BST vs Hash Map Trade-offs for Ordered Data in DSA Rust - Compare & Choose

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why choose a BST over a Hash Map for ordered data?

Which of the following is the main reason to use a Binary Search Tree (BST) instead of a Hash Map when you need to keep data ordered?

A Hash Map uses less memory than BST for storing ordered data.

B BST provides faster average lookup time than Hash Map for all cases.

C BST maintains elements in sorted order, allowing in-order traversal to get sorted data easily.

D Hash Map automatically sorts keys, so BST is unnecessary.

💡 Hint

Think about how each data structure stores and retrieves data in order.

A BST keeps elements sorted by design, so you can traverse it in order to get sorted data. Hash Maps do not maintain order.

❓ Predict Output

intermediate

Output of in-order traversal on BST vs Hash Map keys

Given the following Rust-like pseudocode, what is the output of the in-order traversal of the BST and the keys iteration of the Hash Map?

DSA Rust

let bst = BST::new();
bst.insert(3);
bst.insert(1);
bst.insert(4);
bst.insert(2);

let hash_map = HashMap::new();
hash_map.insert(3, "c");
hash_map.insert(1, "a");
hash_map.insert(4, "d");
hash_map.insert(2, "b");

let bst_output = bst.in_order_traversal();
let hash_map_keys: Vec<_> = hash_map.keys().cloned().collect();

println!("BST in-order: {:?}", bst_output);
println!("Hash Map keys: {:?}", hash_map_keys);

A

BST in-order: [1, 2, 3, 4]
Hash Map keys: [1, 2, 3, 4]

B

BST in-order: [3, 1, 4, 2]
Hash Map keys: [4, 3, 2, 1]

C

BST in-order: [4, 3, 2, 1]
Hash Map keys: [1, 2, 3, 4]

D

BST in-order: [1, 2, 3, 4]
Hash Map keys: [3, 1, 4, 2]

💡 Hint

BST in-order traversal returns sorted keys; Hash Map keys order is not guaranteed.

BST in-order traversal visits nodes in ascending order. Hash Map keys have no guaranteed order.

🔧 Debug

advanced

Why does this Hash Map code fail to maintain order?

Consider this Rust code snippet that tries to maintain insertion order using a standard Hash Map. Why does it fail to keep keys ordered?

DSA Rust

let mut map = HashMap::new();
map.insert(10, "ten");
map.insert(5, "five");
map.insert(20, "twenty");

for key in map.keys() {
    println!("{}", key);
}

A Keys are sorted automatically, so the output is always sorted.

B Standard Hash Map does not guarantee any order of keys during iteration.

C The code has a syntax error in the for loop.

D Hash Map keys are always printed in reverse insertion order.

💡 Hint

Think about how Hash Maps store keys internally.

Rust's standard Hash Map does not preserve insertion order or sort keys. Iteration order is unpredictable.

🚀 Application

advanced

Choosing data structure for range queries on ordered data

You need to store a large set of numbers and frequently perform queries like "find all numbers between 100 and 200" efficiently. Which data structure is best suited for this task?

A Binary Search Tree (BST) because it supports efficient range queries via in-order traversal.

B Hash Map because it provides constant time lookup for any key.

C Array because it uses less memory than BST or Hash Map.

D Stack because it allows easy access to the last inserted element.

💡 Hint

Think about which structure keeps data sorted and allows partial range access.

BSTs keep data sorted and allow efficient range queries by traversing only relevant nodes. Hash Maps do not support range queries efficiently.

🧠 Conceptual

expert

Trade-offs of BST vs Hash Map for ordered data with frequent insertions and lookups

Which statement best describes the trade-offs between using a balanced BST and a Hash Map for storing ordered data with frequent insertions and lookups?

A Balanced BST offers O(log n) insertions and lookups with order, while Hash Map offers average O(1) lookups but no order guarantee.

B Hash Map offers O(log n) insertions and lookups with order, while BST offers average O(1) lookups but no order guarantee.

C Both BST and Hash Map offer O(1) insertions and lookups and maintain order automatically.

D BST offers constant time insertions but linear time lookups, while Hash Map offers logarithmic time for both.

💡 Hint

Consider time complexities and order guarantees of each data structure.

Balanced BSTs keep data sorted and provide O(log n) insertions and lookups. Hash Maps provide average O(1) lookups but do not maintain order.

Practice

1. Which data structure is best suited when you need to maintain elements in sorted order and perform fast range queries?

easy

A. Stack

B. Hash Map

C. Binary Search Tree (BST)

D. Queue

Solution

1. Step 1: Understand the need for sorted order

   Maintaining sorted order requires a data structure that keeps elements arranged by key value.

