LatticeDB: Deterministic Storage for Time-Travel Simulations

LatticeDB: Deterministic Storage for Time-Travel Simulations

Version: 1.0.0 Status: In Development Last Updated: 2026-01-10

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Overview

LatticeDB is Orix’s deterministic storage system built for time-travel simulations. It provides tick-aware storage, built-in Chronicle time-travel, CRDT support, and deterministic query ordering—features that traditional databases lack.

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The Problem

Traditional databases don’t understand simulation time. LatticeDB was built to solve specific problems that arise when you need deterministic, time-aware storage for simulations:

1. No Simulation Time Awareness

• PostgreSQL, MongoDB, and others track wall-clock time (DateTime.Now)
• They don’t understand ticks - discrete simulation time units
• Querying “what was the state at tick 5000?” requires custom temporal extensions
• Time-travel queries are bolted on, not built in

2. No Built-In Time-Travel

• Historical state requires custom versioning tables
• Reconstructing past states means replaying change logs
• Branching timelines (“what if” scenarios) requires application-level complexity
• No native snapshot/restore primitives

3. CRDT Support Missing

• Conflict-free data types (counters, sets, maps) require custom layers
• Multi-master replication needs manual conflict resolution
• No guarantees about merge convergence

4. Non-Deterministic Query Ordering

• Dictionary<K,V> iteration order varies between runs
• HashSet<T> enumeration is non-deterministic
• Index scan order can differ based on implementation details
• This breaks simulation determinism

5. Schema Enforcement Gaps

• MongoDB allows arbitrary document shapes
• PostgreSQL requires migrations for schema changes
• No compile-time guarantee that code matches database schema

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How Lattice Solves It

Core Principles

1. Tick-Aware by Default - Every query understands simulation time
2. Chronicle Built-In - Time-travel is a first-class feature, not an extension
3. Deterministic Ordering - Query results are stable across runs
4. Schema Authority - Axion schemas define storage structures
5. CRDT Native - Conflict-free data types for distributed consistency

Technical Approach

Tick-Aware Storage

// Storage engine tracks tick metadata
public interface IStorageEngine
{
    void Put(ReadOnlySpan<byte> key, ReadOnlySpan<byte> value);
    bool TryGet(ReadOnlySpan<byte> key, out byte[] value);
    IEnumerable<KeyValuePair<byte[], byte[]>> Scan(ReadOnlySpan<byte> prefix);
}

// Chronicle wraps storage with temporal versioning
public sealed class ChronicleEngine
{
    Snapshot CreateSnapshot(ulong tick, byte[] stateData, ulong stateHash);
    TravelResult TravelTo(ulong targetTick);
    BranchResult Branch(ulong snapshotId, BranchOptions options);
}

Deterministic Collections

All Lattice storage operations return results in sorted order:

• Keys are ordered lexicographically
• Scan operations return deterministic sequences
• CRDT merge operations preserve commutativity

Schema-Driven Types

schemas/lattice/storage.axion

// Axion schema defines structure
entity Transaction {
    @key id: uuid;
    state: TransactionState;
    start_tick: int64;
    commit_tick: int64?;
}

// Generated code implements storage interface
// ILatticeEntity<T> for ORM integration
public interface ILatticeEntity<TSelf> where TSelf : struct, ILatticeEntity<TSelf>
{
    uint Id { get; set; }
    void Serialize(ref BitWriter writer);
    static abstract TSelf Deserialize(ref BitReader reader);
}

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What Lattice Provides

Architecture Overview

┌─────────────────────────────────────────────────────────┐
│                    Application Layer                     │
├─────────────────────────────────────────────────────────┤
│  Lattice.ORM      │  Lattice.QL       │  Lattice.Client │
│  (Active Record)  │  (Query Language) │  (Network API)  │
├─────────────────────────────────────────────────────────┤
│          Lattice.Chronicle (Time-Travel Layer)          │
│  - Snapshots      - Branching       - Version Tracking  │
├─────────────────────────────────────────────────────────┤
│             Lattice.DB (Storage Engine Layer)           │
│  - Memory         - File            - Binary Pages      │
├─────────────────────────────────────────────────────────┤
│          Lattice.CRDT (Conflict-Free Data Types)        │
│  - Counters       - Sets            - Maps              │
├─────────────────────────────────────────────────────────┤
│       Lattice.Replication (Multi-Node Sync Layer)       │
│  - Sync Protocol  - Conflict Resolution - Delta Logs    │
└─────────────────────────────────────────────────────────┘

