chore: use PostgreSQL for local environment and update README.md (#1729)
This commit is contained in:
Mikaël Ducharme
GitHub
Родитель
94a2208f71
Коммит
a34971d6e6
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@ -31,7 +31,10 @@ jobs:
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path: test-results
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test-local-environment:
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executor: docker/machine
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executor:
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name: docker/machine
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image: ubuntu-2204:2023.04.2
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resource_class: large
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steps:
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- checkout
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- attach_workspace:
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@ -192,8 +195,6 @@ workflows:
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- docker-build-artifact:
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name: 🛠️ Docker build test
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filters: *ci-filter
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requires:
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- 🧪 Validate requirements
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- test-local-environment:
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name: 🧪 Validate local environment
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89
README.md
89
README.md
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@ -19,6 +19,25 @@ Some links relevant to users and developers of WTMO:
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[WTMO Developer Guide](https://mana.mozilla.org/wiki/display/DOPS/WTMO+Developer+Guide)
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(behind SSO)
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## Writing DAGs
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See the Airflow's [Best Practices guide](https://airflow.apache.org/docs/apache-airflow/stable/best-practices.html#best-practices) to help you write DAGs.
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**⚠ Note: How to import DAGs and modules ⚠**
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**Modules should be imported from the project directory, such as `from dags.my_dag import load_data`
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rather than `from my_dag import load_data`.**
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In Airflow, the `dags`, `config`, and `plugins` folders are [automatically added](https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/modules_management.html#built-in-pythonpath-entries-in-airflow) to the
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`PYTHONPATH` to ensure they can be imported and accessed by Airflow's execution environment.
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However, this default configuration can cause problems when running unit tests located
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in the `tests` directory. Since the `PYTHONPATH` includes the `dags` directory,
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but not the project directory itself, the unit tests will not be able to import code from
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the `dags` directory. This limitation restricts the ability to test the DAGs effectively
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within the project structure. It is also generally expected that imports should work from the
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project directory rather than from any of its subdirectories. For this reason, `telemetry-airflow`'s
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Dockerfile adds the project directory to `PYTHONPATH`.
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## Prerequisites
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This app is built and deployed with
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@ -27,7 +46,7 @@ This app is built and deployed with
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Dependencies are managed with
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[pip-tools](https://pypi.org/project/pip-tools/) `pip-compile`.
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You'll also need to install MySQL to build the database container.
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You'll also need to install PostgreSQL to build the database container.
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### Installing dependencies locally
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**_⚠ Make sure you use the right Python version. Refer to Dockerfile for current supported Python Version ⚠_**
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@ -124,80 +143,20 @@ make build && make up
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You can then connect to Airflow [locally](localhost:8080). Enable your DAG and see that it runs correctly.
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### Production Setup
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When deploying to production make sure to set up the following environment
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variables:
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- `AWS_ACCESS_KEY_ID` -- The AWS access key ID to spin up the Spark clusters
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- `AWS_SECRET_ACCESS_KEY` -- The AWS secret access key
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- `AIRFLOW_DATABASE_URL` -- The connection URI for the Airflow database, e.g.
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`mysql://username:password@hostname:port/database`
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- `AIRFLOW_BROKER_URL` -- The connection URI for the Airflow worker queue, e.g.
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`redis://hostname:6379/0`
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- `AIRFLOW_BROKER_URL` -- The connection URI for the Airflow result backend, e.g.
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`redis://hostname:6379/1`
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- `AIRFLOW_GOOGLE_CLIENT_ID` -- The Google Auth client id used for
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authentication.
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- `AIRFLOW_GOOGLE_CLIENT_SECRET` -- The Google Auth client secret used for
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authentication.
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- `AIRFLOW_GOOGLE_APPS_DOMAIN` -- The domain(s) to restrict Google Auth login
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to e.g. `mozilla.com`
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- `AIRFLOW_SMTP_HOST` -- The SMTP server to use to send emails e.g.
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`email-smtp.us-west-2.amazonaws.com`
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- `AIRFLOW_SMTP_USER` -- The SMTP user name
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- `AIRFLOW_SMTP_PASSWORD` -- The SMTP password
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- `AIRFLOW_SMTP_FROM` -- The email address to send emails from e.g.
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`telemetry-alerts@workflow.telemetry.mozilla.org`
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- `URL` -- The base URL of the website e.g.
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`https://workflow.telemetry.mozilla.org`
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- `DEPLOY_ENVIRONMENT` -- The environment currently running, e.g.
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`stage` or `prod`
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Also, please set
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- `AIRFLOW_SECRET_KEY` -- A secret key for Airflow's Flask based webserver
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- `AIRFLOW__CORE__FERNET_KEY` -- A secret key to saving connection passwords in the DB
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This repository was structured to be deployed using the [offical Airflow Helm Chart.](https://airflow.apache.org/docs/helm-chart/stable/index.html).
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See the [Production Guide](https://airflow.apache.org/docs/helm-chart/stable/production-guide.html) for best practices.
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### Debugging
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Some useful docker tricks for development and debugging:
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```bash
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# Stop all docker containers:
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docker stop $(docker ps -aq)
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make clean
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# Remove any leftover docker volumes:
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docker volume rm $(docker volume ls -qf dangling=true)
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# Purge docker volumes (helps with mysql container failing to start)
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# Purge docker volumes (helps with postgres container failing to start)
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# Careful as this will purge all local volumes not used by at least one container.
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docker volume prune
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```
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### Triggering a task to re-run within the Airflow UI
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- Check if the task / run you want to re-run is visible in the DAG's Tree View UI
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- For example, [the `main_summary` DAG tree view](http://workflow.telemetry.mozilla.org/admin/airflow/tree?num_runs=25&root=&dag_id=main_summary).
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- Hover over the little squares to find the scheduled dag run you're looking for.
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- If the dag run is not showing in the Dag Tree View UI (maybe deleted)
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- Browse -> Dag Runs
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- Create (you can look at another dag run of the same dag for example values too)
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- Dag Id: the name of the dag, for example, `main_summary`
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- Execution Date: The date the dag should have run, for example, `2018-05-14 00:00:00`
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- Start Date: Some date between the execution date and "now", for example, `2018-05-20 00:00:05`
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- End Date: Leave it blank
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- State: success
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- Run Id: `scheduled__2018-05-14T00:00:00`
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- External Trigger: unchecked
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- Click Save
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- Click on the Graph view for the dag in question. From the main DAGs view, click the name of the DAG
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- Select the "Run Id" you just entered from the drop-down list
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- Click "Go"
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- Click each element of the DAG and "Mark Success"
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- The tasks should now show in the Tree View UI
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- If the dag run is showing in the DAG's Tree View UI
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- Click on the small square for the task you want to re-run
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- **Uncheck** the "Downstream" toggle
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- Click the "Clear" button
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- Confirm that you want to clear it
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- The task should be scheduled to run again straight away.
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982
airflow.cfg
982
airflow.cfg
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@ -1,982 +0,0 @@
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[core]
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# Hostname by providing a path to a callable, which will resolve the hostname.
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# The format is "package.function".
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#
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# For example, default value "socket.getfqdn" means that result from getfqdn() of "socket"
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# package will be used as hostname.
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#
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# No argument should be required in the function specified.