2. Step 2: Compare BST and Hash Map for sorted data

   BST keeps elements in sorted order naturally, allowing range queries. Hash Map does not maintain order.

3. Final Answer:

   Binary Search Tree (BST) -> Option C

4. Quick Check:

   Sorted order + range queries = BST [OK]

Hint: Sorted order needs BST, not Hash Map [OK]

Common Mistakes:

• Choosing Hash Map for ordered data
• Confusing Stack or Queue with sorted structures
• Ignoring range query requirements

2. Which Rust syntax correctly declares a HashMap with keys as integers and values as strings?

easy

A. let map = HashMap();

B. let map: HashMap = HashMap::new();

C. let map: HashMap = new HashMap();

D. let map = HashMap::new();

Solution

1. Step 1: Recall Rust HashMap declaration syntax

   Rust uses HashMap::new() to create a new map, with type annotation for key and value.

2. Step 2: Check each option for correct syntax

   let map: HashMap = HashMap::new(); uses correct syntax with type annotation and new() method. Others have syntax errors.

3. Final Answer:

   let map: HashMap = HashMap::new(); -> Option B

4. Quick Check:

   Rust HashMap declaration = let map: HashMap = HashMap::new(); [OK]

Hint: Use HashMap::new() with type annotation [OK]

Common Mistakes:

• Omitting type annotation
• Using parentheses instead of ::new()
• Incorrect generic syntax

3. Consider this Rust code using a BST-like structure for ordered keys:

let mut bst = BTreeMap::new();
bst.insert(3, "three");
bst.insert(1, "one");
bst.insert(2, "two");
for (key, value) in &bst {
    println!("{} -> {}", key, value);
}

What is the printed output order?

medium

A. 3 -> three
1 -> one
2 -> two

B. 2 -> two
3 -> three
1 -> one

C. Insertion order: 3 -> three
1 -> one
2 -> two

D. 1 -> one
2 -> two
3 -> three

Solution

1. Step 1: Understand BTreeMap ordering

   BTreeMap stores keys in sorted order, so iteration yields keys in ascending order.

2. Step 2: Trace insertion and iteration

   Keys inserted: 3, 1, 2. Iteration prints keys sorted: 1, 2, 3 with their values.

3. Final Answer:

   1 -> one 2 -> two 3 -> three -> Option D

4. Quick Check:

   BTreeMap iteration = sorted keys [OK]

Hint: BTreeMap iterates keys in sorted order [OK]

Common Mistakes:

• Assuming insertion order is preserved
• Confusing HashMap with BTreeMap
• Expecting reverse order

4. This Rust code tries to use a HashMap to get sorted keys:

let mut map = HashMap::new();
map.insert(10, "ten");
map.insert(5, "five");
map.insert(7, "seven");
for key in map.keys() {
    println!("{}", key);
}

What is the main problem with this code if the goal is to print keys in ascending order?

medium

A. HashMap keys() does not guarantee order

B. Keys are printed in reverse order

C. HashMap does not support integer keys

D. Code will cause a compile-time error

Solution

1. Step 1: Understand HashMap key ordering

   HashMap stores keys without any order; keys() returns keys in arbitrary order.

2. Step 2: Identify issue with printing keys

   Since keys() order is arbitrary, printing keys will not be sorted ascending.

3. Final Answer:

   HashMap keys() does not guarantee order -> Option A

4. Quick Check:

   HashMap unordered keys = HashMap keys() does not guarantee order [OK]

Hint: HashMap keys unordered, use BTreeMap for sorting [OK]

Common Mistakes:

• Expecting sorted output from HashMap
• Thinking keys() returns reverse order
• Assuming compile error due to key type

5. You need to store a large dataset with unique integer keys and values, and you want to perform fast lookups and also quickly find all keys within a range. Which data structure should you choose in Rust, and why?

hard

A. BTreeMap, because it keeps keys sorted and supports efficient range queries

B. HashMap, because it offers O(1) average lookup but no order for range queries

C. Vec, because it stores data contiguously and supports binary search

D. LinkedList, because it allows easy insertion and traversal

Solution

1. Step 1: Analyze lookup and range query needs

   Fast lookups and range queries require a data structure that supports both efficiently.

2. Step 2: Compare data structures for these needs

   HashMap offers fast lookups but no order for range queries. Vec supports binary search but slow insertions. LinkedList is slow for lookups. BTreeMap keeps keys sorted and supports efficient range queries.

3. Final Answer:

   BTreeMap, because it keeps keys sorted and supports efficient range queries -> Option A

4. Quick Check:

   Fast lookup + range queries = BTreeMap [OK]

Hint: Use BTreeMap for sorted keys and range queries [OK]

Common Mistakes:

• Choosing HashMap ignoring range queries
• Using Vec without considering insertion cost
• Selecting LinkedList for fast lookups