Alternatives to Ambassador logo

Alternatives to Ambassador

Consul, Envoy, Istio, Kong, and JavaScript are the most popular alternatives and competitors to Ambassador.
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What is Ambassador and what are its top alternatives?

Ambassador is an API gateway tool that helps in managing and securing APIs. It provides features like traffic management, authentication, rate limiting, and observability. However, some limitations of Ambassador include limited support for advanced routing capabilities and lack of dynamic service discovery.

  1. Kong: Kong is an open-source API gateway with features like traffic control, access control, and analytics. Pros: Community support, easy to set up. Cons: Limited documentation.
  2. Apigee: Apigee is a full lifecycle API management platform by Google Cloud. Key features include API design, security, analytics, and monetization. Pros: Robust security features, scalability. Cons: Expensive pricing.
  3. Tyk: Tyk is an open-source API gateway with features like access control, traffic management, and analytics. Pros: High performance, enterprise features. Cons: Steeper learning curve for beginners.
  4. AWS API Gateway: Amazon's API Gateway is a fully managed service to create, publish, maintain, monitor, and secure APIs. Pros: Seamless integration with AWS services, scalability. Cons: Vendor lock-in.
  5. Azure API Management: Azure API Management is a full-featured API gateway by Microsoft Azure. Key features include API design, publishing, and analytics. Pros: Integration with Azure services, developer portal. Cons: Complex pricing structure.
  6. NGINX: NGINX is a web server that can also function as an API gateway with features like load balancing, security, and caching. Pros: High performance, flexibility. Cons: Requires complex configurations.
  7. Wso2 API Manager: Wso2 API Manager is an open-source API management platform with features like API design, publishing, monitoring, and security. Pros: Open-source, comprehensive API lifecycle management. Cons: Complex setup process.
  8. 3scale: 3scale by Red Hat is an API management platform with features like API traffic control, billing, and developer portal. Pros: Scalability, developer-friendly. Cons: Limited customization options.
  9. MuleSoft Anypoint Platform: MuleSoft offers an API management platform with features like API design, publishing, security, and analytics. Pros: Integration capabilities, extensive connectors. Cons: High pricing.
  10. Traefik: Traefik is a modern HTTP reverse proxy and load balancer that can also function as an API gateway with features like dynamic configuration and TLS termination. Pros: Easy to use, automatic configuration. Cons: Limited advanced routing capabilities.

Top Alternatives to Ambassador

  • Consul
    Consul

    Consul is a tool for service discovery and configuration. Consul is distributed, highly available, and extremely scalable. ...

  • Envoy
    Envoy

    Originally built at Lyft, Envoy is a high performance C++ distributed proxy designed for single services and applications, as well as a communication bus and “universal data plane” designed for large microservice “service mesh” architectures. ...

  • 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. ...

  • Kong
    Kong

    Kong is a scalable, open source API Layer (also known as an API Gateway, or API Middleware). Kong controls layer 4 and 7 traffic and is extended through Plugins, which provide extra functionality and services beyond the core platform. ...

  • 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. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

  • GitHub
    GitHub

    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

Ambassador alternatives & related posts

Consul logo

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    John Kodumal

    As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

    We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

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    ConsulConsulElixirElixirErlangErlang
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    Postmates built a tool called Bazaar that helps onboard new partners and handles several routine tasks, like nightly emails to merchants alerting them about items that are out of stock.

    Since they ran Bazaar across multiple instances, the team needed to avoid sending multiple emails to their partners by obtaining lock across multiple hosts. To solve their challenge, they created and open sourced ConsulMutEx, and an Elixir module for acquiring and releasing locks with Consul and other backends.

    It works with Consul’s KV store, as well as other backends, including ets, Erlang’s in-memory database.

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    Envoy logo

    Envoy

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      related Envoy posts

      Noah Zoschke
      Engineering Manager at Segment · | 30 upvotes · 276.5K views

      We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. Behind the scenes the Config API is built with Go , GRPC and Envoy.

