First Unique Number


10 Prerequisite LeetCode Problems
For “1429. First Unique Number”, the following are a good preparation:
“387. First Unique Character in a String”  This problem is the most basic form of finding unique characters in a string. It will help you understand the process and logic behind identifying unique elements.
“136. Single Number”  This problem involves finding a number that appears only once in an array. It will give you the idea of how to track unique numbers and what data structures can be helpful in this regard.
“448. Find All Numbers Disappeared in an Array”  This problem can help you understand how to keep track of numbers in a sequence and how to handle missing numbers.
“350. Intersection of Two Arrays II”  It involves dealing with two arrays and finding common elements, which can be useful for understanding how to compare elements.
“217. Contains Duplicate”  This problem requires you to find duplicates in an array. It will help you understand how to track elements and their repetitions.
“260. Single Number III”  An advanced version of the single number problem where you have to find two numbers that appear only once. It can help you understand how to handle multiple unique elements.
“283. Move Zeroes”  It will help you understand how to handle and move certain elements in an array, which can be useful when maintaining the order of the queue.
“380. Insert Delete GetRandom O(1)”  This problem involves implementing a data structure with different operations. It can help you prepare for implementing the operations of the FirstUnique class.
“202. Happy Number”  This problem involves repeated operations on numbers in a sequence, which can give you a good exercise in dealing with number sequences and sets.
“141. Linked List Cycle”  Even though it is about linked lists, the problem will give you insight into handling sequences and identifying uniqueness.
These cover array operations, handling unique elements, and working with data structures, which are important for solving the “First Unique Number” problem.
Problem Classification
This is a data structures problem dealing with efficient lookup and manipulation of a collection with constraints. The key ‘What’ components are:
 Queue  Ordered collection with enqueue and dequeue operations
 Integers  Values held in the queue
 Uniqueness  Constraint that values should be distinct
 Retrieval  Getting the first unique element
 Operations  Methods to show and add elements
Based on these aspects, we can further classify this problem as:
 Data structure design  Creating a custom data structure
 Constraint enforcement  Maintaining uniqueness invariant
 Ordered retrieval  Getting first by insertion order
 Query optimization  Efficient lookups and updates
The core challenge is building a data structure that provides efficient operations to retrieve the first unique element in a queue with duplicates while enforcing the uniqueness constraint.
This requires skills in data structures, enforcing invariants, query optimization, and ordered retrieval  all focused on creating a custom queue with efficient lookups. The key is balancing retrieval speed with constraint enforcement.
Clarification Questions
Here are some clarification questions that could be asked about this queue with unique elements problem:
Are duplicate elements allowed to be enqueued?
Does uniqueness apply only to consecutive elements or across the entire queue?
For integer values, is the range limited or unlimited?
Can we assume the queue is initialized with at least one element?
Can we modify the input queue or does it need to be unchanged?
Is retrieval order firstinfirstout (FIFO) or can it differ?
Should showFirstUnique() return 1 if queue is empty?
Can showFirstUnique() and add() be called concurrently by multiple threads?
Are there constraints on space complexity of data structure?
Does showFirstUnique() need to be constant time O(1) or is linear O(N) allowed?
Can we apply additional buffers, queues or storage beyond given queue?
Asking clarifying questions helps validate assumptions, exposes edge cases, and provides insights into intended behavior, constraints, and performance expectations. This additional context improves and focuses the problem analysis.
Problem Analysis and Key Insights
Here are some key insights gained from analyzing the problem statement:
Need to model the queue and uniqueness constraint, which suggests a custom data structure.
Retrieving the first unique element indicates fast lookup time is important.
add() and showFirstUnique() imply common operations needed are enqueue, dequeue, and retrieval.
Large number ranges suggest efficient storage like hash tables is needed.
No constraint on space complexity means we can optimize for time complexity.
Tracking uniqueness across entire queue rather than just consecutive values requires global knowledge.
