Voxxed Days Belgrade - Spring Boot & Kotlin, a match made in Heaven
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Nicolas Frankel presents on the match between Spring Boot and Kotlin. He discusses why Spring Boot is useful for convention over configuration and out-of-the-box features. Kotlin is also discussed as being more expressive than Java with improved object oriented and functional features. Key benefits of Kotlin include being more expressive than Java, improved object oriented programming, and adding functional programming capabilities. Extension methods are highlighted as a killer feature that allows adding new methods on existing types.
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The document outlines an agenda for a Cloud Study Jam workshop to teach participants how to set up development and production environments in Google Cloud Platform (GCP) using Docker and Kubernetes, including hands-on labs over 4 hours to learn GCP essentials and Kubernetes followed by homework to complete additional labs for badges to demonstrate their cloud computing skills. Participants would get free access to GCP and the Qwiklabs training platform to continue learning on their own.
SPS NYC Using the Office Dev PnP to Accelerate Your Productivity
This document summarizes Ryan Schouten's presentation about using the Office 365 Dev PnP tools to accelerate productivity in SharePoint. It introduces the Patterns and Practices (PnP) initiative which provides documentation, guidance, samples and reusable components. It highlights some key PnP tools including PowerShell cmdlets for provisioning and managing SharePoint sites, the remote provisioning engine, the JavaScript core library, and using Azure Functions with SharePoint. Demo links are provided and questions are invited at the end.
An Introduction to CoffeeScript
A CoffeeScript introduction presentation, which deals about the basic and basic concepts of CoffeeScript.
Discovery Phase: Planing Your Web Project
Making a website is more then making pretty picture and some sales jargon. You have to fully understand the project, your audience, current traffic trends and the even more important – the business goals before every writing a line of code.
萬國之津梁
This document summarizes Kenichi Ishigaki's presentations and activities related to Perl conferences from 2016 to 2018. It discusses his plans and experiences attending conferences like YAPC::Okinawa 2018, London Perl Workshop 2017, The Perl Conference 2017, YAPC::Asia 2016, and Perl QA Hackathon 2016. It also mentions talks he enjoyed and discussions he had on topics like PAUSE, JSON, and DBIx::Class at these various events.
Untangling - fall2017 - week 7
This document provides an overview of various topics related to developing a NodeJS application with a database. It discusses mindsets for developers, resources for learning to code like online courses and communities, and technologies involved in web development like front-end versus back-end programming. It also introduces NodeJS, databases like MongoDB and Cloudant, and provides instructions for an example app using Cloudant on Bluemix. Students are assigned homework to deploy this example app and modify it for their own purposes.
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SnowCamp - Adding search to a legacy application
Most applications evolve to a point where they need to provide search capabilities. But updating an application is always a risk. Plus, sometimes, you don’t have access to the source code. The easiest way to access the data is by getting them directly from the database.
The initial load is the easiest step. However, how do you keep the search index in sync with the database? How do you keep the latency between the search store and the source of truth, so your users don’t have to wait for the next run of the batch to access the newest changes?
In this live coding session, we will show you how you can solve this issue by connecting Elasticsearch to the database with a touch of Hazelcast.
Un CV de dévelopeur toujours a jour
On dit que GitHub est le CV d'un développeur. Un rapide coup d'œil à votre historique de commit et les recruteurs savent tout de vous. Cette approche comporte quelques problèmes. La plupart des entreprises ne publient même pas leur code sous une licence Open Source. Si vous travaillez pour l'une d'entre elles, et si vous n'êtes pas un développeur Open Source les soirs et les week-ends, alors vous n'avez aucune chance.
Récemment, GitHub a permis un certain degré de personnalisation de son profil. Ainsi, même si votre historique de commit a plus de blanc que de vert, vous pouvez fournir un bon point d'entrée pour les employeurs potentiels. Mais ça ne vaut que l'effort que vous y mettez et les données perdent leur valeur rapidement. Pourtant, avec un peu de travail et l'aide d'outils d'automatisation (tels que GitHub Actions), vous pouvez présenter un profil toujours à jour.
Zero-downtime deployment on Kubernetes with Hazelcast
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.
jLove - A Change-Data-Capture 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.
BigData conference - Introduction to stream processing
This document discusses stream processing and summarizes a presentation about the topic. It introduces Hazelcast Jet as a stream processing engine and covers open data standards like GTFS. It also describes a demo that uses GTFS data to enrich public transit vehicle position updates in real-time using Hazelcast Jet. The presentation discusses streaming approaches, benefits over batch processing, and provides an overview of stream processing concepts.
