1. Profile before optimizing - Use performance profiling tools to identify bottlenecks rather than guessing. 2. Cache expensive operations - Implement caching for database queries, API calls, and complex calculations. 3. Database optimization - Add proper indexes, optimize queries, and implement connection pooling. 4. Lazy loading - Load resources only when needed rather than upfront. 5. Code isolation - Encapsulate legacy components behind clean interfaces to limit their impact. 6. Parallel processing - Identify tasks that can run concurrently and implement parallelization. 7. Memory management - Fix memory leaks and reduce excessive object creation. 8. External services - Offload intensive operations to specialized services.

Optimizing legacy code is like improving a busy restaurant’s workflow like you don’t rebuild the kitchen; you fix inefficiencies. By identifying bottlenecks, like tasks being repeated unnecessarily, and introducing solutions like caching (storing commonly used data for quick access), we can significantly improve performance. Small adjustments, like reorganizing workflows or fine-tuning critical areas, deliver noticeable results without disrupting operations. This approach ensures smoother performance while keeping the system stable and efficient.

You change the underlying framework so that the old code only needs small modifications to become fast. Let new memory allocations be aligned and padded by default, because replacing something arbitrary with something specified can not break compatibility. Replace pointers with reference counted handles or bound checked pointers. Replace scalar types with portable SIMD vectors, loop over multiples of elements, and let the framework generate intrinsic assembler instructions. Then you identify the slowest parts such as histograms, quaternions and integer divisions and rewrite the worst bits.

for optimising sql queries we can have a look over custom execution plans. modern framework versions provide functions that can reduce the code size significantly,ex: promise.allsettled in express etc can reduce nested callbacks completely.

Optimizing legacy code without rewriting it requires strategic enhancements. For e.g, while working on a financial data pipeline, legacy SQL queries were causing performance bottlenecks. Instead of a full rewrite, I identified inefficiencies using query profiling tools and optimized indexes, reducing execution time by 35%. Adding caching layers for frequently accessed data cut repetitive processing. I implemented parallel processing with tools like Apache Spark to handle large datasets, significantly boosting throughput. Monitoring tools, like CloudWatch, helped track performance metrics in real-time. These targeted improvements preserved the core functionality while dramatically enhancing performance without the cost of a complete overhaul