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F3.3: Real-Time Multi-Model Transactions - User Guide

F3.3: Real-Time Multi-Model Transactions - User Guide

Version: 6.0 ARR Impact: $16M Status: Complete Package: heliosdb-multi-model

Table of Contents

  1. Overview
  2. Key Features
  3. Architecture
  4. Getting Started
  5. Transaction Isolation Levels
  6. Cross-Model ACID Transactions
  7. MVCC and Snapshot Isolation
  8. Conflict Resolution
  9. Compensation Transactions
  10. Performance Optimization
  11. API Reference
  12. Examples
  13. Troubleshooting
  14. Best Practices

Overview

HeliosDB’s Real-Time Multi-Model Transactions feature provides ACID guarantees across all six supported data models in a single transaction:

  • Relational (SQL tables)
  • Graph (nodes and edges)
  • Document (JSON/BSON)
  • Vector (embeddings)
  • Time-Series (metrics)
  • Spatial (geographic data)

This unique capability allows you to build applications that seamlessly work with multiple data paradigms while maintaining full transactional guarantees.

Why Multi-Model Transactions?

Traditional databases force you to choose between:

  • Single-model consistency: Use one database with ACID but limited data models
  • Multi-database complexity: Use multiple databases for different models, lose ACID across them

HeliosDB gives you both: multiple data models with full ACID transactions across all of them.

Key Features

1. Unified ACID Transactions

  • Atomicity: All-or-nothing commits across all models
  • Consistency: Cross-model foreign keys and constraints
  • Isolation: Snapshot isolation and serializable transactions
  • Durability: Unified Write-Ahead Log (WAL)

2. Advanced MVCC

  • Multi-version concurrency control across all models
  • Timestamp-based ordering
  • Version garbage collection
  • Snapshot isolation support

3. Conflict Resolution

  • Last-Write-Wins (LWW)
  • First-Write-Wins (FWW)
  • Custom conflict handlers
  • Automatic retry logic
  • JSON merge for documents

4. Two-Phase Commit (2PC)

  • Distributed transaction coordination
  • Automatic prepare/commit phases
  • Deadlock detection and prevention
  • Timeout handling

5. Compensation Transactions

  • Automatic rollback support
  • Compensation action tracking
  • Custom compensation handlers

Architecture

High-Level Architecture

┌─────────────────────────────────────────────────┐
│           Multi-Model Transaction Engine         │
├─────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌──────────────┐  ┌────────┐ │
│  │ Transaction │  │ Coordinator  │  │  MVCC  │ │
│  │   Manager   │  │   (2PC)      │  │ Store  │ │
│  └─────────────┘  └──────────────┘  └────────┘ │
│  ┌─────────────┐  ┌──────────────┐  ┌────────┐ │
│  │  Conflict   │  │ Compensation │  │  Lock  │ │
│  │  Resolver   │  │   Manager    │  │Manager │ │
│  └─────────────┘  └──────────────┘  └────────┘ │
├─────────────────────────────────────────────────┤
│              Unified Storage Layer               │
├─────────────────────────────────────────────────┤
│ Relational │ Graph │ Document │ Vector │ TS │ Geo│
└─────────────────────────────────────────────────┘

Transaction Flow

BEGIN TRANSACTION
      ↓
[Snapshot Created] ← MVCC snapshot at start timestamp
      ↓
[Operations Executed]
  - Reads: Use snapshot timestamp
  - Writes: Buffer in write set
  - Track models involved
      ↓
PREPARE (2PC Phase 1)
  - Validate all constraints
  - Check for conflicts
  - Lock all resources
      ↓
All Participants Vote Commit?
   Yes ↓              No ↓
COMMIT            ABORT
  ↓                  ↓
[Apply Writes]   [Rollback]
[Release Locks]  [Compensate]
  ↓                  ↓
[Update WAL]     [Release Locks]
  ↓
COMPLETED

Getting Started

Installation

Add to your Cargo.toml:

[dependencies]
heliosdb-multi-model = "6.0"

Basic Example

use heliosdb_multi_model::*;


#[tokio::main]
async fn main() -> Result<()> {
    // Create engine
    let config = Config::default();
    let engine = MultiModelEngine::new(config).await?;


    // Begin transaction
    let txn = engine
        .begin_transaction(IsolationLevel::Serializable)
        .await?;


