Data Engineering Pipelines play a vital role in managing the flow of business data for companies. Organizations spend a significant amount of money for developing and managing Data Pipelines, so they can get streamlined data to accomplish their daily activities with ease. Many tools in the market help companies manage their messaging and task distributions for scalability.
Apache Airflow is a workflow management platform that helps companies orchestrate their Data Pipeline tasks and save time. As data and the number of backend tasks grow, the need to scale up and make resources available for all the tasks becomes a necessity. Airflow Celery Executor makes it easier for developers to build a scalable application by distributing the tasks on multiple machines. With the help of Airflow Celery, companies can send multiple messages for execution without any lag.
Apache Airflow Celery Executor is easy to use as it uses Python scripts and DAGs for scheduling, monitoring, and executing tasks. In this article, you will learn about Airflow Celery Executor and how it helps in scaling out and easily distributing the tasks. Also, you will read about the architecture of the Airflow Celery Executor and how its process to execute the tasks.
Table of Contents
- Introduction to Apache Airflow
- Introduction to the Airflow Celery Executor
- Airflow Celery Executor Setup
- Airflow Celery Executor Architecture
- Task Execution Process of Airflow Celery
- A brief overview of Apache Airflow.
Introduction to Apache Airflow
Apache Airflow is an open-source workflow management platform to programmatically author, schedule, and monitor workflows. In Airflow, workflows are the DAGs (Directed Acyclic Graphs) of tasks and are widely used to schedule and manage the Data Pipelines. Airflow is written in Python, and workflows are created in Python scripts. Airflow using Python allows Developers to make use of libraries in creating workflows.
The airflow workflow engine executes the complex Data Pipeline jobs with ease and ensures that each task executes on time in the correct order, and makes sure every task gets the required resources. Airflow can run DAGs on the defined schedule and based on the external event triggers.
Key Features of Apache Airflow
Some of the main features of Apache Airflow are listed below.
- Open Source: Airflow is free to use and has a lot of active users and an interactive community. Its resources are easily available over the web.
- Robust Integrations: Airflow comes with ready-to-use operators and many plug-and-play integrations that offer users to run tasks on Google Cloud Platform, Amazon AWS, Microsoft Azure, etc.
- Easy to Use: Airflow is written in Python which means, users can create workflows using Python scripts and import libraries that make the job easier.
- Interactive UI: Airflow also offers a web application that allows users to monitor the real-time status of tasks running and DAGs. Users can schedule and manage their workflows with ease.
To know more about Apache Airflow, click here.
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Introduction to the Airflow Celery Executor
Airflow Celery is a task queue that helps users scale and integrate with other languages. It comes with the tools and support you need to run such a system in production.
Executors in Airflow are the mechanism by which users can run the task instances. Airflow comes with various executors, but the most widely used among those is Airflow Celery Executor used for scaling out by distributing the workload on multiple Celery workers that can run on different machines.
CeleryExecutor works with some workers it has to distribute the tasks with the help of messages. The deployment scheduler adds a message to the queue and the Airflow Celery broker delivers that message to the Airflow Celerty worker to execute the task. If due to any failure the assigned Airflow Celery worker on the job goes down then Airflow Celery quickly adapts to it and assigns the task to another worker.
Airflow Celery Executor Setup
To set up the Airflow Celery Executor, first, you need to set up an Airflow Celery backend using the message broker services such as RabbitMQ, Redis, etc. After that, you need to change the airflow.cfg file to point the executor parameters to CeleryExecutor and enter all the required configurations for it.
The prerequisites for setting up the Airflow Celery Executor Setup are listed below:
- Airflow installed on the local machine and properly configured.
- Airflow configuration should be homogeneous across the cluster.
- Before executing any Operators, the workers need to have their dependencies met in that context by importing the Python library.
- The workers should have access to the DAGs directory – DAGS_FOLDER.
To start the Airflow Celery worker, use the following command given below.
airflow celery worker
As the service is started and ready to receive tasks. As soon as a task is triggered in its direction, it will start its job.
