Entity Framework: Optimizing Large Dataset Inserts
Efficiently inserting large datasets into Entity Framework is crucial for performance. A common challenge arises when using TransactionScope with a large number of records (e.g., 4000 ), potentially exceeding the default transaction timeout (10 minutes). The key is to avoid frequent calls to SaveChanges(), which significantly slows down the process.
Several strategies can dramatically improve bulk insert speed:
-
Batch
SaveChanges(): Instead of saving after each record, callSaveChanges()once after all records have been added to the context. -
Periodic
SaveChanges(): CallSaveChanges()after a predetermined number of records (e.g., 100 or 1000). -
Context Recycling: Call
SaveChanges(), dispose of the context, and create a new one. This clears the context's change tracker, further enhancing performance.
Disabling change tracking (AutoDetectChangesEnabled = false) also boosts efficiency during bulk operations.
Example Implementation:
The following code demonstrates a high-performance bulk insert approach using batching and context recycling:
using (TransactionScope scope = new TransactionScope())
{
MyDbContext context = null;
try
{
context = new MyDbContext();
context.Configuration.AutoDetectChangesEnabled = false;
int count = 0;
foreach (var entityToInsert in someCollectionOfEntitiesToInsert)
{
++count;
context = AddToContext(context, entityToInsert, count, 1000, true); // Commit every 1000 records
}
context.SaveChanges();
}
finally
{
context?.Dispose();
}
scope.Complete();
}
private MyDbContext AddToContext(MyDbContext context, Entity entity, int count, int commitCount, bool recreateContext)
{
context.Set<Entity>().Add(entity);
if (count % commitCount == 0)
{
context.SaveChanges();
if (recreateContext)
{
context.Dispose();
context = new MyDbContext();
context.Configuration.AutoDetectChangesEnabled = false;
}
}
return context;
}
This example commits every 1000 records and recreates the context after each commit. Experimentation may reveal that different commitCount values (e.g., 100, 500, 1000) yield optimal results depending on your specific environment and data. The key is to find the balance between minimizing SaveChanges() calls and managing memory usage effectively.
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