csharp-optimization

name	csharp-optimization
description	Use when optimizing C# code in MCC, reducing GC pressure, profiling hot paths, fixing latency spikes, or reviewing code for allocation or throughput issues.
metadata	{"category":"technique","triggers":"performance, allocations, GC, hot path, latency, throughput, memory pressure, optimize, slow, freeze, lag spike, packet processing speed"}

C# Performance Optimization for MCC

Hands-on optimization recipes for Minecraft Console Client hot paths. Complements csharp-best-practices (conventions) with measurement-driven performance work.

When to Use

• Profiling or reducing GC pressure in a running MCC session
• Optimizing per-packet code (Protocol18.HandlePacket, DataTypes.ReadNext*)
• Optimizing per-tick code (PlayerPhysics.Tick, CollisionDetector.Collide)
• Speeding up chunk decoding (Protocol18Terrain.ProcessChunkColumnData)
• Improving A* pathfinding (Movement.CalculatePath)
• Reviewing any code change for allocation or throughput regressions

NOT for:

• Login, config parsing, or one-shot command handlers (prefer clarity there)
• Style/convention questions (use csharp-best-practices instead)

---

Iron Rule: Measure First

NEVER optimize without profiling data.

Guessing which code is slow is wrong more often than right. Measure, change, re-measure. If you cannot show a before/after number, the optimization is not justified.

Rationalization	Reality
"This is obviously slow"	Obvious to you is not obvious to the JIT. Measure.
"I'll profile later"	Later never comes. Profile now or don't optimize.
"It's just one allocation"	On a 20 TPS tick, one allocation = 20 per second = GC pressure. Measure.
"AggressiveInlining everywhere"	The JIT already inlines small methods. Prove it helps before adding.

---

MCC Hot-Path Map

Know which code runs at which frequency before deciding where to invest:

Frequency	Key paths (actual files)	Priority
Per-packet (100s/sec)	Protocol/Handlers/Protocol18.cs HandlePacket, Protocol/Handlers/DataTypes.cs ReadNext*	High
Per-tick (20/sec)	Physics/PlayerPhysics.cs Tick, Physics/CollisionDetector.cs Collide, ChatBot Update()	High
Per-chunk-load	Protocol/Handlers/Protocol18Terrain.cs ProcessChunkColumnData, ReadBlockStatesField	Medium
Per-pathfind	Mapping/Movement.cs CalculatePath (A*)	Medium
Per-connection	Login, registry sync, config	Low
Per-user-action	Commands, chat	Low

---

Profiling Recipes

1. Live GC monitoring

dotnet-counters ps                     # find MinecraftClient PID
dotnet-counters monitor --process-id <PID> \
  --counters System.Runtime[gen-0-gc-count,gen-1-gc-count,gen-2-gc-count,alloc-rate]

Healthy idle MCC: near-zero Gen-1/Gen-2 collections. Frequent Gen-0 during idle means a hot-path allocation needs attention.

2. Allocation tracking

dotnet-trace collect --process-id <PID> \
  --providers Microsoft-Windows-DotNETRuntime:0x1:5

Open .nettrace in PerfView to find top-allocated types and call stacks.

3. Isolated benchmarks (BenchmarkDotNet)

Extract the hot method, add [MemoryDiagnoser]. Key columns: Mean, Allocated, Gen0.

---

Allocation Reduction (Highest Impact)

Reducing GC pressure directly reduces latency spikes in a long-running client.

Pattern: Reuse per-tick buffers

// BEFORE: new List every tick (20 allocations/sec)
var result = new List<Aabb>();

// AFTER: thread-local reuse (0 allocations/sec)
[ThreadStatic] private static List<Aabb>? t_buf;
var result = t_buf ??= new List<Aabb>(64);
result.Clear();

[ThreadStatic] works when single-threaded and non-reentrant (physics tick). If reentrant: use ObjectPool<T>. If cross-thread: use ArrayPool<T>.

Pattern: stackalloc for small fixed buffers

MCC already does this in DataTypes.cs for endian-swapped reads:

Span<byte> rawValue = stackalloc byte[8];
for (int i = 7; i >= 0; --i) rawValue[i] = cache.Dequeue();
return BitConverter.ToDouble(rawValue);

Rules: under 512 bytes, known size at compile time, never inside loops or recursion.

