HackerRank Coding Challenges: 7 Patterns That Solve 80% of Problems

HackerRank problems look diverse on the surface, but most of them use the same underlying patterns. Learn to recognise which pattern applies, and the solution becomes straightforward.

Pattern 1: Hash Map Lookup

When to use: "Find pairs," "count occurrences," "check for duplicates."

Build a frequency map or a lookup table. O(n) time instead of O(n²).

Pattern 2: Sliding Window

When to use: "Maximum/minimum sum of subarray," "longest substring with K distinct characters."

def max_subarray_sum(arr, k):
    window = sum(arr[:k])
    best = window
    for i in range(k, len(arr)):
        window += arr[i] - arr[i - k]
        best = max(best, window)
    return best

Pattern 3: Two Pointers

When to use: Sorted array problems, "pair with target sum," "remove duplicates."

Start from both ends, move inward based on the comparison.

Pattern 4: BFS/DFS (Graph Traversal)

When to use: "Shortest path," "connected components," "flood fill," anything on a grid or tree.

BFS for shortest path (use a queue). DFS for exhaustive search (use recursion or a stack).

Pattern 5: Dynamic Programming

When to use: "Count ways," "minimum cost," "longest/shortest" with overlapping subproblems.

Identify the state, write the recurrence, memoise or tabulate.

Pattern 6: Greedy

When to use: "Minimum number of X to cover Y," interval scheduling, making change.

Sort by the right criterion, then take the locally optimal choice at each step.

Pattern 7: Sorting as Preprocessing

When to use: Problems that become trivial after sorting — "find the closest pair," "merge intervals," "find the median."

The Recognition Strategy

1. Read the problem. What's the input shape? (Array, string, graph, tree?)
2. What's being asked? (Count, find, optimise, check?)
3. Match to a pattern from the list above.
4. Implement the pattern with the problem's specific constraints.

Practice 3-5 problems per pattern, and you'll recognise them instantly in interviews.

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