Previous Serverless Version 0.5.x VS examples

Compare Previous Serverless Version 0.5.x vs examples and see what are their differences.

Previous Serverless Version 0.5.x

⚡ Serverless Framework – Use AWS Lambda and other managed cloud services to build apps that auto-scale, cost nothing when idle, and boast radically low maintenance. (by serverless)

examples

Serverless Examples – A collection of boilerplates and examples of serverless architectures built with the Serverless Framework on AWS Lambda, Microsoft Azure, Google Cloud Functions, and more. (by serverless)
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Previous Serverless Version 0.5.x examples
90 23
45,956 11,237
0.4% 0.5%
8.4 3.3
7 days ago 9 days ago
JavaScript JavaScript
MIT License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Previous Serverless Version 0.5.x

Posts with mentions or reviews of Previous Serverless Version 0.5.x. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-20.
  • The Top 10 GitHub Repositories Making Waves 🌊📊
    22 projects | dev.to | 20 Dec 2023
    Github | Website
    22 projects | dev.to | 20 Dec 2023
  • [p] I built an open source platform to deploy computationally intensive Python functions as serverless jobs, with no timeouts
    2 projects | /r/MachineLearning | 2 Feb 2023
    - With Lambda, you manage creating and building the container yourself, as well as updating the Lambda function code. There are tools out there such as sst or serverless.com which help streamline this.
  • AWS Lambda, a good host for a rest API?
    2 projects | /r/aws | 27 Jan 2023
    If you'd like to use Lambda, usually you need to engineer FOR it, from day one, you don't (often) get to choose some other framework and shoehorn it into Lambda and Serverless. There's some great frameworks to help deploy code into Lambda easily and create REST endpoints for things, one such frameworks is serverless.com that helps easily deploy to it, but it lacks a framework for doing REST that also supports local emulation (as easily). For that, I recommend a framework by AWS called Chalice. This is an amazing REST framework that runs a proxy that works locally and deploys exactly the same on Lambda, it is Python however.
  • First time building microservice-based application
    3 projects | /r/node | 8 Jan 2023
  • Key learnings after 10h diving into Lambda, js and Github Actions
    3 projects | dev.to | 6 Jan 2023
    After knocking out a README with a set of goals and a list of TODOs to check off as I made progress, I spent about 10 hours over a weekend trying to get something to work. I used serverless for making Lambda easier, Github Actions for the deploy pipeline and store my credentials; and sadly I rolled my own access_token refresh logic because I couldn't find a helper that just did that for me! wtf!?
  • What tech-stack to use for a solo dev that can prioritize product iteration and scale?
    3 projects | /r/aws | 3 Jan 2023
    The backend is built with serverless.com (lambda, dynamodb, sqs, appsync). The good thing is that all the backend is stored in a file and you can deploy multiple stacks on the same account using seed.run . You don't really need EC2/Fargate when you have lambdas and you know that most of the time will be idle time. The same with cache I wouldn't think of it right now until you see the workload you are facing. Dynamodb once you understand it and have a proper design it's the fastest thing you can have. On my appsync calls I'm using Dynamodb as a cache because it's cheaper...
  • Creating and managing an AWS MSK Cluster and Configuration
    5 projects | dev.to | 23 Dec 2022
    Apache Kafka allows for asynchronous communication in a distributed ecosystem. It allows producers to publish messages on topics that are then ingested by consumers interested in those topics. As a concept, pub-sub models have been around for ages. However, the beauty of Kafka is in the how — using partitions and consumer groups, Kafka can scale the rate of consumption of messages with minimal dev and economic overhead. In this tutorial, I’ll take you through how to provision a managed Kafka cluster using the AWS Managed Stream for Kafka (MSK) service. We’ll use the serverless framework to create and maintain the infrastructure for MSK and the supporting VPCs, subnets, etc.
  • Do some developers actually, REALLY, have no local environment and run everything in AWS? Is the individual cloud dev environment a real alternative to having things running locally?
    3 projects | /r/aws | 20 Oct 2022
    I run my personal project on AWS. I has been running for 4+ years now and I never had a local environment. I took the serverless route. That is appsync, lambda, dynamodb, sqs to build the stack. I'm using serverless.com to have all the resources defined in a yaml files which will deploy multiple stacks. I'm using seed.run to manage that part because it's much more simple than to do it manually.
  • Use IAM Identity Center (AWS SSO) to protect your Cloudfront served application
    4 projects | dev.to | 4 Oct 2022
    The solution is deployed using serverless.com

