Kubernetes–What is the difference between resource requests and limits?

In Kubernetes it is a best practice to configure resource limits for your containers. VSCode will even warn you if it couldn’t detect resource limits in your manifest files:

Setting resource limits prevents a container consuming too much resources and impacting other workloads. By setting limits, pods will be terminated by Kubernetes when their limits are exceeded. This helps in keeping the cluster healthy and stable.

The most common resources to specify are CPU and memory, but others exists.

Here is a short example on how to configure this at the container level:

In the example above, the CPU usage is limited to 250m or 250 milliCPU (1/4th of a vCPU/Core) and memory usage is limited to 512Mi or 512MiB.

Next to resource limits, it is also possible to configure resource requests. Setting a resource request indicates the amount of that resource that you expect the container will use. Kubernetes will use this information when determining which node to schedule the pod on.

A node will be ineligible to host a new container if the sum of the workload requests, including the new container’s request, exceeds the available capacity. This remains the case even if the real-time memory use is actually very low. This is the reason why it is best to keep the requests values as low as possible and setting the limits as high as possible(without bringing other workloads into trouble). Using a low resource request value gives your pods the best chance of getting scheduled to a node.

Limits are also configured in the resource section of your manifest file:

More information: https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME ...

Cache stampede: when our cache turned against us

While investigating some performance issues, we ran into an ASP.NET Core API that cached a fairly expensive aggregation query for 60 seconds. Under normal load, that was fine: one request rebuilds the cache, everyone else reads from it. Under peak load, dozens of requests would arrive in that same expiry window, all see a cache miss, and all fire the same expensive query in parallel. The database didn't like that. That was the moment when our caching layer stopped helping and started hurting. A burst of requests comes in at the same time, all miss the cache, and all go hammer the database or the downstream API at once. That's a cache stampede . The cache was supposed to protect our backend, and for a few hundred milliseconds it did the opposite. Why this happens IMemoryCache.GetOrCreate (and its async sibling) looks like it protects you, but it doesn't add any locking on its own. Look at the naive version: public async Task<Report> GetReportAsync(string key) ...

A complex system designed from scratch never works

A few years ago, I worked as an architect on a big mainframe rewrite. I still count it as one of my failures. Not because the technology was wrong, but because I couldn't convince the management team to simplify the approach. Years later, the organization is still struggling to get the new system up and running. I left the project at the time, because I couldn't put my name behind an approach that would take very long and cost a lot of money without a working system to show for it along the way. Gall’s Law That memory keeps coming back to me, because it's a textbook case of Gall's Law playing out in real life. Gall's Law , from John Gall's Systemantics , states it plainly: A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works, and it cannot be patched to make it work. You have to start over with a simple system that works. What does that mean in practice,...

The art of simplicity

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