Metadata-Version: 2.5
Name: dagrun-proxy
Version: 0.2.0
Summary: Bulk external runs projected into Airflow proxy DAGRuns.
Project-URL: Homepage, https://github.com/oboki/dagrun-proxy
Project-URL: Repository, https://github.com/oboki/dagrun-proxy
Project-URL: Issues, https://github.com/oboki/dagrun-proxy/issues
Author-email: oboki <oboki@kakao.com>
License:                               Apache License
                                Version 2.0, January 2004
                             http://www.apache.org/licenses/
        
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License-File: LICENSE
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.11
Requires-Dist: apache-airflow-providers-http>=5.0
Requires-Dist: apache-airflow<4,>=3.1
Provides-Extra: postgres
Requires-Dist: apache-airflow-providers-postgres>=6.0; extra == 'postgres'
Provides-Extra: test
Requires-Dist: pytest>=8.0; extra == 'test'
Description-Content-Type: text/markdown

# DAGRun Proxy

DAGRun Proxy is a Python library for relaying batch execution dependencies
from Airflow and pluggable external execution sources into a target Airflow
cluster through proxy DAGRuns.

It collects successful runs from an external system in one scheduled operator
and projects them into the target Airflow cluster as proxy DAGRuns. Downstream
DAGs can then use Airflow-native `ExternalTaskSensor`
dependencies without requiring each consumer sensor to poll the external
system independently or manage a separate external connection.

## How It Works

```typograms
+---------------------+    +---------------------+
| UPSTREAM SYSTEMS    | -> | PROJECTION TASK     |
|---------------------|    |---------------------|
| Cross-system runs   |    | Scheduled bulk      |
| Airflow & plugins   |    | history projection  |
+---------------------+    +---------------------+
                                      |
                                      v
+---------------------+    +---------------------+
| CONSUMER            | <- | PROXY DAGRUN        |
|---------------------|    |---------------------|
| Downstream DAGs     |    | Target Airflow      |
| ExternalTaskSensor  |    | Duplicate-safe      |
+---------------------+    +---------------------+
```

- `DAGRunProxyOperator` gathers successful external runs for the configured
  dependency aliases and collection period.
- External run histories are normalized into a common run-event model, so
  Airflow and pluggable execution sources can use the same dependency flow.
- Each successful source partition is represented by a proxy DAGRun in the
  target Airflow cluster.
- Proxy delivery is duplicate-safe for repeated observations and collector task
  retries: the same delivery key does not create duplicate proxy DAGRuns.
- Each dependency alias has a lightweight proxy DAG whose completion task is
  referenced by downstream `ExternalTaskSensor`s.

The collection process is schedule-driven, not daemon-driven. The DAGRun Proxy
collection DAG runs on its configured Airflow schedule, and each collection
DAGRun's `data_interval` becomes the source observation window. By default,
this is the half-open interval `[data_interval_start, data_interval_end)`;
`lookback_seconds` can extend its start for late source updates. No additional
always-on service is required. The scheduled collector still performs the
source query, while the integration uses Airflow's existing scheduling, retry,
and execution model.

The resulting proxy DAGRun uses the source data interval start, when present,
as its logical date for dependency matching. Source logical-date and interval
details are retained in the proxy DAGRun configuration. The target Airflow
cluster owns the dependency state, and downstream DAGs interact only with its
native DAGRun interface.

## Install

Requirements: Python 3.11 or newer and Airflow 3.1 or newer.

```bash
python -m pip install .
```

After a release is published to PyPI, install the package with:

```bash
python -m pip install dagrun-proxy
```

For the PostgreSQL-based `awesome_execution` origin, install the optional
PostgreSQL extra:

```bash
python -m pip install ".[postgres]"
```

## Configuration

Use stable aliases as the dependency contract consumed by downstream DAGs.
Subscription strings use the same value for the Origin job ID and alias.
Mappings can add an alias and JSON-serializable metadata.

```python
from dagrun_proxy.operators.proxy import DAGRunProxyOperator

DAGRunProxyOperator(
    task_id="collect_external_runs",
    conn_id="producer_airflow",
    target_conn_id="target_airflow",
    origin="airflow",
    subscriptions=[
        "producer.daily_orders",
        {
            "origin_job_id": "producer.monthly_orders",
            "alias": "orders.monthly",
            "metadata": {"owner": "finance"},
        },
    ],
    lookback_seconds=300,
    page_size=500,
)
```

`conn_id` is used to read the source system. `target_conn_id` identifies an
Airflow HTTP Connection used to create proxy DAGRuns through the target
cluster's public Airflow REST API. In a single-cluster deployment, this can
point to the current Airflow API.

