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:
Load all data.
Store data in memory.
Begin iteration.
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:
Simple to implement
Easy to understand
Excellent for in-memory collections
Low complexity
Supported throughout the .NET ecosystem
Ideal for small and moderate datasets
Limitations of IEnumerable
Despite its popularity, IEnumerable<T> has some drawbacks.
Entire collection must be loaded first
Higher memory consumption for large datasets
Not suitable for asynchronous data retrieval
Can cause delays when processing slow data sources
May impact application scalability
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:
Streams data gradually
Lower memory usage
Asynchronous processing
Faster first result
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:
Faster processing
Reduced memory usage
Better scalability
When to Use IEnumerable
Choose IEnumerable<T> when:
Data already exists in memory
Collections are small
Operations are synchronous
Simplicity is important
Performance is not impacted by loading all records
Examples:
When to Use IAsyncEnumerable
Choose IAsyncEnumerable<T> when:
Working with large datasets
Reading files asynchronously
Streaming API responses
Processing database records
Retrieving cloud data
Handling real-time data feeds
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
| Feature | IEnumerable | IAsyncEnumerable |
|---|
| Processing Type | Synchronous | Asynchronous |
| Memory Usage | Higher | Lower |
| Large Dataset Support | Limited | Excellent |
| Data Streaming | No | Yes |
| Requires await | No | Yes |
| First Result Availability | Delayed | Immediate |
| Scalability | Moderate | High |
| API Streaming | Not Ideal | Excellent |
| Database Streaming | Limited | Excellent |
Before and After Scenario
Let's consider a reporting application.
Before Using IEnumerable
Load 500,000 records
High memory consumption
Long wait time
Slow user experience
After Using IAsyncEnumerable
Stream records gradually
Lower memory usage
Faster response time
Better user experience
This is one reason modern cloud-native applications increasingly adopt asynchronous streaming.
Best Practices
When working with these interfaces:
Use IEnumerable<T> for small in-memory collections.
Use IAsyncEnumerable<T> for large datasets.
Avoid loading entire datasets before streaming.
Use await foreach properly.
Test performance under realistic workloads.
Monitor memory usage when handling large records.
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.