this is necessarily the best approach. But so
far
it
seems a reasonable design principle to tell the developers.
Thanks,
Jiangjie (becket) Qin
On Wed, Mar 25, 2020 at 11:53 AM Kurt Young <ykt...@gmail.com>
<ykt...@gmail.com> wrote:
Hi Becket,
I don't think we should discuss this in pure engineering aspects. Your
proposal is trying
to let SQL connector developers understand as less SQL concepts as
possible. But quite
the opposite, we are designing those interfaces to emphasize the SQL
concept, to bridge
high level concepts into real interfaces and classes.
We keep talking about time-varying relations and dynamic table when
introduce SQL concepts,
sources and sinks are most critical part playing with those concepts.
It's
essential to let
Flink SQL developers to learn these concepts and connect them with
real
codes by introducing
these connector interfaces and can further write *correct* connectors
based
on such domain
knowledge.
So this FLIP is a very important chance to express these concepts and
make
most SQL developers
be align with concepts and on same page. It's mostly for different
level of
abstractions and for domains
like SQL, it's becoming more important. It helps Flink SQL go
smoothly
in
the future, and also
make it easier for new contributors. But I would admit this is not
that
obvious for others who don't work
with SQL frequently.
Best,
Kurt
On Wed, Mar 25, 2020 at 11:07 AM Becket Qin <becket....@gmail.com>
<becket....@gmail.com>
wrote:
Hi Jark,
It is good to know that we do not expect the end users to touch those
interfaces.
Then the question boils down to whether the connector developers
should
be
aware of the interfaces that are only used by the SQL optimizer. It
seems a
win if we can avoid that.
Two potential solutions off the top of my head are:
1. An internal helper class doing the instanceOf based on DataStream
source
interface and create pluggables for that DataStream source.
2. codegen the set of TableSource interfaces given a DataStream
Source
and
its corresponding TablePluggablesFactory.
Thanks,
Jiangjie (Becket) Qin
On Wed, Mar 25, 2020 at 10:07 AM Jark Wu <imj...@gmail.com> <imj...@gmail.com>
wrote:
Hi Becket,
Regarding to Flavor1 and Flavor2, I want to clarify that user will
never
use table source like this:
{
MyTableSource myTableSource = MyTableSourceFactory.create();
myTableSource.setSchema(mySchema);
myTableSource.applyFilterPredicate(expression);
...
}
TableFactory and TableSource are not directly exposed to end users,
all
the
methods are called by planner, not users.
Users always use DDL or descriptor to register a table, and planner
will
find the factory and create sources according to the properties.
All the optimization are applied automatically, e.g.
filter/projection
pushdown, users don't need to call `applyFilterPredicate`
explicitly.
On Wed, 25 Mar 2020 at 09:25, Becket Qin <becket....@gmail.com>
<becket....@gmail.com>
wrote:
Hi Timo and Dawid,
Thanks for the clarification. They really help. You are right that
we
are
on the same page regarding the hierarchy. I think the only
difference
between our view is the flavor of the interfaces. There are two
flavors
of
the source interface for DataStream and Table source.
*Flavor 1. Table Sources are some wrapper interfaces around
DataStream
source.*
Following this way, we will reach the design of the current
proposal,
i.e.
each pluggable exposed in the DataStream source will have a
corresponding
TableSource interface counterpart, which are at the Factory level.
Users
will write code like this:
{
MyTableSource myTableSource = MyTableSourceFactory.create();
myTableSource.setSchema(mySchema);
myTableSource.applyFilterPredicate(expression);
...
}
The good thing for this flavor is that from the SQL / Table's
perspective,
there is a dedicated set of Table oriented interface.
The downsides are:
A. From the user's perspective, DataStream Source and Table Source
are
just
two different sets of interfaces, regardless of how they are the
same
internally.
B. The source developers have to develop for those two sets of
interfaces
in order to support both DataStream and Table.
C. It is not explicit that DataStream can actually share the
pluggable
in
Table / SQL. For example, in order to provide a filter pluggable
with
SQL
expression, users will have to know the actual converter class that
converts the expression to the filter predicate and construct that
converter by themselves.
---------------
*Flavor 2. A TableSource is a DataStream source with a bunch of
pluggables.
No Table specific interfaces at all.*
Following this way, we will reach another design where you have a
SourceFactory and a single Pluggable factory for all the table
pluggables.
And users will write something like:
{
Deserializer<Row> myTableDeserializer =
MyTablePluggableFactory.createDeserializer(schema)
MySource<Row> mySource = MySourceFactory.create(properties,
myTableDeserializer);
mySource.applyPredicate(MyTablePluggableFactory.createFilterPredicate(expression));
}
The good thing for this flavor is that there is just one set of
interface
that works for both Table and DataStream. There is no difference
between
creating a DataStream source and creating a Table source.
