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AWS Lambda
AWS Lambda

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AWS Lambda vs CouchDB: What are the differences?

Developers describe AWS Lambda as "Automatically run code in response to modifications to objects in Amazon S3 buckets, messages in Kinesis streams, or updates in DynamoDB". 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. On the other hand, CouchDB is detailed as "HTTP + JSON document database with Map Reduce views and peer-based replication". Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.

AWS Lambda can be classified as a tool in the "Serverless / Task Processing" category, while CouchDB is grouped under "Databases".

"No infrastructure" is the top reason why over 121 developers like AWS Lambda, while over 42 developers mention "JSON" as the leading cause for choosing CouchDB.

CouchDB is an open source tool with 4.24K GitHub stars and 835 GitHub forks. Here's a link to CouchDB's open source repository on GitHub.

According to the StackShare community, AWS Lambda has a broader approval, being mentioned in 1022 company stacks & 612 developers stacks; compared to CouchDB, which is listed in 61 company stacks and 32 developer stacks.

- No public GitHub repository available -

What is 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.

What is CouchDB?

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.
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    What are some alternatives to AWS Lambda and CouchDB?
    Serverless
    Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.
    AWS Elastic Beanstalk
    Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.
    Azure Functions
    Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.
    AWS Step Functions
    AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
    Google App Engine
    Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
    See all alternatives
    Decisions about AWS Lambda and CouchDB
    Josh Dzielak
    Josh Dzielak
    Developer Advocate at DeveloperMode · | 5 upvotes · 14.6K views
    Cloudant
    Cloudant
    CouchDB
    CouchDB
    Pouchdb
    Pouchdb
    Firebase
    Firebase

    As a side project, I was building a note taking app that needed to synchronize between the client and the server so that it would work offline. At first I used Firebase to store the data on the server and wrote my own code to cache Firebase data in local storage and synchronize it. This was brittle and not performant. I figured that someone else must have solved this in a better way so I went looking for a better solution.

    I needed a tool where I could write the data once and it would write to client and server, and when clients came back on line they would automatically catch the client up. I also needed conflict resolution. I was thrilled to discover Pouchdb and its server-side counterpart CouchDB. Together, they met nearly all of my requirements and were very easy to implement - I was able to remove a ton of custom code and have found the synchronization to be very robust. Pouchdb 7 has improved mobile support too, so I can run the app on iOS or Android browsers.

    My Couchdb instance is actually a Cloudant instance running on IBM Bluemix. For my fairly low level of API usage, it's been totally free, and it has a decent GUI for managing users and replications.

    See more
    Kestas Barzdaitis
    Kestas Barzdaitis
    Entrepreneur & Engineer · | 12 upvotes · 44.7K views
    atCodeFactorCodeFactor
    Google Cloud Functions
    Google Cloud Functions
    Azure Functions
    Azure Functions
    AWS Lambda
    AWS Lambda
    Docker
    Docker
    Google Compute Engine
    Google Compute Engine
    Microsoft Azure
    Microsoft Azure
    Amazon EC2
    Amazon EC2
    CodeFactor.io
    CodeFactor.io
    Kubernetes
    Kubernetes
    #SAAS
    #IAAS
    #Containerization
    #Autoscale
    #Startup
    #Automation
    #Machinelearning
    #AI
    #Devops

    CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

    CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

    AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

    It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

    The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

    In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

    Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

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    Nitzan Shapira
    Nitzan Shapira
    at Epsagon · | 10 upvotes · 93K views
    atEpsagonEpsagon
    AWS Lambda
    AWS Lambda
    GitHub
    GitHub
    Java
    Java
    Go
    Go
    Node.js
    Node.js
    npm
    npm
    Serverless
    Serverless
    Python
    Python

    At Epsagon, we use hundreds of AWS Lambda functions, most of them are written in Python, and the Serverless Framework to pack and deploy them. One of the issues we've encountered is the difficulty to package external libraries into the Lambda environment using the Serverless Framework. This limitation is probably by design since the external code your Lambda needs can be usually included with a package manager.

