Recently I wanted to learn a bit about Apache Kafka. It is often used as a way to do event sourcing (or similar message-driven architectures). An “add-on” to the simple publish/subscribe pattern in Kafka is Kafka Streams, which provides ways to process unbounded data sets.

I wanted to write something slightly more complicated than the examples in the Kafka documentation.

Estimating π (Pi) by throwing darts

Circle inside square Back in the day we all learned the formula $A=\pi r^2$ for the area of a circle. We place the circle of radius 1 inside the unit square $[-1,1]^2$ (see the picture). The unit square has an area of $2 \times 2 = 4$, so the area of the circle is $\pi \cdot r^2 = \pi \cdot 1^2 = \pi$. The circle covers $\pi / 4 \approx 0.7853$ of the square’s area.

One (silly, very bad) way to estimate the area of the circle is to sample random points inside the square, and count how many of them land inside the circle. We end up with this formula:

[ \lim_{n \to \infty} \frac{ \#( \text{hits inside the circle} )}{n} = \large\pi ]

We need two ingredients to estimate Pi this way:

  • A way of generating (pseudo-)random numbers (which any normal computer can do).
  • A way to decide if a given point is inside the circle.

To decide if a given point is inside a circle, we just check that its absolute value is less than one:

[ (x,y) \mapsto x^2+y^2 \leq 1 ]

We realize now that this algorithm can be implemented in 25 lines of Python (with plenty of spacing), but let us use our skills as engineers to over-engineer instead.

Implementation in Kafka

Glossing over details, Kafka consists of topics and messages. Clients can publish and subscribe to these topics. It also promises “high throughput”, “scalability”, and “high availability”. Since I’m doing this on my laptop, none of this applies.

Kafka Streams is a framework for working with the Kafka messages as a stream of events, and provides utilities for real time aggregation.

Below is a high-level overview of the Kafka streams topology that we will build:

We produce a stream of random pairs of numbers to a Kafka topic named randoms. Then, in the aggregate step, we use a state store to keep track of the numbers of hits so far (together with the total number of random points processed).

The code to build the topology is quite simple:

public static Topology getTopology() {
    final StreamsBuilder builder = new StreamsBuilder();

    var randoms =,
                                 new Tuple.TupleSerde()));

    KStream<String, Double> fractionStream = getPiEstimationStream(randoms);

    // Output result to a topic,
                     Produced.with(Serdes.String(), Serdes.Double()));

    // Also make a topic with the error
    fractionStream.mapValues(v -> Math.abs(Math.PI - v) / Math.PI)
                     Produced.with(Serdes.String(), Serdes.Double()));


We consume input from the randoms topic (making it into a stream). Then we use the getPiEstimationStream method to calculate a running estimation of π. Finally, we output the estimation to the Kafka topic pi-estimation. We also output the relative error to another topic.

The code for calculating the estimate is also quite short:

private static KStream<String, Double> getPiEstimationStream(KStream<String, Tuple> stream) {
    var fractionStore = Materialized
                          .<String, FractionAggregator>as(Stores.persistentKeyValueStore("average-store"))
                          .withValueSerde(new FractionAggregator.FractionAggregatorSerde());

    return stream
            .mapValues(Tuple::insideCircle) // Map Tuple to boolean
            .aggregate(() -> new FractionAggregator(0, 0),
                       (key, value, agg) -> FractionAggregator.aggregate(
                               value ? 1 : 0),

private static double getEstimate(FractionAggregator fractionAggregator) {
    return == 0
            ? 0
            : 4. * fractionAggregator.trues() /;

The FractionAggregator class is just a simple Java record that keeps track of the total number of messages consumed, and how many landed inside the circle.

I also set up a simple Javalin app that publishes the messages via websockets to localhost. To do this, one writes a Kafka consumer for each topic, and use a standard consumer.poll loop. Then I used µPlot to continuously update the current estimate.


As always, I put my code on Github. To run the project, first start Kafka (for example with docker run -it --rm -p 9092:9092 apache/kafka:3.7.0), and then run mvn exec:java to run the project. Open your browser on localhost:8081, and start seeing estimations come in.

Below is a screen recording I did.

What I learned

Even though this little project was embarrassingly simple, I learned more than just the Kafka Java API.

Maybe write tests earlier

I decided to also write tests, mostly to increase my own learning. For example, writing the tests for the stream topology (see source here), made me realize that the topology is completely stateless. It is just a specification of inputs, transformations, and outputs.

If I had written the tests earlier, I would have avoided having to restart the Kafka container again and again.

Documentation of test utilities

I spent an awful lot of time writing the single test I have for the EstimationConsumer class. It uses Kafka’s MockConsumer class to mock a consumer. The only documentation I could find of this class were some examples on Baeldung’s pages.

(in general, I find that documentation for “beginners” is often too basic, outdated, or lacking)

Kafka not as fast as I thought (locally)

The first thing I tried when I started exploring Kafka, was to publish a message on some topic, and subscribe to the same topic in another terminal. About one second later, I got the message. I was a bit surprised that I wouldn’t see the message immediately, but when meditating on this for a little while, I realized that there is a difference between latecy and throughput.

There is also tons of configuration that I did not explore.

Serialization can be complicated

I chose to not take serialization seriously, so I just used Java’s built-in serialization interface (using implements Serializable). This is of course fine for testing applications, but it turned out to be cumbersome when I changed the classes involved. The solution was usually to delete and restart the Kafka container.

In a real application one would rather use a serialization method that don’t crash on schema changes (Protobuf, Avro, JSON, …). Also, one must be mindful about what to do with invalid messages. The default is to crash if any message is invalid.

There is even a whole chapter about this in the book Designing Data-Intensive Applications (Chapter 4).


Take a look at the code, and if I you found any errors here, don’t hesitate to contact me.