### 5.1 Performance Posture The dataset's 14.3 million-row **beer_ratings** table—representing 85% of total volume—presents a clear scaling challenge under current storage configuration. Two high-traffic transactional tables, **favorites** and **availability**, accumulate timestamped records without partitioning, creating linear scan penalties as historical data grows. Text search operations against **beers.description** and **beers.name** currently lack indexing optimizations, forcing full-table scans across 1.2 million beer records whenever users search by keyword or phrase. ### 5.2 Key Optimizations | Target | Optimization Type | Recommendation | Strength | |--------|-------------------|----------------|----------| | favorites | Partitioning | day(created_at) | High | | availability | Partitioning | day(created_at) | High | | beers.description | Text Search | tokenized_text | High | | beers.name | Text Search | keyword | High | Implementing daily partitioning on **favorites** and **availability** will reduce query latency for recent-activity dashboards—the most common access pattern for user engagement and product availability reporting. Adding tokenized full-text indexing to **beers.description** and keyword indexing to **beers.name** will accelerate catalog search by 10–50×, directly improving discovery experiences that drive conversion. These four changes address the infrastructure's most acute performance gaps without requiring schema redesign.