vJUG24 - Spring Boot and Kotlin, a match made in Heaven
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A Kotlin online training course is designed to teach individuals and organizations how to use Kotlin, a programming language that is increasingly popular for developing Android applications and other software. The course typically covers topics such as basic syntax, object-oriented programming concepts, and functional programming concepts.
The course is usually delivered through a combination of instructor-led training, hands-on exercises, and online resources. Participants will learn how to use Kotlin to create Android applications, server-side applications, and other types of software. They will also learn how to leverage Kotlin's features such as null safety, extension functions, and coroutines to write more efficient and maintainable code.
The course may also cover advanced topics such as Kotlin's interoperability with Java, testing with Kotlin, and advanced language features such as DSLs and type-safe builders. Upon completion of the course, participants should have a comprehensive understanding of Kotlin and be able to use it effectively to develop high-quality software.
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At a point in the past, it was forecast that Java would die, but the JVM platform would be its legacy. And in fact, for a long time, the JVM has been tremendously successful. Wikipedia itself lists a bunch of languages that run on it, some of them close to Java e.g. Kotlin, some of them very remote e.g. Clojure.
But nowadays, the Cloud is becoming ubiquitous. Containerization is the way to go to alleviate some of the vendor lock-in issues. Kubernetes is a de facto platform. If a container needs to be killed for whatever reason (resource consumption, unhealthy, etc.), a new one needs to replace it as fast as possible. In that context, the JVM seems to be a dead-end: its startup time is huge in comparison to a native process. Likewise, it consumes a lot of memory that just increase the monthly bill.
What does that mean for us developers? Has all the time spent in learning the JVM ecosystem been invested with no hope of return over investment? Shall we need to invest even more time in new languages, frameworks, libraries, etc.? That is one possibility for sure. But we can also leverage our existing knowledge, and embrace the Cloud and containers ways with the help of some tools.
In this talk, I’ll create a simple URL shortener with a “standard” stack: Kotlin, JAX-RS and Hazelcast. Then, with the help of Quarkus and GraalVM, I’ll turn this application into a native executable with all Cloud/Container related work has been moved to the build process.
Devclub.lv - Introduction to stream processing
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top.
The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision-makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing – or data streaming – exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available.
OSCONF Koshi - Zero downtime deployment with Kubernetes, Flyway and Spring Boot
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. You need to think about application backward compatibility, possible rollback, database schema migration, etc. I believe the later is one of the biggest pain point. In this talk, I’ll demo how to update a Spring Boot app deployed on a Kubernetes cluster with a non-trivial database schema migration with the help of Flyway, while keeping the service up during the entire update process.
JOnConf - A CDC use-case: designing an Evergreen Cache
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.
London In-Memory Computing Meetup - A Change-Data-Capture use-case: designing...
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.
In this talk, I will describe an easy-to-setup architecture that leverages CDC to have an evergreen cache.
JUG Tirana - Introduction to data streaming
This talk will be about the reasons behind the new stream processing model, how it compare to the old batch model, what are their pros and cons, and a list of existing technologies implementing stream processing with their most prominent characteristics. It will contain details of one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map, beginning with an overview of all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform,showing a working demo implementation of it.
Java.IL - Your own Kubernetes controller, not only in Go!
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.
vJUG - Introduction to data streaming
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top.
The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing - or data streaming - exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available.
In this talk, I’ll define the context in which the old batch processing model was born, the reasons that are behind the new stream processing one, how they compare, what are their pros and cons, and a list of existing technologies implementing the latter with their most prominent characteristics. I’ll conclude by describing in detail one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map. I’ll go through the all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform, I’ll try to impress attendees with a working demo implementation of it.
London Java Community - An Experiment in Continuous Deployment of JVM applica...
A couple of years ago, continuous integration in the JVM ecosystem meant Jenkins. Since that time, a lot of other tools have been made available. But new tools don’t mean new features, just new ways. Besides that, what about continuous deployment? There’s no tool that allows deploying new versions of a JVM-based application without downtime. The only way to achieve zero downtime is to have multiple nodes deployed on a platform, and let that platform achieve that e.g. Kubernetes.
And yet, achieving true continuous deployment of bytecode on one single JVM instance is possible if one changes one’s way of looking at things. What if the compilation could be seen as changes? What if those changes could be stored in a data store, and a listener on this data store could stream those changes to the running production JVM via the Attach API?
In this talk, we'll demo exactly that using Hazelcast and Hazelcast Jet - but it’s possible to re-use the principles that will be shown using other streaming technologies.
