Alternatives to Azure Service Fabric logo

Alternatives to Azure Service Fabric

Docker, Kubernetes, Azure Container Service, AWS Lambda, and Azure App Service are the most popular alternatives and competitors to Azure Service Fabric.
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What is Azure Service Fabric and what are its top alternatives?

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.
Azure Service Fabric is a tool in the Microservices Tools category of a tech stack.
Azure Service Fabric is an open source tool with 2.8K GitHub stars and 361 GitHub forks. Here’s a link to Azure Service Fabric's open source repository on GitHub

Top Alternatives to Azure Service Fabric

  • Docker

    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 ...

  • 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. ...

  • Azure Container Service

    Azure Container Service

    Azure Container Service optimizes the configuration of popular open source tools and technologies specifically for Azure. You get an open solution that offers portability for both your containers and your application configuration. You select the size, the number of hosts, and choice of orchestrator tools, and Container Service handles everything else. ...

  • AWS Lambda

    AWS Lambda

    AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security. ...

  • Azure App Service

    Azure App Service

    Quickly build, deploy, and scale web apps created with popular frameworks .NET, .NET Core, Node.js, Java, PHP, Ruby, or Python, in containers or running on any operating system. Meet rigorous, enterprise-grade performance, security, and compliance requirements by using the fully managed platform for your operational and monitoring tasks. ...

  • AWS Fargate

    AWS Fargate

    AWS Fargate is a technology for Amazon ECS and EKS* that allows you to run containers without having to manage servers or clusters. With AWS Fargate, you no longer have to provision, configure, and scale clusters of virtual machines to run containers. ...

  • Azure Kubernetes Service

    Azure Kubernetes Service

    Deploy and manage containerized applications more easily with a fully managed Kubernetes service. It offers serverless Kubernetes, an integrated continuous integration and continuous delivery (CI/CD) experience, and enterprise-grade security and governance. Unite your development and operations teams on a single platform to rapidly build, deliver, and scale applications with confidence. ...

  • Istio

    Istio

    Istio is an open platform for providing a uniform way to integrate microservices, manage traffic flow across microservices, enforce policies and aggregate telemetry data. Istio's control plane provides an abstraction layer over the underlying cluster management platform, such as Kubernetes, Mesos, etc. ...

Azure Service Fabric alternatives & related posts

Docker logo

Docker

91.9K
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Enterprise Container Platform for High-Velocity Innovation.
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PROS OF DOCKER
  • 817
    Rapid integration and build up
  • 688
    Isolation
  • 515
    Open source
  • 502
    Testa­bil­i­ty and re­pro­ducibil­i­ty
  • 457
    Lightweight
  • 215
    Standardization
  • 180
    Scalable
  • 104
    Upgrading / down­grad­ing / ap­pli­ca­tion versions
  • 86
    Security
  • 83
    Private paas environments
  • 33
    Portability
  • 25
    Limit resource usage
  • 15
    I love the way docker has changed virtualization
  • 15
    Game changer
  • 12
    Fast
  • 11
    Concurrency
  • 7
    Docker's Compose tools
  • 4
    Because its fun
  • 4
    Easy setup
  • 4
    Fast and Portable
  • 3
    Makes shipping to production very simple
  • 2
    It's dope
  • 1
    Open source and highly configurable
  • 1
    Simplicity, isolation, resource effective
  • 1
    Highly useful
  • 1
    MacOS support FAKE
  • 1
    Its cool
  • 1
    Docker hub for the FTW
  • 1
    Package the environment with the application
  • 1
    Very easy to setup integrate and build
CONS OF DOCKER
  • 7
    New versions == broken features
  • 4
    Documentation not always in sync
  • 3
    Moves quickly
  • 3
    Unreliable networking

related Docker posts

Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 2.2M views

Our whole DevOps stack consists of the following tools:

  • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
  • Respectively Git as revision control system
  • SourceTree as Git GUI
  • Visual Studio Code as IDE
  • CircleCI for continuous integration (automatize development process)
  • Prettier / TSLint / ESLint as code linter
  • SonarQube as quality gate
  • Docker as container management (incl. Docker Compose for multi-container application management)
  • VirtualBox for operating system simulation tests
  • Kubernetes as cluster management for docker containers
  • Heroku for deploying in test environments
  • nginx as web server (preferably used as facade server in production environment)
  • SSLMate (using OpenSSL) for certificate management
  • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
  • PostgreSQL as preferred database system
  • Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

  • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
  • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
  • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
  • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
  • Scalability: All-in-one framework for distributed systems.
  • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
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Tymoteusz Paul
Devops guy at X20X Development LTD · | 21 upvotes · 4M 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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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
  • 152
    Leading docker container management solution
  • 121
    Simple and powerful
  • 96
    Open source
  • 71
    Backed by google
  • 55
    The right abstractions
  • 24
    Scale services
  • 17
    Replication controller
  • 9
    Permission managment
  • 6
    Simple
  • 5
    Cheap
  • 5
    Supports autoscaling
  • 3
    Promotes modern/good infrascture practice
  • 3
    Reliable
  • 3
    No cloud platform lock-in
  • 3
    Self-healing
  • 3
    Open, powerful, stable
  • 3
    Scalable
  • 2
    Quick cloud setup
  • 2
    A self healing environment with rich metadata
  • 2
    Captain of Container Ship
  • 1
    Custom and extensibility
  • 1
    Expandable
  • 1
    Easy setup
  • 1
    Gke
  • 1
    Golang
  • 1
    Backed by Red Hat
  • 1
    Everything of CaaS
  • 1
    Runs on azure
  • 1
    Cloud Agnostic
  • 1
    Sfg
CONS OF KUBERNETES
  • 13
    Poor workflow for development
  • 10
    Steep learning curve
  • 5
    Orchestrates only infrastructure
  • 2
    High resource requirements for on-prem clusters

related Kubernetes posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 37 upvotes · 3.5M 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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Azure Container Service logo

Azure Container Service

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Deploy and manage containers using the tools you choose
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PROS OF AZURE CONTAINER SERVICE
  • 5
    Easy to setup, very agnostic
  • 3
    It supports Kubernetes, Mesos DC/OS and Docker Swarm
  • 2
    It has a nice command line interface (CLI) tool
CONS OF AZURE CONTAINER SERVICE
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    related Azure Container Service posts

    AWS Lambda logo

    AWS Lambda

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    Automatically run code in response to modifications to objects in Amazon S3 buckets, messages in Kinesis streams, or...
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    PROS OF AWS LAMBDA
    • 126
      No infrastructure
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      Cheap
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      Quick
    • 57
      Stateless
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      No deploy, no server, great sleep
    • 9
      AWS Lambda went down taking many sites with it
    • 5
      Extensive API
    • 5
      Event Driven Governance
    • 5
      Easy to deploy
    • 4
      Auto scale and cost effective
    • 3
      VPC Support
    • 1
      Integrated with various AWS services
    CONS OF AWS LAMBDA
    • 5
      Cant execute ruby or go
    • 0
      Can't execute PHP w/o significant effort

    related AWS Lambda posts

    Jeyabalaji Subramanian

    Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

    We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

    Based on the above criteria, we selected the following tools to perform the end to end data replication:

    We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

    We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

    In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

    Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

    In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

    See more
    Tim Nolet

    Heroku Docker GitHub Node.js hapi Vue.js AWS Lambda Amazon S3 PostgreSQL Knex.js Checkly is a fairly young company and we're still working hard to find the correct mix of product features, price and audience.

    We are focussed on tech B2B, but I always wanted to serve solo developers too. So I decided to make a $7 plan.

    Why $7? Simply put, it seems to be a sweet spot for tech companies: Heroku, Docker, Github, Appoptics (Librato) all offer $7 plans. They must have done a ton of research into this, so why not piggy back that and try it out.

