Hand of Straights
A medium Greedy problem included in Love Babbar 450. Below: the roles whose interviews prioritise this topic, and how to practise it.
- Topic
- Greedy
- Sheets
- 1
- Core for
- 3 roles
- Platform
- LeetCode
The problem
You have an integer array hand where hand[i] is the value of the ith card, and an integer groupSize. Check if you can rearrange the cards into groups of groupSize consecutive cards. Return true if possible.
Example 1
- Input
- hand = [1,2,3,6,2,3,4,7,8], groupSize = 3
- Output
- true
- Why
- Rearrange into groups [1,2,3], [2,3,4], and [6,7,8]. Each group has consecutive cards.
Example 2
- Input
- hand = [1,2,3,4,5], groupSize = 4
- Output
- false
- Why
- Cannot rearrange 5 cards into groups of 4 consecutive cards.
Constraints
- 1 <= hand.length <= 10^5
- 1 <= hand[i] <= 10^9
- 1 <= groupSize <= hand.length
How to think about it
Updated 2026-09-09The smallest available card cannot be part of any group other than one where it serves as the opening value. Once you lock in the minimum card, its succeeding group members are completely predetermined, leaving no choices to deliberate.
Approaches, worst first
Sort and frequency map
time O(n log n) · space O(n)
Count frequencies of all cards and sort unique values. Repeatedly take the smallest remaining card and decrement counts for the next groupSize consecutive values. Fails immediately if any expected consecutive card is missing.
Queue-based greedy matchingWrite this one
time O(n log n) · space O(n)
Sort unique keys, maintain a queue of active group starts with counts, and verify consecutive transitions in a sliding window manner. Avoids nested lookups for each individual card in a group.
Where people lose marks · 3
- Failing to check `hand.length % groupSize === 0` at the very beginning wastes execution time on arrays that cannot partition evenly.
- Card values can reach 10^9, so an array-based frequency counter will exhaust memory; use a hash table or tree map.
- Re-checking depleted card values: skipping counts that have already hit zero is essential for maintaining efficiency.
The theory behind it
Greedy — the ground this problem stands on. All Greedy problems
What Greedy is
A greedy algorithm makes the best-looking choice available right now, at every step, without ever looking back or second-guessing its decision. Think of a cashier making change by handing over the largest possible coin first, repeatedly, until the total is reached. Unlike dynamic programming, which saves and compares answers to multiple overlapping paths, a greedy strategy commits to one immediate option and keeps moving forward.
When to reach for it
Reach for greedy when problems ask for minimum jumps, interval scheduling, assigning resources to maximize satisfaction, or finding fractional values. Key signals include sorted orders where greedily taking the next item never hurts future options, or gas station round trips where running balances prove reachability. If you can prove that taking the immediate best choice never leaves you worse off than any alternative, greedy gives the fastest answer.
How the pattern works
Start by sorting the input to bring the most promising candidates to the front. At each position, evaluate your local rule, take the best available piece, and update your running state. The crucial mental step is proving the greedy choice property: demonstrate that picking this immediate winner cannot block a better global solution down the road. If choosing an item now forces you to reconsider past decisions when conditions change later, greedy fails and you must switch to dynamic programming instead.
What each operation costs
| Operation | Time |
|---|---|
| sort elements to enable greedy selection | O(n log n) |
| greedy single-pass scan through sorted input | O(n) |
| greedy choice using a priority queue | O(n log n) |
What usually goes wrong with Greedy
- Applying a greedy choice without proving it yields the global optimum, such as picking the largest coin first for arbitrary denominations where dynamic programming was required.
- Forgetting to sort the input before running the greedy loop, making local decisions on unordered elements that produce invalid answers.
- Picking items based on only one attribute when the optimal decision depends on a ratio or combination of multiple attributes.
Which roles need this problem
Greedy is a core topic for these 3 roles — if you're targeting one of them, this problem is early in your path, not optional.
Secondary for 4 more roles, including Site Reliability Engineer, Search Engineer, Quant Developer.
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