dotnet-linq-optimization

dotnet-linq-optimization

LINQ performance patterns for .NET applications. Covers the critical distinction between IQueryable<T> server-side evaluation and IEnumerable<T> client-side materialization, compiled queries for EF Core hot paths, deferred execution pitfalls, LINQ-to-Objects allocation patterns and when to drop to manual loops, and Span-based alternatives for zero-allocation processing.

Out of scope: EF Core DbContext lifecycle, migrations, interceptors, and connection resiliency -- see [skill:dotnet-efcore-patterns]. Strategic data architecture (repository patterns, read/write split, N+1 governance) -- see [skill:dotnet-efcore-architecture]. Span and Memory fundamentals -- see [skill:dotnet-performance-patterns]. Microbenchmarking setup -- see [skill:dotnet-benchmarkdotnet].

Cross-references: [skill:dotnet-efcore-patterns] for compiled queries in EF Core context and DbContext usage, [skill:dotnet-performance-patterns] for Span/Memory foundations and ArrayPool patterns, [skill:dotnet-benchmarkdotnet] for measuring LINQ optimization impact.

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IQueryable vs IEnumerable Materialization

The most impactful LINQ performance decision is where evaluation happens: on the database server (IQueryable<T>) or in application memory (IEnumerable<T>).

The Problem

// DANGEROUS: Materializes entire table into memory, then filters in C#
IEnumerable<Order> orders = dbContext.Orders;
var recent = orders.Where(o => o.CreatedAt > cutoff).ToList();
// SQL: SELECT * FROM Orders  (no WHERE clause!)

// CORRECT: Filter executes on the database server
IQueryable<Order> orders = dbContext.Orders;
var recent = orders.Where(o => o.CreatedAt > cutoff).ToList();
// SQL: SELECT ... FROM Orders WHERE CreatedAt > @cutoff

When Materialization Happens

Operation	Effect
ToList(), ToArray(), ToDictionary()	Executes query, loads results into memory
foreach / await foreach	Executes query, streams results
AsEnumerable()	Switches from server to client evaluation
Count(), Any(), First(), Single()	Executes query, returns scalar
Where(), Select(), OrderBy() on IQueryable	Builds expression tree (no execution)
Where(), Select(), OrderBy() on IEnumerable	Deferred in-memory evaluation

Common Mistakes

// MISTAKE 1: AsEnumerable() before filtering
var results = dbContext.Orders
    .AsEnumerable()           // <-- switches to client evaluation
    .Where(o => o.Total > 100)  // runs in memory, not SQL
    .ToList();

// MISTAKE 2: Calling a C# method in IQueryable predicate
var results = dbContext.Orders
    .Where(o => IsHighValue(o))  // Cannot translate to SQL; throws or falls back
    .ToList();

// FIX: Use expression-compatible predicates or call after materialization
var results = dbContext.Orders
    .Where(o => o.Total > 100)   // SQL-translatable
    .AsEnumerable()
    .Where(o => IsHighValue(o))  // C# logic after materialization
    .ToList();

// MISTAKE 3: Projecting too many columns
var names = dbContext.Orders.ToList().Select(o => o.CustomerName);
// Loads ALL columns, then picks one in memory

// FIX: Project before materializing
var names = dbContext.Orders.Select(o => o.CustomerName).ToList();
// SQL: SELECT CustomerName FROM Orders

Detection Checklist

• Any AsEnumerable() or cast to IEnumerable<T> before Where/Select is a potential server-bypass
• EF Core logs Microsoft.EntityFrameworkCore.Query at Warning level when it falls back to client evaluation
• Enable ConfigureWarnings(w => w.Throw(RelationalEventId.MultipleCollectionIncludeWarning)) during development

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Compiled Queries for EF Core Hot Paths

Compiled queries eliminate the per-call expression tree compilation overhead. For queries executed thousands of times per second, this can reduce overhead significantly.

Standard Compiled Query

public sealed class OrderRepository(AppDbContext db)
{
    // Compiled once, reused across all calls
    private static readonly Func<AppDbContext, Guid, Task<Order?>>
        s_findById = EF.CompileAsyncQuery(
            (AppDbContext ctx, Guid id) =>
                ctx.Orders.FirstOrDefault(o => o.Id == id));

    private static readonly Func<AppDbContext, DateTime, IAsyncEnumerable<Order>>
        s_findRecent = EF.CompileAsyncQuery(
            (AppDbContext ctx, DateTime cutoff) =>
                ctx.Orders
                    .Where(o => o.CreatedAt > cutoff)
                    .OrderByDescending(o => o.CreatedAt));

    public Task<Order?> FindByIdAsync(Guid id) =>
        s_findById(db, id);

    public IAsyncEnumerable<Order> FindRecentAsync(DateTime cutoff) =>
        s_findRecent(db, cutoff);
}