The default policy is observed-only. To synthesize missing target partitions,
configure a timetable and a per-gap limit:

```python
{
  "mode": "synthesize",
  "cron": "0 0 * * *",
  "timezone": "UTC",
  "max_synthetic_dagruns": 7,
}
```

Synthesis starts from the latest earlier proxy partition and walks observed
partitions in order, filling only timetable partitions between adjacent
boundaries. Each gap is calculated once. If no durable baseline exists, the
first observed partition is delivered directly; later observed partitions in
the same collection can still fill gaps from the preceding observed partition.
`lookback_seconds` defaults to `0`. Set `bounded_by_data_interval_end=False`
only for an unbounded one-time history load.

`build_proxy_dags()` creates one `EmptyOperator` completion marker per proxy
DAG. Its task ID defaults to `done`; use the same ID in downstream
`ExternalTaskSensor`s.

## Quickstart

The quickstart starts two ephemeral Airflow 3 clusters:

1. Producer runs the real `daily_orders` DAG.
2. Target collects the producer's run history.
3. Target creates a `daily_orders` proxy DAGRun.
4. `orders_consumer` completes through `ExternalTaskSensor`.
5. A second projection demonstrates duplicate-safe delivery.

Prerequisites: Docker Desktop with at least 8 GB available, a POSIX shell,
and a `python3` command.

```bash
./quickstart/demo.sh
```

Open the UIs at <http://localhost:8082> (producer) and
<http://localhost:8081> (target). Architecture and contract details are in
[ARCHITECTURE.md](ARCHITECTURE.md).

Reset the environment with:

```bash
docker compose -f quickstart/compose.yml down --volumes --remove-orphans
```

## Developer Guide

Create a virtual environment, install the test extra, and run the suite:

```bash
python3 -m venv .venv
.venv/bin/python -m pip install --upgrade pip
.venv/bin/python -m pip install -e ".[test]"
.venv/bin/python -m pytest -q
```

The main implementation is under `src/dagrun_proxy`; tests are under `tests`.
The architecture, data contracts, Origin contract, and design boundaries are
documented in [ARCHITECTURE.md](ARCHITECTURE.md).

To add an Origin, implement the structural `RunOrigin` contract:

```python
def fetch_runs(request: FetchRunsRequest) -> FetchRunsResult: ...
```

The origin constructor receives one `conn_id` and must retrieve all requested
subscriptions in grouped, page-aware queries. Register third-party origins
with the `dagrun_proxy.origins` entry point group. See
[ARCHITECTURE.md](ARCHITECTURE.md) for the built-in Origin contracts.

## Operations

Schedule the collector with an explicit data-interval timetable and
`catchup=True` so normal scheduling, backfills, pause recovery, and retries
cover the same source history. Use a pool for scarce sources and size
`page_size` for source limits and worker memory; source query and filtering
cost depend on both result pages and the size of the subscription filter.

The source connection needs read-only access to the supported API or history
relation. The target connection needs permission to read target DAGRuns and
trigger proxy DAGRuns. For JWT-protected Airflow APIs, put an `Authorization`
header such as `{"Authorization": "Bearer <token>"}` in the HTTP Connection
extra. Run a canary against a disposable target before enabling proxy creation
and monitor metadata database growth because each represented partition
creates a DAGRun.

## Scope

The project creates DAGRuns only. It is not an asset publisher, data catalog,
lineage platform, data-product contract system, or general data-mesh service.

## License

Licensed under the [Apache License 2.0](LICENSE).