Component Status

Component	Status	Description
Lattice.DB	✅ Implemented	Core key-value storage engine
Lattice.Chronicle	✅ Implemented	Time-travel snapshots and branching
Lattice.ORM	🚧 In Progress	Object-relational mapping layer
Lattice.QL	🚧 In Progress	Query language parser (GraphQL-like)
Lattice.CRDT	✅ Implemented	GCounter, PNCounter, OR-Set, G-Set, 2P-Set
Lattice.Search	📋 Planned	Full-text search indexing
Lattice.Replication	🚧 In Progress	Multi-node synchronization
Lattice.Server	🚧 In Progress	HTTP/gRPC/WebSocket transports
Lattice.Client	🚧 In Progress	Client library for remote access
Lattice.Crypto	🚧 In Progress	Capability-based access control
Lattice.Optimization	🚧 In Progress	Query planner and batch executor

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Lattice.DB: Core Storage

Storage Engines

LatticeDB supports multiple backend engines:

public enum StorageEngine
{
    Lmdb = 0,      // Lightning Memory-Mapped Database (default)
    Sqlite = 1,    // SQLite for compatibility
    RocksDb = 2,   // RocksDB for high-throughput
    Memory = 3     // In-memory for testing
}

Key-Value Operations

// Open storage
var engine = new MemoryStorageEngine();
engine.Open(":memory:");

// Store data
engine.Put("user:123"u8, userData);

// Retrieve data
if (engine.TryGet("user:123"u8, out var data))
{
    // Process data
}

// Range scan (deterministic order)
foreach (var kvp in engine.Scan("user:"u8))
{
    Console.WriteLine($"Key: {Encoding.UTF8.GetString(kvp.Key)}");
}

// Count keys with prefix
int userCount = engine.Count("user:"u8);

// Delete range
int deleted = engine.DeleteRange("user:"u8);

Transaction Support

// Use transactional storage for atomic operations
if (engine is ITransactionalStorage txStorage)
{
    using var tx = txStorage.BeginTransaction();
    try
    {
        tx.Put("balance:123"u8, newBalance);
        tx.Put("history:123:5000"u8, historyEntry);
        tx.Commit();
    }
    catch
    {
        tx.Rollback();
        throw;
    }
}

Configuration

config StorageConfig {
    engine: StorageEngine = Lmdb;
    max_size_bytes: int64 = 0;           // 0 = unlimited
    page_size: int32 = 4096;             // 4KB pages
    mmap: bool = true;                   // Memory-mapped I/O
    sync_mode: SyncMode = Normal;        // Write synchronization
    wal_enabled: bool = true;            // Write-ahead logging
}

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Lattice.Chronicle: Time-Travel Engine

Chronicle enables snapshot-based time travel and timeline branching.

Creating Snapshots

var chronicle = new ChronicleEngine(storageEngine);
chronicle.EnsureMainTimeline();

// Capture state at current tick
var snapshot = chronicle.CreateSnapshot(
    tick: 5000,
    stateData: SerializeGameState(),
    stateHash: ComputeStateHash(),
    options: new SnapshotOptions
    {
        Label = "Before boss fight",
        Compress = true
    }
);

Console.WriteLine($"Snapshot {snapshot.Id} at tick {snapshot.Tick}");
Console.WriteLine($"Size: {snapshot.Data.Length} bytes (compressed from {snapshot.UncompressedSize})");

Time-Travel Queries

// Travel to a specific tick
var result = chronicle.TravelTo(targetTick: 3500);

if (result.Success)
{
    Console.WriteLine($"Found snapshot at tick {result.Snapshot.Tick}");
    Console.WriteLine($"Need to replay {result.TicksReplayed} ticks");
    Console.WriteLine($"Retrieved in {result.ElapsedMicroseconds} µs");

    // Restore state
    RestoreGameState(result.StateData);
}

Timeline Branching

// Branch from a snapshot to explore alternate outcomes
var branchResult = chronicle.Branch(
    snapshotId: snapshot.Id,
    options: new BranchOptions
    {
        Name = "What if player chose door B?",
        Description = "Alternate timeline where player made different choice",
        SwitchToNewTimeline = true
    }
);

if (branchResult.Success)
{
    Console.WriteLine($"Created timeline {branchResult.Timeline.Id}");
    Console.WriteLine($"Branched from tick {branchResult.BranchPoint.Tick}");