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# If using IP address as hostname is preferred, use value ``airflow.utils.net.get_host_ip_address``
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# hostname_callable = socket.getfqdn
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default_timezone = utc
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hide_sensitive_var_conn_fields = True
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sensitive_var_conn_names = 'cred,CRED,secret,SECRET,pass,PASS,password,Password,PASSWORD,private,PRIVATE,key,KEY,cert,CERT,token,TOKEN,AKIA'
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# This setting would not have any effect in an existing deployment where the default_pool already exists.
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default_pool_task_slot_count = 50
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# The folder where your airflow pipelines live, most likely a
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# subfolder in a code repository
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dags_folder = $AIRFLOW_HOME/dags
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# The executor class that airflow should use. Choices include
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# SequentialExecutor, LocalExecutor, CeleryExecutor
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executor = CeleryExecutor
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# The amount of parallelism as a setting to the executor. This defines
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# the max number of task instances that should run simultaneously
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# on this airflow installation
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parallelism = 16
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# The number of task instances allowed to run concurrently by the scheduler
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max_active_tasks_per_dag = 16
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# Are DAGs paused by default at creation
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dags_are_paused_at_creation = True
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# The maximum number of active DAG runs per DAG
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max_active_runs_per_dag = 5
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# Whether to load the examples that ship with Airflow. It's good to
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# get started, but you probably want to set this to False in a production
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# environment
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load_examples = False
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# Where your Airflow plugins are stored
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plugins_folder = $AIRFLOW_HOME/plugins
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# Should tasks be executed via forking of the parent process ("False",
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# the speedier option) or by spawning a new python process ("True" slow,
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# but means plugin changes picked up by tasks straight away)
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execute_tasks_new_python_interpreter = False
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# Secret key to save connection passwords in the db
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# Setting this to $AIRFLOW_FERNET_KEY is broken in 1.9 for initdb. Set $AIRFLOW__CORE__FERNET_KEY instead
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# fernet_key =
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# Whether to disable pickling dags
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donot_pickle = False
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# How long before timing out a python file import while filling the DagBag
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dagbag_import_timeout = 30.0
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# Should a traceback be shown in the UI for dagbag import errors,
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# instead of just the exception message
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dagbag_import_error_tracebacks = True
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# If tracebacks are shown, how many entries from the traceback should be shown
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dagbag_import_error_traceback_depth = 2
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# How long before timing out a DagFileProcessor, which processes a dag file
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dag_file_processor_timeout = 50
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# The class to use for running task instances in a subprocess.
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# Choices include StandardTaskRunner, CgroupTaskRunner or the full import path to the class
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# when using a custom task runner.
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task_runner = StandardTaskRunner
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# If set, tasks without a ``run_as_user`` argument will be run with this user
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# Can be used to de-elevate a sudo user running Airflow when executing tasks
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# default_impersonation =
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# What security module to use (for example kerberos)
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# security =
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# Turn unit test mode on (overwrites many configuration options with test
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# values at runtime)
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unit_test_mode = False
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# Whether to enable pickling for xcom (note that this is insecure and allows for
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# RCE exploits).
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enable_xcom_pickling = False
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# When a task is killed forcefully, this is the amount of time in seconds that
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# it has to cleanup after it is sent a SIGTERM, before it is SIGKILLED
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killed_task_cleanup_time = 60
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# Whether to override params with dag_run.conf. If you pass some key-value pairs
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# through ``airflow dags backfill -c`` or
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# ``airflow dags trigger -c``, the key-value pairs will override the existing ones in params.
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dag_run_conf_overrides_params = True
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# When discovering DAGs, ignore any files that don't contain the strings ``DAG`` and ``airflow``.
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dag_discovery_safe_mode = False
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# The pattern syntax used in the ".airflowignore" files in the DAG directories. Valid values are
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# ``regexp`` or ``glob``.
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dag_ignore_file_syntax = regexp
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# The number of retries each task is going to have by default. Can be overridden at dag or task level.
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default_task_retries = 0
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# The weighting method used for the effective total priority weight of the task
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default_task_weight_rule = downstream
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# The default task execution_timeout value for the operators. Expected an integer value to
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# be passed into timedelta as seconds. If not specified, then the value is considered as None,
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# meaning that the operators are never timed out by default.
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default_task_execution_timeout =
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# We will override the next 2 intervals in prod via env vars.
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# Updating serialized DAG can not be faster than a minimum interval to reduce database write rate.
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# This flag sets the minimum interval (in seconds) after which the serialized DAGs in the DB should be updated.
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# This helps in reducing database write rate.
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min_serialized_dag_update_interval = 10
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# If True, serialized DAGs are compressed before writing to DB.
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# Note: this will disable the DAG dependencies view
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compress_serialized_dags = False
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# Fetching serialized DAG can not be faster than a minimum interval to reduce database
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# read rate. This config controls when your DAGs are updated in the Webserver
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min_serialized_dag_fetch_interval = 5
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# Whether to persist DAG files code in DB.
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# If set to True, Webserver reads file contents from DB instead of
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# trying to access files in a DAG folder.
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# (Default is ``True``)
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# Example: store_dag_code = True
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# store_dag_code =
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# Maximum number of Rendered Task Instance Fields (Template Fields) per task to store
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# in the Database.
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# All the template_fields for each of Task Instance are stored in the Database.
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# Keeping this number small may cause an error when you try to view ``Rendered`` tab in
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# TaskInstance view for older tasks.
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max_num_rendered_ti_fields_per_task = 30
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# On each dagrun check against defined SLAs
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check_slas = True
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# Path to custom XCom class that will be used to store and resolve operators results
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# Example: xcom_backend = path.to.CustomXCom
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xcom_backend = airflow.models.xcom.BaseXCom
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# By default Airflow plugins are lazily-loaded (only loaded when required). Set it to ``False``,
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# if you want to load plugins whenever 'airflow' is invoked via cli or loaded from module.
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lazy_load_plugins = True
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# By default Airflow providers are lazily-discovered (discovery and imports happen only when required).
|
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# Set it to False, if you want to discover providers whenever 'airflow' is invoked via cli or
|
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# loaded from module.
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lazy_discover_providers = True
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# The maximum list/dict length an XCom can push to trigger task mapping. If the pushed list/dict has a
|
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# length exceeding this value, the task pushing the XCom will be failed automatically to prevent the
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# mapped tasks from clogging the scheduler.
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max_map_length = 1024
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[database]
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# Whether to load the default connections that ship with Airflow. It's good to
|
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# get started, but you probably want to set this to ``False`` in a production
|
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# environment
|
||||
# We have configured google_cloud_default, so hopefully this wont remove it.
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load_default_connections = False
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# The SqlAlchemy connection string to the metadata database.
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# SqlAlchemy supports many different database engine, more information
|
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# their website
|
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sql_alchemy_conn = $AIRFLOW_DATABASE_URL
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# The SqlAlchemy pool size is the maximum number of database connections
|
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# in the pool.
