Dataflow writing a pCollection of GenericRecords to Parquet files

In apache beam step I have a PCollection of KV<String, Iterable<KV<Long, GenericRecord>>>>. I want to write all the records in the iterable to the same parquet file. My code snippet is given below

p.apply(ParDo.of(new MapWithAvroSchemaAndConvertToGenericRecord())) // PCollection<GenericRecord>
.apply(ParDo.of(new MapKafkaGenericRecordValue(formatter, options.getFileNameDelimiter()))) //PCollection<KV<String, KV<Long, GenericRecord>>>
.apply(GroupByKey.create()) //PCollection<KV<String, Iterable<KV<Long, GenericRecord>>>>>

now I want to write all the Records in the Iterable in the same parquet file(derive the file name by the key of KV).

Answer

I found out the solution to the problem. at the step –

apply(GroupByKey.create()) //PCollection<KV<String, Iterable<KV<Long, GenericRecord>>>>>

I will apply another transform that will return only the Iterable as the output pCollection. `.apply(ParDo.of(new GetIterable())) //PCollection>> where key is the name of the file I have to write to. then remaining snippet is

.apply(Flatten.iterables())
                .apply(
                        FileIO.<String, KV<String, GenericRecord>>writeDynamic()
                                .by((SerializableFunction<KV<String, GenericRecord>, String>) KV::getKey)
                                .via(
                                        Contextful.fn(
                                                (SerializableFunction<KV<String, GenericRecord>, GenericRecord>) KV::getValue
                                        ),
                                        ParquetIO.sink(schema)
                                                .withCompressionCodec(CompressionCodecName.SNAPPY)


                                )

                                .withTempDirectory("/tmp/temp-beam")
                                .to(options.getGCSBucketUrl())
                                .withNumShards(1)
                                .withDestinationCoder(StringUtf8Coder.of())
                )