Best Coding Pattern Resources to Pair With System Design Preparation

Preparing for tech interviews means you need to be ready for both coding challenges and system design discussions. Focusing only on system design without sharpening your coding skills (or vice versa) can leave gaps in your preparation.

The smart approach is to pair system design prep with mastering key coding interview patterns. This way, you build a strong foundation in problem-solving techniques that will complement your ability to design scalable systems.

Coding patterns are recurring solutions or techniques for common algorithmic problems.

Instead of trying to brute-force hundreds of random LeetCode questions, studying patterns lets you recognize problem types and apply proven strategies.

Think of it like learning a handful of strategies that you can apply to dozens of problems, rather than treating each problem as unique.

By recognizing an underlying pattern, you can reduce a complex question to a familiar scenario and solve it efficiently.

In today's competitive tech interviews, understanding and mastering coding patterns can significantly enhance your problem-solving skills and boost your performance.

Below, we’ll cover some essential coding patterns that frequently appear in coding interviews.

These patterns nicely complement system design preparation – while system design focuses on high-level architecture, coding patterns hone your algorithmic thinking for tackling coding rounds.

We’ll also highlight the best resources (books, courses, websites) to learn and practice these patterns.

Using these resources alongside your system design studies will ensure you’re thoroughly prepared for all aspects of your interview.

Essential Coding Patterns for Interview Success

When preparing for coding interviews, focus on these important patterns which cover a broad range of problems.

Mastering these will give you a toolkit to solve new questions by recognizing similarities to ones you've seen before:

• Sliding Window: Involves moving a window (fixed or flexible size) across a data structure (usually an array or string) to solve problems involving subarrays or substrings. Great for finding subarray sums, longest substrings, or average of subarrays efficiently by reusing previous computations.

• Two Pointers: Uses two indices (or pointers) to iterate through a structure from different ends or at different speeds. Useful for tasks like pairing numbers (e.g. two-sum in a sorted array), comparing two sequences, or removing duplicates in-place by avoiding nested loops.

• Fast and Slow Pointers (Tortoise and Hare): Uses two pointers moving at different speeds (usually in linked lists or cyclic structures). Handy for detecting cycles (loop in a linked list), finding the middle of a list, or other problems where one pointer skipping ahead helps find a condition (like the start of a cycle).

• Merge Intervals: Pattern of dealing with intervals (start and end times) by sorting and then merging overlapping intervals. Common in scheduling problems, calendar merges, or anytime you need to consolidate ranges.

• Cyclic Sort: A clever array sorting strategy for situations where numbers are in a known range (like 1 to n). You "cycle" numbers to their correct index by swapping, which efficiently sorts or finds missing numbers with O(n) time and O(1) space.

• In-Place Reversal of a Linked List: Reversing a linked list (or a part of it) by rearranging pointers in-place instead of using extra memory. This pattern helps in problems like reversing the entire list, reversing every k-elements, or checking for palindrome lists, all in one pass.

• Stacks: Uses the Last-In-First-Out (LIFO) principle to solve problems. A stack is great for evaluating expressions, undo/redo operations, or balancing parentheses because it naturally handles nested or sequential dependencies (the most recent element is processed first).

• Monotonic Stack: A specialized stack that keeps elements in sorted order (either all increasing or all decreasing). Useful for problems like "next greater element", stock span, or temperature span problems, where you need to efficiently find next/previous larger or smaller elements.

• Hash Maps: Leverages key-value storage for O(1) lookups. Many patterns use hash maps to count frequencies (e.g. anagrams, palindrome permutation), check for duplicates (two-sum, subarray sums), or map relationships (like parent pointers in trees) quickly. Hash maps often complement other patterns (e.g. using a map to augment two-pointer or DP solutions).

• Dynamic Programming (DP): Breaks down problems into overlapping subproblems and solves each just once, storing the results. This pattern is crucial for optimization problems (knapsack, coin change, longest subsequences) where brute force is too slow. By using techniques like memoization or tabulation, DP finds optimal solutions by reusing past computations. Example: computing Fibonacci numbers or edit distance efficiently instead of recursively recalculating subcases.

• Breadth First Search (BFS): Traverses level-by-level in trees or graphs. BFS is ideal for shortest path problems in unweighted graphs, or any scenario where you need to explore neighbors first (like finding the nearest something). In tree problems, BFS (also called level-order traversal) is used for operations like zigzag level order, finding minimum depth, etc.

• Depth First Search (DFS): Explores as far as possible down one path before backtracking, using recursion or an explicit stack. DFS is the basis for backtracking solutions (like generating combinations or solving puzzles) and for graph traversal tasks like detecting connected components, cycles, or performing topological sorts.

• Graphs: Broadly covers problems involving nodes connected by edges. Key graph algorithms include traversal (BFS/DFS), but also concepts like topological sorting (for dependency resolution), Union-Find (Disjoint Set Union) for connectivity, and Graph Shortest Paths (Dijkstra's or BFS for unweighted). Graph patterns help solve network problems, dependency graphs, and pathfinding in maps or mazes.

