Code Optimization in Java: How to Minimize Latency

In software development, code optimization plays a crucial role in improving performance, especially in latency-sensitive applications. A well-optimized code/algorithm can mean the difference between a sluggish application and a lightning-fast one. Here I ‘ll show you an instance of code optimization in Java

Recently, while solving a problem that involved merging two strings in alternating order, I encountered an interesting performance challenge. My initial solution had a latency of 2ms, but after applying key optimizations, I achieved 0ms execution time. Here, I’ll break down the optimizations that made this possible and explain why they matter for high-performance Java applications.

The Problem: Merging Two Strings Alternately

The task was simple:

1. Given two strings, word1 and word2, merging them by alternating their characters.
2. If one string is longer than the other, append the remaining characters at the end.

Initial Solution

Here was my first straightforward approach using StringBuilder:

class Solution {
public String mergeAlternately(String word1, String word2) {
StringBuilder result = new StringBuilder();
int i = 0;
while (i < word1.length() || i < word2.length()) {
if (i < word1.length()) {
result.append(word1.charAt(i));
}
if (i < word2.length()) {
result.append(word2.charAt(i));
}
i++;
}
return result.toString();
}
}

This simpli works

Performance: ~2ms

While this worked, I wondered: Can I make it even faster?

Optimized Solution

After analyzing the bottlenecks, I restructured the solution using a pre-allocated char array instead of StringBuilder:

class Solution {
public String mergeAlternately(String word1, String word2) {
int m = word1.length();
int n = word2.length();
char[] result = new char[m + n];
int i = 0, j = 0, k = 0;
while (i < m || j < n) {
if (i < m) {
result[k++] = word1.charAt(i++);
}
if (j < n) {
result[k++] = word2.charAt(j++);
}
}
return new String(result);
}
}

Performance: 0ms

What changed you may ask.

By replacing StringBuilder with a pre-allocated char array, I eliminated dynamic resizing overhead and reduced latency from 2ms to 0ms.

• No Dynamic Resizing: Unlike StringBuilder, the char[] doesn’t need to grow.
• Reduced Method Calls: Avoiding append() minimizes stack operations.
• Better Cache Locality: Arrays have contiguous memory access, improving CPU cache efficiency.
• JVM Optimizations: The JIT compiler optimizes array operations aggressively.

Best Practices for Code Optimization in Java

✔ Pre-allocate memory where possible.
✔ Reduce method calls in loops.
✔ Leverage primitives for speed.

This small program demonstrates how small, thoughtful changes can lead to significant performance gains. While both solutions produce the same output, the optimized version highlights crucial principles every developer should remember: understanding underlying data structures, minimizing overhead, and working with the JVM rather than against it. The difference between 2ms and 0ms might seem trivial in isolation, but applied across a large-scale system, these micro-optimizations compound into measurable improvements in throughput, scalability, and user experience and eventually the bottom line. Nonetheless, we must balance readability with performance. Not every line of code needs this level of tuning, but recognizing when and how to optimize separates functional code from exceptional engineering.

“Good development isn’t just about making it work, it’s about making it blazing fast.” – Ranchoddas Shamaldas Chanchad 😛