DataStream
can
easily reuse the pluggables from the Table sources.
The downside is that Table / SQL won't have a dedicated API for
optimization. Instead of writing:
if (MyTableSource instanceOf FilterableTableSource) {
// Some filter push down logic.
MyTableSource.applyPredicate(expression)
}
One have to write:
if (MySource instanceOf FilterableSource) {
// Some filter push down logic.
mySource.applyPredicate(MyTablePluggableFactory.createFilterPredicate(expression));
}
-------------------------
Just to be clear, I am not saying flavor 2 is necessarily better
than
flavor 1, but I want to make sure flavor 2 is also considered and
discussed.
Thanks,
Jiangjie (Becket) Qin.
On Tue, Mar 24, 2020 at 10:53 PM Dawid Wysakowicz <
dwysakow...@apache.org>
wrote:
Hi Becket,
I really think we don't have a differing opinions. We might not
see
the
changes in the same way yet. Personally I think of the
DynamicTableSource
as of a factory for a Source implemented for the DataStream API.
The
important fact about the DynamicTableSource and all feature traits
(SupportsFilterablePushDown, SupportsProjectPushDown etc.) work
with
Table
API concepts such as e.g. Expressions, SQL specific types etc. In
the
end
what the implementation would resemble is (bear in mind I
tremendously
simplified the example, just to show the relation between the two
APIs):
SupportsFilterablePushDown {
applyFilters(List<ResolvedExpression> filters) {
this.filters = convertToDataStreamFilters(filters);
}
Source createSource() {
return Source.create()
.applyFilters(this.filters);
}
}
or exactly as you said for the computed columns:
SupportsComputedColumnsPushDown {
applyComputedColumn(ComputedColumnConverter converter) {
this.deserializationSchema = new DeserializationSchema<Row>
{
Row deserialize(...) {
RowData row = format.deserialize(bytes); // original
format,
e.g
json, avro, etc.
RowData enriched = converter(row)
}
}
}
Source createSource() {
return Source.create()
.withDeserialization(deserializationSchema);
}
}
So to sum it up again, all those interfaces are factories that
configure
appropriate parts of the DataStream API using Table API concepts.
Finally
to answer you question for particular comparisons:
DynamicTableSource v.s. Source<Row, SourceSplitT, EnumChkT>
SupportsFilterablePushDown v.s. FilterableSource
SupportsProjectablePushDown v.s. ProjectableSource
SupportsWatermarkPushDown v.s. WithWatermarkAssigner
SupportsComputedColumnPushDown v.s. ComputedColumnDeserializer
ScanTableSource v.s. ChangeLogDeserializer.
pretty much you can think of all on the left as factories for the
right
side, left side works with Table API classes (Expressions,
DataTypes).
I
hope this clarifies it a bit.
Best,
Dawid
On 24/03/2020 15:03, Becket Qin wrote:
Hey Kurt,
I don't think DataStream should see some SQL specific concepts
such
as
Filtering or ComputedColumn.
Projectable and Filterable seems not necessarily SQL concepts, but
could
be
applicable to DataStream source as well to reduce the network
load.
For
example ORC and Parquet should probably also be readable from
DataStream,
right?
ComputedColumn is not part of the Source, it is an interface
extends
the
Deserializer, which is a pluggable for the Source. From the SQL's
perspective it has the concept of computed column, but from the
Source
perspective, It is essentially a Deserializer which also converts
the
records internally, assuming we allow some conversion to be
embedded
to
the source in addition to just deserialization.
Thanks,
Jiangjie (Becket) Qin
On Tue, Mar 24, 2020 at 9:36 PM Jark Wu <imj...@gmail.com> <imj...@gmail.com> <
imj...@gmail.com> wrote:
Thanks Timo for updating the formats section. That would be very
helpful
for changelog supporting (FLIP-105).
I just left 2 minor comment about some method names. In general,
I'm
+1
to
start a voting.
--------------------------------------------------------------------------------------------------
Hi Becket,
I agree we shouldn't duplicate codes, especiall the runtime
implementations.
However, the interfaces proposed by FLIP-95 are mainly used during
optimization (compiling), not runtime.
I don't think there is much to share for this. Because table/sql
is declarative, but DataStream is imperative.
For example, filter push down, DataStream FilterableSource may
allow
to
accept a FilterFunction (which is a black box for the source).
However, table sources should pick the pushed filter expressions,
some
sources may only support "=", "<", ">" conditions.
