win845 commented on issue #33268:
URL: https://github.com/apache/beam/issues/33268#issuecomment-2514512912
While the implementation may take a while. What is a current strategy to
deal with a sequentially emiting source as linked above? Bigqueryio is reading
records in loop and emiting them sequentially without also implementing a
progress method.
This leads to pipelines on dataflow never being autoscaled .
Can something be done in subsequent step to make the subsequent processing
being redistributed to multipe workers.
Example Code:
```
bigqueryRows := bigqueryio.Query(scope, config.Project, itemOptionsQuery,
reflect.TypeOf(ItemOptionRow{}),
bigqueryio.UseStandardSQL())
mutations := beam.ParDo(scope, func(bigqueryRow ItemRow, emit
func(bigtableio.Mutation)) {
rowKey := bigqueryRow.Id
mutation := bigtableio.NewMutation(rowKey)
// ...
emit(*mutation)
}, bigqueryRows)
```
Can bigqueryRows here be somehow consumed in chunks which are distributed on
multiple workers for further transform?
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