João Esperancinha
Developer, Vereniging COIN B.V.
Nieuwegein, The Netherlands
15 years as a Software Developer, Studied Telecom Engineering at ISEL Lisboa, and worked in different companies in Portugal, Spain, and The Netherlands.
https://konghq.com/kong-champions
Area of Expertise
Topics
Kotlin Streams Potentially Good and Potentially Bad Ideas
In the last 5 years, we have not seen such a sophisticated and fast evolution of technology as the one Kotlin is going through. Such a speed in evolution can lead us sometimes to think that Kotlin can do anything including replacing external event streaming / queueing systems such as Kafka and RabbitMQ. In real life, this remains something that we must decide by considering what we want to implement. In this session, we'll have a look at the pitfalls of event streaming using pure Kotlin code, where this can be used, and when we definitely need to use an external event streaming system.
From command line to Spring Native - Working with the Kotlin-Native compiler and GraalVM
This session is about Native Development.
Lightning development times using Kong's own Unirest Library
Developing a REST client can be very easy but in some cases, it can be a very daunting experience. In this session, I will bring in a complex REST application service and a Kong gateway portal and we'll experience together and live how the development of a client can be done quite fast with increasing complexity with the Java Unirest Library. The code will be developed in Kotlin for the client whereas the service will be already available running in the background.
Collecting events from an Event Stream in Kotlin
The Kotlin language allows us to easily create streams where events can be emitted and then collected via, for example, REST endpoints. In this session we'll take a look at examples of how to work with event streams in Kotlin, how can we apply algorithms with it, and make it work within a Spring Framework environment.
Real time Kong Gateway Configuration For Kotlin Applications
Kotlin allows us to create beautiful, easy-to-read, and generic code in different ways. In this session, we will have a look at the different ways Kong can provide extra features to our Kotlin applications. We will have a look at quick examples of how these features can be implemented in the back-end via Kotlin and how some of these features can work together with Kong. The highlights are how can we access our application via OAuth2 with a pure Kotlin implementation or via Kong, apply rate-limiting, and use StatsD, CORS, and metric analytics with Prometheus and Moesif API. This session intends especially to show how can we, with services implemented with Spring and/or Micronaut and Kotlin, can get an optimal workflow between our applications, Kong, and the outside world. Further, we will also see, with examples, how can we get this configured in real-time with an angular administration application, speaking to our own Kotlin implemented back-end service in Spring. For example, we will see how can we limit traffic to an application and visualize that with Locust in real-time, and observe how traffic easily gets limited at a push of a button with our own custom application.
Making a Distributed Cache System with Hazelcast with combined Data processing in Kotlin
Working with a distributed cache system provides leverage to make applications faster by avoiding too many database operations. Not only that, but a cache system can also play a crucial role in avoiding unnecessary repeated calculations. In this session, we're going to go to the insides and outs of data processing in Kotlin with generics, flow, data streams, and a couple of surprises, to get systems optimal. We're going to have a look at a fictional banking system and have a look at different situations where crucial decisions can be made to allow our Bank to be efficient and still remain reliable in reaching an optimal SLA. This session combines a thorough review of data processing in Kotlin and how can we apply it considering a distributed cache system like Hazelcast. We'll have a look at data grids and how can we populate them efficiently using Kotlin as the backbone of our services.
Event Streaming in the context of Kotlin Reactive Web Applications
Event streaming is not a new concept. Coroutines are also not a new concept. However, these two concepts, together, are only now being widely used and considered for new developments and updates of old applications. Kotlin provides a way to implement applications in a reactive way using coroutines, and implementing event streams. We will have a look at different running application examples and how they can respond to event triggers, how they can digest event messages and how that plays out in terms of performance. We will also draw a few comparisons to understand how a reactive architecture works.
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