airflow-providers-orchesjob 0.1.6

Apache Airflow provider for orchesjob – lightweight idempotent job runner

airflow-providers-orchesjob

⚠️ EXPERIMENTAL

This package is an experimental implementation. Do not use in production. APIs and behaviour may change without notice.

Apache Airflow provider for orchesjob. Starts and monitors orchesjob jobs on a remote host over SSH.

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⚠️ Known Limitations

mode="reschedule" is required

You must use OrchesJobSensor with mode="reschedule". The default mode="poke" does not work.

# ❌ BROKEN: poke mode (default) — do not use
OrchesJobSensor(
    task_id="wait",
    job_id="...",
    ssh_conn_id="my_ssh",
    poke_interval=30.0,
)

# ✅ CORRECT: always specify mode="reschedule"
OrchesJobSensor(
    task_id="wait",
    job_id="...",
    ssh_conn_id="my_ssh",
    poke_interval=30.0,
    mode="reschedule",  # required
)

In poke mode the worker process stays alive for the entire duration of the sensor. In environments such as MWAA, the Airflow server responds with Task Instance not found for tasks that remain in running state for a long time, causing the worker to forcibly terminate itself.

In reschedule mode the worker exits normally after each False return from poke(), and the scheduler re-queues the task after poke_interval seconds, avoiding this problem.

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Requirements

• Apache Airflow ≥ 2.6
• apache-airflow-providers-ssh ≥ 3.0
• orchesjob installed on the remote host

Installation

pip install airflow-providers-orchesjob

Setup

Register an SSH Connection in Airflow (Admin → Connections):

Field	Value
Conn Id	any name (e.g. my_ssh)
Conn Type	SSH
Host	remote host address
Username	SSH username

Usage

Use OrchesJobOperator to start a job and OrchesJobSensor to wait for completion.

from airflow.decorators import dag
from airflow_providers_orchesjob.operators.orchesjob import OrchesJobOperator
from airflow_providers_orchesjob.sensors.orchesjob import OrchesJobSensor

@dag(dag_id="my_dag", ...)
def my_dag():
    start = OrchesJobOperator(
        task_id="run_job",
        command=["/jobs/import.sh", "--date", "{{ ds }}"],
        ssh_conn_id="my_ssh",
    )

    wait = OrchesJobSensor(
        task_id="wait_job",
        job_id="{{ ti.xcom_pull(task_ids='run_job', key='job_id') }}",
        ssh_conn_id="my_ssh",
        poke_interval=30.0,
        timeout=3600.0,
        mode="reschedule",  # required
    )

    start >> wait

Idempotency

run_key defaults to {dag_id}__{task_id}__{run_id}. Re-triggering the same DAG run will not re-execute the job if it is still active.

# Explicit run_key
OrchesJobOperator(
    task_id="import",
    command=["/jobs/import.sh"],
    ssh_conn_id="my_ssh",
    run_key="daily-import-{{ ds }}",
)

Set strict=True to prevent any re-execution with the same run_key, even after the previous job has finished.

Parameters

OrchesJobOperator

Parameter	Type	Default	Description
command	list[str]	required	Command to run on the remote host
ssh_conn_id	str	required	Airflow SSH Connection ID
run_key	str | None	auto	orchesjob idempotency key
strict	bool	False	Prevent re-execution with the same run_key
orchesjob_home	str | None	None	Override ORCHESJOB_HOME on the remote host

OrchesJobSensor

Parameter	Type	Default	Description
job_id	str	required	orchesjob job ID to monitor
ssh_conn_id	str	required	Airflow SSH Connection ID
orchesjob_home	str | None	None	Override ORCHESJOB_HOME on the remote host
poke_interval	float	30.0	Seconds between polls
timeout	float	3600.0	Sensor timeout in seconds
mode	str	"poke"	Must be set to "reschedule"

Error Handling

Event	Airflow behaviour
Job FAILED or LOST	AirflowException → task retries apply
Job CANCELLED	AirflowException
SSH connection error	AirflowException → task retries apply
Sensor timeout exceeded	AirflowSensorTimeout → task retries apply

License

MIT