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From "Anton Kedin (JIRA)" <>
Subject [jira] [Commented] (BEAM-3157) BeamSql transform should support other PCollection types
Date Fri, 01 Dec 2017 17:38:00 GMT


Anton Kedin commented on BEAM-3157:

This will align with schema-aware pcollections:

I also need something like this for Nexmark, so I am working on a code generation solution
to infer the schema from pojo, so that you can do things like:

BeamRecordSqlTypeProxy recordType =

BeamRecord record = recordType.newRecordCopyOf(pojoInstance);

Current draft commit:

I expect to have a PR out today or tomorrow for this piece.

> BeamSql transform should support other PCollection types
> --------------------------------------------------------
>                 Key: BEAM-3157
>                 URL:
>             Project: Beam
>          Issue Type: Improvement
>          Components: dsl-sql
>            Reporter: Ismaël Mejía
> Currently the Beam SQL transform only supports input and output data represented as a
BeamRecord. This seems to me like an usability limitation (even if we can do a ParDo to prepare
objects before and after the transform).
> I suppose this constraint comes from the fact that we need to map name/type/value from
an object field into Calcite so it is convenient to have a specific data type (BeamRecord)
for this. However we can accomplish the same by using a PCollection of JavaBean (where we
know the same information via the field names/types/values) or by using Avro records where
we also have the Schema information. For the output PCollection we can map the object via
a Reference (e.g. a JavaBean to be filled with the names of an Avro object).
> Note: I am assuming for the moment simple mappings since the SQL does not support composite
types for the moment.
> A simple API idea would be something like this:
> A simple filter:
> PCollection<MyPojo> col = BeamSql.query("SELECT * FROM .... WHERE ...").from(MyPojo.class);
> A projection:
> PCollection<MyNewPojo> newCol = BeamSql.query("SELECT id, name").from(MyPojo.class).as(MyNewPojo.class);
> A first approach could be to just add the extra ParDos + transform DoFns however I suppose
that for memory use reasons maybe mapping directly into Calcite would make sense.

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