Alternatives to Mesosphere logo

Alternatives to Mesosphere

Kubernetes, Rancher, Red Hat OpenShift, CoreOS, and Apache Mesos are the most popular alternatives and competitors to Mesosphere.
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What is Mesosphere and what are its top alternatives?

Mesosphere offers a layer of software that organizes your machines, VMs, and cloud instances and lets applications draw from a single pool of intelligently- and dynamically-allocated resources, increasing efficiency and reducing operational complexity.
Mesosphere is a tool in the Cluster Management category of a tech stack.

Mesosphere alternatives & related posts

related Kubernetes posts

Yshay Yaacobi
Yshay Yaacobi
Software Engineer · | 30 upvotes · 661.6K views
atSolutoSoluto
Docker Swarm
Docker Swarm
.NET
.NET
F#
F#
C#
C#
JavaScript
JavaScript
TypeScript
TypeScript
Go
Go
Visual Studio Code
Visual Studio Code
Kubernetes
Kubernetes

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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Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 24 upvotes · 1.2M views
atUber TechnologiesUber Technologies
Jaeger
Jaeger
Python
Python
Java
Java
Node.js
Node.js
Go
Go
C++
C++
Kubernetes
Kubernetes
JavaScript
JavaScript
Red Hat OpenShift
Red Hat OpenShift
C#
C#
Apache Spark
Apache Spark

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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related Red Hat OpenShift posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 24 upvotes · 1.2M views
atUber TechnologiesUber Technologies
Jaeger
Jaeger
Python
Python
Java
Java
Node.js
Node.js
Go
Go
C++
C++
Kubernetes
Kubernetes
JavaScript
JavaScript
Red Hat OpenShift
Red Hat OpenShift
C#
C#
Apache Spark
Apache Spark

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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Michael Ionita
Michael Ionita
CTO at Walls.io GmbH · | 6 upvotes · 85.5K views
atWalls.ioWalls.io
Kubernetes
Kubernetes
Red Hat OpenShift
Red Hat OpenShift

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.

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CoreOS logo

CoreOS

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Linux for Massive Server Deployments
CoreOS logo
CoreOS
VS
Mesosphere logo
Mesosphere
Apache Mesos logo

Apache Mesos

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Develop and run resource-efficient distributed systems
Apache Mesos logo
Apache Mesos
VS
Mesosphere logo
Mesosphere

related Apache Mesos posts

StackShare Editors
StackShare Editors
| 1 upvotes · 119.3K views
atUber TechnologiesUber Technologies
Apache Mesos
Apache Mesos
Docker
Docker
Apache Aurora
Apache Aurora

Docker containers on Mesos run their microservices with consistent configurations at scale, along with Aurora for long-running services and cron jobs.

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Cloud Foundry logo

Cloud Foundry

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Deploy and scale applications in seconds on your choice of private or public cloud
Cloud Foundry logo
Cloud Foundry
VS
Mesosphere logo
Mesosphere
Nomad logo

Nomad

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A cluster manager and scheduler
Nomad logo
Nomad
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Mesosphere logo
Mesosphere

related Nomad posts

Robert Zuber
Robert Zuber
CTO at CircleCI · | 6 upvotes · 64.9K views
atCircleCICircleCI
Docker
Docker
Kubernetes
Kubernetes
Nomad
Nomad
Helm
Helm

Our backend consists of two major pools of machines. One pool hosts the systems that run our site, manage jobs, and send notifications. These services are deployed within Docker containers orchestrated in Kubernetes. Due to Kubernetes’ ecosystem and toolchain, it was an obvious choice for our fairly statically-defined processes: the rate of change of job types or how many we may need in our internal stack is relatively low.

The other pool of machines is for running our users’ jobs. Because we cannot dynamically predict demand, what types of jobs our users need to have run, nor the resources required for each of those jobs, we found that Nomad excelled over Kubernetes in this area.

We’re also using Helm to make it easier to deploy new services into Kubernetes. We create a chart (i.e. package) for each service. This lets us easily roll back new software and gives us an audit trail of what was installed or upgraded.

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DC/OS logo

DC/OS

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The Datacenter Operating System. The easiest way to run microservices, big data, and containers in production.
DC/OS logo
DC/OS
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Mesosphere logo
Mesosphere