Race conditions are one of the trickiest and most elusive bugs in multithreaded programming. They occur when multiple threads access and modify shared data simultaneously, leading to unpredictable results.
In this article, we will explore how to intentionally create a race condition, analyze why it happens, and demonstrate the best ways to properly simulate a race condition in C#.
Understanding a Race Condition with a Simple Counter
Before diving into real-world examples, let’s start with a simple demonstration using a counter. This example shows a basic race condition where multiple threads increment a shared variable.
using System;
using System.Threading;
class Program
{
static int counter = 0;
static void IncrementCounter()
{
for (int i = 0; i < 1000; i++)
{
counter++; // NOT ATOMIC → Race condition occurs
}
}
static void Main()
{
Thread thread1 = new Thread(IncrementCounter);
Thread thread2 = new Thread(IncrementCounter);
thread1.Start();
thread2.Start();
thread1.Join();
thread2.Join();
Console.WriteLine($"Final counter value: {counter} (Expected: 2000)");
}
}
Why is this a race condition?
The statement counter++ is not atomic; it consists of three operations:
- Read counter value.
- Increment the value.
- Store the updated value back. When multiple threads execute this statement simultaneously, they may read the same initial value before either writes it back, leading to lost updates.
This example demonstrates a race condition, but in practical applications, race conditions often involve more than just a counter. Let’s now look at a real-world scenario and test different ways to create a race condition.
A More Real-World Example: Bank Account Transactions
In this example, we will use a bank account withdrawal system where multiple users attempt to withdraw money simultaneously. This allows us to observe how race conditions might affect financial transactions.
If a race condition occurs, we might see an incorrect final balance, such as a negative value, indicating that multiple threads withdrew money at the same time without proper synchronization. If no race condition occurs, we would expect the final balance to be exactly 0 after all withdrawals are complete.
using System;
using System.Threading;
class BankAccount
{
private int _balance;
public int GetBalance() => _balance;
public BankAccount(int initialBalance) {
_balance = initialBalance;
}
public void Withdraw(int amount) {
if (_balance > 0) {
_balance -= amount;
}
}
}
class Program
{
static void Main() {
BankAccount account = new BankAccount(1000);
int threadCount = 15;
Thread[] threads = new Thread[threadCount];
for (int i = 0; i < threadCount; i++) {
threads[i] = new Thread(() => account.Withdraw(100));
threads[i].Start();
}
foreach (var thread in threads)
thread.Join();
Console.WriteLine($"Final account balance: {account.GetBalance()} (Expected: 0 or negative value if a race condition occurs)");
}
}
If a race condition occurs, we might see an incorrect final balance, such as a negative value, indicating that multiple threads withdrew money at the same time without proper synchronization. If no race condition occurs, we would expect the final balance to be exactly 0 after all withdrawals are complete.
Is this guaranteed to create a race condition however? The answer is no, because of thread-scheduling, CPU allocation and memory optimizations.
Thread Scheduling is Non-Deterministic
The OS scheduler decides when to switch between threads, meaning that execution order is unpredictable. Sometimes, one thread may complete all its operations before another even starts, reducing contention. Other times, two or more threads might execute _balance -= amount concurrently, leading to lost updates or corrupted values.
CPU Speed and Core Allocation
If the CPU switches context too slowly, a single thread may complete multiple operations before another thread gets a chance to run, effectively eliminating the possibility of interleaved execution. Additionally, if the CPU schedules each thread to a separate core, operations may be executed in a sequential rather than interleaved manner, further reducing the likelihood of a race condition occurring.
JIT Optimizations & Memory Reordering
The .NET JIT compiler may optimize code execution differently across runs. This means that even identical code can produce varying results based on how the compiler decides to optimize memory accesses. Additionally, modern CPUs may reorder memory writes, meaning that updates made by one thread may not be immediately visible to another, sometimes preventing or delaying expected conflicts.
Evidently, this means that while race conditions can happen, they are not guaranteed every time the program is run.
What if we increase thread count to 1000?
In an attempt to increase the likelihood of encountering a race condition, we can significantly raise the number of concurrent threads operating on the shared resource. The idea is that a higher number of threads will lead to more simultaneous access, increasing contention.
static void Main()
{
BankAccount account = new BankAccount(1000);
int threadCount = 1000;
Thread[] threads = new Thread[threadCount];
for (int i = 0; i < threadCount; i++)
{
threads[i] = new Thread(() => account.Withdraw(100));
threads[i].Start();
}
foreach (var thread in threads)
thread.Join();
Console.WriteLine($"Final account balance: {account.GetBalance()} (Expected: 0 or negative value if a race condition occurs)");
}
Increasing thread count makes issues more likely, but it does not guarantee a race condition every time.
