Intervals Pattern: LeetCode Guide & Problems

Merge, insert, and count overlapping intervals after sorting by start time.

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4 Problem 1 Easy 2 Medium 1 Hard

Overview

Interval problems become tractable once you sort by start time. The key insight is comparing each interval's start with the previous interval's end to detect overlaps.

When to Use

Merge intervals
Insert interval
Meeting rooms (can attend all?)
Meeting rooms II (min rooms needed)
Non-overlapping intervals

Common Mistakes

Not sorting by start time first
Off-by-one when checking overlap: start <= end vs start < end
Forgetting to add the last interval after merging

Complexity

Time

O(n log n) — dominated by sorting

Space

O(n)

public int[][] mergeIntervals(int[][] intervals) {
    Arrays.sort(intervals, (a, b) -> a[0] - b[0]);
    List<int[]> merged = new ArrayList<>();
    merged.add(intervals[0]);

    for (int i = 1; i < intervals.length; i++) {
        int[] last = merged.get(merged.size() - 1);
        if (intervals[i][0] <= last[1]) {
            last[1] = Math.max(last[1], intervals[i][1]); // overlap: extend
        } else {
            merged.add(intervals[i]); // no overlap: new interval
        }
    }
    return merged.toArray(new int[0][]);
}

Problems (4)

Hide completed

Problem	Difficulty	Companies	Freq	Solution	Link
Merge Intervals #Array #Sorting	Medium	FacebookAmazon +3	High	Solution
Insert Interval #Array	Medium	LinkedInGoogle +2	High	Solution
Minimum Interval to Include Each Query #Array #Binary Search #Line Sweep	Hard	GoogleAmazon	Low	Solution
Meeting Rooms ★ #Array #Sorting	Easy	FacebookAmazon +2	High	Solution	★

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Mastering the Intervals Pattern for Coding Interviews

1. Mental Model & Visual Intuition

The Intervals pattern is designed to eliminate redundant work and reduce search spaces. Rather than evaluating every possible state using a naive brute-force approach, this pattern relies on an invariant that allows you to safely discard subproblems that cannot lead to an optimal answer. When encountering an interview question, look for core triggers such as monotonicity, contiguous constraints, optimal substructure, or overlapping subproblems.

2. Algorithmic Invariants & Step-by-Step Execution

To implement this pattern flawlessly under interview conditions, structure your thought process into three discrete steps:

State Initialization: Define your boundaries, pointers, or memoization tables clearly before entering loops.
State Transition: Update tracking variables incrementally as new elements enter or leave your active evaluation window.
Feasibility Check: When constraints are violated, systematically shrink or adjust your state until the valid invariant is restored.

3. Time and Space Complexity Trade-Offs

In top-tier technical interviews at Google, Meta, and Amazon, arriving at a working solution is only half the battle. You must explicitly justify why your approach achieves optimal Big-O bounds. For Intervals, the target time complexity is typically O(n log n) — dominated by sorting while maintaining an auxiliary space complexity of O(n). Always discuss whether an in-place modification or two-pass traversal can save memory.

4. Common Interview Edge Cases to Test

Before informing your interviewer that you have completed coding, trace your logic against these standard high-failure test cases:

Empty inputs, single-element collections, or all-identical values.
Inputs with negative numbers, duplicates, or extreme values that could cause integer overflow.
Odd versus even length inputs when dealing with pointer convergence or tree midpoints.