When to Use Compiled Queries

Scenario	Use compiled query?
High-frequency lookups (auth, caching)	Yes
Admin dashboard queries (low frequency)	No -- overhead is negligible
Queries with dynamic predicates (user search)	No -- cannot parameterize shape
Queries with Include() that varies	No -- includes change expression tree shape

Limitations

• Compiled queries cannot use dynamic Include() or conditional Where() clauses that change the expression tree shape
• Parameters must be simple types (no complex objects or collections)
• EF.CompileAsyncQuery returns Task<T> for single results or IAsyncEnumerable<T> for collections

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Deferred Execution Pitfalls

LINQ uses deferred execution: query operators build a pipeline that executes only when results are consumed. This is powerful but creates subtle bugs.

Multiple Enumeration

// BUG: Enumerates the database query twice
IQueryable<Order> query = dbContext.Orders.Where(o => o.Status == Status.Active);

var count = query.Count();         // Executes SQL (1st query)
var items = query.ToList();        // Executes SQL again (2nd query)

// FIX: Materialize once
var items = dbContext.Orders
    .Where(o => o.Status == Status.Active)
    .ToList();

var count = items.Count;           // In-memory, no SQL

Closure Capture in Loops

// BUG: All queries capture the same loop variable 'i' by reference
var queries = new List<IQueryable<Order>>();
for (int i = 0; i < statuses.Length; i++)
{
    queries.Add(dbContext.Orders.Where(o => o.Status == statuses[i]));
    // 'i' is captured by reference -- all queries use final value of i
}

// FIX: Copy to a local variable inside the loop body
for (int i = 0; i < statuses.Length; i++)
{
    var localStatus = statuses[i];
    queries.Add(dbContext.Orders.Where(o => o.Status == localStatus));
}

Note: C# 5+ foreach loop variables are scoped per iteration and do not exhibit this bug. The for loop index variable is shared across iterations, making this a common pitfall when building deferred LINQ queries in a loop.

Deferred Execution in Method Returns

// DANGEROUS: Returns an unevaluated query -- caller may not realize
// the DbContext could be disposed before enumeration
public IEnumerable<Order> GetActiveOrders()
{
    return dbContext.Orders.Where(o => o.Status == Status.Active);
    // Not evaluated yet -- DbContext may be disposed when caller iterates
}

// SAFE: Materialize before returning
public async Task<List<Order>> GetActiveOrdersAsync(CancellationToken ct)
{
    return await dbContext.Orders
        .Where(o => o.Status == Status.Active)
        .ToListAsync(ct);
}

---

LINQ-to-Objects Allocation Patterns

LINQ operators on in-memory collections allocate iterators, delegates, and intermediate collections. For hot paths processing thousands of items per second, these allocations can cause GC pressure.

Allocation Sources

Operation	Allocations
Where(), Select()	Iterator object + delegate
ToList(), ToArray()	New collection + possible resizing
OrderBy()	Full copy for sorting
GroupBy()	Dictionary + grouping objects
SelectMany()	Iterator + inner iterators
Lambda capture of local variable	Closure object per captured scope

When LINQ Allocation Matters

LINQ allocations are negligible for most code. Optimize only when:

• Processing is on a hot path (called thousands of times per second)
• BenchmarkDotNet shows significant Allocated bytes
• GC metrics (Gen0 collections/sec) indicate pressure

Manual Loop Alternatives

// LINQ: Allocates iterator + delegate + List<T>
var result = items
    .Where(x => x.IsActive)
    .Select(x => x.Name)
    .ToList();

// Manual loop: Single List<T> allocation, no iterator/delegate overhead
var result = new List<string>(items.Count);
foreach (var item in items)
{
    if (item.IsActive)
    {
        result.Add(item.Name);
    }
}

// LINQ: Allocates iterator + delegate + bool boxing (Any)
var hasActive = items.Any(x => x.IsActive);

// Manual loop: Zero allocations beyond the enumerator
var hasActive = false;
foreach (var item in items)
{
    if (item.IsActive)
    {
        hasActive = true;
        break;
    }
}

Reducing Allocations Without Abandoning LINQ

Before dropping to manual loops, consider these intermediate steps:

// 1. Use Array.Find / Array.Exists for arrays (no iterator allocation)
var first = Array.Find(items, x => x.IsActive);
var exists = Array.Exists(items, x => x.IsActive);

// 2. Pre-size collections when count is known
var result = new List<string>(items.Length);
result.AddRange(items.Where(x => x.IsActive).Select(x => x.Name));

// 3. Use static lambdas to avoid delegate allocation (C# 9+)
var result = items.Where(static x => x.IsActive).ToList();
// Note: static lambdas prevent accidental closure capture
// but the delegate is already cached by the compiler for
// non-capturing lambdas; the main benefit is enforcement

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Span-Based Alternatives for Collection Processing

For the highest-performance scenarios, Span<T> and ReadOnlySpan<T> enable stack-based, zero-allocation processing. These APIs are not LINQ-compatible but cover common patterns.