Input queue being passed in means we may be able to leverage it rather than recreate storage.
Reasonable constraints on number of operations allows more complex data structures.
Order needs to be maintained to return first unique, ruling out unordered sets.
Described as a queue suggests maintaining FIFO order.
The key insights are around optimizing lookups and updates via efficient data structures while enforcing uniqueness globally. The constraints allow flexibility in design choices to achieve these goals.
Problem Boundary
Based on analyzing the problem statement, here is how I would summarize the scope:
Inputs:
 Integer array to initialize queue
 Individual integers to add to queue
Outputs:
 First unique integer in queue
 1 if none exists
Objective:
 Design data structure supporting enqueue, dequeue, and retrieval ops
 Retrieve first unique element in queue
 Maintain uniqueness of elements
Focus:
 Efficient lookups, updates, and retrieval
 Enforcing uniqueness
 Queue/FIFO order
Out of Scope:
 Exact implementation details
 Memory usage unless excessive
 Retrieval ops other than first unique
 Concurrent add/retrieval
So in essence, the scope focuses on efficiently implementing enqueue, dequeue, and ordered retrieval while maintaining global uniqueness of elements. The problem formulation gives flexibility in implementation details.
Here are some ways we can establish clear boundaries for the scope of this unique elements queue problem:
The inputs are limited to initializing with an integer array, and individual integer operations for add and retrieval. No other inputs.
The only outputs are the first unique integer, or 1 if none exists. We don’t need to output the full queue state.
We only need to implement enqueue, dequeue and first unique retrieval functionality. Other queries like peeking, editing etc. are out of scope.
The uniqueness constraint is global to the entire queue, not just consecutive values. Duplicate elements can be enqueued.
No specific constraints are provided on time or space complexity, so we have freedom to optimize as needed.
The order of returned elements must be firstinfirstout (FIFO) queue order.
Concurrency and thread safety are not required based on the problem description.
Only integers are used, so no need to handle generic data types.
Reasonable constraints on number of ops allows more advanced data structures.
Keeping the scope narrowly focused on the required operations, data types, and uniqueness constraint enables solving the core problem without getting distracted by unnecessary generalizations or embellishments. The boundaries help isolate what matters most.
Distilling the Problem to Its Core Elements
Here are my thoughts on analyzing this unique elements queue problem:
Fundamental concept:
 This problem boils down to constraint enforcement  maintaining an invariant that elements must be unique while efficiently supporting operations. The core technique is optimized data structures.
Simple explanation:
 We want to store numbers uniquely in the order they are added, and get the first unique one quickly.
Core problem:
 Retrieve first unique element in a queue efficiently while enforcing uniqueness. We can simplify it to optimizing retrieval under a uniqueness constraint.
Key components:
 Storage for queue elements
 Uniqueness checking on add
 Fast retrieval of first unique
Minimal operations:
 Store elements
 Check uniqueness on add
 On retrieval, iterate to find first unique
 Return 1 if none exists
So in essence, this problem involves using appropriate data structures and constraints to optimize retrieving the first unique element in an evolving queue.
Visual Model of the Problem
Here are some ways we could visualize this queue with unique elements problem to provide more intuition:
Queue diagram  Show enqueue at end, dequeue from front. Cross out duplicates. Circle first unique.
Timeline  Horizontal timeline showing elements being added and removed from queue.
Set visualization  Show set of elements with outline color indicating uniqueness. Highlight first unique.
Animated bars  Animated vertical bars representing elements, color coded by uniqueness. Bar for first unique stands out.
Factory analogy  Visualize queue as conveyor belt in factory, with quality control removing duplicate elements.
Graph  Model queue as graph with nodes for elements and edges indicating order. Color by uniqueness.
Interactive UI  Show queue visually with ability to enqueue/dequeue and highlight first unique element.
Visualizing the queue operations and uniqueness constraint over time provides intuition. Interactivity allows playing with different conditions. Graphs and analogies map concepts.