      At Segment, we build new services in Go by default. The language is simple so new team members quickly ramp up on a codebase. The tool chain is fast so developers get immediate feedback when they break code, tests or integrations with other systems. The runtime is fast so it performs great at scale.

      For the newest round of APIs we adopted the GRPC service #framework.

      The Protocol Buffer service definition language makes it easy to design type-safe and consistent APIs, thanks to ecosystem tools like the Google API Design Guide for API standards, uber/prototool for formatting and linting .protos and lyft/protoc-gen-validate for defining field validations, and grpc-gateway for defining REST mapping.

      With a well designed .proto, its easy to generate a Go server interface and a TypeScript client, providing type-safe RPC between languages.

      For the API gateway and RPC we adopted the Envoy service proxy.

      The internet-facing segmentapis.com endpoint is an Envoy front proxy that rate-limits and authenticates every request. It then transcodes a #REST / #JSON request to an upstream GRPC request. The upstream GRPC servers are running an Envoy sidecar configured for Datadog stats.

      The result is API #security , #reliability and consistent #observability through Envoy configuration, not code.

      We experimented with Swagger service definitions, but the spec is sprawling and the generated clients and server stubs leave a lot to be desired. GRPC and .proto and the Go implementation feels better designed and implemented. Thanks to the GRPC tooling and ecosystem you can generate Swagger from .protos, but it’s effectively impossible to go the other way.

      See more
      Joseph Irving
      DevOps Engineer at uSwitch · | 7 upvotes · 537.8K views
      Shared insights
      on
      KubernetesKubernetesEnvoyEnvoyGolangGolang
      at

      At uSwitch we wanted a way to load balance between our multiple Kubernetes clusters in AWS to give us added redundancy. We already had ingresses defined for all our applications so we wanted to build on top of that, instead of creating a new system that would require our various teams to change code/config etc.

      Envoy seemed to tick a lot of boxes:

      • Loadbalancing capabilities right out of the box: health checks, circuit breaking, retries etc.
      • Tracing and prometheus metrics support
      • Lightweight
      • Good community support

      This was all good but what really sold us was the api that supported dynamic configuration. This would allow us to dynamically configure envoy to route to ingresses and clusters as they were created or destroyed.

      To do this we built a tool called Yggdrasil using their Go sdk. Yggdrasil effectively just creates envoy configuration from Kubernetes ingress objects, so you point Yggdrasil at your kube clusters, it generates config from the ingresses and then envoy can loadbalance between your clusters for you. This is all done dynamically so as soon as new ingress is created the envoy nodes get updated with the new config. Importantly this all worked with what we already had, no need to create new config for every application, we just put this on top of it.

      See more
      Istio logo

      Istio

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      Open platform to connect, manage, and secure microservices, by Google, IBM, and Lyft
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        Great flexibility
      • 5
        Resiliency
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        Ingress controller
      • 4
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        Full Security
      CONS OF ISTIO
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        Performance

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      At my company, we are trying to move away from a monolith into microservices led architecture. We are now stuck with a problem to establish a communication mechanism between microservices. Since, we are planning to use service meshes and something like Dapr/Istio, we are not sure on how to split services between the two. Service meshes offer Traffic Routing or Splitting whereas, Dapr can offer state management and service-service invocation. At the same time both of them provide mLTS, Metrics, Resiliency and tracing. How to choose who should offer what?

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      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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      Kong logo

      Kong

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      Open Source Microservice & API Management Layer
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      PROS OF KONG
      • 37
        Easy to maintain
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        Easy to install
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        Flexible
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        Great performance
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        Api blueprint
      • 4
        Custom Plugins
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        Security
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        Has a good plugin infrastructure
      • 2
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        Load balancing
      • 1
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      CONS OF KONG
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        related Kong posts

        Shared insights
        on
        GrafanaGrafanaKongKongDatadogDatadog

        Hello :) We are using Datadog on Kong to monitor the metrics and analytics.

