Pattern hub system
LeetCode hubs organized by solve pattern
If you want reusable solve frames before scaling into many problem families, this layer is a cleaner starting point than random browsing. Each hub is organized around recognition signals, ladders, and linked topics.
Start here
136
pattern hubs
2,808
problems covered
If you are not sure where to begin, start with sliding window, two pointers, binary search, and dynamic programming.
Featured patterns
Start from the four most reusable patterns
These four patterns make the strongest first pass because they give you a shared frame that transfers back into the most common interview topics.
Featured patternarrow_forward
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Sliding window with running state updates
Recognition cues + ladder + linked topics
Strong for interval maintenance, rolling state updates, and common string/array interview scenarios.
hash tablestringsliding window
66 problems · 24 linked topics
Featured patternarrow_forward
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Graph indegree plus topological ordering
Recognition cues + ladder + linked topics
Ideal for drilling state definition, transitions, and full complexity trade-off articulation.
arraydynamic programmingdepth first search
11 problems · 18 linked topics
Featured patternarrow_forward
search
Graph indegree plus topological ordering
Recognition cues + ladder + linked topics
Sharpens monotonicity checks, boundary control, and decision-function reasoning.
arraybinary searchdynamic programming
1 problems · 7 linked topics
All patterns
All pattern entries
Keep the full pattern surface area, but route it through lighter entries instead of turning the page into another information wall.
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State transition dynamic programming
12219223·454
Array scanning plus hash lookup
10623577·418
Greedy choice plus invariant validation
3712833·198
Binary search over the valid answer space
1910177·197
Binary-tree traversal and state tracking
388835·161
Array plus Math
315030·111
Two-pointer scanning with invariant tracking
37566·99
Stack-based state management
124821·81
Hash Table plus String
24722·71
Linked-list pointer manipulation
8456·59
Hash Table plus String
35221·58
String-driven solution strategy
4891·58
Array-driven solution strategy
44100·54
Backtracking search with pruning
23416·52
Array plus String
24187·49
Array plus Matrix
21261·48
Show remaining pattern hubs (117)expand_more
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Graph traversal with depth-first search
12616·43
Math-driven solution strategy
29113·43
Array plus Sorting
18231·42
Array plus Prefix Sum
11224·37
Array plus Divide and Conquer
32210·35
Math plus String
16127·35
Array plus Simulation
1880·26
Array plus Bit Manipulation
8142·24
Hash Table plus Math
4114·19
Math plus Simulation
1140·15
String plus Simulation
851·14
Graph-driven solution strategy
148·13
Math plus Bit Manipulation
751·13
Math plus Enumeration
543·12
Bit Manipulation-driven solution strategy
830·11
Array plus Union Find
038·11
Array plus Enumeration
630·9
Math plus Geometry
250·7
Math plus Recursion
142·7
Queue-driven state processing
330·6
Array plus Binary Indexed Tree
023·5
Math plus Number Theory
131·5
String plus Rolling Hash
014·5
String plus String Matching
311·5
Array plus Design
040·4
Array plus Segment Tree
004·4
Math plus Combinatorics
112·4
String plus Enumeration
220·4
String plus Sorting
130·4
Math plus Brainteaser
120·3
String plus Counting
210·3
Array plus Divide and Conquer
011·2
Array plus Rolling Hash
011·2
Design plus Segment Tree
002·2
Design plus Simulation
110·2
Divide and Conquer plus Bit Manipulation
200·2
Math plus Sorting
110·2
String plus Bit Manipulation
020·2
String plus Recursion
011·2
Add Two Promises core interview pattern
100·1
Allow One Function Call core interview pattern
100·1
Apply Transform Over Each Element in Array core interview pattern
100·1
Array plus Brainteaser
010·1
Array Prototype Last core interview pattern
100·1
Array Reduce Transformation core interview pattern
100·1
Array Wrapper core interview pattern
100·1
Bit Manipulation plus Brainteaser
010·1
Cache With Time Limit core interview pattern
010·1
Calculator with Method Chaining core interview pattern
100·1
Call Function with Custom Context core interview pattern
010·1
Change Data Type core interview pattern
100·1
Check if Object Instance of Class core interview pattern
