jedcunningham commented on code in PR #578: URL: https://github.com/apache/airflow-site/pull/578#discussion_r857750485
########## landing-pages/site/content/en/blog/airflow-2.3.0/index.md: ########## @@ -0,0 +1,139 @@ +--- +title: "Apache Airflow 2.3.0 is here" +linkTitle: "Apache Airflow 2.3.0 is here" +author: "Ephraim Anierobi" +github: "ephraimbuddy" +linkedin: "ephraimanierobi" +description: "We're proud to announce that Apache Airflow 2.3.0 has been released." +tags: [Release] +date: "TBD" +--- + +Apache Airflow 2.3.0 contains over TBD commits since 2.2.0 and includes TBD new features, TBD improvements, TBD bug fixes, and several doc changes. + +**Details**: + +π¦ PyPI: https://pypi.org/project/apache-airflow/2.3.0/ \ +π Docs: https://airflow.apache.org/docs/apache-airflow/2.3.0/ \ +π οΈ Changelog: https://airflow.apache.org/docs/apache-airflow/2.3.0/changelog.html \ +π³ Docker Image: docker pull apache/airflow:2.3.0 \ +π Constraints: https://github.com/apache/airflow/tree/constraints-2.3.0 + +As the changelog is quite large, the following are some notable new features that shipped in this release. + +## Dynamic Task Mapping(AIP-42) + +There's now first-class support for dynamic tasks in Airflow. What this means is that you can generate tasks dynamically at runtime. Much like using a `for` loop +to create a list of tasks, here you can create the same tasks without having to know the exact number of tasks ahead of time. + +You can have a `task` generate the list to iterate over, which is not possible with a `for` loop. + +Here is an example: + +```python +@task +def make_list(): + # This can also be from an API call, checking a database, -- almost anything you like, as long as the + # resulting list/dictionary can be stored in the current XCom backend. + return [1, 2, {"a": "b"}, "str"] + + +@task +def consumer(arg): + print(list(arg)) + + +with DAG(dag_id="dynamic-map", start_date=datetime(2022, 4, 2)) as dag: + consumer.expand(arg=make_list()) +``` + +More information can be found here: [Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/latest/concepts/dynamic-task-mapping.html) Review Comment: ```suggestion More information can be found here: [Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html) ``` Probably better to link to the specific version? Or it at least should be to `stable`. ########## landing-pages/site/content/en/blog/airflow-2.3.0/index.md: ########## @@ -0,0 +1,139 @@ +--- +title: "Apache Airflow 2.3.0 is here" +linkTitle: "Apache Airflow 2.3.0 is here" +author: "Ephraim Anierobi" +github: "ephraimbuddy" +linkedin: "ephraimanierobi" +description: "We're proud to announce that Apache Airflow 2.3.0 has been released." +tags: [Release] +date: "TBD" +--- + +Apache Airflow 2.3.0 contains over TBD commits since 2.2.0 and includes TBD new features, TBD improvements, TBD bug fixes, and several doc changes. + +**Details**: + +π¦ PyPI: https://pypi.org/project/apache-airflow/2.3.0/ \ +π Docs: https://airflow.apache.org/docs/apache-airflow/2.3.0/ \ +π οΈ Changelog: https://airflow.apache.org/docs/apache-airflow/2.3.0/changelog.html \ +π³ Docker Image: docker pull apache/airflow:2.3.0 \ +π Constraints: https://github.com/apache/airflow/tree/constraints-2.3.0 + +As the changelog is quite large, the following are some notable new features that shipped in this release. + +## Dynamic Task Mapping(AIP-42) + +There's now first-class support for dynamic tasks in Airflow. What this means is that you can generate tasks dynamically at runtime. Much like using a `for` loop +to create a list of tasks, here you can create the same tasks without having to know the exact number of tasks ahead of time. + +You can have a `task` generate the list to iterate over, which is not possible with a `for` loop. + +Here is an example: + +```python +@task +def make_list(): + # This can also be from an API call, checking a database, -- almost anything you like, as long as the + # resulting list/dictionary can be stored in the current XCom backend. + return [1, 2, {"a": "b"}, "str"] + + +@task +def consumer(arg): + print(list(arg)) + + +with DAG(dag_id="dynamic-map", start_date=datetime(2022, 4, 2)) as dag: + consumer.expand(arg=make_list()) +``` + +More