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

Concept Flow - Trie vs Hash Map for Prefix Matching

Start with prefix

↓

Return matched words

No↓

Traverse Trie from root

↓

For each char in prefix

↓

No match found

Yes↓

Move to child node

↓

After prefix end

↓

Collect all words below node

↓

Return matched words

This flow shows how prefix matching works differently in a Hash Map and a Trie: Hash Map checks keys starting with prefix, Trie traverses nodes for each prefix character then collects words.

Execution Sample

DSA Javascript

const words = ['cat', 'car', 'dog'];
const prefix = 'ca';
// Hash Map keys: 'cat', 'car', 'dog'
// Trie nodes: c -> a -> t, r
// Find words starting with 'ca'

This code sets up words and a prefix, then uses both Hash Map and Trie to find words starting with 'ca'.

Execution Table

Step	Operation	Data Structure	State/Pointer Changes	Visual State
1	Build Hash Map keys	Hash Map	Keys added: 'cat', 'car', 'dog'	{"cat":true, "car":true, "dog":true}
2	Build Trie nodes	Trie	Insert 'cat': root->c->a->t (end node) Insert 'car': root->c->a->r (end node) Insert 'dog': root->d->o->g (end node)	root ├─ c │ └─ a │ ├─ t (end) │ └─ r (end) └─ d └─ o └─ g (end)
3	Hash Map prefix check	Hash Map	Check keys starting with 'ca'	Matches found: ['cat', 'car']
4	Trie prefix traversal	Trie	Traverse root->c->a	At node 'a' under 'c'
5	Trie collect words	Trie	Collect words below node 'a': 'cat', 'car'	Collected words: ['cat', 'car']
6	Return results	Both	Return matched words	Output: ['cat', 'car']
7	End	Both	No more operations	Process complete

💡 All words processed and matched words returned for prefix 'ca'

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	Final
Hash Map keys	{}	{"cat":true, "car":true, "dog":true}	{"cat":true, "car":true, "dog":true}	{"cat":true, "car":true, "dog":true}	{"cat":true, "car":true, "dog":true}	{"cat":true, "car":true, "dog":true}	{"cat":true, "car":true, "dog":true}
Trie nodes	root empty	root empty	root with c->a->t, c->a->r, d->o->g	same	pointer at node 'a' under 'c'	words collected: ['cat', 'car']	same
Matched words	[]	[]	[]	['cat', 'car']	['cat', 'car']	['cat', 'car']	['cat', 'car']
Current Trie node	root	root	root	root	node 'a' under 'c'	node 'a' under 'c'	node 'a' under 'c'

Key Moments - 3 Insights

Why does the Trie traversal stop after reaching the node for the last prefix character?

Because the Trie stores words as paths, once we reach the node for the last prefix character (step 4), all words starting with that prefix are below this node. We then collect all words from here (step 5). This is shown in execution_table rows 4 and 5.

How does the Hash Map find words starting with a prefix without a tree structure?

The Hash Map checks all keys to see which start with the prefix (step 3). It does not store characters separately, so it scans keys directly. This is less efficient for large data but simpler to implement.

What is the visual difference between Trie and Hash Map states after building them?

The Trie visually shows nodes connected by characters forming branches (step 2), while the Hash Map is a flat collection of keys (step 1). This difference is clear in the Visual State column of execution_table rows 1 and 2.

Visual Quiz - 3 Questions

Look at the execution_table at step 4, which node does the Trie traversal pointer reach?

A Node 'a' under 'c'

B Node 't' under 'a'

C Root node

D Node 'd' under root

💡 Hint

Check the 'State/Pointer Changes' and 'Visual State' columns at step 4 in execution_table

At which step does the Hash Map find all keys starting with the prefix?

A Step 5

B Step 2

C Step 3

D Step 6

💡 Hint

Look at the 'Operation' column for Hash Map prefix check in execution_table

If the prefix was 'do', what would the Trie pointer be after traversal?

A Node 'g' under 'o'

B Node 'o' under 'd'

C Node 'a' under 'c'

D Root node

💡 Hint

Refer to the Trie structure in execution_table step 2 and the traversal logic in concept_flow

Concept Snapshot

Trie vs Hash Map for Prefix Matching:
- Trie stores words as paths of characters (nodes).
- Hash Map stores full words as keys.
- Trie traversal follows prefix chars node-by-node.
- Hash Map scans keys for prefix matches.
- Trie is efficient for large datasets with shared prefixes.
- Hash Map is simpler but less efficient for prefix queries.

Full Transcript

This visual execution compares how Trie and Hash Map find words starting with a prefix. First, both data structures are built: Hash Map stores full words as keys, Trie builds nodes for each character. For prefix matching, Hash Map checks keys starting with the prefix directly, while Trie traverses nodes for each prefix character. After reaching the last prefix node in Trie, it collects all words below. The execution table shows each step, pointer positions, and the visual state of data structures. Key moments clarify why Trie traversal stops at the prefix end, how Hash Map scans keys, and the visual difference between structures. The quiz tests understanding of pointer positions and steps. The snapshot summarizes the main differences and use cases.