Update local MWAA to 2.10

README.md

@@ -1,9 +1,15 @@
+## Note
+Starting from Airflow version 2.9, MWAA has open-sourced the original Docker image used in our production deployments. You can refer to our open-source image repository at https://github.com/aws/amazon-mwaa-docker-images to create a local environment identical to that of MWAA.
+You can also continue to use the MWAA Local Runner for testing and packaging requirements for all Airflow versions supported on MWAA.
+
 # About aws-mwaa-local-runner
 
 This repository provides a command line interface (CLI) utility that replicates an Amazon Managed Workflows for Apache Airflow (MWAA) environment locally.
 
-*Please note: MWAA/AWS/DAG/Plugin issues should be raised through AWS Support or the Airflow Slack #airflow-aws channel.  Issues here should be focused on this local-runner repository.*
+_Please note: MWAA/AWS/DAG/Plugin issues should be raised through AWS Support or the Airflow Slack #airflow-aws channel. Issues here should be focused on this local-runner repository._
 
+_Please note: The dynamic configurations which are dependent on the class of an environment are
+aligned with the Large environment class in this repository._
 
 ## About the CLI
 
@@ -14,7 +20,7 @@ The CLI builds a Docker container image locally that’s similar to a MWAA produ
 ```text
 dags/
   example_lambda.py
-  example_dag_with_taskflow_api.py    
+  example_dag_with_taskflow_api.py
   example_redshift_data_execute_sql.py
 docker/
   config/
@@ -34,7 +40,7 @@ docker/
   Dockerfile
 plugins/
   README.md
-requirements/  
+requirements/
   requirements.txt
 .gitignore
 CODE_OF_CONDUCT.md
@@ -102,7 +108,7 @@ The following section describes where to add your DAG code and supporting files.
 
 #### Requirements.txt
 
-1. Add Python dependencies to `requirements/requirements.txt`.  
+1. Add Python dependencies to `requirements/requirements.txt`.
 2. To test a requirements.txt without running Apache Airflow, use the following script:
 
 ```bash
@@ -117,7 +123,7 @@ Collecting aws-batch (from -r /usr/local/airflow/dags/requirements.txt (line 1))
   Downloading https://files.pythonhosted.org/packages/5d/11/3aedc6e150d2df6f3d422d7107ac9eba5b50261cf57ab813bb00d8299a34/aws_batch-0.6.tar.gz
 Collecting awscli (from aws-batch->-r /usr/local/airflow/dags/requirements.txt (line 1))
   Downloading https://files.pythonhosted.org/packages/07/4a/d054884c2ef4eb3c237e1f4007d3ece5c46e286e4258288f0116724af009/awscli-1.19.21-py2.py3-none-any.whl (3.6MB)
-    100% |████████████████████████████████| 3.6MB 365kB/s 
+    100% |████████████████████████████████| 3.6MB 365kB/s
 ...
 ...
 ...
@@ -136,7 +142,7 @@ For example usage see [Installing Python dependencies using PyPi.org Requirement
 
 #### Custom plugins
 
-- There is a directory at the root of this repository called plugins. 
+- There is a directory at the root of this repository called plugins.
 - In this directory, create a file for your new custom plugin.
 - Add any Python dependencies to `requirements/requirements.txt`.
 
@@ -165,7 +171,7 @@ The following section contains common questions and answers you may encounter wh
 ### Can I test execution role permissions using this repository?
 
 - You can setup the local Airflow's boto with the intended execution role to test your DAGs with AWS operators before uploading to your Amazon S3 bucket. To setup aws connection for Airflow locally see [Airflow | AWS Connection](https://airflow.apache.org/docs/apache-airflow-providers-amazon/stable/connections/aws.html)
-To learn more, see [Amazon MWAA Execution Role](https://docs.aws.amazon.com/mwaa/latest/userguide/mwaa-create-role.html).
+  To learn more, see [Amazon MWAA Execution Role](https://docs.aws.amazon.com/mwaa/latest/userguide/mwaa-create-role.html).
 - You can set AWS credentials via environment variables set in the `docker/config/.env.localrunner` env file. To learn more about AWS environment variables, see [Environment variables to configure the AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-envvars.html) and [Using temporary security credentials with the AWS CLI](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_use-resources.html#using-temp-creds-sdk-cli). Simply set the relevant environment variables in `.env.localrunner` and `./mwaa-local-env start`.
 