Problem Restatement
Here’s how I would paraphrase the problem statement in my own words:
We need to implement a queue data structure that stores integer numbers and supports adding and removing elements, with the additional constraint that elements must be unique.
The queue starts out initialized with an array of integer numbers.
We need to provide two key operations:
showFirstUnique()  Return the first element in the queue that appears only once, or 1 if no unique element exists. The order should follow queue insertion order.
add(value)  Insert the given integer value into the queue, unless it already exists. Maintain uniqueness.
The key challenge is efficiently retrieving the first unique element in insertion order, while also enforcing the uniqueness constraint when adding new elements.
We can leverage the initialization array as internal storage, but need to build data structures on top to optimize the required operations under the uniqueness constraint.
By paraphrasing, we can focus on the core challenge of efficiently retrieving the first unique element from an evolving queue with duplicates. The problem revolves around optimized data structures and constraints.
Abstract Representation of the Problem
Here is one way we could formulate an abstract representation of this queue with unique elements problem:
We want to implement an ordered collection C of elements with the following constraints:
C supports insertion of an element e if no identical element already exists in C
C supports removing and returning the first element by order of insertion
C supports querying for the first element in insertion order that has only one occurrence in C
The goal is to build C with an interface supporting these operations in an optimized way.
More formally:
C  Ordered collection of elements
C.insert(e)  Insert e into C if e does not already exist in C
C.removeFirst()  Remove and return first element by insertion order
C.firstUnique()  Return first element in C with exactly one occurrence, or null if none
By representing the problem in terms of an abstract ordered collection C, we can focus on what operations it needs to support and the uniqueness constraint. This helps relate it to general concepts in data structures and algorithms rather than specifics of queues and integers.
Terminology
Here are some key terms and concepts that are important for this queue with unique elements problem:
Queue  Ordered collection where elements are inserted at end (enqueue) and removed from front (dequeue). Maintaining insertion order is key.
Firstinfirstout (FIFO)  Queue property where first element inserted is first removed. Important for correct retrieval.
Hash table  Data structure that supports fast insertion, deletion, and lookup of values. Can help enforce uniqueness.
Invariant  Condition that must always be maintained, like uniqueness of elements here.
Amortized analysis  Method of analyzing overall cost over sequence of operations. Can model repeated lookups/insertions.
Time complexity  Run time efficiency of operations like insertion and retrieval. Critical for optimization.
Space complexity  Memory usage of supporting data structures. May influence design decisions.
Ordered retrieval  Returning elements in same order as inserted. Required for first unique.
The core concepts center around leveraging data structures like queues, hash tables, and heuristics like amortized analysis to optimize time complexity of operations under the invariant that elements must remain unique.
Problem Simplification and Explanation
Here’s one way I could break down this unique elements queue problem into simpler concepts using a metaphor:
Let’s compare the queue to a line of people waiting to buy concert tickets.
Each person in line represents a number in the queue. A person’s spot in line is like the number’s position in the queue.
The ticket seller only sells one ticket per show to each person. This is like only allowing unique numbers in the queue.
When a new person joins the line, the ticket seller checks if they already sold them a ticket. If so, they make the person leave. This is like checking uniqueness before enqueueing.
The ticket seller always helps the person at the front of the line first. This is like dequeuing from the front.
The manager wants to know who is the first person in line who hasn’t bought a ticket yet. This is like finding the first unique number.
So the queue is like the line of people, with position indicating order. Uniqueness is enforced by the ticket seller. The manager’s query is like finding the first unique element.
This concert ticket line analogy maps the concepts of ordered storage, enforced uniqueness, and optimized retrieval into a more concrete scenario.
Constraints
Here are some characteristics and constraints from the problem statement that could help optimize finding an efficient solution:
Integer values  Allows using fixed size storage and direct addressing with arrays/hash tables. More efficient than generic data.
Large value range (up to 10^8)  Implies need for efficient storage like hash tables rather than linear array search.
