AdvancedAdvanced
Trie
A tree-like data structure where each node represents a character and paths from root to nodes form prefixes. Tries enable O(m) insert, search, and prefix queries (m = word length), making them ideal for dictionaries, autocomplete, spell checking, and word search problems where multiple strings share common prefixes.
Empty Trie
Start with an empty trie (root node only)
Regular Node
End of Word
Step 1/8
Speed:
When to Use
- Prefix-based search
- Autocomplete / word suggestions
- Word search in a board
- Longest common prefix
Key Indicators
- Prefix matching
- Dictionary/word search
- Autocomplete
- Multiple string queries
Complexity
Time
O(m) per operation (m = word length)
Space
O(total characters)
Common Problems
Implement TrieWord Search IIDesign Add and Search WordsLongest Word in Dictionary
Code
class TrieNode:
children = {}
is_end = False
def insert(word):
node = root
for char in word:
if char not in node.children:
node.children[char] = TrieNode()
node = node.children[char]
node.is_end = TrueWant all 29 patterns with detailed visual walkthroughs?
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