Features and Functionality MEV Exposure Monitoring

MEV Exposure Monitoring

**Referenced Files in This Document** - [rpcProber.js](file://backend/src/services/rpcProber.js) - [rpc.js](file://backend/src/routes/rpc.js) - [solanaRpc.js](file://backend/src/services/solanaRpc.js) - [helius.js](file://backend/src/services/helius.js) - [validatorsApp.js](file://backend/src/services/validatorsApp.js) - [queries.js](file://backend/src/models/queries.js) - [criticalPoller.js](file://backend/src/jobs/criticalPoller.js) - [routinePoller.js](file://backend/src/jobs/routinePoller.js) - [index.js](file://backend/src/config/index.js) - [db.js](file://backend/src/models/db.js) - [migrate.js](file://backend/src/models/migrate.js) - [MevTracker.jsx](file://frontend/src/pages/MevTracker.jsx) - [RpcRecommendationBanner.jsx](file://frontend/src/components/rpc/RpcRecommendationBanner.jsx) - [Validators.jsx](file://frontend/src/pages/Validators.jsx) - [validatorStore.js](file://frontend/src/stores/validatorStore.js) - [validatorApi.js](file://frontend/src/services/validatorApi.js)

Table of Contents

1. Introduction
2. Project Structure
3. Core Components
4. Architecture Overview
5. Detailed Component Analysis
6. Dependency Analysis
7. Performance Considerations
8. Troubleshooting Guide
9. Conclusion
10. Appendices

Introduction

This document explains the MEV (Maximal Extractable Value) Exposure Monitoring feature in InfraWatch. It covers:

• Jito validator detection and MEV-protected node identification
• Tip floor tracking and market manipulation indicators
• MEV-protected RPC identification for secure transaction submission
• MEV exposure scoring for RPC providers
• Relationship between MEV activity and network congestion
• Real-time MEV monitoring with alert thresholds and risk warnings
• Educational content on MEV concepts, risks, and mitigation strategies

The system integrates live network telemetry, RPC health monitoring, validator metadata, and congestion analytics to provide actionable insights for reducing MEV exposure and maintaining secure, efficient transaction submission.

Project Structure

The MEV monitoring capability spans backend services, polling jobs, database models, and frontend presentation layers. The backend collects network telemetry and probes RPC providers, while the frontend surfaces validator and RPC health information, including Jito-enabled validator status and RPC recommendations.

graph TB
subgraph "Backend"
CP["Critical Poller<br/>every 30s"]
RP["Routine Poller<br/>every 5min"]
SR["Solana RPC Service"]
HP["Helius Service"]
RPCT["RPC Prober"]
VA["Validators.app Service"]
Q["Queries (DAL)"]
CFG["Config"]
DB["PostgreSQL"]
end
subgraph "Frontend"
VT["Validators Page"]
MR["MEV Tracker Page"]
RR["RPC Recommendation Banner"]
end
CP --> SR
CP --> HP
CP --> RPCT
CP --> Q
CP --> DB
RP --> VA
RP --> Q
RP --> DB
VA --> Q
SR --> Q
HP --> SR
RPCT --> Q
Q --> DB
CFG --> SR
CFG --> VA
CFG --> RPCT
DB --> Q
VT --> VA
MR --> CP
RR --> CP

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Diagram sources

• criticalPoller.js:21-103
• routinePoller.js:20-111
• solanaRpc.js:1-340
• helius.js:1-188
• rpcProber.js:1-342
• validatorsApp.js:1-388
• queries.js:1-459
• index.js:1-68
• Validators.jsx:1-179
• MevTracker.jsx:1-43
• RpcRecommendationBanner.jsx:1-63

Section sources

• criticalPoller.js:1-108
• routinePoller.js:1-116
• solanaRpc.js:1-340
• helius.js:1-188
• rpcProber.js:1-342
• validatorsApp.js:1-388
• queries.js:1-459
• index.js:1-68

Core Components

• Network telemetry collection and congestion scoring
• RPC health probing and rolling statistics
• Helius priority fee integration for tip floor estimation
• Validator metadata ingestion with Jito detection
• Real-time streaming and recommendations

Key backend services and their roles:

• Solana RPC Service: health, TPS, slot latency, epoch info, delinquent validators, confirmation time, congestion scoring
• Helius Service: priority fee estimates and enhanced TPS data
• RPC Prober: provider health checks, latency measurements, rolling percentiles, best provider recommendation
• Validators.app Service: validator list, normalization, caching, rate limiting, commission change detection
• Data Access Layer (DAL): database operations for snapshots, RPC checks, validators, and alerts
• Polling Jobs: periodic collection and persistence of telemetry and validator data

Section sources

• solanaRpc.js:1-340
• helius.js:1-188
• rpcProber.js:1-342
• validatorsApp.js:1-388
• queries.js:1-459
• criticalPoller.js:1-108
• routinePoller.js:1-116

Architecture Overview

The system operates on two cadences:

• Critical Poller (every 30 seconds): collects network snapshot, probes RPC providers, writes to DB and cache, emits WebSocket updates
• Routine Poller (every 5 minutes): fetches validators, detects changes, persists snapshots, creates alerts

sequenceDiagram
participant Cron as "Scheduler"
participant CP as "Critical Poller"
participant SR as "Solana RPC Service"
participant HP as "Helius Service"
participant RPCT as "RPC Prober"
participant Q as "Queries (DAL)"
participant DB as "PostgreSQL"
Cron->>CP : Schedule tick (every 30s)
CP->>SR : collectNetworkSnapshot()
CP->>HP : getPriorityFeeEstimate()
SR-->>CP : Snapshot (TPS, slot, epoch, delinquent, congestion)
HP-->>CP : Priority fees (if configured)
CP->>RPCT : probeAllProviders()
RPCT-->>CP : Provider health + latency
CP->>Q : insertNetworkSnapshot()
Q->>DB : Persist snapshot
CP->>Q : insertRpcHealthCheck() x N
Q->>DB : Persist RPC checks
CP-->>CP : Update caches and emit WS events

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Diagram sources

• criticalPoller.js:21-103
• solanaRpc.js:275-328
• helius.js:13-70
• rpcProber.js:140-180
• queries.js:27-118

Detailed Component Analysis

Jito Validator Detection and MEV-Protected Nodes

• Validators.app provides a field indicating whether Jito is enabled for a validator. The backend normalizes this into the validator record and persists it to the database.
• The frontend displays Jito status alongside validator details, enabling users to identify MEV-protected validator nodes.

Implementation highlights:

• Field mapping includes a Jito flag during normalization
• Database schema includes a dedicated column for Jito status
• Frontend validator detail panel renders Jito status visually

flowchart TD
Start(["Fetch Validators"]) --> CallVA["Call Validators.app API"]
CallVA --> Normalize["Normalize validator data<br/>including jito_enabled"]
Normalize --> Upsert["Upsert to validators table"]
Upsert --> Cache["Cache top validators"]
Cache --> Render["Render in Validators page<br/>with Jito badge"]

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Diagram sources

• validatorsApp.js:156-179
• queries.js:180-220
• Validators.jsx:158-175

Section sources

• validatorsApp.js:156-179
• queries.js:180-220
• Validators.jsx:158-175

Tip Floor Tracking and Market Manipulation Indicators

• Helius service provides priority fee estimates, including low, medium, high, and very high levels, along with a 90th percentile proxy.
• The backend calculates congestion score using TPS, priority fee 90th percentile, and slot latency. This serves as a market manipulation indicator and helps infer tip floors.

flowchart TD
Start(["Collect Network Snapshot"]) --> GetTPS["Get TPS"]
GetTPS --> GetFees["Get Priority Fees (Helius)"]
GetFees --> GetLatency["Get Slot Latency"]
GetLatency --> Score["calculateCongestionScore()<br/>Weighted combination"]
Score --> Store["Attach congestion_score to snapshot"]

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Diagram sources

• solanaRpc.js:275-328
• helius.js:13-70
• criticalPoller.js:32-43

Section sources

• helius.js:13-70
• solanaRpc.js:228-268
• criticalPoller.js:32-43

MEV-Protected RPC Identification

• RPC Prober continuously probes configured providers and computes rolling latency percentiles and uptime statistics.
• The system recommends the best healthy provider based on p95 latency, enabling users to select MEV-protected or low-latency RPCs.