OSCONF - Your own Kubernetes controller: not only in Go
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 favourite language?
In this talk, I will describe what an operator is, how they work, how to design one, and finally demo a Java-based operator that is as good as a Go one.
More from Nicolas Fränkel (20)
Zero-downtime deployment on Kubernetes with Hazelcast
Zero-downtime deployment on Kubernetes with Hazelcast
jLove - A Change-Data-Capture use-case: designing an evergreen cache
jLove - A Change-Data-Capture use-case: designing an evergreen cache
BigData conference - Introduction to stream processing
BigData conference - Introduction to stream processing
ADDO - Your own Kubernetes controller, not only in Go
ADDO - Your own Kubernetes controller, not only in Go
TestCon Europe - Mutation Testing to the Rescue of Your Tests
TestCon Europe - Mutation Testing to the Rescue of Your Tests
OSCONF Jaipur - A Hitchhiker's Tour to Containerizing a Java application
OSCONF Jaipur - A Hitchhiker's Tour to Containerizing a Java application
GeekcampSG 2020 - A Change-Data-Capture use-case: designing an evergreen cache
GeekcampSG 2020 - A Change-Data-Capture use-case: designing an evergreen cache
JavaDay Istanbul - 3 improvements in your microservices architecture
JavaDay Istanbul - 3 improvements in your microservices architecture
OSCONF Koshi - Zero downtime deployment with Kubernetes, Flyway and Spring Boot
OSCONF Koshi - Zero downtime deployment with Kubernetes, Flyway and Spring Boot
JOnConf - A CDC use-case: designing an Evergreen Cache
JOnConf - A CDC use-case: designing an Evergreen Cache
London In-Memory Computing Meetup - A Change-Data-Capture use-case: designing...
London In-Memory Computing Meetup - A Change-Data-Capture use-case: designing...
Java.IL - Your own Kubernetes controller, not only in Go!
Java.IL - Your own Kubernetes controller, not only in Go!
London Java Community - An Experiment in Continuous Deployment of JVM applica...
London Java Community - An Experiment in Continuous Deployment of JVM applica...
OSCONF - Your own Kubernetes controller: not only in Go
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👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
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vJUG24 - Spring Boot and Kotlin, a match made in Heaven
- 1. SPRING BOOT AND KOTLIN, A MATCH MADE IN HEAVEN @NICOLAS_FRANKEL
- 3. WHY SPRING BOOT? @nicolas_frankel #kotlin #springboot 4 Convention over configuration • Kickstart a project in minutes, not days Out-of-the-box features • Actuator
- 6. KOTLIN @nicolas_frankel #kotlin #springboot 7 Open Source Compiles to • JVM bytecode • JavaScript • Native (experimental) A "simpler Scala" @nicolas_frankel #kotlin #springboot
- 7. KOTLIN MAIN FEATURES @nicolas_frankel #kotlin #springboot 8 Functional and object-oriented Statically typed Null safety No checked exceptions Named & optional arguments Lambdas Extension functions more later Java compatibility (And more...) @nicolas_frankel #kotlin #springboot
- 8. KOTLIN MAIN BENEFITS @nicolas_frankel #kotlin #springboot 9 More expressive than Java Improved OO Functional too
- 9. HELLO KOTLIN! package hello // no semicolons // namespace-level functions // types on the right // no special syntax for arrays // optional return type fun main(args: Array<String>) { println("Hello Kotlin!") } @nicolas_frankel #kotlin #springboot 10 @nicolas_frankel #kotlin #springboot
- 10. THE KILLER FEATURE: EXTENSIONS @nicolas_frankel #kotlin #springboot 11 Extension methods Extension properties
- 11. EXTENSION METHOD @nicolas_frankel #kotlin #springboot Add new methods on an existing type Called like a method on the type Translated to static Util method in the bytecode 12
- 12. ENOUGH TALK… @nicolas_frankel #kotlin #springboot 13
- 13. SPRING WEB 5 FUNCTIONAL & KOTLIN @nicolas_frankel #kotlin #springboot 14 https://docs.spring.io/sprin g/docs/current/spring- framework- reference/languages.html#kot lin-bean-definition-dsl
- 14. REACTIVE SPRING @nicolas_frankel #kotlin #springboot 15
- 15. KEY TAKEAWAY @nicolas_frankel #kotlin #springboot 16
- 16. Q&A @nicolas_frankel #kotlin #springboot http://blog.frankel.ch/ @nicolas_frankel @nfrankel (#vjug24) https://git.io/vVSLG 17