Up to 50,000 operations  Constraint allows more complex data structures like trees and graphs that have higher per operation costs. Simple linked lists likely insufficient.
Duplicate values allowed  This changes the typical FIFO queue implementation and requires tracking uniqueness as an added constraint.
Uniqueness across entire queue  Requires global knowledge rather than just checking consecutive values. Impacts efficiency.
Retrieval by insertion order  Maintaining order will factor into the data structure design. Sets alone insufficient.
No specified complexity constraints  Gives flexibility to optimize for faster time at the expense of more space.
These characteristics suggest hashed based data structures to efficiently enforce uniqueness globally while maintaining FIFO order. The large domain requires efficient lookup and storage. Relatively small operations count allows more complex structures to optimize retrieval.
Here are some key insights gained from analyzing the constraints:
Integer values allow direct storage and addressing without serialization. More efficient.
Large domain signals need for hashing or indexing to efficiently find elements, rather than just iterating.
Specified upper bound on operations allows more complex data structures like balanced trees. Simple linked lists likely too slow.
Allowing duplicate element enqueueing requires explicitly tracking uniqueness as an added constraint.
Need for first unique retrieval implies maintaining insertion ordering, so unordered sets alone insufficient.
Uniqueness across entire queue requires global knowledge about all elements. Can’t just check adjacent.
Lack of specific complexity constraints gives freedom to optimize for faster lookups by using more memory.
Can leverage provided initialization array as a starting point for storage.
Overall, the constraints suggest hashing structures combined with linked storage to efficiently enforce global uniqueness and insertion ordering. The limitations allow flexibility in optimizing lookup speed, which is a priority for first unique retrieval.
Case Analysis
Here are some additional test cases covering edge cases and different scenarios:
Empty queue
 Input: []
 Output: 1
 Reasoning: No elements so no first unique.
One element
 Input: [5]
 Output: 5
 Reasoning: Single element is trivially unique.
All unique
 Input: [1, 2, 3]
 Output: 1
 Reasoning: All unique so first is output.
Contains duplicates
 Input: [1, 2, 2, 3]
 Output: 3
 Reasoning: Skips over duplicate.
All duplicates
 Input: [1, 1, 1]
 Output: 1
 Reasoning: No uniqueness.
Large inputs
 Input: [1, 2, …, 100000]
 Output: 1
 Reasoning: Scalability.
Edge cases:
 Empty queue
 Queue with no uniqueness
The cases validate handling empty queues, large inputs, lack of uniqueness, presence of duplicates, and that first unique element is properly returned.
Here are some ways to visualize the test cases for this unique elements queue problem:
Empty queue  Empty array/blank space.
One element  Single node.
All unique  Distinct nodes with each connected to next.
Contains duplicates  Graph with duplicate nodes clustered together.
All duplicates  Graph with all nodes overlapping.
Large input  Zoomed out graph with small nodes.
First unique returned  Graph with node colored differently.
Sequence over time  Animation showing elements being enqueued and dequeued.
We can represent cases using:
 Graphs with nodes as elements
 Animations of operations over time
 Queue diagrams with elements shown sequentially
Visualizing requests as graphs and animations provides intuition about uniqueness and order. Diagrams help highlight output.
Here are some key insights gained by analyzing the different test cases:
Empty and single element cases reveal need for defensive coding and edge handling.
Unique and duplicate elements cases validate uniqueness constraint is enforced properly.
Duplicate edge case validates first unique logic handles lack of uniqueness correctly.
Large input validates scalability and performance.
First unique return case confirms output matches first unique in order.
Animation over time shows sequence of operations is correct.
Visual graphs illustrate duplicate detection and ordering logic works.
Diagrams help validate enqueue/dequeue order is properly maintained.
Overall, the cases validate the core logic and constraints around uniqueness and ordered operations are handled correctly across diverse conditions. Defensive coding for edge cases is highlighted. Visualizing the cases provides further confidence the implementation is robust.
