1. Real-time Prediction Outputs Caching with DynamoDB


    To set up real-time prediction outputs caching with DynamoDB, you would typically create a DynamoDB table to store the prediction outputs. This table can act as a cache, allowing you to quickly retrieve predictions without having to recompute them if they've already been calculated.

    Here's how you can achieve this with Pulumi and AWS:

    1. DynamoDB Table: You'll define a DynamoDB table to store your prediction outputs. The primary key for the table can be a unique identifier for each prediction, like a prediction ID.
    2. Table Attributes and Types: Each attribute in DynamoDB has a name and a type. Common types include strings (S), numbers (N), and binary (B). Depending on the nature of your predictions, you may set these accordingly.
    3. Provisioned Throughput: This specifies the read and write throughput for the table. AWS DynamoDB allows you to specify provisioned throughput to manage performance and costs; however, with on-demand mode, AWS manages the provisioning of capacity to meet your workloads.

    Let's write a Pulumi program in Python to create a DynamoDB table for caching prediction outputs. We'll use two key attributes for simplicity: predictionId as the partition key and timestamp as the sort key. This allows you to look up predictions quickly by ID and sort or query by the time they were generated.

    Before running the following Pulumi program, make sure you have the Pulumi CLI installed, and your AWS credentials configured.

    import pulumi import pulumi_aws as aws # Define a DynamoDB table for caching prediction outputs prediction_cache_table = aws.dynamodb.Table("predictionCacheTable", attributes=[ # Primary key for the predictions, a unique identifier for each prediction aws.dynamodb.TableAttributeArgs( name="predictionId", type="S" ), # Timestamp for each prediction - can be used for sorting or TTL aws.dynamodb.TableAttributeArgs( name="timestamp", type="N" ) ], billing_mode="PAY_PER_REQUEST", # Utilize on-demand capacity mode hash_key="predictionId", # Partition key range_key="timestamp", # Sort key ttl=aws.dynamodb.TableTtlArgs( enabled=True, attribute_name="timestamp" # Set the TTL to use the 'timestamp' attribute ), tags={ "Environment": "Production", "Purpose": "CachePredictionOutputs" } ) # Export the name of the table pulumi.export("prediction_cache_table_name", prediction_cache_table.name)

    This program creates a DynamoDB table with the specified attributes and configuration:

    • attributes: Defines the schema for the DynamoDB table.
    • billing_mode: Set to "PAY_PER_REQUEST" which indicates that on-demand capacity mode is used instead of manually provisioned capacity.
    • hash_key: The primary key that uniquely identifies each item.
    • range_key: A secondary key that is used to sort items with the same primary key.
    • ttl: A Time-To-Live setting which allows for the automatic expiration of items based on a timestamp.
    • tags: Metadata that provides additional information about the table.

    With this table, you can insert items where predictionId is your unique identifier and timestamp could be the current Unix timestamp. When you query the table, you can retrieve cached predictions by predictionId and ensure stale data is automatically removed based on the TTL.

    To deploy this infrastructure, you would run pulumi up in your CLI, which would prompt Pulumi to provision the resources as per the code. Once created, the output will display the name of the DynamoDB table.

    Remember, you should be aware of the AWS pricing model for DynamoDB, especially for on-demand capacity, to manage costs effectively.