CLI reference

celine-utils
├── governance
│   ├── generate marquez
│   └── graph
└── pipeline
    ├── init app
    └── run  (envs | meltano | seed | dbt | build | prefect)

Installed as the celine-utils entry point. From a checkout of this repository, task cli -- <args> runs the same CLI from the working tree.

This page describes what each command is for and the traps in using it. For the exact current options, ask the CLI:

celine-utils <command> --help

An option table copied into a document is stale the next time an option is added, with nothing to detect it. The tables below are orientation, not the source of truth.

governance

`governance generate marquez`

Generate a governance.yaml scaffold from the datasets Marquez already knows about, so a governance file starts from what the pipeline actually produces rather than from a blank page.

celine-utils governance generate marquez --app <app-name> [options]

Option	Meaning
`--app`	CELINE app name. Required
`--output`, `-o`	Output path. Defaults to `PIPELINES_ROOT/apps/<app>/governance.yaml`
`--marquez`	Marquez base URL, overriding `OPENLINEAGE_URL`
`--namespace`	OpenLineage namespace, overriding `OPENLINEAGE_NAMESPACE`
`--yes`, `-y`	Non-interactive — write a skeleton using defaults

Interactively it prompts per dataset for licence, access level, classification, ownership and tags, and offers pattern scopes (exact name, schema wildcard, prefix wildcard) so one answer can cover many datasets. With --yes it writes a skeleton with an empty rule per discovered dataset.

# Interactive, against the configured Marquez
celine-utils governance generate marquez --app om

# Scaffold only, no prompts
celine-utils governance generate marquez --app om --yes

# Explicit endpoint, namespace and destination
celine-utils governance generate marquez \
  --app om \
  --marquez http://marquez.internal:5000 \
  --namespace ds_prod_silver \
  --output ./governance.yaml

The generated file is a starting point, not a finished one: it cannot know licence terms or who owns the data. Review it, then validate it.

Reads OPENLINEAGE_URL, OPENLINEAGE_NAMESPACE, PIPELINES_ROOT, and the KEYCLOAK_* variables when Marquez requires authentication — see environment.

`governance graph`

Show which pipelines feed which, resolved from the depends_on and sources blocks of every governance.yaml matched.

celine-utils governance graph [PATHS...] [--format tree|order|json|mermaid]
                              [--schedules FILE] [--strict]

PATHS are shell globs naming pipelines or governance files. A directory contributes the governance.yaml inside it, so apps/* and apps/*/governance.yaml are equivalent; governance.<name>.yaml is a deployer overlay and is skipped with a note. The default is apps/*.

# one repository
celine-utils governance graph 'apps/*'

# a deployment and the open-source pipelines together
celine-utils governance graph 'apps/*' '/path/to/deployment/pipelines/apps/*'

# the run order, flattened — what to run, in sequence
celine-utils governance graph 'apps/*' --format order

# a diagram, or something to feed a scheduler
celine-utils governance graph 'apps/*' --format mermaid
celine-utils governance graph 'apps/*' --format json

The tree output is a topological ordering: everything in tier N may run in parallel once tier N-1 is done. Findings and the summary go to stderr, so the graph itself stays pipeable.

Which trees you glob is a judgement, not a detail. A deployment repository may hold unmaintained copies of open-source apps, and including them reports every dataset those copies declare as having two producers.

Findings

Finding	Meaning
`unresolved`	No scanned pipeline produces the dataset and it is not marked `external: true` — a typo, or a tree you did not glob
`multiple-producers`	Two governance files declare the same dataset: two answers to who owns it, and an ambiguous producer for anything depending on it
`cycle`	No run order exists for the pipelines named
`self-dependency`	A pipeline depends on a dataset it also declares as its own output
`inactive-producer`	An active pipeline reads from one marked `active: false` — whatever it reads is as old as the last time that pipeline ran
`external-satisfied`	An entry marked `external: true` that a producer in this scan satisfies. Informational — it means a wider scan closed the graph

--strict exits 1 when anything other than external-satisfied or schedule-unverified is reported, which is the form for CI.

