Summary
In this chapter, we learned how unbounded streams of data can be viewed as time-varying relations and, as such, are suitable to be queried using SQL. We saw how standard SQL needs to be adjusted to fit streaming needs – we introduced three special functions called TUMBLE, HOP, and SESSION to be used in the GROUP BY clauses of SQL to apply a windowing strategy within SQL statements.
We explored that the prerequisite of applying Apache Beam SQL to PCollection is to create a PCollection<Row>, where Row represents the relational view of a stream, broken down to a structure with a given Schema, which represents the individual (possibly nested) fields of data elements inside PCollection. We also learned how to either automatically infer a schema from the given type using the @DefaultSchema annotation with a SchemaProvider such as JavaFieldSchema or JavaBeanSchema. When we cannot (or do not want to) use a @DefaultSchema, we can set the schema to a PCollection manually...
