That's a cool feature. Are there any limitations for the schema
inference apart from being a Pojo/Bean? Does it supported nested PoJos,
e.g. "wrapper.field"?
-Max
On 29.08.18 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
