Event Sourcing for AI Application States with Scheduled Backups

Question

Pulumi · Accepted Answer

Event Sourcing is a design pattern in which all changes to application state are stored as a sequence of events. Instead of storing just the current state, the application maintains the sequence of actions taken, which provides a history of these states over time. This can be particularly useful in AI applications where you may want to trace decisions or model training progresses.

To implement event sourcing with scheduled backups in the context of a cloud infrastructure, you can use managed services that handle data storage, event streams, and scheduled backups. One possible approach is to use a combination of cloud resources such as a database for storing events, a message queue or stream for processing events, and a backup service to periodically take snapshots of your event data.

Here's how you can set up these components in Pulumi using AWS as the cloud provider:

- **Amazon DynamoDB**: A high-performance NoSQL database service for all events generated by your application.
- **Amazon Kinesis**: A scalable and durable real-time data streaming service which can reliably process and move data at scale.
- **AWS Lambda**: To process the data and events in real-time.
- **AWS Backup**: A fully managed backup service to protect your data across AWS services.

Below is a basic Pulumi program in Python that sets up a DynamoDB table to handle event sourcing, a Kinesis stream to manage the flow of these events, an AWS Lambda function for event processing, and an AWS Backup plan to schedule backups.

```python
import pulumi
import pulumi_aws as aws

# This creates a new DynamoDB table to store events. Events will be identified by an ID.
dynamo_db_table = aws.dynamodb.Table("eventSourcingTable",
    attributes=[aws.dynamodb.TableAttributeArgs(
        name="id",
        type="S",
    )],
    hash_key="id",
    read_capacity=1,
    write_capacity=1)

# This provision an Amazon Kinesis stream that can ingest events produced by the application.
kinesis_stream = aws.kinesis.Stream("eventStream",
    shard_count=1)

# A simple AWS Lambda function that gets triggered by new records
# in the Kinesis stream. For illustration, it just logs the new records.
lambda_function = aws.lambda_.Function("processor",
    runtime="python3.8",
    code=pulumi.AssetArchive({
        '.': pulumi.FileArchive('./processor'),  # The directory './processor' should contain your Lambda code and any dependencies
    }),
    handler="processor.handler",               # 'processor' is the module name and 'handler' is the function
    role=lambda_role.arn,                      # Assign the IAM role created above to the Lambda
    timeout=300,
    # Trigger the Lambda for new records in the Kinesis stream
    event_source_mappings=[aws.lambda_.EventSourceMappingArgs(
        event_source_arn=kinesis_stream.arn,
        starting_position="LATEST"
    )]
)

# AWS Backup plan to automatically take backups of the DynamoDB table.
backup_plan = aws.backup.Plan("scheduledBackupPlan",
    rules=[aws.backup.PlanRuleArgs(
        rule_name="daily",
        target_vault_name=vault.name,
        schedule="cron(0 12 * * ? *)",  # Scheduled to run at 12:00 every day
        lifecycle=aws.backup.PlanRuleLifecycleArgs(
            delete_after=90,  # Deletes backups after 90 days
        ),
        recovery_point_tags={
            "Application": "event-sourcing"
        }
    )])

# Creation of the Backup Vault
vault = aws.backup.Vault("backupVault")

# Pulumi stack outputs
pulumi.export('dynamodb_table_name', dynamo_db_table.name)
pulumi.export('kinesis_stream_name', kinesis_stream.name)
pulumi.export('lambda_function_name', lambda_function.name)
pulumi.export('backup_plan_id', backup_plan.id)
```

Explanation of program components:

- **DynamoDB Table**: We create an Amazon DynamoDB table with a string type hash key `id`. This is the primary key for the table and each entry is uniquely identified by this ID.

- **Kinesis Stream**: An Amazon Kinesis stream is created to handle the flow of events. The stream has one shard, which can be scaled up depending on the throughput required.

- **AWS Lambda**: A simple AWS Lambda function is defined with Python 3.8 runtime. The lambda function processes records from the Kinesis stream and acts upon them. The code and dependencies for the lambda function should be located in the './processor' directory.

- **AWS Backup Plan**: An AWS Backup plan is created to schedule periodic backups of the DynamoDB table. For demonstration purposes, we run the backup daily at 12 noon. The lifecycle policy setting ensures that backups are deleted after 90 days.

- **Backup Vault**: A Backup Vault is where the snapshots taken by AWS Backup are stored. The vault should be created before defining the backup plan as it needs the vault's name for the target.

- **Stack Outputs**: The program exports several stack outputs, such as the names of the created DynamoDB table, Kinesis Stream, Lambda function, and the ID of the backup plan, that can be used to reference these resources later.

The provided Pulumi program is a starting point and you might want to customize the Lambda function processing logic, adjust the backup plan schedule, or DynamoDB table throughput settings based on your specific use case and requirements.