Catching up on this thread sorry if late to the party :) and my excuses because
this is going to be loooong but worth.
> It does look like BEAM-11460 could work for you. Note that relies on a dynamic
> object which won't work with schema-aware transforms and SqlTransform. It's
> likely this isn't a problem for you, I just wanted to point it out.
We may be missing in this discussion the existence of the
`withBeamSchemas(true)` method on the IOs that produce Avro objects. This method
sets up a Schema-based coder for the output of the PCollection generated by the
read. This allows both SQL and Schema-based transforms just afterwards by
auto-infering the Beam Row schema and auto-transforming everything into Rows
when needed.
PCollection<GenericRecord> input =
p.apply(
ParquetIO.read(SCHEMA)
.from(path)
.withBeamSchemas(true));
Now input can be used by SQL/Schema-based PTransforms.
> @Kobe Feng thank you so much for the insights. Agree that it may be a good
> practice to read all sorts of file formats (e.g. parquet, avro etc) into a
> PCollection<Row> and then perform the schema aware transforms that you are
> referring to.
This is not the case at the moment because most IOs precede the schema-based
APIs, but more and more PTransforms are supporting it. Notice that for dynamic
objects or Schema-aware PCollection you don't even need them to produce
PCollection<Row>. You can take a PCollection<GenericRecord> (like above) and
connect directly to schema-aware transformations as if it was a PCollection<Row>
the transformation is done automatically for the user because of the
Schema-based coder.
You can do this manually if you have a non-schema PCollection of GenericRecords
by setting explicitly a Schema-based coder for the PCollection:
mycollection.setCoder(AvroUtils.schemaCoder(schema));
Beam also includes the schema-based `Convert` transform to convert different
types from/to Rows so this could be handy for cases when you need to transform
in both directions and it is not supported. Beam 2.28.0 introduces an
improvement that allows to Convert from any Schema-based PCollection (Rows or
others) into GenericRecords. This is really useful because Avro/Parquet based
writes expect a PCollection<GenericRecord> not one of rows, and now you can just
transform a schema-based PCollection (e.g. PCollection<Row> or of other objects)
into a PCollection<GenericRecord> like this:
myrowcollection.apply(Convert.to(GenericRecord.class)).apply(AnAvroBasedSinkIO.write(...))
https://issues.apache.org/jira/browse/BEAM-11571
So now the full scenario is covered for reads via .withBeamSchemas(true) or by
setting manually an AvroCoder for schemas and for writes by preceding the Sinks
with `Convert.to`. That's the beauty of Beam's bidirectional Schema coders.
Note that this probably can be better documented in the programming guide or in
the javadocs so contributions welcome!
And now back to the initial question:
> Quick question about ParquetIO. Is there a way to avoid specifying the avro
> schema when reading parquet files?
No, you cannot at the moment. BEAM-11460 allows you to parametrize the
transformation from a GenericRecord (with a schema you expect in advance even if
you don't specify it) into your own type of objects.
In Parquet/Avro the schema you use to write can differ from the schema you use
to read, this is done to support schema evolution, so the most general use case
is to allow users to read from specific versions of the Schema provided into
their objects. That's probably one of the reasons why this is not supported.
Since the Schema is part of the Parquet file metadata I suppose we could somehow
use it and produce the Schema for the output collection, notice however that if
the schema differs on the files this will break in runtime.
Filled https://issues.apache.org/jira/browse/BEAM-11650 to track this.
On Wed, Jan 13, 2021 at 7:42 PM Tao Li <t...@zillow.com> wrote:
>
> @Kobe Feng thank you so much for the insights. Agree that it may be a good
> practice to read all sorts of file formats (e.g. parquet, avro etc) into a
> PCollection<Row> and then perform the schema aware transforms that you are
> referring to.
>
>
>
> The new dataframe APIs for Python SDK sound pretty cool and I can imagine it
> will save a lot of hassles during a beam app development. Hopefully it will
> be added to Java SDK as well.
>
>
>
> From: Kobe Feng <flllbls...@gmail.com>
> Reply-To: "user@beam.apache.org" <user@beam.apache.org>
> Date: Friday, January 8, 2021 at 11:39 AM
> To: "user@beam.apache.org" <user@beam.apache.org>
> Subject: Re: Quick question regarding ParquetIO
>
>
>
> Tao,
> I'm not an expert, and good intuition, all you want is schema awareness
> transformations or let's say schema based transformation in Beam not only for
> IO but also for other DoFn, etc, and possibly have schema revolution in
> future as well.
>
>
> This is how I try to understand and explain in other places before: Not like
> spark, flink to leverage internal/built-in types (e.g, catalyst struct type)
> for built-in operators as more as possible to infer the schema when IOs could
> convert to, beam is trying to have capable to handle any type during
> transforms for people to migrate existing ones to beam (Do spark map
> partition func with own type, Encoder can't be avoided as well, right). Also
> yes, we could leverage beam own type "Row" to do all transformations and
> converting all in/out types like parquet, avro, orc, etc at IO side, and then
> do schema inferring in built-in operators base on row type when we know they
> will operate on internal types, that's how to avoid the coder or explicit
> schema there, more further, provide IO for schema registry capability and
> then transform will lookup when necessary for the revolution. I saw beam put
> schema base transformation in goals last year which will be convenient for
> people (since normally people would rather use builtin types instead of
> providing their own types' coder for following operators until we have to),
> that's why dataframe APIs for python SDK here I think.
