78 строки
3.0 KiB
ReStructuredText
78 строки
3.0 KiB
ReStructuredText
.. Licensed to the Apache Software Foundation (ASF) under one
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or more contributor license agreements. See the NOTICE file
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distributed with this work for additional information
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regarding copyright ownership. The ASF licenses this file
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to you under the Apache License, Version 2.0 (the
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"License"); you may not use this file except in compliance
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with the License. You may obtain a copy of the License at
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.. http://www.apache.org/licenses/LICENSE-2.0
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.. Unless required by applicable law or agreed to in writing,
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software distributed under the License is distributed on an
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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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KIND, either express or implied. See the License for the
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specific language governing permissions and limitations
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under the License.
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Quick Start
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-----------
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The installation is quick and straightforward.
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.. code-block:: bash
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# airflow needs a home, ~/airflow is the default,
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# but you can lay foundation somewhere else if you prefer
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# (optional)
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export AIRFLOW_HOME=~/airflow
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# install from pypi using pip
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pip install apache-airflow
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# initialize the database
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airflow db init
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# if you build with master
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airflow users create --username admin --firstname Peter --lastname Parker --role Admin --email spiderman@superhero.org
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# start the web server, default port is 8080
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airflow webserver -p 8080
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# start the scheduler
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airflow scheduler
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# visit localhost:8080 in the browser and use the admin account you just
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# created to login. Enable the example_bash_operator dag in the home page
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Upon running these commands, Airflow will create the ``$AIRFLOW_HOME`` folder
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and lay an "airflow.cfg" file with defaults that get you going fast. You can
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inspect the file either in ``$AIRFLOW_HOME/airflow.cfg``, or through the UI in
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the ``Admin->Configuration`` menu. The PID file for the webserver will be stored
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in ``$AIRFLOW_HOME/airflow-webserver.pid`` or in ``/run/airflow/webserver.pid``
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if started by systemd.
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Out of the box, Airflow uses a sqlite database, which you should outgrow
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fairly quickly since no parallelization is possible using this database
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backend. It works in conjunction with the :class:`airflow.executors.sequential_executor.SequentialExecutor` which will
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only run task instances sequentially. While this is very limiting, it allows
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you to get up and running quickly and take a tour of the UI and the
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command line utilities.
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Here are a few commands that will trigger a few task instances. You should
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be able to see the status of the jobs change in the ``example_bash_operator`` DAG as you
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run the commands below.
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.. code-block:: bash
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# run your first task instance
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airflow tasks run example_bash_operator runme_0 2015-01-01
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# run a backfill over 2 days
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airflow dags backfill example_bash_operator -s 2015-01-01 -e 2015-01-02
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What's Next?
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''''''''''''
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From this point, you can head to the :doc:`tutorial` section for further examples or the :doc:`howto/index` section if you're ready to get your hands dirty.
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