Alternatives to Serverless logo

Alternatives to Serverless

AWS Lambda, Terraform, Zappa, Kubernetes, and JavaScript are the most popular alternatives and competitors to Serverless.
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What is Serverless and what are its top alternatives?

Serverless is a cloud computing model where developers can build and run applications without managing servers. Key features of Serverless include automatic scaling, pay-per-use pricing model, and reduced operational overhead. However, limitations of Serverless include potential performance issues, vendor lock-in, and limitations in terms of customization.

  1. AWS Lambda: AWS Lambda is a serverless computing service provided by Amazon Web Services. Key features include support for multiple programming languages, built-in monitoring and logging, and integration with other AWS services. Pros include high scalability and easy deployment, while cons include cold start latency and potential costs at scale.

  2. Azure Functions: Azure Functions is a serverless compute service provided by Microsoft Azure. Key features include support for multiple programming languages, automatic scaling, and integration with Azure services. Pros include seamless integration with other Azure services and good monitoring capabilities, while cons include potential vendor lock-in and limited support for certain programming languages.

  3. Google Cloud Functions: Google Cloud Functions is a serverless computing service provided by Google Cloud. Key features include support for multiple triggers, automatic scaling, and integration with Google Cloud services. Pros include seamless integration with other Google Cloud services and cost-effective pricing, while cons include cold start latency and limited support for some programming languages.

  4. IBM Cloud Functions: IBM Cloud Functions is a serverless platform provided by IBM Cloud. Key features include support for multiple programming languages, seamless integration with IBM Cloud services, and automatic scaling. Pros include strong security features and flexible pricing options, while cons include limited support for some programming languages and potential complexity in managing larger applications.

  5. Alibaba Cloud Function Compute: Alibaba Cloud Function Compute is a serverless computing service provided by Alibaba Cloud. Key features include support for multiple programming languages, high scalability, and seamless integration with other Alibaba Cloud services. Pros include strong performance and reliability, while cons include limited availability outside of China and potential language support limitations.

  6. Oracle Functions: Oracle Functions is a serverless compute service provided by Oracle Cloud. Key features include support for multiple triggers, automatic scaling, and seamless integration with Oracle Cloud services. Pros include strong security features and advanced monitoring capabilities, while cons include potential complexity in setting up and managing functions.

  7. Kubeless: Kubeless is an open-source serverless framework built on Kubernetes. Key features include support for multiple programming languages, easy deployment and scaling, and compatibility with Kubernetes ecosystem. Pros include flexibility in deployment options and integration with Kubernetes tools, while cons include potential complexity in setup and maintenance for users not familiar with Kubernetes.

  8. OpenWhisk: Apache OpenWhisk is an open-source serverless platform that can run on any cloud or on-premises environment. Key features include support for multiple programming languages, event-driven architecture, and scalability. Pros include high flexibility and compatibility with various cloud providers, while cons include potential complexity in configuration and integration with other services.

  9. Fn Project: Fn Project is an open-source serverless platform that can run on any cloud or on-premises environment. Key features include support for multiple programming languages, seamless Docker integration, and high performance. Pros include easy deployment with Docker containers and flexibility in managing functions, while cons include potential learning curve for users new to Docker and serverless concepts.

  10. Serverless Framework: Serverless Framework is an open-source tool that helps developers build, deploy, and manage serverless applications across multiple cloud providers. Key features include support for multiple cloud platforms, easy configuration management, and plugin ecosystem. Pros include cross-cloud compatibility and streamlined development process, while cons include potential limitations in customization compared to cloud provider-specific solutions.

Top Alternatives to Serverless

  • 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. ...

  • Terraform
    Terraform

    With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel. ...

  • Zappa
    Zappa

    Zappa makes it super easy to deploy all Python WSGI applications on AWS Lambda + API Gateway. Think of it as "serverless" web hosting for your Python web apps. That means infinite scaling, zero downtime, zero maintenance - and at a fraction of the cost of your current deployments! ...

  • 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. ...

  • 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. ...

Serverless alternatives & related posts

AWS Lambda logo

AWS Lambda

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PROS OF AWS LAMBDA
  • 129
    No infrastructure
  • 83
    Cheap
  • 70
    Quick
  • 59
    Stateless
  • 47
    No deploy, no server, great sleep
  • 12
    AWS Lambda went down taking many sites with it
  • 6
    Event Driven Governance
  • 6
    Extensive API
  • 6
    Auto scale and cost effective
  • 6
    Easy to deploy
  • 5
    VPC Support
  • 3
    Integrated with various AWS services
CONS OF AWS LAMBDA
  • 7
    Cant execute ruby or go
  • 3
    Compute time limited
  • 1
    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
Terraform logo

Terraform

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Describe your complete infrastructure as code and build resources across providers
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PROS OF TERRAFORM
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    Infrastructure as code
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    Declarative syntax
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    Planning
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    Simple
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    Parallelism
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    Well-documented
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    Cloud agnostic
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    It's like coding your infrastructure in simple English
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    Immutable infrastructure
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    Platform agnostic
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    Extendable
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    Automation
  • 4
    Automates infrastructure deployments
  • 4
    Portability
  • 2
    Lightweight
  • 2
    Scales to hundreds of hosts
CONS OF TERRAFORM
  • 1
    Doesn't have full support to GKE

related Terraform posts

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
Emanuel Evans
Senior Architect at Rainforest QA · | 20 upvotes · 1.5M views

We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).