• Island Problems: A specific application of DFS/BFS on a grid matrix. "Island" style problems involve counting connected regions in a 2D grid (often counting islands of 1's in a sea of 0's). The island pattern uses BFS/DFS to mark all connected cells and count clusters. These teach you to systematically explore matrices and are common in interviews (e.g. Number of Islands, Connected Components in a matrix).

• Two Heaps: Involves maintaining two heaps (usually a max-heap and a min-heap) to keep track of elements on either side of a median or threshold. This pattern is famously used for the "Median of a Data Stream" problem, where you continuously insert numbers and need to get the median quickly. It's also useful for any scenario where you want to balance two parts (like smallest and largest halves of a dataset).

• Subsets (Backtracking): This pattern involves generating all combinations or subsets of a given set (power set). It's the basis for combinatorial search problems (subsets, permutations, combinations, palindrome partitioning). By using recursion (DFS) and backtracking (undoing choices), you explore all possibilities systematically. Knowing the subsets pattern helps in any problem that asks for "all possible X".

• Modified Binary Search: Variations of binary search to handle special cases like rotated sorted arrays, searching in sorted matrices, or finding specific boundaries (first/last occurrence). The binary search idea remains – halve the search space each time – but with tweaks to handle conditions beyond a simple sorted list. Mastering this pattern lets you solve search problems with O(log n) efficiency even when the data isn't perfectly sorted in the usual way.

• Bitwise XOR: Uses the XOR operation for clever trick solutions, often to find unique elements in a set of duplicates. The property x ^ x = 0 and x ^ 0 = x means XOR-ing all numbers cancels out pairs. This pattern appears in problems like "find the single number that isn't repeated" or "find two non-repeating numbers in an array of pairs". It's a neat bit-manipulation tool to have in your kit.

• Top K Elements: Focuses on efficiently finding the K largest or smallest elements in a collection. Often achieved with a heap (priority queue) – for example, maintaining a min-heap of size K for largest K elements, or using a max-heap for smallest K. Common scenarios include finding top K frequent elements, K closest points, or streaming data where you need to keep track of the top scores.

• K-Way Merge: A pattern to merge K sorted lists (or streams) into one sorted output, typically using a min-heap to always pick the smallest current head among the lists. This underlies problems like merging multiple sorted linked lists, iterating over sorted iterators, or finding smallest ranges covering elements from multiple lists. It generalizes the two-list merge procedure to K lists.

• Greedy Algorithms: Greedy pattern involves making the best local choice at each step with the hope of finding a global optimum. Many optimization problems have greedy solutions – e.g. interval scheduling (choose earliest finishing interval first), coin change (choose largest coin first, for the canonical coin systems), or constructing Huffman trees. Greedy algorithms build solutions step-by-step by always choosing the most favorable option available at the moment. The key is knowing when a greedy approach yields an optimal result and when it doesn't, which comes from practice.

These patterns cover the gamut of coding interview questions – from array and string manipulation to linked lists, trees, graphs, and math tricks.

By practicing them, you'll recognize that new problems are often just variations of these themes.

This pattern-based preparation not only makes you faster in coding interviews, but also boosts your confidence when tackling system design, since you can break down big design problems into smaller known patterns or algorithms as needed.

For a detailed explanation, check 20 Coding Patterns for Interviews.

Best Resources to Learn Coding Patterns (and Complement Your System Design Prep)

To effectively learn and practice these coding patterns, it's important to use quality resources.

Here is a mix of books, courses, and websites that will help you master coding interview patterns and pair nicely with your system design study plan.

(As a rule of thumb, work through coding pattern problems daily while also brushing up on system design fundamentals – the combination will make you a well-rounded candidate.)

Books for Coding Interview Patterns and Algorithms

• Cracking the Coding Interview by Gayle Laakmann McDowell (6th Edition): The classic interview prep book with 189 programming interview questions ranging from easy to hard. Each question comes with a thorough solution walkthrough, which often highlights the pattern or data structure used. While not explicitly organized by pattern, you'll cover all the common patterns by solving its problems. This book also offers great explanations of fundamental concepts and even dedicates chapters to system design and behavioral questions, making it a comprehensive prep resource.

• Elements of Programming Interviews (in Java/Python/C++ by Aziz, Lee, & Prakash): A collection of 300+ interview problems with detailed solutions. Problems are grouped by topics like arrays, linked lists, sorting, dynamic programming, etc. By working through these categories, you'll encounter all of the patterns we listed (and more) in a rigorous way. EPI is known for its challenging problems and is a great way to deepen your understanding once you're comfortable with the basics. Solving EPI problems can solidify pattern recognition because you'll see multiple variations of each pattern. (They also have a handbook section on how to approach problems, which aligns well with a pattern-based approach.)