information can be found here: [Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/latest/concepts/dynamic-task-mapping.html) + +## Grid View replaces Tree View + +Grid view replaces tree view in Airflow 2.3.0. + +**Screenshots**: + + + +## LocalKubernetesExecutor + +Airflow 2.3.0, features a new executor called LocalKubernetesExecutor. This executor helps you run some tasks using LocalExecutor and run another set of tasks using the KubernetesExecutor in the same deployment using task's queue to coordinate the switching. Review Comment: ```suggestion There is a new executor names LocalKubernetesExecutor. This executor helps you run some tasks using LocalExecutor and run another set of tasks using the KubernetesExecutor in the same deployment based on the task's queue. ``` nit ########## landing-pages/site/content/en/blog/airflow-2.3.0/index.md: ########## @@ -0,0 +1,139 @@ +--- +title: "Apache Airflow 2.3.0 is here" +linkTitle: "Apache Airflow 2.3.0 is here" +author: "Ephraim Anierobi" +github: "ephraimbuddy" +linkedin: "ephraimanierobi" +description: "We're proud to announce that Apache Airflow 2.3.0 has been released." +tags: [Release] +date: "TBD" +--- + +Apache Airflow 2.3.0 contains over TBD commits since 2.2.0 and includes TBD new features, TBD improvements, TBD bug fixes, and several doc changes. + +**Details**: + +π¦ PyPI: https://pypi.org/project/apache-airflow/2.3.0/ \ +π Docs: https://airflow.apache.org/docs/apache-airflow/2.3.0/ \ +π οΈ Changelog: https://airflow.apache.org/docs/apache-airflow/2.3.0/changelog.html \ Review Comment: ```suggestion π οΈ Release Notes: https://airflow.apache.org/docs/apache-airflow/2.3.0/release_notes.html \ ``` ########## landing-pages/site/content/en/blog/airflow-2.3.0/index.md: ########## @@ -0,0 +1,139 @@ +--- +title: "Apache Airflow 2.3.0 is here" +linkTitle: "Apache Airflow 2.3.0 is here" +author: "Ephraim Anierobi" +github: "ephraimbuddy" +linkedin: "ephraimanierobi" +description: "We're proud to announce that Apache Airflow 2.3.0 has been released." +tags: [Release] +date: "TBD" +--- + +Apache Airflow 2.3.0 contains over TBD commits since 2.2.0 and includes TBD new features, TBD improvements, TBD bug fixes, and several doc changes. + +**Details**: + +π¦ PyPI: https://pypi.org/project/apache-airflow/2.3.0/ \ +π Docs: https://airflow.apache.org/docs/apache-airflow/2.3.0/ \ +π οΈ Changelog: https://airflow.apache.org/docs/apache-airflow/2.3.0/changelog.html \ +π³ Docker Image: docker pull apache/airflow:2.3.0 \ +π Constraints: https://github.com/apache/airflow/tree/constraints-2.3.0 + +As the changelog is quite large, the following are some notable new features that shipped in this release. + +## Dynamic Task Mapping(AIP-42) + +There's now first-class support for dynamic tasks in Airflow. What this means is that you can generate tasks dynamically at runtime. Much like using a `for` loop +to create a list of tasks, here you can create the same tasks without having to know the exact number of tasks ahead of time. + +You can have a `task` generate the list to iterate over, which is not possible with a `for` loop. + +Here is an example: + +```python +@task +def make_list(): + # This can also be from an API call, checking a database, -- almost anything you like, as long as the + # resulting list/dictionary can be stored in the current XCom backend. + return [1, 2, {"a": "b"}, "str"] + + +@task +def consumer(arg): + print(list(arg)) + + +with DAG(dag_id="dynamic-map", start_date=datetime(2022, 4, 2)) as dag: + consumer.expand(arg=make_list()) +``` + +More information can be found here: [Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/latest/concepts/dynamic-task-mapping.html) + +## Grid View replaces Tree View + +Grid view replaces tree view in Airflow 2.3.0. + +**Screenshots**: + + + +## LocalKubernetesExecutor + +Airflow 2.3.0, features a new executor called LocalKubernetesExecutor. This executor helps you run some tasks using LocalExecutor and run another set of tasks using the KubernetesExecutor in the same deployment using task's queue to coordinate the switching. + +More information can be found here: [LocalKubernetesExecutor](https://airflow.apache.org/docs/apache-airflow/latest/executor/local_kubernetes.html) + + +## DagProcessorManager