    In order to overcome this issue, we've developed a tool, which we also published as open-source (see link below), which automatically packs these libraries using a simple npm package and a YAML configuration file. Support for Node.js, Go, and Java will be available soon.

    The GitHub respoitory: https://github.com/epsagon/serverless-package-external

    See more
    Michal Nowak
    Michal Nowak
    Co-founder at Evojam · | 7 upvotes · 50K views
    atEvojamEvojam
    Azure Functions
    Azure Functions
    Firebase
    Firebase
    AWS Lambda
    AWS Lambda
    Serverless
    Serverless

    In a couple of recent projects we had an opportunity to try out the new Serverless approach to building web applications. It wasn't necessarily a question if we should use any particular vendor but rather "if" we can consider serverless a viable option for building apps. Obviously our goal was also to get a feel for this technology and gain some hands-on experience.

    We did consider AWS Lambda, Firebase from Google as well as Azure Functions. Eventually we went with AWS Lambdas.

    PROS
    • No servers to manage (obviously!)
    • Limited fixed costs – you pay only for used time
    • Automated scaling and balancing
    • Automatic failover (or, at this level of abstraction, no failover problem at all)
    • Security easier to provide and audit
    • Low overhead at the start (with the certain level of knowledge)
    • Short time to market
    • Easy handover - deployment coupled with code
    • Perfect choice for lean startups with fast-paced iterations
    • Augmentation for the classic cloud, server(full) approach
    CONS
    • Not much know-how and best practices available about structuring the code and projects on the market
    • Not suitable for complex business logic due to the risk of producing highly coupled code
    • Cost difficult to estimate (helpful tools: serverlesscalc.com)
    • Difficulty in migration to other platforms (Vendor lock⚠️)
    • Little engineers with experience in serverless on the job market
    • Steep learning curve for engineers without any cloud experience

    More details are on our blog: https://evojam.com/blog/2018/12/5/should-you-go-serverless-meet-the-benefits-and-flaws-of-new-wave-of-cloud-solutions I hope it helps 🙌 & I'm curious of your experiences.

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    Jonathan Pugh
    Jonathan Pugh
    Software Engineer / Project Manager / Technical Architect · | 17 upvotes · 125.1K views
    Pouchdb
    Pouchdb
    CouchDB
    CouchDB
    Font Awesome
    Font Awesome
    CSS 3
    CSS 3
    Apache Cordova
    Apache Cordova
    PhoneGap
    PhoneGap
    HTML5
    HTML5
    Ruby
    Ruby
    Babel
    Babel
    Webpack
    Webpack
    Visual Studio Code
    Visual Studio Code
    Figma
    Figma
    TypeScript
    TypeScript
    JavaScript
    JavaScript
    Framework7
    Framework7
    #Css
    #CSS3
    #SCSS
    #Sass
    #Less
    #Electron
    #HandleBars
    #Template7
    #Sketch
    #GraphQL
    #HTML5
    #GraphCool

    I needed to choose a full stack of tools for cross platform mobile application design & development. After much research and trying different tools, these are what I came up with that work for me today:

    For the client coding I chose Framework7 because of its performance, easy learning curve, and very well designed, beautiful UI widgets. I think it's perfect for solo development or small teams. I didn't like React Native. It felt heavy to me and rigid. Framework7 allows the use of #CSS3, which I think is the best technology to come out of the #WWW movement. No other tech has been able to allow designers and developers to develop such flexible, high performance, customisable user interface elements that are highly responsive and hardware accelerated before. Now #CSS3 includes variables and flexboxes it is truly a powerful language and there is no longer a need for preprocessors such as #SCSS / #Sass / #less. React Native contains a very limited interpretation of #CSS3 which I found very frustrating after using #CSS3 for some years already and knowing its powerful features. The other very nice feature of Framework7 is that you can even build for the browser if you want your app to be available for desktop web browsers. The latest release also includes the ability to build for #Electron so you can have MacOS, Windows and Linux desktop apps. This is not possible with React Native yet.