Online Course for Coding Patterns

• Grokking the Coding Interview: Patterns for Coding Questions (DesignGurus): An excellent online course that is entirely focused on teaching coding interview patterns. It covers 28 essential coding patterns and includes 500+ practice problems in multiple languages. Each pattern in the course is explained with several example problems, starting from easy and gradually increasing in difficulty, which helps you truly grok the pattern. Grokking the Coding Interview: Patterns for Coding Questions is highly recommended because it trains you to identify patterns behind new problems quickly. Many interview candidates credit pattern-based courses like this for their success, since it “teaches you how to find the underlying pattern which can then be used to solve many more coding problems” instead of just individual solutions. Another advantage is that DesignGurus.io courses are created by the original authors of the Grokking series, ensuring the content is tried-and-tested. Tip: After learning a pattern from the course, go to a site like LeetCode and solve a few more problems tagged with that pattern to reinforce your learning.

Note: DesignGurus also offers Grokking the System Design Interview, a popular resource for system design concepts and case studies. Using their coding patterns course in tandem with the system design course can give you a one-two punch in your interview prep – you'll be practicing coding and design with a consistent teaching style and depth.

Websites and Practice Platforms

• LeetCode (Practice by Patterns): LeetCode is a must-use platform for coding interview prep. Use LeetCode's search and discuss sections to find problems for each of the patterns above. For example, you can search the Discuss forum for lists of questions by pattern (many users compile pattern-wise problem lists). LeetCode also has explore cards and study plans for specific topics (like a card for "Two Pointers" or "Binary Search"). By practicing a cluster of LeetCode problems on, say, the Sliding Window pattern, you’ll internalize the approach. LeetCode is also great for simulating the actual coding interview environment with its online judge. Try to time yourself and write clean, well-tested solutions. Check out Top LeetCode patterns to crack FAANG interviews.

• GeeksforGeeks (Algorithms and Patterns Articles): GeeksforGeeks is an extensive free resource that has tutorials and problems for every algorithm and data structure imaginable. If you find yourself stuck on a pattern, you can read GfG’s explanatory articles – for instance, “Sliding Window Technique”, “Two Pointer Technique”, or dynamic programming guides. They break down the concept with examples and often provide multiple sample problems. Once you understand the pattern, you can solve the practice problems on GfG related to that topic to cement your understanding. GeeksforGeeks is especially useful for clearing up theoretical concepts (like how BFS vs DFS works, or various greedy algorithm applications) which strengthens your fundamentals for both coding and design interviews.

• Interactive Coding Challenge Platforms: Aside from the big two above, other platforms can add value as supplementary practice:

  • HackerRank has an Interview Preparation Kit that groups challenges by topic (Arrays, Dictionaries, Graphs, etc.), which correlates with patterns. It’s a good way to practice in a structured manner and track your progress.

  • InterviewBit (now part of Scaler) offers a guided set of coding problems categorized by techniques like two-pointers, DP, graphs, etc. It provides hints and a progression system that keeps you motivated to cover all patterns. This can be useful if you prefer a bit of gamification in your practice.

  • FreeCodeCamp and YouTube channels – There are free video series (for example, Abdul Bari’s algorithms playlist or NeetCode’s pattern explanations) that explain patterns visually. Watching a pattern walkthrough and then implementing it yourself on a coding platform can reinforce the learning.

When using these resources, remember to simulate real interview conditions occasionally: discuss your thought process out loud, and write code on a whiteboard or simple text editor for practice.

This will help you translate pattern knowledge into clear communication during interviews.

Understand how to follow coding patterns for smarter interview prep.

Check out Coding Patterns Cheat Sheet.

Combining Coding Patterns with System Design Prep

Mastering coding patterns will make you more confident and efficient in the coding interviews, but it also complements your system design preparation in subtle ways.

As you work through pattern-based problems, you learn to break down complex tasks, optimize for constraints, and justify your decisions – all skills that are invaluable in system design discussions.

For example, understanding the BFS/DFS pattern not only helps in coding questions but also in designing systems that might require graph traversal logic (like social network friend suggestions or web crawlers).

Knowing dynamic programming teaches you to think in terms of trade-offs (time vs space) which is useful when evaluating design choices. Even a simple pattern like caching results (memoization) in DP parallels the idea of caching in system architecture for performance.

While you practice these coding patterns, continue to read up on system design principles (scalability, consistency, etc.) and design case studies.

A great approach is alternating your study sessions: one day focus on a coding pattern and solving related problems, the next day work on a system design scenario.

You'll find that having strong algorithmic skills allows you to suggest more concrete solutions in system design (for instance, using a Trie data structure for an autocompletion system, or recognizing when a heap could optimize a scheduling system).

Summary

Pairing coding pattern practice with system design prep is a proven strategy to ace tech interviews. Use the resources we listed – grab a book like Cracking the Coding Interview for breadth, take the DesignGurus.io pattern course for focused learning, and consistently solve problems on LeetCode and GfG to reinforce each pattern.

At the same time, keep building your system design knowledge (DesignGurus' system design course or blog posts on system architecture are excellent for this).

This dual preparation approach will ensure you can tackle the 10 am coding round and the 2 pm system design round with equal confidence.

With these coding pattern resources at your side and a solid system design foundation, you'll be well-equipped to solve any coding puzzle and design a system under pressure.