Iterating Collections – The Power of Simplicity

“Machine-Near” Java: Iteration Done Right

Even after 25 years in Java, one lesson remains: simplicity often outperforms abstraction. Iterating over collections is a perfect example. Streams are elegant but creating them repeatedly incurs measurable overhead.

Benchmark (1,000 elements, repeated 100,000 times):

For loop: ~25–40 ms total
Stream: ~800–1,200 ms total

Example

Instead of:

list.stream().mapToInt(Integer::intValue).sum();

Use:

int sum = 0;
for (int i = 0; i < list.size(); i++)
{
sum += list.get(i);
}

Why this matters

• Minimal object creation, predictable memory usage
• JVM optimizations fully apply
• Easier to debug and maintain

Streams Make Sense When

• You need functional composition across multiple transformations
• Processing large or parallelizable datasets
• Code readability and maintainability outweigh micro-performance concerns

Cloud Considerations

When running in cloud-based Lambda functions, resource consumption matters even more. Each extra object, temporary allocation, or pipeline setup can increase cold-start times, memory footprint, and billing. Lean loops reduce this overhead, improving efficiency and cost-effectiveness.

Takeaway

For repeated or performance-critical iterations, a machine-near for loop is faster, lighter, and keeps code clean—ideal for both local and serverless/cloud environments.

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