        We feel that the cost associated with Datadog is high in terms of custom metrics and indexations. So, we planned to find an alternative for Datadog and we are looking into Grafana implementation with kong.

        Will the shift from Datadog to Grafana be a wise move and flawless?

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        Anas MOKDAD
        Shared insights
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        KongKongIstioIstio

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

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        JavaScript logo

        JavaScript

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        Lightweight, interpreted, object-oriented language with first-class functions
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          Lots of great frameworks
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          Fast
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          Light weight
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          Flexible
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          You can't get a device today that doesn't run js
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          Non-blocking i/o
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          Ubiquitousness
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          Expressive
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          Extended functionality to web pages
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          Relatively easy language
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          Executed on the client side
        • 30
          Relatively fast to the end user
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          Pure Javascript
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          Functional programming
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          Async
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          Full-stack
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          Setup is easy
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          Future Language of The Web
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          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
          Everyone use it
        • 9
          Expansive community
        • 9
          Easy
        • 9
          Can be used in backend, frontend and DB
        • 8
          Easy to hire developers
        • 8
          No need to use PHP
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          For the good parts
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          Can be used both as frontend and backend as well
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          Powerful
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          Most Popular Language in the World
        • 7
          Evolution of C
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          Hard not to use
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          Versitile
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          Its fun and fast
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          Supports lambdas and closures
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          Love-hate relationship
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          Photoshop has 3 JS runtimes built in
        • 7
          Nice
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          It's fun
        • 7
          Popularized Class-Less Architecture & Lambdas
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          Agile, packages simple to use
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          Can be used on frontend/backend/Mobile/create PRO Ui
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          1.6K Can be used on frontend/backend
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          Client side JS uses the visitors CPU to save Server Res
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          It let's me use Babel & Typescript
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          Easy to make something
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          Client processing
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          Everywhere
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          Scope manipulation
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          Function expressions are useful for callbacks
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          What to add
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          Because it is so simple and lightweight
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          Easy to understand
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          Test
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          Subskill #4
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          Easy to learn
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          Hard to learn
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          Not the best
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        CONS OF JAVASCRIPT
        • 22
          A constant moving target, too much churn
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          Horribly inconsistent
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          Javascript is the New PHP
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          Thinks strange results are better than errors
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        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 · 11.3M 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
        Git logo

        Git

        293.8K
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          Distributed version control system
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          Efficient branching and merging
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          Fast
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          Open source
        • 726
          Better than svn
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          Great command-line application
        • 306
          Simple
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          Free
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          Small & Fast
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          Feature based workflow
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          Staging Area
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          Most wide-spread VSC
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        • 2
          Compatible
        • 2
          Flexible
        • 2
          Possible to lose history and commits
        • 1
          Rebase supported natively; reflog; access to plumbing
        • 1
          Light
        • 1
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        • 1
          Fast, scalable, distributed revision control system
        • 1
          Easy
        • 1
          Flexible, easy, Safe, and fast
        • 1
          CLI is great, but the GUI tools are awesome
        • 1
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        • 16
          Hard to learn
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          Inconsistent command line interface
        • 9
          Easy to lose uncommitted work
        • 7
          Worst documentation ever possibly made
        • 5
          Awful merge handling
        • 3
          Unexistent preventive security flows
        • 3
          Rebase hell
        • 2
          When --force is disabled, cannot rebase
        • 2
          Ironically even die-hard supporters screw up badly
        • 1
          Doesn't scale for big data

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        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 10M 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.
        See more
        Tymoteusz Paul
        Devops guy at X20X Development LTD · | 23 upvotes · 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.

