Launching a MEV Dashboard for Protocol Monitoring

MEV, or Maximal Extractable Value, represents the profit that can be extracted from block production beyond standard block rewards and gas fees. For protocol developers, this is not just an abstract concept—it directly impacts user experience and protocol health. Common forms of MEV include front-running user transactions, sandwich attacks on DEX trades, and arbitrage between liquidity pools. A dedicated MEV dashboard provides the visibility needed to quantify this activity, identify attack patterns, and assess their financial impact on your users.

Building a monitoring system requires aggregating and analyzing on-chain data. You'll need to track events like large slippage on swaps, failed transactions due to gas price wars, and profitable arbitrage loops that drain protocol-owned liquidity. Tools like the Flashbots MEV-Share API, EigenPhi, and Etherscan's labeled address datasets are valuable starting points. The core technical challenge is efficiently processing and querying this data to surface actionable insights in real-time, moving from raw blockchain logs to a clear visualization of MEV risk.

This tutorial will walk through constructing a full-stack MEV dashboard. We'll cover data sourcing from RPC nodes and specialized MEV APIs, structuring a database (using TimescaleDB for time-series data), and building a backend service to process transactions. The frontend will use a framework like Next.js with Recharts or D3.js to create visualizations for metrics such as daily extracted value, top extracting addresses (searchers), and the most commonly exploited transaction patterns within your protocol's smart contracts.

To effectively monitor MEV, you must first understand what it is. Maximal Extractable Value (MEV) refers to the profit that can be extracted by reordering, including, or censoring transactions within a block, beyond the standard block reward and gas fees. This includes common strategies like arbitrage, liquidations, and sandwich attacks. Familiarity with the Ethereum Virtual Machine (EVM) execution model, the role of validators (or miners), and the concept of the mempool (the pool of pending transactions) is essential, as these are the arenas where MEV occurs.

Your dashboard will rely on accessing and processing raw blockchain data. You should be comfortable interacting with JSON-RPC endpoints from providers like Alchemy, Infura, or a self-hosted node (e.g., Geth, Erigon). For historical MEV data analysis, you'll need to query The Graph subgraphs (e.g., Flashbots' MEV-Share or EigenPhi's datasets) or use specialized APIs from EigenPhi, Flashbots, or Blocknative. Understanding the structure of blocks, transactions, and event logs is crucial for parsing this data.

A functional dashboard requires backend logic to fetch, process, and serve data. Proficiency in a backend language like Node.js (JavaScript/TypeScript) or Python is recommended. You will use libraries such as ethers.js, web3.js, or web3.py to interact with the blockchain. For storing and querying processed data efficiently, knowledge of a database like PostgreSQL (with TimescaleDB for time-series data) or ClickHouse is valuable. Basic API design principles (REST or GraphQL) are needed to connect your backend to the frontend.

On the frontend, you'll build the visualization interface. Experience with a modern framework like React (with Next.js for full-stack capabilities) or Vue.js is typical. You will use charting libraries such as Recharts, Chart.js, or D3.js to render time-series graphs of MEV volume, profit distribution, and searcher activity. Understanding state management and asynchronous data fetching is key to creating a dynamic, real-time dashboard that updates as new blocks are produced.

Finally, consider the operational and ethical context. You should understand how Flashbots Protect and MEV-Share aim to democratize access to MEV and reduce negative externalities. From an infrastructure standpoint, be prepared to handle rate limits from data providers, manage WebSocket connections for real-time block updates, and implement caching strategies to optimize performance. Setting up a project with environment variables for API keys and using Docker for containerization can streamline development and deployment.

A production-ready MEV monitoring dashboard requires a multi-layered architecture designed for low-latency data processing. The core system ingests raw blockchain data from sources like Ethereum execution clients (Geth, Erigon) and specialized MEV-relay APIs (Flashbots, bloXroute). This data stream is then processed by a real-time analytics engine, often built with frameworks like Apache Kafka or Apache Flink, to detect MEV opportunities such as arbitrage, liquidations, and sandwich attacks. The processed insights are stored in a time-series database like TimescaleDB or InfluxDB for historical analysis and served to a frontend dashboard via a GraphQL or REST API.

