airflow-plugins

作成者: astronomer

Airflow 3.1+のプラグインを構築し、FastAPIアプリ、カスタムUIページ、Reactコンポーネント、ミドルウェア、マクロ、オペレーターリンクをAirflow UIに直接埋め込みます。使用…

npx skills add https://github.com/astronomer/agents --skill airflow-plugins

Airflow 3 Plugins

Airflow 3 plugins let you embed FastAPI apps, React UIs, middleware, macros, operator buttons, and custom timetables directly into the Airflow process. No sidecar, no extra server.

CRITICAL: Plugin components (fastapi_apps, react_apps, external_views) require Airflow 3.1+. NEVER import flask, flask_appbuilder, or use appbuilder_views / flask_blueprints — these are Airflow 2 patterns and will not work in Airflow 3. If existing code uses them, rewrite the entire registration block using FastAPI.

Security: FastAPI plugin endpoints are not automatically protected by Airflow auth. If your endpoints need to be private, implement authentication explicitly using FastAPI's security utilities.

Restart required: Changes to Python plugin files require restarting the API server. Static file changes (HTML, JS, CSS) are picked up immediately. Set AIRFLOW__CORE__LAZY_LOAD_PLUGINS=False during development to load plugins at startup rather than lazily.

Relative paths always: In external_views, href must have no leading slash. In HTML and JavaScript, use relative paths for all assets and fetch() calls. Absolute paths break behind reverse proxies.

Before writing any code, verify

  1. Am I using fastapi_apps / FastAPI — not appbuilder_views / Flask?
  2. Are all HTML/JS asset paths and fetch() calls relative (no leading slash)?
  3. Are all synchronous SDK or SQLAlchemy calls wrapped in asyncio.to_thread()?
  4. Do the static/ and assets/ directories exist before the FastAPI app mounts them?
  5. If the endpoint must be private, did I add explicit FastAPI authentication?

Step 1: Choose plugin components

A single plugin class can register multiple component types at once.

ComponentWhat it doesField
Custom API endpointsFastAPI app mounted in Airflow processfastapi_apps
Nav / page linkEmbeds a URL as an iframe or links outexternal_views
React componentCustom React app embedded in Airflow UIreact_apps
API middlewareIntercepts all Airflow API requests/responsesfastapi_root_middlewares
Jinja macrosReusable Python functions in DAG templatesmacros
Task instance buttonExtra link button in task Detail viewoperator_extra_links / global_operator_extra_links
Custom timetableCustom scheduling logictimetables
Event hooksListener callbacks for Airflow eventslisteners

Step 2: Plugin registration skeleton

Project file structure

Give each plugin its own subdirectory under plugins/ — this keeps the Python file, static assets, and templates together and makes multi-plugin projects manageable:

plugins/
  my-plugin/
    plugin.py       # AirflowPlugin subclass — auto-discovered by Airflow
    static/
      index.html
      app.js
    assets/
      icon.svg

BASE_DIR = Path(__file__).parent in plugin.py resolves to plugins/my-plugin/ — static and asset paths will be correct relative to that. Create the subdirectory and any static/assets folders before starting Airflow, or StaticFiles will raise on import.

from pathlib import Path
from airflow.plugins_manager import AirflowPlugin
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse

BASE_DIR = Path(__file__).parent

app = FastAPI(title="My Plugin")

# Both directories must exist before Airflow starts or FastAPI raises on import
app.mount("/static", StaticFiles(directory=BASE_DIR / "static"), name="static")
app.mount("/assets", StaticFiles(directory=BASE_DIR / "assets"), name="assets")


class MyPlugin(AirflowPlugin):
    name = "my_plugin"

    fastapi_apps = [
        {
            "app": app,
            "url_prefix": "/my-plugin",   # plugin available at {AIRFLOW_HOST}/my-plugin/
            "name": "My Plugin",
        }
    ]

    external_views = [
        {
            "name": "My Plugin",
            "href": "my-plugin/ui",              # NO leading slash — breaks on Astro and reverse proxies
            "destination": "nav",                # see locations table below
            "category": "browse",                # nav bar category (nav destination only)
            "url_route": "my-plugin",            # unique route name (required for React apps)
            "icon": "/my-plugin/static/icon.svg" # DOES use a leading slash — served by FastAPI
        }
    ]

External view locations

destinationWhere it appears
"nav"Left navigation bar (also set category)
"dag"Extra tab on every Dag page
"dag_run"Extra tab on every Dag run page
"task"Extra tab on every task page
"task_instance"Extra tab on every task instance page

Nav bar categories (destination: "nav")

Set "category" to place the link under a specific nav group: "browse", "admin", or omit for top-level.