Span Search and Filter

// Zero-allocation contains check on an array
ReadOnlySpan<int> values = stackalloc int[] { 1, 2, 3, 4, 5 };
bool found = values.Contains(3);

// Zero-allocation index search
int index = values.IndexOf(4);

MemoryExtensions for String Processing

// Zero-allocation split and iterate
ReadOnlySpan<char> csv = "alice,bob,charlie";
foreach (var segment in csv.Split(','))
{
    ReadOnlySpan<char> value = csv[segment];
    // Process each value without allocating strings
}

// Zero-allocation trim and compare
ReadOnlySpan<char> input = "  hello  ";
bool match = input.Trim().SequenceEqual("hello");

When to Use Span Over LINQ

Scenario	Approach
Parsing CSV/log lines in a tight loop	ReadOnlySpan<char> + Split
Searching sorted arrays	Span<T>.BinarySearch
Processing byte buffers from I/O	ReadOnlySpan<byte> slicing
General business logic on collections	LINQ (readability over micro-optimization)

See [skill:dotnet-performance-patterns] for comprehensive Span/Memory patterns and ArrayPool usage.

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Query Optimization Patterns

Projection Before Materialization

Always select only the columns you need:

// BAD: Loads entire entity graph
var orders = await dbContext.Orders
    .Include(o => o.Lines)
    .Include(o => o.Customer)
    .ToListAsync(ct);

var summaries = orders.Select(o => new
{
    o.Id,
    o.Customer.Name,
    Total = o.Lines.Sum(l => l.Price * l.Quantity)
});

// GOOD: Project in the query -- single SQL with computed columns
var summaries = await dbContext.Orders
    .Select(o => new
    {
        o.Id,
        CustomerName = o.Customer.Name,
        Total = o.Lines.Sum(l => l.Price * l.Quantity)
    })
    .ToListAsync(ct);

Pagination with Keyset (Seek) Method

// Offset pagination: O(N) -- server must skip rows
var page = await dbContext.Orders
    .OrderBy(o => o.Id)
    .Skip(pageSize * pageNumber)
    .Take(pageSize)
    .ToListAsync(ct);

// Keyset pagination: O(1) -- index seek
var page = await dbContext.Orders
    .Where(o => o.Id > lastSeenId)
    .OrderBy(o => o.Id)
    .Take(pageSize)
    .ToListAsync(ct);

Batch Operations

// BAD: N UPDATE statements (one per tracked entity change)
foreach (var order in orders)
{
    order.Status = OrderStatus.Archived;
}
await dbContext.SaveChangesAsync(ct);
// Generates N individual UPDATE statements in a single round-trip

// GOOD: EF Core 7+ ExecuteUpdateAsync (single SQL statement)
await dbContext.Orders
    .Where(o => o.CreatedAt < cutoff)
    .ExecuteUpdateAsync(
        s => s.SetProperty(o => o.Status, OrderStatus.Archived),
        ct);

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Agent Gotchas

1. Do not cast IQueryable to IEnumerable before filtering -- this silently switches from server-side SQL evaluation to client-side in-memory evaluation, potentially loading entire tables. Check for AsEnumerable(), explicit casts, or method signatures that accept IEnumerable<T>.
2. Do not return IQueryable from repository methods -- callers can compose additional operators, but the DbContext may be disposed before enumeration. Return materialized collections (List<T>) or use IAsyncEnumerable<T>.
3. Do not optimize LINQ allocations without benchmarks -- LINQ iterator overhead is negligible for most business logic. Use [skill:dotnet-benchmarkdotnet] [MemoryDiagnoser] to prove allocations matter before replacing LINQ with manual loops.
4. Do not use compiled queries with dynamic predicates -- compiled queries cache the expression tree shape. If the query shape changes per call (conditional includes, dynamic filters), the compiled query throws or produces wrong results.
5. Do not enumerate a deferred query multiple times -- each enumeration re-executes the underlying source (database query, network call). Materialize with ToList() when the result will be consumed more than once.
6. Do not use Skip()/Take() for deep pagination -- offset pagination is O(N) on the database. Use keyset (seek) pagination with a Where clause on the last-seen key for consistent performance regardless of page depth.

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References

• EF Core query evaluation
• EF Core compiled queries
• EF Core efficient querying
• LINQ execution model (deferred vs immediate)
• MemoryExtensions class
• EF Core ExecuteUpdate and ExecuteDelete
• Keyset pagination in EF Core