How to process a KStream in a batch of max size or fallback to a time window? - apache-kafka

Question

I would like to create a Kafka stream-based application that processes a topic and takes messages in batches of size X (i.e. 50) but if the stream has low flow, to give me whatever the stream has within Y seconds (i.e. 5).
So, instead of processing messages one by one, I process a List[Record] where the size of the list is 50 (or maybe less).
This is to make some I/O bound processing more efficient.
I know that this can be implemented with the classic Kafka API but was looking for a stream-based implementation that can also handle offset committing natively, taking errors/failures into account.
I couldn't find anything related int he docs or by searching around and was wondering if anyone has a solution to this problem.

Answer 1

#Matthias J. Sax answer is nice, I just want to add an example for this, I think it might be useful for someone.
let's say we want to combine incoming values into the following type:
public class MultipleValues { private List<String> values; }
To collect messages into batches with max size, we need to create transformer:
public class MultipleValuesTransformer implements Transformer<String, String, KeyValue<String, MultipleValues>> {
private ProcessorContext processorContext;
private String stateStoreName;
private KeyValueStore<String, MultipleValues> keyValueStore;
private Cancellable scheduledPunctuator;
public MultipleValuesTransformer(String stateStoreName) {
this.stateStoreName = stateStoreName;
}
#Override
public void init(ProcessorContext processorContext) {
this.processorContext = processorContext;
this.keyValueStore = (KeyValueStore) processorContext.getStateStore(stateStoreName);
scheduledPunctuator = processorContext.schedule(Duration.ofSeconds(30), PunctuationType.WALL_CLOCK_TIME, this::doPunctuate);
}
#Override
public KeyValue<String, MultipleValues> transform(String key, String value) {
MultipleValues itemValueFromStore = keyValueStore.get(key);
if (isNull(itemValueFromStore)) {
itemValueFromStore = MultipleValues.builder().values(Collections.singletonList(value)).build();
} else {
List<String> values = new ArrayList<>(itemValueFromStore.getValues());
values.add(value);
itemValueFromStore = itemValueFromStore.toBuilder()
.values(values)
.build();
}
if (itemValueFromStore.getValues().size() >= 50) {
processorContext.forward(key, itemValueFromStore);
keyValueStore.put(key, null);
} else {
keyValueStore.put(key, itemValueFromStore);
}
return null;
}
private void doPunctuate(long timestamp) {
KeyValueIterator<String, MultipleValues> valuesIterator = keyValueStore.all();
while (valuesIterator.hasNext()) {
KeyValue<String, MultipleValues> keyValue = valuesIterator.next();
if (nonNull(keyValue.value)) {
processorContext.forward(keyValue.key, keyValue.value);
keyValueStore.put(keyValue.key, null);
}
}
}
#Override
public void close() {
scheduledPunctuator.cancel();
}
}
and we need to create key-value store, add it to StreamsBuilder, and build KStream flow using transform method
Properties props = new Properties();
...
Serde<MultipleValues> multipleValuesSerge = Serdes.serdeFrom(new JsonSerializer<>(), new JsonDeserializer<>(MultipleValues.class));
StreamsBuilder builder = new StreamsBuilder();
String storeName = "multipleValuesStore";
KeyValueBytesStoreSupplier storeSupplier = Stores.persistentKeyValueStore(storeName);
StoreBuilder<KeyValueStore<String, MultipleValues>> storeBuilder =
Stores.keyValueStoreBuilder(storeSupplier, Serdes.String(), multipleValuesSerge);
builder.addStateStore(storeBuilder);
builder.stream("source", Consumed.with(Serdes.String(), Serdes.String()))
.transform(() -> new MultipleValuesTransformer(storeName), storeName)
.print(Printed.<String, MultipleValues>toSysOut().withLabel("transformedMultipleValues"));
KafkaStreams kafkaStreams = new KafkaStreams(builder.build(), props);
kafkaStreams.start();
with such approach we used the incoming key for which we did aggregation. if you need to collect messages not by key, but by some message's fields, you need the following flow to trigger rebalancing on KStream (by using intermediate topic):
.selectKey(..)
.through(intermediateTopicName)
.transform( ..)

Answer 2

The simplest way might be, to use a stateful transform() operation. Each time you receive a record, you put it into the store. When you have received 50 records, you do your processing, emit output, and delete the records from the store.
To enforce processing if you don't read the limit in a certain amount of time, you can register a wall-clock punctuation.

Answer 3

It seems that there is no need to use Processors or Transformers and transform() to batch events by count. Regular groupBy() and reduce()/aggregate() should do the trick:
KeyValueSerde keyValueSerde = new KeyValueSerde(); // simple custom Serde
final AtomicLong batchCount = new AtomicLong(0L);
myKStream
.groupBy((k,v) -> KeyValue.pair(k, batchCount.getAndIncrement() / batchSize),
Grouped.keySerde(keyValueSerde))
.reduce(this::windowReducer) // <-- how you want to aggregate values in batch
.toStream()
.filter((k,v) -> /* pass through full batches only */)
.selectKey((k,v) -> k.key)
...
You'd also need to add straightforward Serde for the standard KeyValue<String, Long>.
This option is obviously only helpful when you don't need a "punctuator" to emit incomplete batches on timeout. It also doesn't guarantee the order of elements in the batch in case of distributed processing.
You can also concatenate count to the key string to form the new key (instead of using KeyValue). That would simplify example even further (to using Serdes.String()).