Fizz Buzz Multithreaded - Solution & Explanation
Problem Statement
You have the four functions:
printFizzthat prints the word"fizz"to the console,printBuzzthat prints the word"buzz"to the console,printFizzBuzzthat prints the word"fizzbuzz"to the console, andprintNumberthat prints a given integer to the console.
You are given an instance of the class FizzBuzz that has four functions: fizz, buzz, fizzbuzz and number. The same instance of FizzBuzz will be passed to four different threads:
- Thread A: calls
fizz()that should output the word"fizz". - Thread B: calls
buzz()that should output the word"buzz". - Thread C: calls
fizzbuzz()that should output the word"fizzbuzz". - Thread D: calls
number()that should only output the integers.
Modify the given class to output the series [1, 2, "fizz", 4, "buzz", ...] where the ith token (1-indexed) of the series is:
"fizzbuzz"ifiis divisible by3and5,"fizz"ifiis divisible by3and not5,"buzz"ifiis divisible by5and not3, oriifiis not divisible by3or5.
Implement the FizzBuzz class:
FizzBuzz(int n)Initializes the object with the numbernthat represents the length of the sequence that should be printed.void fizz(printFizz)CallsprintFizzto output"fizz".void buzz(printBuzz)CallsprintBuzzto output"buzz".void fizzbuzz(printFizzBuzz)CallsprintFizzBuzzto output"fizzbuzz".void number(printNumber)Callsprintnumberto output the numbers.
Example 1:
Input: n = 15 Output: [1,2,"fizz",4,"buzz","fizz",7,8,"fizz","buzz",11,"fizz",13,14,"fizzbuzz"]
Example 2:
Input: n = 5 Output: [1,2,"fizz",4,"buzz"]
Constraints:
1 <= n <= 50
Approach Overview
Problem Overview: Four threads must cooperatively print numbers from 1 to n. One thread prints fizz for multiples of 3, another prints buzz for multiples of 5, another prints fizzbuzz for multiples of both, and the last prints the number itself. The challenge is coordinating these threads so output appears in the correct order without race conditions.
This is a classic concurrency control problem. Multiple workers want to act on the same sequence of integers. Without synchronization, threads can interleave unpredictably and produce incorrect output. The goal is to enforce ordering while keeping the logic simple and efficient.
Approach 1: Synchronize with Locks (Time: O(n), Space: O(1))
A shared counter tracks the current number from 1 to n. Each thread repeatedly acquires a mutual exclusion lock, checks whether the current number satisfies its condition, prints if appropriate, and increments the counter. If the condition does not match, the thread releases the lock so another worker can check. The lock guarantees that only one thread evaluates and updates the shared state at a time, preventing race conditions. This pattern is common in multithreading problems where threads coordinate through a shared variable.
The key idea is that the condition check and counter increment must be atomic. The lock ensures ordering and correctness, though threads may repeatedly wake and check the condition until it matches. The algorithm processes each number exactly once, so total work is O(n) with constant auxiliary memory.
Approach 2: Semaphore for Synchronization (Time: O(n), Space: O(1))
A more structured solution uses semaphores to explicitly control which thread runs next. A main semaphore handles the number-checking thread. Depending on the value of the current number, it releases one of three semaphores: fizz, buzz, or fizzbuzz. The corresponding worker prints its output and signals back so the next number can be processed.
This design avoids busy checking because each thread sleeps until its semaphore is released. The number thread determines which worker should run, ensuring the correct order and condition handling. Each integer triggers exactly one worker action, so time complexity remains O(n) and memory usage stays constant.
Semaphore-based coordination is common in interview problems involving ordered thread execution. It clearly separates responsibilities between threads and avoids repeated condition polling.
Recommended for interviews: The semaphore approach is usually preferred. It demonstrates clear reasoning about thread coordination and avoids inefficient lock polling. The lock-based method still shows you understand mutual exclusion and shared state protection, but semaphores communicate intent more directly in concurrency design.
Approach 1: Synchronize with Locks
One way to solve this challenge is by using locks or mutexes. We can lock each section of code until the appropriate printing function is executed. Here, Java's intrinsic locking via `synchronized` blocks, C#'s `lock` statement, Python's `threading.Lock`, or C++'s `std::mutex` (similarly in C with Pthreads) can be utilized. The key is to ensure only one block of code runs at a time, corresponding to the appropriate thread (fizz, buzz, fizzbuzz, number). By doing this, threads can access shared data (current number) but only when it's their turn to do so based on the divisibility rules.
This implementation uses the `threading.Lock()` in Python to synchronize access to the current number being evaluated. Each method (fizz, buzz, fizzbuzz, number) acquires the lock, checks if the shared counter needs to be accessed, performs its operation, and then releases the lock. This ensures only one thread can modify the shared state at a time and only under correct conditions.
Complexity
Time Complexity: O(n) – each number from 1 to n is checked once.
Space Complexity: O(1) – constant space is used aside from input n.
Approach 2: Semaphore for Synchronization
Another approach is to use semaphore mechanisms. Semaphores control access based on counters, which can track how many threads can access a particular resource or section of code. Here, using three semaphores for fizz, buzz, and fizzbuzz allows fine-grained control over which function gets executed when. Depending on the number being evaluated, relevant semaphores are released or acquired.
This Python solution uses `threading.Semaphore` to enable or block the execution of tasks. The `number_sem` initially allows number processing but each operation concludes with determining the next permissible operation and releasing the respective semaphore. This controls thread execution order and access to shared data.
Complexity
Time Complexity: O(n)
Space Complexity: O(1)
Approach 3: Default Approach
Try this approach in the editor →Complexity Comparison
| Approach | Complexity |
|---|---|
| Synchronize with Locks | Time Complexity: O(n) – each number from 1 to n is checked once. |
| Semaphore for Synchronization | Time Complexity: O(n) |
| Default Approach | — |
Detailed Complexity Analysis
| Approach | Time | Space | When to Use |
|---|---|---|---|
| Synchronize with Locks | O(n) | O(1) | When coordinating threads using a shared counter with mutual exclusion |
| Semaphore for Synchronization | O(n) | O(1) | Preferred when explicit thread signaling is needed to control execution order |
Video Solution
1195. Fizz Buzz Multithreaded (Leetcode Concurrency Section) • EverydayLeetcode • 4,042 views views
Watch 3 more video solutions →Frequently Asked Questions
Is Fizz Buzz Multithreaded easy or hard?
How to solve Fizz Buzz Multithreaded in O(n)?
Fizz Buzz Multithreaded Python or Java solution?
What is the best approach for Fizz Buzz Multithreaded?
What data structure is used in Fizz Buzz Multithreaded?
What is the time complexity of Fizz Buzz Multithreaded?
Is Fizz Buzz Multithreaded asked at Google, Amazon, or Meta?
Ready to solve this problem?
Practice Fizz Buzz Multithreaded with our built-in code editor and test cases.
Practice on FleetCode