    Framework7 runs on top of Apache Cordova. Cordova and webviews have been slated as being slow in the past. Having a game developer background I found the tweeks to make it run as smooth as silk. One of those tweeks is to use WKWebView. Another important one was using srcset on images.

    I use #Template7 for the for the templating system which is a no-nonsense mobile-centric #HandleBars style extensible templating system. It's easy to write custom helpers for, is fast and has a small footprint. I'm not forced into a new paradigm or learning some new syntax. It operates with standard JavaScript, HTML5 and CSS 3. It's written by the developer of Framework7 and so dovetails with it as expected.

    I configured TypeScript to work with the latest version of Framework7. I consider TypeScript to be one of the best creations to come out of Microsoft in some time. They must have an amazing team working on it. It's very powerful and flexible. It helps you catch a lot of bugs and also provides code completion in supporting IDEs. So for my IDE I use Visual Studio Code which is a blazingly fast and silky smooth editor that integrates seamlessly with TypeScript for the ultimate type checking setup (both products are produced by Microsoft).

    I use Webpack and Babel to compile the JavaScript. TypeScript can compile to JavaScript directly but Babel offers a few more options and polyfills so you can use the latest (and even prerelease) JavaScript features today and compile to be backwards compatible with virtually any browser. My favorite recent addition is "optional chaining" which greatly simplifies and increases readability of a number of sections of my code dealing with getting and setting data in nested objects.

    I use some Ruby scripts to process images with ImageMagick and pngquant to optimise for size and even auto insert responsive image code into the HTML5. Ruby is the ultimate cross platform scripting language. Even as your scripts become large, Ruby allows you to refactor your code easily and make it Object Oriented if necessary. I find it the quickest and easiest way to maintain certain aspects of my build process.

    For the user interface design and prototyping I use Figma. Figma has an almost identical user interface to #Sketch but has the added advantage of being cross platform (MacOS and Windows). Its real-time collaboration features are outstanding and I use them a often as I work mostly on remote projects. Clients can collaborate in real-time and see changes I make as I make them. The clickable prototyping features in Figma are also very well designed and mean I can send clickable prototypes to clients to try user interface updates as they are made and get immediate feedback. I'm currently also evaluating the latest version of #AdobeXD as an alternative to Figma as it has the very cool auto-animate feature. It doesn't have real-time collaboration yet, but I heard it is proposed for 2019.

    For the UI icons I use Font Awesome Pro. They have the largest selection and best looking icons you can find on the internet with several variations in styles so you can find most of the icons you want for standard projects.

    For the backend I was using the #GraphCool Framework. As I later found out, #GraphQL still has some way to go in order to provide the full power of a mature graph query language so later in my project I ripped out #GraphCool and replaced it with CouchDB and Pouchdb. Primarily so I could provide good offline app support. CouchDB with Pouchdb is very flexible and efficient combination and overcomes some of the restrictions I found in #GraphQL and hence #GraphCool also. The most impressive and important feature of CouchDB is its replication. You can configure it in various ways for backups, fault tolerance, caching or conditional merging of databases. CouchDB and Pouchdb even supports storing, retrieving and serving binary or image data or other mime types. This removes a level of complexity usually present in database implementations where binary or image data is usually referenced through an #HTML5 link. With CouchDB and Pouchdb apps can operate offline and sync later, very efficiently, when the network connection is good.

    I use PhoneGap when testing the app. It auto-reloads your app when its code is changed and you can also install it on Android phones to preview your app instantly. iOS is a bit more tricky cause of Apple's policies so it's not available on the App Store, but you can build it and install it yourself to your device.

    So that's my latest mobile stack. What tools do you use? Have you tried these ones?