External URLs and minimal plugins

href can be a relative path to an internal endpoint ("my-plugin/ui") or a full external URL. A plugin with only external_views and no fastapi_apps is valid — no backend needed for a simple link or tab:

from airflow.plugins_manager import AirflowPlugin

class LearnViewPlugin(AirflowPlugin):
    name = "learn_view_plugin"

    external_views = [
        {
            "name": "Learn Airflow 3",
            "href": "https://www.astronomer.io/docs/learn",
            "destination": "dag",   # adds a tab to every Dag page
            "url_route": "learn"
        }
    ]

The no-leading-slash rule applies to internal paths only — full https:// URLs are fine.


Step 3: Serve the UI entry point

@app.get("/ui", response_class=FileResponse)
async def serve_ui():
    return FileResponse(BASE_DIR / "static" / "index.html")

In HTML, always use relative paths. Absolute paths break when Airflow is mounted at a sub-path:

<!-- correct -->
<link rel="stylesheet" href="static/app.css" />
<script src="static/app.js?v=20240315"></script>

<!-- breaks behind a reverse proxy -->
<script src="/my-plugin/static/app.js"></script>

Same rule in JavaScript:

fetch('api/dags')           // correct — relative to current page
fetch('/my-plugin/api/dags') // breaks on Astro and sub-path deploys

Step 4: Call the Airflow API from your plugin

Only needed if your plugin calls the Airflow REST API. Plugins that only serve static files, register external_views, or use direct DB access do not need this step — skip to Step 5 or Step 6.

Add the dependency

Only if REST API communication is being implemented: add apache-airflow-client to the project's dependencies. Check which file exists and act accordingly:

File foundAction
requirements.txtAppend apache-airflow-client
pyproject.toml (uv / poetry)uv add apache-airflow-client or poetry add apache-airflow-client
None of the aboveTell the user: "Add apache-airflow-client to your dependencies before running the plugin."

Use apache-airflow-client to talk to Airflow's own REST API. The SDK is synchronous but FastAPI routes are async — never call blocking SDK methods directly inside async def or you will stall the event loop and freeze all concurrent requests.

JWT token management

Cache one token per process. Refresh 5 minutes before the 1-hour expiry. Use double-checked locking so multiple concurrent requests don't all race to refresh simultaneously:

Replace MYPLUGIN_ with a short uppercase prefix derived from the plugin name (e.g. if the plugin is called "Trip Analyzer", use TRIP_ANALYZER_). If no plugin name has been given yet, ask the user before writing env var names.

import asyncio
import os
import threading
import time
import airflow_client.client as airflow_sdk
import requests

AIRFLOW_HOST  = os.environ.get("MYPLUGIN_HOST",     "http://localhost:8080")
AIRFLOW_USER  = os.environ.get("MYPLUGIN_USERNAME", "admin")
AIRFLOW_PASS  = os.environ.get("MYPLUGIN_PASSWORD", "admin")
AIRFLOW_TOKEN = os.environ.get("MYPLUGIN_TOKEN")    # Astronomer Astro: Deployment API token

_cached_token: str | None = None
_token_expires_at: float  = 0.0
_token_lock = threading.Lock()


def _fetch_fresh_token() -> str:
    """Exchange username/password for a JWT via Airflow's auth endpoint."""
    response = requests.post(
        f"{AIRFLOW_HOST}/auth/token",
        json={"username": AIRFLOW_USER, "password": AIRFLOW_PASS},
        timeout=10,
    )
    response.raise_for_status()
    return response.json()["access_token"]


def _get_token() -> str:
    # Astronomer Astro production: use static Deployment API token directly
    if AIRFLOW_TOKEN:
        return AIRFLOW_TOKEN
    global _cached_token, _token_expires_at
    now = time.monotonic()
    # Fast path — no lock if still valid
    if _cached_token and now < _token_expires_at:
        return _cached_token
    # Slow path — one thread refreshes, others wait
    with _token_lock:
        if _cached_token and now < _token_expires_at:
            return _cached_token
        _cached_token = _fetch_fresh_token()
        _token_expires_at = now + 55 * 60  # refresh 5 min before 1-hour expiry
    return _cached_token


def _make_config() -> airflow_sdk.Configuration:
    config = airflow_sdk.Configuration(host=AIRFLOW_HOST)
    config.access_token = _get_token()
    return config

After implementing auth, tell the user:

  • Local development: set MYPLUGIN_USERNAME and MYPLUGIN_PASSWORD in .env — JWT exchange happens automatically.

