IronWorker vs Serverless: What are the differences?
IronWorker: High-Scale Async Task Processing. IronWorker provides the muscle for modern applications by efficiently isolating the code and dependencies of individual tasks to be processed on demand. Run in a multi-language containerized environment with streamlined orchestration, IronWorker gives you the flexibility to power any task in parallel at massive scale; Serverless: The most widely-adopted toolkit for building serverless applications. 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.
IronWorker and Serverless can be primarily classified as "Serverless / Task Processing" tools.
Serverless is an open source tool with 30.9K GitHub stars and 3.43K GitHub forks. Here's a link to Serverless's open source repository on GitHub.
What is IronWorker?
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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
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
- 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.
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.
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.
In #Aliadoc, we're exploring the crowdfunding option to get traction before launch. We are building a SaaS platform for website design customization.
For the Admin UI and website editor we use React and we're currently transitioning from a Create React App setup to a custom one because our needs have become more specific. We use CloudFlare as much as possible, it's a great service.
For routing dynamic resources and proxy tasks to feed websites to the editor we leverage CloudFlare Workers for improved responsiveness. We use Firebase for our hosting needs and user authentication while also using several Cloud Functions for Firebase to interact with other services along with Google App Engine and Google Cloud Storage, but also the Real Time Database is on the radar for collaborative website editing.
We generally hate configuration but honestly because of the stage of our project we lack resources for doing heavy sysops work. So we are basically just relying on Serverless technologies as much as we can to do all server side processing.
Visual Studio Code definitively makes programming a much easier and enjoyable task, we just love it. We combine it with Bitbucket for our source code control needs.
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.
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