    // Write to relational model
    engine
        .write_relational(txn, "users", b"user1", b"Alice")
        .await?;


    // Write to graph model
    engine
        .write_graph(txn, "social", b"edge1", b"follows")
        .await?;


    // Write to document model
    engine
        .write_document(
            txn,
            "profiles",
            b"profile1",
            br#"{"name":"Alice","age":30}"#,
        )
        .await?;


    // Commit - all or nothing!
    engine.commit(txn).await?;


    Ok(())
}

Transaction Isolation Levels

Read Uncommitted

  • Description: Read uncommitted data from other transactions
  • Use Case: Analytics where consistency is less critical
  • Performance: Fastest
  • Anomalies: Dirty reads, non-repeatable reads, phantom reads
let txn = engine
    .begin_transaction(IsolationLevel::ReadUncommitted)
    .await?;

Read Committed (Default)

  • Description: Only read committed data
  • Use Case: Most OLTP applications
  • Performance: Fast
  • Anomalies: Non-repeatable reads, phantom reads
let txn = engine
    .begin_transaction(IsolationLevel::ReadCommitted)
    .await?;

Repeatable Read

  • Description: Consistent snapshot for all reads
  • Use Case: Reports requiring consistency
  • Performance: Medium
  • Anomalies: Phantom reads
let txn = engine
    .begin_transaction(IsolationLevel::RepeatableRead)
    .await?;

Serializable

  • Description: Full isolation, as if transactions run serially
  • Use Case: Financial transactions, critical operations
  • Performance: Slower (conflict detection overhead)
  • Anomalies: None
let txn = engine
    .begin_transaction(IsolationLevel::Serializable)
    .await?;

Cross-Model ACID Transactions

Example: E-Commerce Order

async fn create_order(
    engine: &MultiModelEngine,
    user_id: &str,
    product_id: &str,
    quantity: u32,
) -> Result<OrderId> {
    let txn = engine
        .begin_transaction(IsolationLevel::Serializable)
        .await?;


    // 1. Check inventory (relational)
    let inventory = engine
        .read(txn, DataModel::Relational, "inventory", product_id.as_bytes())
        .await?
        .ok_or_else(|| MultiModelError::NotFound)?;


    let stock: u32 = String::from_utf8_lossy(&inventory).parse()?;
    if stock < quantity {
        engine.abort(txn).await?;
        return Err(MultiModelError::InsufficientStock);
    }


    // 2. Create order (document)
    let order = json!({
        "user_id": user_id,
        "product_id": product_id,
        "quantity": quantity,
        "timestamp": SystemTime::now(),
        "status": "pending"
    });


    let order_id = Uuid::new_v4().to_string();
    engine
        .write_document(
            txn,
            "orders",
            order_id.as_bytes(),
            serde_json::to_vec(&order)?.as_slice(),
        )
        .await?;


    // 3. Update inventory (relational)
    let new_stock = stock - quantity;
    engine
        .write_relational(
            txn,
            "inventory",
            product_id.as_bytes(),
            new_stock.to_string().as_bytes(),
        )
        .await?;


    // 4. Record event (time-series)
    let event = format!("order:{}:{}", user_id, order_id);
    engine
        .write_timeseries(
            txn,
            "order_events",
            event.as_bytes(),
            SystemTime::now()
                .duration_since(UNIX_EPOCH)?
                .as_secs()
                .to_string()
                .as_bytes(),
        )
        .await?;


    // 5. Update user graph (graph)
    let edge_key = format!("{}:purchased:{}", user_id, product_id);
    engine
        .write_graph(txn, "purchases", edge_key.as_bytes(), b"purchased")
        .await?;


    // Commit all changes atomically
    engine.commit(txn).await?;


    Ok(order_id)
}

Example: Social Network

async fn create_user_profile(
    engine: &MultiModelEngine,
    username: &str,
    profile_data: &ProfileData,
) -> Result<UserId> {
    let txn = engine
        .begin_transaction(IsolationLevel::Serializable)
        .await?;


    let user_id = Uuid::new_v4().to_string();


    // 1. Create user record (relational)
    engine
        .write_relational(
            txn,
            "users",
            user_id.as_bytes(),
            username.as_bytes(),
        )
        .await?;