Identification of Applicable Theoretical Concepts
Here are some mathematical and algorithmic concepts that could help simplify and solve this queue with unique elements problem:
Hashing functions  Could hash the integers for efficient lookups, inserts and deletes. Useful for enforcing uniqueness.
Linked lists  Allow efficient insertion and deletions and maintaining order. Useful for implementing queues.
Graph theory  Could model as graph problem with edges indicating order constraints.
Amortized analysis  Helps consider aggregate cost over sequence of operations. Useful for modeling cost tradeoffs.
Queue abstract data type  Provides interface for standard queue operations like enqueue, dequeue.
Trees  Potentially balanced binary trees or heaps could efficiently track ordering.
Set theory  Uniqueness constraint maps directly to mathematical sets which cannot contain duplicates.
Modular arithmetic  Useful for large integer ranges when storing in arrays.
In particular, hashing, linked data structures, amortized analysis, and leveraging set and queue theory seem like potentially useful mathematical tools that could simplify modeling the problem and lead to an optimized solution.
Simple Explanation
Here is how I would explain this queue with unique elements problem in simple nontechnical terms:
Let’s imagine you operate a jewelry store that engravings names on rings for customers.
Customers give you their name when they order a ring to be engraved. This is like numbers being added to the queue.
You only engrave each name once for every customer since it’s their personal ring. This is like the queue only allowing unique numbers.
Customers pick up their rings in the order they ordered them. The first person in line takes their ring first. This is like removing numbers from the queue in insertion order.
Your manager asks who is the first customer in line whose name has only been engraved once so far. This is like finding the first unique number in the queue.
If no customer has a uniquely engraved name yet, you tell your manager no such customer exists. This is like returning 1 if no unique number.
So in plain terms, the queue is like the customer line with unique engraving names. Requests to add and find the first unique name correspond to queue operations.
Problem Breakdown and Solution Methodology
Here is how I would approach solving this unique elements queue problem:
Create a Queue class to represent the queue. Internally use a dynamic array to store elements.
To add elements, first check if element already exists using a HashSet. If not, add to array.
To remove, take first element from array.
For first unique, linearly scan array comparing counts in HashSet. Return first with count 1.
If array exhausted, no unique so return 1.
For example:
Init queue: [1, 2, 2]
Add(3): [1, 2, 2, 3]
First unique: 1
Add(2): No change since duplicate First unique: 1
Remove(): [2, 2, 3] First unique: 3
This uses array to preserve order, hash set for fast lookups to enforce uniqueness, and linear scan to find first unique.
Changes affecting complexity:
 Larger allowed range increases hashing time
 More operations increases scan time
The hybrid data structure balances ordered storage and fast uniqueness checking for correctness and reasonable complexity.
Inference of ProblemSolving Approach from the Problem Statement
Here are the key terms and how they guide the solution approach:
Queue  FIFO structure guides using array or linked list for ordered storage.
Unique  Uniqueness constraint guides using a hash table or set for fast duplicate checking.
Integer  Discrete numeric values allow array indexing and hashing functions.
Retrieval  Need for efficient retrieval of first unique guides linear scans or indexing.
Add/remove  Standard queue operations guide implementing enqueue and dequeue logic.
Large values  Big input range guides using hashing for efficient storage and lookup.
Ordered  Maintaining queue order guides array or linked list with inherent ordering.
The core terms like queue, unique, retrieval, and ordered signal marrying data structures like arrays, linked lists, and hash tables to get both correct queue behavior and efficient uniqueness checking and retrieval. Discrete integers allow direct hashing and array storage. Ordering and uniqueness constraints guide the implementation.