    // Continue simulation in alternate timeline
    // Original timeline remains unchanged
}

Timeline Management

// List all timelines
foreach (var timeline in chronicle.Timelines)
{
    Console.WriteLine($"Timeline {timeline.Id}: {timeline.Name}");
    Console.WriteLine($"  Created at tick: {timeline.CreatedAtTick}");
    Console.WriteLine($"  Current tick: {timeline.CurrentTick}");
    Console.WriteLine($"  Active: {timeline.IsActive}");
}

// Switch between timelines
chronicle.SwitchTimeline(timelineId: 2);

// Compare states across timelines
var comparison = chronicle.Compare(
    timelineA: 1,
    timelineB: 2,
    tick: 5000
);

Console.WriteLine($"Timeline 1 hash: {comparison.StateHashA:X16}");
Console.WriteLine($"Timeline 2 hash: {comparison.StateHashB:X16}");
Console.WriteLine($"Diverged: {comparison.StateHashA != comparison.StateHashB}");

Snapshot Verification

// Verify snapshot integrity
bool valid = chronicle.VerifySnapshot(
    snapshotId: snapshot.Id,
    hashFunction: data => ComputeStateHash(data)
);

if (!valid)
{
    Console.WriteLine("WARNING: Snapshot integrity check failed!");
}

Chronicle Statistics

var stats = chronicle.GetStats();
Console.WriteLine($"Timelines: {stats.TimelineCount}");
Console.WriteLine($"Snapshots: {stats.SnapshotCount}");
Console.WriteLine($"Total data: {stats.TotalDataBytes / 1024} KB");
Console.WriteLine($"Uncompressed: {stats.TotalUncompressedBytes / 1024} KB");
Console.WriteLine($"Compression ratio: {stats.CompressionRatio:P1}");

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Lattice.QL: Query Language

Lattice Query Language (LatticeQL) is a GraphQL-inspired language with temporal extensions.

Query Syntax

# Basic query
query GetPlayer {
    player(id: "123") {
        name
        score
        level
    }
}

# Time-travel query
query GetPlayerHistory {
    player(id: "123") @at(tick: 5000) {
        name
        score
    }
}

# Range query
query HighScorers {
    players(where: { score_gt: 1000 }, orderBy: score_DESC) {
        name
        score
    }
}

# Temporal range
query PlayerScoreHistory {
    player(id: "123") @between(start: 1000, end: 5000) {
        tick
        score
    }
}

Query Execution (In Progress)

var parser = QueryParser.Parse(@"
    query GetPlayer {
        player(id: ""123"") {
            name
            score
        }
    }
");

if (!parser.HasErrors)
{
    var document = parser.Document;
    // Execute query (executor not yet implemented)
}

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Lattice.CRDT: Conflict-Free Data Types

CRDTs enable distributed state that converges without coordination.

Counters

GCounter (Grow-Only Counter)

// Create counter for this node
var counter = new GCounter(nodeId: "node-1");

// Increment locally
counter.Increment(10);
counter.Increment(5);

Console.WriteLine($"Value: {counter.Value}"); // 15

// Merge from another node
var otherCounter = new GCounter(nodeId: "node-2");
otherCounter.Increment(20);

counter.Merge(otherCounter);
Console.WriteLine($"Merged value: {counter.Value}"); // 35

// Serialize for network transmission
byte[] bytes = counter.ToBytes();
var restored = GCounter.FromBytes(bytes, "node-1");

PNCounter (Positive-Negative Counter)

// Counter that supports both increment and decrement
var counter = new PnCounter(nodeId: "node-1");

counter.Increment(100);
counter.Decrement(30);

Console.WriteLine($"Value: {counter.Value}"); // 70
Console.WriteLine($"Total increments: {counter.TotalIncrements}"); // 100
Console.WriteLine($"Total decrements: {counter.TotalDecrements}"); // 30

// Merge from another node
var other = new PnCounter(nodeId: "node-2");
other.Increment(50);
counter.Merge(other);

Console.WriteLine($"Merged: {counter.Value}"); // 120

BoundedCounter (Lower-Bounded Counter)

// Counter that cannot go below zero (useful for inventory)
var inventory = new BoundedCounter(nodeId: "node-1", lowerBound: 0);

inventory.Increment(100);

// Try to decrement
if (inventory.TryDecrement(30))
{
    Console.WriteLine("Decremented successfully");
}

// This would fail (returns false)
bool success = inventory.TryDecrement(200);