Pushing a FilterFunction doesn't work in table ecosystem. That
means,
the
connectors have to have some table-specific implementations.
Best,
Jark
On Tue, 24 Mar 2020 at 20:41, Kurt Young <ykt...@gmail.com> <ykt...@gmail.com> <
ykt...@gmail.com> wrote:
Hi Becket,
I don't think DataStream should see some SQL specific concepts
such
as
Filtering or ComputedColumn. It's
better to stay within SQL area and translate to more generic
concept
when
translating to DataStream/Runtime
layer, such as use MapFunction to represent computed column logic.
Best,
Kurt
On Tue, Mar 24, 2020 at 5:47 PM Becket Qin <becket....@gmail.com>
<becket....@gmail.com>
<
becket....@gmail.com> wrote:
Hi Timo and Dawid,
It's really great that we have the same goal. I am actually
wondering
if
we
can go one step further to avoid some of the interfaces in Table
as
well.
For example, if we have the FilterableSource, do we still need the
FilterableTableSource? Should DynamicTableSource just become a
Source<*Row*,
SourceSplitT, EnumChkT>?
Can you help me understand a bit more about the reason we need the
following relational representation / wrapper interfaces v.s. the
interfaces that we could put to the Source in FLIP-27?
DynamicTableSource v.s. Source<Row, SourceSplitT, EnumChkT>
SupportsFilterablePushDown v.s. FilterableSource
SupportsProjectablePushDown v.s. ProjectableSource
SupportsWatermarkPushDown v.s. WithWatermarkAssigner
SupportsComputedColumnPushDown v.s. ComputedColumnDeserializer
ScanTableSource v.s. ChangeLogDeserializer.
LookUpTableSource v.s. LookUpSource
Assuming we have all the interfaces on the right side, do we still
need
the
interfaces on the left side? Note that the interfaces on the right
can
be
used by both DataStream and Table. If we do this, there will only
be
one
set of Source interfaces Table and DataStream, the only difference
is
that
the Source for table will have some specific plugins and
configurations.
An
omnipotent Source can implement all the the above interfaces and
take a
Deserializer that implements both ComputedColumnDeserializer and
ChangeLogDeserializer.
Would the SQL planner work with that?
Thanks,
Jiangjie (Becket) Qin
On Tue, Mar 24, 2020 at 5:03 PM Jingsong Li <
jingsongl...@gmail.com>
<
jingsongl...@gmail.com>
wrote:
+1. Thanks Timo for the design doc.
We can also consider @Experimental too. But I am +1 to
@PublicEvolving,
we
should be confident in the current change.
Best,
Jingsong Lee
On Tue, Mar 24, 2020 at 4:30 PM Timo Walther <twal...@apache.org>
<twal...@apache.org>
<
twal...@apache.org>
wrote:
@Becket: We totally agree that we don't need table specific
connectors
during runtime. As Dawid said, the interfaces proposed here are
just
for
communication with the planner. Once the properties (watermarks,
computed column, filters, projecttion etc.) are negotiated, we can
configure a regular Flink connector.
E.g. setting the watermark assigner and deserialization schema of
a
Kafka connector.
For better separation of concerns, Flink connectors should not
include
relational interfaces and depend on flink-table. This is the
responsibility of table source/sink.
@Kurt: I would like to mark them @PublicEvolving already because
we
need
to deprecate the old interfaces as early as possible. We cannot
redirect
to @Internal interfaces. They are not marked @Public, so we can
still
evolve them. But a core design shift should not happen again, it
would
leave a bad impression if we are redesign over and over again.
Instead
we should be confident in the current change.
Regards,
Timo
On 24.03.20 09:20, Dawid Wysakowicz wrote:
Hi Becket,
Answering your question, we have the same intention not to
duplicate
connectors between datastream and table apis. The interfaces
proposed
in
the FLIP are a way to describe relational properties of a source.
The
intention is as you described to translate all of those expressed
as
expressions or other Table specific structures into a DataStream
source.
In other words I think what we are doing here is in line with
what
you
described.
Best,
Dawid
On 24/03/2020 02:23, Becket Qin wrote:
Hi Timo,
Thanks for the proposal. I completely agree that the current
Table
connectors could be simplified quite a bit. I haven't finished
reading
everything, but here are some quick thoughts.
Actually to me the biggest question is why should there be two
different
connector systems for DataStream and Table? What is the
fundamental
reason
that is preventing us from merging them to one?
The basic functionality of a connector is to provide
capabilities
to
do
IO
and Serde. Conceptually, Table connectors should just be
DataStream
connectors that are dealing with Rows. It seems that quite a few
of
the
special connector requirements are just a specific way to do IO
/
Serde.