  • Astronomer Astro (production): create a Deployment API token and set it as MYPLUGIN_TOKEN — the JWT exchange is skipped entirely:

    1. Astro UI → open the Deployment → AccessAPI Tokens+ Deployment API Token
    2. Copy the token value (shown only once)
    3. astro deployment variable create MYPLUGIN_TOKEN=<token>

    MYPLUGIN_USERNAME and MYPLUGIN_PASSWORD are not needed on Astro.

Wrapping SDK calls with asyncio.to_thread

from fastapi import HTTPException
from airflow_client.client.api import DAGApi

@app.get("/api/dags")
async def list_dags():
    try:
        def _fetch():
            with airflow_sdk.ApiClient(_make_config()) as client:
                return DAGApi(client).get_dags(limit=100).dags
        dags = await asyncio.to_thread(_fetch)
        return [{"dag_id": d.dag_id, "is_paused": d.is_paused, "timetable_summary": d.timetable_summary} for d in dags]
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

API field names: Never guess response field names — verify against the REST API reference. Key DAGResponse fields: dag_id, dag_display_name, description, is_paused, timetable_summary, timetable_description, fileloc, owners, tags.

The pattern is always: define a plain inner def _fetch() with all SDK logic, then await asyncio.to_thread(_fetch).

Alternative: Direct database access

Warning — use with caution and tell the user. The Airflow metadb is not a public interface. Direct writes or poorly-formed queries can corrupt scheduler state. Whenever you use this pattern, explicitly tell the user: "This accesses Airflow's internal database directly. The internal models are not part of the public API, can change between Airflow versions, and incorrect queries can cause issues in the metadb. Prefer apache-airflow-client unless the operation is not exposed via the REST API."

Since FastAPI plugin endpoints run inside the API server process (not in a task worker), they have direct access to Airflow's internal SQLAlchemy models — no HTTP round-trip or JWT needed. Use only for read operations not exposed via the REST API, or when the extra HTTP overhead genuinely matters. Always wrap DB calls in asyncio.to_thread() — SQLAlchemy queries are blocking.

from airflow.models import DagBag, DagModel
from airflow.utils.db import provide_session

@app.get("/api/dags/status")
async def dag_status():
    def _fetch():
        @provide_session
        def _query(session=None):
            dagbag = DagBag()
            paused = sum(
                1 for dag_id in dagbag.dags
                if (m := session.query(DagModel).filter(DagModel.dag_id == dag_id).first())
                and m.is_paused
            )
            return {"total": len(dagbag.dags), "paused": paused}
        return _query()
    return await asyncio.to_thread(_fetch)

Step 5: Common API endpoint patterns

If you need an SDK method or field not shown in the examples below, verify it before generating code — do not guess. Either run python3 -c "from airflow_client.client.api import <Class>; print([m for m in dir(<Class>) if not m.startswith('_')])" in any environment where the SDK is installed, or search the apache/airflow-client-python repo for the class definition.

from airflow_client.client.api import DAGApi, DagRunApi
from airflow_client.client.models import TriggerDAGRunPostBody, DAGPatchBody


@app.post("/api/dags/{dag_id}/trigger")
async def trigger_dag(dag_id: str):
    def _run():
        with airflow_sdk.ApiClient(_make_config()) as client:
            return DagRunApi(client).trigger_dag_run(dag_id, TriggerDAGRunPostBody())
    result = await asyncio.to_thread(_run)
    return {"run_id": result.dag_run_id, "state": normalize_state(result.state)}


@app.patch("/api/dags/{dag_id}/pause")
async def toggle_pause(dag_id: str, is_paused: bool):
    def _run():
        with airflow_sdk.ApiClient(_make_config()) as client:
            DAGApi(client).patch_dag(dag_id, DAGPatchBody(is_paused=is_paused))
    await asyncio.to_thread(_run)
    return {"dag_id": dag_id, "is_paused": is_paused}


