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From Ryan Blue <rb...@netflix.com.INVALID>
Subject Re: [discuss][data source v2] remove type parameter in DataReader/WriterFactory
Date Thu, 19 Apr 2018 20:47:30 GMT
Wenchen, thanks for clarifying.

I think it is valuable to consider the API as a whole because it’s
difficult to think about the impact of these changes otherwise. With that
in mind, here’s a snapshot of the relevant portion of the batch API, which
I think is pretty reasonable:

v2.ReadSupport // source supports reading
  DataSourceReader createReader(options)

v2.DataSourceReader // created to configure and perform a read
  List<DataReaderFactory<InternalRow>> createDataReaderFactories()

v2.SupportsScanColumnarBatch // Reader mix-in to create vector readers
  List<DataReaderFactory<ColumnarBatch>> createBatchDataReaderFactories()

v2.DataReaderFactory<D> // each one is a unique read task; equivalent
to Iterable<D>
  DataReader<D> createDataReader()

v2.DataReader<D> // equivalent to an Iterator<D>

And here’s the current streaming side, I think (not including Microbatch
classes):

v2.ContinuousReadSupport // source supports continuous reading
  ContinuousReader createContinuousReader(schema, checkpointLocation, options)

v2.ContinuousReader extends DataSourceReader // configure/perform a
continuous read
  // inherits createDataReaderFactories() from DataSourceReader
  // may have createBatchDataReaderFactories() from SupportsScanColumnarBatch

v2.ContinuousDataReaderFactory<D> // a continuous read task
  DataReader<D> createDataReaderWithOffset(PartitionOffset)

Looks like the reason why casting is required is that
ContinuousReader#createDataReaderFactories is inherited and doesn’t return
ContinuousDataReaderFactory even though it actually needs to. In that case,
why reuse DataSourceReader when ContinuousReader could expose create for a
list of continuous factories/tasks? Then we have just one mix-in trait for
batch and one for streaming. This looks like a consequence of partially
reusing classes. I don’t think there is enough reason to refactor the API
here.

Not refactoring has a few benefits:

   - Keeping the mix-in structure maintains consistency with the rest of
   the API, which uses mix-ins for optional traits.
   - It also keeps the API small for simple or basic implementations:
   mix-ins bring in more options, but they are entirely optional. Adding 4
   methods to each factory/task is more complicated.
   - This maintains the intent of the task and data-reader classes, which
   is to provide an API like Iterable/Iterator.

I think the second problem can be solved by inheritance where necessary,
but I don’t know how big of a problem this is. How many implementations are
going to provide both row and vector reads? Why would an implementation
provide both? If streaming and batch need to be separate, then the
constructors will probably be different as well. I don’t think changing the
API is going to be useful for this.

In addition, *I think this discussion is very likely a consequence of not
proposing and discussing the v2 streaming API publicly*. There’s no
published design that gives a high-level overview of the streaming API, and
I’m really concerned because problems with it are resulting in proposed
refactors to the batch API that *was* discussed and is already available.

The write-side design doc that Joseph put together is a good start,
especially the diagram because it gives a great visual to help reason about
it. Could you please put together a doc for the read side as well?

rb



On Wed, Apr 18, 2018 at 10:20 PM, Wenchen Fan <cloud0fan@gmail.com> wrote:

