Introduction

When working with collections in C#, developers frequently use IEnumerable<T> to iterate through data. It is one of the most commonly used interfaces in .NET applications for looping through lists, arrays, database records, and other collections.

However, modern applications often deal with large datasets, cloud services, APIs, and database operations that may take time to return results. Loading all data into memory before processing can negatively impact performance and increase resource consumption.

To solve this challenge, C# introduced IAsyncEnumerable<T>, which enables asynchronous streaming of data. Instead of waiting for an entire collection to load, data can be processed one item at a time as it becomes available.

In this article, we'll explore the differences between IEnumerable<T> and IAsyncEnumerable<T>, understand their performance implications, and learn when to use each one in real-world applications.

What Is IEnumerable?

IEnumerable<T> is an interface that represents a collection of items that can be iterated using a foreach loop.

It provides a simple and efficient way to traverse data sequentially.

Example

var products = new List<string>
{
    "Laptop",
    "Mouse",
    "Keyboard"
};

foreach (var product in products)
{
    Console.WriteLine(product);
}

In this example, all data is already available in memory before iteration begins.

How IEnumerable Works

When you use IEnumerable<T>, the application retrieves data synchronously.

The process generally follows these steps:

  1. Load all data.

  2. Store data in memory.

  3. Begin iteration.

  4. Process records.

Visual representation:

Database/API
      ↓
Load Entire Collection
      ↓
Store In Memory
      ↓
Process Records

This approach works well for small and medium-sized datasets.

Real-World Example of IEnumerable

Imagine a company employee directory containing 100 employee records.

The application loads all employees and displays them on a page.

public IEnumerable<Employee> GetEmployees()
{
    return employeeRepository.GetAll();
}

Since the dataset is relatively small, loading everything at once is perfectly acceptable.

Advantages of IEnumerable

Using IEnumerable<T> offers several benefits:

Limitations of IEnumerable

Despite its popularity, IEnumerable<T> has some drawbacks.

These limitations become noticeable when working with large databases, APIs, or file processing systems.

What Is IAsyncEnumerable?

IAsyncEnumerable<T> is an interface that allows asynchronous iteration over a collection.

Instead of waiting for the complete dataset, items are returned one by one as they become available.

This feature was introduced in C# 8.0 to support asynchronous data streaming.

Example:

public async IAsyncEnumerable<int> GetNumbersAsync()
{
    for (int i = 1; i <= 5; i++)
    {
        await Task.Delay(1000);

        yield return i;
    }
}

Consuming the data:

await foreach (var number in GetNumbersAsync())
{
    Console.WriteLine(number);
}

Each value becomes available immediately after it is produced.

How IAsyncEnumerable Works

The execution flow is different from IEnumerable<T>.

Instead of waiting for the entire collection, records are streamed asynchronously.

Database/API
      ↓
Retrieve First Record
      ↓
Process Record
      ↓
Retrieve Next Record
      ↓
Process Record

This reduces memory consumption and improves responsiveness.

Real-World Example of IAsyncEnumerable

Imagine an e-commerce platform generating a report containing one million orders.

Using IEnumerable<T> would require loading all records into memory before processing.

With IAsyncEnumerable<T>, records are streamed one at a time.

public async IAsyncEnumerable<Order> GetOrdersAsync()
{
    await foreach (var order in context.Orders.AsAsyncEnumerable())
    {
        yield return order;
    }
}

The application starts processing immediately instead of waiting for the entire dataset.

Comparing Syntax

IEnumerable Example

IEnumerable<string> products = GetProducts();

foreach (var product in products)
{
    Console.WriteLine(product);
}

IAsyncEnumerable Example

IAsyncEnumerable<string> products = GetProductsAsync();

await foreach (var product in products)
{
    Console.WriteLine(product);
}

The main difference is the use of await foreach.

Performance Comparison

Performance is often the primary reason developers choose IAsyncEnumerable<T>.

Let's compare both approaches.

