┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Customer API │ │ Query Router │ │ Network Nodes │
│ │───▶│ │───▶│ (Racing Pool) │
│ - REST/GraphQL │ │ - Load Balance │ │ - 3-5 per query │
│ - WebSocket │ │ - Node Selection│ │ - Independent │
│ - Performance │ │ - SLA Enforce │ │ - Specialized │
│ Guarantees │ │ - Rate Limiting │ │ - Competitive │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │
▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Settlement │ │ Consensus │ │ Distributed │
│ Engine │◀───│ Engine │◀───│ Query Engine │
│ │ │ │ │ │
│ - Token Payment │ │ - Result Verify │ │ - Sub-queries │
│ - Node Rewards │ │ - Racing Judge │ │ - Result Merge │
│ - Batch Solana │ │ - Fraud Detect │ │ - Cache Coord │
└─────────────────┘ └─────────────────┘ └─────────────────┘
Customer Query Request
│
▼
┌─────────────────┐
│ Query Router │ ──── Authenticate Customer
│ │ ──── Analyze Query Complexity
│ │ ──── Select Racing Nodes
└─────────────────┘
│
▼
┌─────────────────┐
│ Distributed │ ──── Generate Sub-queries
│ Query Planner │ ──── Find Data Locations
│ │ ──── Optimize Execution Plan
└─────────────────┘
│
▼
┌─────────────────┐
│ Racing Nodes │ ──── Execute Sub-queries in Parallel
│ (3-5 nodes) │ ──── Merge Partial Results
│ │ ──── Race for Best Response
└─────────────────┘
│
▼
┌─────────────────┐
│ Consensus │ ──── Verify Results Match
│ Verification │ ──── Check Performance Target
│ │ ──── Detect Anomalies
└─────────────────┘
│
▼
┌─────────────────┐
│ Economic │ ──── Distribute Rewards
│ Settlement │ ──── Process Customer Payment
│ │ ──── Update Reputation Scores
└─────────────────┘
│
▼
Customer Response
pub struct QueryRouter {
// Customer-facing interface
customer_api: CustomerAPI,
// Performance and load management
performance_monitor: PerformanceMonitor,
load_balancer: IntelligentLoadBalancer,
rate_limiter: CustomerRateLimiter,
// Node selection and routing
node_registry: NodeRegistry,
routing_algorithm: RoutingAlgorithm,
// SLA enforcement
sla_manager: SLAManager,
timeout_manager: TimeoutManager,
}
impl QueryRouter {
pub async fn route_customer_query(&self, request: CustomerRequest) -> RoutingDecision {
// 1. Authenticate and validate customer
let auth_result = self.customer_api.authenticate(&request).await?;
// 2. Analyze query requirements
let query_analysis = self.analyze_query_requirements(&request.query).await;
// 3. Select optimal racing nodes
let racing_nodes = self.select_racing_nodes(&query_analysis).await;
// 4. Create execution plan
RoutingDecision {
selected_nodes: racing_nodes,
execution_strategy: query_analysis.execution_strategy,
performance_target: request.performance_requirements,
timeout_config: self.timeout_manager.for_query(&query_analysis),
}
}
async fn select_racing_nodes(&self, analysis: &QueryAnalysis) -> Vec<NodeId> {
// Find nodes capable of handling this query type
let capable_nodes = self.node_registry
.find_capable_nodes(&analysis.requirements).await;
// Weight by reputation, performance, and current load
let weighted_nodes = capable_nodes.iter()
.map(|node| (node.id, self.calculate_selection_weight(node)))
.collect();
// Select 3-5 nodes for racing based on weights
self.routing_algorithm.weighted_selection(weighted_nodes, 5)
}
}pub struct DistributedQueryEngine {
// Local database instance
local_db: DuckDBConnection,
// Query planning and optimization
query_planner: DistributedQueryPlanner,
query_optimizer: QueryOptimizer,
// Inter-node communication
network_interface: NNGNetworkInterface,
// Result processing
result_merger: ResultMerger,
cache_manager: CacheManager,
}
impl DistributedQueryEngine {
pub async fn execute_distributed_query(&self, query: NetworkQuery) -> QueryResult {
// 1. Plan query execution across available data
