[AIRFLOW-3933] Fix various typos (apache#4747)

Fix typos

CONTRIBUTING.md

@@ -264,7 +264,7 @@ meets these guidelines:
 1. The pull request should include tests, either as doctests, unit tests, or both. The airflow repo uses [Travis CI](https://travis-ci.org/apache/airflow) to run the tests and [codecov](https://codecov.io/gh/apache/airflow) to track coverage. You can set up both for free on your fork (see the "Testing on Travis CI" section below). It will help you making sure you do not break the build with your PR and that you help increase coverage.
 1. Please [rebase your fork](http://stackoverflow.com/a/7244456/1110993), squash commits, and resolve all conflicts.
 1. Every pull request should have an associated [JIRA](https://issues.apache.org/jira/browse/AIRFLOW/?selectedTab=com.atlassian.jira.jira-projects-plugin:summary-panel). The JIRA link should also be contained in the PR description.
-1. Preface your commit's subject & PR's title with **[AIRFLOW-XXX]** where *XXX* is the JIRA number. We compose release notes (i.e. for Airflow releases) from all commit titles in a release. By placing the JIRA number in the commit title and hence in the release notes, Airflow users can look into JIRA and Github PRs for more details about a particular change.
+1. Preface your commit's subject & PR's title with **[AIRFLOW-XXX]** where *XXX* is the JIRA number. We compose release notes (i.e. for Airflow releases) from all commit titles in a release. By placing the JIRA number in the commit title and hence in the release notes, Airflow users can look into JIRA and GitHub PRs for more details about a particular change.
 1. Add an [Apache License](http://www.apache.org/legal/src-headers.html) header to all new files
 1. If the pull request adds functionality, the docs should be updated as part of the same PR. Doc string are often sufficient.  Make sure to follow the Sphinx compatible standards.
 1. The pull request should work for Python 2.7 and 3.5. If you need help writing code that works in both Python 2 and 3, see the documentation at the [Python-Future project](http://python-future.org) (the future package is an Airflow requirement and should be used where possible).

UPDATING.md

@@ -38,7 +38,7 @@ Sensors are now accessible via `airflow.sensors` and no longer via `airflow.oper
 For example: `from airflow.operators.sensors import BaseSensorOperator` 
 becomes `from airflow.sensors.base_sensor_operator import BaseSensorOperator`
 
-### Renamed "extra" requirments for cloud providers
+### Renamed "extra" requirements for cloud providers
 
 Subpackages for specific services have been combined into one variant for
 each cloud provider.
@@ -191,7 +191,7 @@ that he has permissions on. If a new role wants to access all the dags, the admi
 We also provide a new cli command(``sync_perm``) to allow admin to auto sync permissions.
 
 ### Modification to `ts_nodash` macro
-`ts_nodash` previously contained TimeZone information alongwith execution date. For Example: `20150101T000000+0000`. This is not user-friendly for file or folder names which was a popular use case for `ts_nodash`. Hence this behavior has been changed and using `ts_nodash` will no longer contain TimeZone information, restoring the pre-1.10 behavior of this macro. And a new macro `ts_nodash_with_tz` has been added which can be used to get a string with execution date and timezone info without dashes.
+`ts_nodash` previously contained TimeZone information along with execution date. For Example: `20150101T000000+0000`. This is not user-friendly for file or folder names which was a popular use case for `ts_nodash`. Hence this behavior has been changed and using `ts_nodash` will no longer contain TimeZone information, restoring the pre-1.10 behavior of this macro. And a new macro `ts_nodash_with_tz` has been added which can be used to get a string with execution date and timezone info without dashes.
 
 Examples:
   * `ts_nodash`: `20150101T000000`

airflow/contrib/example_dags/example_qubole_operator.py

@@ -65,7 +65,7 @@ def compare_result(ds, **kwargs):
     fetch_logs=True,
     # If `fetch_logs`=true, will fetch qubole command logs and concatenate
     # them into corresponding airflow task logs
-    tags='aiflow_example_run',
+    tags='airflow_example_run',
     # To attach tags to qubole command, auto attach 3 tags - dag_id, task_id, run_id
     qubole_conn_id='qubole_default',
     # Connection id to submit commands inside QDS, if not set "qubole_default" is used
@@ -220,7 +220,7 @@ def main(args: Array[String]) {
     program=prog,
     language='scala',
     arguments='--class SparkPi',
-    tags='aiflow_example_run',
+    tags='airflow_example_run',
     dag=dag)
 