Pattern: Span slicing instead of array copies

// BEFORE: allocates
byte[] sub = new byte[length];
Array.Copy(source, offset, sub, 0, length);

// AFTER: zero-copy
ReadOnlySpan<byte> sub = source.AsSpan(offset, length);

Critical in packet parsing where many fields are sliced from one buffer.

---

Hot-Path Tuning

MethodImpl attributes

MCC uses [MethodImpl] on its hottest paths. Match the attribute to the method:

Attribute	When	MCC examples
AggressiveInlining	Tiny methods (< ~32 bytes IL), called millions of times	Vec3d.Add, Aabb.Intersects, Chunk.SetWithoutCheck
AggressiveOptimization	Larger critical-path methods	ReadBlockStatesField, ProcessChunkColumnData
Both	Medium methods, very high frequency	DataTypes.ReadNextVarInt, ReadDataReverse
Neither	Infrequent code	Login, config, commands

Do not scatter AggressiveInlining without profiling evidence. The JIT already inlines small methods.

BinaryPrimitives over BitConverter

// BEFORE: manual endian swap
(buf[0], buf[3]) = (buf[3], buf[0]);
int val = BitConverter.ToInt32(buf);

// AFTER: direct big-endian read, no branch
int val = BinaryPrimitives.ReadInt32BigEndian(buf);

MemoryMarshal for bulk reads

Already used in chunk decoding for zero-copy packed-long reads:

ReadOnlySpan<long> longs = MemoryMarshal.Cast<byte, long>(entryData);

---

Data Structure Selection

Frozen collections for palettes

Palette maps are built once and read millions of times. FrozenDictionary gives ~50% faster reads than Dictionary:

private static readonly FrozenDictionary<int, Material> s_palette =
    new Dictionary<int, Material> { ... }.ToFrozenDictionary();

Apply to: BlockPalettes/*.cs, EntityPalettes/*.cs, ItemPalettes/*.cs, PacketPalettes/*.cs, any static readonly Dictionary populated once.

PriorityQueue for A*

Movement.cs has a custom BinaryHeap. The built-in PriorityQueue<TElement, TPriority> (.NET 6+) is well-optimized and avoids maintenance burden.

ConcurrentDictionary sizing

Pre-size World.chunks to avoid rehashing:

new ConcurrentDictionary<(int, int), ChunkColumn>(
    concurrencyLevel: Environment.ProcessorCount, capacity: 1024);

---

Threading

Minimize lock scope

Copy data out under the lock, process outside:

List<Item> snapshot;
lock (_lock) { snapshot = [.. _items]; }
foreach (var item in snapshot) ExpensiveProcess(item);

Batch InvokeOnMainThread

Each InvokeOnMainThread() call blocks until the main thread runs it. In loops, batch into a single call:

handler.InvokeOnMainThread(() =>
{
    foreach (var entity in entities) UpdateEntity(entity);
});

Channel<T> over BlockingCollection<T>

Lower overhead, async-friendly:

var ch = Channel.CreateUnbounded<(int Id, Memory<byte> Data)>(
    new UnboundedChannelOptions { SingleReader = true });

---

Common Optimization Anti-Patterns

These are things agents (and humans) rationalize doing. Every one of them makes performance worse or wastes effort.

Anti-pattern	Why it's wrong
Adding AggressiveInlining to large methods	Bloats call sites, causes more cache misses, makes code slower
Optimizing login/config code	Runs once per session; clarity matters more than speed
Using ConcurrentDictionary where a plain Dictionary + lock suffices	Concurrent overhead on uncontested paths costs more than a lock
Replacing LINQ with manual loops on cold paths	No measurable gain, worse readability
Caching mutable state to avoid re-reads	Stale cache bugs are harder to diagnose than the perf hit
Task.Result / .Wait() on hot paths	Deadlock risk and thread-pool starvation

---

Pre-Commit Checklist

ALWAYS verify before submitting a performance change:

• Hot path identified with profiling data, not guesswork
• Before/after measurements recorded (allocation count, throughput, or latency)
• No new allocations inside per-tick or per-packet methods
• [MethodImpl] attributes match method call frequency and IL size
• Frozen collections used for any static lookup table
• Lock scopes contain no I/O or expensive work
• No Task.Result, .Wait(), or GetAwaiter().GetResult() on hot paths
• Thread safety preserved (checked existing lock/concurrent patterns)
• Optimization comments explain non-obvious choices
• Code still compiles and passes all existing checks