Optimizing Ef Core Queries

Optimizing EF Core Queries

When to Use

• EF Core queries are slow or generating too many SQL statements
• Database CPU/IO is high due to ORM inefficiency
• N+1 query patterns are detected in logs
• Large result sets cause memory pressure

When Not to Use

• The user is using Dapper or raw ADO.NET (not EF Core)
• The performance issue is database-side (missing indexes, bad schema)
• The user is building a new data access layer from scratch

Inputs

Workflow

Step 1: Enable query logging to see the actual SQL

// In Program.cs or DbContext configuration:
optionsBuilder
    .UseSqlServer(connectionString)
    .LogTo(Console.WriteLine, LogLevel.Information)
    .EnableSensitiveDataLogging()  // shows parameter values (dev only!)
    .EnableDetailedErrors();

Or use the Microsoft.EntityFrameworkCore log category:

{
  "Logging": {
    "LogLevel": {
      "Microsoft.EntityFrameworkCore.Database.Command": "Information"
    }
  }
}

Step 2: Fix N+1 query patterns

The #1 EF Core performance killer. Happens when loading related entities in a loop.

Before (N+1 — 1 query for orders + N queries for items):

var orders = await db.Orders.ToListAsync();
foreach (var order in orders)
{
    // Each access triggers a lazy-load query!
    var items = order.Items.Count;
}

After (eager loading — 1 or 2 queries total):

// Option 1: Include (JOIN)
var orders = await db.Orders
    .Include(o => o.Items)
    .ToListAsync();

// Option 2: Split query (separate SQL, avoids cartesian explosion)
var orders = await db.Orders
    .Include(o => o.Items)
    .AsSplitQuery()
    .ToListAsync();

// Option 3: Explicit projection (best - only fetches needed columns)
var orderSummaries = await db.Orders
    .Select(o => new OrderSummary
    {
        OrderId = o.Id,
        Total = o.Items.Sum(i => i.Price),
        ItemCount = o.Items.Count
    })
    .ToListAsync();

When to use Split vs Single query:

Step 3: Use NoTracking for read-only queries

Change tracking overhead is significant. Disable it when you don't need to update entities:

// Per-query
var products = await db.Products
    .AsNoTracking()
    .Where(p => p.IsActive)
    .ToListAsync();

// Global default for read-heavy apps
services.AddDbContext<AppDbContext>(options =>
    options.UseSqlServer(connectionString)
           .UseQueryTrackingBehavior(QueryTrackingBehavior.NoTracking));

Use AsNoTrackingWithIdentityResolution() when the query returns duplicate entities to avoid duplicated objects in memory.

Step 4: Use compiled queries for hot paths

// Define once as static
private static readonly Func<AppDbContext, int, Task<Order?>> GetOrderById =
    EF.CompileAsyncQuery((AppDbContext db, int id) =>
        db.Orders
            .Include(o => o.Items)
            .FirstOrDefault(o => o.Id == id));

// Use repeatedly — skips query compilation overhead
var order = await GetOrderById(db, orderId);

Step 5: Avoid common query traps

Step 6: Use raw SQL or FromSql for complex queries

When LINQ can't express it efficiently:

var results = await db.Orders
    .FromSqlInterpolated($@"
        SELECT o.* FROM Orders o
        INNER JOIN (
            SELECT OrderId, SUM(Price) as Total
            FROM OrderItems
            GROUP BY OrderId
            HAVING SUM(Price) > {minTotal}
        ) t ON o.Id = t.OrderId")
    .AsNoTracking()
    .ToListAsync();

Validation

• SQL logging shows expected number of queries (no N+1)
• Read-only queries use AsNoTracking()
• Hot-path queries use compiled queries
• No client-side evaluation warnings in logs
• Include/split strategy matches data shape

Common Pitfalls