as standalone process (AIP-43) + +As of 2.3.0, you can run the DagProcessorManager as a standalone process. Because DagProcessorManager runs user code, separating it from the scheduler process and running it as an independent process in a different host is a good idea. + +`airflow dag-processor` cli command will start a new process that will run the DagProcessorManager in a separate process. Before you can run the DagProcessorManager as a standalone process, you need to set the `AIRFLOW__SCHEDULER__STANDALONE_DAG_PROCESSOR` to `True`. + +More information can be found here: [dag-processor CLI command](https://airflow.apache.org/docs/apache-airflow/latest/cli-and-env-variables-ref.html#dag-processor) + +## JSON serialization for connections +You can now create connections using the `json` serialization format. + +```bash +airflow connections add 'my_prod_db' \ + --conn-json '{ + "conn_type": "my-conn-type", + "login": "my-login", + "password": "my-password", + "host": "my-host", + "port": 1234, + "schema": "my-schema", + "extra": { + "param1": "val1", + "param2": "val2" + } + }' +``` +You can also use `json` serialization format when setting the connection in environment variables. + +More information can be found here: [JSON serialization for connections](https://airflow.apache.org/docs/apache-airflow/latest/howto/connection.html) + +## Airflow `db downgrade` and Offline generation of SQL scripts + +Airflow 2.3.0 introduced a new command `airflow db downgrade` that will downgrade the database to your chosen version. + +You can also generate the downgrade/upgrade SQL scripts for your database and manually run it against your database or just view the SQL scripts that would be run by the downgrade/upgrade command. + +More information can be found here: [Airflow `db downgrade` and Offline generation of SQL scripts](https://airflow.apache.org/docs/apache-airflow/latest/usage-cli.html#downgrading-airflow) + +## Reuse of decorated tasks + +You can now reuse decorated tasks across your dag files. A decorated task has an `override` method that allows you to override it's arguments. + +Here's an example: + +```python +@task +def add_task(x, y): + print(f"Task args: x={x}, y={y}") + return x + y + + +@dag(start_date=datetime(2022, 1, 1)) +def mydag(): + start = add_task.override(task_id="start")(1, 2) + for i in range(3): + start >> add_task.override(task_id=f"add_start_{i}")(start, i) +``` + +More information can be found here: [Reuse of decorated DAGs](https://airflow.apache.org/docs/apache-airflow/latest/tutorial_taskflow_api.html#reusing-a-decorated-task) + +## Other small features + +This isnβt a comprehensive list, but some noteworthy or interesting small features include: + +- Support different timeout value for dag file parsing +- `airflow dags reserialize` command to reserialize dags +- `db clean` CLI command for purging old data Review Comment: I think this should get a full section! ########## landing-pages/site/content/en/blog/airflow-2.3.0/index.md: ########## @@ -0,0 +1,139 @@ +--- +title: "Apache Airflow 2.3.0 is here" +linkTitle: "Apache Airflow 2.3.0 is here" +author: "Ephraim Anierobi" +github: "ephraimbuddy" +linkedin: "ephraimanierobi" +description: "We're proud to announce that Apache Airflow 2.3.0 has been released." +tags: [Release] +date: "TBD" +--- + +Apache Airflow 2.3.0 contains over TBD commits since 2.2.0 and includes TBD new features, TBD improvements, TBD bug fixes, and several doc changes. + +**Details**: + +π¦ PyPI: https://pypi.org/project/apache-airflow/2.3.0/ \ +π Docs: https://airflow.apache.org/docs/apache-airflow/2.3.0/ \ +π οΈ Changelog: https://airflow.apache.org/docs/apache-airflow/2.3.0/changelog.html \ +π³ Docker Image: docker pull apache/airflow:2.3.0 \ +π Constraints: https://github.com/apache/airflow/tree/constraints-2.3.0 + +As the changelog is quite large, the following are some notable new features that shipped in this release. + +## Dynamic Task Mapping(AIP-42) + +There's now first-class support for dynamic tasks in Airflow. What this means is that you can generate tasks dynamically at runtime. Much like using a `for` loop +to create a list of