ADDO - 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.
TestCon Europe - Mutation Testing to the Rescue of Your Tests
Unit testing ensures your production code is relevant. But what does ensure your testing code is relevant? Come discover mutation testing and make sure your never forget another assert again.
In the realm of testing, the code coverage metrics is the most often talked about. However, it doesn’t mean that the test has been useful or even that an assert has been coded. Mutation testing is a strategy to make sure that the test code is relevant.
In this talk, Nicolas will explain how Code Coverage is computed and what its inherent flaw is. Afterwards, he will describe how Mutation Testing work and how it helps pointing out code that is tested but leave out corner cases. He will also demo PIT, a Java production-grade framework that enables Mutation Testing.
OSCONF Jaipur - A Hitchhiker's Tour to Containerizing a Java application
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.
GeekcampSG 2020 - A Change-Data-Capture use-case: designing an evergreen cache
CDC is a brand new approach that "turns the database inside out": it allows to get events out of the database state. This can be leveraged to get a cache that is never stale.
JavaDay Istanbul - 3 improvements in your microservices architecture
While a microservices architecture is more scalable than a monolith, it has a direct hit on performance.
To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I’ll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it. Hint: it’s much easier than one would expect.
OSCONF Hyderabad - Shorten all URLs!
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
This document discusses using change data capture (CDC) and Hazelcast Jet to build an evergreen cache that remains in sync with a database. It covers alternatives to cache invalidation like polling and triggers, introduces CDC and the Debezium implementation, and proposes a Jet job that watches database change events, analyzes them, and updates the cache accordingly to solve the cache freshness problem.
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
The document discusses stream processing and provides an overview of Hazelcast Jet. It begins with explaining why streaming is useful and describes different streaming approaches like event-driven programming. It then provides details on Hazelcast Jet, including its concepts of pipelines and jobs. The document also discusses open data standards like GTFS and demonstrates a sample streaming pipeline that enriches public transportation data from open APIs.
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
This document discusses creating Kubernetes operators and controllers using different programming languages besides Go. It suggests that a Java-based controller is possible using the GraalVM, which allows creating native executables from Java bytecode. Key points covered include what controllers and operators are, that no specific technology stack is required, and that the JVM could be a good option for controller development with GraalVM's support for polyglot programming and creating native applications.
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Zero-downtime deployment on Kubernetes with Hazelcast
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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
OSCONF - Your own Kubernetes controller: not only in Go
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Voxxed Days Belgrade - Spring Boot & Kotlin, a match made in Heaven
- 1. SPRING BOOT AND KOTLIN, A MATCH MADE IN HEAVEN @NICOLAS_FRANKEL
- 2. ME, MYSELF AND I @nicolas_frankel #kotlin #springboot By day • Consultant By night • Developer • Blogger • Book author • Teacher/trainer 2
- 3. HYBRIS, AN SAP COMPANY @nicolas_frankel #kotlin #springboot 3
- 4. HCP STARTUP CHALLENGE @nicolas_frankel #kotlin #springboot 4 Preparation Workshop • October 4th, 10:00 AM • StartIt Centar, Savska 5, 11000 Beograd, Serbia http://www.hcpstartupchallenge.com/
- 7. WHY SPRING BOOT? @nicolas_frankel #kotlin #springboot Convention over configuration • Kickstart a project in minutes, not days Out-of-the-box features • Actuator 7
- 9. KOTLIN @nicolas_frankel #kotlin #springboot Open Source Compiles to • JVM bytecode • JavaScript (experimental) A "simpler Scala" @nicolas_frankel #kotlin #springboot 9
- 10. KOTLIN MAIN FEATURES @nicolas_frankel #kotlin #springboot Functional and object-oriented Statically typed Null safety No checked exceptions Named & optional arguments Lambdas Extension functions Java compatibility (And more...) @nicolas_frankel #kotlin #springboot 1
- 11. KOTLIN MAIN BENEFITS @nicolas_frankel #kotlin #springboot More expressive than Java Improved OO Functional too 1
- 12. 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@nicolas_frankel #kotlin #springboot 1
- 13. THE KILLER FEATURE: EXTENSIONS @nicolas_frankel #kotlin #springboot Extension methods Extension properties 1
- 14. 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 1
- 15. ENOUGH TALK… @nicolas_frankel #kotlin #springboot 1
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