Loguru
vector
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Loguru | vector | |
---|---|---|
31 | 96 | |
18,080 | 16,512 | |
- | 5.7% | |
8.6 | 9.9 | |
29 days ago | 4 days ago | |
Python | Rust | |
MIT License | Mozilla Public 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.
Loguru
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Loguru VS polog - a user suggested alternative
2 projects | 9 Dec 2023
- a few comments and questions about loguru - the most popular 3rd party logging module for Python
- What libraries do you use the most alongside django?
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Linus is being reasonable and wise and well-mannered once again. Wouldn't mind reading a few juicy expletives, to be honest.
Because you get to see the simultaneous mix of arguments about objective verifiable facts and arguments about yelling at each other. Plus I don't understand how to cook but I do understand Delgan/loguru#563.
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library to log methods and function calls.
How can we integrate with current logging libraries such as logging, logges, loguru? And how would you compare your library with ic
- Is adding logging to a library good design?
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Where can I apply logging specifically with loguru?
I found loguru online and was thinking if it's relevant for my code. As far as I understand it would be preferable as opposed to me printing my exceptions with print.
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Logging in Python Like a Pro
You should try the loguru library. I was able to roll a rolling-upload-to-s3 adapter in under an hour. Switching to json logs is one bool flag away. Plus it's gorgeous
https://github.com/Delgan/loguru
Also iirc s3's "file-like interface" does not actually obey the file protocol, which is obnoxious.
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NameError: name 'logger' is not defined
If you have a choice, save yourself some heartache and install loguru
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The Boilerplate for Logging in Python
This? First time to hear about it. Thanks!
vector
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Docker Log Observability: Analyzing Container Logs in HashiCorp Nomad with Vector, Loki, and Grafana
job "vector" { datacenters = ["dc1"] # system job, runs on all nodes type = "system" group "vector" { count = 1 network { port "api" { to = 8686 } } ephemeral_disk { size = 500 sticky = true } task "vector" { driver = "docker" config { image = "timberio/vector:0.30.0-debian" ports = ["api"] volumes = ["/var/run/docker.sock:/var/run/docker.sock"] } env { VECTOR_CONFIG = "local/vector.toml" VECTOR_REQUIRE_HEALTHY = "false" } resources { cpu = 100 # 100 MHz memory = 100 # 100MB } # template with Vector's configuration template { destination = "local/vector.toml" change_mode = "signal" change_signal = "SIGHUP" # overriding the delimiters to [[ ]] to avoid conflicts with Vector's native templating, which also uses {{ }} left_delimiter = "[[" right_delimiter = "]]" data=<
- FLaNK AI Weekly 18 March 2024
- Vector: A high-performance observability data pipeline
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Hacks to reduce cloud spend
we are doing something similar with OTEL but we are looking at using https://vector.dev/
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About reading logs
We don't pull logs, we forward logs to a centralized logging service.
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Self hosted log paraer
opensearch - amazon fork of Elasticsearch https://opensearch.org/docs/latestif you do this an have distributed log sources you'd use logstash for, bin off logstash and use vector (https://vector.dev/) its better out of the box for SaaS stuff.
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creating a centralize syslog server with elastic search
I have done something similar in the past: you can send the logs through a centralized syslog servers (I suggest syslog-ng) and from there ingest into ELK. For parsing I am advice to use something like Vector, is a lot more faster than logstash. When you have your logs ingested correctly, you can create your own dashboard in Kibana. If this fit your requirements, no need to install nginx (unless you want to use as reverse proxy for Kibana), php and mysql.
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Show HN: Homelab Monitoring Setup with Grafana
I think there's nothing currently that combines both logging and metrics into one easy package and visualizes it, but it's also something I would love to have.
Vector[1] would work as the agent, being able to collect both logs and metrics. But the issue would then be storing it. I'm assuming the Elastic Stack might now be able to do both, but it's just to heavy to deal with in a small setup.
A couple of months ago I took a brief look at that when setting up logging for my own homelab (https://pv.wtf/posts/logging-and-the-homelab). Mostly looking at the memory usage to fit it on my synology. Quickwit[2] and Log-Store[3] both come with built in web interfaces that reduce the need for grafana, but neither of them do metrics.
- [1] https://vector.dev
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Retaining Logs generated by service running in pod.
Log to stdout/stderr and collect your logs with a tool like vector (vector.dev) and send it to something like Grafana Loki.
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Lightweight logging on RPi?
I would recommend that you run vector as a systems service so you don't have to worry about managing it. Here is a basic config to do that - https://github.com/vectordotdev/vector/blob/master/distribution/systemd/vector.service .
What are some alternatives?
structlog - Simple, powerful, and fast logging for Python.
graylog - Free and open log management
Sentry - Developer-first error tracking and performance monitoring
Fluentd - Fluentd: Unified Logging Layer (project under CNCF)
logzero - Robust and effective logging for Python 2 and 3.
agent - Vendor-neutral programmable observability pipelines.
logbook - A cool logging replacement for Python.
syslog-ng - syslog-ng is an enhanced log daemon, supporting a wide range of input and output methods: syslog, unstructured text, queueing, SQL & NoSQL.
Eliot - Eliot: the logging system that tells you *why* it happened
OpenSearch - 🔎 Open source distributed and RESTful search engine.
icecream - 🍦 Never use print() to debug again.
tracing - Application level tracing for Rust.