Why is My Entity Framework 6 Async Operation 10x Slower Than its Synchronous Counterpart?

Why are asynchronous operations ten times slower than synchronous operations in Entity Framework 6?

Introduction

This article explores a common problem encountered when using Entity Framework (EF) 6 asynchronous operations: significant performance degradation compared to synchronous operations. We'll dive into the causes of this behavior and provide solutions.

Problem Overview

Users often experience significant slowdowns, even ten times or more, when switching Entity Framework queries to their asynchronous equivalents (e.g. using ToListAsync() instead of ToList()).

Research and Analysis

After investigating using SQL Server Profiler, I found that the underlying SQL query remains the same for both synchronous and asynchronous EF calls. However, asynchronous operations take significantly longer to execute.

Root cause

The performance difference is due to internal implementation details of EF 6 asynchronous operations. Unlike the synchronous version, the asynchronous version uses a buffering mechanism to read data from the database in a non-sequential manner. This approach is intended to improve performance in some cases, but can have adverse effects when dealing with large columns of binary data.

The buffering process involves creating a large number of asynchronous tasks, which creates a lot of overhead in terms of task creation, synchronization, and thread usage. This overhead, combined with non-sequential data reads, results in significant performance degradation.

Suggestion

To mitigate this issue, it is recommended to avoid using EF 6's asynchronous operations with tables containing binary data columns. Instead, execute the query synchronously, using TaskCompletionSource to wrap the results in an asynchronous callback if needed.

Conclusion

While EF 6's asynchronous operations can improve performance in some cases, the way they are currently implemented can cause significant overhead when processing large binary data. Developers should be aware of this limitation and consider using alternatives if necessary.

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