What is the Tree BFS pattern?

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

When should I use the Tree BFS pattern?

Use the Tree BFS 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).

What is the time complexity of Tree BFS?

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

Is Tree BFS important for coding interviews?

Yes — the Tree BFS 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 Tree BFS 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

Tree BFS.

Level-order traversal of trees

complexity

time

O(n)

space

O(n) for queue

practice questions

browse and solve all 0 pattern questions →

02' — visual

interactive

Sliding window — k = 3

Maximum sum subarray

Window

[0, 2]

Sum

Step

0 / 3

03'

mental
model.

Use a queue. Add root, then process each level by tracking queue size at each iteration.

Tree BFS uses a queue to traverse trees level by level. Essential for problems requiring level-order information.

when to use ✓

01Level order traversal
02Minimum depth of binary tree
03Zigzag level order
04Average of levels

when not ✗

01When path information is more important than level

04'

recognition signals

Level-order traversal needed

Find minimum depth

Right side view of tree

Connect nodes at same level

Standard level orderZigzag level orderVertical orderBottom-up level order

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 tracking level size correctly
02Confusing with DFS for depth problems

interview tips

01Master the 'level size' pattern
02Use marker for zigzag and other variants

06'

practice this pattern

0 curated problems from LeetCode

See all

No questions found for this pattern yet.

07'

faq

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