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sql_alchemy_pool_size = 5
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# The SqlAlchemy pool recycle is the number of seconds a connection
|
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# can be idle in the pool before it is invalidated. This config does
|
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# not apply to sqlite.
|
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sql_alchemy_pool_recycle = 3600
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# Number of times the code should be retried in case of DB Operational Errors.
|
||||
# Not all transactions will be retried as it can cause undesired state.
|
||||
# Currently it is only used in ``DagFileProcessor.process_file`` to retry ``dagbag.sync_to_db``.
|
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max_db_retries = 3
|
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|
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# Collation for ``dag_id``, ``task_id``, ``key`` columns in case they have different encoding.
|
||||
# By default this collation is the same as the database collation, however for ``mysql`` and ``mariadb``
|
||||
# the default is ``utf8mb3_general_ci`` so that the index sizes of our index keys will not exceed
|
||||
# the maximum size of allowed index when collation is set to ``utf8mb4`` variant
|
||||
# (see https://github.com/apache/airflow/pull/17603#issuecomment-901121618).
|
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# and https://github.com/apache/airflow/pull/17729/
|
||||
# sql_engine_collation_for_ids =
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||||
|
||||
|
||||
[logging]
|
||||
# The folder where airflow should store its log files. This location
|
||||
base_log_folder = $AIRFLOW_HOME/logs
|
||||
|
||||
# Logging level.
|
||||
#
|
||||
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
|
||||
logging_level = INFO
|
||||
|
||||
# Logging level for celery. If not set, it uses the value of logging_level
|
||||
#
|
||||
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
|
||||
# celery_logging_level =
|
||||
|
||||
# Logging level for Flask-appbuilder UI.
|
||||
#
|
||||
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
|
||||
fab_logging_level = WARNING
|
||||
|
||||
# When you start an airflow worker, airflow starts a tiny web server
|
||||
# subprocess to serve the workers local log files to the airflow main
|
||||
# web server, who then builds pages and sends them to users. This defines
|
||||
# the port on which the logs are served. It needs to be unused, and open
|
||||
# visible from the main web server to connect into the workers.
|
||||
worker_log_server_port = 8793
|
||||
|
||||
# Logging class
|
||||
# Specify the class that will specify the logging configuration
|
||||
# This class has to be on the python classpath
|
||||
# Example: logging_config_class = my.path.default_local_settings.LOGGING_CONFIG
|
||||
# logging_config_class =
|
||||
|
||||
# Flag to enable/disable Colored logs in Console
|
||||
# Colour the logs when the controlling terminal is a TTY.
|
||||
colored_console_log = True
|
||||
|
||||
# Log format for when Colored logs is enabled
|
||||
colored_log_format = [%%(blue)s%%(asctime)s%%(reset)s] {{%%(blue)s%%(filename)s:%%(reset)s%%(lineno)d}} %%(log_color)s%%(levelname)s%%(reset)s - %%(log_color)s%%(message)s%%(reset)s
|
||||
colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter
|
||||
|
||||
# Format of Log line
|
||||
log_format = [%%(asctime)s] {{%%(filename)s:%%(lineno)d}} %%(levelname)s - %%(message)s
|
||||
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s
|
||||
|
||||
# Specify prefix pattern like mentioned below with stream handler TaskHandlerWithCustomFormatter
|
||||
# Example: task_log_prefix_template = {{ti.dag_id}}-{{ti.task_id}}-{{execution_date}}-{{try_number}}
|
||||
# task_log_prefix_template =
|
||||
|
||||
# Formatting for how airflow generates file names/paths for each task run.
|
||||
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ execution_date.strftime("%%Y-%%m-%%dT%%H:%%M:%%S") }}/{{ try_number }}.log
|
||||
|
||||
# Formatting for how airflow generates file names for log
|
||||
log_processor_filename_template = {{ filename }}.log
|
||||
|
||||
# full path of dag_processor_manager logfile
|
||||
dag_processor_manager_log_location = $AIRFLOW_HOME/logs/dag_processor_manager/dag_processor_manager.log
|
||||
|
||||
# Name of handler to read task instance logs.
|
||||
# Defaults to use ``task`` handler.
|
||||
task_log_reader = task
|
||||
|
||||
# A comma\-separated list of third-party logger names that will be configured to print messages to
|
||||
# consoles\.
|
||||
# Example: extra_logger_names = connexion,sqlalchemy
|
||||
# extra_logger_names =
|
||||
|
||||
|
||||
[webserver]