 t11.set_upstream(branching)

airflow/contrib/hooks/azure_data_lake_hook.py

@@ -77,7 +77,7 @@ def upload_file(self, local_path, remote_path, nthreads=64, overwrite=True,
             are not supported.
         :type local_path: str
         :param remote_path: Remote path to upload to; if multiple files, this is the
-            dircetory root to write within.
+            directory root to write within.
         :type remote_path: str
         :param nthreads: Number of threads to use. If None, uses the number of cores.
         :type nthreads: int

airflow/contrib/hooks/fs_hook.py

@@ -30,7 +30,7 @@ class FSHook(BaseHook):
     example:
     Conn Id: fs_test
     Conn Type: File (path)
-    Host, Shchema, Login, Password, Port: empty
+    Host, Schema, Login, Password, Port: empty
     Extra: {"path": "/tmp"}
     """
 

airflow/contrib/hooks/qubole_hook.py

@@ -194,7 +194,7 @@ def get_jobs_id(self, ti):
         """
         Get jobs associated with a Qubole commands
         :param ti: Task Instance of the dag, used to determine the Quboles command id
-        :return: Job informations assoiciated with command
+        :return: Job information associated with command
         """
         if self.cmd is None:
             cmd_id = ti.xcom_pull(key="qbol_cmd_id", task_ids=self.task_id)

airflow/contrib/hooks/salesforce_hook.py

@@ -276,7 +276,7 @@ def write_object_to_file(
 
             schema = self.describe_object(object_name)
 
-            # possible columns that can be convereted to timestamps
+            # possible columns that can be converted to timestamps
             # are the ones that are either date or datetime types
             # strings are too general and we risk unintentional conversion
             possible_timestamp_cols = [

airflow/contrib/operators/awsbatch_operator.py

@@ -33,7 +33,7 @@ class AWSBatchOperator(BaseOperator):
     """
     Execute a job on AWS Batch Service
 
-    .. warning: the queue parameter was renamed to job_queue to segreggate the
+    .. warning: the queue parameter was renamed to job_queue to segregate the
                 internal CeleryExecutor queue from the AWS Batch internal queue.
 
     :param job_name: the name for the job that will run on AWS Batch (templated)

airflow/contrib/operators/bigquery_check_operator.py

@@ -48,7 +48,7 @@ class BigQueryCheckOperator(CheckOperator):
     This operator can be used as a data quality check in your pipeline, and
     depending on where you put it in your DAG, you have the choice to
     stop the critical path, preventing from
-    publishing dubious data, or on the side and receive email alterts
+    publishing dubious data, or on the side and receive email alerts
     without stopping the progress of the DAG.
 
     :param sql: the sql to be executed

airflow/contrib/operators/cassandra_to_gcs.py

@@ -266,7 +266,7 @@ def convert_tuple_type(cls, name, value):
         """
         Converts a tuple to RECORD that contains n fields, each will be converted
         to its corresponding data type in bq and will be named 'field_<index>', where
-        index is determined by the order of the tuple elments defined in cassandra.
+        index is determined by the order of the tuple elements defined in cassandra.
         """
         names = ['field_' + str(i) for i in range(len(value))]
         values = [cls.convert_value(name, value) for name, value in zip(names, value)]
@@ -276,7 +276,7 @@ def convert_tuple_type(cls, name, value):
     def convert_map_type(cls, name, value):
         """
         Converts a map to a repeated RECORD that contains two fields: 'key' and 'value',
-        each will be converted to its corresopnding data type in BQ.
+        each will be converted to its corresponding data type in BQ.
         """
         converted_map = []
         for k, v in zip(value.keys(), value.values()):

airflow/contrib/operators/dataflow_operator.py

@@ -92,7 +92,7 @@ class DataFlowJavaOperator(BaseOperator):
         Cloud Platform for the dataflow job status while the job is in the
         JOB_STATE_RUNNING state.
     :type poll_sleep: int
-    :param job_class: The name of the dataflow job class to be executued, it
+    :param job_class: The name of the dataflow job class to be executed, it
         is often not the main class configured in the dataflow jar file.
     :type job_class: str
 

airflow/contrib/operators/dataproc_operator.py

@@ -1376,7 +1376,7 @@ def execute(self, context):
         self.hook.wait(self.start())
 
     def start(self, context):
-        raise AirflowException('plese start a workflow operation')
+        raise AirflowException('Please start a workflow operation')
 