        See more
        GitHub logo

        GitHub

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        Powerful collaboration, review, and code management for open source and private development projects
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        PROS OF GITHUB
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          Open source friendly
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          Easy source control
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        • 867
          Easy setup
        • 504
          Issue tracker
        • 486
          Great community
        • 483
          Remote team collaboration
        • 451
          Great way to share
        • 442
          Pull request and features planning
        • 147
          Just works
        • 132
          Integrated in many tools
        • 121
          Free Public Repos
        • 116
          Github Gists
        • 112
          Github pages
        • 83
          Easy to find repos
        • 62
          Open source
        • 60
          It's free
        • 60
          Easy to find projects
        • 56
          Network effect
        • 49
          Extensive API
        • 43
          Organizations
        • 42
          Branching
        • 34
          Developer Profiles
        • 32
          Git Powered Wikis
        • 30
          Great for collaboration
        • 24
          It's fun
        • 23
          Clean interface and good integrations
        • 22
          Community SDK involvement
        • 20
          Learn from others source code
        • 16
          Because: Git
        • 14
          It integrates directly with Azure
        • 10
          Standard in Open Source collab
        • 10
          Newsfeed
        • 8
          It integrates directly with Hipchat
        • 8
          Fast
        • 8
          Beautiful user experience
        • 7
          Easy to discover new code libraries
        • 6
          Smooth integration
        • 6
          Cloud SCM
        • 6
          Nice API
        • 6
          Graphs
        • 6
          Integrations
        • 6
          It's awesome
        • 5
          Quick Onboarding
        • 5
          Reliable
        • 5
          Remarkable uptime
        • 5
          CI Integration
        • 5
          Hands down best online Git service available
        • 4
          Uses GIT
        • 4
          Version Control
        • 4
          Simple but powerful
        • 4
          Unlimited Public Repos at no cost
        • 4
          Free HTML hosting
        • 4
          Security options
        • 4
          Loved by developers
        • 4
          Easy to use and collaborate with others
        • 3
          Ci
        • 3
          IAM
        • 3
          Nice to use
        • 3
          Easy deployment via SSH
        • 2
          Easy to use
        • 2
          Leads the copycats
        • 2
          All in one development service
        • 2
          Free private repos
        • 2
          Free HTML hostings
        • 2
          Easy and efficient maintainance of the projects
        • 2
          Beautiful
        • 2
          Easy source control and everything is backed up
        • 2
          IAM integration
        • 2
          Very Easy to Use
        • 2
          Good tools support
        • 2
          Issues tracker
        • 2
          Never dethroned
        • 2
          Self Hosted
        • 1
          Dasf
        • 1
          Profound
        CONS OF GITHUB
        • 54
          Owned by micrcosoft
        • 38
          Expensive for lone developers that want private repos
        • 15
          Relatively slow product/feature release cadence
        • 10
          API scoping could be better
        • 9
          Only 3 collaborators for private repos
        • 4
          Limited featureset for issue management
        • 3
          Does not have a graph for showing history like git lens
        • 2
          GitHub Packages does not support SNAPSHOT versions
        • 1
          No multilingual interface
        • 1
          Takes a long time to commit
        • 1
          Expensive

        related GitHub posts

        Johnny Bell

        I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.

        I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!

        I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.

        Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.

        Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.

        With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.

        If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.

