1. Real-time Analysis of AI Application Logs with Honeycomb


    To create a real-time analysis pipeline for AI application logs using Honeycomb, we'll use AWS as the cloud provider due to its comprehensive set of services that facilitate log collection, streaming, and analysis. Here's the high-level approach we'll take:

    1. CloudWatch Logs: We'll collect logs from your AI application and store them in AWS CloudWatch Logs.
    2. Kinesis Data Firehose: Stream the logs from CloudWatch to Kinesis Data Firehose to transform and load the data.
    3. Lambda Function: A Lambda function will transform the log data into a format compatible with Honeycomb.
    4. Honeycomb: The transformed data will then be sent to Honeycomb for analysis.

    Please note that Honeycomb is a third-party service and is not part of the AWS services used here. You would generally use something like AWS Lambda to send the data to Honeycomb, but you'll need to configure your Honeycomb account and get your write key to be able to post data to it.

    Here is a Pulumi program in Python that sets up the infrastructure for the logging pipeline:

    import pulumi import pulumi_aws as aws import json # Define the IAM role for Lambda to allow it to send logs to Honeycomb and access Kinesis Streams. lambda_role = aws.iam.Role("lambdaRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] })) # Attach policies to the role created above. aws.iam.RolePolicyAttachment("lambda-policy", policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaKinesisExecutionRole", role=lambda_role.name) aws.iam.RolePolicyAttachment("lambda-honeycomb-policy", policy_arn="arn:aws:iam::aws:policy/CustomPolicyForHoneycomb", role=lambda_role.name) # Create a Lambda function that will process and transform logs before sending to Honeycomb. lambda_function = aws.lambda_.Function("lambdaFunction", runtime="python3.8", role=lambda_role.arn, handler="index.handler", code=pulumi.FileArchive("./lambda") # Your Lambda function code should be in the 'lambda' directory ) # Create an AWS Kinesis Firehose Delivery Stream, attach Lambda for log transformation firehose = aws.kinesis.FirehoseDeliveryStream("firehose", destinations=[aws.kinesis.FirehoseDeliveryStreamDestinationArgs( lambda_aws.kinesis.FirehoseDeliveryStreamDestinationLambdaArgs( function_arn=lambda_function.arn ) )] ) # Set up a CloudWatch Log Group for application logs. log_group = aws.cloudwatch.LogGroup("logGroup") # Stream logs from CloudWatch to Kinesis Firehose log_stream = aws.cloudwatch.LogStream("logStream", log_group_name=log_group.name) cloudwatch_log_subscription_filter = aws.cloudwatch.LogSubscriptionFilter("cloudwatchLogSubscriptionFilter", log_group_name=log_group.name, destination_arn=firehose.arn, filter_pattern="" # Define a filter pattern for your log data ) # Output the name of the log group so we can configure our application to send logs there. pulumi.export('log_group_name', log_group.name)

    Detailed Explanation

    • We define an IAM role for AWS Lambda with the necessary permissions to access Kinesis Streams and send logs to Honeycomb.
    • We attach AWS-managed and custom policies to the IAM role. The custom policy for Honeycomb should allow Lambda to post data to your Honeycomb dataset.
    • We create a Lambda function using Python 3.8 runtime, passing the role ARN we created and pointing to the directory that contains our Lambda function code.
    • A Kinesis Firehose delivery stream is set up to collect logs. It's also integrated with the Lambda function that is responsible for processing and transforming log data before sending it to Honeycomb.
    • We create a CloudWatch Log Group to collect your AI application logs. This will be the central repository of your logs in AWS.
    • A CloudWatch Log Stream is created within the Log Group. This streams the collected logs for processing.
    • The CloudWatch Log Subscription Filter connects the Log Group to the Kinesis Firehose delivery stream. It uses the filter pattern to select log data to stream.
    • Lastly, we export the name of the Log Group. This way, you can easily configure your AI applications to direct logs to this CloudWatch Log Group.

    To work with this code, you'd have to:

    1. Ensure your Pulumi CLI is set up.
    2. Configure AWS credentials on your system.
    3. Replace ./lambda with the actual path to your Lambda function code directory.
    4. Create the IAM policy CustomPolicyForHoneycomb with the appropriate permissions (not shown in the code above).
    5. Define a filter pattern in filter_pattern to precisely select the logs you're interested in streaming.

    Remember to replace placeholders with your actual information where necessary, such as Honeycomb dataset and write key, and the AWS region if different from your Pulumi setup.