Alternatives to Fabric logo

Alternatives to Fabric

Ansible, Azure Service Fabric, Kubernetes, Liquid, and Forge are the most popular alternatives and competitors to Fabric.
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What is Fabric and what are its top alternatives?

Fabric is a Python (2.5-2.7) library and command-line tool for streamlining the use of SSH for application deployment or systems administration tasks. It provides a basic suite of operations for executing local or remote shell commands (normally or via sudo) and uploading/downloading files, as well as auxiliary functionality such as prompting the running user for input, or aborting execution.
Fabric is a tool in the Server Configuration and Automation category of a tech stack.
Fabric is an open source tool with 14.7K GitHub stars and 1.9K GitHub forks. Here’s a link to Fabric's open source repository on GitHub

Top Alternatives to Fabric

  • Ansible
    Ansible

    Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use. ...

  • Azure Service Fabric
    Azure Service Fabric

    Azure Service Fabric is a distributed systems platform that makes it easy to package, deploy, and manage scalable and reliable microservices. Service Fabric addresses the significant challenges in developing and managing cloud apps. ...

  • 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. ...

  • Liquid
    Liquid

    It is an open-source template language written in Ruby. It is the backbone of Shopify themes and is used to load dynamic content on storefronts. It is safe, customer facing template language for flexible web apps. ...

  • Forge
    Forge

    Fastest possible way to host lighting-fast static websites for small businesses, web startups, and app developers. ...

  • Material
    Material

    Express your creativity with Material, an animation and graphics framework for Google's Material Design and Apple's Flat UI in Swift. ...

  • Fiber
    Fiber

    Fiber UI Kit is the perfect starting place for your next project. Each element has been designed to work independently or as one seamless flow. It’s a full-fledged prototype with customizable components. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

Fabric alternatives & related posts

Ansible logo

Ansible

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Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
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PROS OF ANSIBLE
  • 284
    Agentless
  • 210
    Great configuration
  • 199
    Simple
  • 176
    Powerful
  • 155
    Easy to learn
  • 69
    Flexible
  • 55
    Doesn't get in the way of getting s--- done
  • 35
    Makes sense
  • 30
    Super efficient and flexible
  • 27
    Powerful
  • 11
    Dynamic Inventory
  • 9
    Backed by Red Hat
  • 7
    Works with AWS
  • 6
    Cloud Oriented
  • 6
    Easy to maintain
  • 4
    Vagrant provisioner
  • 4
    Simple and powerful
  • 4
    Multi language
  • 4
    Simple
  • 4
    Because SSH
  • 4
    Procedural or declarative, or both
  • 4
    Easy
  • 3
    Consistency
  • 2
    Well-documented
  • 2
    Masterless
  • 2
    Debugging is simple
  • 2
    Merge hash to get final configuration similar to hiera
  • 2
    Fast as hell
  • 1
    Manage any OS
  • 1
    Work on windows, but difficult to manage
  • 1
    Certified Content
CONS OF ANSIBLE
  • 8
    Dangerous
  • 5
    Hard to install
  • 3
    Doesn't Run on Windows
  • 3
    Bloated
  • 3
    Backward compatibility
  • 2
    No immutable infrastructure

related Ansible posts

Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 8.9M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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Sebastian Gębski

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.

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Azure Service Fabric logo

Azure Service Fabric

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Distributed systems platform that simplifies build, package, deploy, and management of scalable microservices apps
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PROS OF AZURE SERVICE FABRIC
  • 5
    Intelligent, fast, reliable
  • 4
    Runs most of Azure core services
  • 3
    Reliability
  • 3
    Superior programming models
  • 3
    More reliable than Kubernetes
  • 3
    Open source
  • 2
    Quickest recovery and healing in the world
  • 1
    Deploy anywhere
  • 1
    Is data storage technology
  • 1
    Battle hardened in Azure > 10 Years
CONS OF AZURE SERVICE FABRIC
    Be the first to leave a con

    related Azure Service Fabric 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
    • 164
      Leading docker container management solution
    • 128
      Simple and powerful
    • 106
      Open source
    • 76
      Backed by google
    • 58
      The right abstractions
    • 25
      Scale services
    • 20
      Replication controller
    • 11
      Permission managment
    • 9
      Supports autoscaling
    • 8
      Cheap
    • 8
      Simple
    • 6
      Self-healing
    • 5
      No cloud platform lock-in
    • 5
      Promotes modern/good infrascture practice
    • 5
      Open, powerful, stable
    • 5
      Reliable
    • 4
      Scalable
    • 4
      Quick cloud setup
    • 3
      Cloud Agnostic
    • 3
      Captain of Container Ship
    • 3
      A self healing environment with rich metadata
    • 3
      Runs on azure
    • 3
      Backed by Red Hat
    • 3
      Custom and extensibility
    • 2
      Sfg
    • 2
      Gke
    • 2
      Everything of CaaS
    • 2
      Golang
    • 2
      Easy setup
    • 2
      Expandable
    CONS OF KUBERNETES
    • 16
      Steep learning curve
    • 15
      Poor workflow for development
    • 8
      Orchestrates only infrastructure
    • 4
      High resource requirements for on-prem clusters
    • 2
      Too heavy for simple systems
    • 1
      Additional vendor lock-in (Docker)
    • 1
      More moving parts to secure
    • 1
      Additional Technology Overhead

    related Kubernetes posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.1M 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
    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