examples

Posts with mentions or reviews of examples. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-14.
  • Deploy a Django application to AWS Lambda using Serverless Framework
    2 projects | dev.to | 14 Dec 2023
    To create a new Serverless service you can run the sls create command and pass a template as a parameter. You can see a list of template examples in the official sls repository. As I write this post, there isn’t a Django template available in this list. Therefore, we are going to create a serverless.yml file in the root directory of our project and manually write the code we need in it.
  • [AskJS] Kicking a dead horse - TS vs JS
    2 projects | /r/javascript | 9 Dec 2023
    Here's an example that deploys a node lambda along with an api gateway with very minimal configuration (check out the serverless.yml).
  • CICD for Serverless Part 2 — AWS CodePipeline Integration
    2 projects | dev.to | 16 May 2022
    Here we’re still using the same Todo list example the folks at the Serverless Framework created as our codebase. But with some variations so that it more cleanly supports automated testing and the CICD toolchain used — AWS CodePipeline.
  • How to use multiple runtimes in a single serverless microservice
    2 projects | dev.to | 27 Apr 2022
    Let’s consider a small application that uses two runtimes and provides two functions. This example will use Python and Node targeting AWS, but the concepts will be broadly applicable in other circumstances. The full the project files can be found here
  • How to Create a Serverless GraphQL API for MySQL, Postgres and Aurora
    4 projects | dev.to | 14 Apr 2022
    You can find the full example project that we’ve been using in this GitHub repo. The easiest way to experiment with the project is to clone the repo and deploy it from your machine using npm run deploy.
  • How to Make a Serverless GraphQL API using Lambda and DynamoDB
    3 projects | dev.to | 11 Apr 2022
    To deploy this service yourself, download the source code and deploy it with the Serverless Framework. Or, take a look at a larger example project for ideas on project structure and factoring.
  • Strategies for implementing user authentication in serverless applications
    3 projects | dev.to | 31 Mar 2022
    You can also find a working implementation of an Authorizer function here in the Serverless Examples repo.
  • Unit testing for Node.js Serverless projects with Jest
    2 projects | dev.to | 29 Mar 2022
    We decided to start with a fresh copy of the aws-node-simple-http-endpoint example in this section:
  • How to publish and use AWS Lambda Layers with the Serverless Framework
    6 projects | dev.to | 24 Mar 2022
    Originally posted at Serverless AWS re:Invent is in full swing, with AWS announcing a slew of new features. Most notably, we’re pretty excited about AWS Lambda’s support for Layers. Layers allows you to include additional files or data for your functions. This could be binaries such as FFmpeg or ImageMagick, or it could be difficult-to-package dependencies, such as NumPy for Python. These layers are added to your function’s zip file when published. In a way, they are comparable to EC2 AMIs, but for functions. The killer feature of Lambda’s Layers is that they can be shared between Lambda functions, accounts, and even publicly! There are two aspects to using Lambda Layers: Publishing a layer that can be used by other functions Using a layer in your function when you publish a new function version. We’re excited to say that the Serverless Framework has day 1 support for both publishing and using Lambda Layers with your functions with Version 1.34.0! See how you can publish and use Lambda Layers with the Serverless Framework below. Example use case: Creating GIFs with FFmpeg For a walkthrough, let’s make a service that takes an uploaded video and converts it to a GIF. We’ll use FFmpeg, a open source tool for manipulating video and audio. FFmpeg is a binary program and a great example use case for a layer as managing the binary falls outside the responsibility of your runtime’s packaging system. In this example, we’ll build and publish a layer that contains FFmpeg. Then, we’ll create a Lambda function that uses the FFmpeg layer to convert videos to GIFs. To get started, create a serverless project for your layer & service: Then at the bottom of your serverless.yml add the following to define your layer that will contain FFmpeg. The path property is a path to a directory that will be zipped up and published as your layer: Run the following commands to download the contents of your layer: You’re ready to test deployment of your layer. Deploy and you’ll see the layer’s ARN in the output info: Next, we’ll add a custom section to serverless.yml to specify the S3 bucket name (choose your own unique bucket name): Now rename