Architecting Efficient AWS Data Stores: A Guide to DynamoDB and DAX for Product APIs

Use Data Stores In Application Development | 🏗️ Build A Product Catalog API

The AWS Certified Developer - Associate exam requires mastery of Domain 1, Task 3 regarding data store selection and optimization. This technical implementation demonstrates how to build a Product Catalog API utilizing DynamoDB single-table design and DAX caching.

Why This Matters

In high-scale application development, the choice between Query and Scan operations directly impacts both latency and cost. While a Scan reads every item in a table and filters results afterward, a Query targeted at a well-designed Global Secondary Index (GSI) ensures that only relevant data is processed, maintaining sub-millisecond performance as datasets grow to millions of items.

Key Insights

• Scan operations consume Read Capacity Units (RCUs) for every item in the table, regardless of FilterExpressions applied, as demonstrated by the 40% efficiency rate in filtered scans.
• Global Secondary Indexes (GSIs) can be created at any time and support separate throughput, whereas Local Secondary Indexes (LSIs) must be defined at table creation and share the base table’s throughput.
• DynamoDB Accelerator (DAX) provides microsecond latency for read-heavy workloads but is restricted to eventually consistent reads and requires a VPC deployment.
• Strongly consistent reads consume double the Read Capacity Units (2x) compared to eventually consistent reads and are unavailable on Global Secondary Indexes.
• DynamoDB TTL (Time to Live) offers a no-cost method for automatic data expiration, though deletions may be delayed by up to 48 hours after the Unix epoch timestamp is reached.

Working Examples

Lambda handler for retrieving product metadata using the primary key and a Decimal encoder for DynamoDB types.

import json\nimport boto3\nfrom decimal import Decimal\nfrom boto3.dynamodb.conditions import Key, Attr\n\ndynamodb = boto3.resource('dynamodb')\ntable = dynamodb.Table('ProductCatalog')\n\nclass DecimalEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, Decimal):\n            return float(obj)\n        return super().default(obj)\n\ndef get_product(product_id):\n    result = table.get_item(\n        Key={\n            'PK': f'PRODUCT#{product_id}',\n            'SK': 'METADATA'\n        }\n    )\n    item = result.get('Item')\n    return item

Example of an overloaded key structure for a single-table design supporting category-based GSI queries.

{\n  \"PK\": {\"S\": \"PRODUCT#laptop-001\"},\n  \"SK\": {\"S\": \"METADATA\"},\n  \"GSI1PK\": {\"S\": \"CATEGORY#electronics\"},\n  \"GSI1SK\": {\"S\": \"PRICE#00999.99\"},\n  \"name\": {\"S\": \"Pro Laptop 15\"},\n  \"price\": {\"N\": \"999.99\"},\n  \"status\": {\"S\": \"active\"}\n}

Practical Applications

• Inventory Management: Implementing strongly consistent reads for stock checks to prevent overselling items during high-traffic events.
• Session Management: Using DynamoDB TTL to automatically purge user session data after 24 hours without incurring manual deletion costs.
• Search Optimization: Replacing expensive Scans with Global Secondary Indexes for category-specific price filtering, resulting in a 100% efficiency rating.
• High-Performance Read Caching: Integrating DAX for frequently accessed product metadata to reduce latency from single-digit milliseconds to microseconds.

References:

• https://dev.to/aws-builders/use-data-stores-in-application-development-build-a-product-catalog-api-404f