defmt VS jaeger

As someone who had only done embedded programming in the Arduino IDE, utilizing the defmt crate for logging with OpenOCD and GDB was an amazing experience. Although I still had no idea on to implement USB-HID for actually sending the key reports, until I discovered the usbd-human-interface-device crate and everything became so much easier. I just needed to create an iterator over Keyboard events and the crate would handle the rest as an added benefit the crate also supports multiple devices, so adding mouse support was as easy as creating a separate iterator over WheelMouseReport.

I think I want to use the QEMU emulator (as suggested in the Embedded Rust Book) and tests created with defmt-test. But I can't figure out how to get them to work together.

On embeded defmt is often used https://github.com/knurling-rs/defmt

defmt is a great framework for general logging.

Great writeup! The defmt logging crate uses a linker script to extract debug symbols so that you get nicely formatted stack traces on embedded systems. It works on linux, macos and windows. I wonder if the same technique can be applied to this project. It needs a runner though so may not be the right approach.

https://github.com/knurling-rs/defmt

Just played with embedded stuff today. defmt can be used for logging instead of println since there's not really a stdout when running on bare metal.

yYeah. For example, instead of printf("Variable X = %d, y=%f", x, y), where the micro then formats the string and pushes it out a serial port, blocking until its sent, I can write LOG("Variable X = %d, Y=%f, x, y), and what actually happens is a unique pointer to the string, and a tagged raw int and float get pushed onto a buffer, which takes about 15 instructions and takes up 16 bytes in the buffer. The buffer is then asynchronously sent out over the serial port, and the PC knows how to map the string ID to the actual string (this can by dynamically fetched from the micro or stored like debug info if there's not enough flash for the strings), and applies the formatting. There's an added bonus that it's super easy to take any variable which is logged and plot it live over time. There's also stuff like if the system crashes and the watchdog resets it, the buffer can be read out from memory to catch anything which wasn't sent out yet. It's a bit more of a complex system to set up but it really makes printf feel like the stone age when you get to using it. For an example of a similar system in rust, https://github.com/knurling-rs/defmt is implementing the same ideas (I don't know of any publicly available equivalent in C or C++, but you can implement it the same, though C++ is easier and it helps to know your way around a linker script to make something properly ergonomic).

If the build constraints are amenable for your project, you might also enjoy knurling-rs' defmt logging framework.

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.