Java Streams: When Functional Programming Hurts Performance

Streams are beautiful but slow. Each intermediate operation allocates stream object. filter() then map() then reduce() = 3 stream objects allocated.

Benchmark: Direct loop 100%, Stream 300% (3x slower).

Why slow: intermediate stream object allocation, lambda allocation, no loop unrolling, JIT can't inline across boundaries, boxing with boxed streams.

Solution 1: Use primitive streams (IntStream not Stream<Integer>). Solution 2: Avoid intermediate operations (filter().map().forEach() = bad pattern). Solution 3: Terminal operation only. Solution 4: Parallel only if >1000 elements AND each operation takes >1 microsecond.

When streams OK: I/O naturally async, chaining many transforms for clarity, building new collections, non-critical paths.

When loops better: hot paths (>1000/sec), latency-critical code, game engines, trading systems, real-time processing.

Production checklist: Profile before using streams, use loops for hot paths, IntStream if boxing possible, avoid intermediate ops.

Summary: Streams allocate 3-4x more than loops. Use loops for hot paths. Use streams for clarity elsewhere.

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