Our great sponsors
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
-
-
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
-
-
-
-
-
-
F#
Discontinued Please file issues or pull requests here: https://github.com/dotnet/fsharp (by fsharp)
-
serverless-application-model
The AWS Serverless Application Model (AWS SAM) transform is a AWS CloudFormation macro that transforms SAM templates into CloudFormation templates.
-
aws-lambda-java-libs
Official mirror for interface definitions and helper classes for Java code running on the AWS Lambda platform.
-
-
-
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
That coginitive dissonance is why I wrote this article. I love how both laguages can shine in what they do best doing the same task, and it’s fascinating to compare them, and how you’d approach doing the same thing in each. For reference, all code is up on my Github.
We need the Promise library as it is the best way to use Promises in ReScript. We utilize the Jzon library for deterministically parsing our JSON into sound ReScript types without using the super verbose Js.Json classify syntax. Finally, we have an Environment module to help our Lambda know if it’s running in a QA, Stage, or Production environment.
We need the Promise library as it is the best way to use Promises in ReScript. We utilize the Jzon library for deterministically parsing our JSON into sound ReScript types without using the super verbose Js.Json classify syntax. Finally, we have an Environment module to help our Lambda know if it’s running in a QA, Stage, or Production environment.
To do that, we’re using rescript-test, which feels natural to those used to Jest, Mocha, etc. The only main difference is using testAsync vs test for async code, and having to define your own assertions vs. “Jest/Chai has ALL THE THINGS”.
The ReScript programming language is like TypeScript; you write with types and it compiles to JavaScript. The shining features are the fastest compiler on the planet, sound vs strict types which leads to more confidence your code works when it compiles, a functional style, yet still has escape hatches to slowly integrate with your existing JavaScript codebase.
If you’re building an API, whether a REST one using API Gateway, or a GraphQL one using AppSync, you’ll have a monorepo with a bunch of Lambda functions in it. Each Lambda function is 1 to many files, and each function typically corresponds to a REST API route, or a GraphQL query/mutation. You’ll use something like Serverless Framework, AWS SAM, or AWS CDK to deploy. The deployment uses CloudFormation behind the scenes which means when you first deploy, you’re deploying “everything”. If you change 1 line of code in 1 Lambda and deploy, you’re only deploying 1 Lambda function update. Using Serverless deploy function, or AWS SAM accelerate, you’re bypassing CloudFormation to quickly test code in a dev/qa/stage environment, something common in Serverless. You can, and should, run and test your code locally, although for some runtimes (Go, Python with C++ libraries, custom runtimes, etc) this can be difficult so you just deploy and test in AWS only.
Enter Lambda error parse from CloudWatch filter pattern subscriptions. In this article, we’ll show 2 languages used to do that in a Functional Programming style. Specifically ReScript and Python. While subjective, we’ll do our best to compare and contrast the different approaches. I don’t specifically recommend either language for the task, I just find it interesting to compare and contrast and hope you do too. I’ll cover each function, what it does, and how it fits into the larger whole.
Enter Lambda error parse from CloudWatch filter pattern subscriptions. In this article, we’ll show 2 languages used to do that in a Functional Programming style. Specifically ReScript and Python. While subjective, we’ll do our best to compare and contrast the different approaches. I don’t specifically recommend either language for the task, I just find it interesting to compare and contrast and hope you do too. I’ll cover each function, what it does, and how it fits into the larger whole.
ReScript code is just like F# or OCAML; it doesn’t have a function parse phase like JavaScript, so we have to define our functions and types first before we can use them. That’s fine, but makes explaining the code backwards (meaning you start at the bottom of the file and work your way up), so we’ll start at our lambda handler and explain each part, regardless of where it’s defined.
To do that, we’re using rescript-test, which feels natural to those used to Jest, Mocha, etc. The only main difference is using testAsync vs test for async code, and having to define your own assertions vs. “Jest/Chai has ALL THE THINGS”.
Instead of writing the above, we can use a pure function lense function from PyDash (the Python version of JavaScript’s Lodash). It has a function called get which allows us to safely access not just properties, but deeply nested ones, including a mix and match of Dictionaries and Lists. Instead of the above style for a deeply nested property:
To do that, we’re using rescript-test, which feels natural to those used to Jest, Mocha, etc. The only main difference is using testAsync vs test for async code, and having to define your own assertions vs. “Jest/Chai has ALL THE THINGS”.
If you’re building an API, whether a REST one using API Gateway, or a GraphQL one using AppSync, you’ll have a monorepo with a bunch of Lambda functions in it. Each Lambda function is 1 to many files, and each function typically corresponds to a REST API route, or a GraphQL query/mutation. You’ll use something like Serverless Framework, AWS SAM, or AWS CDK to deploy. The deployment uses CloudFormation behind the scenes which means when you first deploy, you’re deploying “everything”. If you change 1 line of code in 1 Lambda and deploy, you’re only deploying 1 Lambda function update. Using Serverless deploy function, or AWS SAM accelerate, you’re bypassing CloudFormation to quickly test code in a dev/qa/stage environment, something common in Serverless. You can, and should, run and test your code locally, although for some runtimes (Go, Python with C++ libraries, custom runtimes, etc) this can be difficult so you just deploy and test in AWS only.
