Database Scaling
Breaking PostgreSQL at Scale
Key PostgreSQL Scaling Insights
- Connection Pooling: Critical for high-concurrency applications
- Query Optimization: Index strategy becomes crucial at scale
- Partitioning: Table partitioning for managing large datasets
- Replication: Read replicas and failover strategies
- Monitoring: Essential metrics for identifying bottlenecks
Algorithm Learning
LeetCode Problem Patterns
- GitHub - SeanPrashad/leetcode-patterns
- Curated list of LeetCode questions grouped by algorithmic patterns
- Organized approach to learning problem-solving techniques
- Helps identify common patterns across different problems
Pattern Categories
- Two Pointers: Array and string manipulation problems
- Sliding Window: Substring and subarray problems
- Dynamic Programming: Optimization and counting problems
- Tree Traversal: Binary tree and graph problems
- Backtracking: Combinatorial and constraint satisfaction problems
Key Takeaways
- Database Scaling: Understanding bottlenecks before they become critical
- Pattern Recognition: Algorithmic problem-solving benefits from pattern
identification
- Systematic Learning: Organized approaches to complex topics improve
retention
- Production Ready: Both database optimization and algorithmic thinking are
essential for real-world applications
These resources provide both practical database management skills and
fundamental algorithmic problem-solving techniques - essential for building
scalable systems.
