fission VS Hey

Fission

This is where I see K8S coming in – teachers can provide dev deployments that are setup for students to learn. Teachers can also provide containers that run automated tests against the student containers for assessment! Plus, we can smooth over some of the git workflow stuff for the ripest of beginners; we can integrate with github to sync their work on our platform to repositories on their github account, so that they can really take ownership of the work they do on the platform. Last, students can graduate their work from development into production very easily, since we can take the base images + student diffs, build a new "prod" image for the student. We can run students' prod work on "serverless" K8S frameworks like fission or OpenFaas to be able to host many low-traffic "production" apps at the same time.

I use https://fission.io/ on Kubernetes to emulate AWS Lambda + API Gateway to run Python functions. I use their YAML Spec functionality to deploy functions. It works well for my use case.

After doing a lot of research, I ended up settling on the Fission.io framework to support this project. Fission is an open-source Serverless framework running in kubernetes. Think AWS Lambdas, but we are in control of every part of the infrastructure. Kubernetes gives us the power to define the environments the containers will be executed in, and any other resources they need. This gives us the control we need to be able to create our very own environment for executing arbitrary JavaScript through the V8 engine. Each function can be isolated as much as we need to and Fission is really great at giving us the ability to quickly create multiple environments.

Nope. I was using https://fission.io/

There are a lot of FaaS service offerings out there namely Lambda, Azure Functions, Google Cloud Functions to name a few. While these offerings run on their respective clouds, there are services like Fission that are open source and allow you to deploy and execute functions on Kubernetes clusters irrespective of where they reside.

Fission

It's super slow (20-50 rps), please try this instead : https://github.com/fission/fission (few hundreds to few thousands rps)

The results of the experiment below were based on reproducing approximately 100 cold starts for the duration of our experiment which ran for approximately 1 hour. For it (and all experiments from my previous articles) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman

The results of the experiment to retrieve the existing product from the database by its id see GetProductByIdViaAuroraServerlessV2DataApiHandler with Lambda function with 1024 MB memory setting were based on reproducing more than 100 cold and approximately 10.000 warm starts with experiment which ran for approximately 1 hour. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. We won't enable SnapStart on the Lambda function first.

The results of the experiment below were based on reproducing more than 100 cold and approximately 100.000 warm starts with experiment which ran for approximately 1 hour. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. I ran all these experiments for all 3 scenarios using 2 different compilation options in template.yaml each:

In our experiment we'll re-use the application introduced in part 9 for this. There are basically 2 Lambda functions which both respond to the API Gateway requests and retrieve product by id received from the API Gateway from DynamoDB. One Lambda function GetProductByIdWithPureJava21Lambda can be used with and without SnapStart and the second one GetProductByIdWithPureJava21LambdaAndPriming uses SnapStart and DynamoDB request invocation priming. We'll measure cold and warm starts using the following memory settings in MBs : 256, 512, 768, 1024, 1536 and 2048. I also put the cold starts measured in the part 12 into the tables to see both cold and warm starts in one place. The results of the experiment below were based on reproducing more than 100 cold and approximately 100.000 warm starts for the duration of our experiment which ran for approximately 1 hour. Here is the code for the sample application. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. Abbreviation c is for the cold start and w is for the warm start.

Por último, podemos executar os testes de carga usando hey.

For running our experiments to provoke anomalies we'll use the stress test tool. You can use the tool of your choice (like Gatling, JMeter, Fiddler or Artillery), I personally prefer to use the tool hey as it is easy to use and similar to curl. On Linux this tool can be installed by executing

Hey: is a fast HTTP load testing tool used to test web applications and APIs. It provides a CLI (command-line interface) and supports concurrent requests.

The client and server nodes are CentOS7.9/X86_64. If the HTTP POST requests were sent directly to the server with hey -c 1, there are about 0.2% of cases that may timeout. If the HTTP POST requests were sent through an NGINX proxy on the client node, there are about 20% of cases will timeout. I've confirmed that only one backend node has this problem. All other nodes are 100% succeeded even with higher throughput.

64 concurrent requests isn't a lot. Modern web apps can typically handle much more than that (depending on what the request does, of course). Try it yourself with a load tester like https://github.com/rakyll/hey against a Go HTTP server, for example the one I've built in https://www.golang.dk/articles/go-and-sqlite-in-the-cloud