tasks, here you can create the same tasks without having to know the exact number of tasks ahead of time. + +You can have a `task` generate the list to iterate over, which is not possible with a `for` loop. + +Here is an example: + +```python +@task +def make_list(): + # This can also be from an API call, checking a database, -- almost anything you like, as long as the + # resulting list/dictionary can be stored in the current XCom backend. + return [1, 2, {"a": "b"}, "str"] + + +@task +def consumer(arg): + print(list(arg)) + + +with DAG(dag_id="dynamic-map", start_date=datetime(2022, 4, 2)) as dag: + consumer.expand(arg=make_list()) +``` + +More information can be found here: [Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/latest/concepts/dynamic-task-mapping.html) + +## Grid View replaces Tree View + +Grid view replaces tree view in Airflow 2.3.0. + +**Screenshots**: + + + +## LocalKubernetesExecutor + +Airflow 2.3.0, features a new executor called LocalKubernetesExecutor. This executor helps you run some tasks using LocalExecutor and run another set of tasks using the KubernetesExecutor in the same deployment using task's queue to coordinate the switching. + +More information can be found here: [LocalKubernetesExecutor](https://airflow.apache.org/docs/apache-airflow/latest/executor/local_kubernetes.html) + + +## DagProcessorManager as standalone process (AIP-43) + +As of 2.3.0, you can run the DagProcessorManager as a standalone process. Because DagProcessorManager runs user code, separating it from the scheduler process and running it as an independent process in a different host is a good idea. + +`airflow dag-processor` cli command will start a new process that will run the DagProcessorManager in a separate process. Before you can run the DagProcessorManager as a standalone process, you need to set the `AIRFLOW__SCHEDULER__STANDALONE_DAG_PROCESSOR` to `True`. Review Comment: ```suggestion The `airflow dag-processor` cli command will start a new process that will run the DagProcessorManager in a separate process. Before you can run the DagProcessorManager as a standalone process, you need to set the [[scheduler] standalone_dag_processor](https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#standalone_dag_processor) to `True`. ``` ########## landing-pages/site/content/en/blog/airflow-2.3.0/index.md: ########## @@ -0,0 +1,139 @@ +--- +title: "Apache Airflow 2.3.0 is here" +linkTitle: "Apache Airflow 2.3.0 is here" +author: "Ephraim Anierobi" +github: "ephraimbuddy" +linkedin: "ephraimanierobi" +description: "We're proud to announce that Apache Airflow 2.3.0 has been released." +tags: [Release] +date: "TBD" +--- + +Apache Airflow 2.3.0 contains over TBD commits since 2.2.0 and includes TBD new features, TBD improvements, TBD bug fixes, and several doc changes. + +**Details**: + +π¦ PyPI: https://pypi.org/project/apache-airflow/2.3.0/ \ +π Docs: https://airflow.apache.org/docs/apache-airflow/2.3.0/ \ +π οΈ Changelog: https://airflow.apache.org/docs/apache-airflow/2.3.0/changelog.html \ +π³ Docker Image: docker pull apache/airflow:2.3.0 \ +π Constraints: https://github.com/apache/airflow/tree/constraints-2.3.0 + +As the changelog is quite large, the following are some notable new features that shipped in this release. + +## Dynamic Task Mapping(AIP-42) + +There's now first-class support for dynamic tasks in Airflow. What this means is that you can generate tasks dynamically at runtime. Much like using a `for` loop +to create a list of tasks, here you can create the same tasks without having to know the exact number of tasks ahead of time. + +You can have a `task` generate the list to iterate over, which is not possible with a `for` loop. + +Here is an example: + +```python +@task +def make_list(): + # This can also be from an API call, checking a database, -- almost anything you like, as long as the + # resulting list/dictionary can be stored in the current XCom backend. + return [1, 2, {"a": "b"}, "str"] + + +@task +def consumer(arg): + print(list(arg)) + + +with DAG(dag_id="dynamic-map", start_date=datetime(2022, 4, 2)) as dag: + consumer.expand(arg=make_list()) +``` + +More information can be found here: [Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/latest/concepts/dynamic-task-mapping.html) + +## Grid View replaces Tree View + +Grid view replaces tree view in Airflow 2.3.0. + +**Screenshots**: + + + +## LocalKubernetesExecutor + +Airflow 2.3.0, features a new executor called LocalKubernetesExecutor. This executor helps you run some tasks using LocalExecutor and run another set of tasks using the KubernetesExecutor in the same deployment using task's queue to coordinate the switching. + +More information can be found here: [LocalKubernetesExecutor](https://airflow.apache.org/docs/apache-airflow/latest/executor/local_kubernetes.html) + + +## DagProcessorManager as standalone process (AIP-43) + +As of 2.3.0, you can run the DagProcessorManager as a standalone process. Because DagProcessorManager runs user code, separating it from the scheduler process and running it as an independent process in a different host is a good idea. + +`airflow dag-processor` cli command will start a new process that will run the DagProcessorManager in a separate process. Before you can run the DagProcessorManager as a standalone process, you need to set the `AIRFLOW__SCHEDULER__STANDALONE_DAG_PROCESSOR` to `True`. + +More information can be found here: [dag-processor CLI command](https://airflow.apache.org/docs/apache-airflow/latest/cli-and-env-variables-ref.html#dag-processor) + +## JSON serialization for connections +You can now create connections using the `json` serialization format. + +```bash +airflow connections add 'my_prod_db' \ + --conn-json '{ + "conn_type": "my-conn-type", + "login": "my-login", + "password": "my-password", + "host": "my-host", + "port": 1234, + "schema": "my-schema", + "extra": { + "param1": "val1", + "param2": "val2" + } + }' +``` +You can also use `json` serialization format when setting the connection in environment variables. + +More information can be found here: [JSON serialization for connections](https://airflow.apache.org/docs/apache-airflow/latest/howto/connection.html) + +## Airflow `db downgrade` and Offline generation of SQL scripts + +Airflow 2.3.0 introduced a new command `airflow db downgrade` that will downgrade the database to your chosen version. + +You can also generate the downgrade/upgrade SQL scripts for your database and manually run it against your database or just view the SQL scripts that would be run by the downgrade/upgrade command. Review Comment: ```suggestion You can also generate the downgrade/upgrade SQL scripts for your database and manually run it against your database or just view the SQL queries that would be run by the downgrade/upgrade command. ``` ########## landing-pages/site/content/en/blog/airflow-2.3.0/index.md: ########## @@ -0,0 +1,139 @@ +--- +title: "Apache Airflow 2.3.0 is here" +linkTitle: "Apache Airflow 2.3.0 is here" +author: "Ephraim Anierobi" +github: "ephraimbuddy" +linkedin: "ephraimanierobi" +description: "We're proud to announce that Apache Airflow 2.3.0 has been released." +tags: [Release] +date: "TBD" +--- + +Apache Airflow 2.3.0 contains over TBD commits since 2.2.0 and includes TBD new features, TBD improvements, TBD bug fixes, and several doc changes. + +**Details**: + +π¦ PyPI: https://pypi.org/project/apache-airflow/2.3.0/ \ +π Docs: https://airflow.apache.org/docs/apache-airflow/2.3.0/ \ +π οΈ Changelog: https://airflow.apache.org/docs/apache-airflow/2.3.0/changelog.html \ +π³ Docker Image: docker pull apache/airflow:2.3.0 \ +π Constraints: https://github.com/apache/airflow/tree/constraints-2.3.0 + +As the changelog is quite large, the following are some notable new features that shipped in this release. + +## Dynamic Task Mapping(AIP-42) + +There's now first-class support for dynamic tasks in Airflow. What this means is that you can generate tasks dynamically at runtime. Much like using a `for` loop +to create a list of tasks, here you can create the same tasks without having to know the exact number of tasks ahead of time. + +You can have a `task` generate the list to iterate over, which is not possible with a `for` loop. + +Here is an example: + +```python +@task +def make_list(): + # This can also be from an API call, checking a database, -- almost anything you like, as long