In fact, instead of a clear race condition, you may just experience performance degradation. The final value of the counter may sometimes be close to the expected value if most threads execute sequentially rather than in parallel. However, due to random interleaving, there may still be occasional incorrect values, making the race condition inconsistent and difficult to reproduce reliably.
So, how do we reliably maximize our chances to create a race condition?
Ensuring a Race Condition Always Occurs
Adding artificial delays such as Thread.Sleep(1) inside the Withdraw method increases the likelihood of a race condition happening every time.
public void Withdraw(int amount)
{
if (_balance > 0)
{
Thread.Sleep(1); // Force a small delay to increase interleaving
_balance -= amount;
Console.WriteLine($"{Thread.CurrentThread.Name} withdrawing... Current Balance After: {_balance}");
}
}
By pausing execution at a crucial moment, this approach ensures that multiple threads access and modify _balance at the same time, making the race condition visible in every execution.
Why does this happen?
Thread.Sleep(1) causes a race condition because it forces the operating system to pause execution of the current thread and allow other threads to run. This interruption occurs after the balance check (_balance > 0) but before updating _balance, creating a window where another thread can enter the method, read the same (stale) balance, and proceed with its own withdrawal.
Since both threads see the same initial balance before either updates it, they perform calculations based on stale data, leading to incorrect final values.
Without Thread.Sleep(1), the race condition might occur unpredictably based on system load and thread scheduling, but with it, the problem is consistently reproduced, making it easier to observe and debug.
A More Natural Way – Using a Semaphore to Force Contention
While adding artificial delays – almost – guarantees a race condition, using a semaphore provides a more structured way to test for it. Instead of relying on CPU timing and artificial delays, we can line up threads at the race point and use a semaphore to release them all simultaneously. This method ensures that multiple threads enter the critical section together, increasing contention naturally.
class BankAccount
{
private int _balance;
public int GetBalance() => _balance;
public BankAccount(int initialBalance) {
_balance = initialBalance;
}
public void Withdraw(int amount) {
if (_balance > 0) {
_balance -= amount;
Console.WriteLine($"{Thread.CurrentThread.Name} withdrawing... Current Balance After: {_balance}");
}
}
}
class Program
{
static void Main() {
BankAccount account = new BankAccount(1000);
int threadCount = 150;
Thread[] threads = new Thread[threadCount];
SemaphoreSlim semaphore = new SemaphoreSlim(0); // Initially locked
for (int i = 0; i < threadCount; i++) {
threads[i] = new Thread(() =>
{
semaphore.Wait();
account.Withdraw(100);
});
threads[i].Start();
}
Thread.Sleep(1000); //Wait for all threads to start
Console.WriteLine("Releasing all threads at once...");
semaphore.Release(threadCount); // Release all threads at once!
foreach (var thread in threads) {
thread.Join();
}
Console.WriteLine($"Final account balance: {account.GetBalance()} (Expected: Negative value due to race condition)");
}
}
By using a semaphore to hold back all threads and then releasing them simultaneously, we increase the chances of multiple threads attempting to modify the shared resource at the exact same time. This forces the race condition to occur more predictably, making it easier to observe and analyze.
Of course, in this specific example, it is likely that no race condition will actually occur when you run the code. The reason is that the decrement operation (_balance -= amount;) executes so quickly that the chances of interleaving between threads are extremely low. Since there are no artificial delays (Thread.Sleep) or additional read-modify-write steps before the subtraction, most CPUs will execute each withdrawal sequentially rather than in parallel.
The OS scheduler may also serialize execution across threads, reducing the possibility of lost updates. This means that while multiple threads are running, each thread is likely seeing and modifying the most recent value of _balance rather than an outdated one.
Despite that, this approach simulates contention in a structured way, allowing you to test for race conditions without relying on unpredictable delays.
Conclusion
Detecting and understanding race conditions is crucial for writing reliable and thread-safe code. The most effective way to reliably reproduce a race condition is to synchronize all competing threads at a single execution point and release them simultaneously, as demonstrated with the semaphore approach.
By structuring your tests to create contention at the right moment, you can gain insight into how concurrent operations interact and identify potential synchronization issues.
If you are developing a multithreaded application, incorporating controlled race condition testing into your workflow can help prevent subtle concurrency bugs before they reach production.
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