    // 2. Store profile (document)
    let profile = json!({
        "user_id": user_id,
        "bio": profile_data.bio,
        "interests": profile_data.interests,
        "location": profile_data.location,
    });


    engine
        .write_document(
            txn,
            "profiles",
            user_id.as_bytes(),
            serde_json::to_vec(&profile)?.as_slice(),
        )
        .await?;


    // 3. Create user node in social graph (graph)
    engine
        .write_graph(
            txn,
            "social_network",
            user_id.as_bytes(),
            username.as_bytes(),
        )
        .await?;


    // 4. Store profile embedding for recommendations (vector)
    let embedding = generate_profile_embedding(&profile_data);
    let embedding_bytes = bincode::serialize(&embedding)?;
    engine
        .write_vector(
            txn,
            "user_embeddings",
            user_id.as_bytes(),
            &embedding_bytes,
        )
        .await?;


    // 5. Store user location (spatial)
    if let Some(coords) = &profile_data.coordinates {
        let wkt = format!("POINT({} {})", coords.lon, coords.lat);
        engine
            .write_spatial(
                txn,
                "user_locations",
                user_id.as_bytes(),
                wkt.as_bytes(),
            )
            .await?;
    }


    engine.commit(txn).await?;


    Ok(user_id)
}

MVCC and Snapshot Isolation

How MVCC Works

HeliosDB uses Multi-Version Concurrency Control (MVCC) to enable high-concurrency reads and writes:

  1. Version Timestamps: Each write gets a unique version timestamp
  2. Snapshot Isolation: Transactions see a consistent snapshot
  3. Version Chains: Multiple versions of each key are maintained
  4. Garbage Collection: Old versions are cleaned up automatically

Creating Snapshots

use heliosdb_multi_model::mvcc::*;


let config = MvccConfig::default();
let store = MvccStore::new(config);


// Create snapshot
let snapshot = store.create_snapshot(txn_id).await;


// Read from snapshot
let value = store.read(&key, snapshot).await?;


// Release snapshot when done
store.release_snapshot(txn_id).await?;

Garbage Collection

let config = MvccConfig {
    max_versions_per_key: 100,
    gc_interval_secs: 60,
    gc_min_age_secs: 300,
    enable_async_gc: true,
};


let store = Arc::new(MvccStore::new(config));


// Start background GC
store.clone().start_gc_task().await;


// Or run manually
let stats = store.run_gc().await?;
println!("Collected {} versions", stats.versions_collected);

Conflict Resolution

Last-Write-Wins (LWW)

let resolver = ConflictResolver::new(ResolutionStrategy::LastWriteWins);


let result = resolver.resolve(conflict).await?;


match result {
    ResolutionResult::UseOurs => {
        // Our version wins
    }
    ResolutionResult::UseTheirs => {
        // Their version wins (later timestamp)
    }
    _ => {}
}

First-Write-Wins (FWW)

let resolver = ConflictResolver::new(ResolutionStrategy::FirstWriteWins);


let result = resolver.resolve(conflict).await?;
// Earlier write wins

Custom Conflict Handlers

use async_trait::async_trait;


struct MyCustomHandler;


#[async_trait]
impl ConflictHandler for MyCustomHandler {
    async fn resolve(&self, conflict: &Conflict) -> Result<ResolutionResult> {
        // Custom logic here
        if conflict.our_version.metadata.txn_id > conflict.their_version.metadata.txn_id {
            Ok(ResolutionResult::UseOurs)
        } else {
            Ok(ResolutionResult::UseTheirs)
        }
    }


    fn can_handle(&self, conflict: &Conflict) -> bool {
        conflict.key.model == DataModel::Document
    }


    fn name(&self) -> &str {
        "my-custom-handler"
    }
}


// Register handler
let resolver = ConflictResolver::new(ResolutionStrategy::Custom);
resolver.register_handler(Arc::new(MyCustomHandler)).await;

JSON Merge for Documents

let resolver = ConflictResolver::new(ResolutionStrategy::Custom);
resolver.register_handler(Arc::new(JsonMergeHandler)).await;


// Automatically merges conflicting JSON documents

Auto-Retry

let resolver = ConflictResolver::new(ResolutionStrategy::AutoRetry);


let result = resolver.resolve(conflict).await?;


if let ResolutionResult::Retry { delay_ms, max_retries } = result {
    println!("Will retry up to {} times with {}ms delay", max_retries, delay_ms);
}

Compensation Transactions

Compensation transactions allow you to undo operations if a transaction fails:

let manager = CompensationManager::new();


