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Deep Dive into Saga Transactions with Kafka Streams and Spring Boot

Article by one of our Subject Matter Expert

In this article, you will learn how to use Kafka Streams and Spring Boot to perform transactions according to the Saga pattern. I will also show how you can easily set up a cloud-managed Kafka on the Upstash.

Introduction

We used Kafka Streams to process order transactions on the order-service side. To handle orders coming to the stock-service and payment-service we used a standard Spring Kafka Listener. There are also two databases – a single database per every service. The stock-service stores data related to the number of available products and updates them after receiving an order. The same with the payment-service. It updates the customer’s account on every single order. Both applications receive orders from Kafka topic. They send responses to other topics. But just to simplify, we will skip it as shown in the figure below. We treat the Kafka orders topic as a stream of events and also as a table with the latest order’s status.

What may go wrong with that approach? In fact, we have two data sources here. We use Kafka as the order store. On the other hand, there are SQL databases (in my case H2, but you can use any other) that store stock and payment data. Once we send an order with a reservation to the Kafka topic, we need to update a database. Since Kafka does not support XA transactions, it may result in data inconsistency. Of course, Kafka doesn’t support XA transactions the same as many other systems including e.g. RabbitMQ.

The question is what can we do with that? One of the possible options you may use is an approach called Change Data Capture (CDC) with the outbox pattern. CDC identifies and tracks changes to data in a database. Then it may emit those changes as events and send them, for example to the Kafka topic.

Architecture with Kafka Streams

The approach I will describe today is fully based on the Kafka Streams. We won’t use any SQL databases. When the order-service sends a new order its id is the message key. With Kafka Streams, we may change a message key in the stream. It results in creating new topics and repartitioning. With new message keys, we may perform calculations just for the specific customerId or productId. The result of such calculation may be saved in the persistent store. For example, Kafka automatically creates and manages such state stores when you are calling stateful operations like count() or aggregate(). We will aggregate the orders related to the particular customer or product. Here’s the illustration of our architecture. Here’s the visualization of our process.

Now, let’s consider a scenario for the payment-service in details. In the incoming stream of orders the payment-service calls the selectKey() operation. It changes the key from the order’s id into the order’s customer Id. Then it groups all the orders by the new key and invokes the aggregate() operation. In the aggregate() method it calculates the available amount and reserved amount based on the order’s price and status (whether it is a new order or a confirmation order). If there are sufficient funds on the customer account it sends the ACCEPT order to the payment-orders topic. Otherwise, it sends the REJECT order. Then the order-service process responses by joining streams from payment-orders and stock-orders by the order’s id. As the result, it sends a confirmation or a rollback order.

Aggregation with Kafka Streams

Let’s begin with the payment-service. The implementation of KStream in not complicated here. In the first step (1), we invoke the selectKey() method and get the customerId value of the Order object as a new key. Then we call groupByKey() method (2) to receive KGroupedStream as a result. While we have KGroupedStream we may invoke one of the calculation methods. In that case, we need to use aggregate(), since we have a little bit more advanced calculation than just a simple count (3). The last two steps are just for printing the value after calculation.

@Bean

public KStream<Long, Order> stream(StreamsBuilder builder) {

   JsonSerde<Order> orderSerde = new JsonSerde<>(Order.class);

   JsonSerde<Reservation> rsvSerde = new JsonSerde<>(Reservation.class);

   KStream<Long, Order> stream = builder

      .stream(“orders”, Consumed.with(Serdes.Long(), orderSerde))

      .peek((k, order) -> LOG.info(“New: {}”, order));

 

   KeyValueBytesStoreSupplier customerOrderStoreSupplier =

      Stores.persistentKeyValueStore(“customer-orders”);

 

   stream.selectKey((k, v) -> v.getCustomerId()) // (1)

      .groupByKey(Grouped.with(Serdes.Long(), orderSerde)) // (2)

      .aggregate(

         () -> new Reservation(random.nextInt(1000)),

         aggregatorService,

         Materialized.<Long, Reservation>as(customerOrderStoreSupplier)

            .withKeySerde(Serdes.Long())

            .withValueSerde(rsvSerde)) // (3)

      .toStream()

      .peek((k, trx) -> LOG.info(“Commit: {}”, trx));

 

   return stream;

}

However, the most important step in the fragment of code visible above is the class called inside the aggregate() method. The aggregate() method takes three input arguments. The first of them indicates the starting value of our compute object. That object represents the current state of the customer’s account. It has two fields: amountAvailable and amountReserved. To clarify, we use that object instead of the entity that stores available and reserved amounts on the customer account. Each customer is represented by the customerId (key) and the Reservation object (value) in Kafka KTable. Just for the test purpose, we are generating the starting value of amountAvailable as a random number between 0 and 1000.