    See more
    Jeyabalaji Subramanian
    Jeyabalaji Subramanian
    CTO at FundsCorner · | 12 upvotes · 309.7K views
    atFundsCornerFundsCorner
    Amazon SQS
    Amazon SQS
    Sentry
    Sentry
    GitLab CI
    GitLab CI
    Slack
    Slack
    Google Compute Engine
    Google Compute Engine
    Netlify
    Netlify
    AWS Lambda
    AWS Lambda
    Zappa
    Zappa
    vuex
    vuex
    Vuetify
    Vuetify
    Vue.js
    Vue.js
    Swagger UI
    Swagger UI
    MongoDB
    MongoDB
    Flask
    Flask
    Python
    Python

    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.

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    Jeyabalaji Subramanian
    Jeyabalaji Subramanian
    CTO at FundsCorner · | 24 upvotes · 199.1K views
    atFundsCornerFundsCorner
    Zappa
    Zappa
    AWS Lambda
    AWS Lambda
    SQLAlchemy
    SQLAlchemy
    Python
    Python
    Amazon SQS
    Amazon SQS
    Node.js
    Node.js
    MongoDB Stitch
    MongoDB Stitch
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB

    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!

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    Julien DeFrance
    Julien DeFrance
    Full Stack Engineering Manager at ValiMail · | 2 upvotes · 12K views
    atSmartZipSmartZip
    Amazon SageMaker
    Amazon SageMaker
    Amazon Machine Learning
    Amazon Machine Learning
    AWS Lambda
    AWS Lambda
    Serverless
    Serverless
    #FaaS
    #GCP
    #PaaS

    Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

    Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

    Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

    Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

    See more
    Aviad Mor
    Aviad Mor
    CTO & Co-Founder at Lumigo · | 5 upvotes · 8.7K views
    atLumigoLumigo
    Serverless
    Serverless
    CircleCI
    CircleCI
    AWS Lambda
    AWS Lambda

    Our backend is serverless based, with many AWS Lambda , with CI/CD, using CircleCI and Serverless. This allows to develop with awesome agility and move fast. Since we update our lambdas daily, we needed a way to make sure we did not run into AWS's max limit of versions per lambda. We use the open source in link below to clear them out and stay clear of the limit.

    See more
    AWS Lambda
    AWS Lambda
    Google Cloud Functions
    Google Cloud Functions

    I use Google Cloud Functions because it's the AWS Lambda equivalent on GCP. It's not as mature compared to lambda because it doesn't have VPC enablement unless done through VPC Service Controls which can be pretty cumbersome.

    Although it feels bare bones compared to lambda, it still gets the job done when you want backend tasks done via serverless.

    Example Use Case

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    Tim Nolet
    Tim Nolet
    Founder, Engineer & Dishwasher at Checkly · | 5 upvotes · 14.3K views
    atChecklyHQChecklyHQ
    Node.js
    Node.js
    Google Cloud Functions
    Google Cloud Functions
    Azure Functions
    Azure Functions
    Amazon CloudWatch
    Amazon CloudWatch
    Serverless
    Serverless
    AWS Lambda
    AWS Lambda

    AWS Lambda Serverless Amazon CloudWatch Azure Functions Google Cloud Functions Node.js

    In the last year or so, I moved all Checkly monitoring workloads to AWS Lambda. Here are some stats:

    • We run three core functions in all AWS regions. They handle API checks, browser checks and setup / teardown scripts. Check our docs to find out what that means.
    • All functions are hooked up to SNS topics but can also be triggered directly through AWS SDK calls.
    • The busiest function is a plumbing function that forwards data to our database. It is invoked anywhere between 7000 and 10.000 times per hour with an average duration of about 179 ms.
    • We run separate dev and test versions of each function in each region.