    Enough biz talk, onto tech. The challenges were:

    • Slice of a portion of the functionality so a $7 plan is still profitable. We call this the "plan limits"
    • Update API and back end services to handle and enforce plan limits.
    • Update the UI to kindly state plan limits are in effect on some part of the UI.
    • Update the pricing page to reflect all changes.
    • Keep the actual processing backend, storage and API's as untouched as possible.

    In essence, we went from strictly volume based pricing to value based pricing. Here come the technical steps & decisions we made to get there.

    1. We updated our PostgreSQL schema so plans now have an array of "features". These are string constants that represent feature toggles.
    2. The Vue.js frontend reads these from the vuex store on login.
    3. Based on these values, the UI has simple v-if statements to either just show the feature or show a friendly "please upgrade" button.
    4. The hapi API has a hook on each relevant API endpoint that checks whether a user's plan has the feature enabled, or not.

    Side note: We offer 10 SMS messages per month on the developer plan. However, we were not actually counting how many people were sending. We had to update our alerting daemon (that runs on Heroku and triggers SMS messages via AWS SNS) to actually bump a counter.

    What we build is basically feature-toggling based on plan features. It is very extensible for future additions. Our scheduling and storage backend that actually runs users' monitoring requests (AWS Lambda) and stores the results (S3 and Postgres) has no knowledge of all of this and remained unchanged.

    Hope this helps anyone building out their SaaS and is in a similar situation.

    See more
    Azure App Service logo

    Azure App Service

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    Build, deploy, and scale web apps on a fully managed platform
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    PROS OF AZURE APP SERVICE
    • 4
      .Net Framework
    • 3
      Visual studio
    CONS OF AZURE APP SERVICE
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      AWS Fargate logo

      AWS Fargate

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      Run Containers Without Managing Infrastructure
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      PROS OF AWS FARGATE
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          Cyril Duchon-Doris

          We build a Slack app using the Bolt framework from slack https://api.slack.com/tools/bolt, a Node.js express app. It allows us to easily implement some administration features so we can easily communicate with our backend services, and we don't have to develop any frontend app since Slack block kit will do this for us. It can act as a Chatbot or handle message actions and custom slack flows for our employees.

          This app is deployed as a microservice on Amazon EC2 Container Service with AWS Fargate. It uses very little memory (and money) and can communicate easily with our backend services. Slack is connected to this app through a ALB ( AWS Elastic Load Balancing (ELB) )

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          Azure Kubernetes Service logo

          Azure Kubernetes Service

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              Farzad Jalali
              Senior Software Architect at BerryWorld · | 8 upvotes · 159.2K views

              Visual Studio Azure DevOps Azure Functions Azure Websites #Azure #AzureKeyVault #AzureAD #AzureApps

              #Azure Cloud Since Amazon is potentially our competitor then we need a different cloud vendor, also our programmers are microsoft oriented so the choose were obviously #Azure for us.

              Azure DevOps Because we need to be able to develop a neww pipeline into Azure environment ina few minutes.

              Azure Kubernetes Service We already in #Azure , also need to use K8s , so let's use AKS as it's a manged Kubernetes in the #Azure

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              Istio logo

              Istio

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              Open platform to connect, manage, and secure microservices, by Google, IBM, and Lyft
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              PROS OF ISTIO
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                Zero code for logging and monitoring
              • 8
                Service Mesh
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                Great flexibility
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                Ingress controller
              • 3
                Easy integration with Kubernetes and Docker
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                Powerful authorization mechanisms
              • 3
                Resiliency
              • 3
                Full Security
              CONS OF ISTIO
              • 8
                Performance

              related Istio posts

              Anas MOKDAD
              Shared insights
              on
              KongKongIstioIstio

              As for the new support of service mesh pattern by Kong, I wonder how does it compare to Istio?

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