sequenceDiagram
participant CP as "Critical Poller"
participant RPCT as "RPC Prober"
participant Route as "RPC Routes"
participant FE as "Frontend"
CP->>RPCT : probeAllProviders()
RPCT-->>CP : Results + rolling stats
CP-->>Route : Cache latest RPC results
FE->>Route : GET /api/rpc/status
Route-->>FE : Providers + stats + recommendation
FE->>FE : Render RPC Recommendation Banner

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Diagram sources

• rpcProber.js:140-180
• rpc.js:17-88
• RpcRecommendationBanner.jsx:3-62

Section sources

• rpcProber.js:256-307
• rpc.js:17-88
• RpcRecommendationBanner.jsx:3-62

MEV Exposure Scoring for RPC Providers

• The scoring is implicit through RPC health metrics:
  • Uptime percentage (>95%) indicates reliability
  • Latency percentiles (p50/p95/p99) reflect responsiveness
  • Best provider selection prioritizes low-latency, healthy endpoints
• These metrics collectively quantify potential MEV exposure risks by highlighting unreliable or slow providers that may expose transactions to front-running.

flowchart TD
Collect["Collect provider checks"] --> Healthy["Filter healthy checks"]
Healthy --> Percentiles["Compute p50/p95/p99"]
Percentiles --> Uptime["Compute uptime %"]
Uptime --> Best["Select best provider by p95"]
Best --> Score["Exposure Risk Score:<br/>Lower p95, higher uptime = Lower risk"]

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Diagram sources

• rpcProber.js:208-250
• rpcProber.js:295-307

Section sources

• rpcProber.js:208-250
• rpcProber.js:295-307

Relationship Between MEV Activity and Network Congestion

• Congestion score combines:
  • TPS (inverse relationship with congestion)
  • Priority fee 90th percentile (proxy for tip pressure)
  • Slot latency (proxy for block production delays)
• Higher congestion often correlates with increased MEV activity and market manipulation attempts.

flowchart TD
TPS["TPS"] --> W1["Weight 0.4"]
Fee["Priority Fee 90th"] --> W2["Weight 0.3"]
Lat["Slot Latency"] --> W3["Weight 0.3"]
W1 --> Sum["Weighted Average"]
W2 --> Sum
W3 --> Sum
Sum --> Score["Congestion Score (0–100)"]

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Diagram sources

• solanaRpc.js:228-268

Section sources

• solanaRpc.js:228-268

Real-Time MEV Activity Monitoring and Alerts

• The MEV Tracker page currently displays placeholders for MEV metrics and indicates future real-time stream integration.
• Alerts are generated for significant validator changes (e.g., commission changes) and can be extended to include MEV-related conditions.

sequenceDiagram
participant RP as "Routine Poller"
participant VA as "Validators.app Service"
participant Q as "Queries (DAL)"
participant WS as "WebSocket"
participant FE as "Frontend"
RP->>VA : getValidators()
VA-->>RP : Validators with jito_enabled
RP->>Q : upsertValidator() x N
RP->>Q : insertValidatorSnapshot() x Top50
RP->>Q : insertAlert() for changes
Q-->>WS : Emit alert : new
WS-->>FE : Notify UI

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Diagram sources

• routinePoller.js:30-100
• validatorsApp.js:186-209
• queries.js:180-220
• queries.js:340-403

Section sources

• MevTracker.jsx:1-43
• routinePoller.js:80-100
• queries.js:340-403

Educational Content: MEV Concepts, Risks, and Mitigation

• MEV refers to the profit extractable by miners/block builders from reordering, inserting, or censoring transactions. On MEV-protected networks, validators commit to submitting transactions in a canonical order, reducing front-running.
• Risks include:
  • Transaction reordering and sandwich attacks
  • Increased gas/tip costs under congestion
  • Slippage and reduced trade quality
• Mitigations:
  • Prefer MEV-protected validators and RPCs with low latency and high uptime
  • Monitor congestion and tip pressure indicators
  • Use priority fee estimates to set competitive tips
  • Diversify RPC providers and monitor health metrics

[No sources needed since this section provides general guidance]

Dependency Analysis

The backend relies on configuration, database, and external APIs. The frontend consumes backend endpoints and displays telemetry and recommendations.

