Workflow orchestration: Google Cloud Composer
Google Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. It allows you to create, schedule, monitor, and manage workflows across your cloud environment. Cloud Composer is designed to be easy to use, scalable, and flexible, integrating seamlessly with many services across Google Cloud Platform (GCP) and external systems.
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Key Features of Cloud Composer
Fully Managed: Cloud Composer abstracts away the underlying infrastructure. Google handles the setup, maintenance, and scaling of the resources required to run your Airflow environment.
Integrated: Seamlessly integrates with services across GCP like BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub, and more, enabling you to orchestrate complex workflows across your data pipelines.
Scalable: Easily scale your environment up or down based on your workflow needs.
Secure: Integrates with Identity and Access Management (IAM) for access control and allows you to define custom IAM roles.
Environment and Configuration
Environment Setup: When you create a Cloud Composer environment, it sets up an instance of Apache Airflow in a Google-managed Kubernetes cluster. You specify the region, machine type, disk size, and Airflow configuration parameters.
Configuration: Cloud Composer environments are highly configurable. You can customize Airflow configurations, Python and OS-level packages, and the Airflow web server’s appearance.
Setting Up Google Cloud Composer
For Google Cloud Composer, it's recommended to employ a unique service account with administrative privileges.
Through the Google Cloud Console:
Utilize the search bar to locate and select Composer.
Authorize the activation of the necessary API.
Proceed to create a new Composer environment.
Configure the environment's resources, including workload setup and core infrastructure, along with the network configuration, according to your requirements.
Using Terraform:
Obtain the initialization code for Google Cloud Composer from the Terraform documentation.
Customize the environment resources, including workload settings and core infrastructure, as well as the network configuration, to fit your needs.
The setup of Google Cloud Composer typically completes in around 20 minutes.
Google Cloud Composer Architecture
Projects in Cloud Composer
Customer Project: Your Google Cloud project where Composer environments are created. Multiple environments can coexist in one customer project.
Tenant Project: A separate, Google-managed project providing additional security and unified access control for each Composer environment.
Components of a Composer Environment
Environment Components: Elements of the managed Airflow infrastructure running in Google Cloud, located in either the tenant or customer project.
Cluster Management: Utilizes a VPC-native GKE cluster in Autopilot mode for managing environment resources, including VMs (nodes) and containers (pods).
Airflow Components: Schedulers, workers, and the Redis queue for task management, alongside Airflow's web server and database for UI and metadata storage.
Database: A Cloud SQL instance in the tenant project stores Airflow's metadata, with access limited to the environment's service account for security.
Storage Bucket: A Cloud Storage bucket in the customer project holds DAGs, plugins, logs, and data dependencies, syncing them across the environment.
Additional Components: Includes Cloud SQL Storage for database backups, Cloud SQL Proxy for database connectivity, monitoring, logging, Pub/Sub subscriptions, and more, all aimed at ensuring the smooth operation and scalability of the environment.
Important Considerations
Do Not Modify the Cluster: Direct modifications to the environment's cluster or database can disrupt your Cloud Composer environment. Managed by Google, these components are optimized for performance and security.
Automated Management Features: Cloud Composer includes node auto-upgrades and auto-repair to maintain security and reliability, with operations scheduled during specified maintenance windows.
Core Concepts
Apache Airflow: Cloud Composer is based on Apache Airflow, an open-source platform to programmatically author, schedule, and monitor workflows. Understanding Airflow concepts like DAGs (Directed Acyclic Graphs), operators, tasks, and the scheduler is essential for using Cloud Composer effectively.
DAGs (Directed Acyclic Graphs): Workflows in Airflow are defined as DAGs. A DAG is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies.
Operators: Operators determine what actually gets done by a task. Airflow offers multiple operator types like PythonOperator, BashOperator, and more, including the ability to create custom operators.
Tasks: A task is an instance of an operator; it represents a node in the DAG and defines a single unit of work.
Scheduler: The Airflow scheduler monitors all tasks and DAGs, then triggers the task instances whose dependencies have been met.
Simple DAG template
# Import necessary modules from the Airflow library
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime, timedelta
# Define the default arguments for the DAG
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2023, 1, 1), # Ensure this is in the past to avoid delays
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
}
# Define the DAG, its ID, and its scheduling interval (daily in this case)
dag = DAG(
'simple_hello_world_dag', # Unique identifier for the DAG
default_args=default_args,
description='A simple Hello World DAG',
schedule_interval=timedelta(days=1), # This DAG will run daily
)
# Define the Python function for the first task
def print_hello():
print("Hello")
# Define the Python function for the second task
def print_world():
print("World")
# Task 1: Use the PythonOperator to execute the print_hello function
task_1 = PythonOperator(
task_id='print_hello', # Unique identifier for this task
python_callable=print_hello, # Function to be executed by the task
dag=dag,
)
# Task 2: Use the PythonOperator to execute the print_world function
task_2 = PythonOperator(
task_id='print_world', # Unique identifier for this task
python_callable=print_world, # Function to be executed by the task
dag=dag,
)
# Define the task execution order
task_1 >> task_2 # task_2 will run after task_1Workflow Management
DAG Deployment: Workflows are deployed as DAGs. You can store your DAGs in Google Cloud Storage and manage them directly from the GCP console or use CI/CD pipelines for automated deployments.
Monitoring and Logging: Cloud Composer integrates with Stackdriver for monitoring and logging, providing visibility into your DAGs’ execution and performance.
Best Practices
Version Control: Keep your DAGs and plugins in a version control system. This helps in tracking changes and automating the deployment of DAGs to your Composer environment.
DAG Design: Keep your DAGs idempotent and deterministic. Ensure tasks are retryable and use sensors wisely to avoid unnecessary resource consumption.
Testing: Test your DAGs locally before deploying them to Cloud Composer. This helps catch issues early in the development cycle.
Resource Management: Monitor your Cloud Composer environment and optimize resources based on your needs. Use Airflow’s dynamic task generation capabilities to manage workflows efficiently.