|
||||
# The base url of your website as airflow cannot guess what domain or
|
||||
# cname you are using. This is use in automated emails that
|
||||
# airflow sends to point links to the right web server
|
||||
base_url = $URL
|
||||
|
||||
# Default timezone to display all dates in the UI, can be UTC, system, or
|
||||
# any IANA timezone string (e.g. Europe/Amsterdam). If left empty the
|
||||
# default value of core/default_timezone will be used
|
||||
# Example: default_ui_timezone = America/New_York
|
||||
default_ui_timezone = UTC
|
||||
|
||||
# The ip specified when starting the web server
|
||||
web_server_host = 0.0.0.0
|
||||
|
||||
# The port on which to run the web server
|
||||
web_server_port = $PORT
|
||||
|
||||
# Secret key used to run your flask app
|
||||
secret_key = $AIRFLOW_SECRET_KEY
|
||||
|
||||
# Number of workers to run the Gunicorn web server
|
||||
workers = 4
|
||||
|
||||
# The worker class gunicorn should use. Choices include
|
||||
# sync (default), eventlet, gevent
|
||||
worker_class = gevent
|
||||
|
||||
# Set to true to turn on authentication : http://pythonhosted.org/airflow/installation.html#web-authentication
|
||||
auth_backend = $AIRFLOW_AUTH_BACKEND
|
||||
|
||||
# Paths to the SSL certificate and key for the web server. When both are
|
||||
# provided SSL will be enabled. This does not change the web server port.
|
||||
# web_server_ssl_cert =
|
||||
|
||||
# Paths to the SSL certificate and key for the web server. When both are
|
||||
# provided SSL will be enabled. This does not change the web server port.
|
||||
# web_server_ssl_key =
|
||||
|
||||
# The type of backend used to store web session data, can be 'database' or 'securecookie'
|
||||
# Example: session_backend = securecookie
|
||||
# session_backend = database
|
||||
|
||||
# Number of seconds the webserver waits before killing gunicorn master that doesn't respond
|
||||
web_server_master_timeout = 300
|
||||
|
||||
# Number of seconds the gunicorn webserver waits before timing out on a worker
|
||||
web_server_worker_timeout = 300
|
||||
|
||||
# Number of workers to refresh at a time. When set to 0, worker refresh is
|
||||
# disabled. When nonzero, airflow periodically refreshes webserver workers by
|
||||
# bringing up new ones and killing old ones.
|
||||
worker_refresh_batch_size = 1
|
||||
|
||||
# Number of seconds to wait before refreshing a batch of workers.
|
||||
worker_refresh_interval = 6000
|
||||
|
||||
# If set to True, Airflow will track files in plugins_folder directory. When it detects changes,
|
||||
# then reload the gunicorn.
|
||||
# We set this to True for local development, and override it with ENV var in prod
|
||||
# False in prod so that changes pushed to plugins folder do not kill currently running backfills
|
||||
reload_on_plugin_change = True
|
||||
|
||||
# Log files for the gunicorn webserver. '-' means log to stderr.
|
||||
access_logfile = -
|
||||
|
||||
# Log files for the gunicorn webserver. '-' means log to stderr.
|
||||
error_logfile = -
|
||||
|
||||
# Access log format for gunicorn webserver.
|
||||
# default format is %%(h)s %%(l)s %%(u)s %%(t)s "%%(r)s" %%(s)s %%(b)s "%%(f)s" "%%(a)s"
|
||||
# documentation - https://docs.gunicorn.org/en/stable/settings.html#access-log-format
|
||||
# access_logformat =
|
||||
|
||||
# Expose the configuration file in the web server
|
||||
expose_config = True
|
||||
|
||||
# Expose hostname in the web server
|
||||
expose_hostname = True
|
||||
|
||||
# Expose stacktrace in the web server
|
||||
expose_stacktrace = True
|
||||
|
||||
# Default DAG view. Valid values are: ``tree``, ``graph``, ``duration``, ``gantt``, ``landing_times``
|
||||
dag_default_view = grid
|
||||
|
||||
# Default DAG orientation. Valid values are:
|
||||
# ``LR`` (Left->Right), ``TB`` (Top->Bottom), ``RL`` (Right->Left), ``BT`` (Bottom->Top)
|
||||
dag_orientation = LR
|
||||
|
||||
# The amount of time (in secs) webserver will wait for initial handshake
|
||||
# while fetching logs from other worker machine
|
||||
log_fetch_timeout_sec = 5
|
||||
|
||||
# Time interval (in secs) to wait before next log fetching.
|
||||
log_fetch_delay_sec = 2
|
||||
|
||||
# Distance away from page bottom to enable auto tailing.
|
||||
log_auto_tailing_offset = 30
|
||||
|
||||
# Animation speed for auto tailing log display.
|
||||
log_animation_speed = 1000
|
||||
|
||||
# By default, the webserver shows paused DAGs. Flip this to hide paused
|
||||
# DAGs by default
|
||||
hide_paused_dags_by_default = False
|
||||
|
||||
# Consistent page size across all listing views in the UI
|
||||
page_size = 100
|
||||
|
||||
# Define the color of navigation bar
|
||||
navbar_color = #fff
|
||||
|
||||
# Default dagrun to show in UI
|
||||
default_dag_run_display_number = 25
|
||||
|
||||
# Enable werkzeug ``ProxyFix`` middleware for reverse proxy
|
||||
enable_proxy_fix = False
|
||||
|
||||
# Number of values to trust for ``X-Forwarded-For``.
|
||||
# More info: https://werkzeug.palletsprojects.com/en/0.16.x/middleware/proxy_fix/
|
||||
proxy_fix_x_for = 1
|
||||
|
||||
# Number of values to trust for ``X-Forwarded-Proto``
|
||||
proxy_fix_x_proto = 1
|
||||
|
||||
# Number of values to trust for ``X-Forwarded-Host``
|
||||
proxy_fix_x_host = 1
|
||||
|
||||
# Number of values to trust for ``X-Forwarded-Port``
|
||||
proxy_fix_x_port = 1
|
||||
|
||||
# Number of values to trust for ``X-Forwarded-Prefix``
|
||||
proxy_fix_x_prefix = 1
|
||||
|
||||
# Set secure flag on session cookie
|
||||
cookie_secure = False
|
||||
|
||||
# Set samesite policy on session cookie
|
||||
cookie_samesite = Lax
|
||||
|
||||
# Default setting for wrap toggle on DAG code and TI log views.
|
||||
default_wrap = False
|
||||
|
||||
# Allow the UI to be rendered in a frame
|
||||
x_frame_enabled = True
|
||||
|
||||
# Send anonymous user activity to your analytics tool
|
||||
# choose from google_analytics, segment, or metarouter
|
||||
# analytics_tool =
|
||||
|
||||
# Unique ID of your account in the analytics tool
|
||||
# analytics_id =
|
||||
|
||||
# 'Recent Tasks' stats will show for old DagRuns if set
|
||||
show_recent_stats_for_completed_runs = True
|
||||
|
||||
# Update FAB permissions and sync security manager roles
|
||||
# on webserver startup
|
||||
update_fab_perms = True
|
||||
|
||||
# The UI cookie lifetime in minutes. User will be logged out from UI after
|
||||
# ``session_lifetime_minutes`` of non-activity
|
||||
session_lifetime_minutes = 43200
|
||||
|
||||
# Sets a custom page title for the DAGs overview page and site title for all pages
|
||||
# instance_name =
|
||||
|
||||
# Whether the custom page title for the DAGs overview page contains any Markup language
|
||||
instance_name_has_markup = False
|
||||
|
||||
# How frequently, in seconds, the DAG data will auto-refresh in graph or grid view
|
||||
# when auto-refresh is turned on
|
||||
auto_refresh_interval = 3
|
||||
|
||||
# Boolean for displaying warning for publicly viewable deployment
|
||||
warn_deployment_exposure = True
|
||||
|
||||
# Comma separated string of view events to exclude from dag audit view.
|
||||
# All other events will be added minus the ones passed here.
|
||||
# The audit logs in the db will not be affected by this parameter.
|
||||
audit_view_excluded_events = gantt,landing_times,tries,duration,calendar,graph,grid,tree,tree_data
|
||||
|
||||
# Comma separated string of view events to include in dag audit view.
|
||||
# If passed, only these events will populate the dag audit view.
|
||||
# The audit logs in the db will not be affected by this parameter.
|
||||
# Example: audit_view_included_events = dagrun_cleared,failed
|
||||
# audit_view_included_events =
|
||||
|
||||
[email]
|
||||
email_backend = $AIRFLOW_EMAIL_BACKEND
|
||||
|
||||
# Email connection to use
|
||||
# email_conn_id = smtp_default
|
||||
|
||||
# Whether email alerts should be sent when a task is retried
|
||||
default_email_on_retry = True
|
||||
|
||||
# Whether email alerts should be sent when a task failed
|
||||
default_email_on_failure = True
|
||||
|
||||
# File that will be used as the template for Email subject (which will be rendered using Jinja2).
|
||||
# If not set, Airflow uses a base template.
|
||||
# Example: subject_template = /path/to/my_subject_template_file
|
||||
# subject_template =
|
||||
|
||||
# File that will be used as the template for Email content (which will be rendered using Jinja2).
|
||||
# If not set, Airflow uses a base template.
|
||||
# Example: html_content_template = /path/to/my_html_content_template_file
|
||||
# html_content_template =
|
||||
|
||||
|
||||
[smtp]
|
||||
# If you want airflow to send emails on retries, failure, and you want to
|
||||
# the airflow.utils.send_email function, you have to configure an smtp
|
||||
# server here
|
||||
smtp_starttls = True
|
||||
smtp_ssl = False
|
||||
smtp_host = $AIRFLOW_SMTP_HOST
|
||||
smtp_port = 587
|
||||
smtp_user = $AIRFLOW_SMTP_USER
|
||||
smtp_password = $AIRFLOW_SMTP_PASSWORD
|
||||
smtp_mail_from = $AIRFLOW_SMTP_FROM
|
||||
# smtp_timeout = 30
|
||||
# smtp_retry_limit = 5
|
||||
|
||||
|
||||
[sentry]
