Systems

/Systems_

Design patterns, distributed systems, and service architecture. The data-modeling, API, and scaling decisions you make under the ML hood.

Software design

• Multi-Dimensional Aggregation. Feature store rollups without reaching for pandas.
• Unbiased Random Permutation. Fisher-Yates for fair sampling in ML pipelines.
• Memory Allocation and Fragmentation. malloc/free patterns behind GPU caching allocators.
• Single-Pass Map-Side Aggregation. Lightweight ETL with dictionary accumulation.
• Grid Traversal Strategies. Greedy, BFS, DFS compared.
• Session Window Feature Engineering. Sessionization for recommendation and churn models.
• Sequential Access Optimization (SCAN). Disk scheduling for data loader performance.
• Composable Unit Expression Parser. Recursive parsing for mixed-unit feature engineering.

Microservices and APIs

• Hierarchical Tree Flattening. Tree-building from flat parent pointers.
• Adjacency List vs Nested Set. Hierarchy storage for cheap reads or cheap writes. You pick.
• Ingest-Transform-Serve Pipeline. Scheduled ingestion to a REST API.
• Queue-Based Stage Decoupling. High-throughput message processing architecture.
• Graph Traversal with Command Pattern. Separating the data graph from traversal strategy.
• Fair Allocation with Priority. Constrained distribution with guaranteed minimums.