as the + # resulting list/dictionary can be stored in the current XCom backend. + return [1, 2, {"a": "b"}, "str"] + + +@task +def consumer(arg): + print(list(arg)) + + +with DAG(dag_id="dynamic-map", start_date=datetime(2022, 4, 2)) as dag: + consumer.expand(arg=make_list()) +``` + +More information can be found here: [Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/latest/concepts/dynamic-task-mapping.html) + +## Grid View replaces Tree View + +Grid view replaces tree view in Airflow 2.3.0. + +**Screenshots**: + + + +## LocalKubernetesExecutor + +Airflow 2.3.0, features a new executor called LocalKubernetesExecutor. This executor helps you run some tasks using LocalExecutor and run another set of tasks using the KubernetesExecutor in the same deployment using task's queue to coordinate the switching. + +More information can be found here: [LocalKubernetesExecutor](https://airflow.apache.org/docs/apache-airflow/latest/executor/local_kubernetes.html) + + +## DagProcessorManager as standalone process (AIP-43) + +As of 2.3.0, you can run the DagProcessorManager as a standalone process. Because DagProcessorManager runs user code, separating it from the scheduler process and running it as an independent process in a different host is a good idea. + +`airflow dag-processor` cli command will start a new process that will run the DagProcessorManager in a separate process. Before you can run the DagProcessorManager as a standalone process, you need to set the `AIRFLOW__SCHEDULER__STANDALONE_DAG_PROCESSOR` to `True`. + +More information can be found here: [dag-processor CLI command](https://airflow.apache.org/docs/apache-airflow/latest/cli-and-env-variables-ref.html#dag-processor) + +## JSON serialization for connections +You can now create connections using the `json` serialization format. + +```bash +airflow connections add 'my_prod_db' \ + --conn-json '{ + "conn_type": "my-conn-type", + "login": "my-login", + "password": "my-password", + "host": "my-host", + "port": 1234, + "schema": "my-schema", + "extra": { + "param1": "val1", + "param2": "val2" + } + }' +``` +You can also use `json` serialization format when setting the connection in environment variables. + +More information can be found here: [JSON serialization for connections](https://airflow.apache.org/docs/apache-airflow/latest/howto/connection.html) + +## Airflow `db downgrade` and Offline generation of SQL scripts + +Airflow 2.3.0 introduced a new command `airflow db downgrade` that will downgrade the database to your chosen version. + +You can also generate the downgrade/upgrade SQL scripts for your database and manually run it against your database or just view the SQL scripts that would be run by the downgrade/upgrade command. + +More information can be found here: [Airflow `db downgrade` and Offline generation of SQL scripts](https://airflow.apache.org/docs/apache-airflow/latest/usage-cli.html#downgrading-airflow) + +## Reuse of decorated tasks + +You can now reuse decorated tasks across your dag files. A decorated task has an `override` method that allows you to override it's arguments. + +Here's an example: + +```python +@task +def add_task(x, y): + print(f"Task args: x={x}, y={y}") + return x + y + + +@dag(start_date=datetime(2022, 1, 1)) +def mydag(): + start = add_task.override(task_id="start")(1, 2) + for i in range(3): + start >> add_task.override(task_id=f"add_start_{i}")(start, i) +``` + +More information can be found here: [Reuse of decorated DAGs](https://airflow.apache.org/docs/apache-airflow/latest/tutorial_taskflow_api.html#reusing-a-decorated-task) + +## Other small features + +This isnβt a comprehensive list, but some noteworthy or interesting small features include: + +- Support different timeout value for dag file parsing +- `airflow dags reserialize` command to reserialize dags +- `db clean` CLI command for purging old data +- Events Timetable +- SmoothOperator - Operator that does literally nothing but it logs YouTube link to + Sade song "Smooth Operator" Review Comment: ```suggestion - SmoothOperator - Operator that does literally nothing except logging a YouTube link to Sade's "Smooth Operator". Enjoy! ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