|
||||
# Sentry (https://docs.sentry.io) integration. Here you can supply
|
||||
# additional configuration options based on the Python platform. See:
|
||||
# https://docs.sentry.io/error-reporting/configuration/?platform=python.
|
||||
# Unsupported options: ``integrations``, ``in_app_include``, ``in_app_exclude``,
|
||||
# ``ignore_errors``, ``before_breadcrumb``, ``before_send``, ``transport``.
|
||||
# Enable error reporting to Sentry
|
||||
# sentry_on = false
|
||||
# sentry_dsn =
|
||||
|
||||
|
||||
[celery_kubernetes_executor]
|
||||
# This section only applies if you are using the ``CeleryKubernetesExecutor`` in
|
||||
# ``[core]`` section above
|
||||
# Define when to send a task to ``KubernetesExecutor`` when using ``CeleryKubernetesExecutor``.
|
||||
# When the queue of a task is the value of ``kubernetes_queue`` (default ``kubernetes``),
|
||||
# the task is executed via ``KubernetesExecutor``,
|
||||
# otherwise via ``CeleryExecutor``
|
||||
# kubernetes_queue = kubernetes
|
||||
|
||||
|
||||
[celery]
|
||||
# This section only applies if you are using the CeleryExecutor in
|
||||
# [core] section above
|
||||
|
||||
# The app name that will be used by celery
|
||||
celery_app_name = airflow.executors.celery_executor
|
||||
|
||||
# The concurrency that will be used when starting workers with the
|
||||
# "airflow worker" command. This defines the number of task instances that
|
||||
# a worker will take, so size up your workers based on the resources on
|
||||
# your worker box and the nature of your tasks
|
||||
worker_concurrency = 32
|
||||
|
||||
# The maximum and minimum concurrency that will be used when starting workers with the
|
||||
# ``airflow celery worker`` command (always keep minimum processes, but grow
|
||||
# to maximum if necessary). Note the value should be max_concurrency,min_concurrency
|
||||
# Pick these numbers based on resources on worker box and the nature of the task.
|
||||
# If autoscale option is available, worker_concurrency will be ignored.
|
||||
# http://docs.celeryproject.org/en/latest/reference/celery.bin.worker.html#cmdoption-celery-worker-autoscale
|
||||
# Example: worker_autoscale = 16,12
|
||||
# worker_autoscale =
|
||||
|
||||
# Used to increase the number of tasks that a worker prefetches which can improve performance.
|
||||
# The number of processes multiplied by worker_prefetch_multiplier is the number of tasks
|
||||
# that are prefetched by a worker. A value greater than 1 can result in tasks being unnecessarily
|
||||
# blocked if there are multiple workers and one worker prefetches tasks that sit behind long
|
||||
# running tasks while another worker has unutilized processes that are unable to process the already
|
||||
# claimed blocked tasks.
|
||||
# https://docs.celeryproject.org/en/stable/userguide/optimizing.html#prefetch-limits
|
||||
# Example: worker_prefetch_multiplier = 1
|
||||
# worker_prefetch_multiplier =
|
||||
|
||||
# Specify if remote control of the workers is enabled.
|
||||
# When using Amazon SQS as the broker, Celery creates lots of ``.*reply-celery-pidbox`` queues. You can
|
||||
# prevent this by setting this to false. However, with this disabled Flower won't work.
|
||||
# worker_enable_remote_control = true
|
||||
|
||||
# Umask that will be used when starting workers with the ``airflow celery worker``
|
||||
# in daemon mode. This control the file-creation mode mask which determines the initial
|
||||
# value of file permission bits for newly created files.
|
||||
# worker_umask = 0o077
|
||||
|
||||
# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally
|
||||
# a sqlalchemy database. Refer to the Celery documentation for more
|
||||
# information.
|
||||
broker_url = $AIRFLOW_BROKER_URL
|
||||
|
||||
# Another key Celery setting
|
||||
result_backend = $AIRFLOW_RESULT_URL
|
||||
|
||||
# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
|
||||
# it ``airflow celery flower``. This defines the IP that Celery Flower runs on
|
||||
flower_host = 0.0.0.0
|
||||
|
||||
# The root URL for Flower
|
||||
# Example: flower_url_prefix = /flower
|
||||
# flower_url_prefix =
|
||||
|
||||
# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
|
||||
# it `airflow flower`. This defines the port that Celery Flower runs on
|
||||
flower_port = $AIRFLOW_FLOWER_PORT
|
||||
|
||||
# Securing Flower with Basic Authentication
|
||||
# Accepts user:password pairs separated by a comma
|
||||
# Example: flower_basic_auth = user1:password1,user2:password2
|
||||
# flower_basic_auth =
|
||||
|
||||
# How many processes CeleryExecutor uses to sync task state.
|
||||
# 0 means to use max(1, number of cores - 1) processes.
|
||||
sync_parallelism = 0
|
||||
|
||||
# Import path for celery configuration options
|
||||
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG
|
||||
ssl_active = False
|
||||
# ssl_key =
|
||||
# ssl_cert =
|
||||
# ssl_cacert =
|
||||
|
||||
# Celery Pool implementation.
|
||||
# Choices include: ``prefork`` (default), ``eventlet``, ``gevent`` or ``solo``.
|
||||
# See:
|
||||
# https://docs.celeryproject.org/en/latest/userguide/workers.html#concurrency
|
||||
# https://docs.celeryproject.org/en/latest/userguide/concurrency/eventlet.html
|
||||
pool = prefork
|
||||
|
||||
# The number of seconds to wait before timing out ``send_task_to_executor`` or
|
||||
# ``fetch_celery_task_state`` operations.
|
||||
operation_timeout = 3.0
|
||||
|
||||
# Celery task will report its status as 'started' when the task is executed by a worker.
|
||||
# This is used in Airflow to keep track of the running tasks and if a Scheduler is restarted
|
||||
# or run in HA mode, it can adopt the orphan tasks launched by previous SchedulerJob.
|
||||
task_track_started = True
|
||||
|
||||
# Time in seconds after which Adopted tasks are cleared by CeleryExecutor. This is helpful to clear
|
||||
# stalled tasks.
|
||||
task_adoption_timeout = 600
|
||||
|
||||
# The Maximum number of retries for publishing task messages to the broker when failing
|
||||
# due to ``AirflowTaskTimeout`` error before giving up and marking Task as failed.
|
||||
task_publish_max_retries = 3
|
||||
|
||||
# Worker initialisation check to validate Metadata Database connection
|
||||
worker_precheck = False
|
||||
|
||||
|
||||
# [dask]
|
||||
# This section only applies if you are using the DaskExecutor in
|
||||
# [core] section above
|
||||
# The IP address and port of the Dask cluster's scheduler.
|
||||
# cluster_address = 127.0.0.1:8786
|
||||
|
||||
# TLS/ SSL settings to access a secured Dask scheduler.
|
||||
# tls_ca =
|
||||
# tls_cert =
|
||||
# tls_key =
|
||||
|
||||
|
||||
[celery_broker_transport_options]