Sets

GSet (Grow-Only Set)

// Set that only supports additions
var achievements = new GSet<string>();

achievements.Add("First Victory");
achievements.Add("Level 10");
achievements.Add("Perfect Score");

Console.WriteLine($"Count: {achievements.Count}"); // 3
Console.WriteLine($"Has 'Level 10': {achievements.Contains("Level 10")}"); // true

// Merge from another node
var otherAchievements = new GSet<string>();
otherAchievements.Add("Speed Run");

achievements.Merge(otherAchievements);
Console.WriteLine($"Merged count: {achievements.Count}"); // 4

ORSet (Observed-Remove Set / Add-Wins Set)

// Set supporting both add and remove
// Uses Hybrid Logical Clock for causality tracking
var clock = new HybridLogicalClock("node-1");
var friends = new OrSet<string>("node-1", clock);

friends.Add("Alice");
friends.Add("Bob");
friends.Add("Charlie");

Console.WriteLine($"Friends: {friends.Count}"); // 3

// Remove a friend
friends.Remove("Bob");
Console.WriteLine($"Friends: {friends.Count}"); // 2

// Merge from another node
var otherFriends = new OrSet<string>("node-2", new HybridLogicalClock("node-2"));
otherFriends.Add("Diana");

friends.Merge(otherFriends);
Console.WriteLine($"Merged friends: {friends.Count}"); // 3

// Tombstone garbage collection (remove old deletion markers)
var cutoff = clock.Now();
int removed = friends.CollectGarbage(cutoff);

TwoPhaseSet (2P-Set)

// Set where removal is permanent (element can never be re-added)
var permissions = new TwoPhaseSet<string>();

permissions.Add("read");
permissions.Add("write");
permissions.Add("execute");

// Revoke permission (permanent)
permissions.Remove("execute");

// Try to re-add (throws exception)
try
{
    permissions.Add("execute");
}
catch (InvalidOperationException)
{
    Console.WriteLine("Cannot re-add removed permission");
}

// Safe version
bool added = permissions.TryAdd("execute"); // Returns false

CRDT Characteristics

All Lattice CRDTs satisfy:

1. Convergence: All replicas that receive the same updates will have identical state
2. Commutativity: Updates can be applied in any order
3. Idempotence: Applying the same update twice has no additional effect
4. Associativity: Merge(A, Merge(B, C)) = Merge(Merge(A, B), C)

Serialization

// All CRDTs implement ISerializableCrdt
ISerializableCrdt crdt = counter;
byte[] serialized = crdt.ToBytes();

// Transmit over network, store to disk, etc.

// Deserialize
var restored = GCounter.FromBytes(serialized, "node-1");

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Lattice.Replication: Multi-Node Sync

Status: In Progress

Enables synchronization across multiple Lattice instances.

// Create replication node
var node = new ReplicationNode(
    nodeId: "node-1",
    storage: storageEngine
);

// Connect to peer
node.ConnectToPeer("node-2", "tcp://192.168.1.100:5000");

// Sync protocol handles:
// - Delta transmission (only changes since last sync)
// - Conflict resolution (using CRDT semantics)
// - Vector clocks (for causality tracking)
// - Compression (LZ4 for efficient network usage)

---

Lattice.ORM: Object-Relational Mapping

Status: In Progress

Provides Active Record pattern for entities.

schemas/game/inventory.axion

// Define entity via Axion schema
entity InventoryItem {
    @key id: uint;
    name: string;
    quantity: int32;
    category: ItemCategory;
}

// Generated code implements ILatticeEntity<T>
// Use Active Record methods:

var item = new InventoryItem
{
    Name = "Health Potion",
    Quantity = 10,
    Category = ItemCategory.Consumable
};

item.Save(); // Persists to storage

// Find by ID
var loaded = InventoryItem.Find(item.Id);

// Update
loaded.Quantity += 5;
loaded.Save();

// Delete
loaded.Delete();

// Query
var consumables = InventoryItem.Where(i => i.Category == ItemCategory.Consumable);

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Advantages

1. Time-Travel Built-In

No need for custom temporal tables or application-level versioning:

// Traditional DB
var state = await db.ExecuteQuery(
    "SELECT * FROM game_state WHERE tick <= @tick ORDER BY tick DESC LIMIT 1",
    new { tick = 5000 }
);

// LatticeDB
var state = chronicle.TravelTo(5000);

