Package: viewflow
ViewflowViewflow is a framework built on the top of Airflow that enables data scientists to create materialized views. It allows data scientists to focus on the logic of the view creation in their preferred tool (e.g., SQL, Python).Viewflow automatically creates Airflow DAGs and tasks based on SQL or Python files.One of the major features of Viewflow is its ability to manage tasks' dependencies, i.e., views used to create another view. Viewflow can automatically extract from the code (SQL query or Python script) the internal and external dependencies. An internal dependency is a view that belongs to the same DAG as a view being created. An external dependency is a view that belongs to a different DAG. The benefits of automatic dependency management are twofold: First, data scientists don't have to manually list dependencies, usually an error-prone process. Second, it makes sure that no view is built on stale data (because all dependent views will be updated beforehand).Currently, Viewflow supports SQL and Python views and PostgreSQL/Redshift as a destination. We will continue improving Viewflow by adding new view types (e.g., R, Jupyter Notebooks, ...) and destination (e.g., Snowflake, BigQuery, ...).Do you want more context on why we built and released Viewflow? Check out our announcement blog post:Data Scientists, don’t worry about data engineering: Viewflow has your back.!Viewflow demoWe created a demo that shows how Viewflow works. The demo creates two DAGs:viewflow-demo-1andviewflow-demo-2. These DAGs create a total of four views in a local Postgres database. Check out the view files indemo/dags/. Some of the following commands are different based on which Airflow version you're using. For new users, Airflow 2 is the best option. However, you can also run the demo using the older Airflow 1.10 version by using the indicated commands.Run the demoWe usedocker-composeto instantiate an Apache Airflow instance and a Postgres database. The Airflow container and the Postgres container are defined in thedocker-compose-airflow<version>.ymlfiles. The first time you want to run the demo, you will first have to build the Apache Airflow docker image that embeds Viewflow:docker-compose-fdocker-compose-airflow2.ymlbuild# Airflow 2docker-compose-fdocker-compose-airflow1.10.ymlbuild# Airflow 1.10Then run the docker containers:docker-compose-fdocker-compose-airflow2.ymlup# Airflow 2docker-compose-fdocker-compose-airflow1.10.ymlup# Airflow 1.10Go to your local Apache Airflow instance onhttp://localhost:8080. There are two DAGs calledviewflow-demo-1andviewflow-demo-2. Notice how Viewflow automatically generated these DAGs based on the example queries in the subfolders ofdemo/dags/!By default, the DAGs are disabled. Turn the DAGs on by clicking on the buttonOff. This will trigger the DAGs.Query the viewsOnce the DAGs have run and all tasks completed, you can query the views created by Viewflow in the local Postgres database created by Docker. You can use any Postgres client (note that Postgres is running locally on port5433):psql-hlocalhost-p5433-Uairflow-dairflowUseairflowwhenpsqlasks you for the user password.There is a schema namedviewflow_rawand a schema namedviewflow_demo. The first one contains three tables:users,courses, anduser_course. They are considered as the raw data. The second schema,viewflow_demo, is the schema in which the views created by Viewflow are stored.\dn+---------------+---------+|Name|Owner||---------------+---------||public|airflow||viewflow_demo|airflow||viewflow_raw|airflow|+---------------+---------+Viewflow created four views:user_xp(SQL),user_enriched(SQL),course_enriched(SQL) andtop_3_user_xp(Python)\dtviewflow_demo.+---------------+-----------------+--------+---------+|Schema|Name|Type|Owner||---------------+-----------------+--------+---------||viewflow_demo|course_enriched|table|airflow||viewflow_demo|top_3_user_xp|table|airflow||viewflow_demo|user_enriched|table|airflow||viewflow_demo|user_xp|table|airflow|+---------------+-----------------+--------+---------+You can query these tables to see their data:select*fromviewflow_demo.user_xp;+-----------+------+-----------------------+|user_id|xp|__view_generated_at||-----------+------+-----------------------||1|750|2021-03-17||2|200|2021-03-17||3|550|2021-03-17||4|500|2021-03-17||5|650|2021-03-17||6|430|2021-03-17||7|300|2021-03-17||8|280|2021-03-17||9|100|2021-03-17||10|350|2021-03-17|+-----------+------+-----------------------+You can also access the tables' comment (both table and columns):selectobj_description('viewflow_demo.user_enriched'::regclass)asview_description;+---------------------------------------------+|view_description||---------------------------------------------||Atablewithenrichedinformationofusers|+---------------------------------------------+selectcolumn_name,col_description((table_schema||'.'