|
||||
# This section is for specifying options which can be passed to the
|
||||
# underlying celery broker transport. See:
|
||||
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-broker_transport_options
|
||||
# The visibility timeout defines the number of seconds to wait for the worker
|
||||
# to acknowledge the task before the message is redelivered to another worker.
|
||||
# Make sure to increase the visibility timeout to match the time of the longest
|
||||
# ETA you're planning to use.
|
||||
# visibility_timeout is only supported for Redis and SQS celery brokers.
|
||||
# See:
|
||||
# http://docs.celeryproject.org/en/master/userguide/configuration.html#std:setting-broker_transport_options
|
||||
# Example: visibility_timeout = 21600
|
||||
# visibility_timeout =
|
||||
|
||||
|
||||
[operators]
|
||||
# Default queue that tasks get assigned to and that worker listen on.
|
||||
default_queue = default
|
||||
|
||||
# The default owner assigned to each new operator, unless
|
||||
# provided explicitly or passed via ``default_args``
|
||||
# default_owner = airflow
|
||||
# default_cpus = 1
|
||||
# default_ram = 512
|
||||
# default_disk = 512
|
||||
# default_gpus = 0
|
||||
|
||||
# Is allowed to pass additional/unused arguments (args, kwargs) to the BaseOperator operator.
|
||||
# If set to False, an exception will be thrown, otherwise only the console message will be displayed.
|
||||
allow_illegal_arguments = False
|
||||
|
||||
|
||||
[scheduler]