We can visualize the key properties and structures of this problem using tables and diagrams:
Queue ordering:
 Show elements in linear order with arrow indicating insertion direction
[1] > [2] > [3]
Hash set membership:
 Draw a circle for set of elements
 Use color to indicate uniqueness
{1, 2, 2}
U D
Sequence over time:
 Horizontal timeline with queue operations shown sequentially
Initialize: [1, 2, 3]
Add(2) > [1, 2, 3, 2]
Remove() > [2, 3, 2]
State transitions:
 Directed graph with nodes as queue states, edges for operations
The core concepts around ordered storage, uniqueness, and sequencing over time can be captured visually. Diagrams provide an intuitive feel for the dynamics of the problem.
How did you infer from the problem statement that this problem can be solved using ?
Simple Explanation of the Proof
I’m having trouble understanding the proof of this algorithm. Could you explain it in a way that’s easy to understand?
Stepwise Refinement
Here is one way we could break down the highlevel solution into more concrete steps:
Create Queue class with backing DynamicArray to store elements
Implement enqueue(element) method
 Check HashSet for existing element
 Insert into DynamicArray
Implement dequeue() method
 Remove from front of DynamicArray
Implement firstUnique()
 Linearly scan DynamicArray
 Return first element with count 1 in HashSet
Initialize by inserting input array into Queue
Expose enqueue and firstUnique externally
This breaks the solution down into:
 Data structure initialization
 Core queue methods (enqueue, dequeue)
 firstUnique retrieval operation
 Initialization
The queue operations like enqueue and dequeue can be developed independently from firstUnique retrieval.
There is a repeating pattern of checking the HashSet for uniqueness on insertions.
Decomposing the solution helps build up each piece before integrating them together.
Solution Approach and Analysis
Here is how I would approach solving this unique elements queue problem:
Create a Queue class with a LinkedList to store elements in insertion order
Maintain a HashSet to check element uniqueness
For enqueue(element):
 Check HashSet if element already exists
 If so, return
 Insert element into LinkedList
 Insert element into HashSet
 Check HashSet if element already exists
For dequeue():
 Remove first node from LinkedList
 Remove element from HashSet
For firstUnique():
 Iterate over LinkedList
 Return first node value with count 1 in HashSet
 If none found, return 1
For example:
Init Queue: [1, 2, 2]
Enqueue(3): [1, 2, 2, 3] First unique: 1
Enqueue(2): No change
First unique: 1
Dequeue(): [2, 2, 3] First unique: 3
The LinkedList preserves order, HashSet enables uniqueness, and iteration finds first unique.
Changes affecting complexity:
 More allowed values increases HashSet time
 More operations increases iteration time
So this leverages complementary data structures to satisfy the requirements.
Identify Invariant
The key invariant in this unique elements queue problem is:
At all times, no duplicate elements exist in the queue data structure.
This can be proven by induction:
Base case: The queue is initially empty, trivially satisfying no duplicates.
Inductive step: Assume no duplicates currently exist. When enqueueing a new element, we only insert if the element does not already exist. This maintains the no duplicates invariant. When dequeuing an element, we remove one instance, which again maintains uniqueness.
Therefore, by induction, the queue elements remain unique throughout all operations.
This invariant is crucial for the correctness of the queue implementation. It ensures:
The uniqueness constraint is always satisfied.
firstUnique() will always return either a valid element or 1 if none exists, never crashing.
The output integrity is maintained as the queue evolves via operations.
By preserving this invariant, we can guarantee the queue behavior remains welldefined and correct.
Identify Loop Invariant
What is the loop invariant in this problem?
Is invariant and loop invariant the same for this problem?
Identify Recursion Invariant
Is there an invariant during recursion in this problem?
Is invariant and invariant during recursion the same for this problem?
Thought Process
Here is how I would think through solving this type of problem:
First, identify cues from the problem statement:
 Maintaining a queue with order
 Ensuring only unique elements
 Efficiently querying first unique
This suggests:
 Using complementary data structures  one for ordering, one for uniqueness
 Checking constraints on insert
 Iterating to find first unique
Some insights about the problem:
 Array or linked list good for maintaining order
 Hash table or set good for checking uniqueness
 Can layer data structures to get both features
 Tradeoff between space and time complexity
I would:
 Create Queue class with linked list to store elements
 Use hash set to check uniqueness
 For enqueue, check set before inserting into linked list
 For dequeue, remove from front of linked list
 For first unique, iterate linked list and return first element with count 1 in set
Here is Python code implementing this approach:


This uses complementary data structures to get both correct queue behavior and efficient uniqueness checking.