ReScript code is just like F# or OCAML; it doesn’t have a function parse phase like JavaScript, so we have to define our functions and types first before we can use them. That’s fine, but makes explaining the code backwards (meaning you start at the bottom of the file and work your way up), so we’ll start at our lambda handler and explain each part, regardless of where it’s defined.
If you’re building an API, whether a REST one using API Gateway, or a GraphQL one using AppSync, you’ll have a monorepo with a bunch of Lambda functions in it. Each Lambda function is 1 to many files, and each function typically corresponds to a REST API route, or a GraphQL query/mutation. You’ll use something like Serverless Framework, AWS SAM, or AWS CDK to deploy. The deployment uses CloudFormation behind the scenes which means when you first deploy, you’re deploying “everything”. If you change 1 line of code in 1 Lambda and deploy, you’re only deploying 1 Lambda function update. Using Serverless deploy function, or AWS SAM accelerate, you’re bypassing CloudFormation to quickly test code in a dev/qa/stage environment, something common in Serverless. You can, and should, run and test your code locally, although for some runtimes (Go, Python with C++ libraries, custom runtimes, etc) this can be difficult so you just deploy and test in AWS only.
When Lambdas log to CloudWatch, it’s a stream a messages. Those streams can be subscribed too. Those streams can also be filtered to “only give me the ones that have errors in them”. You can then have that filtered stream invoke a Lambda with log error message, parse it, and send the error somewhere. This allows you to build better reactive, and proactive, monitoring, like sending error messages to Slack/PagerDuty/xMatters. Instead of “something broke” you get “the database couldn’t be connected to” or “an upstream service had a blip of downtime, but all is fine now”. Much more useful and actionable and nice to get English on your phone when it’s 3am you’re out of it vs “some error, please acknowledge page”. “NO, I demand you tell me what is exactly wrong waking me up at an un-godly hour!” “pLe@$3 ACK-kN0wl3DgE p@ge” “Stupid phone……”
If you’re building an API, whether a REST one using API Gateway, or a GraphQL one using AppSync, you’ll have a monorepo with a bunch of Lambda functions in it. Each Lambda function is 1 to many files, and each function typically corresponds to a REST API route, or a GraphQL query/mutation. You’ll use something like Serverless Framework, AWS SAM, or AWS CDK to deploy. The deployment uses CloudFormation behind the scenes which means when you first deploy, you’re deploying “everything”. If you change 1 line of code in 1 Lambda and deploy, you’re only deploying 1 Lambda function update. Using Serverless deploy function, or AWS SAM accelerate, you’re bypassing CloudFormation to quickly test code in a dev/qa/stage environment, something common in Serverless. You can, and should, run and test your code locally, although for some runtimes (Go, Python with C++ libraries, custom runtimes, etc) this can be difficult so you just deploy and test in AWS only.
If you’re building an API, whether a REST one using API Gateway, or a GraphQL one using AppSync, you’ll have a monorepo with a bunch of Lambda functions in it. Each Lambda function is 1 to many files, and each function typically corresponds to a REST API route, or a GraphQL query/mutation. You’ll use something like Serverless Framework, AWS SAM, or AWS CDK to deploy. The deployment uses CloudFormation behind the scenes which means when you first deploy, you’re deploying “everything”. If you change 1 line of code in 1 Lambda and deploy, you’re only deploying 1 Lambda function update. Using Serverless deploy function, or AWS SAM accelerate, you’re bypassing CloudFormation to quickly test code in a dev/qa/stage environment, something common in Serverless. You can, and should, run and test your code locally, although for some runtimes (Go, Python with C++ libraries, custom runtimes, etc) this can be difficult so you just deploy and test in AWS only.
If you’re building an API, whether a REST one using API Gateway, or a GraphQL one using AppSync, you’ll have a monorepo with a bunch of Lambda functions in it. Each Lambda function is 1 to many files, and each function typically corresponds to a REST API route, or a GraphQL query/mutation. You’ll use something like Serverless Framework, AWS SAM, or AWS CDK to deploy. The deployment uses CloudFormation behind the scenes which means when you first deploy, you’re deploying “everything”. If you change 1 line of code in 1 Lambda and deploy, you’re only deploying 1 Lambda function update. Using Serverless deploy function, or AWS SAM accelerate, you’re bypassing CloudFormation to quickly test code in a dev/qa/stage environment, something common in Serverless. You can, and should, run and test your code locally, although for some runtimes (Go, Python with C++ libraries, custom runtimes, etc) this can be difficult so you just deploy and test in AWS only.