 ### How do I add libraries to requirements.txt and test install?

VERSION

@@ -1 +1 @@
-2.8.1
+2.10.1

docker/Dockerfile

@@ -8,9 +8,9 @@ LABEL maintainer="amazon"
 
 # Airflow
 ## Version specific ARGs
-ARG AIRFLOW_VERSION=2.8.1
-ARG WATCHTOWER_VERSION=3.0.1
-ARG PROVIDER_AMAZON_VERSION=8.16.0
+ARG AIRFLOW_VERSION=2.10.1
+ARG WATCHTOWER_VERSION=3.3.1
+ARG PROVIDER_AMAZON_VERSION=8.28.0
 
 ## General ARGs
 ARG AIRFLOW_USER_HOME=/usr/local/airflow

docker/config/airflow.cfg

@@ -157,7 +157,7 @@ sensitive_var_conn_names =
 # Task Slot counts for ``default_pool``. This setting would not have any effect in an existing
 # deployment where the ``default_pool`` is already created. For existing deployments, users can
 # change the number of slots using Webserver, API or the CLI
-default_pool_task_slot_count = 10000
+default_pool_task_slot_count = 200
 
 [database]
 # Collation for ``dag_id``, ``task_id``, ``key`` columns in case they have different encoding.
@@ -342,7 +342,7 @@ backend = airflow.providers.amazon.aws.secrets.secrets_manager.SecretsManagerBac
 # See documentation for the secrets backend you are using. JSON is expected.
 # Example for AWS Systems Manager ParameterStore:
 # ``{{"connections_prefix": "/airflow/connections", "profile_name": "default"}}``
-backend_kwargs = {"connections_prefix" : "airflow-prod/connection", "variables_prefix" : "airflow-prod/variable", "config_prefix": "airflow-prod/config"}
+backend_kwargs = {"connections_prefix" : "airflow-prod/connection", "variables_prefix" : "airflow-prod/variable", "config_prefix": "airflow-prod/config", "connections_lookup_pattern":"^(?!aws_default$).*$"}
 
 [cli]
 # In what way should the cli access the API. The LocalClient will use the
@@ -815,7 +815,7 @@ catchup_by_default = True
 # complexity of query predicate, and/or excessive locking.
 # Additionally, you may hit the maximum allowable query length for your db.
 # Set this to 0 for no limit (not advised)
-max_tis_per_query = 512
+max_tis_per_query = 16
 
 # Should the scheduler issue ``SELECT ... FOR UPDATE`` in relevant queries.
 # If this is set to False then you should not run more than a single
@@ -832,7 +832,7 @@ max_dagruns_per_loop_to_schedule = 20
 # Should the Task supervisor process perform a "mini scheduler" to attempt to schedule more tasks of the
 # same DAG. Leaving this on will mean tasks in the same DAG execute quicker, but might starve out other
 # dags in some circumstances
-schedule_after_task_execution = True
+schedule_after_task_execution = False
 
 # The scheduler can run multiple processes in parallel to parse dags.
 # This defines how many processes will run.
@@ -1031,3 +1031,21 @@ shards = 5
 
 # comma separated sensor classes support in smart_sensor.
 sensors_enabled = NamedHivePartitionSensor
+
+[usage_data_collection]
+# Airflow integrates `Scarf <https://about.scarf.sh/>`__ to collect basic platform and usage data
+# during operation. This data assists Airflow maintainers in better understanding how Airflow is used.
+# Insights gained from this telemetry are critical for prioritizing patches, minor releases, and
+# security fixes. Additionally, this information supports key decisions related to the development road map.
+# Check the FAQ doc for more information on what data is collected.
+#
+# Deployments can opt-out of analytics by setting the ``enabled`` option
+# to ``False``, or the ``SCARF_ANALYTICS=false`` environment variable.
+# Individual users can easily opt-out of analytics in various ways documented in the
+# `Scarf Do Not Track docs <https://docs.scarf.sh/gateway/#do-not-track>`__.
+
+# Enable or disable usage data collection and sending.
+#
+# Variable: AIRFLOW__USAGE_DATA_COLLECTION__ENABLED
+#
+enabled = False