Establishing Preconditions and Postconditions
Here is how I would analyze the enqueue() method:
 Parameters:
 element: Integer  The value to add to the queue
 Preconditions:
 The queue data structure must be initialized
 element must be within allowed integer range
 Functionality:
 Checks if element already exists in queue using hash set
 If not, inserts element into backing linked list to preserve order
 Also inserts into hash set to track uniqueness
 Postconditions:
 If element was unique, it is now added to the queue
 The uniqueness invariant is maintained
 The insertion order queue property is maintained
 Error handling:
 If element already exists, do nothing
 If preconditions fail, throw IllegalArgumentException
In summary, enqueue() attempts to insert a value while maintaining queue and uniqueness invariants. The method depends on and maintains the state of the underlying data structures.
Problem Decomposition
Here is how I would break down this problem into logical components:
Problem Understanding: We need to implement a queue that stores unique integer values and provides efficient operations to add, remove, and find the first unique element.
Initial Breakdown:
 Maintain queue with order
 Check uniqueness on add
 Efficiently find first unique
 Implement standard queue operations like add and remove
 Subproblem Refinement:
 Use linked list or array to store queue values
 Leverage hash table or set for fast uniqueness checking
 Iterate and track counts to find first unique
 Expose standard API: add(), remove(), firstUnique()
 Task Identification:
 Queue storage
 Uniqueness tracking
 Finding first unique
 Adding element
 Removing element
 Task Abstraction:
 Ordered storage
 Uniqueness checking
 First unique finding
 Insertion
 Deletion
 Method Naming:
 storeOrdered()
 checkUniqueness()
 findFirstUnique()
 insert()
 delete()
 Subproblem Interactions:
 Implement ordered storage and uniqueness checking
 Layer them to provide queue operations:
 insert() checks uniqueness before inserting
 remove() deletes from ordered storage
 findFirstUnique() iterates ordered storage and checks counts
This breaks the problem down into logical focused subtasks that can be assembled to solve the overall problem.
From Brute Force to Optimal Solution
Here is one approach to optimize a brute force solution:
Brute force:
 Use array to store queue values
 On add, linear scan to check uniqueness O(n)
 On find first unique, scan array counting frequencies O(n)
This is inefficient, taking O(n) time for both operations.
Optimizations:
 Hash table for uniqueness check:
 On add, check hash table for existence O(1)
 Reduce insertions to O(1) amortized
Avoiding repeated linear scans improves insertion.
 Linked list for efficient inserts and deletes:
 Doubly linked list allows O(1) insert and deletes
 Improves over array shifting on deletions
Linked list improves deletions compared to arrays.
 Track frequencies in hash table:
 Increment counts on insert
 First unique can early exit when count is 1
 Avoid rescanning entire structure
Track frequencies for efficient first unique finding.
 Hybrid data structure:
 Use linked list for storage, ordering
 Use hash table for fast uniqueness and frequency checks
Get benefits of both data structures.
Together these optimizations improve overall complexity from O(n) to O(1) for core operations by using appropriate data structures.
Code Explanation and Design Decisions
Here is how I would analyze this code’s logic:
The queue array contains the initial state to process. Hashset tracks uniqueness.
The main loop iterates over the queue array, adding elements to the hashset. This builds up knowledge useful later.
Inside this loop, if the element already exists in the hashset, the conditional skips it to enforce uniqueness.
The hashset is updated within the loop to keep the uniqueness state uptodate.
The invariant is that the hashset tracks the uniqueness of elements seen so far. This directly satisfies the core problem constraint.