public class Reservation {

   private int amountAvailable;

   private int amountReserved;

   public Reservation() {

   }

   public Reservation(int amountAvailable) {

      this.amountAvailable = amountAvailable;

   }

   // GETTERS AND SETTERS …

}

Ok, let’s take a look at our aggregation method. It needs to implement the Kafka Aggregate interface and its method apply(). It may handle three types of orders. One of them is a confirmation of the order (1). It confirms the distributed transaction, so we just need to cancel a reservation by subtracting the order’s price from the amountReserved field. On the other, in the case of rollback, we need to increase the value of amountAvailable by the order’s price and decrease the value amountRerserved accordingly (2). Finally, if we receive a new order we need to perform a reservation if there are sufficient funds on the customer account, or otherwise, reject an order.

Aggregator<Long, Order, Reservation> aggregatorService = (id, order, rsv) -> {

   switch (order.getStatus()) {

      case “CONFIRMED” -> // (1)

         rsv.setAmountReserved(rsv.getAmountReserved()

               – order.getPrice());

      case “ROLLBACK” -> { // (2)

         if (!order.getSource().equals(“PAYMENT”)) {

            rsv.setAmountAvailable(rsv.getAmountAvailable()

                  + order.getPrice());

            rsv.setAmountReserved(rsv.getAmountReserved()

                  – order.getPrice());

         }

      }

      case “NEW” -> { // (3)

         if (order.getPrice() <= rsv.getAmountAvailable()) {

            rsv.setAmountAvailable(rsv.getAmountAvailable()

                  – order.getPrice());

            rsv.setAmountReserved(rsv.getAmountReserved()

                  + order.getPrice());

            order.setStatus(“ACCEPT”);

         } else {

            order.setStatus(“REJECT”);

         }

         template.send(“payment-orders”, order.getId(), order);

      }

   }

   LOG.info(“{}”, rsv);

   return rsv;

};

State Store with the Kafka Streams Table

The implementation of the stock-service is pretty similar to the payment-service. With the difference that we count a number of available products on stock instead of available funds on the customer account. Here’s our Reservation object:

public class Reservation {

   private int itemsAvailable;

   private int itemsReserved;

 

   public Reservation() {

   

   }

 

   public Reservation(int itemsAvailable) {

      this.itemsAvailable = itemsAvailable;

   }

   // GETTERS AND SETTERS …

}

The implementation of the aggregation method is also very similar to the payment-service. However, this time, let’s focus on another thing. Once we process a new order we need to send a response to the stock-orders topic. We use KafkaTemplate for that. In the case of payment-service we also send a response, but to the payment-orders topic. The send method from the KafkaTemplate does not block the thread. It returns the ListenableFuture objects. We may add a callback to the send method using it and the result after sending the message (1). Finally, let’s log the current state of the Reservation object (2).

Aggregator<Long, Order, Reservation> aggrSrv = (id, order, rsv) -> {

   switch (order.getStatus()) {

      case “CONFIRMED” -> rsv.setItemsReserved(rsv.getItemsReserved()

            – order.getProductCount());

      case “ROLLBACK” -> {

         if (!order.getSource().equals(“STOCK”)) {

            rsv.setItemsAvailable(rsv.getItemsAvailable()

                  + order.getProductCount());

            rsv.setItemsReserved(rsv.getItemsReserved()

                  – order.getProductCount());

         }

      }

      case “NEW” -> {

         if (order.getProductCount() <= rsv.getItemsAvailable()) {

            rsv.setItemsAvailable(rsv.getItemsAvailable()

                  – order.getProductCount());

            rsv.setItemsReserved(rsv.getItemsReserved()

                  + order.getProductCount());

            order.setStatus(“ACCEPT”);

         } else {

            order.setStatus(“REJECT”);

         }

         // (1)

         template.send(“stock-orders”, order.getId(), order)

            .addCallback(r -> LOG.info(“Sent: {}”,

               result != null ? result.getProducerRecord().value() : null),

               ex -> {});

      }

   }

   LOG.info(“{}”, rsv); // (2)

   return rsv;

};

After that, we are also logging the value of the Reservation object (1). In order to do that we need to convert KTable into KStream and then call the peek method. This log is printed just after Kafka Streams commits the offset in the source topic.