graph LR
CFG["Config"] --> SR["Solana RPC Service"]
CFG --> VA["Validators.app Service"]
CFG --> RPCT["RPC Prober"]
SR --> Q["Queries (DAL)"]
VA --> Q
RPCT --> Q
HP["Helius Service"] --> SR
Q --> DB["PostgreSQL"]
CP["Critical Poller"] --> SR
CP --> HP
CP --> RPCT
RP --> VA

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Diagram sources

• index.js:27-65
• solanaRpc.js:1-340
• validatorsApp.js:1-388
• rpcProber.js:1-342
• helius.js:1-188
• queries.js:1-459
• db.js:15-47

Section sources

• index.js:27-65
• db.js:15-47
• migrate.js:11-94

Performance Considerations

• Polling cadence: Critical Poller runs every 30 seconds; Routine Poller runs every 5 minutes. Adjust intervals via configuration for desired responsiveness vs. resource usage.
• Caching: Redis caches current network snapshot and RPC results; database serves as fallback and persistence layer.
• Rolling statistics: Efficiently computed percentiles and uptime metrics enable quick provider comparisons.
• External API limits: Validators.app rate limiter prevents throttling; ensure API keys are configured for optimal data flow.

[No sources needed since this section provides general guidance]

Troubleshooting Guide

Common issues and resolutions:

• Database not configured: The system logs a warning and disables database-dependent features. Set the database URL to enable persistence.
• Redis unavailability: Cache updates are skipped gracefully; endpoints still serve from database fallback.
• Missing Helius API key: Priority fee data is unavailable; congestion scoring falls back to basic metrics.
• Validators.app API key missing: Validator data is limited; configure the API key for full validator metadata.
• RPC provider timeouts: Probes mark providers unhealthy; review endpoint URLs and network connectivity.

Section sources

• db.js:20-23
• criticalPoller.js:80-86
• helius.js:14-18
• validatorsApp.js:116-119
• rpcProber.js:119-133

Conclusion

InfraWatch’s MEV Exposure Monitoring integrates network telemetry, RPC health metrics, and validator metadata to help users and developers reduce MEV risks. By leveraging Jito-enabled validators, tip floor indicators, congestion scoring, and real-time recommendations, the platform supports informed decisions for secure and efficient transaction submission.

[No sources needed since this section summarizes without analyzing specific files]

Appendices

Database Schema Overview

Tables supporting MEV monitoring:

• Network snapshots: time-series metrics including congestion score
• RPC health checks: provider latency, health, and error tracking
• Validators: current validator state including Jito flag
• Validator snapshots: historical state for trend analysis
• Alerts: event log for operational and MEV-related signals

erDiagram
NETWORK_SNAPSHOTS {
int id PK
timestamptz timestamp
numeric tps
bigint slot_height
numeric slot_latency_ms
integer epoch
numeric epoch_progress
integer delinquent_count
integer active_validators
numeric confirmation_time_ms
numeric congestion_score
}
RPC_HEALTH_CHECKS {
int id PK
timestamptz timestamp
varchar provider_name
text endpoint_url
numeric latency_ms
boolean is_healthy
bigint slot_height
text error_message
}
VALIDATORS {
varchar vote_pubkey PK
varchar identity_pubkey
varchar name
text avatar_url
numeric score
numeric stake_sol
numeric commission
boolean is_delinquent
numeric skip_rate
varchar software_version
varchar data_center
varchar asn
boolean jito_enabled
timestamptz last_updated
}
VALIDATOR_SNAPSHOTS {
int id PK
timestamptz timestamp
varchar vote_pubkey FK
boolean is_delinquent
numeric skip_rate
numeric commission
numeric stake_sol
integer vote_distance
integer root_distance
}
ALERTS {
int id PK
varchar type
varchar severity
varchar entity
text message
jsonb details_json
timestamptz created_at
timestamptz resolved_at
}
VALIDATORS ||--o{ VALIDATOR_SNAPSHOTS : "has snapshots"

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Diagram sources

• migrate.js:11-94

Frontend Data Flow

• Validators page fetches top validators and displays Jito status and other metrics.
• RPC recommendation banner shows the fastest healthy provider and suggests switching thresholds.
• MEV Tracker page presents placeholder metrics and indicates future real-time integration.

Section sources

• Validators.jsx:1-179
• validatorStore.js:1-28
• validatorApi.js:1-8
• RpcRecommendationBanner.jsx:1-63
• MevTracker.jsx:1-43