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  1. Stackups
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  5. Argo vs Testcontainers

Argo vs Testcontainers

OverviewComparisonAlternatives

Overview

Testcontainers
Testcontainers
Stacks139
Followers59
Votes0
GitHub Stars8.5K
Forks1.8K
Argo
Argo
Stacks761
Followers470
Votes6

Argo vs Testcontainers: What are the differences?

  1. Key difference 1: Purpose and focus: Argo is a Kubernetes-native workflow engine that aims to simplify the creation and management of complex workflows in a cloud-native environment. It provides a declarative way to define and run tasks, using YAML files to describe the sequence of steps. On the other hand, Testcontainers is a Java library that helps with integration testing by providing lightweight, disposable containers for application dependencies. It focuses on providing a convenient way to spin up containers for databases, web servers, and other dependencies needed during testing.

  2. Key difference 2: Scope of testing: Argo is primarily used for testing and deploying workflows in a Kubernetes cluster. It is designed to handle large-scale, long-running workflows that involve multiple steps and dependencies. On the other hand, Testcontainers is specifically tailored for integration testing of applications. It focuses on providing isolated environments for application dependencies, allowing developers to test the integration between their application and these dependencies.

  3. Key difference 3: Language support: Argo workflows are defined using YAML files, which makes it language-agnostic. It can be used with any programming language that can interact with Kubernetes APIs. Testcontainers, on the other hand, is a Java library and is primarily used with Java-based applications. It leverages the power of Docker containers, which are widely supported across different programming languages.

  4. Key difference 4: Automation and scalability: Argo provides a robust automation framework for managing workflows. It has features like the ability to automatically retry failed steps, parallel execution of independent steps, and fine-grained control over resource allocation. Testcontainers, on the other hand, focuses on providing lightweight containers for testing application dependencies. While it can be used in an automated testing environment, its main focus is on simplifying integration testing at the developer level.

  5. Key difference 5: Community and ecosystem: Argo has a large and active community of contributors and users. It is supported by the Cloud Native Computing Foundation (CNCF) and has a growing ecosystem of plugins and integrations with other tools in the Kubernetes ecosystem. Testcontainers, being a Java library, also has a strong community support and a wide range of integrations with Java testing frameworks like JUnit and TestNG.

  6. Key difference 6: Deployment and infrastructure requirements: Argo is typically deployed in a Kubernetes cluster and requires a certain level of infrastructure to run effectively. It leverages the capabilities of Kubernetes to schedule and orchestrate workflows. On the other hand, Testcontainers can be used on any system that supports Docker containers, providing more flexibility in terms of deployment options.

In summary, Argo and Testcontainers differ in their purpose and focus, with Argo being a Kubernetes-native workflow engine and Testcontainers focusing on providing lightweight containers for integration testing. Argo is designed for managing complex workflows at scale, using YAML files for defining workflows, while Testcontainers simplifies the testing of application dependencies by providing disposable containers. Argo has a broader language support, a robust automation framework, a larger community, and requires a Kubernetes cluster for deployment, whereas Testcontainers is primarily used with Java applications, focuses on integration testing, and can be used on any system that supports Docker containers.

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Detailed Comparison

Testcontainers
Testcontainers
Argo
Argo

It is a Java library that supports JUnit tests, providing lightweight, throwaway instances of common databases, Selenium web browsers, or anything else that can run in a Docker container.

Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition).

Data access layer integration tests; Application integration tests; UI/Acceptance tests
DAG or Steps based declaration of workflows;Artifact support (S3, Artifactory, HTTP, Git, raw);Step level input & outputs (artifacts/parameters);Loops;Parameterization;Conditionals;Timeouts (step & workflow level);Retry (step & workflow level);Resubmit (memoized);Suspend & Resume;Cancellation;K8s resource orchestration;Exit Hooks (notifications, cleanup);Garbage collection of completed workflow;Scheduling (affinity/tolerations/node selectors);Volumes (ephemeral/existing);Parallelism limits;Daemoned steps;DinD (docker-in-docker);Script steps
Statistics
GitHub Stars
8.5K
GitHub Stars
-
GitHub Forks
1.8K
GitHub Forks
-
Stacks
139
Stacks
761
Followers
59
Followers
470
Votes
0
Votes
6
Pros & Cons
No community feedback yet
Pros
  • 3
    Open Source
  • 2
    Autosinchronize the changes to deploy
  • 1
    Online service, no need to install anything
Integrations
Oracle
Oracle
Docker
Docker
PostgreSQL
PostgreSQL
MySQL
MySQL
Spock Framework
Spock Framework
JUnit
JUnit
Kubernetes
Kubernetes
Docker
Docker

What are some alternatives to Testcontainers, Argo?

Kubernetes

Kubernetes

Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.

Rancher

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

Docker Compose

Docker Compose

With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.

Docker Swarm

Docker Swarm

Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself.

Tutum

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

Codefresh

Codefresh

Automate and parallelize testing. Codefresh allows teams to spin up on-demand compositions to run unit and integration tests as part of the continuous integration process. Jenkins integration allows more complex pipelines.

CAST.AI

CAST.AI

It is an AI-driven cloud optimization platform for Kubernetes. Instantly cut your cloud bill, prevent downtime, and 10X the power of DevOps.

k3s

k3s

Certified Kubernetes distribution designed for production workloads in unattended, resource-constrained, remote locations or inside IoT appliances. Supports something as small as a Raspberry Pi or as large as an AWS a1.4xlarge 32GiB server.

Flocker

Flocker

Flocker is a data volume manager and multi-host Docker cluster management tool. With it you can control your data using the same tools you use for your stateless applications. This means that you can run your databases, queues and key-value stores in Docker and move them around as easily as the rest of your app.

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