Alternatives to Apache Aurora logo

Alternatives to Apache Aurora

Kubernetes, Marathon, Apache Mesos, Nomad, and DC/OS are the most popular alternatives and competitors to Apache Aurora.
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What is Apache Aurora and what are its top alternatives?

Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.
Apache Aurora is a tool in the Cluster Management category of a tech stack.
Apache Aurora is an open source tool with 625 GitHub stars and 241 GitHub forks. Here’s a link to Apache Aurora's open source repository on GitHub

Top Alternatives to Apache Aurora

  • 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. ...

  • Marathon

    Marathon

    Marathon is an Apache Mesos framework for container orchestration. Marathon provides a REST API for starting, stopping, and scaling applications. Marathon is written in Scala and can run in highly-available mode by running multiple copies. The state of running tasks gets stored in the Mesos state abstraction. ...

  • Apache Mesos

    Apache Mesos

    Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers. ...

  • Nomad

    Nomad

    Nomad is a cluster manager, designed for both long lived services and short lived batch processing workloads. Developers use a declarative job specification to submit work, and Nomad ensures constraints are satisfied and resource utilization is optimized by efficient task packing. Nomad supports all major operating systems and virtualized, containerized, or standalone applications. ...

  • DC/OS

    DC/OS

    Unlike traditional operating systems, DC/OS spans multiple machines within a network, aggregating their resources to maximize utilization by distributed applications. ...

  • YARN Hadoop

    YARN Hadoop

    Its fundamental idea is to split up the functionalities of resource management and job scheduling/monitoring into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). ...

  • kops

    kops

    It helps you create, destroy, upgrade and maintain production-grade, highly available, Kubernetes clusters from the command line. AWS (Amazon Web Services) is currently officially supported, with GCE in beta support , and VMware vSphere in alpha, and other platforms planned. ...

  • Mesosphere

    Mesosphere

    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. ...

Apache Aurora alternatives & related posts

Kubernetes logo

Kubernetes

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Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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PROS OF KUBERNETES
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    Leading docker container management solution
  • 124
    Simple and powerful
  • 101
    Open source
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    Backed by google
  • 56
    The right abstractions
  • 24
    Scale services
  • 18
    Replication controller
  • 9
    Permission managment
  • 7
    Simple
  • 7
    Supports autoscaling
  • 6
    Cheap
  • 4
    Self-healing
  • 4
    Reliable
  • 4
    No cloud platform lock-in
  • 3
    Open, powerful, stable
  • 3
    Scalable
  • 3
    Quick cloud setup
  • 3
    Promotes modern/good infrascture practice
  • 2
    Backed by Red Hat
  • 2
    Runs on azure
  • 2
    Cloud Agnostic
  • 2
    Custom and extensibility
  • 2
    Captain of Container Ship
  • 2
    A self healing environment with rich metadata
  • 1
    Golang
  • 1
    Easy setup
  • 1
    Everything of CaaS
  • 1
    Sfg
  • 1
    Expandable
  • 1
    Gke
CONS OF KUBERNETES
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    Poor workflow for development
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    Steep learning curve
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    Orchestrates only infrastructure
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    High resource requirements for on-prem clusters

related Kubernetes posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M 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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Marathon logo

Marathon

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Deploy and manage containers (including Docker) on top of Apache Mesos at scale
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PROS OF MARATHON
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    High Availability
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    Powerful UI
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    Service Discovery
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    Load Balancing
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    Health Checks
CONS OF MARATHON
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    Apache Mesos logo

    Apache Mesos

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    Develop and run resource-efficient distributed systems
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    PROS OF APACHE MESOS
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      Easy scaling
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      Web UI
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      Fault-Tolerant
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      Elastic Distributed System
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      High-Available
    CONS OF APACHE MESOS
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      Not for long term
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      Depends on Zookeeper

    related Apache Mesos posts

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

    See more
    Nomad logo

    Nomad

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    A cluster manager and scheduler
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    PROS OF NOMAD
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      Built in Consul integration
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      Easy setup
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      Bult-in Vault integration
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      Built-in federation support
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      Self-healing
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      Flexible
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      Managable by terraform
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      Open source
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      Multiple workload support
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      Nice ACL
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      Bult-in Vault inegration
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      Stable
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      Simple
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      Autoscaling support
    CONS OF NOMAD
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      Easy to start with
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      HCL language for configuration, an unpopular DSL
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      Small comunity

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    Robert Zuber

    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.

    See more
    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.
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    PROS OF DC/OS
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      Easy to setup a HA cluster
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      Open source
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      Has templates to install via AWS and Azure
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      Easy Setup
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      Easy to get services running and operate them
    CONS OF DC/OS
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      YARN Hadoop logo

      YARN Hadoop

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      Resource management and job scheduling technology
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      PROS OF YARN HADOOP
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        Batch processing with commodity machine
      CONS OF YARN HADOOP
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        kops logo

        kops

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        Production Grade K8s Installation, Upgrades, and Management
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        PROS OF KOPS
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          CONS OF KOPS
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            Mesosphere logo

            Mesosphere

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            Combine your datacenter servers and cloud instances into one shared pool
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            PROS OF MESOSPHERE
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              Devops
            CONS OF MESOSPHERE
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