astro-sdk-python 1.8.1

Description:

astrosdkpython 1.8.1

astro


workflows made easy










Astro Python SDK is a Python SDK for rapid development of extract, transform, and load workflows in Apache Airflow. It allows you to express your workflows as a set of data dependencies without having to worry about ordering and tasks. The Astro Python SDK is maintained by Astronomer.
Prerequisites

Apache Airflow >= 2.1.0.

Install
The Astro Python SDK is available at PyPI. Use the standard Python
installation tools.
To install a cloud-agnostic version of the SDK, run:
pip install astro-sdk-python

You can also install dependencies for using the SDK with popular cloud providers:
pip install astro-sdk-python[amazon,google,snowflake,postgres]

Quickstart


Ensure that your Airflow environment is set up correctly by running the following commands:
export AIRFLOW_HOME=`pwd`
export AIRFLOW__CORE__XCOM_BACKEND=astro.custom_backend.astro_custom_backend.AstroCustomXcomBackend
export AIRFLOW__ASTRO_SDK__STORE_DATA_LOCAL_DEV=true
airflow db init


Note: AIRFLOW__CORE__ENABLE_XCOM_PICKLING no longer needs to be enabled for astro-sdk-python. This functionality is now deprecated as our custom xcom backend handles serialization.

The AIRFLOW__ASTRO_SDK__STORE_DATA_LOCAL_DEV should only be used for local development. The XCom backend docs give further details about how to set this up in non-local environments.
Currently, custom XCom backends are limited to data types that are json serializable. Since Dataframes are not json serializable, we need to enable XCom pickling to store dataframes.
The data format used by pickle is Python-specific. This has the advantage that there are no restrictions imposed by external standards such as JSON or XDR (which can’t represent pointer sharing); however it means that non-Python programs may not be able to reconstruct pickled Python objects.
Read more: enable_xcom_pickling and pickle:


Create a SQLite database for the example to run with:
# The sqlite_default connection has different host for MAC vs. Linux
export SQL_TABLE_NAME=`airflow connections get sqlite_default -o yaml | grep host | awk '{print $2}'`
sqlite3 "$SQL_TABLE_NAME" "VACUUM;"



Copy the following workflow into a file named calculate_popular_movies.py and add it to the dags directory of your Airflow project:
https://github.com/astronomer/astro-sdk/blob/d5aa768b2d4bca72ef98f8d533fe3f99624b172f/example_dags/calculate_popular_movies.py#L1-L37
Alternatively, you can download calculate_popular_movies.py
curl -O https://raw.githubusercontent.com/astronomer/astro-sdk/main/python-sdk/example_dags/calculate_popular_movies.py



Run the example DAG:
airflow dags test calculate_popular_movies `date -Iseconds`



Check the result of your DAG by running:
sqlite3 "$SQL_TABLE_NAME" "select * from top_animation;" ".exit"

You should see the following output:
$ sqlite3 "$SQL_TABLE_NAME" "select * from top_animation;" ".exit"
Toy Story 3 (2010)|8.3
Inside Out (2015)|8.2
How to Train Your Dragon (2010)|8.1
Zootopia (2016)|8.1
How to Train Your Dragon 2 (2014)|7.9



Supported technologies



Databases




Databricks Delta


Google BigQuery


Postgres


Snowflake


SQLite


Amazon Redshift


Microsoft SQL


DuckDB






File types




CSV


JSON


NDJSON


Parquet






File stores




Amazon S3


Filesystem


Google GCS


Google Drive


SFTP


FTP


Azure WASB


Azure WASBS



Available operations
The following are some key functions available in the SDK:

load_file: Load a given file into a SQL table
transform: Applies a SQL select statement to a source table and saves the result to a destination table
drop_table: Drops a SQL table
run_raw_sql: Run any SQL statement without handling its output
append: Insert rows from the source SQL table into the destination SQL table, if there are no conflicts
merge: Insert rows from the source SQL table into the destination SQL table, depending on conflicts:

ignore: Do not add rows that already exist
update: Replace existing rows with new ones


export_file: Export SQL table rows into a destination file
dataframe: Export given SQL table into in-memory Pandas data-frame

For a full list of available operators, see the SDK reference documentation.
Documentation
The documentation is a work in progress--we aim to follow the Diátaxis system:

Getting Started Tutorial: A hands-on introduction to the Astro Python SDK
How-to guides: Simple step-by-step user guides to accomplish specific tasks
Reference guide: Commands, modules, classes and methods
Explanation: Clarification and discussion of key decisions when designing the project

Changelog
The Astro Python SDK follows semantic versioning for releases. Check the changelog for the latest changes.
Release managements
To learn more about our release philosophy and steps, see Managing Releases.
Contribution guidelines
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
Read the Contribution Guideline for a detailed overview on how to contribute.
Contributors and maintainers should abide by the Contributor Code of Conduct.
License
Apache Licence 2.0