Checking the deployed schedules

Pass a deployment's scheduled flows and the crons are checked against the graph:

celine-utils governance graph 'apps/*' --schedules staging-flows.yaml

flows:
  - name: weather
    path: /pipelines/apps/weather/flows/pipeline.py   # or: app: weather
    schedule:
      cron: "15 * * * *"

app may be given directly or derived from path, which is the shape a Prefect deployment manifest already has. An entry with no cron is skipped — a flow triggered by hand has no ordering to check.

Finding	Meaning
`schedule-inversion`	Both fire hourly, never in the same minute, and the consumer is always earlier in the hour. It succeeds every time, on the previous run's output
`schedule-collision`	Producer and consumer can start in the same minute; which run the consumer sees depends on which starts first
`schedule-unverified`	The same two problems, on a pair where an app deploys several flows. Governance is per app and cannot say which flow produces which dataset, so the pairing may not be the one that moves the data. Advisory, and excluded from `--strict`
`not-deployed`	A pipeline marked active that no scheduled flow in this deployment runs

Schedules are not read from governance.yaml, deliberately. A cron is a deployment fact: one app runs several flows on independent schedules, and the same pipeline runs on different schedules in different deployments. A governance file is one per app and is shared by every deployment that installs it, so it is the wrong place for either.

pipeline

`pipeline init app`

Scaffold a new pipeline application.

celine-utils pipeline init app <app-name> [--force]

Produces:

<app-name>/
├── meltano/
├── dbt/
├── flows/pipeline.py
├── .env
└── README.md

Templates are rendered with the app name substituted, and database values in .env are populated from POSTGRES_* in the environment when present. The command aborts if the target already looks like a CELINE pipeline app; --force overwrites it.

See the pipeline tutorial for what to do with the result.

`pipeline run`

Executes one stage of a pipeline. Every subcommand discovers the app root by walking upward from the current directory looking for meltano/, dbt/ or flows/, so run these from inside the app.

Command	Does
`pipeline run envs`	Print the pipeline run environment as `export` lines
`pipeline run meltano [command]`	Run a Meltano command in the app's `meltano/`. Default: `run import`
`pipeline run seed`	`dbt seed` in the app's `dbt/`
`pipeline run dbt <spec>`	One dbt stage in the app's `dbt/` — see the spec grammar below
`pipeline run build [select]`	`dbt build` — every model followed immediately by its own tests
`pipeline run prefect`	Load and execute a `@flow` function from `flows/`

# Make the pipeline's own environment available to your shell
source <(celine-utils pipeline run envs)

celine-utils pipeline run meltano
celine-utils pipeline run meltano "run import --select my_stream"

celine-utils pipeline run seed
celine-utils pipeline run dbt staging
celine-utils pipeline run dbt "test -s tag:meters"
celine-utils pipeline run build silver

celine-utils pipeline run prefect
celine-utils pipeline run prefect --flow pipeline --function om_flow

The dbt spec grammar

pipeline run dbt takes one string, not a bare tag, and the same string that a flow passes to dbt_run(). It may open with a dbt subcommand — run, build, test, seed, snapshot — and defaults to run when it does not:

Spec	Runs
`silver`	`dbt run --select silver`
`staging --exclude tag:meters`	`dbt run --select staging --exclude tag:meters`
`-s gold,tag:wind`	`dbt run -s gold,tag:wind`
`build -s silver`	`dbt build -s silver`
`test`	`dbt test`
`test -s tag:meters`	`dbt test -s tag:meters`

--select is injected only when the spec names nodes without a selection flag of its own. A consequence of the leading-subcommand rule: a model actually named run, build, test, seed or snapshot is shadowed, and has to be named through an explicit -s.

Which verb. build is the one to use when the point is to populate the database: it interleaves each model's tests with the model, so a layer that populated badly fails where it broke rather than several layers downstream. run is for iterating on a single model, where the tests are noise until the model is right.

pipeline run prefect auto-detects both the flow module and the decorated function when --flow / --function are omitted. Pass them explicitly when a module holds more than one flow.

Each run emits OpenLineage events and resolves governance for the datasets it touches; see governance.

Configuration

Every command is configured by environment variables, loaded from the process environment and from .env, .env.dev, .env.prod where present. The full list is in the environment reference.