Scalable Log Analytics for AI Applications with AWS MSK

Question

Pulumi · Accepted Answer

To create a scalable log analytics solution for AI applications using AWS MSK (Managed Streaming for Kafka), you'll typically need the following resources:

1. **AWS MSK ServerlessCluster**: This provides a fully managed Apache Kafka service without the need to manage the underlying Kafka infrastructure and is designed to scale as your application scales.
2. **Kinesis Data Analytics Application**: To process the streaming data in real-time. Kinesis Data Analytics can run SQL queries against incoming streams and can be used for complex analytics, including machine learning inference.
3. **AWS Lambda**: For running code in response to triggers such as changes in data or system state.
4. **Amazon S3**: As a place to store and retrieve any type of data.
5. **Amazon CloudWatch**: For monitoring and logging to gain system-wide visibility into resource utilization, application performance, and operational health.

Here's how you can provision these resources with Pulumi in Python:

```python
import pulumi
import pulumi_aws as aws

# Define the VPC for your MSK Serverless Cluster. 
# Typically, you would create or reference an existing VPC. Here's how you could declare a new one:
vpc = aws.ec2.Vpc("ai_log_analytics_vpc",
    cidr_block="10.0.0.0/16",
    tags={
        "Name": "ai_log_analytics_vpc",
    })

# Create an Amazon MSK Serverless Cluster:
# Documentation: https://www.pulumi.com/registry/packages/aws/api-docs/msk/serverlesscluster/
msk_cluster = aws.msk.ServerlessCluster("ai_log_analytics_msk_cluster",
    cluster_name="ai-log-analytics-cluster",
    vpc_configs=[aws.msk.ServerlessClusterVpcConfigArgs(
        subnet_ids=vpc.public_subnets.apply(lambda subnets: [s.id for s in subnets]),
        security_group_ids=[vpc.default_security_group.id],
    )],
    client_authentication=aws.msk.ServerlessClusterClientAuthenticationArgs(
        sasl=aws.msk.ServerlessClusterClientAuthenticationSaslArgs(
            iam=aws.msk.ServerlessClusterClientAuthenticationSaslIamArgs(
                enabled=True,
            )
        )
    ),
    tags={
        "Environment": "production",
        "Project": "AI Log Analytics",
    })

# Create a Kinesis Data Analytics Application for real-time processing:
# Documentation: https://www.pulumi.com/registry/packages/aws/api-docs/kinesisanalyticsv2/application/
analytics_app = aws.kinesisanalyticsv2.Application("ai_log_analytics_app",
    name="ai-log-analytics-app",
    runtime_environment="FLINK-1_13", # Define the runtime environment (FLINK for real-time stream processing)
    service_execution_role=aws.iam.Role("analytics_exec_role", # Role that Kinesis Data Analytics can assume to access other AWS services
        assume_role_policy="""{
            "Version": "2012-10-17",
            "Statement": [{
                "Action": "sts:AssumeRole",
                "Principal": {"Service": "kinesisanalytics.amazonaws.com"},
                "Effect": "Allow",
                "Sid": ""
            }]
        }""").arn,
    application_configuration=aws.kinesisanalyticsv2.ApplicationApplicationConfigurationArgs(
        application_code_configuration=aws.kinesisanalyticsv2.ApplicationApplicationConfigurationApplicationCodeConfigurationArgs(
            code_content=aws.kinesisanalyticsv2.ApplicationApplicationConfigurationApplicationCodeConfigurationCodeContentArgs(
                text_content= """
                // Your Flink code or SQL query goes here.
                // This defines the application's logic for processing the incoming streaming data.
                """),
            code_content_type="PLAINTEXT", # Specify the content type of the code
        ),
        flink_application_configuration=aws.kinesisanalyticsv2.ApplicationApplicationConfigurationFlinkApplicationConfigurationArgs(
            checkpoint_configuration=aws.kinesisanalyticsv2.ApplicationApplicationConfigurationFlinkApplicationConfigurationCheckpointConfigurationArgs(
                checkpointing_enabled=True,
                checkpoint_interval=60000, # Time interval between checkpoints
                min_pause_between_checkpoints=5000, # Minimum pause between checkpoints
            ),
            parallelism_configuration=aws.kinesisanalyticsv2.ApplicationApplicationConfigurationFlinkApplicationConfigurationParallelismConfigurationArgs(
                parallelism=1, # Start parallelism
                auto_scaling_enabled=True, # Enable auto scaling
            ),
        ),
        # Other configurations such as VPC, monitoring, etc. can be added here
    ))

# Export the relevant outputs:
pulumi.export("msk_cluster_arn", msk_cluster.arn)
pulumi.export("analytics_app_arn", analytics_app.application_arn)
```

This program creates an AWS MSK ServerlessCluster, which scales on-demand and a Kinesis Data Analytics Application to process streaming data in real-time using Apache Flink.

MSK ServerlessCluster is ideal for log analytics as it allows for scalable, high-throughput data ingestion necessary for collecting logs. The Kafka cluster can grow and shrink automatically with your data production, ensuring that you only pay for the capacity you use.

The Kinesis Data Analytics Application is provisioned with Flink runtime for stream processing and can execute complex queries, including those for AI applications like machine learning inferences on the log data in real-time.

Both the MSK cluster and the Kinesis application are tagged appropriately for identification and billing purposes within AWS. The IAM role `analytics_exec_role` is required to allow the Kinesis Data Analytics application to assume the necessary permissions to interact with other AWS services.

Finally, the ARNs (Amazon Resource Names) for both the MSK cluster and the Kinesis application are exported. These can be used to integrate with other AWS services or Pulumi stacks.