Taking SupportsFilterPushDown as an example, imagine we have the
following
interface:
interface FilterableSource<PREDICATE> {
void applyFilterable(Supplier<PREDICATE> predicate);
}
And if a ParquetSource would like to support filterable, it will
become:
class ParquetSource implements Source,
FilterableSource(FilterPredicate> {
...
}
For Table, one just need to provide an predicate supplier that
converts
an
Expression to the specified predicate type. This has a few
benefit:
1. Same unified API for filterable for sources, regardless of
DataStream or
Table.
2. The DataStream users now can also use the
ExpressionToPredicate
supplier if they want to.
To summarize, my main point is that I am wondering if it is
possible
to
have a single set of connector interface for both Table and
DataStream,
rather than having two hierarchies. I am not 100% sure if this
would
work,
but if it works, this would be a huge win from both code
maintenance
and
user experience perspective.
Thanks,
Jiangjie (Becket) Qin
On Tue, Mar 24, 2020 at 2:03 AM Dawid Wysakowicz <
dwysakow...@apache.org>
wrote:
Hi Timo,
Thank you for the proposal. I think it is an important
improvement
that
will benefit many parts of the Table API. The proposal looks
really
good
to me and personally I would be comfortable with voting on the
current
state.
Best,
Dawid
On 23/03/2020 18:53, Timo Walther wrote:
Hi everyone,
I received some questions around how the new interfaces play
together
with formats and their factories.
Furthermore, for MySQL or Postgres CDC logs, the format should
be
able
to return a `ChangelogMode`.
Also, I incorporated the feedback around the factory design in
general.
I added a new section `Factory Interfaces` to the design
document.
This should be helpful to understand the big picture and
connecting
the concepts.
Please let me know what you think?
Thanks,
Timo
On 18.03.20 13:43, Timo Walther wrote:
Hi Benchao,
this is a very good question. I will update the FLIP about
this.
The legacy planner will not support the new interfaces. It
will
only
support the old interfaces. With the next release, I think
the
Blink
planner is stable enough to be the default one as well.
Regards,
Timo
On 18.03.20 08:45, Benchao Li wrote:
Hi Timo,
Thank you and others for the efforts to prepare this FLIP.
The FLIP LGTM generally.
+1 for moving blink data structures to table-common, it's
useful
to
udf too
in the future.
A little question is, do we plan to support the new
interfaces
and
data
types in legacy planner?
Or we only plan to support these new interfaces in blink
planner.
And using primary keys from DDL instead of derived key
information
from
each query is also a good idea,
we met some use cases where this does not works very well
before.
This FLIP also makes the dependencies of table modules more
clear, I
like
it very much.
Timo Walther <twal...@apache.org> <twal...@apache.org> <twal...@apache.org>
<twal...@apache.org>
于2020年3月17日周二
上午1:36写道：
Hi everyone,
I'm happy to present the results of long discussions that
we
had
internally. Jark, Dawid, Aljoscha, Kurt, Jingsong, me, and
many
more
have contributed to this design document.
We would like to propose new long-term table source and
table
sink
interfaces:
https://cwiki.apache.org/confluence/display/FLINK/FLIP-95%3A+New+TableSource+and+TableSink+interfaces
This is a requirement for FLIP-105 and finalizing FLIP-32.
The goals of this FLIP are:
- Simplify the current interface architecture:
- Merge upsert, retract, and append sinks.
- Unify batch and streaming sources.
- Unify batch and streaming sinks.
- Allow sources to produce a changelog:
- UpsertTableSources have been requested a lot by
users.
Now
is the
time to open the internal planner capabilities via the new
interfaces.
- According to FLIP-105, we would like to support
changelogs for
processing formats such as Debezium.
- Don't rely on DataStream API for source and sinks:
- According to FLIP-32, the Table API and SQL should
be
independent
of the DataStream API which is why the `table-common`
module
has
no
dependencies on `flink-streaming-java`.
- Source and sink implementations should only depend
on
the
`table-common` module after FLIP-27.
- Until FLIP-27 is ready, we still put most of the
interfaces in
`table-common` and strictly separate interfaces that
communicate
with a
planner and actual runtime reader/writers.
- Implement efficient sources and sinks without planner
dependencies:
- Make Blink's internal data structures available to
connectors.
- Introduce stable interfaces for data structures
that
can
be
marked as `@PublicEvolving`.
- Only require dependencies on `flink-table-common`
in
the
future
It finalizes the concept of dynamic tables and consideres
how
all
source/sink related classes play together.
We look forward to your feedback.
Regards,
Timo
--
Best, Jingsong Lee