        See more

        Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

        Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

        Check Out My Architecture: CLICK ME

        Check out the GitHub repo attached

        See more
        Python logo

        Python

        241.9K
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        A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
        241.9K
        197.3K
        + 1
        6.9K
        PROS OF PYTHON
        • 1.2K
          Great libraries
        • 961
          Readable code
        • 846
          Beautiful code
        • 787
          Rapid development
        • 689
          Large community
        • 435
          Open source
        • 393
          Elegant
        • 282
          Great community
        • 272
          Object oriented
        • 220
          Dynamic typing
        • 77
          Great standard library
        • 59
          Very fast
        • 55
          Functional programming
        • 49
          Easy to learn
        • 45
          Scientific computing
        • 35
          Great documentation
        • 29
          Productivity
        • 28
          Easy to read
        • 28
          Matlab alternative
        • 23
          Simple is better than complex
        • 20
          It's the way I think
        • 19
          Imperative
        • 18
          Very programmer and non-programmer friendly
        • 18
          Free
        • 17
          Machine learning support
        • 17
          Powerfull language
        • 16
          Fast and simple
        • 14
          Scripting
        • 12
          Explicit is better than implicit
        • 11
          Ease of development
        • 10
          Clear and easy and powerfull
        • 9
          Unlimited power
        • 8
          Import antigravity
        • 8
          It's lean and fun to code
        • 7
          Python has great libraries for data processing
        • 7
          Print "life is short, use python"
        • 6
          Although practicality beats purity
        • 6
          Readability counts
        • 6
          Rapid Prototyping
        • 6
          Fast coding and good for competitions
        • 6
          There should be one-- and preferably only one --obvious
        • 6
          Now is better than never
        • 6
          High Documented language
        • 6
          I love snakes
        • 6
          Flat is better than nested
        • 6
          Great for tooling
        • 5
          Lists, tuples, dictionaries
        • 5
          Great for analytics
        • 4
          Beautiful is better than ugly
        • 4
          Multiple Inheritence
        • 4
          Socially engaged community
        • 4
          CG industry needs
        • 4
          Easy to learn and use
        • 4
          Simple and easy to learn
        • 4
          Easy to setup and run smooth
        • 4
          Complex is better than complicated
        • 4
          Web scraping
        • 4
          Plotting
        • 3
          No cruft
        • 3
          It is Very easy , simple and will you be love programmi
        • 3
          Many types of collections
        • 3
          If the implementation is easy to explain, it may be a g
        • 3
          If the implementation is hard to explain, it's a bad id
        • 3
          Special cases aren't special enough to break the rules
        • 3
          Pip install everything
        • 3
          List comprehensions
        • 3
          Generators
        • 3
          Import this
        • 2
          Good for hacking
        • 2
          Flexible and easy
        • 2
          Batteries included
        • 2
          Can understand easily who are new to programming
        • 2
          Powerful language for AI
        • 2
          Should START with this but not STICK with This
        • 2
          A-to-Z
        • 2
          Because of Netflix
        • 2
          Only one way to do it
        • 2
          Better outcome
        • 1
          Automation friendly
        • 1
          Securit
        • 1
          Slow
        • 1
          Sexy af
        • 1
          Procedural programming
        • 0
          Powerful
        • 0
          Ni
        CONS OF PYTHON
        • 53
          Still divided between python 2 and python 3
        • 28
          Performance impact
        • 26
          Poor syntax for anonymous functions
        • 22
          GIL
        • 19
          Package management is a mess
        • 14
          Too imperative-oriented
        • 12
          Hard to understand
        • 12
          Dynamic typing
        • 12
          Very slow
        • 8
          Indentations matter a lot
        • 8
          Not everything is expression
        • 7
          Incredibly slow
        • 7
          Explicit self parameter in methods
        • 6
          Requires C functions for dynamic modules
        • 6
          Poor DSL capabilities
        • 6
          No anonymous functions
        • 5
          Fake object-oriented programming
        • 5
          Threading
        • 5
          The "lisp style" whitespaces
        • 5
          Official documentation is unclear.
        • 5
          Hard to obfuscate
        • 5
          Circular import
        • 4
          Lack of Syntax Sugar leads to "the pyramid of doom"
        • 4
          The benevolent-dictator-for-life quit
        • 4
          Not suitable for autocomplete
        • 2
          Meta classes
        • 1
          Training wheels (forced indentation)

        related Python posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.3M 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
        Nick Parsons
        Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 4M views

        Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

        We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

        We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

        Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

        #FrameworksFullStack #Languages

        See more