D2iQ (formerly Mesosphere) still supports the Mesosphere solution, which is designed for operations at a very large scale. It's powered by DC/OS, a production-proven cloud native platform that runs containers and data services on the same infrastructure.
D2iQ rebranded to reflect their change and broadening of focus towards Kubernetes but other services such as Cassandra, Kafka, and Spark. D2iQ also now offers IT professional services in tandem with its products.
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HashiCorp Nomad
Score 8.0 out of 10
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Nomad, from HashiCorp, is presented as a simple, flexible, and production-grade workload orchestrator that enables organizations to deploy, manage, and scale any application, containerized, legacy or batch jobs, across multiple regions, on private and public clouds. Nomad's workload support enables an organization to run containerized, non containerized, and batch applications through a single workflow. Nomad is available open source, or via a supported enterprise plan.
Mesosphere is well suited for orchestrating workloads. It supports Docker as a container as well as support others. It is highly suitable for running resilient and auto recovering big data/application containers. Mesosphere has proven time and again to be production ready at a massive scale. It supports native single button/API call scale up and scale down and supports various deployment patterns like Blue-Green and others.
Nomad is well suited for organizations who wish to tackle the problem of cloud computing with as little opinion as possible. Where competing tools like Kubernetes limit the concept of "batteries included," Nomad relies on engineers understanding the missing components and filling them in as necessary. The benefit of Nomad is the ability to build a system out of small pieces with the cost of having more complexity at a system level compared to alternatives.
Setting up is a bit of a hassle, especially ZooKeeper state management and mesos and marathon quorum.
Occasionally, I observed some failures when deploying something onto Marathon. Logging or detailed error reporting can help.
Stale containers and inconsistent states resultant of the cluster failure are hard to solve and need a complete system restart to get it back to normal state.
Nomad only handles one part of a full platform. Expertise and vision are required in implementing an entire system that is functional enough for an organization to rely on. This includes other tools to handle things like secrets, service discovery, network routing, etc.
Nomad is delayed in some modern functionality, like features for service-mesh and open tracing. These features are on the tool's roadmap, but there's currently no native support. These paradigms can be established still, but require more expertise outside of Nomad itself.
Nomad is not the leading tool for this space, and as such risks being left behind by tools with much greater support, such as Kubernetes.
I happen to like mesosphere because it integrates well with a Jenkins based workflow, Deis is a little more Heroku like and it's not clear how to fit that model into a continuous-integration process. Kubernetes has also been criticized for being complicated.
Nomad's primary competitor is Kubernetes, specifically its scheduling component. Kubernetes is a much more complete system that will handle more things than job scheduling, including service discovery, secrets management, and service routing. There also exists a much larger community support for Kubernetes vs Nomad. One might say Kubernetes is the safer choice between the two. Kubernetes is the complete "operating system" for cloud computing, but with it includes complexities that are "Kubernetes" specific. The decision really comes down to a mindset of monolith vs components. With Kubernetes, I would argue you choose the entire system as a whole. With Nomad, you design your system piece by piece. There is no wrong answer.
Nomad has allowed our organization to deploy quicker and more frequently with a lower failure rate.
Nomad has brought in consistency from an operations perspective.
Nomad's performance allows us to scale infinitely while providing functionality that reduces mean time to repair (canary deploys, versioning, rollbacks, etc).