@app.delete("/api/dags/{dag_id}")
async def delete_dag(dag_id: str):
    def _run():
        with airflow_sdk.ApiClient(_make_config()) as client:
            DAGApi(client).delete_dag(dag_id)
    await asyncio.to_thread(_run)
    return {"deleted": dag_id}


def normalize_state(raw) -> str:
    """Convert SDK enum objects to plain strings before sending to the frontend."""
    if raw is None:
        return "never_run"
    return str(raw).lower()

DAG runs, task instances, and logs

These are the most common calls beyond basic DAG CRUD. For anything not shown here, consult the REST API reference for available endpoints and the matching Python SDK class/method names.

from airflow_client.client.api import DagRunApi, TaskInstanceApi

# Latest run for a DAG
@app.get("/api/dags/{dag_id}/runs/latest")
async def latest_run(dag_id: str):
    def _fetch():
        with airflow_sdk.ApiClient(_make_config()) as client:
            runs = DagRunApi(client).get_dag_runs(dag_id, limit=1, order_by="-start_date").dag_runs
            return runs[0] if runs else None
    run = await asyncio.to_thread(_fetch)
    if not run:
        return {"state": "never_run"}
    return {"run_id": run.dag_run_id, "state": normalize_state(run.state)}


# Task instances for a specific run
@app.get("/api/dags/{dag_id}/runs/{run_id}/tasks")
async def task_instances(dag_id: str, run_id: str):
    def _fetch():
        with airflow_sdk.ApiClient(_make_config()) as client:
            return TaskInstanceApi(client).get_task_instances(dag_id, run_id).task_instances
    tasks = await asyncio.to_thread(_fetch)
    return [{"task_id": t.task_id, "state": normalize_state(t.state)} for t in tasks]


# Task log (try_number starts at 1)
@app.get("/api/dags/{dag_id}/runs/{run_id}/tasks/{task_id}/logs/{try_number}")
async def task_log(dag_id: str, run_id: str, task_id: str, try_number: int):
    def _fetch():
        with airflow_sdk.ApiClient(_make_config()) as client:
            return TaskInstanceApi(client).get_log(
                dag_id, run_id, task_id, try_number, map_index=-1
            )
    result = await asyncio.to_thread(_fetch)
    return {"log": result.content if hasattr(result, "content") else str(result)}

Streaming proxy

Use StreamingResponse to proxy binary content from an external URL through the plugin — useful when the browser can't fetch the resource directly (CORS, auth, etc.):

import requests
from starlette.responses import StreamingResponse

@app.get("/api/files/{filename}")
async def proxy_file(filename: str):
    def _stream():
        r = requests.get(f"https://files.example.com/{filename}", stream=True)
        r.raise_for_status()
        return r
    response = await asyncio.to_thread(_stream)
    return StreamingResponse(
        response.iter_content(chunk_size=8192),
        media_type="application/octet-stream",
        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
    )

Note that requests.get() is blocking — fetch in asyncio.to_thread so the event loop isn't stalled while waiting for the remote server.


Step 6: Other plugin component types

Macros

Macros are loaded by the scheduler (and DAG processor), not the API server. Restart the scheduler after changes.

from airflow.plugins_manager import AirflowPlugin

def format_confidence(confidence: float) -> str:
    return f"{confidence * 100:.2f}%"

class MyPlugin(AirflowPlugin):
    name = "my_plugin"
    macros = [format_confidence]

Use in any templated field — including with XCom:

{{ macros.my_plugin.format_confidence(0.95) }}

{{ macros.my_plugin.format_confidence(ti.xcom_pull(task_ids='score_task')['confidence']) }}

The naming pattern is always macros.{plugin_name}.{function_name}.

Middleware

Middleware applies to all Airflow API requests, including the built-in REST API and any FastAPI plugins. Use sparingly and filter requests explicitly if needed:

from starlette.middleware.base import BaseHTTPMiddleware
from fastapi import Request, Response

class AuditMiddleware(BaseHTTPMiddleware):
    async def dispatch(self, request: Request, call_next) -> Response:
        # runs before every request to the Airflow API server
        response = await call_next(request)
        return response

class MyPlugin(AirflowPlugin):
    name = "my_plugin"
    fastapi_root_middlewares = [
        {"middleware": AuditMiddleware, "args": [], "kwargs": {}, "name": "Audit"}
    ]

