Merge pull request #237 from mistercrunch/artwr/amending_example_dag_docs_and_typos
Docs and docstrings edits
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@ -48,9 +48,11 @@ class PrestoHook(BaseHook):
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session.close() # currently only a pass in pyhive
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def get_cursor(self):
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'''
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"""
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Returns a cursor.
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'''
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"""
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return self.cursor
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@staticmethod
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@ -58,9 +60,11 @@ class PrestoHook(BaseHook):
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return sql.strip().rstrip(';')
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def get_records(self, hql, parameters=None):
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'''
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"""
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Get a set of records from Presto
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'''
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"""
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try:
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self.cursor.execute(self._strip_sql(hql), parameters)
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records = self.cursor.fetchall()
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@ -70,10 +74,12 @@ class PrestoHook(BaseHook):
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return records
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def get_first(self, hql, parameters=None):
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'''
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"""
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Returns only the first row, regardless of how many rows the query
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returns.
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'''
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"""
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try:
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self.cursor.execute(self._strip_sql(hql), parameters)
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record = self.cursor.fetchone()
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@ -83,9 +89,11 @@ class PrestoHook(BaseHook):
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return record
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def get_pandas_df(self, hql, parameters=None):
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'''
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"""
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Get a pandas dataframe from a sql query.
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'''
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"""
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import pandas
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cursor = self.get_cursor()
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cursor.execute(self._strip_sql(hql), parameters)
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@ -103,7 +111,9 @@ class PrestoHook(BaseHook):
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return df
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def run(self, hql, parameters=None):
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'''
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"""
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Execute the statement against Presto. Can be used to create views.
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'''
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"""
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self.cursor.execute(self._strip_sql(hql), parameters)
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@ -18,7 +18,7 @@ class HiveToMySqlTransfer(BaseOperator):
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:type mysql_table: str
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:param mysql_conn_id: source mysql connection
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:type mysql_conn_id: str
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:param hive_conn_id: desctination hive connection
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:param hive_conn_id: destination hive connection
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:type hive_conn_id: str
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:param mysql_preoperator: sql statement to run against mysql prior to
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import, typically use to truncate of delete in place of the data
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@ -30,6 +30,7 @@ There are 3 main types of operators:
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Hive2SambaOperator,
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HiveOperator,
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HivePartitionSensor,
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HiveToMySqlTransfer,
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MySqlOperator,
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MySqlToHiveTransfer,
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PostgresOperator,
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@ -37,6 +38,7 @@ There are 3 main types of operators:
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PrestoIntervalCheckOperator,
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PrestoValueCheckOperator,
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PythonOperator,
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S3KeySensor,
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S3ToHiveTransfer,
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SqlSensor,
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SubDagOperator,
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@ -108,7 +110,7 @@ Hooks
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-----
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.. automodule:: airflow.hooks
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:show-inheritance:
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:members: MySqlHook, PostgresHook, PrestoHook, HiveCliHook, HiveServer2Hook, HiveMetastoreHook, S3Hook
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:members: MySqlHook, PostgresHook, PrestoHook, HiveCliHook, HiveServer2Hook, HiveMetastoreHook, S3Hook, SqliteHook
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Executors
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---------
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@ -1,10 +1,10 @@
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Quick Start
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'''''''''''
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The installation is quick and straightforward.
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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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# 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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@ -18,28 +18,28 @@ The installation is quick and straightforward.
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# start the web server, default port is 8080
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airflow webserver -p 8080
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Upon running these commands, airflow will create the ``$AIRFLOW_HOME`` folder
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Upon running these commands, airflow will create the ``$AIRFLOW_HOME`` folder
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and lay a "airflow.cfg" files with defaults that get you going fast. You can
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inspect the file either in ``$AIRFLOW_HOME/airflow.cfg``, or through the UI in
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inspect the file either in ``$AIRFLOW_HOME/airflow.cfg``, or through the UI in
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the ``Admin->Configuration`` menu.
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Out of the box, airflow uses a sqlite database, which you should outgrow
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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 ``SequentialExecutor`` which will
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backend. It works in conjunction with the ``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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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 ``example1`` DAG as you
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be able to see the status of the jobs change in the ``example1`` 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 run example1 runme_0 2015-01-01
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airflow run example_bash_operator runme_0 2015-01-01
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# run a backfill over 2 days
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airflow backfill example1 -s 2015-01-01 -e 2015-01-02
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airflow backfill example_bash_operator -s 2015-01-01 -e 2015-01-02
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From this point, you can move on to the :doc:`tutorial` section, and come back
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if/when you are ready to make your Airflow sandbox more of a serious
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