    Moving all this to AWS Lambda took some work and considerations. The blog post linked below goes into the following topics:

    • Why Lambda is an almost perfect match for SaaS. Especially when you're small.
    • Why I don't use a "big" framework around it.
    • Why distributed background jobs triggered by queues are Lambda's raison d'être.
    • Why monitoring & logging is still an issue.

    https://blog.checklyhq.com/how-i-made-aws-lambda-work-for-my-saas/

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    Praveen Mooli
    Praveen Mooli
    Technical Leader at Taylor and Francis · | 11 upvotes · 95.7K views
    MongoDB Atlas
    MongoDB Atlas
    Amazon S3
    Amazon S3
    Amazon DynamoDB
    Amazon DynamoDB
    Amazon RDS
    Amazon RDS
    Serverless
    Serverless
    Docker
    Docker
    Terraform
    Terraform
    Travis CI
    Travis CI
    GitHub
    GitHub
    RxJS
    RxJS
    Angular 2
    Angular 2
    AWS Lambda
    AWS Lambda
    Amazon SQS
    Amazon SQS
    Amazon SNS
    Amazon SNS
    Amazon Kinesis Firehose
    Amazon Kinesis Firehose
    Amazon Kinesis
    Amazon Kinesis
    Flask
    Flask
    Python
    Python
    ExpressJS
    ExpressJS
    Node.js
    Node.js
    Spring Boot
    Spring Boot
    Java
    Java
    #Data
    #Devops
    #Webapps
    #Eventsourcingframework
    #Microservices
    #Backend

    We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

    To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

    To build #Webapps we decided to use Angular 2 with RxJS

    #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

    See more
    Interest over time
    Reviews of AWS Lambda and CouchDB
    Review ofAWS LambdaAWS Lambda

    I switched my auto chatbot to run in lambda and it was peace !

    How developers use AWS Lambda and CouchDB
    Avatar of Nathan Heffley
    Nathan Heffley uses AWS LambdaAWS Lambda

    To use Pusher's presence channel each client must be connected through a backend authentication system. While Pointer doesn't actually have any login based authentication it still needed a backend system to connect users to the proper channel.

    A small function was built that only gets called when a user first joins a session. After the user is authenticated they can communicate directly with other clients on the same channel. This made the authentication code the perfect candidate for a serverless function. Using AWS Lambda through Netlify's Functions feature made it a breeze to host.

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    Simple Merchant uses AWS LambdaAWS Lambda

    We're moving almost the entirety of our backend processes into Lambda. This has given us vast cost savings in terms of pure infrastructure billing - and time worrying about platform and scale. This move has also made our architecture almost entirely event-driven - another huge benefit as our business itself is inherently event-driven.

    Avatar of Volkan Özçelik
    Volkan Özçelik uses AWS LambdaAWS Lambda

    I mostly use AWS Lambda for triggering DevOps-related actions, like triggering an alarm or a deployment, or scheduling a backup.

    I haven’t gone totally “serverless” and I’m not planning to go 100% serverless anytime soon.

    But when I do, AWS Lambda will be an important element in my serverless setup.

    Avatar of King's Digital Lab
    King's Digital Lab uses CouchDBCouchDB

    Document (JSON) DB.

    • - queries must be pre-defined as views (not as flexible as query formulation on the fly)
    • - community and ecosystem not as large as mongodb
    • + PouchDB is an excellent JS library to interact with CouchDB or even work in offline-then-sync moce
    Avatar of Smileupps
    Smileupps uses CouchDBCouchDB

    By being built on, of, in and around CouchDB, Smileupps offers to its customers secure and reliable CouchDB hosting and a CouchDB-based app store to build and sell serious business-enabled web applications

    Avatar of Promethean TV
    Promethean TV uses AWS LambdaAWS Lambda

    PrometheanTV uses various Lambda functions to provide back-end capabilities to the platform without the need of deploying servers. Examples include, geo lookup services, and data aggregation services.

    Avatar of Flux Work
    Flux Work uses AWS LambdaAWS Lambda

    Serverless is the future. And AWS Lambda is the most mature FaaS out there. AWS SAM makes it easy to package Lambda as micro-apps.

    Avatar of Giant Swarm
    Giant Swarm uses CouchDBCouchDB

    We use CouchDB in an internal analysis tool for usage data.

    Avatar of Mathias Vonende
    Mathias Vonende uses CouchDBCouchDB

    Storage for unstructured, linked and timeseries data.

    Avatar of Aaron Buchanan
    Aaron Buchanan uses CouchDBCouchDB

    json store + geo + _changes

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