Operator extra links

from airflow.sdk.bases.operatorlink import BaseOperatorLink

class MyDashboardLink(BaseOperatorLink):
    name = "Open in Dashboard"

    def get_link(self, operator, *, ti_key, **context) -> str:
        return f"https://my-dashboard.example.com/tasks/{ti_key.task_id}"

class MyPlugin(AirflowPlugin):
    name = "my_plugin"
    global_operator_extra_links = [MyDashboardLink()]  # appears on every task
    # operator_extra_links = [MyDashboardLink()]       # attach to specific operator instead

React apps

React apps are embedded as JavaScript bundles served via FastAPI. The bundle must expose itself as a global variable matching the plugin name:

// In your bundle (e.g. my-app.js)
globalThis['My Plugin'] = MyComponent;   // matches plugin name
globalThis.AirflowPlugin = MyComponent;  // fallback Airflow looks for
class MyPlugin(AirflowPlugin):
    name = "my_plugin"
    fastapi_apps = [{"app": app, "url_prefix": "/my-plugin", "name": "My Plugin"}]
    react_apps = [
        {
            "name": "My Plugin",
            "bundle_url": "/my-plugin/my-app.js",
            "destination": "nav",
            "category": "browse",
            "url_route": "my-plugin",
        }
    ]

The same bundle can be registered to multiple destinations by adding multiple entries — each needs a unique url_route:

react_apps = [
    {"name": "My Widget", "bundle_url": "/my-plugin/widget.js", "destination": "nav",  "url_route": "my-widget-nav"},
    {"name": "My Widget", "bundle_url": "/my-plugin/widget.js", "destination": "dag",  "url_route": "my-widget-dag"},
]

React app integration is experimental in Airflow 3.1. Interfaces may change in future releases.


Step 7: Environment variables and deployment

Never hardcode credentials:

AIRFLOW_HOST = os.environ.get("MYPLUGIN_HOST",     "http://localhost:8080")
AIRFLOW_USER = os.environ.get("MYPLUGIN_USERNAME", "admin")
AIRFLOW_PASS = os.environ.get("MYPLUGIN_PASSWORD", "admin")

Local Astro CLI:

# .env
MYPLUGIN_HOST=http://localhost:8080
MYPLUGIN_USERNAME=admin
MYPLUGIN_PASSWORD=admin
astro dev restart              # required after any Python plugin change

# Check logs by component (Astro CLI):
astro dev logs --api-server    # FastAPI apps, external_views — plugin import errors show here
astro dev logs --scheduler     # macros, timetables, listeners, operator links
astro dev logs --dag-processor # DAG parsing errors

# Non-Astro:
airflow plugins                # CLI — lists all loaded plugins

Production Astronomer:

astro deployment variable create --deployment-id <id> MYPLUGIN_HOST=https://airflow.example.com

Auto-reload during development (skips lazy loading):

AIRFLOW__CORE__LAZY_LOAD_PLUGINS=False

Cache busting for static files after deploy:

<script src="static/app.js?v=20240315-1"></script>

Verify the plugin loaded: open Admin > Plugins in the Airflow UI.

OpenAPI docs are auto-generated for FastAPI plugins:

  • Swagger UI: {AIRFLOW_HOST}/{url_prefix}/docs
  • OpenAPI JSON: {AIRFLOW_HOST}/{url_prefix}/openapi.json

Common pitfalls

ProblemCauseFix
Nav link goes to 404Leading / in href"my-plugin/ui" not "/my-plugin/ui"
Nav icon not showingMissing / in iconicon takes an absolute path: "/my-plugin/static/icon.svg"
Event loop freezes under loadSync SDK called directly in async defWrap with asyncio.to_thread()
401 errors after 1 hourJWT expires with no refreshUse the 5-minute pre-expiry refresh pattern
StaticFiles raises on startupDirectory missingCreate assets/ and static/ before starting
Plugin not showing upPython file changed without restartastro dev restart
Endpoints accessible without loginFastAPI apps are not auto-authenticatedAdd FastAPI security (e.g. OAuth2, API key) if endpoints must be private
Middleware affecting wrong routesMiddleware applies to all API trafficFilter by request.url.path inside dispatch()
JS fetch() breaks on AstroAbsolute path in fetch()Always use relative paths: fetch('api/dags')

