skopeo VS jaeger

I decided to go searching for an alternative means to pull a docker image. In my search I discovered Skopeo, an alternative method to download Docker images that proved to be surprisingly effective. It not only downloaded the image faster, it also allowed me to save my image in a tar file, which means you can pull an image on one system and share that image to another system, loading it easily to docker instance on that system. This can be very beneficial if you have multiple systems and don't want to download an image multiple times.

But I'd suggest looking into if it's solved by other tools already, like regclient/regclient and their regsync features or something like containers/skopeo.

Have a use case where we have a CLI (built with cobra) for our dev teams which can execute common tasks. One of those tasks we want to implement is to copy docker images from the internet to our internal registry. A tool such as skopeo can do this and much more. Instead of essentially re-writing the functionality directly into our CLI we'd like to embed it. This would also negate the need for the dev teams to manage multiple CLI tools.

Self hoisting here, I put this together to make it easier to generate single (extra) layer docker images without needing a docker agent, capabilities, chroot, etc: https://github.com/andrewbaxter/dinker

Caveat: it doesn't work on Fly.io. They seem to be having some issue with OCI manifests: https://github.com/containers/skopeo/issues/1881 . They're also having issues with new docker versions pushing from CI: https://community.fly.io/t/deploying-to-fly-via-github-actio... ... the timing of this post seems weird.

FWIW the article says

> create a Docker image, also known as an OCI image

I don't think this is quite right. From my investigation, Docker and OCI images are basically content addressed trees, starting with a root manifest that points to other files and their hashes (root -> images -> layers -> layer configs + files). The OCI manifests and configs are separate to Docker manifests and configs and basically Docker will support both side by side.

skopeo is another tool worth looking into. we've started deploying amd and arm nodes into our k8s clusters, and this tool was incredibly easy to build around for getting multi-arch images into our container registry.

I would use skopeo, the tool is quite handy for working with remote images. https://github.com/containers/skopeo

Using distroless images not only reduces the size of the container image it also reduces the surface attack. The need for container image signing is because even with the distroless images there is a chance of facing some security threats such as receiving a malicious image. We can use cosign or skopeo for container signing and verifying. You can read more about securing containers with Cosign and Distroless Images in this blog.

Look at using tools like skopeo or crane

You could try some commandline tool like skopeo to fetch the image tags regularly and do some shell magic to notify you on any change you want

This is what Podman, an open-source daemonless and rootless container engine, was developed with in mind. Podman runs using the runC container runtime process, directly on the Linux kernel, and launches containers and pods as child processes. In addition, it was developed for the Docker developer, with most commands and syntax seamlessly mirroring Docker's. Buildah, an image builder, and Skopeo, the image utility tool, are both complimentary to Podman as well, and extend the range of operations able to be performed.

Jaeger maps the flow of requests and data as they traverse a distributed system. These requests may make calls to multiple services, which may introduce their own delays or errors. https://www.jaegertracing.io/

As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had an idea of what API route was slow, there wasn’t much visibility into the actual bottleneck.

This is where our performance product comes in: we’re rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.

What’s unique about our approach is that we lean heavily into creating traces from the frontend. For example, if you’re using our Next.js SDK, we automatically connect browser HTTP requests with server-side code execution, all from the perspective of a user. We find this much more powerful because you can understand what part of your frontend codebase causes a given trace to occur. There’s an example here [3].

From an instrumentation perspective, we’ve built our SDKs on-top of OTel, so you can create custom spans to expand highlight-created traces in server routes that will transparently roll up into the flame graph you see in our UI. You can also send us raw OTel traces and manually set up the client-server connection if you want. [4] Here’s an example of what a trace looks like with a database integration using our Golang GORM SDK, triggered by a frontend GraphQL query [5] [6].

In terms of how it's built, we continue to rely heavily on ClickHouse as our time-series storage engine. Given that traces require that we also query based on an ID for specific groups of spans (more akin to an OLTP db), we’ve leveraged the power of CH materialized views to make these operations efficient (described here [7]).

To try it out, you can spin up the project with our self hosted docs [8] or use our cloud offering at app.highlight.io. The entire stack runs in docker via a compose file, including an OpenTelemetry collector for data ingestion. You’ll need to point your SDK to export data to it by setting the relevant OTLP endpoint configuration (ie. environment variable OTEL_EXPORTER_OTLP_LOGS_ENDPOINT [9]).

Overall, we’d really appreciate feedback on what we’re building here. We’re also all ears if anyone has opinions on what they’d like to see in a product like this!

[1] https://github.com/highlight/highlight/blob/main/LICENSE

[2] https://www.jaegertracing.io

[3] https://app.highlight.io/1383/sessions/COu90Th4Qc3PVYTXbx9Xe...

[4] https://www.highlight.io/docs/getting-started/native-opentel...

[5] https://static.highlight.io/assets/docs/gorm.png

[6] https://github.com/highlight/highlight/blob/1fc9487a676409f1...

[7] https://highlight.io/blog/clickhouse-materialized-views

[8] https://www.highlight.io/docs/getting-started/self-host/self...

[9] https://opentelemetry.io/docs/concepts/sdk-configuration/otl...

For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements

Have you checked out Jaeger [1]? It is lightweight enough for a personal project, but featureful enough to really help "turn on the lightbulb" with other engineers to show them the difference between logging/monitoring and tracing.

[1] https://www.jaegertracing.io/

From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.

Jaeger is an open-source, distributed tracing system that monitors and troubleshoots the flow of requests through complex, microservices-based applications, providing a comprehensive view of system interactions.

However, ensuring fault tolerance in distributed systems is not at all easy. These systems are complex, with multiple nodes or components working together. A failure in one node can cascade across the system if not addressed timely. Moreover, the inherently distributed nature of these systems can make it challenging to pinpoint the exact location and cause of fault - that is why modern systems rely heavily on distributed tracing solutions pioneered by Google Dapper and widely available now in Jaeger and OpenTracing. But still, understanding and implementing fault tolerance becomes not just about addressing the failure but predicting and mitigating potential risks before they escalate.

In this article, we'll use HoneyComb.io as our tracing backend. While there are other tools in the market, some of which can be run on your local machine (e.g., Jaeger), I chose HoneyComb because of their complementary tools that offer improved monitoring of the service and insights into its behavior.

The best way to do this, is with the help of tracing tools such as paid tools such as Honeycomb, or your own instance of the open source Jaeger offering, or perhaps Encore's built in tracing system.

In this example, I will create 3 Node.js services (shipping, notification, and courier) using Amplication, add traces to all services, and show how to analyze trace data using Jaeger.