Apache Mesos vs rkt

Overview

Apache Mesos

Stacks306

Followers418

Votes31

GitHub Stars5.3K

Forks1.7K

rkt

Stacks29

Followers112

Votes10

Apache Mesos vs rkt: What are the differences?

Apache Mesos and rkt are both technologies used in distributed systems, but they have key differences that make them unique.

Containerization Approach: Apache Mesos is a cluster manager that abstracts CPU, memory, storage, and other resources to efficiently deploy and scale applications. On the other hand, rkt is a container runtime developed by CoreOS with a focus on security and composability.
Community Support: Apache Mesos has a larger community and extensive support from companies like Twitter, Airbnb, and Apple. rkt, while innovative, has a smaller community and is not as widely adopted in comparison.
Architecture: Apache Mesos follows a master-slave architecture where the master node schedules tasks on slave nodes based on available resources. In contrast, rkt uses a Pod concept for grouping containers, providing more flexibility and isolation for workloads.
Integration with Orchestration Tools: Apache Mesos is often integrated with frameworks like Marathon and Kubernetes for container orchestration, making it a preferred choice for large-scale deployments. On the other hand, rkt can be used with orchestrators like Kubernetes, but it is not as commonly used in production environments.
Focus on Security: rkt puts a strong emphasis on security by implementing features like image signature verification and running containers in isolated environments. While Apache Mesos also offers security features, rkt is known for its robust security model.
Container Image Format: Apache Mesos primarily uses Docker containers, making it easier to adopt existing Dockerized applications. In contrast, rkt uses its own container image format (ACI), providing an alternative to Docker images.

In summary, Apache Mesos and rkt differ in their containerization approach, community support, architecture, integration with orchestration tools, focus on security, and container image format.

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Detailed Comparison

Apache Mesos	rkt
Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers.	Rocket is a cli for running App Containers. The goal of rocket is to be composable, secure, and fast.
Fault-tolerant replicated master using ZooKeeper;Scalability to 10,000s of nodes;Isolation between tasks with Linux Containers;Multi-resource scheduling (memory and CPU aware);Java, Python and C++ APIs for developing new parallel applications;Web UI for viewing cluster state	Composable. All tools for downloading, installing, and running containers should be well integrated, but independent and composable.;Security. Isolation should be pluggable, and the crypto primitives for strong trust, image auditing and application identity should exist from day one.;Image distribution. Discovery of container images should be simple and facilitate a federated namespace, and distributed retrieval. This opens the possibility of alternative protocols, such as BitTorrent, and deployments to private environments without the requirement of a registry.;Open. The format and runtime should be well-specified and developed by a community. We want independent implementations of tools to be able to run the same container consistently.
Statistics
GitHub Stars 5.3K	GitHub Stars -
GitHub Forks 1.7K	GitHub Forks -
Stacks 306	Stacks 29
Followers 418	Followers 112
Votes 31	Votes 10
Pros & Cons
Pros 21 Easy scaling 6 Web UI 2 Fault-Tolerant 1 High-Available 1 Elastic Distributed System Cons 1 Depends on Zookeeper 1 Not for long term	Pros 5 Security 3 Robust container portability 2 Composable containers
Integrations
Apache Aurora	No integrations available

What are some alternatives to Apache Mesos, rkt?

Docker

The Docker Platform is the industry-leading container platform for continuous, high-velocity innovation, enabling organizations to seamlessly build and share any application — from legacy to what comes next — and securely run them anywhere

LXD

LXD isn't a rewrite of LXC, in fact it's building on top of LXC to provide a new, better user experience. Under the hood, LXD uses LXC through liblxc and its Go binding to create and manage the containers. It's basically an alternative to LXC's tools and distribution template system with the added features that come from being controllable over the network.

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.

LXC

LXC is a userspace interface for the Linux kernel containment features. Through a powerful API and simple tools, it lets Linux users easily create and manage system or application containers.

DC/OS

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

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.

Vagrant Cloud

Vagrant Cloud pairs with Vagrant to enable access, insight and collaboration across teams, as well as to bring exposure to community contributions and development environments.

Gardener

Many Open Source tools exist which help in creating and updating single Kubernetes clusters. However, the more clusters you need the harder it becomes to operate, monitor, manage and keep all of them alive and up-to-date. And that is exactly what project Gardener focuses on.

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).

Atmosly

AI-powered Kubernetes platform for developers & DevOps. Deploy applications without complexity, with intelligent automation and one-click environments.

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Apache Mesos vs rkt: What are the differences?

Apache Mesos and rkt are both technologies used in distributed systems, but they have key differences that make them unique.

Containerization Approach: Apache Mesos is a cluster manager that abstracts CPU, memory, storage, and other resources to efficiently deploy and scale applications. On the other hand, rkt is a container runtime developed by CoreOS with a focus on security and composability.
Community Support: Apache Mesos has a larger community and extensive support from companies like Twitter, Airbnb, and Apple. rkt, while innovative, has a smaller community and is not as widely adopted in comparison.
Architecture: Apache Mesos follows a master-slave architecture where the master node schedules tasks on slave nodes based on available resources. In contrast, rkt uses a Pod concept for grouping containers, providing more flexibility and isolation for workloads.
Integration with Orchestration Tools: Apache Mesos is often integrated with frameworks like Marathon and Kubernetes for container orchestration, making it a preferred choice for large-scale deployments. On the other hand, rkt can be used with orchestrators like Kubernetes, but it is not as commonly used in production environments.
Focus on Security: rkt puts a strong emphasis on security by implementing features like image signature verification and running containers in isolated environments. While Apache Mesos also offers security features, rkt is known for its robust security model.
Container Image Format: Apache Mesos primarily uses Docker containers, making it easier to adopt existing Dockerized applications. In contrast, rkt uses its own container image format (ACI), providing an alternative to Docker images.

In summary, Apache Mesos and rkt differ in their containerization approach, community support, architecture, integration with orchestration tools, focus on security, and container image format.