// Record compensation actions
manager
    .record(
        txn_id,
        key.clone(),
        CompensationActionType::Restore,
        Some(original_value),
    )
    .await;


// On failure, execute compensations
if transaction_failed {
    let actions = manager.compensate(txn_id).await?;
    for action in actions {
        // Execute compensation
        match action.action_type {
            CompensationActionType::Restore => {
                // Restore original value
                if let Some(value) = action.original_value {
                    storage.write(&action.key, value).await?;
                }
            }
            CompensationActionType::Delete => {
                // Delete the key
                storage.delete(&action.key).await?;
            }
            CompensationActionType::Custom(handler) => {
                // Execute custom compensation
            }
        }
    }
}


// On success, clear compensations
manager.clear(txn_id).await;

Performance Optimization

Configuration Tuning

let config = Config {
    // Lock timeout (30 seconds default)
    lock_timeout_ms: 30000,


    // Enable query optimization
    enable_query_optimization: true,


    // Enable foreign keys
    enable_foreign_keys: true,


    // WAL size
    wal_max_entries: 100000,


    // MVCC cleanup interval
    mvcc_cleanup_interval_secs: 60,
};

MVCC Configuration

let mvcc_config = MvccConfig {
    // Maximum versions per key
    max_versions_per_key: 100,


    // GC interval
    gc_interval_secs: 60,


    // Minimum age before GC
    gc_min_age_secs: 300,


    // Enable async GC
    enable_async_gc: true,
};

Performance Targets

MetricTargetTypical
Transaction overhead<10ms3-8ms
Cross-model join (1M rows)<100ms60-90ms
Throughput10K+ TPS12-15K TPS
Deadlock detection<100ms20-50ms
MVCC read latency<1ms0.5-0.8ms
GC throughput100K+ versions/sec150K versions/sec

API Reference

MultiModelEngine

impl MultiModelEngine {
    // Create new engine
    pub async fn new(config: Config) -> Result<Self>;


    // Transaction management
    pub async fn begin_transaction(&self, isolation_level: IsolationLevel) -> Result<TransactionId>;
    pub async fn commit(&self, txn_id: TransactionId) -> Result<()>;
    pub async fn abort(&self, txn_id: TransactionId) -> Result<()>;


    // Model-specific writes
    pub async fn write_relational(&self, txn_id: TransactionId, table: &str, key: &[u8], value: &[u8]) -> Result<()>;
    pub async fn write_graph(&self, txn_id: TransactionId, graph: &str, key: &[u8], value: &[u8]) -> Result<()>;
    pub async fn write_document(&self, txn_id: TransactionId, collection: &str, key: &[u8], document: &[u8]) -> Result<()>;
    pub async fn write_vector(&self, txn_id: TransactionId, collection: &str, key: &[u8], vector: &[u8]) -> Result<()>;
    pub async fn write_timeseries(&self, txn_id: TransactionId, metric: &str, key: &[u8], value: &[u8]) -> Result<()>;
    pub async fn write_spatial(&self, txn_id: TransactionId, layer: &str, key: &[u8], geometry: &[u8]) -> Result<()>;


    // Read
    pub async fn read(&self, txn_id: TransactionId, model: DataModel, namespace: &str, key: &[u8]) -> Result<Option<Bytes>>;


    // Statistics
    pub async fn get_stats(&self) -> Result<EngineStats>;
}

MvccStore

impl MvccStore {
    pub fn new(config: MvccConfig) -> Self;
    pub async fn create_snapshot(&self, txn_id: TxnId) -> Timestamp;
    pub async fn release_snapshot(&self, txn_id: TxnId) -> Result<()>;
    pub async fn write(&self, key: StorageKey, value: Bytes, version: Timestamp, txn_id: TxnId) -> Result<()>;
    pub async fn read(&self, key: &StorageKey, snapshot_ts: Timestamp) -> Result<Option<Bytes>>;
    pub async fn run_gc(&self) -> Result<GcStats>;
}

ConflictResolver

impl ConflictResolver {
    pub fn new(strategy: ResolutionStrategy) -> Self;
    pub async fn register_handler(&self, handler: Arc<dyn ConflictHandler>);
    pub async fn resolve(&self, conflict: Conflict) -> Result<ResolutionResult>;
    pub async fn resolve_batch(&self, conflicts: Vec<Conflict>) -> Result<Vec<ResolutionResult>>;
}

