velero VS Hey

Openshift last I looked used Velero under the covers for the functionality, which works fine in standard kubernetes. Most if not all that Openshift does is Open source.

Valero

velero is also a popular solution to for k8s backup that is 3rd party you might check out.

Logical backups using pre and post hooks thanks to this suggestion https://github.com/vmware-tanzu/velero/issues/2763 working way better than kanister blueprints.

While not discounting OP or the work in this repo (seems like a fun k8s/go project), folks might check out Velero for this purpose if they're looking to rely on this kind of export in prod: https://github.com/vmware-tanzu/velero

Hyper-V itself does not directly support backing up container-based platforms like Kubernetes clusters. To back up a Kubernetes cluster, you would typically use tools that interact with the Kubernetes API to capture the necessary data and metadata for backup purposes. Some of those tools are Velero https://velero.io/ (formerly Heptio Ark), Kasten K10, and Stash.

For Kubernetes-land, https://velero.io/ is awesome - but I haven't used it for online-database backups yet. If you're exploring, I'd checkout Velero - if you just need something to work reliably, I'd checkout Percona.

If you're looking for a backup solution for managed kubernetes, check out Velero. It is great for non-managed kube as well (but you've got other options like etcd backups)

You can also leverage https://velero.io/ to backup both cluster state and pvc state to s3

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