References

astronomerのその他のスキル

airflow
astronomer
Apache AirflowのDAG、実行、タスク、システム設定をクエリ、管理、トラブルシューティングします。DAG検査、実行管理、タスクログ、設定クエリ、REST API直接アクセスを含む30以上のコマンドをサポート。複数のAirflowインスタンスを永続的な設定で管理し、ローカルおよびAstroデプロイメントを自動検出。DAG実行を同期的(完了待機)または非同期的にトリガーし、障害を診断、再試行のために実行をクリア、リトライ/マップインデックスフィルタリング付きでタスクログにアクセス。出力...
official
airflow-hitl
astronomer
人間による承認ゲート、フォーム入力、およびAirflow DAG内での分岐を、遅延可能オペレーターを使用して実現。4種類のオペレーター:承認/却下の判断を行うApprovalOperator、フォームによる複数選択肢の選択を行うHITLOperator、人間主導のタスクルーティングを行うHITLBranchOperator、フォームデータ収集を行うHITLEntryOperator。すべてのオペレーターは遅延可能であり、Airflow UIのRequired ActionsタブまたはREST APIを介して人間の応答を待つ間、ワーカースロットを解放します。カスタム...を含むオプション機能をサポート。
official
analyzing-data
astronomer
データウェアハウスにクエリを実行し、キャッシュされたパターンと概念マッピングを使用してビジネス上の質問に回答します。繰り返し発生する質問タイプのパターン検索とキャッシュをサポートし、結果を記録して将来のクエリを改善します。概念からテーブルへのマッピングキャッシュと、INFORMATION_SCHEMAまたはコードベースのgrepによるテーブルスキーマ検出を含みます。分析用にPolarsまたはPandas DataFrameを返すrun_sql()およびrun_sql_pandas()カーネル関数を提供します。概念、パターン、テーブルキャッシュを管理するCLIコマンド、さらに...
official
annotating-task-lineage
astronomer
Airflowタスクにデータ系列を注釈付けし、インレットとアウトレットを使用します。OpenLineage Datasetオブジェクト、Airflow Assets、Airflow Datasetsをサポートし、データベース、データウェアハウス、クラウドストレージ間での入出力を定義します。オペレーターに組み込みのOpenLineage抽出機能がない場合のフォールバックとして使用し、カスタム抽出機能とOpenLineageメソッドが優先される4段階の優先順位システムに従います。Snowflake、BigQuery、S3、PostgreSQL向けのデータセット命名ヘルパーを含み、一貫性を確保します。
official
authoring-dags
astronomer
Apache Airflow DAGを作成するためのガイド付きワークフローで、検証とテストの統合を備えています。構造化された6フェーズのアプローチ:環境と既存のパターンを発見し、DAG構造を計画し、ベストプラクティスに従って実装し、af CLIコマンドで検証し、ユーザーの同意を得てテストし、修正を繰り返します。発見用のCLIコマンド(af config connections、af config providers、af dags list)と検証用のCLIコマンド(af dags errors、af dags get、af dags explore)は、DAGに関する即時フィードバックを提供します。
official
blueprint
astronomer
Pydanticバリデーションを使用して再利用可能なAirflowタスクグループテンプレートを定義し、YAMLからDAGを構成します。ブループリントテンプレートの作成時や、DAGの構成時に使用します。
official
checking-freshness
astronomer
テーブルのタイムスタンプと更新パターンを陳腐化スケールに照らして確認し、データの鮮度を検証します。一般的なETL命名パターン(_loaded_at、_updated_at、created_atなど)を使用してタイムスタンプカラムを特定し、その最大値をクエリして経過時間を判定します。データを4つの鮮度ステータスに分類します:Fresh(4時間未満)、Stale(4~24時間)、Very Stale(24時間超)、またはUnknown(タイムスタンプなし)。最近の日数における最終更新時刻と行数トレンドを確認するためのSQLテンプレートを提供します...
official
cosmos-dbt-core
astronomer
Astronomer Cosmosを使用して、dbt CoreプロジェクトをAirflow DAGまたはTaskGroupに変換します。3つのアセンブリパターン(スタンドアロンのDbtDag、既存のDAG内のDbtTaskGroup、細かい制御が可能な個別のCosmosオペレーター)をサポートします。分離とパフォーマンスのニーズに基づいて、8つの実行モード(WATCHER、LOCAL、VIRTUALENV、KUBERNETES、AIRFLOW_ASYNCなど)から選択できます。速度とセレクターの複雑さのバランスを取るために、3つの解析戦略(dbt_manifest、dbt_ls、dbt_ls_file、automatic)を提供します...
official