Managing Bursty Traffic for AI Chatbots with RDS Proxy

Question

Pulumi · Accepted Answer

When creating an AI chatbot, especially one that may experience bursty traffic patterns, it's important to design your infrastructure in a way that can handle sudden spikes in traffic without compromising performance or availability. One of the ways you can achieve this on AWS is by using Amazon RDS Proxy.

Amazon RDS Proxy is a fully managed, highly available database proxy for Amazon RDS that makes applications more scalable, more resilient to database failures, and more secure. It sits between your application and your relational database to efficiently manage connections to the database and improve scalability of the application.

Here's why you would use RDS Proxy for AI Chatbots managing bursty traffic:
- **Connection Pooling**: RDS Proxy allows you to pool and share connections established with the database, which can reduce the time and memory overhead needed to establish new connections.
- **Handling Traffic Spikes**: By pooling connections, RDS Proxy can help your chatbot handle a large number of concurrent database connections during traffic spikes without overloading the database.
- **Improved Failover**: In the event of a database failover, RDS Proxy automatically connects to the new database instance without any application changes, reducing downtime.

Let's implement a Pulumi program in Python that provisions an RDS Proxy to be used with a chatbot application.

First, we will create an RDS instance that our Proxy will manage connections for. Then, we will set up the RDS Proxy and target it at our database. We'll also configure the security groups to control the traffic to our database and proxy.

```python
import pulumi
import pulumi_aws as aws

# Create a new security group for the RDS database
vpc_security_group = aws.ec2.SecurityGroup('db-security-group',
    description='Allow all inbound traffic',
    ingress=[
        {
            'description': 'Allow all inbound traffic',
            'from_port': 0,
            'to_port': 0,
            'protocol': '-1',
            'cidr_blocks': ['0.0.0.0/0'],
        }
    ]
)

# Create an RDS instance that we will connect via Proxy
rds_instance = aws.rds.Instance('chatbot-db',
    instance_class='db.t2.small',
    allocated_storage=20,
    # Choose a believable password and username in real setup
    username='chatbot_user',
    password='chatbot_password',
    db_name='chatbotdb',
    vpc_security_group_ids=[vpc_security_group.id],
    skip_final_snapshot=True,
)

# Create the RDS Proxy
rds_proxy = aws.rds.Proxy('chatbot-rds-proxy',
    debug_logging=False,
    engine_family='MYSQL',  # Assuming the Chatbot uses MySQL
    idle_client_timeout=1800,
    require_tls=True,
    role_arn="arn:aws:iam::123456789012:role/rds-proxy-role",  # Replace with the actual IAM role
    vpc_security_group_ids=[vpc_security_group.id],
    vpc_subnet_ids=["subnet-12345678", "subnet-87654321"],  # Replace with actual VPC Subnet IDs
    auths=[{
        'auth_scheme': 'SECRETS',
        'description': 'example',
        'iam_auth': 'DISABLED',
        'secret_arn': f'arn:aws:secretsmanager:us-west-2:123456789012:secret:rds-db-credentials/cluster-ABC12345678/dbuser',  # Replace with the actual secret ARN
    }]
)

# Create a target group
proxy_default_target_group = aws.rds.ProxyDefaultTargetGroup('default-target-group',
    db_proxy_name=rds_proxy.name,
    connection_pool_config={
        'max_connections_percent': 90,
        'max_idle_connections_percent': 10,
    }
)

# Associate the RDS instance with the target group
proxy_target = aws.rds.ProxyTarget('db-proxy-target',
    db_proxy_name=rds_proxy.name,
    target_group_name=proxy_default_target_group.name,
    db_instance_identifier=rds_instance.id,
)

# Export the RDS Proxy endpoint to be used by the application
pulumi.export('rds_proxy_endpoint', rds_proxy.endpoint)
```

In this program, we are setting up an RDS Proxy `chatbot-rds-proxy` which is configured to use connection pooling with the Max connections percentage at 90%, and Max idle connections percentage at 10%. We have used a dummy secret ARN and IAM role ARN which you would replace with your actual AWS Secrets Manager and IAM role ARNs.

We've associated our RDS instance with the target group of our proxy using the `ProxyTarget` resource. By exporting the RDS Proxy endpoint, we're able to connect our application to this proxy endpoint which ensures that all connections go through RDS Proxy and benefit from the improved management and scalability it provides.