Thanks to the code snippet provided by the Google support team I figured it out:
To get a reshuffled PCollection:
PCollection<T> reshuffled = data.apply(Repartition.of());
The Repartition class used:
import com.google.cloud.dataflow.sdk.transforms.DoFn;
import com.google.cloud.dataflow.sdk.transforms.GroupByKey;
import com.google.cloud.dataflow.sdk.transforms.PTransform;
import com.google.cloud.dataflow.sdk.transforms.ParDo;
import com.google.cloud.dataflow.sdk.values.KV;
import com.google.cloud.dataflow.sdk.values.PCollection;
import java.util.concurrent.ThreadLocalRandom;
public class Repartition<T> extends PTransform<PCollection<T>, PCollection<T>> {
private Repartition() {}
public static <T> Repartition<T> of() {
return new Repartition<T>();
}
@Override
public PCollection<T> apply(PCollection<T> input) {
return input
.apply(ParDo.named("Add arbitrary keys").of(new AddArbitraryKey<T>()))
.apply(GroupByKey.<Integer, T>create())
.apply(ParDo.named("Remove arbitrary keys").of(new RemoveArbitraryKey<T>()));
}
private static class AddArbitraryKey<T> extends DoFn<T, KV<Integer, T>> {
@Override
public void processElement(ProcessContext c) throws Exception {
c.output(KV.of(ThreadLocalRandom.current().nextInt(), c.element()));
}
}
private static class RemoveArbitraryKey<T> extends DoFn<KV<Integer, Iterable<T>>, T> {
@Override
public void processElement(ProcessContext c) throws Exception {
for (T s : c.element().getValue()) {
c.output(s);
}
}
}
}