Introduction

Algorithmic patterns are the difference between staring at a new problem and knowing what shape of solution to reach for.

This article explains why patterns work, how patterns relate to algorithms, and how I build pattern recognition without memorizing a thousand one-off solutions.

If you want a lookup table of patterns with “when to use” cues and templates, use Fundamental Algorithmic Patterns.

Type: Explanation (understanding-oriented). Primary audience: intermediate developers learning to think in patterns while building real software.

Scope

This page is about building understanding and intuition:

  • How patterns differ from algorithms and techniques.
  • Why patterns reduce time-to-solution in both interviews and production work.
  • How multiple patterns combine in one problem.
  • How to practice pattern recognition in a way that sticks.

Prerequisites

This article assumes you already recognize common algorithmic patterns (for example, sliding window, two pointers, basic graph traversal) and are comfortable reading high-level algorithm descriptions. If you are new to these patterns, start with Fundamental Algorithmic Patterns.

What This Article Is Not

This article does not provide coding templates, step-by-step solutions, or interview checklists. Its goal is to help you understand why patterns work so you can recognize and apply them in unfamiliar problems.

Patterns, Algorithms, and Techniques

A consistent taxonomy prevents confusion when you learn or teach this material.

Pattern

A pattern is a reusable shape of solution. It tells you what kind of state to track, and what kind of loop or recursion structure to use.

  • Sliding window is a pattern, it tells you to grow and shrink a contiguous range while tracking window state.
  • Two pointers is a pattern, it tells you to use multiple indexes to converge, compare, or partition.

Algorithm

An algorithm is a specific, concrete procedure with defined steps.

  • Dijkstra’s algorithm is a procedure for single-source shortest paths with non-negative edge weights.
  • Kruskal’s algorithm is a procedure for building a minimum spanning tree from sorted edges.

Technique

A technique is a common implementation move that supports patterns and algorithms.

  • A dummy node simplifies linked list edge cases.
  • In-place reversal is a technique you use inside several list and array patterns.

This matters because many “pattern lists” mix these categories, which makes it harder to transfer what you learned to a new problem.

Why Patterns Work

Patterns compress problem solving into a smaller decision tree.

When you recognize a pattern, you get three things immediately:

  • A set of assumptions. For example, two pointers often assumes sorted data or monotonic movement.
  • A state model. Sliding window implies a window state like a sum or frequency map.
  • A failure mode list. Every pattern has predictable footguns, like forgetting to shrink the window or mishandling duplicates.

That is why patterns help in production work too. When a system slows down, I do not start with “which algorithm do I need”, I start with “what pattern is hiding inside this code path”.

Patterns are not magic. If you force a pattern onto a problem without matching its assumptions, you often end up with code that is harder to read and no faster than a straightforward approach.

Pattern Combinations

Real problems frequently combine patterns. The goal is to identify the primary pattern first, then layer supporting patterns as needed.

Common combinations

  • Sliding window + hash map: Longest substring with at most k distinct characters.
  • Graph traversal + backtracking: Find all paths that satisfy constraints.
  • Binary search + greedy: Find the minimum feasible value.
  • Two pointers + sorting: Find pairs/triplets across many candidates.

How I decompose combination problems

  • Identify what must be contiguous, what can be reordered, and what is a graph.
  • Write down the invariant you need to maintain, for example, “window is valid” or “heap contains the k best candidates so far”, and keep it true.
  • Decide which pattern owns the loop, then attach the others as helpers.

This is the skill that turns “I know patterns” into “I can solve new problems quickly”.

Practicing Pattern Recognition Without Memorizing Solutions

I use practice to train recognition, not recall.

A simple practice loop

  • Read the prompt and underline the strongest cues, like “contiguous”, “at most”, “shortest”, “dependencies”, “overlap”, “sorted”, and other constraint words.
  • Name the pattern before writing any code.
  • Write a minimal template, then plug in problem-specific details.
  • After you finish, write one sentence explaining why this was the right pattern.

Common failure modes while practicing

  • Jumping to code too early. This builds speed at typing, not speed at thinking.
  • Treating every problem as unique. You miss transfer learning, which is the point.
  • Overcomplicating with advanced tools. If a hash map and a loop works, a segment tree is a distraction.

Patterns in Production Code

Patterns show up in normal codebases all the time.

  • Sliding window: Rate limiting windows, streaming aggregations, “last 5 minutes” dashboards.
  • Two pointers: Merging sorted lists, deduplication, validation scans.
  • Graph traversals: Dependency resolution, import graphs, build ordering.
  • Prefix sums: Time-series deltas and fast range rollups.
  • Monotonic stacks: “Next change” computations and span-based aggregations.

When I review production code, I look for patterns that are half-implemented. That is where bugs hide, and that is where performance degrades when load increases.

This article does not teach how to implement patterns. It explains how to recognize and reason about them.

References

Algorithm Pattern Resources

Books

  • Skiena, S. S. (2020). The Algorithm Design Manual (3rd ed.). Springer.
  • Laakmann McDowell, G. (2015). Cracking the Coding Interview (6th ed.). CareerCup.

Note on Verification

  • These patterns and the best ways to teach them evolve over time. Verify approaches against your language, libraries, and constraints.