Knative Event-Driven Predictive Analytics

Question

Pulumi · Accepted Answer

Event-driven predictive analytics is a complex scenario that involves several components in a cloud environment. With Pulumi, you can provision and manage these components using infrastructure as code, which allows you to define resources in a programming language and create, update, or delete them in a coordinated way.

To achieve an event-driven predictive analytics setup with Knative, we would typically require a Kubernetes cluster where Knative is installed, along with an event source (such as Kafka for streaming events), a serverless function to process events (such as a Python function running on Knative), and perhaps a storage or database system to store the results of your predictive analysis.

However, from the provided Pulumi Registry Results, there's no direct resource for Knative. Instead, we will set up a simple event processing system on AWS using the available AWS resources for event streaming and processing, since there's no direct match for Knative in the Pulumi AWS provider.

Here's how we will set up the infrastructure for event-driven predictive analytics:

1. AWS Kinesis for streaming event data.
2. AWS Lambda functions for reacting to and processing event data.
3. AWS Kinesis Analytics to run SQL queries on streaming data for predictive analysis.
4. AWS S3 for storage of event data and processing results.

The Pulumi program I will write will:

- Create an AWS Kinesis stream to ingest real-time event data.
- Provision a Lambda function to process these events.
- Set up an AWS Kinesis Analytics Application to perform predictive analytics on the ingested event stream.
- Optionally, define an S3 bucket to store the results.

Please ensure you have the AWS provider configured with the necessary access rights to create these resources.

Here's the program that you can use to set up this event-driven predictive analytics infrastructure:

```python
import pulumi
import pulumi_aws as aws

# Create an AWS Kinesis stream for real-time event data ingestion.
kinesis_stream = aws.kinesis.Stream("eventStream",
    shard_count=1  # Define the number of shards for the stream.
)

# Define the IAM role that will be used by AWS Lambda and Kinesis Analytics.
iam_role = aws.iam.Role("analyticsLambdaRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": ["lambda.amazonaws.com", "kinesisanalytics.amazonaws.com"]
            }
        }]
    }"""
)

# Attach a policy that allows the role to access the resources it needs.
iam_policy = aws.iam.RolePolicy("analyticsLambdaPolicy",
    role=iam_role.id,
    policy=kinesis_stream.arn.apply(lambda arn: f"""{{
        "Version": "2012-10-17",
        "Statement": [
            {{"Action": "kinesis:*", "Effect": "Allow", "Resource": "{arn}" }},
            {{"Action": "lambda:*", "Effect": "Allow", "Resource": "*"}}
        ]
    }}"""
    )
)

# Create a Lambda function that will be triggered by Kinesis events.
lambda_function = aws.lambda_.Function("predictiveAnalyticsProcessor",
    code=pulumi.AssetArchive({'index.py': pulumi.FileAsset('index.py')}),  # Replace 'index.py' with path to your Lambda code.
    role=iam_role.arn,
    handler="index.handler",  # Replace 'index.handler' with the appropriate handler.
    runtime="python3.8",  # Use the runtime compatible with your Lambda code.
    event_source_mappings=[aws.lambda_.EventSourceMappingArgs(
        event_source_arn=kinesis_stream.arn,
        starting_position="LATEST"
    )]
)

# Set up the AWS Kinesis Analytics Application to perform predictive analytics.
analytics_app = aws.kinesis.AnalyticsApplication("predictiveAnalyticsApp",
    inputs=[{
        'namePrefix': 'source',
        'kinesisStream': {
            'resourceArn': kinesis_stream.arn,
            'roleArn': iam_role.arn
        }
    }],
    code="""CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
        "EVENT_TIME" TIMESTAMP,
        "EVENT_VALUE" DOUBLE);
        -- Replace the SQL below with your predictive analytics SQL code.
        CREATE OR REPLACE PUMP "STREAM_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM"
        SELECT STREAM "EVENT_TIME", "EVENT_VALUE" FROM "SOURCE_SQL_STREAM_001";
    """
)

# Export the stream name and Lambda ARN which can be used to track the stack's resources.
pulumi.export('stream_name', kinesis_stream.name)
pulumi.export('lambda_arn', lambda_function.arn)
```

In this program:

- "*kinesis_stream*" is our event stream source that collects data in real-time.
- "*iam_role*" and "*iam_policy*" are created to grant necessary permissions to Lambda and Kinesis Analytics to operate on data streams.
- "*lambda_function*" is triggered by events in the Kinesis stream, enabling you to run custom processing or filtering on the event data.
- "*analytics_app*" is the Kinesis Analytics Application where you would write your SQL code to process and analyze the streaming data in real-time.

Make sure to replace `'index.py'` with the path to your Lambda function code and `'index.handler'` with the function within your code that handles events. Also, replace the SQL placeholder comment with your actual real-time predictive analytics SQL code.

This is a basic illustration and starting point, and you would need to enhance this system to suit your particular predictive analytics needs, including more complex stream processing, error handling, and long-term storage.