we have a webpage on it https://www.astronomer.io/airflow and a blogpost https://www.astronomer.io/blog/introducing-airflow-2-0
On Thu, Dec 17, 2020 at 12:54 PM Shaw, Damian P. < damian.sha...@credit-suisse.com> wrote: > Great news! Is there a single web page that highlights these major > features as you’ve listed them? > > > > Damian > > > > *From:* Ash Berlin-Taylor <a...@apache.org> > *Sent:* Thursday, December 17, 2020 12:36 > *To:* us...@airflow.apache.org > *Cc:* annou...@apache.org; dev@airflow.apache.org > *Subject:* Apache Airflow 2.0.0 is released! > > > > 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. > > > ============================================================================== > Please access the attached hyperlink for an important electronic > communications disclaimer: > http://www.credit-suisse.com/legal/en/disclaimer_email_ib.html > > ============================================================================== >
