Health checks are a fundamental part of our APIs. I guess they fall in that category of "non-functional-but-heavily-required" things. More or less like a good part of the infrastructure code.
They don’t add business value per se but have an enormous impact for those in IT, like DDD and design patterns. You can normally see them in conjunction with container orchestration or monitoring tools to ensure that the system is alive and kicking.
Docker is powerful and simple to use. Docker allows developers to create portable, self-contained images for the software they create. These images can be reliably and repeatably deployed. You can get a lot of value out of Docker very easily, but to get the most out of Docker there are some concepts that are important to understand. How you build your Docker image has a measurable impact when you are doing continuous integration and continuous delivery. In this article, I will focus on how to take a more efficient approach to building Docker images for Spring Boot applications when doing iterative development and deployment. The standard approach has some drawbacks so here we look at what they are and how to do it better.
There are four key Docker concepts at play: images, layers, the Dockerfile and the Docker cache. Simply put, the Dockerfile describes how to build the Docker image. An image consists of a number of layers. The Dockerfile starts with a base image and adds on additional layers. A new layer is generated when new content is added to the image. Each layer that is built is cached so it can be re-used on subsequent builds. When a Docker build runs, it will re-use any existing layers that it can from the cache. This reduces the overall time and space needed for each build. Anything that has changed, or has not been built before, will be built as needed.
I am very excited to share my experiences building Continuous Integration/Continuous Delivery (CI/CD) into Spring-Boot-based Java applications. First, let’s establish everything we will learn in this tutorial:
Step 1) Create a Spring Boot Java App using Spring Initializr
You may also enjoy: Integrating Docker Solutions Into Your CI/CD Pipeline
Nancy is now wrapped up as a Docker image for execution in a pipeline or via an alias in a terminal.
Nancy is a tool to check for vulnerabilities in your Golang dependencies, powered by Sonatype OSS Index. docker-nancy wraps the nancy executable in a Docker image.
The GraalVM compiler is a replacement to HotSpot’s server-side JIT compiler, widely known as the C2 compiler. It is written in Java with the goal of better performance compared to the C2 compiler. New changes, starting with Java 9, mean that we can now plug in our own hand-written C2 compiler into the JVM thanks to JVMCI. The researchers and engineers at Oracle Labs have created a variant of JDK8 with JVMCI enabled, which can be used to build the GraalVM compiler. The GraalVM compiler is open source and is available on GitHub (along with the HotSpot JVMCI sources needed to build the GraalVM compiler). This gives us the ability to fork/clone it and build our own version of the GraalVM compiler.
In this post, we are going to build the GraalVM compiler with JDK8 on CircleCI. The resulting artifacts are going to be:
This is part 4 in a series on monitoring Kubernetes and Docker. Catch up on parts 1, 2, and 3.
Photo by Nareeta Martin on Unsplash
If you’re one of the more than ten million developers who downloaded the official Docker images for OpenJDK 8 and 11 from mid April until about a month ago, you’ll really want to make sure you get those updated stat.
Due to innocent enough confusion over tagging protocols, Debian volunteers populated their repos with unreleased versions of the JDKs some weeks before the final versions were ready to go, but failed to clearly label them as such.
Welcome to our latest episode of Tom’s Tech Notes! In this episode, we’ll hear advice from a host of industry experts about how to secure your containers. Learn what they have to say about security, ease of use, ecosystem maturity, and the talent gap.
As a primer and reminder from our intial post, these podcasts are compiled from conversations our analyst Tom Smith has had with industry experts from around the world as part of his work on our research guides.
See the basics of how to automate database builds into a Linux SQL Server container running on Windows and then back up the containerized database and restore it into dedicated containerized development copies for each developer and tester.
An obvious use for Docker images of SQL Server is to run up a working database from a backup quickly, maybe to test it or possibly to mask the data. We’ll start by doing that in this article. We’ll then use SQL Change Automation (SCA) to synchronize an empty copy of a development database in a Docker container with the latest build in source control and fill it with data ready for testing. Finally, we’ll do a backup of the containerized database so we can restore it into each developer’s local container. These techniques, combined with ‘glue scripts,’ can be used for supporting continuous delivery of databases.
From the introduction of Docker to LXC to Kubernetes, the world of containers has been constantly evolving — and it is not going to slow down any time soon. As the adoption of containers and their counterparts increases drastically, engineers and enterprises have had to increasingly put work into ensuring not only container security but also general functionality and maintenance. Download this guide to learn major orchestration trends, Kubernetes security, and more!
Source de l’article sur DZONE