|
||||
# Task instances listen for external kill signal (when you clear tasks
|
||||
# from the CLI or the UI), this defines the frequency at which they should
|
||||
# listen (in seconds).
|
||||
job_heartbeat_sec = 5
|
||||
|
||||
# The scheduler constantly tries to trigger new tasks (look at the
|
||||
# scheduler section in the docs for more information). This defines
|
||||
# how often the scheduler should run (in seconds).
|
||||
scheduler_heartbeat_sec = 5
|
||||
|
||||
# The number of times to try to schedule each DAG file
|
||||
# -1 indicates unlimited number
|
||||
num_runs = -1
|
||||
|
||||
# The number of seconds to wait between consecutive DAG file processing
|
||||
# Deprecated since version 2.2.0: The option has been moved to scheduler.scheduler_idle_sleep_time
|
||||
scheduler_idle_sleep_time = 1
|
||||
|
||||
# Number of seconds after which a DAG file is parsed. The DAG file is parsed every
|
||||
# ``min_file_process_interval`` number of seconds. Updates to DAGs are reflected after
|
||||
# this interval. Keeping this number low will increase CPU usage.
|
||||
min_file_process_interval = 60
|
||||
|
||||
# How often (in seconds) to check for stale DAGs (DAGs which are no longer present in
|
||||
# the expected files) which should be deactivated.
|
||||
deactivate_stale_dags_interval = 120
|
||||
|
||||
# How often (in seconds) to scan the DAGs directory for new files. Default to 5 minutes.
|
||||
# This is set via env var to 300 in prod, but 30 for local testing
|
||||
dag_dir_list_interval = 30
|
||||
|
||||
# How often should stats be printed to the logs. Setting to 0 will disable printing stats
|
||||
print_stats_interval = 30
|
||||
|
||||
# How often (in seconds) should pool usage stats be sent to statsd (if statsd_on is enabled)
|
||||
pool_metrics_interval = 20.0
|
||||
|
||||
# If the last scheduler heartbeat happened more than scheduler_health_check_threshold
|
||||
# ago (in seconds), scheduler is considered unhealthy.
|
||||
# This is used by the health check in the "/health" endpoint
|
||||
scheduler_health_check_threshold = 30
|
||||
|
||||
# How often (in seconds) should the scheduler check for orphaned tasks and SchedulerJobs
|
||||
orphaned_tasks_check_interval = 300.0
|
||||
child_process_log_directory = $AIRFLOW_HOME/logs/scheduler
|
||||
|
||||
# Local task jobs periodically heartbeat to the DB. If the job has
|
||||
# not heartbeat in this many seconds, the scheduler will mark the
|
||||
# associated task instance as failed and will re-schedule the task.
|
||||
scheduler_zombie_task_threshold = 300
|
||||
|
||||
# How often (in seconds) should the scheduler check for zombie tasks.
|
||||
zombie_detection_interval = 60.0
|
||||
|
||||
# Turn off scheduler catchup by setting this to False.
|
||||
# Default behavior is unchanged and
|
||||
# Command Line Backfills still work, but the scheduler
|
||||
# will not do scheduler catchup if this is False,
|
||||
# however it can be set on a per DAG basis in the
|
||||
# DAG definition (catchup)
|
||||
catchup_by_default = False
|
||||
|
||||
# Setting this to True will make first task instance of a task
|
||||
# ignore depends_on_past setting. A task instance will be considered
|
||||
# as the first task instance of a task when there is no task instance
|
||||
# in the DB with an execution_date earlier than it., i.e. no manual marking
|
||||
# success will be needed for a newly added task to be scheduled.
|
||||
ignore_first_depends_on_past_by_default = True
|
||||
|
||||
# This changes the batch size of queries in the scheduling main loop.
|
||||
# If this is too high, SQL query performance may be impacted by one
|
||||
# or more of the following:
|
||||
# - reversion to full table scan
|
||||
# - complexity of query predicate
|
||||
# - excessive locking
|
||||
# Additionally, you may hit the maximum allowable query length for your db.
|
||||
# Set this to 0 for no limit (not advised)
|
||||
max_tis_per_query = 512
|
||||
|
||||
# Should the scheduler issue ``SELECT ... FOR UPDATE`` in relevant queries.
|
||||
# If this is set to False then you should not run more than a single
|
||||
# scheduler at once
|
||||
use_row_level_locking = True
|
||||
|
||||
# Max number of DAGs to create DagRuns for per scheduler loop
|
||||
#
|
||||
# Default: 10
|
||||
# max_dagruns_to_create_per_loop =
|
||||
|
||||
# How many DagRuns should a scheduler examine (and lock) when scheduling
|
||||
# and queuing tasks.
|
||||
#
|
||||
# Default: 20
|
||||
# max_dagruns_per_loop_to_schedule =
|
||||
|
||||
# Should the Task supervisor process perform a "mini scheduler" to attempt to schedule more tasks of the
|
||||
# same DAG. Leaving this on will mean tasks in the same DAG execute quicker, but might starve out other
|
||||
# dags in some circumstances
|
||||
#
|
||||
# Default: True
|
||||
# schedule_after_task_execution =
|
||||
|
||||
# The scheduler can run multiple processes in parallel to parse dags.
|
||||
# This defines how many processes will run.
|
||||
parsing_processes = 2
|
||||
|
||||
# One of ``modified_time``, ``random_seeded_by_host`` and ``alphabetical``.
|
||||
# The scheduler will list and sort the dag files to decide the parsing order.
|
||||
#
|
||||
# * ``modified_time``: Sort by modified time of the files. This is useful on large scale to parse the
|
||||
# recently modified DAGs first.
|
||||
# * ``random_seeded_by_host``: Sort randomly across multiple Schedulers but with same order on the
|
||||
# same host. This is useful when running with Scheduler in HA mode where each scheduler can
|
||||
# parse different DAG files.
|
||||
# * ``alphabetical``: Sort by filename
|
||||
file_parsing_sort_mode = modified_time
|
||||
|
||||
# Whether the dag processor is running as a standalone process or it is a subprocess of a scheduler
|
||||
# job.
|
||||
standalone_dag_processor = False
|
||||
|
||||
# Only applicable if `[scheduler]standalone_dag_processor` is true and callbacks are stored
|
||||
# in database. Contains maximum number of callbacks that are fetched during a single loop.
|
||||
max_callbacks_per_loop = 20
|
||||
|
||||
# Turn off scheduler use of cron intervals by setting this to False.
|
||||
# DAGs submitted manually in the web UI or with trigger_dag will still run.
|
||||
use_job_schedule = True
|
||||
|
||||
# Allow externally triggered DagRuns for Execution Dates in the future
|
||||
# Only has effect if schedule_interval is set to None in DAG
|
||||
allow_trigger_in_future = False
|
||||
|
||||
# DAG dependency detector class to use
|
||||
dependency_detector = airflow.serialization.serialized_objects.DependencyDetector
|
||||
|
||||
# How often to check for expired trigger requests that have not run yet.
|
||||
trigger_timeout_check_interval = 15
|
||||
|
||||
[triggerer]
|
||||
# How many triggers a single Triggerer will run at once, by default.
|
||||
default_capacity = 1000
|
||||
|
||||
[metrics]
|
||||
# Statsd (https://github.com/etsy/statsd) integration settings
|
||||
# statsd_on = False
|
||||
# statsd_host = localhost
|
||||
# statsd_port = 8125
|
||||
# statsd_prefix = airflow
|
||||
|
||||
# To enable datadog integration to send airflow metrics.
|
||||
statsd_datadog_enabled = False
|
||||
|
||||
# List of datadog tags attached to all metrics(e.g: key1:value1,key2:value2)
|
||||
# statsd_datadog_tags =
|
||||
|
||||
|
||||
# [secrets]
|
||||
# Full class name of secrets backend to enable (will precede env vars and metastore in search path)
|
||||
# Example: backend = airflow.providers.amazon.aws.secrets.systems_manager.SystemsManagerParameterStoreBackend
|
||||
# backend =
|
||||
|
||||
# The backend_kwargs param is loaded into a dictionary and passed to __init__ of secrets backend class.
|
||||
# See documentation for the secrets backend you are using. JSON is expected.
|
||||
# Example for AWS Systems Manager ParameterStore:
|
||||
# ``{{"connections_prefix": "/airflow/connections", "profile_name": "default"}}``
|
||||
# backend_kwargs =
|
||||
|
||||
# [cli]
|
||||
# In what way should the cli access the API. The LocalClient will use the
|
||||
# database directly, while the json_client will use the api running on the
|
||||
# webserver
|
||||
# api_client = airflow.api.client.local_client
|
||||
|
||||
# If you set web_server_url_prefix, do NOT forget to append it here, ex:
|
||||
# ``endpoint_url = http://localhost:8080/myroot``
|
||||
# So api will look like: ``http://localhost:8080/myroot/api/experimental/...``
|
||||
# endpoint_url = http://localhost:8080
|
||||
|
||||
|
||||
[debug]
|
||||
# Used only with ``DebugExecutor``. If set to ``True`` DAG will fail with first
|
||||
# failed task. Helpful for debugging purposes.
|
||||
fail_fast = False
|
||||
|
||||
|
||||
[api]
|
||||
# Enables the deprecated experimental API. Please note that these APIs do not have access control.
|
||||
# The authenticated user has full access.
|
||||
#
|
||||
# .. warning::
|
||||
#
|
||||
# This `Experimental REST API <https://airflow.readthedocs.io/en/latest/rest-api-ref.html>`__ is
|
||||
# deprecated since version 2.0. Please consider using
|
||||
# `the Stable REST API <https://airflow.readthedocs.io/en/latest/stable-rest-api-ref.html>`__.
|
||||
# For more information on migration, see
|
||||
# `UPDATING.md <https://github.com/apache/airflow/blob/master/UPDATING.md>`_
|
||||
enable_experimental_api = False
|
||||
|
||||
# How to authenticate users of the API. See
|
||||
# https://airflow.apache.org/docs/apache-airflow/stable/security.html for possible values.
|
||||
# ("airflow.api.auth.backend.default" allows all requests for historic reasons)
|
||||
auth_backends = airflow.api.auth.backend.session
|
||||
|
||||
# Used to set the maximum page limit for API requests
|
||||
maximum_page_limit = 100
|
||||
|
||||
# Used to set the default page limit when limit is zero. A default limit
|
||||
# of 100 is set on OpenApi spec. However, this particular default limit
|
||||
# only work when limit is set equal to zero(0) from API requests.
|
||||
# If no limit is supplied, the OpenApi spec default is used.
|
||||
fallback_page_limit = 100
|
||||
|
||||
# The intended audience for JWT token credentials used for authorization. This value must match on the client and server sides. If empty, audience will not be tested.
|
||||
# Example: google_oauth2_audience = project-id-random-value.apps.googleusercontent.com
|
||||
# google_oauth2_audience =
|
||||
|
||||
# Path to Google Cloud Service Account key file (JSON). If omitted, authorization based on
|
||||
# `the Application Default Credentials
|
||||
# <https://cloud.google.com/docs/authentication/production#finding_credentials_automatically>`__ will
|
||||
# be used.
|
||||
# Example: google_key_path = /files/service-account-json
|
||||
# google_key_path =
|
||||
|
||||
# Used in response to a preflight request to indicate which HTTP
|
||||
# headers can be used when making the actual request. This header is
|
||||
# the server side response to the browser's
|
||||
# Access-Control-Request-Headers header.
|
||||
# access_control_allow_headers =
|
||||
|
||||
# Specifies the method or methods allowed when accessing the resource.
|
||||
# access_control_allow_methods =
|
||||
|
||||
# Indicates whether the response can be shared with requesting code from the given origin.
|
||||
# access_control_allow_origin =
|
||||
|
||||
[lineage]
|
||||
# what lineage backend to use
|
||||
# backend =
|
||||
|
||||
[mesos]