@Bean

public KStream<Long, Order> stream(StreamsBuilder builder) {

   JsonSerde<Order> orderSerde = new JsonSerde<>(Order.class);

   JsonSerde<Reservation> rsvSerde = new JsonSerde<>(Reservation.class);

   KStream<Long, Order> stream = builder

      .stream(“orders”, Consumed.with(Serdes.Long(), orderSerde))

      .peek((k, order) -> LOG.info(“New: {}”, order));

 

   KeyValueBytesStoreSupplier stockOrderStoreSupplier =

      Stores.persistentKeyValueStore(“stock-orders”);

 

   stream.selectKey((k, v) -> v.getProductId())

      .groupByKey(Grouped.with(Serdes.Long(), orderSerde))

      .aggregate(() -> new Reservation(random.nextInt(100)), aggrSrv,

         Materialized.<Long, Reservation>as(stockOrderStoreSupplier)

            .withKeySerde(Serdes.Long())

            .withValueSerde(rsvSerde))

      .toStream()

      .peek((k, trx) -> LOG.info(“Commit: {}”, trx)); // (1)

 

   return stream;

}

What will happen if you send the test order? Let’s see the logs. You can see the difference in time between processing the message and offset commit. You won’t have any problems with that until your application is running or it has been stopped gracefully. But if you, for example, kill the process using the kill -9 command? After restart, our application will receive the same messages once again. Since we use KafkaTemplate to send the response to the stock-orders topic, we need to commit the offset as soon as possible.

What can we do to avoid such problems? We may override the default value (30000) of the commit.interval.ms Kafka Streams property. If you set it to 0, it commits immediately after processing finishes.

spring.kafka: 

  streams:

    properties:

      commit.interval.ms: 0

Running Kafka on Upstash

For the purpose of today’s exercise, we will use a serverless Kafka cluster on Upstash. You can create it with a single click. If you would like to test JAAS authentication for your application I’ve got good news The authentication on that cluster is enabled by default. You can find and copy username and password from the cluster’s main panel.

Now, let’s configure Kafka connection settings and credentials for the Spring Boot application. There is a developer free tier on Upstash up to 10k messages per day. It will be enough for our tests.

spring.kafka:

  bootstrap-servers: topical-gar-11460-us1-kafka.upstash.io:9092

  properties:

    security.protocol: SASL_SSL

    sasl.mechanism: SCRAM-SHA-256

    sasl.jaas.config: org.apache.kafka.common.security.scram.ScramLoginModule required username=”${USERNAME}” password=”${PASSWORD}”;

 

With Upstash you can easily display a list of topics. In total, there are 10 topics used in our sample system. Three of them are used directly by the Spring Boot applications, while the rest of them by the Kafka Streams in order to process stateful operations.

After starting the order-service application we can call its REST endpoint to create and send an order to the Kafka topic.

private static final Logger LOG =

   LoggerFactory.getLogger(OrderController.class);

private AtomicLong id = new AtomicLong();

private KafkaTemplate<Long, Order> template;

 

@PostMapping

public Order create(@RequestBody Order order) {

   order.setId(id.incrementAndGet());

   template.send(“orders”, order.getId(), order);

   LOG.info(“Sent: {}”, order);

   return order;

}

Let’s call the endpoint using the following curl command. You can use any customerId or productId you want.

$ curl -X ‘POST’ \

  ‘http://localhost:8080/orders’ \

  -H ‘Content-Type: application/json’ \

  -d ‘{

    “customerId”: 20,

    “productId”: 20,

    “productCount”: 2,

    “price”: 10,

    “status”: “NEW”

  }’

All three sample applications use Kafka Streams to process distributed transactions. Once the order is accepted by both stock-service and payment-service you should see the following entry in the order-service logs.

You can easily simulate rejection of transactions with Kafka Streams just by setting e.g. productCount higher than the value generated by the product-service as available items.

With Upstash UI you can also easily verify the number of messages incoming to the topics. Let’s see the current statistics for the orders topic.

Final Thoughts

First of all, each stream partition is a totally ordered sequence of data records and maps to a Kafka topic partition. It means, that even if we have multiple orders at the same time related to e.g. same product, they are all processed sequentially since they have the same message key (productId in that case).

Moreover, by default, there is only a single stream thread that handles all the partitions. You can see this in the logs below. However, there are stream tasks that act as the lowest-level units of parallelism. As a result, stream tasks can be processed independently and in parallel without manual intervention.

I hope this article helps you to better understand Kafka Streams. I just wanted to give you a simple example of how you can use Kafka Streams with Saga transactions in order to simplify your current architecture.

 

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