(airflow-site) 01/01: [WIP] Add blog post for 2.10.0

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utkarsharma pushed a commit to branch 2.10.0-blog-post
in repository https://gitbox.apache.org/repos/asf/airflow-site.git

commit 56dd03ab618922532caeeeef34eb1a40b9511b4a
Author: utkarsh sharma <ut...@gmail.com>
AuthorDate: Thu Aug 8 20:06:37 2024 +0530

    [WIP] Add blog post for 2.10.0
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+---
+title: "Apache Airflow 2.10.0 is here"
+linkTitle: "Apache Airflow 2.10.0 is here"
+author: "Utkarsh Sharma"
+github: "utkarsharma2"
+linkedin: "utkarsh-sharma-5791ab8a"
+description: "Apache Airflow 2.10.0 is a game-changer, with powerful Dataset improvements and the groundbreaking Hybrid Executor, set to redefine your workflow capabilities!"
+tags: [Release]
+date: "2024-08-08"
+---
+
+'m happy to announce that Apache Airflow 2.10.0 is now available, bringing an array of noteworthy enhancements and new features that will greatly serve our community.
+
+Apache Airflow 2.10.0 contains over 135 commits, which include 43 new features,  85 improvements, 43 bug fixes, and 26 documentation changes.
+
+**Details**:
+
+📦 PyPI: https://pypi.org/project/apache-airflow/2.10.0/ \
+📚 Docs: https://airflow.apache.org/docs/apache-airflow/2.10.0/ \
+🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/2.10.0/release_notes.html \
+🐳 Docker Image: "docker pull apache/airflow:2.10.0" \
+🚏 Constraints: https://github.com/apache/airflow/tree/constraints-2.10.0
+
+
+## Hybrid Execution
+
+Each executor comes with its unique set of strengths and weaknesses, typically balancing latency, isolation, and compute efficiency. Traditionally, an Airflow environment is limited to a single executor, requiring users to make trade-offs, as no single executor is perfectly suited for all types of tasks.
+
+We are introducing a new feature that allows for the concurrent use of multiple executors within a single Airflow environment. This flexibility enables users to take advantage of the specific strengths of different executors for various tasks, improving overall efficiency and mitigating weaknesses. Users can set a default executor for the entire environment and, if necessary, assign particular executors to individual DAGs or tasks.
+
+To configure multiple executors we can pass comma separated list in airflow configuration. The first executor in the list will be the default executor for the environment.
+
+```
+[core]
+executor = 'LocalExecutor,CeleryExecutor'
+```
+To make it easier for dag authors, we can also specify aliases for executors that can be specified in the executor configuration
+```commandline
+[core]
+executor = 'LocalExecutor,my.custom.module.ExecutorClass:ShortName'
+```
+
+DAG authors can specify executors to use at the task
+```python
+BashOperator(
+    task_id="hello_world",
+    executor="LocalExecutor",
+    bash_command="echo 'hello world!'",
+)
+
+@task(executor="LocalExecutor")
+def hello_world():
+    print("hello world!")
+```
+
+We can also specify executors on the DAG level
+
+```python
+def hello_world():
+	print("hello world!")
+
+def hello_world_again():
+	print("hello world again!")
+
+with DAG(
+    dag_id="hello_worlds",
+    default_args={"executor": "LocalExecutor"},  # Applies to all tasks in the DAG
+) as dag:
+    # All tasks will use the executor from default args automatically
+    hw = hello_world()
+    hw_again = hello_world_again()
+```
+
+## Dynamic Dataset scheduling through DatasetAlias
+
+Airflow 2.10 comes with `DatasetAlias` class which can be passed as a value in the `outlets`, `inlets` on a task, and `schedule` on a DAG. An instance of `DatasetAlias` is resolved dynamically to a real dataset. Downstream can depend on either the resolved dataset or on an alias itself.
+
+`DatasetAlias` has one argument `name` that uniquely identifies the dataset. The task must first declare the alias as an outlet, and use `outlet_events` or `yield Metadata` to add events to it.
+
+### Emit a dataset event during task execution through outlet_events
+```python
+from airflow.datasets import DatasetAlias
+
+@task(outlets=[DatasetAlias("my-task-outputs")])
+def my_task_with_outlet_events(*, outlet_events):
+    outlet_events["my-task-outputs"].add(Dataset("s3://bucket/my-task"), extra={"k": "v"})
+```
+### Emit a dataset event during task execution by yielding Metadata
+```python
+from airflow.datasets.metadata import Metadata
+
+@task(outlets=[DatasetAlias("my-task-outputs")])
+def my_task_with_metadata():
+    s3_dataset = Dataset("s3://bucket/my-task}")
+    yield Metadata(s3_dataset, extra={"k": "v"}, alias="my-task-outputs")
+```
+
+There are two options for scheduling based on dataset aliases. Schedule based on `DatasetAlias` or real datasets.
+
+```python
+with DAG(dag_id="dataset-producer"):
+    @task(outlets=[Dataset("example-alias")])
+    def produce_dataset_events():
+        pass
+
+with DAG(dag_id="dataset-alias-producer"):
+    @task(outlets=[DatasetAlias("example-alias")])
+    def produce_dataset_events(*, outlet_events):
+        outlet_events["example-alias"].add(Dataset("s3://bucket/my-task"))
+
+with DAG(dag_id="dataset-consumer", schedule=Dataset("s3://bucket/my-task")):
+    ...
+
+with DAG(dag_id="dataset-alias-consumer", schedule=DatasetAlias("example-alias")):
+    ...
+```
+
+## Dark mode for Airflow UI
+
+Airflow 2.10 comes with new Dark Mode feature which is designed to enhance user experience by offering an alternative visual theme that is easier on the eyes, especially in low-light conditions. You can toggle the crescent icon on the right side of the navigation bar to switch between light and dark mode.
+
+![Airflow Dark mode](airflow_dark_mode.png)
+
+![Airflow Light mode](airflow_light_mode.png)
+
+## Contributors
+Thanks to everyone who contributed to this release, including Amogh Desai, Andrey Anshin, Brent Bovenzi, Daniel Standish, Ephraim Anierobi, Hussein Awala, Jarek Potiuk, Jed Cunningham, Jens Scheffler, Tzu-ping Chung, Vincent Beck, Wei Lee, and over 120 others!
+
+I hope you enjoy using Apache Airflow 2.10.0!