1. High-performance Online Feature Store with AWS DAX.


    To build a high-performance online feature store with Amazon DynamoDB Accelerator (DAX), you'll first set up a DynamoDB table to store your feature data. DynamoDB provides a scalable and fast NoSQL database service, which is ideal for handling feature data that may require frequent and fast read and write operations.

    However, when you require microsecond response times for your DynamoDB queries, that's where DAX comes in. DAX is an in-memory cache for DynamoDB, designed for applications needing extremely fast response times. It's compatible with DynamoDB API calls, making it a drop-in acceleration layer for your existing DynamoDB applications.

    In short, you'll define your data schema and provision a DynamoDB table, then set up a DAX cluster to provide caching for that table.

    I'll guide you through creating both resources using Pulumi, starting with the DynamoDB table and then the DAX cluster. Here's how you could define both in a Pulumi program using Python:

    import pulumi import pulumi_aws as aws # Define a new DynamoDB table for your feature store. # For throughput, DynamoDB's on-demand capacity mode provides flexible billing options that align with your application's needs. dynamodb_table = aws.dynamodb.Table("feature-store-table", attributes=[ aws.dynamodb.TableAttributeArgs( name="id", type="S", ), ], billing_mode="PAY_PER_REQUEST", hash_key="id", tags={ "Environment": "production", "Purpose": "FeatureStore", } ) # With your DynamoDB table defined, the next step is to set up a DAX cluster to accelerate read performance. # This config assumes you have a suitable IAM role and subnet group configured for your DAX cluster. dax_cluster = aws.dax.Cluster("feature-store-dax-cluster", node_type="dax.r4.large", replication_factor=1, # For production use cases, you may want a higher replication factor. iam_role_arn="arn:aws:iam::<ACCOUNT_ID>:role/DaxAccessRole", # Replace <ACCOUNT_ID> with your AWS account ID. subnet_group_name="my-dax-subnet-group", security_group_ids=["sg-12345678"], # Replace with your security group ID. tags={ "Environment": "production", "Purpose": "FeatureStoreCache", } ) # Export the DynamoDB table name and DAX cluster endpoint for easy access. pulumi.export("dynamodb_table_name", dynamodb_table.name) pulumi.export("dax_cluster_endpoint", dax_cluster.cluster_address)

    In this program:

    • aws.dynamodb.Table creates a new DynamoDB table for the feature store with a single attribute id as the hash key. The table uses the pay-per-request billing mode, which is suitable for unpredictable workloads.
    • aws.dax.Cluster creates a DAX cluster that acts as an in-memory cache for the DynamoDB table to achieve faster read operations.
    • The replication_factor in the DAX cluster is set to 1 for the example, but in a production environment, you would want a higher replication factor for high availability.
    • The iam_role_arn is a placeholder and should be replaced with the actual IAM role ARN that grants DAX permission to your DynamoDB table.
    • The security_group_ids should be replaced with the security group ID that is appropriate for your VPC network configuration.
    • We export the names of both the DynamoDB table and the DAX cluster endpoint, which will be required when configuring your application to use these resources.

    Make sure to replace placeholders like <ACCOUNT_ID> with actual values from your AWS environment. Before running this program, ensure that you have a DAX-compatible subnet group and IAM role configured in AWS.

    Keep in mind that while a DAX cluster can significantly speed up the read performance of your DynamoDB table, it incurs additional costs, and you should size your cluster according to your application's performance and throughput needs.