Common Cloud Dataflow pattern - is there a better way? - google-cloud-dataflow

Question

We are finding ourselves frequently using the following pattern in Dataflow:
Perform a key extract ParDo from a BigQuery TableRow
Perform a GroupByKey on the result of 1
Perform a flatten ParDo on the result of 2
Is there an operation in Dataflow to achieve this in one hit (at least from the API perspective)?
I've had a look at Combine operation, but that seems more suited to be used when calculating values e.g. sums/averages etc.

Answer 1

Without much details in your question I can only give general advise.
You could create a PTransform that combines the above pattern into a single Composite Transform. This allows you to put together the frequently used operations into a single reusable component.
The following code should give you an idea of what I mean:
import com.google.api.services.bigquery.model.TableRow;
import com.google.cloud.dataflow.sdk.Pipeline;
import com.google.cloud.dataflow.sdk.io.BigQueryIO;
import com.google.cloud.dataflow.sdk.options.PipelineOptions;
import com.google.cloud.dataflow.sdk.transforms.*;
import com.google.cloud.dataflow.sdk.values.KV;
import com.google.cloud.dataflow.sdk.values.PCollection;
class ExtractKeyFn extends DoFn<TableRow, KV<String, TableRow>> {
#Override
public void processElement(ProcessContext c) throws Exception {
TableRow row = c.element();
Object key = row.get("key");
if (key != null) {
c.output(KV.of(key.toString(), row));
}
}
}
class CompositeTransform extends PTransform<PCollection<TableRow>, PCollection<TableRow>> {
public CompositeTransform(String name) {
super(name);
}
public static CompositeTransform named(String name) {
return new CompositeTransform(name);
}
#Override
public PCollection<TableRow> apply(PCollection<TableRow> input) {
return input.apply(ParDo.named("parse").of(new ExtractKeyFn()))
.apply(GroupByKey.create())
// potentially more transformations
.apply(Values.create()) // get only the values ( because we have a kv )
.apply(Flatten.iterables()); // flatten them out
}
}
public class Main {
public static void run(PipelineOptions options) {
Pipeline p = Pipeline.create(options);
// read input
p.apply(BigQueryIO.Read.from("inputTable...").named("inputFromBigQuery"))
// apply fancy transform
.apply(CompositeTransform.named("FancyKeyGroupAndFlatten"))
// write output
.apply(BigQueryIO.Write.to("outputTable...").named("outputToBigQuery"));
p.run();
}
}