serverless-java-container VS PostgreSQL

The key dependency to make it work and translate between Spring Boot 3 (web annotation) model and AWS Lambda is the dependency to the artifact aws-serverless-java-container-springboot3 defined in the pom.xml. It's based on Serverless Java Container which natively supports API Gateway's proxy integration models for requests and responses, and we can create and inject custom models for methods that use custom mappings.

The AWS Serverless Java Container makes it easier to run Java applications written with frameworks such as Spring, Spring Boot 2 and 3, or JAX-RS/Jersey in Lambda. We have already seen that Micronaut framework also uses AWS Serverless Java Container.

Serverless has grown to mean many things and can even mean something different depending on whose cloud you are paying for. But for our team and for the purpose of this article, serverless is a way of building and running applications without having to manage the underlying infrastructure on AWS.

Read the Quick Start here: https://github.com/awslabs/aws-serverless-java-container/wiki/Quick-start---Spring-Boot3 Thanks to Mark Sailes for all of his help with this tuto

Having knowledge about microservices architectures, serverless, and related concepts is highly beneficial. This includes understanding chaos testing techniques with AWS Fault Injection Simulator.

I've heard that server providers like Amazon Web Services offer dynamic scaling serverless architecture https://aws.amazon.com/serverless/

AWS Docs - Official documentation for all things AWS serverless

Although not what I would consider 'core' cloud services, the orchestration and communication services are key to event driven development and robust application design. If you are structuring your application to take advantage of event flow, these are tools you are going to want to be familiar with, and will be instrumental in your success by saving you time, money, complexity, and management overhead. This about wraps up what I want to cover with serverless cloud services, though there is so much out there to explore. Tune in tomorrow as we start to put all of this together with best practices.*

If you're new to the application development world, API stands for Application Programming Interface, an interface that offers a 'service' of sorts for applications to consume. Google Maps, for instance, has a public API that you or I could use if we're building an application to locate all of the restaurants in our town. They surface this, and many, many other data points via documented endpoints, and we can use things like API keys to access this data. If you're a developer, chances are you will build an API at some point in your career, if not several, and you will definitely work with them. As a key building block in application development, AWS provides a few API solutions.

As an AWS Hero and a Principal Software engineer at a large Fortune 100 enterprise, I have been focused solely on serverless technologies and enablement over the last 3 years. I have spoken quite extensively about our serverless journey, what serverless means in a large enterprise, and how to be successful in a corporate setting. Everything I have learned has been through experience, on-the-job. The serverless definition I resonate with most states that serverless is event-driven, your resources scale up and down without you needing to manage them, and follows an "only pay for what you use" model. Let's break this down a bit further:

Every time I buy a new computer, the first thing I install is the servers for MySql and Postgres, the two most common databases. This way, I can start the databases with a simple command like this:

Data Handling: Utilizes Windmill for data pipelines, with a primary database powered by PostgreSQL. Auxiliary data storage is handled by MongoDB, with Redis for caching to optimize performance

PostgreSQL

The LMS Moodle Operator serves as a meta-operator, orchestrating the deployment and management of Moodle instances in Kubernetes. It handles the entire stack required to run Moodle, including components like Postgres, Keydb, NFS-Ganesha, and Moodle itself. Each of these components has its own Kubernetes Operator, ensuring seamless integration and management.

Another key feature of txtai is being able to quickly move from prototyping to production. This article will demonstrate how txtai can integrate with Postgres, a powerful, production-ready and open source object-relational database system. After txtai persists content to Postgres, we'll show it can be directly queried with SQL from any Postgres client

SQL (Structured Query Language) databases are relational databases. They organize data into tables with rows and columns, and they use SQL for querying and managing data. Examples include MySQL, PostgreSQL, and SQLite.

Node.js, MySQL and PostgreSQL servers installed on your machine

Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).