lib-ruby-parser VS vector

I realize this might not be for everyone, but I'm writing it in Rust using Lib-ruby-parser and tower-lsp: two existing libraries that handle a bunch of the heavy lifting for me. I'm more productive in Rust than with Ruby at this point, despite doing Ruby full time for 15 years, plus I really really don't want to have to deal with a slow LSP--that was the whome impetus for this project. I started in the spring, made a bunch of headway, then backtracked to redo the internals to make it easier to handle monkeypatching, overriding/redefining of methods, etc. across your project.

For now Artichoke leans on mruby’s parser, but I’m looking forward to using https://github.com/lib-ruby-parser/lib-ruby-parser which is a Rust port of MRI’s parse.y.

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=<

we are doing something similar with OTEL but we are looking at using https://vector.dev/

We don't pull logs, we forward logs to a centralized logging service.

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.

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.

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

Log to stdout/stderr and collect your logs with a tool like vector (vector.dev) and send it to something like Grafana Loki.

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 .