Kotlin on Jaehyeon Kim

Building a Real-Time Industrial Digital Twin with Apache Flink and Online Machine Learning

Tue, 21 Apr 2026 00:00:00 +0000

Overview Imagine using a rolling pin to flatten out a thick piece of dough. A Hot Strip Mill does the exact same thing, but with glowing red-hot steel slabs (often heated over 1000°C) and massive mechanical rollers. The steel is passed through a series of these rollers, crushing it down from a thick block into a long, thin sheet. Calculating the exact Rolling Force required to crush the steel is critical.

Stream Processing with Flink in Kotlin

Wed, 10 Dec 2025 00:00:00 +0000

A couple of years ago, I read Stream Processing with Apache Flink and worked through the examples using PyFlink. While the book offered a solid introduction to Flink, I frequently hit limitations with the Python API, as many features from the book weren’t supported. This time, I decided to revisit the material, but using Kotlin. The experience has been much more rewarding and fun.

In porting the examples to Kotlin, I also took the opportunity to align the code with modern Flink practices. The complete source for this post is available in the stream-processing-with-flink directory of the flink-demos GitHub repository.

Flink Table API - Declarative Analytics for Supplier Stats in Real Time

Tue, 17 Jun 2025 00:00:00 +0000

In the last post, we explored the fine-grained control of Flink’s DataStream API. Now, we’ll approach the same problem from a higher level of abstraction using the Flink Table API. This post demonstrates how to build a declarative analytics pipeline that processes our continuous stream of Avro-formatted order events. We will define a Table on top of a DataStream and use SQL-like expressions to perform windowed aggregations. This example highlights the power and simplicity of the Table API for analytical tasks and showcases Flink’s seamless integration between its different API layers to handle complex requirements like late data.

Flink DataStream API - Scalable Event Processing for Supplier Stats

Tue, 10 Jun 2025 00:00:00 +0000

Building on our exploration of stream processing, we now transition from Kafka’s native library to Apache Flink, a powerful, general-purpose distributed processing engine. In this post, we’ll dive into Flink’s foundational DataStream API. We will tackle the same supplier statistics problem - analyzing a stream of Avro-formatted order events - but this time using Flink’s robust features for stateful computation. This example will highlight Flink’s sophisticated event-time processing with watermarks and its elegant, built-in mechanisms for handling late-arriving data through side outputs.

Kafka Streams - Lightweight Real-Time Processing for Supplier Stats

Tue, 03 Jun 2025 00:00:00 +0000

In this post, we shift our focus from basic Kafka clients to real-time stream processing with Kafka Streams. We’ll explore a Kotlin application designed to analyze a continuous stream of Avro-formatted order events, calculate supplier statistics in tumbling windows, and intelligently handle late-arriving data. This example demonstrates the power of Kafka Streams for building lightweight, yet robust, stream processing applications directly within your Kafka ecosystem, leveraging event-time processing and custom logic.

Kafka Clients with Avro - Schema Registry and Order Events

Tue, 27 May 2025 00:00:00 +0000

In this post, we’ll explore a practical example of building Kafka client applications using Kotlin, Apache Avro for data serialization, and Gradle for build management. We’ll walk through the setup of a Kafka producer that generates mock order data and a consumer that processes these orders. This example highlights best practices such as schema management with Avro, robust error handling, and graceful shutdown, providing a solid foundation for your own Kafka-based projects. We’ll dive into the build configuration, the Avro schema definition, utility functions for Kafka administration, and the core logic of both the producer and consumer applications.

Kafka Clients with JSON - Producing and Consuming Order Events

Tue, 20 May 2025 00:00:00 +0000

This post explores a Kotlin-based Kafka project, meticulously detailing the construction and operation of both a Kafka producer application, responsible for generating and sending order data, and a Kafka consumer application, designed to receive and process these orders. We’ll delve into each component, from build configuration to message handling, to understand how they work together in an event-driven system.