Tutorials Entity Framework Core Tutorial
Memory Optimization in EF Core
Memory Optimization in EF Core: free step-by-step lesson with examples, common mistakes, and interview tips — part of Entity Framework Core Tutorial on Toolliyo Academy.
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Entity Framework Core Tutorial · Lesson 69 of 100
Memory Optimization in EF Core
Beginner ✓ → Intermediate ✓ → Advanced → Professional
Advanced · 3 — Production skills · ~10 min · Module 7: Performance Optimization
What is this?
Memory optimization reduces change tracker footprint — AsNoTracking, projection, streaming IAsyncEnumerable, and avoiding loading large graphs unnecessarily.
Why should you care?
ShopNest export job reading 500k orders tracked would OOM the API pod — streaming untracked rows keeps memory flat.
See it live — copy this example
Paste into a .NET project with EF Core packages, then run with LocalDB/SQL Server (dotnet ef / dotnet run).
await foreach (var row in _context.Orders
.AsNoTracking()
.Where(o => o.OrderDate >= start)
.Select(o => new { o.Id, o.CustomerId, o.OrderDate })
.AsAsyncEnumerable())
{
await writer.WriteLineAsync($"{row.Id},{row.CustomerId},{row.OrderDate:O}");
}What happened?
- AsAsyncEnumerable streams rows one at a time without ToList allocating giant list.
- AsNoTracking avoids tracker dictionaries growing with each row.
Practice next
- Use AsNoTracking on bulk reads.
- Project minimal columns for exports.
- Prefer await foreach AsAsyncEnumerable over ToListAsync for large sets.
- Compare working set in dotMemory for tracked vs streaming export.
- Batch ClearChangeTracker every 1000 tracked updates if unavoidable.
Remember
Stream large reads with AsAsyncEnumerable. No tracking + projection minimizes footprint. Never turn into C# objects unbounded lists.
ShopNest finance export
Monthly order export streams 400k rows to CSV without loading into List.
Outcome: Pod memory stays under 200MB instead of spiking to 4GB.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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