Start Load Testing Azure PostgreSQL Flexible Server with Read-Only Replica Using Azure Load Testing in Minutes
Ensuring the performance and scalability of your database infrastructure is paramount, especially when dealing with read-heavy workloads. The GitHub repository TechPreacher/azure_loadtest_terraform provides a comprehensive solution to automate the deployment and load testing of an Azure Database for PostgreSQL Flexible Server configured with a read-only replica.
Overview of the Solution
This project leverages Terraform and scripts (bash & powershell) to provision the necessary Azure resources and includes a Python application for database management. The key components deployed are:
- Azure Load Testing Resource: Facilitates the execution of load tests using Apache JMeter.
- All settings of the test are parametrized to allow configuring all aspects of the test without having to re-deploy anything.
- Azure Key Vault: Securely stores sensitive information such as user credentials and JMeter script parameters.
- Azure Database for PostgreSQL Flexible Server with Replica: Sets up the primary database along with a read-only replica to distribute read operations.
- User Assigned Managed Identity: Enables secure access to the Key Vault from the load testing resource.
The load test utilizes an Apache JMeter script to simulate database operations, with all necessary configurations and secrets managed through Azure Key Vault. The load test will write to the main database and read from the replica - a typical scenario in high-performance solutions where monitoring is done off the replica to ease the load on the main database.
Prerequisites
Before deploying this solution, ensure you have the following tools installed:
- Python 3.11 or higher
- Terraform
- Azure CLI
Deployment Steps
- Clone the Repository:
git clone https://github.com/TechPreacher/azure_loadtest_terraform.git
cd azure_loadtest_terraform
- Configure Azure Credentials: Ensure you are logged in to Azure CLI, select the right subscription and have the necessary permissions to create resources.
- Initialize, Plan and Apply Terraform Configuration:
make init
make plan
make apply
This will provision all the required Azure resources as defined in the Terraform scripts.
- Prepare, provision and load test data in the test tables in the PostgreSQL database:
# Initialize the virtual environment
poetry install
poetry shell
# Initialize and populate the database
python create_database/database_setup.py
# Verify replication between primary and replica databases
python create_database/verify_replication.py
# Start the Streamlit web application to edit data (optional)
streamlit run create_database/streamlit_app.py
- Run the provided script to configure the load test (something Terraform unforunatly can't do at the moment):
# Navigate to the Terraform directory
cd terraform
# Run the setup script
./setup_load_test.sh
or
# Navigate to the Terraform directory
cd terraform
# Run the setup script
.\Setup-LoadTest.ps1
- Run the Load Test:After deployment, you can initiate the load test using the Azure Load Testing resource. The test will execute the JMeter script against the PostgreSQL database, utilizing the read-only replica for read operations.
Benefits of Load Testing with Read-Only Replicas
Implementing load testing in this manner offers several advantages:
- Performance Validation: Assess how your database handles concurrent read operations, ensuring it meets performance expectations.
- Scalability Assessment: Determine the effectiveness of read-only replicas in distributing the load and improving response times.
- Cost Efficiency: Identify potential bottlenecks and optimize resource allocation, potentially reducing operational costs.
- Reliability: Ensure that your database setup can handle peak loads without compromising data integrity or availability.
Conclusion
The azure_loadtest_terraform project offers a streamlined approach to deploying and testing an Azure PostgreSQL Flexible Server with a read-only replica. By automating the setup and leveraging Azure Load Testing, you can gain valuable insights into your database’s performance and make informed decisions to enhance scalability and reliability.
For more details and to access the source code, visit the GitHub repository.