010·1
Chunk Array core interview pattern
100·1
Compact Object core interview pattern
010·1
Complement lookup with a hash map
100·1
Counter core interview pattern
100·1
Counter II core interview pattern
100·1
Create a DataFrame from List core interview pattern
100·1
Create a New Column core interview pattern
100·1
Create Hello World Function core interview pattern
100·1
Debounce core interview pattern
010·1
Design Cancellable Function core interview pattern
001·1
Display the First Three Rows core interview pattern
100·1
Drop Duplicate Rows core interview pattern
100·1
Drop Missing Data core interview pattern
100·1
Earliest Finish Time for Land and Water Rides I core interview pattern
100·1
Earliest Finish Time for Land and Water Rides II core interview pattern
010·1
Enumeration-driven solution strategy
010·1
Event Emitter core interview pattern
010·1
Execute Asynchronous Functions in Parallel core interview pattern
010·1
Fill Missing Data core interview pattern
100·1
Filter Elements from Array core interview pattern
100·1
Find Stores with Inventory Imbalance core interview pattern
010·1
Find Students with Study Spiral Pattern core interview pattern
001·1
Flatten Deeply Nested Array core interview pattern
010·1
Function Composition core interview pattern
100·1
Generate Fibonacci Sequence core interview pattern
100·1
Get the Size of a DataFrame core interview pattern
100·1
Queue-driven state processing
010·1
Group By core interview pattern
010·1
Hash Table plus Design
010·1
Interval Cancellation core interview pattern
100·1
Is Object Empty core interview pattern
100·1
Join Two Arrays by ID core interview pattern
010·1
Iterative pointer reversal
100·1
Math plus Design
001·1
Math plus Divide and Conquer
010·1
Math plus Rejection Sampling
010·1
Maximum Balanced Shipments core interview pattern
010·1
Maximum Median Sum of Subsequences of Size 3 core interview pattern
010·1
Maximum Number of Subsequences After One Inserting core interview pattern
010·1
Memoize core interview pattern
010·1
Memoize II core interview pattern
001·1
Method Chaining core interview pattern
100·1
Minimum Jumps to Reach End via Prime Teleportation core interview pattern
010·1
Minimum Removals to Balance Array core interview pattern
010·1
Minimum Time to Activate String core interview pattern
010·1
Modify Columns core interview pattern
100·1
Nested Array Generator core interview pattern
010·1
Promise Time Limit core interview pattern
010·1
Rename Columns core interview pattern
100·1
Reshape Data: Concatenate core interview pattern
100·1
Reshape Data: Melt core interview pattern
100·1
Reshape Data: Pivot core interview pattern
100·1
Return Length of Arguments Passed core interview pattern
100·1
Select Data core interview pattern
100·1
Sleep core interview pattern
100·1
Snail Traversal core interview pattern
010·1
Sort By core interview pattern
100·1
Stack-based bracket matching
100·1
String plus Trie
001·1
Threshold Majority Queries core interview pattern
001·1
Timeout Cancellation core interview pattern
100·1
To Be Or Not To Be core interview pattern
100·1
Trie-driven solution strategy
001·1
Trionic Array I core interview pattern
100·1
Trionic Array II core interview pattern
001·1
Product bridge
Carry pattern practice into a live interview workflow
Use pattern hubs to internalize the signals and solve frame first, then move into the LeetCode Interview Copilot or desktop download path when you want to carry that structure into live interview conditions.
FAQ
A few common questions about pattern hubs
Use these questions to decide whether you should start from patterns, topics, or the live product workflow next.
When should I use pattern hubs?add
Use pattern hubs when you want reusable solving frames instead of only collecting isolated accepted solutions.
Should I start from topic hubs or pattern hubs?add
Start from topic hubs if you already know the weak area. Start from pattern hubs if you want a cleaner shared skeleton across many problem families.
What does a pattern hub include?add
Pattern hubs include recognition cues, recommended ladders, linked topic families, and a progressive-loading problem bank so you can learn the frame before expanding volume.
How does pattern practice connect to the product CTA?add
Build the frame here first, then move into the LeetCode Interview Copilot or desktop download flow when you want to carry that practice into live interview conditions.