Scalable Event Data Storage for ML Event-Driven Architectures with AWS Elasticsearch

Question

Pulumi · Accepted Answer

To accomplish scalable event data storage for machine learning (ML) in an event-driven architecture using AWS Elasticsearch, you'll need to set up a few AWS services that work together:

1. **Amazon Kinesis**: This service will collect and process large streams of data records in real time. You can configure data producers to continually add data to a Kinesis data stream.

2. **AWS Elasticsearch Service**: Elasticsearch is a popular open-source search and analytics engine for log and event data analysis. AWS offers it as a managed service, simplifying operations like provisioning, patching, and backups.

3. **AWS Lambda**: This enables you to process the data in real time as it arrives in Kinesis. After processing, you can send the data to Elasticsearch for storage and indexing.

4. **Amazon CloudWatch**: This is used to monitor your entire stack, provide insights, and ensure performance health.

This program outlines setting up these AWS services with Pulumi. The Elasticsearch cluster is configured for scalability with considerations like the number of instances and the type of instances. Kinesis is set up to stream the data, and Lambda functions are deployed to process it and send it to Elasticsearch. CloudWatch is used to monitor the logs.

Here is a high-level Pulumi Python program to set up such an architecture:

```python
import pulumi
import pulumi_aws as aws

# Define the Kinesis Stream for ingesting event data
kinesis_stream = aws.kinesis.Stream("eventStream",
    shard_count=1, # Adjust the shard count according to throughput requirements
    retention_period=24, # In hours; can be increased as needed
)

# Define the Elasticsearch domain
es_domain = aws.elasticsearch.Domain("esDomain",
    domain_name="events",
    cluster_config=aws.elasticsearch.DomainClusterConfigArgs(# Adjust these configs based on requirements
        instance_type="r5.large.elasticsearch",
        instance_count=2,
    ),
    ebs_options=aws.elasticsearch.DomainEbsOptionsArgs(
        ebs_enabled=True,
        volume_size=10, # In GB; adjust as needed
    ),
    elasticsearch_version="7.1", # Specify the Elasticsearch version
    snapshot_options=aws.elasticsearch.DomainSnapshotOptionsArgs(
        automated_snapshot_start_hour=23, # 23:00 UTC
    ),
    tags={
        "Name": "MLEventStorage",
    }
)

# Define IAM role for Lambda to interact with Kinesis and Elasticsearch
lambda_exec_role = aws.iam.Role("lambdaExecRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{"Action": "sts:AssumeRole",
                       "Principal": {"Service": "lambda.amazonaws.com"},
                       "Effect": "Allow"}]
    }"""
)

# Attach policies to the Lambda role
aws.iam.RolePolicyAttachment("lambdaKinesisAttachment",
    role=lambda_exec_role.name,
    policy_arn="arn:aws:iam::aws:policy/AmazonKinesisFullAccess",
)

aws.iam.RolePolicyAttachment("lambdaLogsAttachment",
    role=lambda_exec_role.name,
    policy_arn="arn:aws:iam::aws:policy/CloudWatchLogsFullAccess",
)

aws.iam.RolePolicyAttachment("lambdaESEntryAttachment",
    role=lambda_exec_role.name,
    policy_arn="arn:aws:iam::aws:policy/AmazonESFullAccess",
)

# Define the Lambda function to process Kinesis events and send to Elasticsearch
lambda_function = aws.lambda_.Function("dataProcessor",
    handler="index.handler",
    role=lambda_exec_role.arn,
    runtime="python3.8",
    environment=aws.lambda_.FunctionEnvironmentArgs(
        variables={
            "ES_ENDPOINT": es_domain.endpoint,
        }
    ),
    code=pulumi.FileArchive("./lambda.zip"),
    timeout=300, # Maximum time for Lambda to process an event
)

# Link the Lambda function with the Kinesis stream
kinesis_event_source_mapping = aws.lambda_.EventSourceMapping("kinesisMapping",
    event_source_arn=kinesis_stream.arn,
    function_name=lambda_function.name,
    starting_position="LATEST",
)

# CloudWatch log group for the Elasticsearch domain
log_group = aws.cloudwatch.LogGroup("esLogGroup",
    name=f"/aws/aes/domains/{es_domain.domain_name}/logs"
)

# Export important resource names and URLs for easy access
pulumi.export("Kinesis Stream ARN", kinesis_stream.arn)
pulumi.export("Elasticsearch Domain Endpoint", es_domain.endpoint)
pulumi.export("Lambda Function ARN", lambda_function.arn)
pulumi.export("CloudWatch Log Group Name", log_group.name)
```

This program will set up a stack for event-driven machine learning architecture with Elasticsearch as the event data store.

**Key Points of the Program**:

- We first create a Kinesis stream designed to ingest a large amount of data in real time.
- A scalable Elasticsearch domain is set up with shard and instance configurations tailored to the expected workload.
- We then define an IAM role with the necessary policies for a Lambda function to access Kinesis and Elasticsearch.
- The Lambda function processes incoming Kinesis events and forwards them to the Elasticsearch domain.
- A mapping is defined to link the Kinesis stream to be processed by the Lambda function.
- Finally, a CloudWatch Log Group is created to store logs generated by the AWS Elasticsearch domain.

This program provides a robust starting point for an ML event-driven architecture; however, further modifications might be needed based on specific use case parameters such as data volume, processing complexity, and specific ML model requirements.