uPlot VS Grafana

Here is a very efficient 2D canvas lib that is extremely efficient https://github.com/leeoniya/uPlot i recommend looking through their readme and seeing if it solves your current problems.

BACKEND:

It's about 3000 lines of Crystal code https://crystal-lang.org/ -- it's been an absolute dream to program in. Ruby-like syntax, statically compiled performance. Nice standard library. Would recommend :)

Performance == caching

Local ephemeral filesystem for shortest-term caching.

Redis for shared medium-term caching and locking.

S3-compatible object storage for longest-term caching (for raw data feeds pulled every evening by a cronjob).

FRONTEND:

Bootstrap

uPlot https://github.com/leeoniya/uPlot

Just a sprinkle of inline JS+CSS on the page.

you can use matchMedia to detect devicePixelRatio changes and re-sync:

https://github.com/leeoniya/uPlot/blob/190134aa844cfa2a0c052...

everything stays crisp even as you browser-zoom. e.g. https://leeoniya.github.io/uPlot/demos/area-fill.html

Safari's slow release cadence (typically tied to OS version upgrades) does leave it more broken and for longer than Firefox, Chrome, Opera and other browsers that are updated more frequently and not tied to the OS.

just recently i ran into incompatibilities with Safari < 14 needing the old-spec matchmedia api:

https://github.com/leeoniya/uPlot/issues/538#issuecomment-87...

uPlot will handle this. https://github.com/leeoniya/uPlot

If you are showing 100s of thousands of points, I'd still go with canvas eg uPlot. It's a really, really excellent library.

The existing node red chart tools is not great. I'd like to be able to use uplot instead. The uplot-charts node should work very much like that charts node built into node-red-dashboard.

super cool!

i've done my share of optimizations and benchmarking charting libs [1] but this goes deeper still.

SVG is not a good choice for large raw datasets when you need performance.

[1] https://github.com/leeoniya/uPlot#performance

Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.

To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.

Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.

Prometheus: Open-source monitoring system. Often used together with Grafana.

Grafana

Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.

So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:

In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:

Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.