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  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. Fission vs Serverless

Fission vs Serverless

OverviewDecisionsComparisonAlternatives

Overview

Serverless
Serverless
Stacks2.2K
Followers1.2K
Votes28
GitHub Stars46.9K
Forks5.7K
Fission
Fission
Stacks27
Followers81
Votes3
GitHub Stars8.8K
Forks788

Fission vs Serverless: What are the differences?

Introduction

Fission and Serverless are two popular serverless computing platforms that allow developers to write and deploy code without worrying about infrastructure management. While they have similar objectives, there are key differences that set them apart. Let's explore these differences in detail:

  1. Scaling: When it comes to scaling, Fission operates on a smaller scale compared to Serverless. Fission allows for granular function-level scaling, meaning only the specific functions that need scaling will be automatically allocated additional resources. In contrast, Serverless operates at a service level, scaling the entire service based on the overall demand.

  2. Resource Allocation: Fission follows an on-demand resource allocation model. It spins up containers only when a function is triggered, ensuring optimal utilization of resources. On the other hand, Serverless maintains a pool of resources, ready to handle requests, resulting in potentially higher costs when resources are underutilized.

  3. Architecture: Fission primarily uses Kubernetes as its underlying infrastructure, making it suitable for environments already leveraging Kubernetes. On the other hand, Serverless utilizes a range of different infrastructure providers, allowing it to be more flexible and compatible with various cloud and on-premises environments.

  4. Programming Languages Support: Fission provides support for multiple programming languages, including Python, Node.js, and Go. It offers a broader range of language options for developers. Serverless, on the other hand, is more focused on JavaScript and Node.js, with limited support for other languages.

  5. Cold Start Performance: Fission generally boasts faster cold start performance compared to Serverless. It achieves this by leveraging a pool of pre-warmed containers for faster function execution. Serverless, though improved, may still experience slightly longer cold start times due to its architecture.

  6. Function Deployment: When it comes to deploying functions, Fission offers the ability to directly deploy code as a source file, making it easier to develop and iterate on code quickly. Serverless, on the other hand, typically requires packaging code and dependencies into a deployment package before deployment, which can add additional complexity to the workflow.

In Summary, Fission and Serverless differ in scaling models, resource allocation, architecture, programming language support, cold start performance, and function deployment methods. These differences make them suitable for different use cases and development preferences.

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Advice on Serverless, Fission

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.

The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

357k views357k
Comments

Detailed Comparison

Serverless
Serverless
Fission
Fission

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.

Write short-lived functions in any language, and map them to HTTP requests (or other event triggers). Deploy functions instantly with one command. There are no containers to build, and no Docker registries to manage.

Statistics
GitHub Stars
46.9K
GitHub Stars
8.8K
GitHub Forks
5.7K
GitHub Forks
788
Stacks
2.2K
Stacks
27
Followers
1.2K
Followers
81
Votes
28
Votes
3
Pros & Cons
Pros
  • 14
    API integration
  • 7
    Supports cloud functions for Google, Azure, and IBM
  • 3
    Lower cost
  • 1
    Auto scale
  • 1
    Openwhisk
Pros
  • 1
    Portability
  • 1
    Open source
  • 1
    Any language
Integrations
Azure Functions
Azure Functions
AWS Lambda
AWS Lambda
Amazon API Gateway
Amazon API Gateway
Kubernetes
Kubernetes
Docker
Docker

What are some alternatives to Serverless, Fission?

AWS Lambda

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.

Azure Functions

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.

Google Cloud Run

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Google Cloud Functions

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Knative

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

OpenFaaS

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

Nuclio

Nuclio

nuclio is portable across IoT devices, laptops, on-premises datacenters and cloud deployments, eliminating cloud lock-ins and enabling hybrid solutions.

Apache OpenWhisk

Apache OpenWhisk

OpenWhisk is an open source serverless platform. It is enterprise grade and accessible to all developers thanks to its superior programming model and tooling. It powers IBM Cloud Functions, Adobe I/O Runtime, Naver, Nimbella among others.

Cloud Functions for Firebase

Cloud Functions for Firebase

Cloud Functions for Firebase lets you create functions that are triggered by Firebase products, such as changes to data in the Realtime Database, uploads to Cloud Storage, new user sign ups via Authentication, and conversion events in Analytics.

AWS Batch

AWS Batch

It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.

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