Articles tagged with "cicd"

When setting up a Jenkins build server on a physical machine, right-sizing can become a challenging task. Long idle times followed by high, irregular loads make it hard to predict the necessary hardware requirements. One solution to this problem is the deployment of a containerized Controller/Agent-based Jenkins setup and to offload workloads to dedicated, transient agents. This is the first post of a three-post series. In this series, I would like to show you how you can leverage AWS Fargate and Terraform to deploy a serverless as well as fault-tolerant, highly available, and scalable Jenkins Controller/Agent deployment pipeline.

When setting up a Jenkins build server on a physical machine, right-sizing can become a challenging task. Long idle times followed by high, irregular loads make it hard to predict the necessary hardware requirements. One solution to this problem is the deployment of a containerized Controller/Agent-based Jenkins setup and to offload workloads to dedicated, transient agents. This is the second post of a three-post series. In this series, I would like to show you how you can leverage AWS Fargate and Terraform to deploy a serverless as well as fault-tolerant, highly available, and scalable Jenkins Controller/Agent deployment pipeline.

When setting up a Jenkins build server on a physical machine, right-sizing can become a challenging task. Long idle times followed by high, irregular loads make it hard to predict the necessary hardware requirements. One solution to this problem is the deployment of a containerized Controller/Agent-based Jenkins setup and to offload workloads to dedicated, transient agents. This is the third post of a three-post series. In this series, I would like to show you how you can leverage AWS Fargate and Terraform to deploy a serverless as well as fault-tolerant, highly available, and scalable Jenkins Controller/Agent deployment pipeline.

Introduction Recently I worked on a project with the Serverless Framework which I really like. I used it do define a serverless application for a customer that runs their workloads mostly on AWS but uses Azure DevOps to build and deploy resources. This came about because they were already building lots of .Net applications and are therefore used to VSTS, the former name of Azure DevOps. After recovering from the initial shock of having to use an Azure tool to deploy resources to AWS I gave it a chance (well, I didn’t really have a choice in the matter).

Vergleich Infrastructure as Code (IaC) Frameworks - tRick Alle Posts Abstraktion und Lines of Code Geschwindigkeit Diversity (polyglott), Tooling, Fazit Benchmark Ausführungsgeschindigkeit Ausführungsgeschwindigkeit Direkt aus dem tRick Repository wird mehrfach (n=10) der Zyklus Build -> Check -> Deploy -> Remove ausgeführt. Damit sollen Cache Effekte statistisch gemittelt werden. Dazu nehme ich das Tool hyperfine zur Hilfe. Es führt Kommandos automatisch mehrfach aus und mittelt die Ergebnisse. Meine Annahme ist es, dass Terraform vorne liegt, da das Programm selber statisch kompiliert in go geschrieben ist. Außerdem geht die Ausführung direkt auf die API.

Vergleich Infrastructure as Code (IaC) Frameworks - tRick Teil 3 Alle Posts Abstraktion und Lines of Code Geschwindigkeit Diversity (polyglott), Tooling, Fazit

Managing multiple environments in Terraform Introduction I recently started learning Terraform. For those who haven’t encountered it: Terraform is in essence a framework to describe Infrastructure as code by Hashicorp. When I began doing that, I was struggling with the staging-concept of Terraform. I did my research and came upon numerous 1 articles and blogs that described ways to manage (multiple) environments or stages in Terraform2. Since I wasn’t really happy with the other solutions and there didn’t seem to be a canonical way to handle multiple environments, I decided to try and figure out my own solution.

Vergleich Infrastructure as Code (IaC) Frameworks - tRick Ein Toolvergleich für Infrastructure as Code. Um effektiv AWS oder generell Cloud Ressourcen zu erzeugen, verwendet man zur Erhöhung des Automatisierungsgrades “Infrastracture as Code”, d.h. die Server, Datenbanken usw. werden in einer Sprache kodiert. Dieser Vorgang wird sinvollerweise über ein Framework, welches Tools dafür zur Verfügung stellt unterstützt. Aber für welches Tool/Framework entscheidet man sich? Hier werden wir mit Dimensionen für den tRick Benchmark Entscheidungshilfen geben.