let execution_plan = self.query_planner.plan_execution(&query).await?;
match execution_plan {
ExecutionPlan::LocalOnly(local_query) => {
// All required data is local
self.execute_local_query(local_query).await
},
ExecutionPlan::Distributed { local_part, remote_parts } => {
// Need to coordinate with other nodes
self.execute_coordinated_query(local_part, remote_parts).await
},
ExecutionPlan::CacheOptimized { cache_query, fallback } => {
// Try cache first, fallback if needed
match self.cache_manager.try_cache_query(&cache_query).await {
Some(cached_result) => Ok(cached_result),
None => self.execute_distributed_query(*fallback).await,
}
}
}
}
async fn execute_coordinated_query(
&self,
local_part: Option<LocalQuery>,
remote_parts: Vec<(NodeId, SubQuery)>
) -> QueryResult {
let mut all_results = Vec::new();
// Execute local part if exists
if let Some(local_query) = local_part {
let local_result = self.execute_local_query(local_query).await?;
all_results.push(local_result);
}
// Execute remote parts in parallel
let remote_futures = remote_parts.into_iter().map(|(node_id, sub_query)| {
self.network_interface.execute_remote_query(node_id, sub_query)
});
let remote_results = join_all(remote_futures).await;
// Filter successful results
for result in remote_results {
if let Ok(sub_result) = result {
all_results.push(sub_result);
}
}
// Merge all partial results
self.result_merger.merge_partial_results(all_results).await
}
}pub struct NNGNetworkInterface {
// Different socket types for different communication patterns
query_sockets: HashMap<NodeId, Socket>, // REQ/REP for queries
broadcast_socket: Socket, // PUB/SUB for announcements
discovery_socket: Socket, // For node discovery
// Connection management
connection_manager: ConnectionManager,
health_monitor: NetworkHealthMonitor,
// Message serialization
message_codec: MessageCodec,
}
impl NNGNetworkInterface {
pub async fn execute_remote_query(&self, node_id: NodeId, sub_query: SubQuery) -> Result<PartialResult, NetworkError> {
// Get or create connection to target node
let socket = self.connection_manager.get_connection(node_id).await?;
// Serialize and send query
let query_message = self.message_codec.encode_sub_query(&sub_query)?;
socket.send(query_message).await
.map_err(|e| NetworkError::SendFailed(e))?;
// Receive and deserialize response with timeout
let response_message = timeout(
Duration::from_millis(15), // 15ms timeout for sub-queries
socket.recv()
).await
.map_err(|_| NetworkError::Timeout)?
.map_err(|e| NetworkError::ReceiveFailed(e))?;
let partial_result = self.message_codec.decode_partial_result(&response_message)?;
Ok(partial_result)
}
pub async fn broadcast_network_announcement(&self, announcement: NetworkAnnouncement) -> Result<(), NetworkError> {
let message = self.message_codec.encode_announcement(&announcement)?;
self.broadcast_socket.send(message).await
.map_err(|e| NetworkError::BroadcastFailed(e))?;
Ok(())
}
pub async fn maintain_network_health(&mut self) {
loop {
// Check all connections health
self.health_monitor.check_all_connections().await;
// Drop unhealthy connections
let unhealthy_nodes = self.health_monitor.get_unhealthy_nodes();
for node_id in unhealthy_nodes {
self.connection_manager.drop_connection(node_id).await;
}
// Discover new nodes
self.discover_new_nodes().await;
sleep(Duration::from_secs(30)).await;
}
}
}pub struct ConsensusEngine {
// Racing result verification
racing_verifier: RacingResultVerifier,
// Fraud detection
fraud_detector: FraudDetectionEngine,
// Reputation management
reputation_manager: ReputationManager,
// Performance verification
performance_verifier: PerformanceVerifier,
}
impl ConsensusEngine {
pub async fn verify_racing_results(&self, race_results: Vec<RaceResult>) -> ConsensusResult {
// 1. Quick consensus check - do majority agree?
