Andrew - the @Experimental tag simply means that we are free to change the interfaces without waiting for the next major Beam version. Once we are happy to freeze these interfaces, we can drop the tag.
On Wed, Aug 29, 2018 at 1:48 PM Andrew Pilloud <[email protected]> wrote: > The work you've done to generalize and expand Schemas has significantly > simplified what we need to do for SQL, I hope they are valuable to everyone > else too. What work remains before we can drop the Experimental designation? > > Andrew > > On Wed, Aug 29, 2018 at 5:31 AM Eugene Kirpichov <[email protected]> > wrote: > >> Wow, this is really coming together, congratulations and thanks for the >> great work! >> >> On Wed, Aug 29, 2018 at 1:40 AM Reuven Lax <[email protected]> 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) >>> >>> public class UserEvent { >>> >>> public String userId; >>> >>> public LatLong location; >>> >>> Public String countryCode; >>> >>> public long transactionCost; >>> >>> public double transactionDuration; >>> >>> public List<String> traceMessages; >>> >>> }; >>> >>> @DefaultSchema(JavaFieldSchema.class) >>> >>> public class LatLong { >>> >>> public double latitude; >>> >>> public double longitude; >>> >>> } >>> >>> 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 >>> >>>
