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API StatusChangelog
Knative
ByGoogle Cloud PlatformGoogle Cloud Platform

Knative

#11in Serverless
Discussions1
Followers342
OverviewDiscussions1

What is 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

Knative is a tool in the Serverless category of a tech stack.

Key Features

Serving - Scale to zero, request-driven compute modelBuild - Cloud-native source to container orchestrationEvents - Universal subscription, delivery and management of eventsServerless add-on on GKE - Enable GCP managed serverless stack on Kubernetes

Knative Pros & Cons

Pros of Knative

  • ✓Portability
  • ✓Autoscaling
  • ✓Eventing
  • ✓On top of Kubernetes
  • ✓Open source
  • ✓Secure Eventing

Cons of Knative

No cons listed yet.

Knative Alternatives & Comparisons

What are some alternatives to Knative?

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.

Serverless

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.

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 Functions

Google Cloud Functions

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

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.

Apex

Apex

Apex is a small tool for deploying and managing AWS Lambda functions. With shims for languages not yet supported by Lambda, you can use Golang out of the box.

Knative Integrations

Google Kubernetes Engine, Gravity, Google Cloud Run, Octo CLI, Otomi are some of the popular tools that integrate with Knative. Here's a list of all 5 tools that integrate with Knative.

Google Kubernetes Engine
Google Kubernetes Engine
Gravity
Gravity
Google Cloud Run
Google Cloud Run
Octo CLI
Octo CLI
Otomi
Otomi

Knative Discussions

Discover why developers choose Knative. Read real-world technical decisions and stack choices from the StackShare community.

Robert Wilson
Robert Wilson

Jun 28, 2023

Needs adviceonOpenFaaSOpenFaaSKafkaKafkaApache OpenWhiskApache OpenWhisk

Currently been using an older version of OpenFaaS, but the new version now requires payment for things we did on the older version. Been looking for alternatives to OpenFaas that have Kafka integrations, and scale to 0 capabilities.

looked at Apache OpenWhisk, but we run on RKE2, and my initial install of Openwhisk appears to be too out of date to support RKE2 and missing images from docker.io. So now looking at Knative. What are your thoughts? We need support to be able to process functions about 10k a min, which can vary on time of execution, between ms and mins. So looking for horizontal scaling that can be controlled by other metrics, than just cpu and ram utilization, but more so, for example if the wait is over 5 scale out.. Issue with older openfaas, was scaling on RKE2 was not working great, for example, I could get it to scale from 5 to 20 pods, but only 12 of them would ever have data, but my backlog would have 100k's of files waiting.. So even though it scaled up, it was as if the distribution of work was only being married to specific pods. If I killed the pods that had no work, they come up again with no work, if I killed one with work, then another pod would scale up and another pod would start to get work. And On occasion with hours, it would reset down to the original deployment allotment of pods, and never scale up again, until I go into Kubernetes and tell it to add more pods.

So hoping to find a solution that doesn't require as much triage, to work with scaling, as points in time we are at higher volume and other points of time could be no volume.

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