if let Some(consensus_result) = self.racing_verifier.find_quick_consensus(&race_results) {
return ConsensusResult::QuickConsensus(consensus_result);
}
// 2. Detailed verification for disagreements
let detailed_verification = self.detailed_result_verification(&race_results).await;
// 3. Fraud detection
let fraud_analysis = self.fraud_detector.analyze_results(&race_results).await;
if fraud_analysis.fraud_detected {
// Handle fraud cases
self.handle_fraud_detection(fraud_analysis).await;
return ConsensusResult::FraudDetected(fraud_analysis);
}
// 4. Reputation-weighted consensus
let weighted_consensus = self.reputation_manager
.weighted_consensus_resolution(&race_results).await;
ConsensusResult::ReputationWeightedConsensus(weighted_consensus)
}
async fn detailed_result_verification(&self, results: &[RaceResult]) -> VerificationResult {
// Check each result for internal consistency
let consistency_checks = results.iter().map(|result| {
self.verify_result_consistency(result)
});
let consistency_results = join_all(consistency_checks).await;
// Check for systematic discrepancies
let discrepancy_analysis = self.analyze_result_discrepancies(results);
VerificationResult {
consistency_scores: consistency_results,
discrepancy_analysis,
recommended_action: self.recommend_consensus_action(&consistency_results, &discrepancy_analysis),
}
}
}pub struct SettlementEngine {
// Solana integration for token operations
solana_client: SolanaClient,
token_program: TokenProgram,
// Payment processing
payment_processor: PaymentProcessor,
credit_manager: CustomerCreditManager,
// Node reward distribution
reward_distributor: NodeRewardDistributor,
reputation_tracker: ReputationTracker,
// Batching for efficiency
settlement_batcher: SettlementBatcher,
}
impl SettlementEngine {
pub async fn process_query_settlement(&mut self, query_result: QueryResult, race_info: RaceInfo) -> SettlementResult {
// 1. Determine payment outcome based on performance
let payment_outcome = self.determine_payment_outcome(&query_result, &race_info);
match payment_outcome {
PaymentOutcome::TargetMet { customer_payment, node_rewards } => {
// Customer pays, nodes get rewarded
self.credit_manager.deduct_credits(race_info.customer_id, customer_payment).await?;
for (node_id, reward) in node_rewards {
self.reward_distributor.queue_reward(node_id, reward).await;
self.reputation_tracker.record_successful_query(node_id, &query_result).await;
}
SettlementResult::Successful { customer_charged: customer_payment, nodes_rewarded: node_rewards }
},
PaymentOutcome::TargetMissed { performance_penalty } => {
// Customer pays nothing, nodes get penalized
self.credit_manager.refund_credits(race_info.customer_id, race_info.original_payment).await?;
for (node_id, penalty) in performance_penalty {
self.reward_distributor.apply_penalty(node_id, penalty).await;
self.reputation_tracker.record_performance_miss(node_id, &query_result).await;
}
SettlementResult::PerformanceMiss { customer_refunded: race_info.original_payment, nodes_penalized: performance_penalty }
},
PaymentOutcome::QueryFailed => {
// Query failed completely, customer pays nothing
self.credit_manager.refund_credits(race_info.customer_id, race_info.original_payment).await?;
SettlementResult::QueryFailed { customer_refunded: race_info.original_payment }
}
}
}
pub async fn execute_batch_settlement(&mut self) -> BatchSettlementResult {
// Collect all pending rewards and penalties
let pending_settlements = self.settlement_batcher.collect_pending_settlements().await;
if pending_settlements.is_empty() {
return BatchSettlementResult::NothingToSettle;
}
// Create batch Solana transaction
let settlement_transaction = self.create_batch_settlement_transaction(&pending_settlements).await?;
// Execute on Solana
let transaction_signature = self.solana_client
.send_and_confirm_transaction(&settlement_transaction).await?;
// Clear pending settlements
self.settlement_batcher.clear_settled(&pending_settlements).await;
BatchSettlementResult::Success {
transaction_signature,
nodes_settled: pending_settlements.len(),
total_amount_settled: pending_settlements.iter().map(|s| s.amount).sum(),
}
}
}pub struct DistributedDuckDBLayer {
// Local DuckDB instance
local_db: DuckDBConnection,
// Data partition management
partition_manager: DataPartitionManager,
// Data synchronization
sync_manager: DataSyncManager,
// Query optimization for distributed data
distributed_optimizer: DistributedQueryOptimizer,
}
pub enum DataPartitionStrategy {
// Partition by account address ranges
AccountRange { start: Pubkey, end: Pubkey },
// Partition by program/token
ProgramData { programs: Vec<Pubkey> },
// Partition by time
TimeRange { start_slot: u64, end_slot: u64 },
