Memory management

Prevents unnecessary memory usage by allocating resources only when they are actually required.

Embedded systems: limited RAM makes upfront allocation of large buffers risky High‑throughput servers: bursty traffic where per‑request buffers should be created on demand Data processing pipelines: reading massive files where allocating the whole buffer upfront would exceed available memory

Systems programming, real‑time firmware, high‑performance network services, data streaming applications

detect need → allocate resource → use resource

• lazy initialization
• on‑demand allocation
• just‑in‑time allocation
• memory pool

• eager allocation – allocates immediately but may waste memory
• preallocation – reserves a fixed size up front
• reducing fragmentation but limiting flexibility
• object pooling – reuses objects to avoid repeated allocations
• at the cost of added management complexity

Allocating after first use without checking for null – leads to runtime crashes Assuming allocation will always succeed – can cause unhandled out‑of‑memory errors under load Delaying allocation too far – introduces latency spikes when the allocation finally occurs Forgetting to free the lazily allocated resource – results in memory leaks

Eager allocation – allocates immediately regardless of actual need Preallocation – reserves a fixed amount up front, trading flexibility for predictability Object pooling – reuses previously allocated objects instead of allocating anew

Coming from Python: assume objects are created automatically on first use — in low‑level languages you must explicitly allocate, and omitting the allocation leads to undefined behavior.

• lazy initialization
• object pooling
• memory arena allocation
• just-in-time allocation

Do not use delayed allocation in hard real‑time code where any allocation latency can violate timing guarantees It reduces peak memory footprint but may add occasional allocation latency, affecting latency‑sensitive paths Thread‑safety must be considered; allocating lazily in a multithreaded context requires proper synchronization to avoid race conditions

Reduces overall memory consumption and improves scalability, while requiring careful handling of allocation latency and thread safety.

Delayed allocation is like ordering a pizza only when you’re hungry, instead of pre‑ordering a whole week’s worth of meals.

In languages with deterministic destructors (e.g., C++), delayed allocation must be paired with explicit deallocation to avoid leaks.

Implement a custom memory pool or arena allocator for high‑frequency delayed allocations.