Operators, predefined tasks that you can string together quickly to build most parts of your DAGs. In the default Airflow installation, this runs everything inside the scheduler, but most production-suitable executors actually push task execution out to workers.Ī webserver, which presents a handy user interface to inspect, trigger and debug the behaviour of DAGs and tasks.Ī folder of DAG files, read by the scheduler and executor (and any workers the executor has)Ī metadata database, used by the scheduler, executor and webserver to store state.Ī DAG runs through a series of Tasks, and there are three common types of task you will see: The task was externally requested to restart when it was runningĪ scheduler, which handles both triggering scheduled workflows, and submitting Tasks to the executor to run.Īn executor, which handles running tasks. The task failed, but has retry attempts left and will be rescheduled.Įx) task run after specific file saved in a s3 bucker The task had an error during execution and failed to run The task was externally requested to shut down when it was running The task is a Sensor that is in reschedule mode The task is running on a worker (or on a local/synchronous executor) The task has been assigned to an Executor and is awaiting a worker The task was skipped due to branching, LatestOnly, or similar The task has vanished from the DAG since the run startedĪn upstream task failed and the Trigger Rule says we needed it The scheduler has determined the Task’s dependencies are met and it should run The Task has not yet been queued for execution (its dependencies are not yet met) (BashOperator, PythonOperator, CustomisedOperator. Each task is an implementation of an operator.A Task(representated as a node in the DAG) defines a unit of work within a DAG.In airflow, workflow is defined as DAG(Directed Acyclic Graph - 방향성 비순환 그래프).A web interface helps manage the state of your workflows.Extensible Python framework enables you to build workflows connecting with virtually any technology.Platform for developing, scheduling, and monitoring batch-oriented workflows.tag extending_airflow:latestĭocker-compose up -d -no-deps -build airflow-webserver airflow-scheduler RUN pip install -no-cache-dir -user -r /requirements.txt docker build. env Initialise the database docker compose up airflow-init Running Airflow docker compose up -d Connect Airflow webserver pluginsĮcho -e "AIRFLOW_UID= $( id -u )" >. ports : # DB 외부 접근 포트 열기 - 5432 : 5432 Setting the right Airflow user mkdir -p. # DAGS 예제 제외 AIRFLOW_CORE_LOAD_EXAMPLES : 'false' services : postgres. Running Airflow In Docker - How-to Guides:įetching docker-compose.yaml: curl -LfO ''ĭocker-compose.yaml 수정 내용 x-airflow-common : environment.
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