2. CRDT for Distributed Consistency

Built-in conflict-free data types eliminate coordination overhead:

// Traditional DB: Manual conflict resolution
try
{
    var current = await db.GetCounter("player:score");
    await db.UpdateCounter("player:score", current + 10);
}
catch (ConcurrencyException)
{
    // Retry or merge manually
}

// LatticeDB: Automatic convergence
counter.Increment(10);
counter.Merge(otherNodeCounter); // Always converges

3. Tick-Aware Queries

First-class support for simulation time:

# Query state at specific tick
query {
    player(id: "123") @at(tick: 5000) {
        position { x, y }
        health
    }
}

# Query changes over time range
query {
    player(id: "123") @between(start: 1000, end: 2000) {
        events {
            tick
            type
        }
    }
}

4. Schema-Driven Development

Axion schemas ensure code and storage are always in sync:

• Compile-time type safety
• No runtime schema mismatches
• Automatic serialization code generation
• Schema versioning and migration support

5. Deterministic Guarantees

All operations produce stable, reproducible results:

• Key iteration is lexicographically ordered
• CRDT merges are commutative and associative
• Query results are deterministic across runs
• No hidden state or ambient time

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Disadvantages

1. Learning Curve

New query language and concepts:

• Developers must learn LatticeQL syntax
• CRDT semantics differ from traditional locks
• Tick-based time model requires mindset shift
• Chronicle timeline management adds complexity

2. More Complex Than Key-Value

Not as simple as Redis or basic key-value stores:

• Chronicle adds versioning overhead
• CRDT metadata increases storage size
• Transaction isolation levels to understand
• Multiple storage engines to choose from

3. Storage Overhead for History

Time-travel requires keeping historical state:

• Snapshots consume disk space
• Delta compression helps but adds CPU cost
• Retention policies need careful tuning
• Garbage collection necessary for long-running systems

4. In-Progress Features

Some components are not yet complete:

• Lattice.QL query executor (parser done, executor in progress)
• Lattice.Search full-text indexing (planned)
• Lattice.Optimization query planner (in progress)
• Network replication protocol (in progress)

5. Specialized Use Case

Not a general-purpose database:

• Optimized for simulation/gaming workloads
• May be overkill for simple CRUD applications
• Tick-based model not suitable for all domains
• Chronicle overhead unnecessary if time-travel not needed

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Comparison with Other Databases

vs PostgreSQL with Temporal Extensions

Feature	PostgreSQL + Extension	LatticeDB
Time-travel	Add-on (pg_temporal)	Built-in (Chronicle)
CRDT support	Manual implementation	Native (Lattice.CRDT)
Determinism	Query order may vary	Guaranteed deterministic
Tick awareness	Application layer	First-class concept
Schema sync	Manual migrations	Axion code generation
Branching	Not supported	Native timeline branching

Winner: LatticeDB for simulation workloads requiring determinism and time-travel

vs MongoDB

Feature	MongoDB	LatticeDB
Schema	Flexible (pro/con)	Enforced via Axion
Versioning	Application-level	Built-in Chronicle
Determinism	Document order varies	Guaranteed stable
CRDT	Not supported	Native types
Query language	MQL (JavaScript-like)	LatticeQL (GraphQL-like)
Replication	Built-in (replica sets)	In progress

Winner: MongoDB for general web apps, LatticeDB for deterministic simulations

vs FoundationDB

Feature	FoundationDB	LatticeDB
Transactions	ACID, serializable	ACID with tick metadata
Abstractions	Low-level key-value	Higher-level (ORM, QL)
Time-travel	Not built-in	Native Chronicle
CRDT	Not supported	Native types
Determinism	Guaranteed	Guaranteed
Complexity	High (bare metal)	Medium (more abstractions)

Winner: FoundationDB for maximum control, LatticeDB for faster development

vs Redis

Feature	Redis	LatticeDB
Persistence	Optional (RDB/AOF)	Primary focus
Data types	String, Hash, Set, etc.	Axion-defined structs + CRDTs
Time-travel	Not supported	Native Chronicle
Determinism	Not guaranteed	Guaranteed
Query language	Commands	LatticeQL (planned)
Use case	Cache/pub-sub	Persistent simulation state

Winner: Redis for caching, LatticeDB for persistent deterministic state

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Performance Targets

These are design goals; actual performance depends on workload:

Write Latency

Operation	Target	Notes
Single Put	<1ms	Local storage, no sync
Transactional Put	<5ms	With durability guarantees
Snapshot Creation	<10ms	For typical game state (1-10 MB)
CRDT Merge	<1ms	Per operation, amortized

Read Latency

Operation	Target	Notes
Single Get	<0.1ms	Cached in memory
Range Scan (1000 items)	<5ms	Sequential read
Time-Travel Query	<10ms	Recent history (last 1000 ticks)
Snapshot Restore	<50ms	Decompression + load

Throughput

Workload	Target	Notes
Writes/sec	100K+	Batch operations, memory engine
Reads/sec	500K+	Hot data cached
Snapshots/sec	100	Concurrent snapshot creation

Storage

Metric	Target	Notes
Compression ratio	2-4x	LZ4 compression on snapshots
Overhead	10-20%	Version metadata, indexes
Compaction	<1%	Amortized write amplification

---

Usage Recommendations

When to Use LatticeDB

✅ Use LatticeDB if you need:

• Deterministic simulation state
• Time-travel debugging (“go back to tick 5000”)
• Timeline branching (“what if” scenarios)
• Distributed state with CRDTs
• Tick-based time model
• Schema-enforced types

When NOT to Use LatticeDB

❌ Don’t use LatticeDB if you need:

• Simple key-value caching (use Redis)
• General CRUD web app (use PostgreSQL/MongoDB)
• Maximum raw throughput (use FoundationDB)
• No schema enforcement (use MongoDB)
• Real-time analytics (use ClickHouse)
• Graph queries (use Neo4j)

Migration Path

If you’re considering migrating to LatticeDB:

1. Prototype Phase: Use Lattice.DB with in-memory storage
2. Schema Design: Define Axion schemas for your entities
3. Incremental Adoption: Start with Chronicle for snapshots only
4. Add CRDTs: Replace coordination with conflict-free types where applicable
5. Full Integration: Switch to LatticeQL for querying

---

Example: Complete Integration

using Lattice.DB;
using Lattice.Chronicle;
using Lattice.CRDT;

// Setup storage
var storage = new FileStorageEngine();
storage.Open("game.ldb");

// Setup Chronicle
var chronicle = new ChronicleEngine(storage);
chronicle.EnsureMainTimeline();

// Game state (serialized to bytes)
var gameState = new GameState
{
    Players = new Dictionary<uint, Player>(),
    CurrentTick = 0
};

// Game loop
for (ulong tick = 0; tick < 10000; tick++)
{
    // Update simulation
    UpdateSimulation(gameState, tick);

    // Snapshot every 100 ticks
    if (tick % 100 == 0)
    {
        var stateBytes = SerializeState(gameState);
        var stateHash = ComputeHash(stateBytes);

        chronicle.CreateSnapshot(
            tick: tick,
            stateData: stateBytes,
            stateHash: stateHash
        );
    }
}

// Time-travel debugging
var buggyTick = 5234;
var travel = chronicle.TravelTo(buggyTick);
if (travel.Success)
{
    Console.WriteLine($"Traveled to tick {travel.Snapshot.Tick}");
    Console.WriteLine($"Need to replay {travel.TicksReplayed} ticks");

    // Restore and investigate
    var restoredState = DeserializeState(travel.StateData);
    DebugGameState(restoredState);
}

// Create alternate timeline
var branchPoint = chronicle.FindLatestSnapshotBefore(1, tick: 3000);
if (branchPoint != null)
{
    var branch = chronicle.Branch(branchPoint.Id, new BranchOptions
    {
        Name = "Alternate ending",
        SwitchToNewTimeline = true
    });

    // Continue simulation in alternate timeline
    // Original timeline unchanged
}

// Cleanup
chronicle.Flush();
storage.Close();

---

Future Roadmap

Short-Term (Q1 2026)

• ✅ Complete Lattice.QL query executor
• ✅ Finish Lattice.ORM Active Record implementation
• ✅ Stabilize replication protocol

Medium-Term (Q2 2026)

• 📋 Add Lattice.Search full-text indexing
• 📋 Implement query optimizer
• 📋 Add PostgreSQL compatibility layer

Long-Term (2026+)

• 📋 Distributed query execution
• 📋 Sharding support for horizontal scaling
• 📋 SQL compatibility layer

---

Related Documents

• Chronicle Time-Travel - Deep dive into temporal features
• Axion Schemas - Schema language specification
• Executive Overview - Platform overview

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Document Version: 1.0.0 Status: Complete Next Review: 2026-02-01