# 📈 Observability

> [!NOTE]
> Grafana Alloy runs natively on every fleet host, shipping logs to Loki and metrics to Prometheus via `remote_write` — pushed outward rather than scraped, a choice made specifically because of the firewall model in [`docs/firewall-and-network.md`](firewall-and-network.md), not despite it.

| | |
|---|---|
| **Agent** | Grafana Alloy, native systemd service on every host, not Docker |
| **Logs** | journald → Loki |
| **Metrics** | Push-based `remote_write`, not pull-based scraping |
| **Coverage** | All fleet hosts, confirmed live |

## Why Push Instead Of Pull — The Actual Eeasoning

A pull-based model (Prometheus scraping every host directly) is the more common default, and it was rejected for a specific reason tied directly to [`docs/firewall-and-network.md`](firewall-and-network.md): scraping means the monitoring host needs inbound network access to every single host it monitors, which means punching a hole through the zero-blanket-allow firewall model for every host, every metrics port. A push model inverts that — only the monitoring host needs to accept inbound writes, and every other host only ever *initiates* outbound connections, which fits the existing "explicit outbound rule, default-deny inbound" posture instead of fighting it. The tradeoff is real: push-based metrics are slightly harder to reason about for ad-hoc debugging (no simple `curl` against a target to see what it'd report), but it was the right call given the network model already in place.

## Pipeline

![Four per-host sources (journald, Docker containers, node metrics, native app metrics) all feed into Grafana Alloy, which ships logs to Loki and metrics to Prometheus via remote_write every 30s](../assets/observability-pipeline.svg)

## Agent Install: deliberate, not default

> [!TIP]
> The agent binary is downloaded directly from upstream releases and run under a hand-written systemd unit — not installed from a package repo or the vendor's own packaged service file. That's a real maintenance cost (manual version bumps instead of `apt upgrade` handling it), accepted because it keeps the exact version pinned and reproducible across the fleet through the same tag-driven Ansible model described in [`docs/iac-pipeline.md`](iac-pipeline.md), rather than whatever version a package repo happens to serve on a given day. Before install, a preflight check fails the run if the host doesn't run systemd, or if the root filesystem has too little free space for journald's own headroom, and configures/waits for time-sync confirmation (skipped on container-based hosts, where the clock can't be locally adjusted anyway).

## Logs: journald only, no double-handling

The system's built-in syslog daemon is explicitly disabled once journald takes over — avoids the same log stream getting handled twice, which would double both storage cost and noise in every downstream query. journald itself is switched from the default in-memory ring buffer to persistent on-disk storage, sized larger on the one host with much higher container-log volume than the rest of the fleet. This is the real config on two different hosts, not a described default:

```
$ cat /etc/systemd/journald.conf.d/*.conf   # the higher-volume host
[Journal]
Storage=persistent
SystemMaxUse=1G
SystemKeepFree=1G
MaxRetentionSec=14day

$ cat /etc/systemd/journald.conf.d/*.conf   # every other host
[Journal]
Storage=persistent
SystemMaxUse=500M
SystemKeepFree=1G
MaxRetentionSec=14day
```

![Three independent log fan-outs, each landing in Loki under its own job label: journald as job=system, Docker container logs (via the container API, not path-tailing) as job=docker, and the DNS query log as job=dns with real event time instead of ingestion time](../assets/observability-log-fanout.svg)

This is real log data flowing through that pipeline, not a mockup — `job=technitium-dns` entries showing real DNS query/answer content, `job=docker` entries from real containers, and `job=system` from journald itself, all landing in the same Loki instance:

![Real Grafana logs view: job=technitium-dns DNS query/answer lines, job=docker container log lines, and a job=system journald line, all in the same log stream](../assets/observability-logs-detail.png)

And the aggregate view across the whole fleet — real log volume, not an estimate:

![A real Grafana dashboard: total log line count, log volume broken down by host, and log rate over time — actual ingested data, not a described capacity](../assets/observability-logs-overview.png)

Every host's logs land in the same Loki instance under its own host label, filterable in Grafana by host or job — the same "one source of truth, not N inconsistent ones" instinct behind the DNS model in [`docs/dns-naming.md`](dns-naming.md). Docker logs are scraped via the container runtime's own API (not by tailing log files on disk) specifically so log collection survives a container being recreated during the update flows in [`docs/registry-and-build.md`](registry-and-build.md) — tailing a file path would silently stop working the moment that container got rebuilt. The DNS query log pipeline (feeding into [`docs/dns-naming.md`](dns-naming.md)'s enforcement story) is kept deliberately separate from general system/container error tracking, with its timestamps extracted from the log line itself rather than ingestion time — the same UTC-vs-wall-clock discipline that matters when querying Wazuh's alert data in [`docs/security-monitoring.md`](security-monitoring.md).

## Metrics: pushed, not pulled

| App | What's exposed |
|---|---|
| Vault | Dedicated read-only metrics token, separate from its normal agent token — see [`docs/secrets-and-pki.md`](secrets-and-pki.md) |
| Kestra | Built-in metrics endpoint |
| Gitea | Built-in metrics endpoint |
| n8n | Built-in metrics endpoint |
| Authentik | Built-in metrics endpoint, localhost-only |

Five apps expose their own Prometheus-format metrics endpoints directly, scraped by that host's own Alloy instance and pushed through the same path as host-level metrics — no separate pipeline needed for app-level visibility, and no separate firewall exception needed either, since it rides the same outbound `remote_write` connection every host already has open. This is that same push path landing in Grafana, for a real host's own metrics — not one of the 5 app endpoints specifically, but proof the pipeline actually delivers real numbers, not placeholder gauges:

![A real Grafana dashboard built entirely from remote_write metrics: live CPU/RAM/swap/disk gauges and real 2-day CPU, memory, network, and disk-usage graphs for an actual fleet host](../assets/observability-metrics-dashboard.png)

---
Related: [`docs/firewall-and-network.md`](firewall-and-network.md), [`docs/dns-naming.md`](dns-naming.md), [`docs/security-monitoring.md`](security-monitoring.md), [`docs/dashy-soc-board.md`](dashy-soc-board.md) · Stacks: [`stacks/platform/grafana-stack/`](../stacks/platform/grafana-stack/)