What is the Intervals pattern?

The Intervals pattern is a systematic approach to solving a class of coding interview problems. By recognizing the Intervals structure, you can solve problems in optimal time complexity using proven techniques.

When should I use the Intervals pattern?

Use the Intervals pattern when the problem structure matches its recognition signals — typically when brute force would be O(n²) or worse, and the pattern reduces complexity to O(n log n).

What is the time complexity of Intervals?

The Intervals pattern typically runs in O(n log n) time and uses O(n) space. The exact complexity depends on the specific sub-variant being applied.

Is Intervals important for coding interviews?

Yes — the Intervals pattern is frequently tested at FAANG companies and competitive engineering interviews. Mastering this pattern gives you a reliable framework to solve many problem variations quickly.

How do I practice the Intervals pattern?

Start by studying the pattern's mental model and recognition signals on DSAPatterns, then practice the curated LeetCode problems below in order from Easy to Hard. Aim for pattern recognition before memorizing solutions.

← back to patterns

pattern / MEDIUM

Intervals.

Merge, overlap, and process interval lists

complexity

time

O(n log n)

space

O(n)

practice questions

browse and solve all 5 pattern questions →

02' — visual

interactive

Sliding window — k = 3

Maximum sum subarray

Window

[0, 2]

Sum

Step

0 / 3

03'

mental
model.

Sort by start time, then process sequentially. Track current interval and update based on overlap.

Interval patterns deal with overlapping, merging, and scheduling problems on ranges of values.

when to use ✓

01Merge intervals
02Insert interval
03Meeting rooms
04Non-overlapping intervals

when not ✗

01When intervals are dynamic and not given as input

04'

recognition signals

List of intervals [start, end]

Merge overlapping intervals

Find conflicts

Meeting rooms problems

Merge intervalsInsert intervalMeeting rooms I/IINon-overlapping intervals

05'

templates

python

1# Beginner: Fixed-size sliding window
2def max_sum_k(nums, k):
3   n = len(nums)
4   if n < k:
5      return 0
6   window_sum = sum(nums[:k])
7   max_sum = window_sum
8   for i in range(k, n):
9      window_sum += nums[i] - nums[i - k]
10      max_sum = max(max_sum, window_sum)
11   return max_sum

common mistakes

01Not sorting first
02Off-by-one in overlap check
03Confusing inclusive vs exclusive

interview tips

01Always sort by start time first
02Use a min-heap for meeting rooms variants

06'

practice this pattern

5 curated problems from LeetCode

See all

Merge Intervals# · Intervals

MEDIUM

Insert Interval# · Intervals

MEDIUM

Minimum Number of Arrows to Burst Balloons# · Intervals

MEDIUM

Maximum Number of Events That Can Be Attended# · Intervals

MEDIUM

Non-overlapping Intervals# · Intervals

MEDIUM

07'

faq

← back to patterns

pattern / MEDIUM

Intervals.

Merge, overlap, and process interval lists

complexity

time

O(n log n)

space

O(n)

practice questions

browse and solve all 5 pattern questions →

02' — visual

interactive

Sliding window — k = 3

Maximum sum subarray

Window

[0, 2]

Sum

Step

0 / 3

03'

mental
model.

Sort by start time, then process sequentially. Track current interval and update based on overlap.

Interval patterns deal with overlapping, merging, and scheduling problems on ranges of values.

when to use ✓

01Merge intervals
02Insert interval
03Meeting rooms
04Non-overlapping intervals

when not ✗

01When intervals are dynamic and not given as input

04'

recognition signals

List of intervals [start, end]

Merge overlapping intervals

Find conflicts

Meeting rooms problems

Merge intervalsInsert intervalMeeting rooms I/IINon-overlapping intervals

05'

templates

python

1# Beginner: Fixed-size sliding window
2def max_sum_k(nums, k):
3   n = len(nums)
4   if n < k:
5      return 0
6   window_sum = sum(nums[:k])
7   max_sum = window_sum
8   for i in range(k, n):
9      window_sum += nums[i] - nums[i - k]
10      max_sum = max(max_sum, window_sum)
11   return max_sum