your function from hello to mkgif, specify that your function uses the layer you’re publishing, and add an S3 event configuration: You’ll also need to give your service permission to read & write your S3 bucket, add the following in the provider section of your serverless.yml file: Your serverless.yml should now look like this. We need to make our handler. Replace the contents of handler.js with the following code, which gets the file from S3, downloads it to disk, runs ffmpeg on it, reads the GIF, and finally puts it back to S3: Now you can deploy both the layer & updated function with sls deploy. Let’s test it out by uploading a video to our S3 bucket: You now have a GIF copy of the mp4 you uploaded! For the full source of this example, check it out in our examples repo. Some tips on working with layers In the example above, instead of specifying an ARN for the layer that the function is using, we used {Ref: FfmpegLambdaLayer}. This is a CloudFormation Reference. The name is derived from your layer’s name, e.g., ffmpeg becomes FfmpegLambdaLayer. If you're not sure what your layer's name will be, you can find it by running sls package then searching for LambdaLayer in .serverless/cloudformation-template-update-stack.json. You may have noticed that every time you deploy your stack, a new layer version is created. This is due to limitations with CloudFormation. The best way to deal with this is by keeping your layer and your function in separate stacks. Let’s try that with the example we just made. First, create a new folder and move the layers directory into it: Remove the top-level layers section in gifmaker/serverless.yml, then create a new serverless.yml in the ffmpeg-layer folder containing: Now you can run sls deploy to publish your layer! Go back to the gifmaker service directory and change {Ref: FfmpegLambdaLayer} in the serverless.yml to ${cf:ffmpeg-layer-dev.FfmpegLayerExport}. You can now run sls deploy and it'll use the layer from the other service. Note that the dev in the variable above is the stage of your layer service. More Examples You can see the following projects for some examples of using this plugin to build a layer. They all leverage Docker and the docker-lambda images to compile for AWS’s Lambda environment on any operating system: geoip-lambda-layer — A layer containing MaxMind’s GeoIP libraries sqlite-lambda-layer — A layer to fix SQLite support in Python 3.6 runtimes Awesome layers Also check out this repository of awesome layers: https://github.com/mthenw/awesome-layers Custom runtime support: even better! Along with layers support, AWS also just announced support for building your own runtime using the Runtime API. This allows you to build, use, and share runtime support for Lambda outside of what AWS officially supports. Custom runtimes with the Serverless Framework To utilize custom runtimes with Serverless, specify the runtime as provided in your serverless.yml and include a layer that provides a custom runtime. For documentation on building your own runtime, see AWS’s documentation here More re:Invent news All the Serverless announcements at re:Invent 2018 DynamoDB On-Demand: When, why and how to use it in your serverless applications Real-time applications with API Gateway WebSockets and AWS Lambda What Firecracker open-source means for the serverless community
  • How I created URL shortener using Serverless and MongoDB
    4 projects | dev.to | 7 Jan 2022
    This template does not include any kind of persistence (database). For a more advanced examples check out the examples repo which includes Typescript, Mongo, DynamoDB and other examples.

What are some alternatives?

When comparing Previous Serverless Version 0.5.x and examples you can also consider the following projects:

Zappa - Serverless Python

apex

python-lambda - A toolkit for developing and deploying serverless Python code in AWS Lambda.

drover - Drover is a command-line utility for deploying Python packages to Lambda functions.

miniflare - 🔥 Fully-local simulator for Cloudflare Workers. For the latest version, see https://github.com/cloudflare/workers-sdk/tree/main/packages/miniflare.

sqlite-lambda-layer - A project providing a Lambda Layer that provides SQLite support in Python3.6 Lambdas

serverless-prune-plugin - Serverless Framework plugin to reap unused versions of deployed functions from AWS

formidable - The most used, flexible, fast and streaming parser for multipart form data. Supports uploading to serverless environments, AWS S3, Azure, GCP or the filesystem. Used in production.

atlantis - Terraform Pull Request Automation

geoip-lambda-layer - An example Lambda Layer containing MaxMind's free GeoIP DBs

arc.codes - The Architect web site! 🌩

serverless-wsgi - Serverless plugin to deploy WSGI applications (Flask/Django/Pyramid etc.) and bundle Python packages