The final output firstUnique uses the iteratively updated hashset to efficiently find and return the first unique element in constant time.
In summary, the loops incrementally build up state knowledge by enforcing uniqueness into the hashset. This state allows firstUnique to leverage the accumulated knowledge for an efficient lookup that satisfies the problem requirements.
Coding Constructs
Here is how I would analyze this code:
The highlevel techniques appear to be linear iteration and hash tables. The queue is processed sequentially while using a hash table for fast lookups.
I would explain the code is putting numbers into a queuelike structure, making sure there are no repeats, and finding the first unique number in the order added.
The logical constructs are:
 Looping for sequential processing
 Hash table for efficient lookup
 Conditional checks for uniqueness
 Returning first match
The algorithm iterates the queue, tracking uniqueness in a hash table, and returns the first element found with a frequency of 1.
The key steps are iteration, hash table uniqueness tracking, conditional checking, and early return when a unique is found. This leverages efficiency of hashing.
The core strategies are linear processing combined with hash tables for efficient lookup. These paradigms could be implemented in any language.
The analysis focuses on the highlevel logic rather than syntactic details. Explaining it in plain English helps reinforce the core algorithmic concepts.
Language Agnostic Coding Drills
Here is how I would break down this code into coding concepts:
Level 1:
 Array iteration  Loop over elements
 Returning value  Early exit
Level 2:
 Hash table update  Add/increment counts
 Membership check  Look up in hash
Level 3:
 Linear search  Scan array for condition
 Conditional return  Return if criteria met
Level 4:
 Amortized analysis  Aggregate complexity over operations
 Time/space tradeoffs  Balance different structures
The overall problem solving approach would be:
 Set up array for queue and hash table for tracking
 Iterate array, update hash table counts
 On query, linearly scan array for hash count of 1
 Return element if condition met, else default
 Analyze time complexity aggregated over operations
 Choose appropriate data structures based on tradeoffs
Each coding concept builds incrementally from simple iteration and returning values to more advanced analysis and tradeoff evaluation. Combining these pieces can solve the full problem.
Targeted Drills in Python
Here are some example Python coding drills for the key concepts:
 Array iteration


 Hash table update


 Conditional return


 Amortized analysis


 Time/space tradeoff


Bringing this together to solve the problem:
 Set up array and hash table
 Populate hash table tracking counts
 Linearly scan array, return if count is 1
 Analyze time complexity across operations
 Choose optimal data structures based on tradeoffs
These drills demonstrate core concepts that can be combined to implement an efficient solution for the problem.
Q&A
Similar Problems
Here are 10 problems that use similar underlying concepts:
Find All Duplicates in an Array (https://leetcode.com/problems/findallduplicatesinanarray/)  Involves tracking element uniqueness using hash table.
Linked List Cycle II (https://leetcode.com/problems/linkedlistcycleii/)  Fast/slow pointers used like hash table for duplicate detection.
Longest Substring Without Repeating Characters (https://leetcode.com/problems/longestsubstringwithoutrepeatingcharacters/)  Sliding window and hash table to track uniqueness.
Remove Duplicates from Sorted Array (https://leetcode.com/problems/removeduplicatesfromsortedarray/)  Mutating array while maintaining uniqueness constraint.
Contiguous Array (https://leetcode.com/problems/contiguousarray/)  Tracking frequency sums using hash table.
Find Pivot Index (https://leetcode.com/problems/findpivotindex/)  Prefix sum tracking analogous to tracking uniqueness.
First Unique Character in a String (https://leetcode.com/problems/firstuniquecharacterinastring/)  First unique element retrieval.
Find Common Characters (https://leetcode.com/problems/findcommoncharacters/)  Hash table used to track uniqueness constraints.
Number of Atoms (https://leetcode.com/problems/numberofatoms/)  Parsing and tracking uniqueness of elements.
Encode and Decode Strings (https://leetcode.com/problems/encodeanddecodestrings/)  Custom data structure with constraints.