 
 class DataprocWorkflowTemplateInstantiateOperator(DataprocWorkflowTemplateBaseOperator):

airflow/contrib/operators/druid_operator.py

@@ -60,5 +60,5 @@ def execute(self, context):
             druid_ingest_conn_id=self.conn_id,
             max_ingestion_time=self.max_ingestion_time
         )
-        self.log.info("Sumitting %s", self.index_spec_str)
+        self.log.info("Submitting %s", self.index_spec_str)
         hook.submit_indexing_job(self.index_spec_str)

airflow/operators/druid_check_operator.py

@@ -47,7 +47,7 @@ class DruidCheckOperator(CheckOperator):
     This operator can be used as a data quality check in your pipeline, and
     depending on where you put it in your DAG, you have the choice to
     stop the critical path, preventing from
-    publishing dubious data, or on the side and receive email alterts
+    publishing dubious data, or on the side and receive email alerts
     without stopping the progress of the DAG.
 
     :param sql: the sql to be executed

airflow/operators/presto_check_operator.py

@@ -48,7 +48,7 @@ class PrestoCheckOperator(CheckOperator):
     This operator can be used as a data quality check in your pipeline, and
     depending on where you put it in your DAG, you have the choice to
     stop the critical path, preventing from
-    publishing dubious data, or on the side and receive email alterts
+    publishing dubious data, or on the side and receive email alerts
     without stopping the progress of the DAG.
 
     :param sql: the sql to be executed

airflow/www/app.py

@@ -135,7 +135,7 @@ def init_views(appbuilder):
                                 href='https://airflow.apache.org/',
                                 category="Docs",
                                 category_icon="fa-cube")
-            appbuilder.add_link("Github",
+            appbuilder.add_link("GitHub",
                                 href='https://github.com/apache/airflow',
                                 category="Docs")
             appbuilder.add_link('Version',

dev/airflow-pr

@@ -63,7 +63,7 @@ AIRFLOW_GIT_LOCATION = os.environ.get(
     "AIRFLOW_GIT",
     os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
 
-# Remote name which points to the Gihub site
+# Remote name which points to the GitHub site
 GITHUB_REMOTE_NAME = os.environ.get("GITHUB_REMOTE_NAME", "github")
 # OAuth key used for issuing requests against the GitHub API. If this is not
 # defined, then requests will be unauthenticated. You should only need to

docs/howto/operator.rst

@@ -534,7 +534,7 @@ it will be retrieved from the GCP connection used. Both variants are shown:
 Advanced
 """"""""
 
-When creating a table, you can specify the optional ``initial_split_keys`` and ``column_familes``.
+When creating a table, you can specify the optional ``initial_split_keys`` and ``column_families``.
 Please refer to the Python Client for Google Cloud Bigtable documentation
 `for Table <https://googleapis.github.io/google-cloud-python/latest/bigtable/table.html>`_ and `for Column
 Families <https://googleapis.github.io/google-cloud-python/latest/bigtable/column-family.html>`_.

docs/installation.rst

@@ -75,7 +75,7 @@ Here's the list of the subpackages and what they enable:
 | gcp_api             | ``pip install apache-airflow[gcp_api]``           | Google Cloud Platform hooks and operators                            |
 |                     |                                                   | (using ``google-api-python-client``)                                 |
 +---------------------+---------------------------------------------------+----------------------------------------------------------------------+
-| github_enterprise   | ``pip install apache-airflow[github_enterprise]`` | Github Enterprise auth backend                                       |
+| github_enterprise   | ``pip install apache-airflow[github_enterprise]`` | GitHub Enterprise auth backend                                       |
 +---------------------+---------------------------------------------------+----------------------------------------------------------------------+
 | google_auth         | ``pip install apache-airflow[google_auth]``       | Google auth backend                                                  |
 +---------------------+---------------------------------------------------+----------------------------------------------------------------------+

docs/project.rst

@@ -23,7 +23,7 @@ History
 
 Airflow was started in October 2014 by Maxime Beauchemin at Airbnb.
 It was open source from the very first commit and officially brought under
-the Airbnb Github and announced in June 2015.
+the Airbnb GitHub and announced in June 2015.
 
 The project joined the Apache Software Foundation's incubation program in March 2016.
 

docs/security.rst

@@ -502,7 +502,7 @@ on limited web views
         'LogModelView',
         'Docs',
         'Documentation',
-        'Github',
+        'GitHub',
         'About',
         'Version',
         'VersionView',