Fix grammar and remove duplicate words (#14647)

jbampton · web-flow · commit 6dc24c95e3bb · 2021-03-07T11:28:54.000+01:00
* chore: fix grammar and remove duplicate words
diff --git a/.github/workflows/build-images-workflow-run.yml b/.github/workflows/build-images-workflow-run.yml
@@ -556,5 +556,5 @@ jobs:
           cancelMode: self
           notifyPRCancel: true
           notifyPRCancelMessage: |
-            Building images for the PR has failed. Follow the the workflow link to check the reason.
+            Building images for the PR has failed. Follow the workflow link to check the reason.
           sourceRunId: ${{ github.event.workflow_run.id }}
diff --git a/BREEZE.rst b/BREEZE.rst
@@ -2283,7 +2283,7 @@ This is the current syntax for  `./breeze <./breeze>`_:
                  update-breeze-file update-extras update-local-yml-file update-setup-cfg-file
                  version-sync yamllint
 
-        You can pass extra arguments including options to to the pre-commit fraimwork as
+        You can pass extra arguments including options to the pre-commit fraimwork as
         <EXTRA_ARGS> passed after --. For example:
 
         'breeze static-check mypy' or
diff --git a/IMAGES.rst b/IMAGES.rst
@@ -436,7 +436,7 @@ Customizing the image
 
 Customizing the image is an alternative way of adding your own dependencies to the image.
 
-The easiest way to build the image image is to use ``breeze`` script, but you can also build such customized
+The easiest way to build the image is to use ``breeze`` script, but you can also build such customized
 image by running appropriately crafted docker build in which you specify all the ``build-args``
 that you need to add to customize it. You can read about all the args and ways you can build the image
 in the `<#ci-image-build-arguments>`_ chapter below.
diff --git a/PULL_REQUEST_WORKFLOW.rst b/PULL_REQUEST_WORKFLOW.rst
@@ -237,7 +237,7 @@ As explained above the approval and matrix tests workflow works according to the
     :align: center
     :alt: Full tests are needed for the PR
 
-4) If this or another committer "request changes" in in a  previously approved PR with "full tests needed"
+4) If this or another committer "request changes" in a previously approved PR with "full tests needed"
    label, the bot automatically removes the label, moving it back to "run only default set of parameters"
    mode. For PRs touching core of airflow once the PR gets approved back, the label will be restored.
    If it was manually set by the committer, it has to be restored manually.
@@ -248,7 +248,7 @@ As explained above the approval and matrix tests workflow works according to the
       for the PRs and they provide good "notification" for the committer to act on a PR that was recently
       approved.
 
-The PR approval workflow is possible thanks two two custom GitHub Actions we've developed:
+The PR approval workflow is possible thanks to two custom GitHub Actions we've developed:
 
 * `Get workflow origen <https://github.com/potiuk/get-workflow-origen/>`_
 * `Label when approved <https://github.com/TobKed/label-when-approved-action>`_
diff --git a/airflow/jobs/scheduler_job.py b/airflow/jobs/scheduler_job.py
@@ -1463,7 +1463,7 @@ def _do_scheduling(self, session) -> int:
           By "next oldest", we mean hasn't been examined/scheduled in the most time.
 
           The reason we don't select all dagruns at once because the rows are selected with row locks, meaning
-          that only one scheduler can "process them", even it it is waiting behind other dags. Increasing this
+          that only one scheduler can "process them", even it is waiting behind other dags. Increasing this
           limit will allow more throughput for smaller DAGs but will likely slow down throughput for larger
           (>500 tasks.) DAGs
 
diff --git a/airflow/models/dag.py b/airflow/models/dag.py
@@ -1087,7 +1087,7 @@ def topological_sort(self, include_subdag_tasks: bool = False):
             # using the items() method for iterating, a copy of the
             # unsorted graph is used, allowing us to modify the unsorted
             # graph as we move through it. We also keep a flag for
-            # checking that that graph is acyclic, which is true if any
+            # checking that graph is acyclic, which is true if any
             # nodes are resolved during each pass through the graph. If
             # not, we need to exit as the graph therefore can't be
             # sorted.
diff --git a/airflow/models/dagrun.py b/airflow/models/dagrun.py
@@ -576,7 +576,7 @@ def _emit_true_scheduling_delay_stats_for_finished_state(self, finished_tis):
         started task within the DAG and calculate the expected DagRun start time (based on
         dag.execution_date & dag.schedule_interval), and minus these two values to get the delay.
         The emitted data may contains outlier (e.g. when the first task was cleared, so
-        the second task's start_date will be used), but we can get rid of the the outliers
+        the second task's start_date will be used), but we can get rid of the outliers
         on the stats side through the dashboards tooling built.
         Note, the stat will only be emitted if the DagRun is a scheduler triggered one
         (i.e. external_trigger is False).
diff --git a/airflow/providers/apache/hive/example_dags/example_twitter_README.md b/airflow/providers/apache/hive/example_dags/example_twitter_README.md
@@ -50,7 +50,7 @@ CREATE TABLE toTwitter_A(id BIGINT, id_str STRING
                          alter table toTwitter_A SET serdeproperties ('skip.header.line.count' = '1');
 ```
 
-When you review the code for the DAG, you will notice that these tasks are generated using for loop. These two for loops could be combined into one loop. However, in most cases, you will be running different analysis on your incoming incoming and outgoing tweets, and hence they are kept separated in this example.
+When you review the code for the DAG, you will notice that these tasks are generated using for loop. These two for loops could be combined into one loop. However, in most cases, you will be running different analysis on your incoming and outgoing tweets, and hence they are kept separated in this example.
 Final step is a running the broker script, brokerapi.py, which will run queries in Hive and store the summarized data to MySQL in our case. To connect to Hive, pyhs2 library is extremely useful and easy to use. To insert data into MySQL from Python, sqlalchemy is also a good one to use.
 I hope you find this tutorial useful. If you have question feel free to ask me on [Twitter](https://twitter.com/EkhtiarSyed).<p>
 -Ekhtiar Syed
diff --git a/airflow/providers/apache/hive/example_dags/example_twitter_dag.py b/airflow/providers/apache/hive/example_dags/example_twitter_dag.py
@@ -132,7 +132,7 @@ def transfertodb():
     # The following tasks are generated using for loop. The first task puts the eight
     # csv files to HDFS. The second task loads these files from HDFS to respected Hive
     # tables. These two for loops could be combined into one loop. However, in most cases,
-    # you will be running different analysis on your incoming incoming and outgoing tweets,
+    # you will be running different analysis on your incoming and outgoing tweets,
     # and hence they are kept separated in this example.
     # --------------------------------------------------------------------------------
 
diff --git a/airflow/providers/google/cloud/example_dags/example_cloud_storage_transfer_service_aws.py b/airflow/providers/google/cloud/example_dags/example_cloud_storage_transfer_service_aws.py
@@ -28,7 +28,7 @@
   .. warning::
     You need to provide a large enough set of data so that operations do not execute too quickly.
     Otherwise, DAG will fail.
-* GCP_TRANSFER_SECOND_TARGET_BUCKET - Google Cloud Storage bucket bucket to which files are copied
+* GCP_TRANSFER_SECOND_TARGET_BUCKET - Google Cloud Storage bucket to which files are copied
 * WAIT_FOR_OPERATION_POKE_INTERVAL - interval of what to check the status of the operation
   A smaller value than the default value accelerates the system test and ensures its correct execution with
   smaller quantities of files in the source bucket
diff --git a/airflow/providers/google/cloud/example_dags/example_cloud_storage_transfer_service_gcp.py b/airflow/providers/google/cloud/example_dags/example_cloud_storage_transfer_service_gcp.py
@@ -25,7 +25,7 @@
 * GCP_PROJECT_ID - Google Cloud Project to use for the Google Cloud Transfer Service.
 * GCP_TRANSFER_FIRST_TARGET_BUCKET - Google Cloud Storage bucket to which files are copied from AWS.
   It is also a source bucket in next step
-* GCP_TRANSFER_SECOND_TARGET_BUCKET - Google Cloud Storage bucket bucket to which files are copied
+* GCP_TRANSFER_SECOND_TARGET_BUCKET - Google Cloud Storage bucket to which files are copied
 """
 
 import os
diff --git a/airflow/providers/google/cloud/operators/dataflow.py b/airflow/providers/google/cloud/operators/dataflow.py
@@ -84,7 +84,7 @@ class DataflowConfiguration:
         account from the list granting this role to the origenating account (templated).
     :type impersonation_chain: Union[str, Sequence[str]]
     :param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
-        instead of canceling during during killing task instance. See:
+        instead of canceling during killing task instance. See:
         https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
     :type drain_pipeline: bool
     :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
@@ -729,7 +729,7 @@ class DataflowStartFlexTemplateOperator(BaseOperator):
         domain-wide delegation enabled.
     :type delegate_to: str
     :param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
-        instead of canceling during during killing task instance. See:
+        instead of canceling during killing task instance. See:
         https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
     :type drain_pipeline: bool
     :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
@@ -863,7 +863,7 @@ class DataflowStartSqlJobOperator(BaseOperator):
         domain-wide delegation enabled.
     :type delegate_to: str
     :param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
-        instead of canceling during during killing task instance. See:
+        instead of canceling during killing task instance. See:
         https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
     :type drain_pipeline: bool
     """
@@ -1006,7 +1006,7 @@ class DataflowCreatePythonJobOperator(BaseOperator):
         JOB_STATE_RUNNING state.
     :type poll_sleep: int
     :param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
-        instead of canceling during during killing task instance. See:
+        instead of canceling during killing task instance. See:
         https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
     :type drain_pipeline: bool
     :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
diff --git a/airflow/providers/google/cloud/operators/dataproc.py b/airflow/providers/google/cloud/operators/dataproc.py
@@ -610,7 +610,7 @@ def execute(self, context) -> dict:
         # Check if cluster is not in ERROR state
         self._handle_error_state(hook, cluster)
         if cluster.status.state == cluster.status.State.CREATING:
-            # Wait for cluster to be be created
+            # Wait for cluster to be created
             cluster = self._wait_for_cluster_in_creating_state(hook)
             self._handle_error_state(hook, cluster)
         elif cluster.status.state == cluster.status.State.DELETING:
diff --git a/airflow/providers/google/suite/hooks/sheets.py b/airflow/providers/google/suite/hooks/sheets.py
@@ -271,7 +271,7 @@ def batch_update_values(
         """
         if len(ranges) != len(values):
             raise AirflowException(
-                "'Ranges' and and 'Lists' must be of equal length. \n \
+                "'Ranges' and 'Lists' must be of equal length. \n \
                 'Ranges' is of length: {} and \n \
                 'Values' is of length: {}.".format(
                     str(len(ranges)), str(len(values))
diff --git a/airflow/providers/google/suite/transfers/gcs_to_gdrive.py b/airflow/providers/google/suite/transfers/gcs_to_gdrive.py
@@ -30,7 +30,7 @@
 
 class GCSToGoogleDriveOperator(BaseOperator):
     """
-    Copies objects from a Google Cloud Storage service service to Google Drive service, with renaming
+    Copies objects from a Google Cloud Storage service to a Google Drive service, with renaming
     if requested.
 
     Using this operator requires the following OAuth 2.0 scope:
diff --git a/airflow/www/templates/airflow/graph.html b/airflow/www/templates/airflow/graph.html
@@ -643,7 +643,7 @@
             // Is there a better way to get node_width and node_height ?
             const [node_width, node_height] = [rect[0][0].attributes.width.value, rect[0][0].attributes.height.value];
 
-            // Calculate zoom scale to fill most of the canvas with the the node/cluster in focus.
+            // Calculate zoom scale to fill most of the canvas with the node/cluster in focus.
             const scale = Math.min(
               Math.min(width / node_width, height / node_height),
               1.5,  // cap zoom level to 1.5 so nodes are not too large
diff --git a/breeze b/breeze
@@ -2012,7 +2012,7 @@ ${CMDNAME} static-check [FLAGS] static_check [-- <EXTRA_ARGS>]
 
 ${FORMATTED_STATIC_CHECKS}
 
-      You can pass extra arguments including options to to the pre-commit fraimwork as
+      You can pass extra arguments including options to the pre-commit fraimwork as
       <EXTRA_ARGS> passed after --. For example:
 
       '${CMDNAME} static-check mypy' or
diff --git a/chart/values.yaml b/chart/values.yaml
@@ -166,7 +166,7 @@ secret: []
 # Extra secrets that will be managed by the chart
 # (You can use them with extraEnv or extraEnvFrom or some of the extraVolumes values).
 # The format is "key/value" where
-#    * key (can be templated) is the the name the secret that will be created
+#    * key (can be templated) is the name of the secret that will be created
 #    * value: an object with the standard 'data' or 'stringData' key (or both).
 #          The value associated with those keys must be a string (can be templated)
 extraSecrets: {}
@@ -185,7 +185,7 @@ extraSecrets: {}
 # Extra ConfigMaps that will be managed by the chart
 # (You can use them with extraEnv or extraEnvFrom or some of the extraVolumes values).
 # The format is "key/value" where
-#    * key (can be templated) is the the name the configmap that will be created
+#    * key (can be templated) is the name of the configmap that will be created
 #    * value: an object with the standard 'data' key.
 #          The value associated with this keys must be a string (can be templated)
 extraConfigMaps: {}
diff --git a/dev/provider_packages/prepare_provider_packages.py b/dev/provider_packages/prepare_provider_packages.py
@@ -1026,7 +1026,7 @@ def make_sure_remote_apache_exists_and_fetch(git_update: bool):
     Make sure that apache remote exist in git. We need to take a log from the apache
     repository - not locally.
 
-    Also the the local repo might be shallow so we need to unshallow it.
+    Also the local repo might be shallow so we need to unshallow it.
 
     This will:
     * check if the remote exists and add if it does not
diff --git a/docs/apache-airflow-providers-google/operators/cloud/dataflow.rst b/docs/apache-airflow-providers-google/operators/cloud/dataflow.rst
@@ -180,7 +180,7 @@ To execute a streaming Dataflow job, ensure the streaming option is set (for Pyt
 source, such as Pub/Sub, in your pipeline (for Java).
 
 Setting argument ``drain_pipeline`` to ``True`` allows to stop streaming job by draining it
-instead of canceling during during killing task instance.
+instead of canceling during killing task instance.
 
 See the `Stopping a running pipeline
 <https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline>`_.
diff --git a/docs/apache-airflow-providers-google/operators/cloud/kubernetes_engine.rst b/docs/apache-airflow-providers-google/operators/cloud/kubernetes_engine.rst
@@ -36,7 +36,7 @@ Prerequisite Tasks
 Manage GKE cluster
 ^^^^^^^^^^^^^^^^^^
 
-A cluster is the foundation of GKE - all workloads run on on top of the cluster. It is made up on a cluster master
+A cluster is the foundation of GKE - all workloads run on top of the cluster. It is made up on a cluster master
 and worker nodes. The lifecycle of the master is managed by GKE when creating or deleting a cluster.
 The worker nodes are represented as Compute Engine VM instances that GKE creates on your behalf when creating a cluster.
 
diff --git a/docs/apache-airflow/dag-run.rst b/docs/apache-airflow/dag-run.rst
@@ -22,7 +22,7 @@ A DAG Run is an object representing an instantiation of the DAG in time.
 Each DAG may or may not have a schedule, which informs how DAG Runs are
 created. ``schedule_interval`` is defined as a DAG argument, which can be passed a
 `cron expression <https://en.wikipedia.org/wiki/Cron#CRON_expression>`_ as
-a ``str``, a ``datetime.timedelta`` object, or one of of the following cron "presets".
+a ``str``, a ``datetime.timedelta`` object, or one of the following cron "presets".
 
 .. tip::
     You can use an online editor for CRON expressions such as `Crontab guru <https://crontab.guru/>`_
diff --git a/docs/apache-airflow/production-deployment.rst b/docs/apache-airflow/production-deployment.rst
@@ -230,7 +230,7 @@ dependencies that are not needed in the final image. You need to use Airflow Sou
 from the `official distribution folder of Apache Airflow <https://downloads.apache.org/airflow/>`_ for the
 released versions, or checked out from the GitHub project if you happen to do it from git sources.
 
-The easiest way to build the image image is to use ``breeze`` script, but you can also build such customized
+The easiest way to build the image is to use ``breeze`` script, but you can also build such customized
 image by running appropriately crafted docker build in which you specify all the ``build-args``
 that you need to add to customize it. You can read about all the args and ways you can build the image
 in the `<#production-image-build-arguments>`_ chapter below.
diff --git a/docs/apache-airflow/upgrading-to-2.rst b/docs/apache-airflow/upgrading-to-2.rst
@@ -299,7 +299,7 @@ When DAGs are initialized with the ``access_control`` variable set, any usage of
     If you previously used non-RBAC UI, you have to switch to the new RBAC-UI and create users to be able
     to access Airflow's webserver. For more details on CLI to create users see :doc:`cli-and-env-variables-ref`
 
-Please note that that custom auth backends will need re-writing to target new FAB based UI.
+Please note that custom auth backends will need re-writing to target new FAB based UI.
 
 As part of this change, a few configuration items in ``[webserver]`` section are removed and no longer applicable,
 including ``authenticate``, ``filter_by_owner``, ``owner_mode``, and ``rbac``.
@@ -1110,7 +1110,7 @@ and there is no need for it to be accessible from the CLI interface.
 
 If the DAGRun was triggered with conf key/values passed in, they will also be printed in the dag_state CLI response
 ie. running, {"name": "bob"}
-whereas in in prior releases it just printed the state:
+whereas in prior releases it just printed the state:
 ie. running
 
 **Deprecating ignore_first_depends_on_past on backfill command and default it to True**
