An Iterator is a design pattern and programming concept that allows you to traverse elements of a collection one at a time without exposing the underlying data structure. Instead of accessing elements directly, an iterator provides controlled access through operations like next() and hasNext(). In data structures and algorithms, iterators are widely used to sequentially process elements in arrays, linked lists, trees, and other collections.
Iterator-based problems frequently appear in technical interviews because they test your understanding of state management, traversal logic, and abstraction. Companies often use these questions to evaluate whether you can design clean interfaces while maintaining efficient iteration over complex structures. For example, interview questions may require building an iterator for a Binary Search Tree, flattening nested structures, or implementing custom traversal logic.
Many iterator problems combine ideas from multiple core DSA topics. You might iterate through a Array or Linked List, maintain traversal order using a Stack, or simulate controlled traversal similar to Depth-First Search. Understanding how these structures interact with iterators helps you design efficient and reusable solutions.
Common techniques used in iterator problems include:
You should use iterators when you need sequential access to elements without exposing internal structure, when dealing with large or nested datasets, or when implementing reusable traversal interfaces. Practicing iterator problems helps you develop strong design thinking and prepares you for common interview questions like BST Iterator, Nested List Iterator, and custom collection iterators.
On FleetCode, you can practice 9 carefully selected Iterator problems that progressively build your understanding of iterator design, traversal strategies, and real interview patterns.
Arrays are the simplest iterable structures. Understanding index-based traversal and sequential access builds the foundation for designing iterator logic.
Stacks are commonly used to simulate traversal state in iterators, especially for tree iterators like in-order BST traversal.
Iterator questions often test object-oriented design skills such as interface design, encapsulation, and maintaining internal iteration state.
Linked lists require pointer-based traversal instead of indexing. Learning how to move through nodes helps you implement iterators that maintain internal state.
Many classic iterator interview problems involve building a BST iterator that returns elements in sorted order using controlled traversal.
| Status | Title | Solution | Practice | Difficulty | Companies | Topics |
|---|---|---|---|---|---|---|
| 173. Binary Search Tree Iterator | Solution | Solve | Medium | Adobe+8 | ||
| 251. Flatten 2D Vector | Solution | Solve | Medium | Airbnb+3 | ||
| 281. Zigzag Iterator | Solution | Solve | Medium | Amazon+3 | ||
| 284. Peeking Iterator | Solution | Solve | Medium | Apple+4 | ||
| 341. Flatten Nested List Iterator | Solution | Solve | Medium | Airbnb+17 | ||
| 900. RLE Iterator | Solution | Solve | Medium | Databricks+2 | ||
| 1286. Iterator for Combination | Solution | Solve | Medium | Google | ||
| 1586. Binary Search Tree Iterator II | Solution | Solve | Medium | Meta |
Frequently appear alongside Iterator.
Common questions about Iterator.
Iterator problems appear in many FAANG-style interviews because they combine data structures with design concepts. Candidates are often asked to implement iterators for trees or nested lists while maintaining efficient time and space complexity.
The best approach is to first understand sequential traversal of arrays and linked lists, then practice designing iterators for more complex structures like binary search trees or nested lists. Focus on maintaining iteration state and implementing next() and hasNext() efficiently.
Common iterator patterns include lazy traversal, stack-based traversal for trees, flattening nested lists, and maintaining a pointer or index as iteration state. These patterns appear repeatedly in interview problems and real-world library implementations.
Most iterator operations such as next() and hasNext() are designed to run in O(1) average time. Some implementations, like BST iterators, may use amortized O(1) time with auxiliary structures such as stacks.
Popular iterator interview problems include BST Iterator, Nested List Iterator, and Peeking Iterator. These problems test traversal design, lazy evaluation, and state management. Practicing 5–10 well-known iterator questions is usually enough to understand the core patterns.
Most candidates gain strong understanding after solving around 8–15 iterator problems. Start with basic collection iterators and then move to tree or nested structure iterators. FleetCode provides 9 curated problems that cover the most common interview patterns.
