Examples with FRAUD_CHECK io.confluent.examples.streams.avro.microservices.OrderValidationType.FRAUD_CHECK used on opensource projects

Example 1 with FRAUD_CHECK

use of io.confluent.examples.streams.avro.microservices.OrderValidationType.FRAUD_CHECK in project kafka-streams-examples by confluentinc.

the class FraudService method processStreams.

private KafkaStreams processStreams(final String bootstrapServers, final String stateDir) {
    // Latch onto instances of the orders and inventory topics
    StreamsBuilder builder = new StreamsBuilder();
    KStream<String, Order> orders = builder.stream(ORDERS.name(), Consumed.with(ORDERS.keySerde(), ORDERS.valueSerde())).filter((id, order) -> OrderState.CREATED.equals(order.getState()));
    // Create an aggregate of the total value by customer and hold it with the order. We use session windows to
    // detect periods of activity.
    KTable<Windowed<Long>, OrderValue> aggregate = orders.groupBy((id, order) -> order.getCustomerId(), Serialized.with(Serdes.Long(), ORDERS.valueSerde())).windowedBy(SessionWindows.with(60 * MIN)).aggregate(OrderValue::new, // Calculate running total for each customer within this window
    (custId, order, total) -> new OrderValue(order, total.getValue() + order.getQuantity() * order.getPrice()), // include a merger as we're using session windows.
    (k, a, b) -> simpleMerge(a, b), Materialized.with(null, Schemas.ORDER_VALUE_SERDE));
    // Ditch the windowing and rekey
    KStream<String, OrderValue> ordersWithTotals = aggregate.toStream((windowedKey, orderValue) -> windowedKey.key()).filter(// When elements are evicted from a session window they create delete events. Filter these out.
    (k, v) -> v != null).selectKey((id, orderValue) -> orderValue.getOrder().getId());
    // Now branch the stream into two, for pass and fail, based on whether the windowed total is over Fraud Limit
    KStream<String, OrderValue>[] forks = ordersWithTotals.branch((id, orderValue) -> orderValue.getValue() >= FRAUD_LIMIT, (id, orderValue) -> orderValue.getValue() < FRAUD_LIMIT);
    forks[0].mapValues(orderValue -> new OrderValidation(orderValue.getOrder().getId(), FRAUD_CHECK, FAIL)).to(ORDER_VALIDATIONS.name(), Produced.with(ORDER_VALIDATIONS.keySerde(), ORDER_VALIDATIONS.valueSerde()));
    forks[1].mapValues(orderValue -> new OrderValidation(orderValue.getOrder().getId(), FRAUD_CHECK, PASS)).to(ORDER_VALIDATIONS.name(), Produced.with(ORDER_VALIDATIONS.keySerde(), ORDER_VALIDATIONS.valueSerde()));
    // disable caching to ensure a complete aggregate changelog. This is a little trick we need to apply
    // as caching in Kafka Streams will conflate subsequent updates for the same key. Disabling caching ensures
    // we get a complete "changelog" from the aggregate(...) step above (i.e. every input event will have a
    // corresponding output event.
    Properties props = baseStreamsConfig(bootstrapServers, stateDir, FRAUD_SERVICE_APP_ID);
    props.setProperty(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, "0");
    return new KafkaStreams(builder.build(), props);
}