||table_name)::regclass::oid,ordinal_position)ascolumn_commentfrominformation_schema.columnswheretable_schema='viewflow_demo'andtable_name='user_enriched';+--------------------------+-----------------------------------------------+|column_name|column_comment||--------------------------+-----------------------------------------------||user_id|Theuserid||xp|TheuseramountofXP||last_course_completed_at|Whenwasthelastcoursecompletedbyauser||last_course_completed|Nameofthelatestcompletedcoursebyauser||number_courses_completed|Numberofcompletedcoursesbyauser||__view_generated_at|<null>|+--------------------------+-----------------------------------------------+And that's it! Congrats on running the demo :rocket:  If you want to play more with Viewflow, follow the installation instructions below.Installation instructions:envelope:If you have any issue with the installation, configuration, or creation of your DAGs, do not hesitate tocontact us!The current installation process requires you to install Viewflow from the GitHub repository:RUNpipinstallgit+https://github.com/datacamp/viewflow.gitCreate a new DAGViewflow creates the DAGs automatically based on configuration files.Here are the steps to create a DAG for the first time.Create the Viewflow main scriptIn your Airflow DAG directory (usually$AIRFLOW_HOME/dags), create a python script calledviewflow-dags.pythat contains the following Python code:fromviewflowimportcreate_dagsDAG=create_dags("./dags",globals(),"<views_schema_name>")This script is executed by Airflow. It calls the main Viewflow function that creates your DAGs. The first parameter is the directory in which your dag folders are located. The third parameter is the schema name in your data warehouse, where your views will be materialized.Create an Airflow connection to your destinationViewflow needs to know where to write the views. It uses an Airflow connection that is referred to in the view files by specifying aconnection_id. Currently, Viewflow supports Postgres (or Redshift) data warehouses. Please look at theAirflow documentationto create a Postgres connection.E.g. the demo's connection is managed using environmemt variables declared indemo/.env. This file is theenv_filespecified in thedocker-compose-airflow<version>.ymlfiles and it allows the scheduler and webserver containers to connect to the Postgres server.Create your DAG directoriesIn Viewflow, the DAGs are created based on a configuration file and on the SQL and Python files in the same directory.In$AIRFLOW_HOME/dags/, create a directory calledmy-first-viewflow-dag. In this directory, create aconfig.ymlfile that contains the following yml fields:default_args:
    owner: <owner>@dag.com
    retries: 1
schedule_interval: 0 6 * * *
start_date: "2021-01-01"Adapt the values of each element to what suits you. Thedefault_argselement contains the Airflowdefault DAG  parameters.Theschedule_intervalandstart_dateelements are the Viewflow counterparts of Airflow'sschedule_intervalandstart_date.You can now add your SQL and Python files in this directory (see sections below). This will create a new DAG in Airflow calledmy-first-viewflow-dagthat will be triggered every day at 6 AM UTC as of January 1, 2021. All failed tasks will be retried once.View metadataViewflow expects some metadata that must be included in the SQL and Python files (examples follow). Here are the fields that should be included in aymlformat:owner: The owner of the view (i.e., who is view responsible). The owner appears in Airflow and allows users to know who they should talk to if they have some questions about the view.description: What is the view about. Viewflow uses this field as a view comment in the database. The description can be retrieved in SQL (see SectionQuery the views).fields (list): Description of each column of the view. Viewflow uses these fields as column comments in the database. The column descriptions can be retrieved in SQL (see SectionQuery the views).schema: The name of the schema in which Viewflow creates the view. It's also used by Viewflow to create the dependencies.connection_id: Airflow connection name used to connect to the database (See SectionCreate an Airflow connection to your destination).SQL viewsA SQL view is created by a SQL file. This SQL file must contain the SQL query (as aSELECTstatement) of your view and the view metadata. Here's an example:/*---owner: name-of-the-view-ownerdescription: A description of your view. It's used as the view's description in the databasefields:email: Description of your column -- used as the view column's description in the databaseschema: schema_name_in_your_destination (e.g. viewflow_demo)connection_id: airflow_destination_connection---*/SELECTDISTINCTemailFROMviewflow_raw.usersPython viewsPlease note that the implementation of the Python view should be considered as beta. It is a newer implementation of the Python view that we use at DataCamp.A Python view is created based on a Python script. This script must contain at least one function with the view's description metadata in its docstring, which returns a Pandas dataframe.Here's an example of a Python view:importpandasaspddefpython_view(db_engine):"""---owner: name-of-the-view-ownerdescription: A description of your view. It's used as the view's description in the databasefields:email: Description of your column -- used as the view column's description in the databaseschema: schema_name_in_your_destination (e.g. viewflow_demo)connection_id: airflow_destination_connection---"""df=pd.read_sql_table("users",db_engine,schema="viewflow_raw")returndf[["email"]]Please note that Viewflow expects the Python function that creates the view to have the parameterdb_engine(used to connect to the database). You don't have to setdb_engineanywhere. Viewflow takes care of setting this variable.Contributing to ViewflowWe welcome all sorts of contributions, be it new features, bug fixes or documentation, we encourage you to create a new PR. To create a new PR or to report new bugs, please read how tocontribute to Viewflow.In the remainder of this section, we show you how to prepare your environment to contribute to Viewflow.Install PoetrySeehttps://python-poetry.org/docs/#osx-linux-bashonwindows-install-instructionsfor comprehensive documentation.curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | pythonInstall the dependenciesYou can automatically install the required dependencies by runningpoetryinstallBy default, this will install Airflow 2 and its corresponding dependencies. If you want to use Airflow 1.10, copy theAirflow@1.10/pyproject.tomlfile to the main directory.Prepare your environment to run the testsPostgresUse docker compose to set up a PostgreSQL database locally (password:passw0rd):docker-compose-fdocker-compose-test.ymlupIf you get a message saying that port 5432 is in use, it means you have a different PostgreSQL server running on your machine. If you used homebrew to install it, you could usebrew services stop postgresqlto stop the other server.Import the fixtures into the local database:psql-Uuser-W-hlocalhost-ftests/fixtures/load_postgres.sql-dviewflowRun PytestBefore you can continue, you will need to set up an Airflow SQLite database.poetryrunairflowdbinit# Airflow 2poetryrunairflowinitdb# Airflow 1.10If running into problems, thislinkcan be helpful. In particular, it's possible you get aSymbol not found: _Py_GetArgcArgverror. This is easily fixed by creating a Python virtual environment as demonstrated in thelink, activating this virtual environment and then runningpoetry installagain.Note for Airflow 1.10.12: if you get anImportError, it can be helpful to refer to thispost.After setting up the database, runpoetryrunpytestIn case the database connection is set up incorrectly, runpoetryrunairflowdbreset# Airflow 2poetryrunairflowresetdb# Airflow 1.10Viewflow architectureWe built Viewflow around three main components: theparser, theadapter, and thedependency extractor.Theparsertransforms a source file (e.g., SQL, Rmd, Python) that contains the view's metadata (e.g., view's owner, view's descriptions, and column's descriptions) and the view's code into a specific Viewflow data structure. The data structure is used by the other components in the Viewflow architecture: the adapter and the dependency creator.Theadapteris the translation layer of Viewflow's views to their corresponding Airflow counterpart. It uses the data structure objects created by the parser to create an Airflow task object (i.e., an Airflow operator).Finally, thedependency extractoruses the parser's data structure objects to set the internal and external dependencies to the Airflow task object created by the adapter.This architecture allows us to add new source file types in the future easily (e.g., Python notebook, R markdown).AcknowledgmentsToday's version of Viewflow is the result of a joint effort of ex and current DataCampers. We would like to thank in particular the following persons who significantly contributed to Viewflow:David RobinsonAnthony BakerMichael Chow