|
||||
# Mesos master address which MesosExecutor will connect to.
|
||||
master = localhost:5050
|
||||
|
||||
# The framework name which Airflow scheduler will register itself as on mesos
|
||||
framework_name = Airflow
|
||||
|
||||
# Number of cpu cores required for running one task instance using
|
||||
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
|
||||
# command on a mesos slave
|
||||
task_cpu = 1
|
||||
|
||||
# Memory in MB required for running one task instance using
|
||||
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
|
||||
# command on a mesos slave
|
||||
task_memory = 256
|
||||
|
||||
# Enable framework checkpointing for mesos
|
||||
# See http://mesos.apache.org/documentation/latest/slave-recovery/
|
||||
checkpoint = False
|
||||
|
||||
# Failover timeout in milliseconds.
|
||||
# When checkpointing is enabled and this option is set, Mesos waits until the configured timeout for
|
||||
# the MesosExecutor framework to re-register after a failover. Mesos shuts down running tasks if the
|
||||
# MesosExecutor framework fails to re-register within this timeframe.
|
||||
# failover_timeout = 604800
|
||||
|
||||
# Enable framework authentication for mesos
|
||||
# See http://mesos.apache.org/documentation/latest/configuration/
|
||||
authenticate = False
|
||||
|
||||
# Mesos credentials, if authentication is enabled
|
||||
# default_principal = admin
|
||||
# default_secret = admin
|
||||
|
||||
|
||||
# [lineage]
|
||||
# what lineage backend to use
|
||||
# backend =
|
||||
|
||||
# [atlas]
|
||||
# sasl_enabled = False
|
||||
# host =
|
||||
# port = 21000
|
||||
# username =
|
||||
# password =
|
||||
|
||||
# [hive]
|
||||
# Default mapreduce queue for HiveOperator tasks
|
||||
# default_hive_mapred_queue =
|
||||
|
||||
# Template for mapred_job_name in HiveOperator, supports the following named parameters
|
||||
# hostname, dag_id, task_id, execution_date
|
||||
# mapred_job_name_template =
|
||||
|
||||
# [kerberos]
|
||||
# ccache = /tmp/airflow_krb5_ccache
|
||||
|
||||
# gets augmented with fqdn
|
||||
# principal = airflow
|
||||
# reinit_frequency = 3600
|
||||
# kinit_path = kinit
|
||||
# keytab = airflow.keytab
|
||||
|
||||
# [github_enterprise]
|
||||
# api_rev = v3
|
||||
|
||||
[sensors]
|
||||
# A sensor will immediately fail without retrying if timeout is reached
|
||||
# Set to 3 days, default is 7 days or 604800
|
||||
default_timeout = 259200
|
||||
|
|
@ -1,15 +1,11 @@
|
|||
from gevent import monkey
|
||||
|
||||
monkey.patch_all()
|
||||
|
||||
STATE_COLORS = {
|
||||
"queued": 'gray',
|
||||
"running": 'lime',
|
||||
"success": '#0000FF',
|
||||
"failed": 'red',
|
||||
"up_for_retry": 'gold',
|
||||
"up_for_reschedule": 'turquoise',
|
||||
"upstream_failed": 'orange',
|
||||
"skipped": 'pink',
|
||||
"scheduled": 'tan',
|
||||
"queued": "gray",
|
||||
"running": "lime",
|
||||
"success": "#0000FF",
|
||||
"failed": "red",
|
||||
"up_for_retry": "gold",
|
||||
"up_for_reschedule": "turquoise",
|
||||
"upstream_failed": "orange",
|
||||
"skipped": "pink",
|
||||
"scheduled": "tan",
|
||||
}
|
||||
|
|
|
|||
|
|
@ -10,22 +10,21 @@
|
|||
# Default: 50000
|
||||
# AIRFLOW_PROJ_DIR - Base path to which all the files will be volumed.
|
||||
# Default: .
|
||||
|
||||
---
|
||||
version: '3'
|
||||
|
||||
x-airflow-common:
|
||||
&airflow-common
|
||||
build: .
|
||||
environment:
|
||||
&airflow-common-env
|
||||
# Compatibility with legacy airflow.cfg
|
||||
AIRFLOW__WEBSERVER__WEB_SERVER_PORT: '8080'
|
||||
AIRFLOW__WEBSERVER__BASE_URL: 'http://localhost:8080'
|
||||
AIRFLOW__CELERY__FLOWER_PORT: '5555'
|
||||
|
||||
AIRFLOW__CORE__EXECUTOR: CeleryExecutor
|
||||
AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: mysql://root:secret@db:3306/airflow
|
||||
AIRFLOW__CELERY__RESULT_BACKEND: redis://:@redis:6379/0
|
||||
AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
|
||||
# For backward compatibility, with Airflow <2.3
|
||||
AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
|
||||
AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow
|
||||
AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0
|
||||
AIRFLOW__CORE__FERNET_KEY: $FERNET_KEY
|
||||
AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'
|
||||
|
|
@ -44,32 +43,30 @@ x-airflow-common:
|
|||
&airflow-common-depends-on
|
||||
redis:
|
||||
condition: service_healthy
|
||||
db:
|
||||
postgres:
|
||||
condition: service_healthy
|
||||
|
||||
services:
|
||||
db:
|
||||
# platform key added for Macbook M1 compatibility, followed:
|
||||
# https://stackoverflow.com/questions/65456814/docker-apple-silicon-m1-preview-mysql-no-matching-manifest-for-linux-arm64-v8
|
||||
platform: linux/x86_64
|
||||
image: mysql:5.7
|
||||
ports:
|
||||
- '3306:3306'
|
||||
command: ['--explicit_defaults_for_timestamp=1']
|
||||
postgres:
|
||||
image: postgres:13
|
||||
environment:
|
||||
MYSQL_ROOT_PASSWORD: secret
|
||||
MYSQL_DATABASE: airflow
|
||||
POSTGRES_USER: airflow
|
||||
POSTGRES_PASSWORD: airflow
|
||||
POSTGRES_DB: airflow
|
||||
volumes:
|
||||
- postgres-db-volume:/var/lib/postgresql/data
|
||||
healthcheck:
|
||||
test: mysqladmin ping -h 127.0.0.1 -u root --password=$$MYSQL_ROOT_PASSWORD
|
||||
timeout: 20s
|
||||
retries: 10
|
||||
test: ["CMD", "pg_isready", "-U", "airflow"]
|
||||
interval: 5s
|
||||
retries: 5
|
||||
restart: always
|
||||
|
||||
redis:
|
||||
image: redis:3.2
|
||||
image: redis:latest
|
||||
expose:
|
||||
- 6379
|
||||
healthcheck:
|
||||
test: [ "CMD", "redis-cli", "ping" ]
|
||||
test: ["CMD", "redis-cli", "ping"]
|
||||
interval: 5s
|
||||
timeout: 30s
|
||||
retries: 50
|
||||
|
|
@ -130,7 +127,7 @@ services:
|
|||
<<: *airflow-common
|
||||
command: triggerer
|
||||
healthcheck:
|
||||
test: [ "CMD-SHELL", 'airflow jobs check --job-type TriggererJob --hostname "$${HOSTNAME}"' ]
|
||||
test: ["CMD-SHELL", 'airflow jobs check --job-type TriggererJob --hostname "$${HOSTNAME}"']
|
||||
interval: 10s
|
||||
timeout: 10s
|
||||
retries: 5
|
||||
|
|
@ -167,7 +164,7 @@ services:
|
|||
echo "If you are on Linux, you SHOULD follow the instructions below to set "
|
||||
echo "AIRFLOW_UID environment variable, otherwise files will be owned by root."
|
||||
echo "For other operating systems you can get rid of the warning with manually created .env file:"
|
||||
echo " See: https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#setting-the-right-airflow-user"
|
||||
echo " See: https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html#setting-the-right-airflow-user"
|
||||
echo
|
||||
fi
|
||||
one_meg=1048576
|
||||
|
|
@ -200,7 +197,7 @@ services:
|
|||
echo
|
||||
echo -e "\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\e[0m"
|
||||
echo "Please follow the instructions to increase amount of resources available:"
|
||||
echo " https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#before-you-begin"
|
||||
echo " https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html#before-you-begin"
|
||||
echo
|
||||
fi
|
||||
mkdir -p /sources/logs /sources/dags /sources/plugins
|
||||
|
|
@ -240,7 +237,7 @@ services:
|
|||
profiles:
|
||||
- flower
|
||||
ports:
|
||||
- '5555:5555'
|
||||
- 5555:5555
|
||||
healthcheck:
|
||||
test: ["CMD", "curl", "--fail", "http://localhost:5555/"]
|
||||
interval: 10s
|
||||
|
|
@ -251,3 +248,6 @@ services:
|
|||
<<: *airflow-common-depends-on
|
||||
airflow-init:
|
||||
condition: service_completed_successfully
|
||||
|
||||
volumes:
|
||||
postgres-db-volume:
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче