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Posted to commits@airflow.apache.org by el...@apache.org on 2022/02/15 07:44:50 UTC
[airflow] branch main updated: Remove unnecessary/stale comments (#21572)
This is an automated email from the ASF dual-hosted git repository.
eladkal pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/airflow.git
The following commit(s) were added to refs/heads/main by this push:
new 2d6282d Remove unnecessary/stale comments (#21572)
2d6282d is described below
commit 2d6282d6b7d8c7603e96f0f28ebe0180855687f3
Author: Jed Cunningham <66...@users.noreply.github.com>
AuthorDate: Tue Feb 15 00:44:09 2022 -0700
Remove unnecessary/stale comments (#21572)
We certainly don't need a stale and incorrect 'Last Updated' comments,
and likely also don't need the original author either, so remove them.
Both can be found via git history.
---
airflow/providers/apache/hive/example_dags/example_twitter_README.md | 3 ---
airflow/providers/apache/hive/example_dags/example_twitter_dag.py | 2 --
airflow/providers/microsoft/winrm/example_dags/example_winrm.py | 2 --
3 files changed, 7 deletions(-)
diff --git a/airflow/providers/apache/hive/example_dags/example_twitter_README.md b/airflow/providers/apache/hive/example_dags/example_twitter_README.md
index 8f88983..a9b7425 100644
--- a/airflow/providers/apache/hive/example_dags/example_twitter_README.md
+++ b/airflow/providers/apache/hive/example_dags/example_twitter_README.md
@@ -52,6 +52,3 @@ CREATE TABLE toTwitter_A(id BIGINT, id_str STRING
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
-Last Update: 8-April-2016
diff --git a/airflow/providers/apache/hive/example_dags/example_twitter_dag.py b/airflow/providers/apache/hive/example_dags/example_twitter_dag.py
index 6cc4b2e..ca1c5d2 100644
--- a/airflow/providers/apache/hive/example_dags/example_twitter_dag.py
+++ b/airflow/providers/apache/hive/example_dags/example_twitter_dag.py
@@ -16,8 +16,6 @@
# specific language governing permissions and limitations
# under the License.
# --------------------------------------------------------------------------------
-# Written By: Ekhtiar Syed
-# Last Update: 8th April 2016
# Caveat: This Dag will not run because of missing scripts.
# The purpose of this is to give you a sample of a real world example DAG!
# --------------------------------------------------------------------------------
diff --git a/airflow/providers/microsoft/winrm/example_dags/example_winrm.py b/airflow/providers/microsoft/winrm/example_dags/example_winrm.py
index 1156099..fcf6fb6 100644
--- a/airflow/providers/microsoft/winrm/example_dags/example_winrm.py
+++ b/airflow/providers/microsoft/winrm/example_dags/example_winrm.py
@@ -16,8 +16,6 @@
# specific language governing permissions and limitations
# under the License.
# --------------------------------------------------------------------------------
-# Written By: Ekhtiar Syed
-# Last Update: 8th April 2016
# Caveat: This Dag will not run because of missing scripts.
# The purpose of this is to give you a sample of a real world example DAG!
# --------------------------------------------------------------------------------