Nice work Reuven!!!
On Thu, Aug 30, 2018 at 6:57 AM Jean-Baptiste Onofré <[email protected]>
wrote:
> Nice feature, thanks Reuven !
>
> I started to revamp the Spark runner with dataset, I will leverage this !
>
> Regards
> JB
>
> On 29/08/2018 07:40, Reuven Lax wrote:
> > I wanted to send a quick note to the community about the current status
> > of schema-aware PCollections in Beam. As some might remember we had a
> > good discussion last year about the design of these schemas, involving
> > many folks from different parts of the community. I sent a summary
> > earlier this year explaining how schemas has been integrated into the
> > DoFn framework. Much has happened since then, and here are some of the
> > highlights.
> >
> >
> > First, I want to emphasize that all the schema-aware classes are
> > currently marked @Experimental. Nothing is set in stone yet, so if you
> > have questions about any decisions made, please start a discussion!
> >
> >
> > SQL
> >
> > The first big milestone for schemas was porting all of BeamSQL to use
> > the framework, which was done in pr/5956. This was a lot of work,
> > exposed many bugs in the schema implementation, but now provides great
> > evidence that schemas work!
> >
> >
> > Schema inference
> >
> > Beam can automatically infer schemas from Java POJOs (objects with
> > public fields) or JavaBean objects (objects with getter/setter methods).
> > Often you can do this by simply annotating the class. For example:
> >
> >
> > @DefaultSchema(JavaFieldSchema.class)
> >
> > publicclassUserEvent{
> >
> > publicStringuserId;
> >
> > publicLatLonglocation;
> >
> > PublicStringcountryCode;
> >
> > publiclongtransactionCost;
> >
> > publicdoubletransactionDuration;
> >
> > publicList<String>traceMessages;
> >
> > };
> >
> >
> > @DefaultSchema(JavaFieldSchema.class)
> >
> > publicclassLatLong{
> >
> > publicdoublelatitude;
> >
> > publicdoublelongitude;
> >
> > }
> >
> >
> > Beam will automatically infer schemas for these classes! So if you have
> > a PCollection<UserEvent>, it will automatically get the following schema:
> >
> >
> > UserEvent:
> >
> > userId: STRING
> >
> > location: ROW(LatLong)
> >
> > countryCode: STRING
> >
> > transactionCost: INT64
> >
> > transactionDuration: DOUBLE
> >
> > traceMessages: ARRAY[STRING]]
> >
> >
> > LatLong:
> >
> > latitude: DOUBLE
> >
> > longitude: DOUBLE
> >
> >
> > Now it’s not always possible to annotate the class like this (you may
> > not own the class definition), so you can also explicitly register this
> > using Pipeline:getSchemaRegistry:registerPOJO, and the same for
> JavaBeans.
> >
> >
> > Coders
> >
> > Beam has a built-in coder for any schema-aware PCollection, largely
> > removing the need for users to care about coders. We generate low-level
> > bytecode (using ByteBuddy) to implement the coder for each schema, so
> > these coders are quite performant. This provides a better default coder
> > for Java POJO objects as well. In the past users were recommended to use
> > AvroCoder for pojos, which many have found inefficient. Now there’s a
> > more-efficient solution.
> >
> >
> > Utility Transforms
> >
> > Schemas are already useful for implementers of extensions such as SQL,
> > but the goal was to use them to make Beam itself easier to use. To this
> > end, I’ve been implementing a library of transforms that allow for easy
> > manipulation of schema PCollections. So far Filter and Select are
> > merged, Group is about to go out for review (it needs some more javadoc
> > and unit tests), and Join is being developed but doesn’t yet have a
> > final interface.
> >
> >
> > Filter
> >
> > Given a PCollection<LatLong>, I want to keep only those in an area of
> > southern manhattan. Well this is easy!
> >
> >
> > PCollection<LatLong>manhattanEvents =allEvents.apply(Filter
> >
> > .whereFieldName("latitude",lat ->lat <40.720&&lat >40.699)
> >
> > .whereFieldName("longitude",long->long<-73.969&&long>-74.747));
> >
> >
> > Schemas along with lambdas allows us to write this transform
> > declaratively. The Filter transform also allows you to register filter
> > functions that operate on multiple fields at the same time.
> >
> >
> > Select
> >
> > Let’s say that I don’t need all the fields in a row. For instance, I’m
> > only interested in the userId and traceMessages, and don’t care about
> > the location. In that case I can write the following:
> >
> >
> > PCollection<Row>selected
> > =allEvents.apply(Select.fieldNames(“userId”,“traceMessages”));
> >
> >
> > BTW, Beam also keeps track of which fields are accessed by a transform
> > In the future we can automatically insert Selects in front of subgraphs
> > to drop fields that are not referenced in that subgraph.
> >
> >
> > Group
> >
> > Group is one of the more advanced transforms. In its most basic form, it
> > provides a convenient way to group by key:
> >
> >
> > PCollection<KV<Row,Iterable<UserEvent>>byUserAndCountry =
> >
> > allEvents.apply(Group.byFieldNames(“userId”,“countryCode”));
> >
> >
> > Notice how much more concise this is than using GroupByKey directly!
> >
> >
> > The Group transform really starts to shine however when you start
> > specifying aggregations. You can aggregate any field (or fields) and
> > build up an output schema based on these aggregations. For example:
> >
> >
> > PCollection<KV<Row,Row>>aggregated =allEvents.apply(
> >
> > Group.byFieldNames(“userId”,“countryCode”)
> >
> > .aggregateField("cost",Sum.ofLongs(),"total_cost")
> >
> > .aggregateField("cost",Top.<Long>largestFn(10),“top_purchases”)
> >
> >
>
> .aggregateField("transationDuration",ApproximateQuantilesCombineFn.create(21),
> >
> > “durationHistogram”)));
> >
> >
> > This will individually aggregate the specified fields of the input items
> > (by user and country), and generate an output schema for these
> > aggregations. In this case, the output schema will be the following:
> >
> >
> > AggregatedSchema:
> >
> > total_cost: INT64
> >
> > top_purchases: ARRAY[INT64]
> >
> > durationHistogram: ARRAY[DOUBLE]
> >
> >
> > There are some more utility transforms I've written that are worth
> > looking at such as Convert (which can convert between user types that
> > share a schema) and Unnest (flattens nested schemas). There are also
> > some others such as Pivot that we should consider writing
> >
> >
> > There is still a lot to do. All the todo items are reflected in JIRA,
> > however here are some examples of current gaps:
> >
> >
> > *
> >
> > Support for read-only POJOs (those with final fields) and JavaBean
> > (objects without setters).
> >
> > *
> >
> > Automatic schema inference from more Java types: protocol buffers,
> > avro, AutoValue, etc.
> >
> > *
> >
> > Integration with sources (BigQueryIO, JdbcIO, AvroIO, etc.)
> >
> > *
> >
> > Support for JsonPath expressions so users can better express nested
> > fields. E.g. support expressions of the form
> > Select.fields(“field1.field2”, “field3.*”, “field4[0].field5”);
> >
> > *
> >
> > Schemas still need to be defined in our portability layer so they
> > can be used cross language.
> >
> >
> > If anyone is interested in helping close these gaps, you'll be helping
> > make Beam a better, more-usable system!
> >
> > Reuven
> >
>
> --
> Jean-Baptiste Onofré
> [email protected]
> http://blog.nanthrax.net
> Talend - http://www.talend.com
>
