pprof
node_exporter
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pprof | node_exporter | |
---|---|---|
12 | 78 | |
7,450 | 10,281 | |
2.5% | 3.0% | |
7.6 | 8.9 | |
about 15 hours ago | 3 days ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pprof
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Profiling Caddy
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)
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Why So Slow? Using Profilers to Pinpoint the Reasons of Performance Degradation
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.
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Improving the performance of your code starting with Go
github.com - google/pprof
- Proposal to Support Timestamps and Labels in Pprof Events
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A Generic Approach to Troubleshooting
The application performances in a specific code path (e.g. gdb, pprof, …).
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Does rust have a visual analysis tool for memory and performance like pprof of golang?
pprof is https://github.com/google/pprof, it's a very useful tool in golang , and really really really convenient
- pprof - tool for visualization and analysis of profiling data
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Tokio Console
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
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Cats and Clouds – There Are No Pillars in Observability with Yoshi Yamaguchi
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.
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Is there a way I can visualize all the function calls made while running the project(C++) in a graphical way?
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.
node_exporter
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Prometheus Fundamentals (Lesson-01)
$ wget https://github.com/prometheus/node_exporter/releases/download/v1.7.0/node_exporter-1.7.0.linux-amd64.tar.gz $ tar -xzvf node_exporter-1.7.0.linux-amd64.tar.gz
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List of your reverse proxied services
Node Exporter
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Best way to monitor disk space, RAM in remote servers and get alerts when full?
The Prometheus node_exporter can provide this information, doesn't require root. You could run it as a systemd user unit if you don't have root.
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Best Course/Learning Path for mastering Prometheus and Grafana
I personally find it best to learn through experimentation. Start with reading a bit about Prometheus and Grafana through their docs, and then familiarise yourself with setting up a local Prometheus + Grafana instance either locally or with docker using docker-compose, along with something to generate /metrics endpoint for Prometheus to scrape from such as a custom Prometheus exporter in Python or using Node Exporter.
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Linux Traffic Monitoring
Your best bet might be to fork node_exporter to get you more verbose socket stats: https://github.com/prometheus/node_exporter/blob/master/collector/sockstat_linux.go
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Tool to monitor disk space
I use Grafana + Prometheus + Node Exporter.
- Is there a dashboard of sorts that can keep track of my linux-based computers and VMs to that I can easily see if any of them have updates or are running low on storage and et cetera?
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Would SNMP present less of a load than SSH to get interface metrics from older cisco 3K series switches?
Crazy idea, can't NX devices run Docker? I wonder if the node_exporter would work.
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Questions about Kubernetes
Kubernetes itself will not notify you, the way I've seen people do this, is to use something like kube-state-metrics or node_exporter, export that to Prometheus (or preferrably VictoriaMetrics because Prometheus is terrible IMO), and then setup alarms on that with alertmanager or equivalent, or just look at dashboards regularly with Grafana. Realistically I recommend only setting alerts on disk usage and application/database latency. CPU and memory utilization isn't a great metric to alert on a lot of the time.
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How to log system usage: RAM, CPU, over a long time to detect which component is slowing down?
You may setup node exporter and collect metrics with prometheus for example. Its not quite "simple" way, but still you may find it useful.
What are some alternatives?
gperftools - Main gperftools repository
cadvisor - Analyzes resource usage and performance characteristics of running containers.
prometheus - The Prometheus monitoring system and time series database.
process-exporter - Prometheus exporter that mines /proc to report on selected processes
jaeger - CNCF Jaeger, a Distributed Tracing Platform
Netdata - The open-source observability platform everyone needs
tracy - Frame profiler
ping_exporter - Prometheus exporter for ICMP echo requests using https://github.com/digineo/go-ping
parca - Continuous profiling for analysis of CPU and memory usage, down to the line number and throughout time. Saving infrastructure cost, improving performance, and increasing reliability.
fortigate_exporter - Prometheus exporter for Fortigate firewalls
massif-visualizer - Visualizer for Valgrind Massif data files
windows_exporter - Prometheus exporter for Windows machines