Thanks to everyone who put an incredible amount of work into making this happen! 🎉 🎊
On Thu, Dec 17, 2020 at 1:58 PM Xinbin Huang <bin.huan...@gmail.com> wrote: > Amazing to see this! 🎉 🎉 🎉 🎉 🎉 🎉 > > On Thu, Dec 17, 2020 at 1:54 PM kumar pavan <pavankumar5...@gmail.com> > wrote: > >> Congrats EveryOne >> >> >> Thanks & Regards >> Pavan >> >> >> On Thu, Dec 17, 2020 at 12:36 PM Ash Berlin-Taylor <a...@apache.org> >> wrote: >> >>> I am proud to announce that Apache Airflow 2.0.0 has been released. >>> >>> The source release, as well as the binary "wheel" release (no sdist this >>> time), are available here >>> >>> We also made this version available on PyPi for convenience (`pip >>> install apache-airflow`): >>> >>> 📦 PyPI: https://pypi.org/project/apache-airflow/2.0.0 >>> >>> The documentation is available on: >>> https://airflow.apache.org/ >>> 📚 Docs: http://airflow.apache.org/docs/apache-airflow/2.0.0/ >>> >>> Docker images will be available shortly -- check out >>> https://hub.docker.com/r/apache/airflow/tags?page=1&ordering=last_updated&name=2.0.0 >>> for it to appear >>> >>> >>> The full changelog is about 3,000 lines long (already excluding >>> everything backported to 1.10), so for now I’ll simply share some of the >>> major features in 2.0.0 compared to 1.10.14: >>> >>> *A new way of writing dags: the TaskFlow API (AIP-31)* >>> >>> (Known in 2.0.0alphas as Functional DAGs.) >>> >>> DAGs are now much much nicer to author especially when using >>> PythonOperator. Dependencies are handled more clearly and XCom is nicer to >>> use >>> >>> Read more here: >>> >>> TaskFlow API Tutorial >>> <http://airflow.apache.org/docs/apache-airflow/stable/tutorial_taskflow_api.html> >>> TaskFlow API Documentation >>> <https://airflow.apache.org/docs/apache-airflow/stable/concepts.html#decorated-flows> >>> >>> A quick teaser of what DAGs can now look like: >>> >>> ``` >>> from airflow.decorators import dag, task >>> from airflow.utils.dates import days_ago >>> >>> @dag(default_args={'owner': 'airflow'}, schedule_interval=None, >>> start_date=days_ago(2)) >>> def tutorial_taskflow_api_etl(): >>> @task >>> def extract(): >>> return {"1001": 301.27, "1002": 433.21, "1003": 502.22} >>> >>> @task >>> def transform(order_data_dict: dict) -> dict: >>> total_order_value = 0 >>> >>> for value in order_data_dict.values(): >>> total_order_value += value >>> >>> return {"total_order_value": total_order_value} >>> >>> @task() >>> def load(total_order_value: float): >>> >>> print("Total order value is: %.2f" % total_order_value) >>> >>> order_data = extract() >>> order_summary = transform(order_data) >>> load(order_summary["total_order_value"]) >>> >>> tutorial_etl_dag = tutorial_taskflow_api_etl() >>> ``` >>> >>> *Fully specified REST API (AIP-32)* >>> >>> We now have a fully supported, no-longer-experimental API with a >>> comprehensive OpenAPI specification >>> >>> Read more here: >>> >>> REST API Documentation >>> <http://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html> >>> . >>> >>> *Massive Scheduler performance improvements* >>> >>> As part of AIP-15 (Scheduler HA+performance) and other work Kamil did, >>> we significantly improved the performance of the Airflow Scheduler. It now >>> starts tasks much, MUCH quicker. >>> >>> Over at Astronomer.io we’ve benchmarked the scheduler—it’s fast >>> <https://www.astronomer.io/blog/airflow-2-scheduler> (we had to triple >>> check the numbers as we don’t quite believe them at first!) >>> >>> *Scheduler is now HA compatible (AIP-15)* >>> >>> It’s now possible and supported to run more than a single scheduler >>> instance. This is super useful for both resiliency (in case a scheduler >>> goes down) and scheduling performance. >>> >>> To fully use this feature you need Postgres 9.6+ or MySQL 8+ (MySQL 5, >>> and MariaDB won’t work with more than one scheduler I’m afraid). >>> >>> There’s no config or other set up required to run more than one >>> scheduler—just start up a scheduler somewhere else (ensuring it has access >>> to the DAG files) and it will cooperate with your existing schedulers >>> through the database. >>> >>> For more information, read the Scheduler HA documentation >>> <http://airflowapache.org/docs/apache-airflow/stable/scheduler.html#running-more-than-one-scheduler> >>> . >>> >>> *Task Groups (AIP-34)* >>> >>> SubDAGs were commonly used for grouping tasks in the UI, but they had >>> many drawbacks in their execution behaviour (primarirly that they only >>> executed a single task in parallel!) To improve this experience, we’ve >>> introduced “Task Groups”: a method for organizing tasks which provides the >>> same grouping behaviour as a subdag without any of the execution-time >>> drawbacks. >>> >>> SubDAGs will still work for now, but we think that any previous use of >>> SubDAGs can now be replaced with task groups. If you find an example where >>> this isn’t the case, please let us know by opening an issue on GitHub >>> >>> For more information, check out the Task Group documentation >>> <http://airflow.apache.org/docs/apache-airflow/stable/concepts.html#taskgroup> >>> . >>> >>> *Refreshed UI* >>> >>> We’ve given the Airflow UI a visual refresh and updated some of the >>> styling. Check out the UI section of the docs >>> <http://0.0.0.0:8000/docs/apache-airflow/stable/ui.html> for >>> screenshots. >>> >>> We have also added an option to auto-refresh task states in Graph View >>> so you no longer need to continuously press the refresh button :). >>> >>> ## Smart Sensors for reduced load from sensors (AIP-17) >>> >>> If you make heavy use of sensors in your Airflow cluster, you might find >>> that sensor execution takes up a significant proportion of your cluster >>> even with “reschedule” mode. To improve this, we’ve added a new mode called >>> “Smart Sensors”. >>> >>> This feature is in “early-access”: it’s been well-tested by AirBnB and >>> is “stable”/usable, but we reserve the right to make backwards incompatible >>> changes to it in a future release (if we have to. We’ll try very hard not >>> to!) >>> >>> Read more about it in the Smart Sensors documentation >>> <https://airflow.apache.org/docs/apache-airflow/stable/smart-sensor.html> >>> . >>> >>> *Simplified KubernetesExecutor* >>> >>> For Airflow 2.0, we have re-architected the KubernetesExecutor in a >>> fashion that is simultaneously faster, easier to understand, and more >>> flexible for Airflow users. Users will now be able to access the full >>> Kubernetes API to create a .yaml pod_template_file instead of specifying >>> parameters in their airflow.cfg. >>> >>> We have also replaced the executor_config dictionary with the >>> pod_override parameter, which takes a Kubernetes V1Pod object for a 1:1 >>> setting override. These changes have removed over three thousand lines of >>> code from the KubernetesExecutor, which makes it run faster and creates >>> fewer potential errors. >>> >>> Read more here: >>> >>> Docs on pod_template_file >>> <https://airflow.apache.org/docs/apache-airflow/stable/executor/kubernetes.html?highlight=pod_override#pod-template-file> >>> Docs on pod_override >>> <https://airflow.apache.org/docs/apache-airflow/stable/executor/kubernetes.html?highlight=pod_override#pod-override> >>> >>> *Airflow core and providers: Splitting Airflow into 60+ packages* >>> >>> Airflow 2.0 is not a monolithic “one to rule them all” package. We’ve >>> split Airflow into core and 61 (for now) provider packages. Each provider >>> package is for either a particular external service (Google, Amazon, >>> Microsoft, Snowflake), a database (Postgres, MySQL), or a protocol >>> (HTTP/FTP). Now you can create a custom Airflow installation from >>> “building” blocks and choose only what you need, plus add whatever other >>> requirements you might have. Some of the common providers are installed >>> automatically (ftp, http, imap, sqlite) as they are commonly used. Other >>> providers are automatically installed when you choose appropriate extras >>> when installing Airflow. >>> >>> The provider architecture should make it much easier to get a fully >>> customized, yet consistent runtime with the right set of Python >>> dependencies. >>> >>> But that’s not all: you can write your own custom providers and add >>> things like custom connection types, customizations of the Connection >>> Forms, and extra links to your operators in a manageable way. You can build >>> your own provider and install it as a Python package and have your >>> customizations visible right in the Airflow UI. >>> >>> Our very own Jarek Potiuk has written about providers in much more >>> detail <https://www.polidea.com/blog/airflow-2-providers/> on the >>> Polidea blog. >>> >>> Docs on the providers concept and writing custom providers >>> <http://airflow.apache.org/docs/apache-airflow-providers/> >>> Docs on the all providers packages available >>> <http://airflow.apache.org/docs/apache-airflow-providers/packages-ref.html> >>> >>> *Security* >>> >>> As part of Airflow 2.0 effort, there has been a conscious focus on >>> Security and reducing areas of exposure. This is represented across >>> different functional areas in different forms. For example, in the new REST >>> API, all operations now require authorization. Similarly, in the >>> configuration settings, the Fernet key is now required to be specified. >>> >>> *Configuration* >>> >>> Configuration in the form of the airflow.cfg file has been rationalized >>> further in distinct sections, specifically around “core”. Additionally, a >>> significant amount of configuration options have been deprecated or moved >>> to individual component-specific configuration files, such as the >>> pod-template-file for Kubernetes execution-related configuration. >>> >>> *Thanks to all of you* >>> >>> We’ve tried to make as few breaking changes as possible and to provide >>> deprecation path in the code, especially in the case of anything called in >>> the DAG. That said, please read throughUPDATING.md to check what might >>> affect you. For example: r We re-organized the layout of operators (they >>> now all live under airflow.providers.*) but the old names should continue >>> to work - you’ll just notice a lot of DeprecationWarnings that need to be >>> fixed up. >>> >>> Thank you so much to all the contributors who got us to this point, in >>> no particular order: Kaxil Naik, Daniel Imberman, Jarek Potiuk, Tomek >>> Urbaszek, Kamil Breguła, Gerard Casas Saez, Xiaodong DENG, Kevin Yang, >>> James Timmins, Yingbo Wang, Qian Yu, Ryan Hamilton and the 100s of others >>> who keep making Airflow better for everyone. >>> >>