For stopping the worker, use the following command given below.
airflow celery stop
You can also use its GUI web application that allows users to monitor the workers. To install the flower Python library, use the following command given below in the terminal.
pip install ‘apache-airflow[celery]’
You can start the Flower web server by using the command given below.
airflow celery flower
Airflow Celery Executor Architecture
The architecture of the Airflow Celery Executor consists of several components, listed below:
- Workers: Its job is to execute the assigned tasks by Airflow Celery.
- Scheduler: It is responsible for adding the necessary tasks to the queue.
- Database: It contains all the information related to the status of tasks, DAGs, Variables, connections, etc.
- Web Server: The HTTP server provides access to information related to DAG and task status.
- Celery: Queue mechanism.
- Broker: This component of the Celery queue stores commands for execution.
- Result Backend: It stores the status of all completed commands.
Now that you have understood the different components of the architecture of Airflow Celery Executor. Now, let’s understand how these components communicate with each other during the execution of any task.
- Web server –> Workers: It fetches all the task execution logs.
- Web server –> DAG files: It reveals the DAG structure.
- Web server –> Database: It fetches the status of the tasks.
- Workers –> DAG files: It reveals the DAG structure and executes the tasks.
- Workers –> Database: Workers get and stores information about connection configuration, variables, and XCOM.
- Workers –> Celery’s result backend: It saves the status of tasks.
- Workers –> Celery’s broker: It stores all the commands for execution.
- Scheduler –> DAG files: It reveals the DAG structure and executes the tasks.
- Scheduler –> Database: It stores information on DAG run and related tasks.
- Scheduler –> Celery’s result backend: It gets the information about the status of completed tasks.
- Scheduler –> Celery’s broker: It puts the commands to be executed.
Task Execution Process of Airflow Celery
In this section, you will learn how the tasks are executed by Airflow Celery Executor. Initially when the task is begin to execute, mainly two processes are running at that time.
- SchedulerProcess: This processes the tasks and runs with the help of Airflow CeleryExecutor.
- WorkerProcess: It overserves that is waiting for new tasks to append in the queue. It also has WorkerChildProcess that waits for new tasks.
Also, The two Databases are involved in the process while executing the task:
Along with the process, the 2 other processes are created. The processes are listed below:
- RawTaskProcess: It is the process with user code.
- LocalTaskJobProcess: The logic for this process is described by the LocalTaskJob. Its job is to monitor the status of RawTaskProcess. All the new processes are started from TaskRunner.
The process of how tasks are executed using the above
process and how the data and instructions flow is given below.
- SchedulerProcess processes the tasks and when it gets a task that needs to be executed, then it sends it to the QueueBroker to add the task in the queue.
- Simultaneously the QueueBroker starts querying the ResultBackend for the status of the task. And when the QueueBroker gets acquainted with the then it sends the information about it to one WorkerProcess.
- When WorkerProcess receives the information, it assigns a single task to one WorkerChildProcess.
- After receiving a task, WorkerChildProcess executes the handling function for the task i.e. execute_command(). This creates the new process LocalTaskJobProcess.
- LocalTaskJobProcess describes the logic for LocalTaskJobProcess. A new process is started using the TaskRunner.
- Both the process RawTaskProcess and LocalTaskJobProcess are stopped when they finish their work.
- Then, WorkerChildProcess notifies this information about completion of task and availability of subsequent tasks to the main process WorkerProcess.
- WorkerProcess then saves the status information back into the ResultBackend.
- Now, whenever SchedulerProcess requests for the status from ResultBackend, it receives the status information of the task.
In this article, you learnt about Apache Airflow, Airflow Celery Executor, and how it helps companies achieve scalability. Also, you read about the architecture of Airflow Celery Executor and its task execution process. Airflow Celery uses message brokers to receive tasks and distributes those on multiple machines for higher performance.Visit our Website to Explore Hevo
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