Prometheus — `prometheus`

Ingests the Prometheus metrics you pick — each series becomes a metric event on every poll tick, joinable with logs, deploys and alerts on the same key. You choose which metrics (by name, or by a label they carry) and NavFlow does the rest: there is no PromQL to write. The numeric value is stored as a number-typed value label, so triggers can aggregate it.

Supports discovery: given a url, it lists the server’s metrics and labels for you to pick from.

Prerequisites: a reachable Prometheus server (its base URL, e.g. http://localhost:9090).

Set up

Agent

Ask a connected agent:

Use navflow to ingest the pg_* metrics from the Prometheus at localhost:9090.

The agent calls discover_source (lists the metrics and labels, samples your picks, proposes the config), test_source (one poll), then create_source. You name the metrics you want — it never writes PromQL.

Console

Sources → Add source → Prometheus. Enter the url (+ any auth) and Discover, then pick the metrics to ingest:

By name — type a metric name or a prefix_* pattern; tick the matches.
By label — pick a label; it lists the metrics that carry it; tick the ones you want.

Confirm, and NavFlow proposes the entity key and labels from a sample. No query language, at any step.

YAML / API

The console and agent generate the config for you — you rarely hand-write it. Your picks compile to a single selector that ingests every chosen metric, each event named by its own metric name:


- name: metrics
  connector: prometheus
  poll: 10s
  config:
    url: http://localhost:9090
    queries:
      - promql: '{__name__=~"(pg_stat_database_xact_commit|pg_up)"}'   # generated from your picks
        by_name: true
    labels:
      - { name: service, field: metric.service, primary: true }
      - { name: value, field: value, type: number }   # the metric value — aggregate it in triggers

Or POST /api/sources with the same object.

Configuration

field	type	description
`url`*	string	Prometheus base URL, e.g. `http://localhost:9090`
`bearer_token`	string · secret	optional `Authorization: Bearer` token (managed Prometheus / auth proxy)
`username`	string	optional HTTP basic-auth username (e.g. a Grafana Cloud instance id)
`password`	string · secret	optional HTTP basic-auth password / API token, paired with `username`
`default_key`	string	entity key for series that carry no key label (default `unknown`)
`queries`*	list	the metrics to ingest — generated from your picks (below)

Each queries entry is a metric selector NavFlow builds from your picks — you don’t author these by hand:

field	type	description
`promql`*	string	the metric selector; for a picked basket, a `{__name__=~"…"}` matcher built for you
`by_name`	bool	basket mode — name each event by its own metric name (set on a generated basket)
`exclude`	string	comma-separated substrings to drop by metric name (e.g. `_bucket`)

The metric value is stored as a number-typed value label (proposed automatically), so a trigger can max / avg / sum it — filter the view to one metric by event_type, then aggregate value. Number labels are excluded from entity faceting.

Advanced — a computed series. A queries row can also be a single PromQL expression (a derived rate, a histogram_quantile p99) with an explicit event_type and field — the escape hatch when you want a computed metric rather than a raw one. The pick-metrics flow above covers raw ingestion without touching PromQL.

Verify

Open the source: Recent events shows one event per series per poll; Fields shows the labels (metric.service, …) and the numeric value. Watch a metric move in Explore by picking the entity the series is keyed on.

Prometheus — prometheus