// Partition by query frequency (hot/cold data)
AccessFrequency { tier: AccessTier },
}
impl DistributedDuckDBLayer {
pub async fn optimize_data_distribution(&mut self) -> OptimizationResult {
// Analyze query patterns
let query_patterns = self.analyze_recent_query_patterns().await;
// Identify hot/cold data splits
let data_temperature_analysis = self.analyze_data_access_patterns().await;
// Optimize partitioning strategy
let new_partitioning = self.distributed_optimizer
.optimize_partitioning(&query_patterns, &data_temperature_analysis).await;
// Execute data rebalancing if beneficial
if new_partitioning.improvement_score > 0.2 {
self.execute_data_rebalancing(new_partitioning).await
} else {
OptimizationResult::NoChangeNeeded
}
}
}This architecture provides:
- High performance through racing and specialization
- Economic incentives aligned with performance
- Fault tolerance through redundancy and consensus
- Scalability through distributed data and computation
- Flexibility for various query types and performance requirements
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Node A │───▶│ Node B │───▶│ Node C │
│ │◀───│ │◀───│ │
│ Gossip Manager │ │ Gossip Manager │ │ Gossip Manager │
│ - Peer tracking │ │ - Peer tracking │ │ - Peer tracking │
│ - Heartbeats │ │ - Heartbeats │ │ - Heartbeats │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────────────────────────────────────────────────┐
│ Gossip Message Flow │
│ │
│ Push: "Here's my peer list" ──────────────────────────────▶│
│ Pull: "What peers do you know?" ◀──────────────────────────│
│ PushPull: Combined for efficiency ◀───────────────────────▶│
│ Heartbeat: "I'm alive" ────────────────────────────────────▶│
└─────────────────────────────────────────────────────────────┘
pub enum GossipMessage {
Push(GossipPush), // Share our peer list
Pull(GossipPull), // Request peer list
PushPull(GossipPushPull), // Combined for efficiency
Heartbeat(GossipHeartbeat), // Liveness check
StateSync(GossipStateSync), // Configuration sync
} ┌──────────────┐
│ Unknown │
└──────┬───────┘
│ First contact
▼
┌──────────────┐
┌────│ Healthy │◀──────────────┐
│ └──────┬───────┘ │
│ │ Missed heartbeat │ Heartbeat received
│ ▼ │
│ ┌──────────────┐ │
│ │ Degraded │───────────────┘
│ └──────┬───────┘
│ │ Multiple missed
│ ▼
│ ┌──────────────┐
│ │ Unavailable │
│ └──────┬───────┘
│ │ Timeout exceeded
│ ▼
│ ┌──────────────┐
└────│ Down │────▶ Remove from pool
└──────────────┘
┌─────────────────────────────────────────────────────────────┐
│ Shard Manager │
│ │
│ ┌────────────────┐ ┌────────────────┐ │
│ │ Health Check │───▶│ Node Status │ │
│ │ Task │ │ Tracker │ │
│ └────────────────┘ └────────────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌────────────────┐ ┌────────────────┐ │
│ │ Heartbeat │ │ Metrics │ │
│ │ Intervals │ │ Recorder │ │
│ └────────────────┘ └────────────────┘ │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Status Transitions │
│ │
│ time_since_heartbeat < timeout → Healthy │
│ timeout < time < timeout*2 → Degraded │
│ timeout*2 < time < timeout*5 → Unavailable │
│ time > timeout*5 → Failed │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ Cluster Rebalancing │
│ │
│ Step 1: Calculate Load Distribution │
│ ┌────────────┐ ┌────────────┐ ┌────────────┐ │
│ │ Node A: 40%│ │ Node B: 35%│ │ Node C: 25%│ │
│ └────────────┘ └────────────┘ └────────────┘ │
│ │
│ Step 2: Identify Imbalance │
│ Target: 33% each │
│ Node A: +7% (overloaded) │
│ Node C: -8% (underloaded) │
│ │
│ Step 3: Plan Migrations │
│ ┌────────────────────────────────────────────────────┐ │
│ │ Move 7 vnodes: Node A ────────────────▶ Node C │ │
│ └────────────────────────────────────────────────────┘ │
│ │
│ Step 4: Execute with Minimal Disruption │
│ - Replicate data first │
│ - Update hash ring │
│ - Remove old replicas │
└─────────────────────────────────────────────────────────────┘
pub enum NodeSpecialization {
SpeedRunner {
target_latency_ms: 1, // Sub-millisecond queries
cache_strategy: CacheStrategy::Aggressive,
},
ReconstructionSpec {
zk_capability: ZKCapability::Full,
merkle_cache_size: 1_000_000,
},
CacheOptimizer {
hot_data_threshold: 10_000,
eviction_policy: EvictionPolicy::LRU,
},
ArchiveNode {
retention_days: 365,
compression_level: 9,
},
} ┌─────────────┐
│Query Router │
└──────┬──────┘
│
┌────────────────┼────────────────┐
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Real-time │ │ Historical │ │ Compressed │
│ Query │ │ Query │ │ Account │
└──────┬──────┘ └──────┬──────┘ └──────┬──────┘
│ │ │
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│SpeedRunners │ │ArchiveNodes │ │Reconstruction│
│ │ │ │ │Specialists │
└─────────────┘ └─────────────┘ └─────────────┘