>
> Kobe
>
>
>
>
> On Fri, Jan 8, 2021 at 9:34 AM Tao Li <t...@zillow.com> wrote:
>
> Thanks Alexey for your explanation. That’s also what I was thinking. Parquet
> files already have the schema built in, so it might be feasible to infer a
> coder automatically (like spark parquet reader). It would be great if we
> have some experts chime in here. @Brian Hulette already mentioned that the
> community is working on new DataFrame APIs in Python SDK, which are based on
> the pandas methods and use those methods at construction time to determine
> the schema. I think this is very close to the schema inference we have been
> discussing. Not sure it will be available to Java SDK though.
>
>
>
> Regarding BEAM-11460, looks like it may not totally solve my problem. As
> @Alexey Romanenko mentioned, we may still need to know the avro or beam
> schema for following operations after the parquet read. A dumb question is,
> with BEAM-11460, after we get a PCollection<GenericRecord> from parquet read
> (without the need to specify avro schema), is it possible to get the attached
> avro schema from a GenericRecord element of this PCollection<GenericRecord>?
>
>
>
> Really appreciate it if you can help clarify my questions. Thanks!
>
>
>
>
>
> From: Alexey Romanenko <aromanenko....@gmail.com>
> Reply-To: "user@beam.apache.org" <user@beam.apache.org>
> Date: Friday, January 8, 2021 at 4:48 AM
> To: "user@beam.apache.org" <user@beam.apache.org>
> Subject: Re: Quick question regarding ParquetIO
>
>
>
> Well, this is how I see it, let me explain.
>
>
>
> Since every PCollection is required to have a Coder to materialize the
> intermediate data, we need to have a coder for "PCollection<GenericRecord>"
> as well. If I’m not mistaken, for “GenericRecord" we used to set AvroCoder
> that is based on Avro (or Beam too?) schema.
>
>
>
> Actually, currently it will throw an exception if you will try to use
> “parseGenericRecords()” with a PCollection<GenericRecord> as output
> pcollection since it can’t infer a Coder based on provided “parseFn”. I guess
> it was done intentially in this way and I doubt that we can have a proper
> coder for PCollection<GenericRecord> without knowing a schema. Maybe some
> Avro experts here can add more on this if we can somehow overcome it.
>
>
>
> On 7 Jan 2021, at 19:44, Tao Li <t...@zillow.com> wrote:
>
>
>
> Alexey,
>
>
>
> Why do I need to set AvroCoder? I assume with BEAM-11460 we don’t need to
> specify a schema when reading parquet files to get
> aPCollection<GenericRecord>. Is my understanding correct? Am I missing
> anything here?
>
>
>
> Thanks!
>
>
>
> From: Alexey Romanenko <aromanenko....@gmail.com>
> Reply-To: "user@beam.apache.org" <user@beam.apache.org>
> Date: Thursday, January 7, 2021 at 9:56 AM
> To: "user@beam.apache.org" <user@beam.apache.org>
> Subject: Re: Quick question regarding ParquetIO
>
>
>
> If you want to get just a PCollection<GenericRecord> as output then you would
> still need to set AvroCoder, but which schema to use in this case?
>
>
>
> On 6 Jan 2021, at 19:53, Tao Li <t...@zillow.com> wrote:
>
>
>
> Hi Alexey,
>
>
>
> Thank you so much for this info. I will definitely give it a try once 2.28 is
> released.
>
>
>
> Regarding this feature, it’s basically mimicking the feature from
> AvroIO:https://beam.apache.org/releases/javadoc/2.26.0/org/apache/beam/sdk/io/AvroIO.html
>
>
>
> I have one more quick question regarding the “reading records of an unknown
> schema” scenario. In the sample code a PCollection<Foo> is being returned and
> the parseGenericRecords requires a parsing logic. What if I just want to get
> a PCollection<GenericRecord> instead of a specific class (e.g. Foo in the
> example)? I guess I can just skip the ParquetIO.parseGenericRecords
> transform? So do I still have to specify the dummy parsing logic like below?
> Thanks!
>
>
>
> p.apply(AvroIO.parseGenericRecords(new SerializableFunction<GenericRecord,
> GenericRecord >() {
>
> public Foo apply(GenericRecord record) {
>
> return record;
>
> }
>
>
>
> From: Alexey Romanenko <aromanenko....@gmail.com>
> Reply-To: "user@beam.apache.org" <user@beam.apache.org>
> Date: Wednesday, January 6, 2021 at 10:13 AM
> To: "user@beam.apache.org" <user@beam.apache.org>
> Subject: Re: Quick question regarding ParquetIO
>
>
>
> Hi Tao,
>
>
>
> This jira [1] looks exactly what you are asking but it was merged recently
> (thanks to Anant Damle for working on this!) and it should be available only
> in Beam 2.28.0.
>
>
>
> [1] https://issues.apache.org/jira/browse/BEAM-11460
>
>
>
> Regards,
>
> Alexey
>
>
>
>
> On 6 Jan 2021, at 18:57, Tao Li <t...@zillow.com> wrote:
>
>
>
> Hi beam community,
>
>
>
> Quick question about ParquetIO. Is there a way to avoid specifying the avro
> schema when reading parquet files? The reason is that we may not know the
> parquet schema until we read the files. In comparison, spark parquet reader
> does not require such a schema specification.
>
>
>
> Please advise. Thanks a lot!
>
>
>
>
>
>
> --
>
> Yours Sincerely
> Kobe Feng