We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .

Read the blog post to go more in depth.

See more
Zappa logo

Zappa

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Deploy all Python WSGI applications on AWS Lambda + API Gateway.
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PROS OF ZAPPA
    Be the first to leave a pro
    CONS OF ZAPPA
      Be the first to leave a con

      related Zappa 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
      Jeyabalaji Subramanian

      At FundsCorner, we are on a mission to enable fast accessible credit to India’s Kirana Stores. We are an early stage startup with an ultra small Engineering team. All the tech decisions we have made until now are based on our core philosophy: "Build usable products fast".

      Based on the above fundamentals, we chose Python as our base language for all our APIs and micro-services. It is ultra easy to start with, yet provides great libraries even for the most complex of use cases. Our entire backend stack runs on Python and we cannot be more happy with it! If you are looking to deploy your API as server-less, Python provides one of the least cold start times.

      We build our APIs with Flask. For backend database, our natural choice was MongoDB. It frees up our time from complex database specifications - we instead use our time in doing sensible data modelling & once we finalize the data model, we integrate it into Flask using Swagger UI. Mongo supports complex queries to cull out difficult data through aggregation framework & we have even built an internal framework called "Poetry", for aggregation queries.

      Our web apps are built on Vue.js , Vuetify and vuex. Initially we debated a lot around choosing Vue.js or React , but finally settled with Vue.js, mainly because of the ease of use, fast development cycles & awesome set of libraries and utilities backing Vue.

      You simply cannot go wrong with Vue.js . Great documentation, the library is ultra compact & is blazing fast. Choosing Vue.js was one of the critical decisions made, which enabled us to launch our web app in under a month (which otherwise would have taken 3 months easily). For those folks who are looking for big names, Adobe, and Alibaba and Gitlab are using Vue.

      By choosing Vuetify, we saved thousands of person hours in designing the CSS files. Vuetify contains all key material components for designing a smooth User experience & it just works! It's an awesome framework. All of us at FundsCorner are now lifelong fanboys of Vue.js and Vuetify.

      On the infrastructure side, all our API services and backend services are deployed as server less micro-services through Zappa. Zappa makes your life super easy by packaging everything that is required to deploy your code as AWS Lambda. We are now addicted to the single - click deploys / updates through Zappa. Try it out & you will convert!

      Also, if you are using Zappa, you can greatly simplify your CI / CD pipelines. Do try it! It's just awesome! and... you will be astonished by the savings you have made on AWS bills at end of the month.

      Our CI / CD pipelines are built using GitLab CI. The documentation is very good & it enables you to go from from concept to production in minimal time frame.

      We use Sentry for all crash reporting and resolution. Pro tip, they do have handlers for AWS Lambda , which made our integration super easy.

      All our micro-services including APIs are event-driven. Our background micro-services are message oriented & we use Amazon SQS as our message pipe. We have our own in-house workflow manager to orchestrate across micro - services.

      We host our static websites on Netlify. One of the cool things about Netlify is the automated CI / CD on git push. You just do a git push to deploy! Again, it is super simple to use and it just works. We were dogmatic about going server less even on static web sites & you can go server less on Netlify in a few minutes. It's just a few clicks away.

      We use Google Compute Engine, especially Google Vision for our AI experiments.

      For Ops automation, we use Slack. Slack provides a super-rich API (through Slack App) through which you can weave magical automation on boring ops tasks.

      See more
      Kubernetes logo

      Kubernetes

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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 · 10.1M 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
      JavaScript logo

      JavaScript

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      PROS OF JAVASCRIPT
      • 1.7K
        Can be used on frontend/backend
      • 1.5K
        It's everywhere
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        Lots of great frameworks
      • 896
        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
        Its everywhere
      • 12
        Future Language of The Web
      • 12
        Setup is easy
      • 11
        JavaScript is the New PHP
      • 11
        Because I love functions
      • 10
        Like it or not, JS is part of the web standard
      • 9
        Expansive community
      • 9
        Can be used in backend, frontend and DB
      • 9
        Easy
      • 9
        Everyone use it
      • 8
        Most Popular Language in the World
      • 8
        Can be used both as frontend and backend as well
      • 8
        Powerful
      • 8
        For the good parts
      • 8
        No need to use PHP
      • 8
        Easy to hire developers
      • 7
        Love-hate relationship
      • 7
        Agile, packages simple to use
      • 7
        Its fun and fast
      • 7
        Hard not to use
      • 7
        Nice
      • 7
        Versitile
      • 7
        Evolution of C
      • 7
        Photoshop has 3 JS runtimes built in
      • 7
        It's fun
      • 7
        Popularized Class-Less Architecture & Lambdas
      • 7
        Supports lambdas and closures
      • 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
        It let's me use Babel & Typescript
      • 6
        Easy to make something
      • 5
        What to add
      • 5
        Clojurescript
      • 5
        Stockholm Syndrome
      • 5
        Function expressions are useful for callbacks
      • 5
        Scope manipulation
      • 5
        Everywhere
      • 5
        Client processing
      • 5
        Promise relationship
      • 4
        Because it is so simple and lightweight
      • 4
        Only Programming language on browser
      • 1
        Easy to learn
      • 1
        Not the best
      • 1
        Hard to learn
      • 1
        Easy to understand
      • 1
        Test
      • 1
        Test2
      • 1
        Subskill #4
      • 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

