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
