How Can Multi-threading Improve C# Applications Accessing SQL Server Databases While Avoiding Deadlocks?

Multi-Threading C# Application with SQL Server Database Calls

When working with large datasets in SQL Server databases, multi-threading can significantly improve performance. One common challenge, however, is avoiding deadlocks when accessing and modifying data concurrently.

Background

The provided scenario involves a single-threaded application that processes records in a "main" table and related "child" tables, performing updates and insertions based on complex criteria. Although this approach is effective, it can be time-consuming for large datasets. To improve performance, the proposed solution aims to parallelize the processing using multiple threads.

Original Approach

The initial approach attempted to create a new data context for each batch of records from the "main" table. However, this led to deadlocks as threads stepped on each other's toes, attempting to add or update the same records simultaneously.

Multi-Threading with Task Parallel Library

To address the deadlock issue and improve performance, it's recommended to leverage the Task Parallel Library (TPL) and adopt a more centralized approach to handling data access. Here's how this can be implemented:

using (var dc = new TestDataContext())
{
    // Get all the ids of interest.
    // ...

    var problematicIds = new List<ErrorType>();

    // Use TPL's Parallel.ForEach() to process ids in parallel.
    Parallel.ForEach(ids, new ParallelOptions {MaxDegreeOfParallelism = 8},
                        id => CalculateDetails(id, problematicIds));
}

In this code, the CalculateDetails method is called for each id in parallel, without the need for multiple data contexts. This minimizes the risk of deadlocks.

Deadlock Handling

To account for potential deadlocks caused by factors such as insufficient indexes or high concurrency, a deadlock retry helper class can be employed. This class can handle deadlocks and automatically retry a certain number of times before failing with an exception.

Partitioning Strategy

If partitioning is feasible, dividing the data into distinct sets can prevent deadlocks altogether. Each partition can be processed independently in its own thread. This eliminates the possibility of contention for locks on the same data.

Conclusion

Optimizing the performance of multi-threaded applications with SQL Server database calls requires careful handling of deadlocks. Using TPL, implementing deadlock handling mechanisms, and leveraging partitioning strategies can significantly improve performance and ensure efficient and reliable data access.

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