 First of all, I think we all agree that data source v2 API should at least
> support InternalRow and ColumnarBatch. With this assumption, the current
> API has 2 problems:
>
> *First problem*: We use mixin traits to add support for different data
> formats.
>
> The mixin traits define API to return DataReader/WriterFactory for
> different formats. It brings a lot of trouble to streaming, as streaming
> has its own factory interface, which we don't want it to extend the batch
> factory. This means we need to duplicate the mixin traits for batch and
> streaming. Keep in mind that duplicating the traits is also a possible
> solution, if there is no better way.
>
> Another possible solution is, remove the mixin traits and put all
> "createFactory" method in DataSourceReader/Writer, with a new method to
> indicate which "createFactory" method Spark should call. Then the API looks
> like
>
> interface DataSourceReader {
>   DataFormat dataFormat;
>
>   default List<DataReaderFactory<Row>> createDataReaderFactories() {
>     throw new IllegalStateException();
>   }
>
>   default List<DataReaderFactory<ColumnarBatch>>
> createColumnarBatchDataReaderFactories() {
>     throw new IllegalStateException();
>   }
> }
>
> or to be more friendly to people who don't care about columnar format
>
> interface DataSourceReader {
>   default DataFormat dataFormat { return DataFormat.INTERNAL_ROW };
>
>   List<DataReaderFactory<Row>> createDataReaderFactories();
>
>   default List<DataReaderFactory<ColumnarBatch>> createColumnarBatchDataReaderFactories()
> {
>     throw new IllegalStateException();
>   }
> }
>
> This solution still brings some trouble to streaming, as the streaming
> specific DataSourceReader needs to re-define all these "createFactory"
> methods, but it's much better than duplicating the mixin traits.
>
> *Second problem*: The DataReader/WriterFactory may have a lot of
> constructor parameters, it's painful to define different factories with the
> same but very long parameter list.
> After a closer look, I think this is the major part of the duplicated
> code. This is not a strong reason, so it's OK if people don't think it's a
> problem. In the meanwhile, I think it might be better to shift the data
> format stuff to the factory so that we can support hybrid storage data
> source in the future, like I mentioned before.
>
>
> Finally, we can also consider Joseph's proposal, to remove the type
> parameter entirely and get rid of this problem.
>
>
>
> On Thu, Apr 19, 2018 at 8:54 AM, Joseph Torres <
> joseph.torres@databricks.com> wrote:
>
>> The fundamental difficulty seems to be that there's a spurious
>> "round-trip" in the API. Spark inspects the source to determine what type
>> it's going to provide, picks an appropriate method according to that type,
>> and then calls that method on the source to finally get what it wants.
>> Pushing this out of the DataSourceReader doesn't eliminate this problem; it
>> just shifts it. We still need an InternalRow method and a ColumnarBatch
>> method and possibly Row and UnsafeRow methods too.
>>
>> I'd propose it would be better to just accept a bit less type safety
>> here, and push the problem all the way down to the DataReader. Make
>> DataReader.get() return Object, and document that the runtime type had
>> better match the type declared in the reader's DataFormat. Then we can get
>> rid of the special Row/UnsafeRow/ColumnarBatch methods cluttering up the
>> API, and figure out whether to support Row and UnsafeRow independently of
>> all our other API decisions. (I didn't think about this until now, but the
>> fact that some orthogonal API decisions have to be conditioned on which set
>> of row formats we support seems like a code smell.)
>>
>> On Wed, Apr 18, 2018 at 3:53 PM, Ryan Blue <rblue@netflix.com.invalid>
>> wrote:
>>
>>> Wenchen, can you explain a bit more clearly why this is necessary? The
>>> pseudo-code you used doesn’t clearly demonstrate why. Why couldn’t this be
>>> handled this with inheritance from an abstract Factory class? Why define
>>> all of the createXDataReader methods, but make the DataFormat a field
>>> in the factory?
>>>
>>> A related issue is that I think there’s a strong case that the v2
>>> sources should produce only InternalRow and that Row and UnsafeRow
>>> shouldn’t be exposed; see SPARK-23325
>>> <https://issues.apache.org/jira/browse/SPARK-23325>. The basic
>>> arguments are:
>>>
>>>    - UnsafeRow is really difficult to produce without using Spark’s
>>>    projection methods. If implementations can produce UnsafeRow, then
>>>    they can still pass them as InternalRow and the projection Spark
>>>    adds would be a no-op. When implementations can’t produce UnsafeRow,
>>>    then it is better for Spark to insert the projection to unsafe. An example
>>>    of a data format that doesn’t produce unsafe is the built-in Parquet
>>>    source, which produces InternalRow and projects before returning the
>>>    row.
>>>    - For Row, I see no good reason to support it in a new interface
>>>    when it will just introduce an extra transformation. The argument that
>>>    Row is the “public” API doesn’t apply because UnsafeRow is already
>>>    exposed through the v2 API.
>>>    - Standardizing on InternalRow would remove the need for these
>>>    interfaces entirely and simplify what implementers must provide and would
>>>    reduce confusion over what to do.
>>>
>>> Using InternalRow doesn’t cover the case where we want to produce
>>> ColumnarBatch instead, so what you’re proposing might still be a good
