compress VS Grafana

There's a pure-go zstd at https://github.com/klauspost/compress - it's likely faster than running the upstream zstd under Wazero.

It's no surprise that "fast" Go libraries are actually just assembly: https://github.com/klauspost/compress/blob/master/zstd/seqdec_amd64.s (just one file out of several, for just one architecture, for just one compression algorithm!)

There is a reasonably feature complete implementation of Zstd for Go: https://github.com/klauspost/compress/tree/master/zstd

It may not offer the same API 1:1, but it has no interoperability issues that I've encountered. So, I just think no one has bothered to implement it in Rust because most use cases don't mind the added bloat you're talking about. Plus, other comments I've seen suggest that you can actually tune the size of the zstd library, although I'm not sure if the Rust bindings expose that.

Thanks again for your review/comment!!! Btw, are you the author of this repo https://github.com/klauspost/compress because I love it!!!

I noticed Github returns "This page is taking too long to load" with status code 502 for https://github.com/klauspost/compress but rest of their urls works fine. Anyone know why would that be the case ?

Cloning the repo works perfectly well.

git clone https://github.com/klauspost/compress

Recent versions of zstd definitely don't obsolete LZ4, or else I don't think the author would still be contributing to both...

And if you're going to play with Snappy, you might find S2, which was linked on HN relatively recently, interesting. [1]

[1] - https://github.com/klauspost/compress/tree/master/s2

Compression method appears to be zstandard and uses https://github.com/klauspost/compress, for those wondering like I was.

OMG u/klauspost is this you? https://github.com/klauspost/compress/tree/master/s2

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.