Trie vs Hash Map for Prefix Matching in DSA Go - Visual Comparison

Concept Flow - Trie vs Hash Map for Prefix Matching

Start: Input prefix

↓

Choose Data Structure

↓

Trie Search

↓

Traverse nodes

↓

Prefix found?

↓

Return matches

↓

Return empty

The flow shows how prefix matching works differently in Trie and Hash Map: Trie traverses nodes by characters, Hash Map checks keys starting with prefix.

Execution Sample

DSA Go

words := []string{"cat", "car", "dog"}
prefix := "ca"
// Trie: traverse nodes c -> a
// Hash Map: check keys starting with "ca"

This code shows prefix matching for 'ca' using Trie traversal and Hash Map key checking.

Execution Table

Step	Operation	Data Structure	Pointer/Key Checked	Action	Resulting Matches
1	Start prefix search	Trie	root	Check child 'c'	None yet
2	Traverse	Trie	node 'c'	Check child 'a'	None yet
3	Traverse	Trie	node 'a'	Collect words under this node	["cat", "car"]
4	Start prefix search	Hash Map	keys: ["cat", "car", "dog"]	Check keys starting with 'ca'	None yet
5	Check key	Hash Map	"cat"	Starts with 'ca'? Yes	Add 'cat'
6	Check key	Hash Map	"car"	Starts with 'ca'? Yes	Add 'car'
7	Check key	Hash Map	"dog"	Starts with 'ca'? No	Ignore
8	Result	Trie	-	Return matches	["cat", "car"]
9	Result	Hash Map	-	Return matches	["cat", "car"]
10	End	Both	-	Search complete	Matches found

💡 Prefix search ends after traversing Trie nodes or checking all Hash Map keys.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 6	Final
Trie Pointer	root	node 'c'	node 'a'	node 'a'	node 'a'
Hash Map Keys Checked	[]	[]	[]	["cat", "car"]	["cat", "car", "dog"]
Matches Found	[]	[]	["cat", "car"]	["cat", "car"]	["cat", "car"]

Key Moments - 3 Insights

Why does Trie search only check nodes along the prefix path?

Because Trie stores words character by character, so traversing nodes matching prefix characters quickly narrows down matches (see execution_table steps 1-3).

Why does Hash Map check all keys instead of traversing nodes?

Hash Map keys are whole words, so to find prefix matches it must check each key's start (see execution_table steps 4-7). This is less efficient for large data.

What happens if prefix is not found in Trie?

Traversal stops when a needed child node is missing, returning no matches immediately (not shown in this example but implied by traversal logic).

Visual Quiz - 3 Questions

Look at the execution_table, what is the Trie pointer after step 2?

A root

B node 'a'

C node 'c'

D node 't'

💡 Hint

Check the 'Trie Pointer' variable in variable_tracker after step 2.

At which step does the Hash Map check the key 'dog'?

A Step 6

B Step 7

C Step 5

D Step 8

💡 Hint

Look at execution_table rows where Hash Map keys are checked.

If the prefix was 'do', how would the Trie traversal change?

A Traverse nodes 'd' -> 'o' instead of 'c' -> 'a'

B Traverse nodes 'c' -> 'a' as before

C No traversal needed

D Traverse all nodes

💡 Hint

Prefix matching in Trie follows nodes matching prefix characters (see concept_flow).

Concept Snapshot

Trie vs Hash Map for Prefix Matching:
- Trie stores words as paths of characters.
- Prefix search in Trie: traverse nodes matching prefix chars.
- Hash Map stores full words as keys.
- Prefix search in Hash Map: check keys starting with prefix.
- Trie is faster for large datasets; Hash Map simpler but slower for prefix.

Full Transcript

This visual execution compares prefix matching using Trie and Hash Map. Trie traverses nodes for each prefix character, quickly narrowing matches. Hash Map checks each key's start to find matches, which is slower for many keys. The execution table shows step-by-step traversal and key checking, with variable tracking of pointers and matches found. Key moments clarify why Trie traversal is efficient and how Hash Map checks keys. The quiz tests understanding of pointer positions and steps. The snapshot summarizes the main differences and usage.