![]() CeleryExecutor: Distributes tasks across a cluster of workers.LocalExecutor: Runs tasks in parallel on the same machine.SequentialExecutor: Runs one task at a time, useful for development.TaskFlow API: Simplifies task definition using Python functions.Sensors: Wait for certain conditions before proceeding.Operators: Define the individual tasks to be executed.Metadata Database: Stores state and metadata for DAGs, tasks, and more.Īirflow can be configured to run Apache Spark jobs through the SparkSubmitOperator, enabling data engineers to create workflows that include Spark tasks alongside other tasks.Webserver: Provides UI for DAG inspection and management.Executor: Responsible for task execution, can be scaled out with workers.Scheduler: Triggers workflows, manages task scheduling, and retries.Here's an in-depth look at the core components and concepts: Architecture An Airflow workflow is represented as a Directed Acyclic Graph (DAG), which consists of a sequence of tasks with defined dependencies. Related documentationĪpache Airflow is a platform designed for orchestrating complex computational workflows and data processing pipelines. Ensure that your Spark jobs are idempotent for better retry and failure handling.īy following these guidelines, you can effectively manage and orchestrate your Spark jobs using Apache Airflow.Use Airflow variables and connections to manage environment-specific configurations.Monitor your Spark jobs through the Airflow UI and access logs for troubleshooting. ![]() Leverage the SparkSubmitOperator to submit Spark jobs directly from Airflow: from .operators.spark_submit import SparkSubmitOperatorĪpplication='/path/to/your/spark_job.py', Set the connection type to Spark and fill in the necessary details such as host, port, and extra parameters.Navigate to Admin > Connections and add a new connection.Use the Airflow UI to create a new connection: Customize your airflow.cfg to include Spark-specific settings under the section.Define Spark-related environment variables in your Airflow environment.To configure Airflow for Spark, follow these steps: Prerequisitesīefore proceeding, ensure that both Apache Airflow and Apache Spark are installed and properly configured on your system. This section provides a comprehensive guide on setting up Airflow to manage Spark jobs, ensuring a seamless workflow. Integrating Apache Airflow with Apache Spark enables robust scheduling and orchestration of Spark jobs. from .operators.spark_sql import SparkSqlOperatorįor more detailed information, refer to the official Apache Spark documentation on submitting applications and running Spark SQL CLI. This operator runs SQL queries on Spark's Hive metastore service. ![]() from .operators.spark_jdbc import SparkJDBCOperator This operator facilitates data transfer between Spark and JDBC databases. ![]() from .operators.spark_submit import SparkSubmitOperator It supports various cluster managers and deploy modes. This operator is used to submit Spark applications using the spark-submit script. The SparkSqlOperator for executing Spark SQL queries.The SparkJDBCOperator for database interactions via JDBC.The SparkSubmitOperator for submitting Spark applications.A configured Spark Connection in Airflow.Here's how to set up and use this powerful combination: Prerequisites This integration allows for the scheduling and monitoring of Spark jobs directly from Airflow's web interface. Apache Airflow can be integrated with Apache Spark to orchestrate complex data processing workflows.
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