To start it up, run airflow webserver and connect to localhost:8080. We can also visualize the DAG in the web UI. args = INFO - Filling up the DagBag from /Users/andrew/airflow/dags The first step is to set some default arguments which will be applied to each task in our DAG. In reality, there are some other details we need to fill in to get a working DAG file. Spark_jar_task = DatabricksSubmitRunOperator(
#Airflow api code#
A skeleton version of the code looks something like this: notebook_task = DatabricksSubmitRunOperator( The first Databricks job will trigger a notebook located at /Users/ /PrepareData, and the second will run a jar located at dbfs:/lib/etl-0.1.jar.įrom a mile high view, the script DAG essentially constructs two DatabricksSubmitRunOperator tasks and then sets the dependency at the end with the set_dowstream method. In the next step, we’ll write a DAG that runs two Databricks jobs with one linear dependency. The SQLite database and default configuration for your Airflow deployment will be initialized in ~/airflow. To perform the initialization run: airflow initdb In a production Airflow deployment, you’ll want to edit the configuration to point Airflow to a MySQL or Postgres database but for our toy example, we’ll simply use the default sqlite database. Airflow will use it to track miscellaneous metadata. The first thing we will do is initialize the sqlite database. In this tutorial, we’ll set up a toy Airflow 1.8.1 deployment which runs on your local machine and also deploy an example DAG which triggers runs in Databricks.
#Airflow api install#
pip install -upgrade "git+git:///databricks/ #egg=apache-airflow"
#Airflow api Patch#
Until then, to use this operator you can install Databricks’ fork of Airflow, which is essentially Airflow version 1.8.1 with our DatabricksSubmitRunOperator patch applied. However, the integrations will not be cut into a release branch until Airflow 1.9.0 is released.
We’ve contributed the DatabricksSubmitRunOperator upstream to the open-source Airflow project. When it completes successfully, the operator will return allowing for downstream tasks to run. After making the initial request to submit the run, the operator will continue to poll for the result of the run. Through this operator, we can hit the Databricks Runs Submit API endpoint, which can externally trigger a single run of a jar, python script, or notebook. We implemented an Airflow operator called DatabricksSubmitRunOperator, enabling a smoother integration between Airflow and Databricks. Since they are simply Python scripts, operators in Airflow can perform many tasks: they can poll for some precondition to be true (also called a sensor) before succeeding, perform ETL directly, or trigger external systems like Databricks.įor more information on Airflow, please take a look at their documentation. The tasks in Airflow are instances of “operator” class and are implemented as small Python scripts. Task D will then be triggered when task B and C both complete successfully. For example, in the example, DAG below, task B and C will only be triggered after task A completes successfully. Dependencies are encoded into the DAG by its edges - for any given edge, the downstream task is only scheduled if the upstream task completed successfully. Besides its ability to schedule periodic jobs, Airflow lets you express explicit dependencies between different stages in your data pipeline.Įach ETL pipeline is represented as a directed acyclic graph (DAG) of tasks (not to be mistaken with Spark’s own DAG scheduler and tasks). Airflow BasicsĪirflow is a generic workflow scheduler with dependency management. We are happy to share that we have also extended Airflow to support Databricks out of the box. Of these, one of the most common schedulers used by our customers is Airflow. To support these complex use cases, we provide REST APIs so jobs based on notebooks and libraries can be triggered by external systems. While this feature unifies the workflow from exploratory data science to production data engineering, some data engineering jobs can contain complex dependencies that are difficult to capture in notebooks. One very popular feature of Databricks’ Unified Data Analytics Platform (UAP) is the ability to convert a data science notebook directly into production jobs that can be run regularly. This blog post illustrates how you can set up Airflow and use it to trigger Databricks jobs. Today, we are excited to announce native Databricks integration in Apache Airflow, a popular open source workflow scheduler.
#Airflow api series#
This blog post is part of our series of internal engineering blogs on Databricks platform, infrastructure management, integration, tooling, monitoring, and provisioning.
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