This talk is meant to demystify how garbage collection (GC) works in a Java runtime. The beginning of the talk will be focused on what GC is and how it is useful for the lifetime of a program. After laying out the basics I’ll explain how each GC algorithm work and how they can be implemented in a real world JVM (e.g. OpenJ9, Hotspot).

How are these algorithms put together to make up a good GC configuration, and how one can better select a configuration/policy for their application?

These are some of the questions that I will be addressing during this talk. I will do so by tying these algorithms to OpenJ9 GC policies and compare them to other policies such as G1, ZGC and Shenandoah. Different programs have different behaviours; by using the more appropriate policy for your application, it can improve its performance considerably.

By the end of the talk you will have a better understanding on how the JVM manages its memory and be able to choose the more appropriate GC policy for your application.

Kafka is the bedrock of Wix's distributed microservices system. For the last 5 years we have learned a lot about how to successfully scale our event-driven architecture to roughly 1400 microservices.

We’ve managed to achieve higher decoupling and independence for our various services and dev teams that have very different use-cases while maintaining a single uniform infrastructure in place.

In this talk you will learn about 10 key decisions and steps you can take in order to safely scale-up your Kafka-based system. These include:
* How to increase dev velocity of event driven style code.
* How to optimize working with Kafka in polyglot setting
* How to support growing amount of traffic and developers.
* How to tackle multiple DCs environment.

An API is what developers use to achieve some task. More precisely it establishes a contract between them and the designers of the software exposing its services through that API. In this sense we're all API designers: our software doesn't work in isolation, but becomes useful only when it interacts with other software written by other developers.

When writing software we're not only consumers, but also providers of one or more API and that's why every developer should know the features of a good API. During this presentation we will go through real-world examples, also taken from the standard Java API, of good and bad API and comment them in order to show the dos and don'ts of API design.

More in general we will discuss the characteristics of an easy and pleasant to use API, like consistency, discoverability and understandability, together with some basic concepts like the principle of least astonishment, and find out how to achieve them.

Developers creating websites and especially backend developers need to know how to build RESTful APIs correctly and effectively. As nowadays those APIs is serving many channels either it is Mobile, Microservices components, IoT, integration or all of them.

This session will help you plan and model your own APIs and understand the six REST design constraints that help guide your architecture. I will start with a simple overview, including advice on identifying the users or "participants" of your system and the activities they might perform with it. I'll help you to paper test your model, validating the design before you build it, provided with a live example to clear up the concept.

You'll then explore the HTTP concepts and REST constraints needed to build your API.

The use of distributed streaming platforms is becoming increasingly popular among developers, but have you ever wonder what exactly this is? Part Pub/Sub messaging system, partly distributed storage, partly event processing engine, the usage of this type of technology brings a whole new perspective on how developers capture, store, and process events.

This talk will explain what distributed streaming platforms are and how it can be a game changer for modern data architectures. It will be discussed the road in IT that led to the need of this type of plataform, the current state of Apache Kafka, as well as scenarios where this technology can be implemented.

Java Modules have been around for a little while now and most of us know deep inside that we need to start doing something with them. Yet most developers are waiting for the momentum to take off. You've probably heard the theory a couple of times already, but putting it into practice sounds like mummy forcing you to eat your veggies. Actually I love my veggies, so I’ll gladly help you out here.


In this session I will demonstrate how you can start using the power of modules in your own application. A Spring Boot application. Because despite the adoption in libraries is not yet great, you can already perfectly make your own application modular.

I will guide you through a step-by-step approach to making your application modular. No magic or module-fu is required to achieve this result. Just everyday technologies we have at our disposal in the Java ecosystem. And as a bonus, it even works with Kotlin....

Last year, two contenders have decided to go on an ambitious mission and compete with Spring Boot as the fastest microservice framework in Java space! Now the battle is on, but Developer productivity is ruled by more than speed alone…

In this session I will discuss Micronaut, Quarkus and compare them with their cousin Spring Boot, who has been travelling the Java space since time immemorial. The question is: who can accelerate and adapt fast enough to outperform the others? Who will fail or prevail? Together, we will find out…

When one’s app is challenged with poor performances, it’s easy to set up a cache in front of one’s SQL database. It doesn’t fix the root cause (e.g. bad schema design, bad SQL query, etc.) but it gets the job done. If the app is the only component that writes to the underlying database, it’s a no-brainer to update the cache accordingly, so the cache is always up-to-date with the data in the database.

Things start to go sour when the app is not the only component writing to the DB. Among other sources of writes, there are batches, other apps (shared databases exist unfortunately), etc. One might think about a couple of ways to keep data in sync i.e. polling the DB every now and then, DB triggers, etc. Unfortunately, they all have issues that make them unreliable and/or fragile.

You might have read about Change-Data-Capture before. It’s been described by Martin Kleppmann as turning the database inside out: it means the DB can send change events (SELECT, DELETE and UPDATE) that one can register to. Just opposite to Event Sourcing that aggregates events to produce state, CDC is about getting events out of states. Once CDC is implemented, one can subscribe to its events and update the cache accordingly. However, CDC is quite in its early stage, and implementations are quite specific.
In this talk, I’ll describe an easy-to-setup architecture that leverages CDC to have an evergreen cache.

Duke needs your help in navigating through the Delta Quadrant. Join us on this incredible journey visiting new features of the JDK 14 system as well as the best features of previous versions of Java. During this journey, you can improve your craft as an engineer. These short challenges will keep your katas going at warp speed! Solve the puzzles to learn the full power of the Java language. Apply the techniques, and develop concise code where bugs will have a hard time hiding. Come to this session to boldly go where no Duke has gone before!

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