Databricks Airflow

 

Cron is great for automation, but when tasks begin to rely on each other (task C can only run after both tasks A and B finish) cron does not do the trick.

Azure

Apache Airflow is open source software (from airbnb) designed to handle the relationship between tasks. I recently setup an airflow server which coordinates automated jobs on databricks (great software for coordinating spark clusters). Connecting databricks and airflow ended up being a little trickier than it should have been, so I am writing this blog post as a resource to anyone else who attempts to do the same in the future.

Databricksconnid (str) – The name of the Airflow connection to use. By default and in the common case this will be databricksdefault. To use token based authentication, provide the key token in the extra field for the connection. Pollingperiodseconds (int) –. Databricks also has a decent tutorial on setting up airflow. The difficulty here is that the airflow software for talking to databricks clusters (DatabricksSubmitRunOperator) was not introduced into airflow until version 1.9 and the A-R-G-O tutorial uses airflow 1.8. Airflow 1.9 uses Celery version = 4.0 (I ended up using Celery version 4.1.1). This repo contains an Astronomer project with multiple example DAGs showing how to use Airflow to orchestrate Databricks jobs. A guide discussing the DAGs and concepts in depth can be found here. Tutorial Overview. This tutorial has one DAGs showing how to use the following Databricks Operators: DatabricksRunNowOperator.

Nowadays, data engineers dread putting their workflows in production. Apache Airflow provides the necessary scheduling primitives but writing the glue scripts, handling Airflow operators and workflow dependencies sucks! Low-Code Development & Deployment can make scheduling Spark workflows much simpler – we’ll show you how. In recent years, Apache Airflow (a Python-based task orchestrator developed at Airbnb) has gained popularity as a collaborative platform between data scientists and infrastructure engineers looking to spare their users from verbose and rigid YAML files.

For the most part I followed this tutorial from A-R-G-O when setting up airflow. Databricks also has a decent tutorial on setting up airflow. The difficulty here is that the airflow software for talking to databricks clusters (DatabricksSubmitRunOperator) was not introduced into airflow until version 1.9 and the A-R-G-O tutorial uses airflow 1.8.

Airflow 1.9 uses Celery version >= 4.0 (I ended up using Celery version 4.1.1). Airflow 1.8 requires Celery < 4.0. In fact, the A-R-G-O tutorial notes that using Celery >= 4.0 will result in the error:

Databricks Airflow

I can attest that this is true! If you use airflow 1.9 with Celery < 4.0, everything might appear to work, but airflow will randomly stop scheduling jobs after awhile (check the airflow-scheduler logs if you run into this). You need to use Celery >= 4.0! Preventing the Wrong destination error is easy, but the fix is hard to find (hence why I wrote this post).

After much ado, here’s the fix! If you follow the A-R-G-O tutorial, install airflow 1.9, celery >=4.0 AND set broker_url in airflow.cfg as follows:

Note that compared to the A-R-G-O tutorial, I am just adding “py” in front of amqp. Easy!

Latest version

Released:

Provider package apache-airflow-providers-databricks for Apache Airflow

Project description

Package apache-airflow-providers-databricks

Release: 1.0.1

Provider package

Databricks Airflow

This is a provider package for databricks provider. All classes for this provider packageare in airflow.providers.databricks python package.

You can find package information and changelog for the providerin the documentation.

Installation

NOTE!

On November 2020, new version of PIP (20.3) has been released with a new, 2020 resolver. This resolverdoes not yet work with Apache Airflow and might lead to errors in installation - depends on your choiceof extras. In order to install Airflow you need to either downgrade pip to version 20.2.4pip install --upgradepip20.2.4 or, in case you use Pip 20.3, you need to add option--use-deprecatedlegacy-resolver to your pip install command.

Databricks Pipeline

You can install this package on top of an existing airflow 2.* installation viapip install apache-airflow-providers-databricks

PIP requirements

PIP packageVersion required
requests>=2.20.0, <3

Release historyRelease notifications RSS feed

1.0.1

1.0.1rc1 pre-release

1.0.0

Airflow Databricks Github

1.0.0rc1 pre-release

1.0.0b2 pre-release

1.0.0b1 pre-release

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