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AWS Lambda vs NSQ: What are the differences?

What is AWS Lambda? 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.

What is NSQ? A realtime distributed messaging platform. NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

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

Some of the features offered by AWS Lambda are:

  • Extend other AWS services with custom logic
  • Build custom back-end services
  • Completely Automated Administration

On the other hand, NSQ provides the following key features:

  • support distributed topologies with no SPOF
  • horizontally scalable (no brokers, seamlessly add more nodes to the cluster)
  • low-latency push based message delivery (performance)

"No infrastructure" is the primary reason why developers consider AWS Lambda over the competitors, whereas "It's in golang" was stated as the key factor in picking NSQ.

NSQ is an open source tool with 15.5K GitHub stars and 2.04K GitHub forks. Here's a link to NSQ's open source repository on GitHub.

According to the StackShare community, AWS Lambda has a broader approval, being mentioned in 1002 company stacks & 585 developers stacks; compared to NSQ, which is listed in 21 company stacks and 8 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 NSQ?

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.
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    What are some alternatives to AWS Lambda and NSQ?
    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 NSQ
    Kestas Barzdaitis
    Kestas Barzdaitis
    Entrepreneur & Engineer · | 12 upvotes · 44.1K 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 · 92.7K 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 · 49.5K 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.

    See more
    Jeyabalaji Subramanian
    Jeyabalaji Subramanian
    CTO at FundsCorner · | 12 upvotes · 308.9K 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.

    See more
    Jeyabalaji Subramanian
    Jeyabalaji Subramanian
    CTO at FundsCorner · | 24 upvotes · 196.2K 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 · 11.9K 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 · 14.1K 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/

    See more
    Praveen Mooli
    Praveen Mooli
    Technical Leader at Taylor and Francis · | 11 upvotes · 93.5K 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 NSQ
    Review ofAWS LambdaAWS Lambda

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

    How developers use AWS Lambda and NSQ
    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.

    Avatar of Simple Merchant
    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 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 InsideSales.com
    InsideSales.com uses NSQNSQ

    The built-in Gamification that comes with our Playbooks application uses NSQ for work queues and microservice communication.

    How much does AWS Lambda cost?
    How much does NSQ cost?
    Pricing unavailable
    News about NSQ
    More news