      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 · 10.1M 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
      Git logo

      Git

      289.9K
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      Fast, scalable, distributed revision control system
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      PROS OF GIT
      • 1.4K
        Distributed version control system
      • 1.1K
        Efficient branching and merging
      • 959
        Fast
      • 845
        Open source
      • 726
        Better than svn
      • 368
        Great command-line application
      • 306
        Simple
      • 291
        Free
      • 232
        Easy to use
      • 222
        Does not require server
      • 27
        Distributed
      • 22
        Small & Fast
      • 18
        Feature based workflow
      • 15
        Staging Area
      • 13
        Most wide-spread VSC
      • 11
        Role-based codelines
      • 11
        Disposable Experimentation
      • 7
        Frictionless Context Switching
      • 6
        Data Assurance
      • 5
        Efficient
      • 4
        Just awesome
      • 3
        Github integration
      • 3
        Easy branching and merging
      • 2
        Compatible
      • 2
        Flexible
      • 2
        Possible to lose history and commits
      • 1
        Rebase supported natively; reflog; access to plumbing
      • 1
        Light
      • 1
        Team Integration
      • 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
        It's what you do
      • 0
        Phinx
      CONS OF GIT
      • 16
        Hard to learn
      • 11
        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

      related Git posts

      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.3M 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 · 8.3M 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

      279.6K
      243.9K
      10.3K
      Powerful collaboration, review, and code management for open source and private development projects
      279.6K
      243.9K
      + 1
      10.3K
      PROS OF GITHUB
      • 1.8K
        Open source friendly
      • 1.5K
        Easy source control
      • 1.3K
        Nice UI
      • 1.1K
        Great for team collaboration
      • 867
        Easy setup
      • 504
        Issue tracker
      • 486
        Great community
      • 482
        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
      • 53
        Owned by micrcosoft
      • 37
        Expensive for lone developers that want private repos
      • 15
        Relatively slow product/feature release cadence
      • 10
        API scoping could be better
      • 8
        Only 3 collaborators for private repos
      • 3
        Limited featureset for issue management
      • 2
        GitHub Packages does not support SNAPSHOT versions
      • 2
        Does not have a graph for showing history like git lens
      • 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
      Russel Werner
      Lead Engineer at StackShare · | 32 upvotes · 2.2M views

      StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

      Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

      #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

      See more
      Python logo

      Python

      239.6K
      195.5K
      6.9K
      A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
      239.6K
      195.5K
      + 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
        Free
      • 18
        Very programmer and non-programmer friendly
      • 17
        Powerfull language
      • 17
        Machine learning support
      • 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
        It's lean and fun to code
      • 8
        Import antigravity
      • 7
        Print "life is short, use python"
      • 7
        Python has great libraries for data processing
      • 6
        Although practicality beats purity
      • 6
        Flat is better than nested
      • 6
        Great for tooling
      • 6
        Rapid Prototyping
      • 6
        Readability counts
      • 6
        High Documented language
      • 6
        I love snakes
      • 6
        Fast coding and good for competitions
      • 6
        There should be one-- and preferably only one --obvious
      • 6
        Now is better than never
      • 5
        Great for analytics
      • 5
        Lists, tuples, dictionaries
      • 4
        Easy to learn and use
      • 4
        Simple and easy to learn
      • 4
        Easy to setup and run smooth
      • 4
        Web scraping
      • 4
        CG industry needs
      • 4
        Socially engaged community
      • 4
        Complex is better than complicated
      • 4
        Multiple Inheritence
      • 4
        Beautiful is better than ugly
      • 4
        Plotting
      • 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
        No cruft
      • 3
        Generators
      • 3
        Import this
      • 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
      • 2
        Batteries included
      • 2
        Should START with this but not STICK with This
      • 2
        Powerful language for AI
      • 2
        Can understand easily who are new to programming
      • 2
        Flexible and easy
      • 2
        Good for hacking
      • 2
        A-to-Z
      • 2
        Because of Netflix
      • 2
        Only one way to do it
      • 2
        Better outcome
      • 1
        Sexy af
      • 1
        Slow
      • 1
        Securit
      • 0
        Ni
      • 0
        Powerful
      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 · 10.1M 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
      Nick Parsons
      Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.5M 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