tracing VS vector

The video description is as follows:

In this stream, we peel back the crust on the tracing crate — https://github.com/tokio-rs/tracing/ — and explore its interface, structure, and mechanisms. We talk about spans, events, their attributes and fields, and how to think about them in async code. We also dig into what subscribers are, how they pick up events, and how you can construct your own subscribers through the layer abstraction. For more details about tracing, see https://docs.rs/tracing/latest/tracing/.

> What's been your biggest issues around ergonomics/amenities for OpenTelemetry?

I can't speak generally, but in the Rust ecosystem the various crates don't play well together. Here's one example: <https://github.com/tokio-rs/tracing/issues/2648> There are four crates involved (tracing-attributes, tracing-opentelemetry, opentelemetry, and opentelemetry-datadog) and none of them fit properly into any of the others.

The tracing (logging) mechanism in an asynchronous codebase (tracing).

Tracing is Tokio's alternative for async code.

At a technical level, in Rust, both [tracing]https://crates.io/crates/tracing) and log are entire ecosystems (though for the latter at least there's also third party logging frameworks), and there's at least a bridge from log to tracing.

I am using https://github.com/tokio-rs/tracing for logging purposes in my application. I would like to develop a feature wherein logs should be saved to a database table (via sea-orm). Something similar is this, but it does not solve my needs fully.

I've used the tracing infrastructure with tracing_flame to profile some hot paths in async code: https://github.com/tokio-rs/tracing/tree/master/tracing-flame

Oh nice! IIRC when I checked, it was the Unicode tables that smashed the code size. I recently hit the same issue with the tracing crate, where a crate feature (for env var filtering) pulled in regex and my binary was suddenly 1MB bigger.

I know about tools such as tracing, jaeger or tracy. While having a complete tracing could be a potential solution, these tools don't work with no_std.

tracing by itself only outputs log data, you need to consume them in a subscriber, the tracing-subscriber crate exists for this. (example)

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 .