How Can I Optimize Entity Framework Inserts for Large Datasets and Avoid Timeouts?

Boosting Entity Framework Insert Performance with Large Datasets

Large-scale data insertion in Entity Framework can lead to significant performance bottlenecks and transaction timeouts. This article outlines strategies to optimize this process.

Minimize SaveChanges() Calls:

Repeated calls to SaveChanges() after each insertion drastically reduces efficiency. Instead, employ these techniques:

• Batch Inserts: Execute SaveChanges() only once after all records are added.
• Interval-Based Inserts: Call SaveChanges() at regular intervals (e.g., every 100 records).
• Context Recycling: SaveChanges() at intervals, then dispose of and recreate the context to release attached entities.

Efficient Bulk Insert Example:

The following code demonstrates a high-performance bulk insertion pattern:

using (TransactionScope scope = new TransactionScope())
{
    MyDbContext context = null;
    try
    {
        context = new MyDbContext();
        context.Configuration.AutoDetectChangesEnabled = false;

        int count = 0;
        foreach (var entityToInsert in largeDataset)
        {
            ++count;
            context = AddToContext(context, entityToInsert, count, 100, true);
        }

        context.SaveChanges();
    }
    finally
    {
        if (context != null)
            context.Dispose();
    }

    scope.Complete();
}

private MyDbContext AddToContext(MyDbContext context, Entity entity, int count, int batchSize, bool recycleContext)
{
    context.Set<Entity>().Add(entity);

    if (count % batchSize == 0)
    {
        context.SaveChanges();
        if (recycleContext)
        {
            context.Dispose();
            context = new MyDbContext();
            context.Configuration.AutoDetectChangesEnabled = false;
        }
    }

    return context;
}

Key Performance Tuning Parameters:

• batchSize: Experiment with values (e.g., 100-1000) to find the optimal batch size for your system.
• recycleContext: Recycling the context improves performance by clearing tracked entities. Test to determine if this offers a benefit in your specific scenario.
• SaveChanges() Frequency: Carefully determine the ideal frequency of SaveChanges() calls for your data volume and database configuration.

By implementing these best practices, you can dramatically improve Entity Framework insert performance and prevent timeouts when working with large datasets.

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