    To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

    Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

    We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

    Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

    Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

    #BigData #AWS #DataScience #DataEngineering

    See more
    Liquid logo

    Liquid

    208
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    Open-source template language written in Ruby
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    PROS OF LIQUID
      Be the first to leave a pro
      CONS OF LIQUID
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        related Liquid posts

        Forge logo

        Forge

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        24
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        Static web hosting made simple
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        PROS OF FORGE
        • 1
          Fgfgf
        CONS OF FORGE
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          related Forge posts

          Material logo

          Material

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          A Graphics Framework for Material Design in Swift
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          PROS OF MATERIAL
          • 1
            Good Documentation
          • 1
            Samples included
          • 1
            IOS benefits
          CONS OF MATERIAL
            Be the first to leave a con

            related Material posts

            Fiber logo

            Fiber

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            An interactive UI Kit by Framer.
            31
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            PROS OF FIBER
              Be the first to leave a pro
              CONS OF FIBER
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                related Fiber posts

                JavaScript logo

                JavaScript

                354K
                269.2K
                8.1K
                Lightweight, interpreted, object-oriented language with first-class functions
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                PROS OF JAVASCRIPT
                • 1.7K
                  Can be used on frontend/backend
                • 1.5K
                  It's everywhere
                • 1.2K
                  Lots of great frameworks
                • 897
                  Fast
                • 745
                  Light weight
                • 425
                  Flexible
                • 392
                  You can't get a device today that doesn't run js
                • 286
                  Non-blocking i/o
                • 237
                  Ubiquitousness
                • 191
                  Expressive
                • 55
                  Extended functionality to web pages
                • 49
                  Relatively easy language
                • 46
                  Executed on the client side
                • 30
                  Relatively fast to the end user
                • 25
                  Pure Javascript
                • 21
                  Functional programming
                • 15
                  Async
                • 13
                  Full-stack
                • 12
                  Setup is easy
                • 12
                  Future Language of The Web
                • 12
                  Its everywhere
                • 11
                  Because I love functions
                • 11
                  JavaScript is the New PHP
                • 10
                  Like it or not, JS is part of the web standard
                • 9
                  Expansive community
                • 9
                  Everyone use it
                • 9
                  Can be used in backend, frontend and DB
                • 9
                  Easy
                • 8
                  Most Popular Language in the World
                • 8
                  Powerful
                • 8
                  Can be used both as frontend and backend as well
                • 8
                  For the good parts
                • 8
                  No need to use PHP
                • 8
                  Easy to hire developers
                • 7
                  Agile, packages simple to use
                • 7
                  Love-hate relationship
                • 7
                  Photoshop has 3 JS runtimes built in
                • 7
                  Evolution of C
                • 7
                  It's fun
                • 7
                  Hard not to use
                • 7
                  Versitile
                • 7
                  Its fun and fast
                • 7
                  Nice
                • 7
                  Popularized Class-Less Architecture & Lambdas
                • 7
                  Supports lambdas and closures
                • 6
                  It let's me use Babel & Typescript
                • 6
                  Can be used on frontend/backend/Mobile/create PRO Ui
                • 6
                  1.6K Can be used on frontend/backend
                • 6
                  Client side JS uses the visitors CPU to save Server Res
                • 6
                  Easy to make something
                • 5
                  Clojurescript
                • 5
                  Promise relationship
                • 5
                  Stockholm Syndrome
                • 5
                  Function expressions are useful for callbacks
                • 5
                  Scope manipulation
                • 5
                  Everywhere
                • 5
                  Client processing
                • 5
                  What to add
                • 4
                  Because it is so simple and lightweight
                • 4
                  Only Programming language on browser
                • 1
                  Test
                • 1
                  Hard to learn
                • 1
                  Test2
                • 1
                  Not the best
                • 1
                  Easy to understand
                • 1
                  Subskill #4
                • 1
                  Easy to learn
                • 0
                  Hard 彤
                CONS OF JAVASCRIPT
                • 22
                  A constant moving target, too much churn
                • 20
                  Horribly inconsistent
                • 15
                  Javascript is the New PHP
                • 9
                  No ability to monitor memory utilitization
                • 8
                  Shows Zero output in case of ANY error
                • 7
                  Thinks strange results are better than errors
                • 6
                  Can be ugly
                • 3
                  No GitHub
                • 2
                  Slow
                • 0
                  HORRIBLE DOCUMENTS, faulty code, repo has bugs

                related JavaScript posts

                Zach Holman

                Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

                But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

                But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

                Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

                See more
                Conor Myhrvold
                Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.1M 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