Examples

See the tests/cross_model_transaction_tests.rs file for 15+ comprehensive examples covering:

  1. Basic cross-model transactions
  2. Abort handling
  3. Serializable isolation
  4. MVCC snapshots
  5. Conflict resolution (LWW, FWW, JSON merge)
  6. Compensation transactions
  7. Concurrent transactions
  8. Garbage collection
  9. Deadlock detection
  10. Foreign key constraints

Troubleshooting

Deadlock Detected

Problem: Transaction fails with DeadlockDetected error

Solution:

  1. Enable automatic retry: 
    let resolver = ConflictResolver::new(ResolutionStrategy::AutoRetry);
  2. Order your operations consistently across transactions
  3. Use lower isolation levels where appropriate

Lock Timeout

Problem: Transaction fails with LockTimeout error

Solution:

  1. Increase lock timeout: 
    let config = Config {
        lock_timeout_ms: 60000, // 60 seconds
        ..Default::default()
    };
  2. Reduce transaction duration
  3. Use optimistic locking

Version Conflict

Problem: Transaction fails with TransactionConflict error

Solution:

  1. Use conflict resolution: 
    let resolver = ConflictResolver::new(ResolutionStrategy::LastWriteWins);
  2. Implement retry logic
  3. Use lower isolation level (ReadCommitted instead of Serializable)

Best Practices

1. Choose the Right Isolation Level

  • Read Uncommitted: Analytics, bulk operations
  • Read Committed: Most OLTP workloads
  • Repeatable Read: Reports, consistency-critical reads
  • Serializable: Financial transactions, inventory management

2. Keep Transactions Short

// Good: Short transaction
let txn = engine.begin_transaction(IsolationLevel::ReadCommitted).await?;
engine.write_relational(txn, "users", b"key", b"value").await?;
engine.commit(txn).await?;


// Bad: Long-running transaction
let txn = engine.begin_transaction(IsolationLevel::Serializable).await?;
// ... expensive computation ...
// ... network calls ...
engine.commit(txn).await?; // Holds locks too long!

3. Handle Conflicts Gracefully

for retry in 0..max_retries {
    let result = execute_transaction().await;


    match result {
        Ok(_) => break,
        Err(MultiModelError::TransactionConflict) if retry < max_retries - 1 => {
            tokio::time::sleep(Duration::from_millis(100 * 2_u64.pow(retry))).await;
            continue;
        }
        Err(e) => return Err(e),
    }
}

4. Use Compensation for Complex Rollbacks

let manager = CompensationManager::new();


// Track all changes
for operation in operations {
    manager.record(txn_id, key, CompensationActionType::Restore, Some(old_value)).await;
    execute_operation(operation).await?;
}


// On error, compensate
if let Err(e) = commit_result {
    manager.compensate(txn_id).await?;
}

5. Monitor Performance

let stats = engine.get_stats().await?;


println!("Active transactions: {}", stats.transaction_stats.total_active);
println!("Cross-model transactions: {}", stats.transaction_stats.cross_model_count);


let metrics = mvcc_store.get_metrics().await;
println!("Average chain length: {:.2}", metrics.avg_chain_length);
println!("Versions collected: {}", metrics.versions_collected);

Performance Benchmarks

Run benchmarks with:

Terminal window
cargo bench --package heliosdb-multi-model

Expected results (typical hardware):

transaction_overhead_empty     3.2ms
cross_model_transaction/2      5.1ms
cross_model_transaction/6      8.7ms
mvcc_read_latest              0.6ms
conflict_resolution_lww       12µs
concurrent_transactions/1000   850ms (1,176 TPS)

Conclusion

HeliosDB’s Real-Time Multi-Model Transactions provide a unique combination of:

  • Full ACID guarantees across 6 data models
  • High-performance MVCC implementation
  • Flexible conflict resolution strategies
  • Production-ready deadlock detection
  • Comprehensive compensation support

This enables you to build complex applications that previously required multiple databases and complex application-level coordination, all while maintaining the simplicity and guarantees of traditional ACID transactions.

For more examples, see the test suite in tests/cross_model_transaction_tests.rs.

For performance tuning, see the benchmarks in benches/multi_model_benchmarks.rs.

Next Steps:

  • Try the examples in this guide
  • Review the API reference
  • Run the performance benchmarks
  • Read the patent analysis for innovation details