stanza VS pprof

Been a while since I looked into fluentd and elasticsearch but I heard Stanza (soon to be moved to the OpenTelemetry Collector) is meant to be a modern replacement for it, but seems it's still in it's early days.

And then they contacted some famous log collection projects such as the Fluent Bit and also others like Stanza as well as Syslog, I guess. And I didn't read the whole thread of the conversation around log collection. But now, they set the Stanza as the first implementation of OpenTelemetry logs. And in Stanza, the observIQ with Stanza is merged under the OpenTelemetry log repository, so that's the status. And then, they try to standardize the format of logs based on the Stanza format, such as what kind of information should be included in log.

observIQ is a hosted log management platform that's powered by our FOSS log agent, Stanza: https://github.com/observIQ/stanza. Stanza is lightweight and robust (written in Go), and can be configured to act as a Syslog receiver in about a minute (guided configuration in our app).

observIQ is a simple, cost-effective hosted log management platform designed with small teams in-mind. Log collection is powered by Stanza: https://github.com/observIQ/stanza , our high-performance OSS log agent. Stanza was also recently adopted as the official log agent of the CNCF's OpenTelemetry project. observIQ offers guided-installation, pre-made dashboards, threshold-based alerts, live tail and more.

We're a new hosted log management platform with simple, native support for gathering logs from Openshift. Deployment typically takes a few minutes. We also automatically enrich Openshift logs metadata like node, namespace, pod, container - no additional configuration required. Logs are gathered with your high-performance OSS log agent, Stanza: https://github.com/observIQ/stanza

We offer a super simple, hosted log management platform that allows you to easily ingest any syslog traffic by setting up high-speed OSS agent as a syslog receiver in just a few click (setup takes about 30 seconds). You can find more about our log agent here: https://github.com/observIQ/stanza

Self-promotion alert, my apologies: Check us out at observIQ: https://observiq.com/ We provide a super-simple host log management platform powered our high-performance OSS Log Agent, Stanza (https://github.com/observIQ/stanza). Average setup time is about 5 minutes, end-to-end. Our platform is designed with start-ups and small businesses in-mind and are priced about half as much as our competitors. Lastly, we offer 14-day free trial, but also offer a completely free plan that provides 3 gigs of ingestion/day and 3 day retention - with access to the full feature set of the platform (Dashboards, Alerts, Live Tail). Shoot me a message or chat if you have any questions!

Similarly, our platform will be rolling out this feature soon, and will utilize our lightweight FOSS log agent Stanza - written in Go. The agent may work for your project https://github.com/observIQ/stanza

The pprof format is not tied to Go. From my understanding, it's used within Google across multiple languages. The format is defined in the pprof repository[0], and the visualization tool is source-language agnostic. I've seen libraries in numerous languages (e.g. Python, Java) to publish profiles in pprof format. This is an indicator the pprof format has become de-facto. Grafana Pyroscope[1] is a tool that's capable of parsing the pprof format, agnostic to the source programming language, and has instructions for Go, Java, Python, Ruby, node.js, Rust, and .NET.

My understanding is that you're searching for a combination of the profiles, metrics, and tracing. Caddy supports all 3.

[0] https://github.com/google/pprof/blob/main/doc/README.md

[1] https://grafana.com/docs/pyroscope/latest/

metrics and tracing need to be manually enabled (for now, perhaps)

Because we couldn't identify the issue using the results we got from Callgrind, we reached for another profiler, gperftools. It's a sampling profiler and therefor it has a smaller impact on the application's performance in exchange for less accurate call statistics. After filtering out the unimportant parts and visualizing the rest with pprof, it was evident that something strange was happening with the send function. It took only 71 milliseconds with the previous implementation and more than 900 milliseconds with the new implementation of our Bolt server. It was very suspicious, but based on Callgrind, its cost was almost the same as before. We were confused as the two results seemed to conflict with each other.

github.com - google/pprof

The application performances in a specific code path (e.g. gdb, pprof, …).

pprof is https://github.com/google/pprof, it's a very useful tool in golang , and really really really convenient

Go also has pretty good out of the box profiling (pprof[0]) and third-party runtime debugging (delv[1]) that can be used both remotely and local.

These tools also have decent editor integration and can be use hand in hand:

https://blog.jetbrains.com/go/2019/04/03/profiling-go-applic...

https://blog.jetbrains.com/go/2020/03/03/how-to-find-gorouti...

[0] https://github.com/google/pprof

[1] https://github.com/go-delve/delve

And what we do in Google Cloud is that we still use the pprof. But it's a kind of forked version of the pprof because the visualization part is totally different. So we give that tool as the Cloud Profiler. So that is the product name. And then, the difference between the pprof and a Cloud Profiler is that Cloud Profiler provides the agent library for each famous programming language such as Java, Python, Node.js, and Go. And then what you need to do is to just write 5 to 10 lines of code in a new application. That launches the profile agent in your application as a subsidiary thread of the main thread. And then, that thread periodically collects the profile data of the application and then sends that data back to Google Cloud and the Cloud Profiler.

gprftools (https://github.com/gperftools/gperftools) can be easily plugged in using LD_PRELOAD and signal, and has nice go implemented visualization tool https://github.com/google/pprof.