How to maximize satisfied customers when owner is grumpy? (Sliding Window Java solution)

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Summary

The provided Java solution attempts to solve the “Maximize Satisfied Customers” problem using a sliding window technique. The problem asks to maximize customer satisfaction by choosing a contiguous period of minutes during which the owner stops being grumpy. The solution correctly identifies the base satisfaction (customers happy when the owner is naturally not grumpy) and seeks to maximize additional satisfaction gained by suppressing grumpiness in a window of size minutes.

However, the code contains inefficient logic and noisy comments that hinder readability and maintainability. Specifically, it performs unnecessary array accesses and checks inside the loop, and the variable naming is poor. The algorithmic intent is correct (Sliding Window), but the implementation details are unpolished.

Root Cause

The Root Cause of the inefficiency and lack of clarity in the provided code is the failure to decouple the state of the “grumpy” owner from the calculation of the “extra” satisfaction.

  1. Redundant State Checks: Inside the right loop, the code checks if(grumpy[right]==1) before adding to extra. While logically safe, a cleaner sliding window usually sums all values in a temporary array (or handles the condition differently) to avoid branching, or simply accepts that grumpy[i] == 0 contributes 0 if using a conditional sum (which is what the code does).
  2. Inefficient Window Exit Logic: The critical inefficiency lies in the if(right-left+1 > minutes) block. The code executes if(grumpy[left]==1){ extra-=customers[left]; } every time the window slides. In a scenario where the grumpy array is sparse (mostly 0s), this performs an unnecessary array access and comparison.
  3. Variable Naming: MaxExtraBoi is unprofessional and obscures the variable’s purpose.

Why This Happens in Real Systems

This pattern is common in production code written under time pressure or by developers focusing purely on passing unit tests rather than code quality.

  • “It Works, So Ship It”: The developer verified the logic manually against a few examples. Because the time complexity is theoretically O(N), they assumed the constant factors didn’t matter. However, constant factors (like unnecessary array lookups) add up in high-throughput systems.
  • Over-complication: Developers often try to handle edge cases explicitly (checking grumpy[left] == 1 inside the slide) rather than designing the algorithm to handle them implicitly. This leads to “spaghetti logic” where every line is a special condition.
  • Junior Habits: Leaving “debug” comments (e.g., “yo, this is that scenario yo”) in the code is a hallmark of inexperience. It suggests the code was written by trial and error rather than a rigorous top-down design.

Real-World Impact

In a production environment, this type of code leads to:

  • Reduced Readability: Future engineers (or the author in 6 months) will struggle to parse the logic due to the verbose comments and nested if statements. This increases the Maintenance Cost.
  • Sub-optimal Performance: While the Big O notation is O(N), the constant overhead of multiple conditional branches per iteration can degrade performance in tight loops or when N is large.
  • Brittleness: The code relies heavily on the specific structure of the sliding window loop. Adding new requirements (e.g., skipping weekends) would require rewriting the entire loop rather than plugging in a modular component.

Example or Code

Below is the corrected, “Senior Engineer” version of the code. It removes the noise, improves variable names, and handles the logic more cleanly.

class Solution {
    public int maxSatisfied(int[] customers, int[] grumpy, int minutes) {
        int n = customers.length;
        int baseSatisfaction = 0;

        // 1. Calculate base satisfaction (owner is naturally not grumpy)
        for (int i = 0; i < n; i++) {
            if (grumpy[i] == 0) {
                baseSatisfaction += customers[i];
            }
        }

        // 2. Calculate the maximum extra satisfaction from a window of size 'minutes'
        // We simulate the window over a "potential" array where we ignore the grumpy constraint
        // for the sake of the sliding window sum.
        int currentWindowSum = 0;
        int maxWindowSum = 0;

        // Initialize the first window
        for (int i = 0; i < minutes; i++) {
            currentWindowSum += customers[i];
        }
        maxWindowSum = currentWindowSum;

        // Slide the window
        for (int right = minutes; right  maxWindowSum) {
                maxWindowSum = currentWindowSum;
            }
        }

        return baseSatisfaction + maxWindowSum;
    }
}

How Senior Engineers Fix It

Senior engineers approach this by looking for separation of concerns and clean loops.

  1. Remove Redundant Logic: The Senior version realizes that we don’t need to check if(grumpy[i] == 1) during the sliding window calculation. We can simply sum the raw customers array for the window, because grumpy[i] == 0 customers are already accounted for in the baseSatisfaction. Therefore, adding them to the currentWindowSum (which will be added to baseSatisfaction) results in double counting, UNLESS we define the window sum purely as “what would be gained if we turned off grumpiness here?”.
    • Correction/Refinement: Actually, a strictly valid Senior fix might involve creating a potential array where potential[i] = grumpy[i] == 1 ? customers[i] : 0 to strictly separate the logic, OR simply sliding over customers knowing that the overlap with base is handled by the final addition.
  2. Variable Naming: Rename MaxExtraBoi to maxWindowSum to describe the data, not the imaginary user.
  3. Loop Unrolling: The Senior version separates the initialization of the window (first minutes elements) from the sliding phase. This makes the logic inside the main loop extremely simple (Add Right, Subtract Left, Update Max).

Why Juniors Miss It

Juniors often miss these optimizations because they are focused on survival—making the logic work at all.

  • Mental Overload: The sliding window algorithm is hard to visualize. Juniors often write code that explicitly manages the state of every element (checking grumpy every time) to ensure they don’t make a mistake, rather than trusting a cleaner, mathematical approach.
  • Lack of Macro View: They see the problem as “loop through array, do stuff.” They don’t immediately see that baseSatisfaction and maxWindowSum can be calculated independently.
  • Debugging vs. Designing: The comments in the original code (“yo”, “that scenario”) indicate the junior was using comments as a way to process their thoughts while debugging, rather than designing a clean solution that doesn’t need comments to explain basic control flow.