>>> idea. I just think that we can simplify either path.
>>> ​
>>>
>>> On Mon, Apr 16, 2018 at 11:17 PM, Wenchen Fan <cloud0fan@gmail.com>
>>> wrote:
>>>
>>>> Yea definitely not. The only requirement is, the
>>>> DataReader/WriterFactory must support at least one DataFormat.
>>>>
>>>> >  how are we going to express capability of the given reader of its
>>>> supported format(s), or specific support for each of “real-time data in
row
>>>> format, and history data in columnar format”?
>>>>
>>>> When DataSourceReader/Writer create factories, the factory must
>>>> contain enough information to decide the data format. Let's take ORC as an
>>>> example. In OrcReaderFactory, it knows which files to read, and which
>>>> columns to output. Since now Spark only support columnar scan for simple
>>>> types, OrcReaderFactory will only output ColumnarBatch if the columns
>>>> to scan are all simple types.
>>>>
>>>> On Tue, Apr 17, 2018 at 11:38 AM, Felix Cheung <
>>>> felixcheung_m@hotmail.com> wrote:
>>>>
>>>>> Is it required for DataReader to support all known DataFormat?
>>>>>
>>>>> Hopefully, not, as assumed by the ‘throw’ in the interface. Then
>>>>> specifically how are we going to express capability of the given reader
of
>>>>> its supported format(s), or specific support for each of “real-time
data in
>>>>> row format, and history data in columnar format”?
>>>>>
>>>>>
>>>>> ------------------------------
>>>>> *From:* Wenchen Fan <cloud0fan@gmail.com>
>>>>> *Sent:* Sunday, April 15, 2018 7:45:01 PM
>>>>> *To:* Spark dev list
>>>>> *Subject:* [discuss][data source v2] remove type parameter in
>>>>> DataReader/WriterFactory
>>>>>
>>>>> Hi all,
>>>>>
>>>>> I'd like to propose an API change to the data source v2.
>>>>>
>>>>> One design goal of data source v2 is API type safety. The FileFormat
>>>>> API is a bad example, it asks the implementation to return InternalRow
>>>>> even it's actually ColumnarBatch. In data source v2 we add a type
>>>>> parameter to DataReader/WriterFactoty and DataReader/Writer, so that
>>>>> data source supporting columnar scan returns ColumnarBatch at API
>>>>> level.
>>>>>
>>>>> However, we met some problems when migrating streaming and file-based
>>>>> data source to data source v2.
>>>>>
>>>>> For the streaming side, we need a variant of DataReader/WriterFactory
>>>>> to add streaming specific concept like epoch id and offset. For details
>>>>> please see ContinuousDataReaderFactory and https://docs.google.com/do
>>>>> cument/d/1PJYfb68s2AG7joRWbhrgpEWhrsPqbhyRwUVl9V1wPOE/edit#
>>>>>
>>>>> But this conflicts with the special format mixin traits like
>>>>> SupportsScanColumnarBatch. We have to make the streaming variant of
>>>>> DataReader/WriterFactory to extend the original
>>>>> DataReader/WriterFactory, and do type cast at runtime, which is
>>>>> unnecessary and violate the type safety.
>>>>>
>>>>> For the file-based data source side, we have a problem with code
>>>>> duplication. Let's take ORC data source as an example. To support both
>>>>> unsafe row and columnar batch scan, we need something like
>>>>>
>>>>> // A lot of parameters to carry to the executor side
>>>>> class OrcUnsafeRowFactory(...) extends DataReaderFactory[UnsafeRow] {
>>>>>   def createDataReader ...
>>>>> }
>>>>>
>>>>> class OrcColumnarBatchFactory(...) extends
>>>>> DataReaderFactory[ColumnarBatch] {
>>>>>   def createDataReader ...
>>>>> }
>>>>>
>>>>> class OrcDataSourceReader extends DataSourceReader {
>>>>>   def createUnsafeRowFactories = ... // logic to prepare the
>>>>> parameters and create factories
>>>>>
>>>>>   def createColumnarBatchFactories = ... // logic to prepare the
>>>>> parameters and create factories
>>>>> }
>>>>>
>>>>> You can see that we have duplicated logic for preparing parameters and
>>>>> defining the factory.
>>>>>
>>>>> Here I propose to remove all the special format mixin traits and
>>>>> change the factory interface to
>>>>>
>>>>> public enum DataFormat {
>>>>>   ROW,
>>>>>   INTERNAL_ROW,
>>>>>   UNSAFE_ROW,
>>>>>   COLUMNAR_BATCH
>>>>> }
>>>>>
>>>>> interface DataReaderFactory {
>>>>>   DataFormat dataFormat;
>>>>>
>>>>>   default DataReader<Row> createRowDataReader() {
>>>>>     throw new IllegalStateException();
>>>>>   }
>>>>>
>>>>>   default DataReader<UnsafeRow> createUnsafeRowDataReader() {
>>>>>     throw new IllegalStateException();
>>>>>   }
>>>>>
>>>>>   default DataReader<ColumnarBatch> createColumnarBatchDataReader()
{
>>>>>     throw new IllegalStateException();
>>>>>   }
>>>>> }
>>>>>
>>>>> Spark will look at the dataFormat and decide which create data reader
>>>>> method to call.
>>>>>
>>>>> Now we don't have the problem for the streaming side as these special
>>>>> format mixin traits go away. And the ORC data source can also be simplified
>>>>> to
>>>>>
>>>>> class OrcReaderFactory(...) extends DataReaderFactory {
>>>>>   def createUnsafeRowReader ...
>>>>>
>>>>>   def createColumnarBatchReader ...
>>>>> }
>>>>>
>>>>> class OrcDataSourceReader extends DataSourceReader {
>>>>>   def createReadFactories = ... // logic to prepare the parameters and
>>>>> create factories
>>>>> }
>>>>>
>>>>> We also have a potential benefit of supporting hybrid storage data
>>>>> source, which may keep real-time data in row format, and history data
in
>>>>> columnar format. Then they can make some DataReaderFactory output
>>>>> InternalRow and some output ColumnarBatch.
>>>>>
>>>>> Thoughts?
>>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>> Ryan Blue
>>> Software Engineer
>>> Netflix
>>>
>>
>>
> ​
-- 
Ryan Blue
Software Engineer
Netflix

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