IEnumerable

Characteristics:

IAsyncEnumerable

Characteristics:

For large datasets, IAsyncEnumerable<T> often provides significantly better scalability.

Memory Usage Comparison

Consider processing one million records.

Using IEnumerable

1 Million Records
       ↓
Load Entire Collection
       ↓
Large Memory Consumption
       ↓
Start Processing

Using IAsyncEnumerable

1 Record
    ↓
Process
    ↓
Next Record
    ↓
Process

Only a small amount of data remains in memory at any given time.

This can dramatically reduce memory pressure.

Database Query Example

Suppose you're retrieving customer records using Entity Framework Core.

Using IEnumerable

var customers = await context.Customers
                             .ToListAsync();

foreach (var customer in customers)
{
    Console.WriteLine(customer.Name);
}

The entire result set loads before processing begins.

Using IAsyncEnumerable

await foreach (var customer in context.Customers
                                      .AsAsyncEnumerable())
{
    Console.WriteLine(customer.Name);
}

Records are processed as they arrive from the database.

This is especially useful when dealing with thousands of rows.

API Streaming Example

Imagine a weather service returning thousands of records.

Using IAsyncEnumerable<T> allows consumers to receive data immediately.

public async IAsyncEnumerable<WeatherData> GetWeatherDataAsync()
{
    foreach (var item in weatherSource)
    {
        await Task.Delay(100);

        yield return item;
    }
}

Users see results sooner instead of waiting for the full response.

File Processing Example

Large file processing is another excellent use case.

public async IAsyncEnumerable<string> ReadFileAsync(string path)
{
    using var reader = new StreamReader(path);

    while (!reader.EndOfStream)
    {
        yield return await reader.ReadLineAsync();
    }
}

The application processes lines one at a time.

Benefits include:

When to Use IEnumerable

Choose IEnumerable<T> when:

Examples:

When to Use IAsyncEnumerable

Choose IAsyncEnumerable<T> when:

Examples:

Common Mistakes Developers Make

Using IAsyncEnumerable for Small Collections

This adds unnecessary complexity.

Bad example:

public async IAsyncEnumerable<int> GetNumbers()
{
    yield return 1;
    yield return 2;
    yield return 3;
}

A simple list would be better.

Loading Data Before Streaming

Incorrect:

var data = await context.Products.ToListAsync();

foreach (var item in data)
{
    yield return item;
}

This defeats the purpose of streaming.

Instead:

await foreach (var item in context.Products.AsAsyncEnumerable())
{
    Console.WriteLine(item.Name);
}

IEnumerable vs IAsyncEnumerable: Side-by-Side Comparison

FeatureIEnumerableIAsyncEnumerable
Processing TypeSynchronousAsynchronous
Memory UsageHigherLower
Large Dataset SupportLimitedExcellent
Data StreamingNoYes
Requires awaitNoYes
First Result AvailabilityDelayedImmediate
ScalabilityModerateHigh
API StreamingNot IdealExcellent
Database StreamingLimitedExcellent

Before and After Scenario

Let's consider a reporting application.

Before Using IEnumerable

After Using IAsyncEnumerable

This is one reason modern cloud-native applications increasingly adopt asynchronous streaming.

Best Practices

When working with these interfaces:

Following these practices helps build scalable and efficient applications.

Conclusion

Both IEnumerable<T> and IAsyncEnumerable<T> are valuable tools in modern C# development, but they serve different purposes.

IEnumerable<T> is ideal for simple, synchronous, in-memory collections where performance and memory usage are not major concerns. It is easy to implement and remains a core part of the .NET ecosystem.

IAsyncEnumerable<T>, on the other hand, is designed for asynchronous data streaming. It allows applications to process records as they become available, reducing memory consumption and improving responsiveness. This makes it particularly useful for large datasets, database queries, API streaming, file processing, and cloud-based applications.

Understanding when to use each interface can significantly improve the performance, scalability, and user experience of your applications.