Speaking

In Kubernetes, operators allow the API to be extended to your heart content. If one task requires too much YAML, it’s easy to create an operator to take care of the repetitive cruft, and only require a minimum amount of YAML. On the other hand, since its beginnings, the Go language has been advertised as closer to the hardware, and is now ubiquitous in low-level programming. Kubernetes has been rewritten from Java to Go, and its whole ecosystem revolves around Go. For that reason, It’s only natural that Kubernetes provides a Go-based framework to create your own operator. While it makes sense, it requires organizations willing to go down this road to have Go developers, and/or train their teams in Go. While perfectly acceptable, this is not the only option. In fact, since Kubernetes is based on REST, why settle for Go and not use your own favorite language? In this talk, I’ll describe what is an operator, how they work, how to design one, and finally demo a Java-based operator that is as good as a Go one.

As 'the Cloud' becomes more and more widespread, now is a good time to assess how you can containerize your Java application. I assume you’re able to write a a Dockerfile around the generated JAR. However, each time the application’s code will change, the whole image will need to be rebuilt. If you’re deploying to a local Kubernetes cluster environment, this increases that much the length of the feedback loop. In this demo-based talk, I’ll present different ways to get your Java app in a container: Dockerfile, Jib, and Cloud Native Buildpacks. We will also have a look at what kind of Docker image they generate, how they layer the images, whether those images are compatible with skaffold, etc.

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.

From Wikipedia, Reactive Programming is 'a declarative programming paradigm concerned with data streams and the propagation of change.' The programming model is complex to master. It requires a lot of experience to feel comfortable with it. However, there’s no denying that it fits the cloud ecosystem perfectly. Since on-premises infrastructure is oversized, running a program that executes a couple of additional CPU cycles won’t change anything. On the other hand, you will pay for them if you host the same program on third-party infrastructure. Depending on the number of those cycles, and the number of nodes the program runs on, it can make a huge difference in your monthly bill. In this talk, I’ll demo how you can migrate a traditional sample Spring Boot application written in Kotlin to coroutines one step at a time via a hands-on demo.

When your application starts slowing down, the reason is probably a bottleneck somewhere in the execution chain. Sometimes, this bottleneck is due to a bug. Sometimes, somebody didn’t set up the optimal configuration. And sometimes, the process of fetching the data is the bottleneck. One option would be to change your whole architecture. Before moving to such a drastic, and probably expensive measure, one can consider a trade-off: instead of getting remote data every time, you can store the data locally after the first read. This is the trade-off that caching offers: stale data vs. speed. Deciding to use caching is just the first step in a long journey. The next step is to think about how your application and the cache will interact. This talk focuses on your options regarding those interactions.

In the industry, it's always an interesting experience to notice the drop in productivity of developers working on the same application across years. Depending on which stage a feature is implemented, it can take double (or triple?) the time. As software rots, there comes a time when it's less expensive to rewrite the application from scratch. Organizations don't want to face this issue: maintenance of existing software is a hot topic among developers of all tech stacks. In this talk, I'll walk through some maintenance-related issues that I've faced in two decades working in different technical roles across various organizations and a couple of industries. I'll tell tales of sweat, tears, and blood. Don't expect a happy end!

The Command-Query Responsability Principle has been defined a while ago. While its benefits are clear, most teams shy away from it because implementing CQRS is a journey in itself, especially in brownfields projects. In this talk, we will define what CQRS is (and what it isn't) and what benefits it can bring to an organization. Then, we will have a look at the different options available to implement it and their respective pros and cons. After the talk, you'll be able to understand whether CQRS is a good fit for a project and which implementation fits your context the best.

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.

Kubernetes allows a lot. After discovering its features, it’s easy to think it can magically transform your application deployment process into a painless no-event. For Hello World applications, that is the case. Unfortunately, not many of us do deploy such applications day-to-day because we need to handle state. Though it would be much easier to have stateless apps, and despite our best efforts in this direction, state is found in (at least) two places: sessions and databases. You need to think keeping the state while stopping and starting application nodes. In this talk, I’ll demo how to update a Spring Boot app deployed on a Kubernetes cluster with a non-trivial database schema change with the help of Hazelcast, while keeping the service up during the entire update process.

Builds require a few properties, chief among them reproducibility. I would consider speed to be low on the order of priorities. However, it’s also one of the most limiting factors to your release cycle: if your build takes T, you cannot release faster than each T. Hence, you’ll probably want to speed up your builds after you’ve reached a certain maturity level to enable more frequent releases. I want to detail some techniques you can leverage to make your Maven builds faster.

Python is ubiquitous in scripts, and it has definite benefits. However, Kotlin developers don't need to migrate to another stack to create scripts. In this talk, I'll show you how I use Kotlin to have an always up-to-date GitHub profile.

Vous avez une source de données représentant la source de vérité. Vous avez besoin d'utiliser ces données d'une autre façon afin de couvrir d'autres cas d'utilisation (cache, indexation, recherche, business intelligence...). Pour cela, pas de magie, il vous faudra dupliquer la donnée, souvent dans un autre format, afin de pouvoir répondre à votre besoin. Mais comment conserver la données toujours synchronisée avec la source de vérité ? Le tout, sans même pouvoir toucher l'application métier. Dans cette session 100% live coding, nous vous donnerons une recette simple pour surmonter ces difficultés en utilisant un pattern de "Change Data Capture".

with David Pilato

Kubernetes allows a lot. After discovering its features, it’s easy to think it can magically transform your application deployment process into a painless no-event. For Hello World applications, that is the case. Unfortunately, not many of us do deploy such applications day-to-day because we need to handle state. Though it would be much easier to have stateless apps, and despite our best efforts in this direction, state is found in (at least) two places: sessions and databases. You need to think keeping the state while stopping and starting application nodes. In this talk, I’ll demo how to update a Spring Boot app deployed on a Kubernetes cluster with a non-trivial database schema change with the help of Hazelcast, while keeping the service up during the entire update process.