Alternatives to ContainerShip logo

Alternatives to ContainerShip

Kubernetes, Codefresh, Red Hat OpenShift, Rancher, and Amazon EC2 Container Service are the most popular alternatives and competitors to ContainerShip.
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What is ContainerShip and what are its top alternatives?

ContainerShip is an open source hosting platform that makes it simple to build, scale and manage containerized web infrastructure. It supports applications with persistent data needs, has built in disaster recovery, and lets you move between clouds with a click.
ContainerShip is a tool in the Containers as a Service category of a tech stack.
ContainerShip is an open source tool with 240 GitHub stars and 32 GitHub forks. Here鈥檚 a link to ContainerShip's open source repository on GitHub

Top Alternatives to ContainerShip

  • 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. ...

  • 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. ...

  • Red Hat OpenShift

    Red Hat OpenShift

    OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications. ...

  • 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. ...

  • Amazon EC2 Container Service

    Amazon EC2 Container Service

    Amazon EC2 Container Service lets you launch and stop container-enabled applications with simple API calls, allows you to query the state of your cluster from a centralized service, and gives you access to many familiar Amazon EC2 features like security groups, EBS volumes and IAM roles. ...

  • Google Kubernetes Engine

    Google Kubernetes Engine

    Container Engine takes care of provisioning and maintaining the underlying virtual machine cluster, scaling your application, and operational logistics like logging, monitoring, and health management. ...

  • Amazon EKS

    Amazon EKS

    Amazon Elastic Container Service for Kubernetes (Amazon EKS) is a managed service that makes it easy for you to run Kubernetes on AWS without needing to install and operate your own Kubernetes clusters. ...

  • AWS Fargate

    AWS Fargate

    AWS Fargate is a technology for Amazon ECS and EKS* that allows you to run containers without having to manage servers or clusters. With AWS Fargate, you no longer have to provision, configure, and scale clusters of virtual machines to run containers. ...

ContainerShip alternatives & related posts

related Kubernetes posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 35 upvotes 路 3.2M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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Yshay Yaacobi

Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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related Red Hat OpenShift posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 35 upvotes 路 3.2M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

See more
Michael Ionita

We use Kubernetes because we decided to migrate to a hosted cluster (not AWS) and still be able to scale our clusters up and down depending on load. By wrapping it with OpenShift we are now able to easily adapt to demand but also able to separate concerns into separate Pods depending on use-cases we have.

See more
Amazon EC2 Container Service logo

Amazon EC2 Container Service

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Container management service that supports Docker containers
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related Amazon EC2 Container Service posts

Cyril Duchon-Doris

We build a Slack app using the Bolt framework from slack https://api.slack.com/tools/bolt, a Node.js express app. It allows us to easily implement some administration features so we can easily communicate with our backend services, and we don't have to develop any frontend app since Slack block kit will do this for us. It can act as a Chatbot or handle message actions and custom slack flows for our employees.

This app is deployed as a microservice on Amazon EC2 Container Service with AWS Fargate. It uses very little memory (and money) and can communicate easily with our backend services. Slack is connected to this app through a ALB ( AWS Elastic Load Balancing (ELB) )

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We started using Amazon EC2 Container Service 3 years ago because it was the easiest containers orchestration tool to start with. At the time it was missing a lot of features compared to other tools, but it was still the fastest way to deploy a container on AWS. As with any AWS product, over time they caught up and improved it significantly. Today it probably one of the best tools in its category. It might not have all the feature Kubernetes has, but it also has less complexity. And it definitely has all the features a small company/team needs.

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Google Kubernetes Engine logo

Google Kubernetes Engine

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Deploy, manage, and scale containerized applications on Kubernetes, powered by Google Cloud
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related Google Kubernetes Engine posts

Omar Mehilba
Co-Founder and COO at Magalix | 18 upvotes 路 236.1K views

We are hardcore Kubernetes users and contributors. We loved the automation it provides. However, as our team grew and added more clusters and microservices, capacity and resources management becomes a massive pain to us. We started suffering from a lot of outages and unexpected behavior as we promote our code from dev to production environments. Luckily we were working on our AI-powered tools to understand different dependencies, predict usage, and calculate the right resources and configurations that should be applied to our infrastructure and microservices. We dogfooded our agent (http://github.com/magalixcorp/magalix-agent) and were able to stabilize as the #autopilot continuously recovered any miscalculations we made or because of unexpected changes in workloads. We are open sourcing our agent in a few days. Check it out and let us know what you think! We run workloads on Microsoft Azure Google Kubernetes Engine and Amazon EC2 and we're all about Go and Python!

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Kir Shatrov
Engineering Lead at Shopify | 14 upvotes 路 482.9K views

At Shopify, over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, Memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.

As we grew into hundreds of shards and pods, it became clear that we needed a solution to orchestrate those deployments. Today, we use Docker, Kubernetes, and Google Kubernetes Engine to make it easy to bootstrap resources for new Shopify Pods.

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Amazon EKS logo

Amazon EKS

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Highly available and scalable Kubernetes service
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PROS OF AMAZON EKS
    No pros available
    CONS OF AMAZON EKS
      No cons available

      related Amazon EKS posts

      Sebastian G臋bski

      Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.

      See more

      We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

      We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

      We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

      You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

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      AWS Fargate logo

      AWS Fargate

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      Run Containers Without Managing Infrastructure
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      PROS OF AWS FARGATE
        No pros available
        CONS OF AWS FARGATE
          No cons available

          related AWS Fargate posts

          Cyril Duchon-Doris

          We build a Slack app using the Bolt framework from slack https://api.slack.com/tools/bolt, a Node.js express app. It allows us to easily implement some administration features so we can easily communicate with our backend services, and we don't have to develop any frontend app since Slack block kit will do this for us. It can act as a Chatbot or handle message actions and custom slack flows for our employees.

          This app is deployed as a microservice on Amazon EC2 Container Service with AWS Fargate. It uses very little memory (and money) and can communicate easily with our backend services. Slack is connected to this app through a ALB ( AWS Elastic Load Balancing (ELB) )

          See more