The data ingestion layer is critical for capturing the full MEV landscape. You must subscribe to pending transaction pools (mempools) to see transactions before they are mined, which is essential for identifying front-running attempts. For Ethereum, this involves connecting to a node's JSON-RPC eth_subscribe method for newPendingTransactions. Additionally, integrating with MEV-Boost relay APIs provides visibility into builder-submitted blocks, revealing the value extracted by searchers. A robust ingestion service must handle reorgs, network latency, and the high volume of data, often requiring a distributed setup with multiple node providers for redundancy.

After ingestion, the event processing layer classifies and enriches transactions. This involves analyzing transaction traces to identify DeFi interactions across protocols like Uniswap, Aave, and Compound. Key detection logic includes calculating profit from arbitrage paths, identifying liquidation triggers, and flagging transaction ordering that suggests sandwich attacks. This processing is computationally intensive and benefits from parallelization. Many teams implement this layer in Go or Rust for performance, using libraries like ethers-rs or ethers.js (in a Node.js context) for blockchain interaction.

The storage and serving layer must support both real-time queries and complex historical analysis. A common pattern uses a dual-database approach: a primary OLTP database (e.g., PostgreSQL) for relational data (like known searcher addresses, protocol contracts) and a time-series database for metric storage (e.g., profit per block, gas used by MEV). The API layer, built with frameworks like FastAPI or Express.js, aggregates data from these sources. It provides endpoints for frontend charts and may stream live events via WebSockets or Server-Sent Events (SSE) to update the dashboard in real-time.

Finally, the frontend visualization dashboard presents the analyzed data. Effective dashboards segment MEV activity by type (e.g., Arbitrage, Liquidations), actor (e.g., Searcher Bots, Block Builders), and affected protocols. Libraries like D3.js or frameworks like React with Recharts are used to build interactive charts showing metrics like MEV revenue per day, top extracted transactions, and network congestion caused by MEV. Alerting systems can be integrated to notify protocol teams of suspicious activity, such as a sudden spike in failed arbitrage attempts targeting their liquidity pools.

MEV monitoring requires ingesting data from multiple sources. The primary source is a reliable Ethereum JSON-RPC node, which provides access to transaction pools, block data, and event logs. For comprehensive coverage, you should also connect to a mem-pool service like Flashbots Protect RPC, BloxRoute, or Blocknative to observe pending transactions before they are mined. This dual-source approach allows your dashboard to detect potential MEV opportunities—such as arbitrage or liquidations—as they emerge in the mempool and confirm their execution on-chain.

To process this data, you need to set up a stream processor. A common pattern is to use Apache Kafka or Amazon Kinesis to handle high-throughput event streams from your RPC connections. For each new block, your processor should subscribe to events like PendingTransaction and NewHeads. Here's a basic Node.js example using the ethers.js library to listen for pending transactions:

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const { ethers } = require('ethers');
const provider = new ethers.providers.WebSocketProvider('wss://mainnet.infura.io/ws/v3/YOUR_KEY');

provider.on('pending', (txHash) => {
  provider.getTransaction(txHash).then(tx => {
    // Analyze transaction for MEV patterns
    console.log(`Pending TX: ${txHash}, Gas Price: ${tx.gasPrice}`);
  });
});

This script captures transaction hashes as they enter the mempool, enabling initial filtering based on gas price or target contracts.

After capturing raw data, you must structure it for analysis. Create a schema in your database (e.g., PostgreSQL or TimescaleDB) with tables for blocks, transactions, and mev_events. Key fields to extract and store include: transaction hash, block number, gasPrice/maxPriorityFeePerGas, to/from addresses, and input data. For identified MEV bundles, store the bundle hash, searcher address, and profit calculated in USD. This structured data is what powers the visualizations and alerts in subsequent dashboard steps. Tools like The Graph for indexing or Dune Analytics for query templates can accelerate this process, but for real-time control, a custom pipeline is essential.

The detection logic is the analytical core of your MEV dashboard. It processes raw blockchain data—primarily transaction receipts and mempool events—to identify patterns indicative of MEV activity. You will implement a series of heuristics and pattern matchers that scan for known MEV strategies. Common targets include sandwich attacks, liquidations, arbitrage, and NFT front-running. This logic runs continuously, either by polling an RPC node or subscribing to events via a WebSocket connection to services like Alchemy or QuickNode.

Start by defining the core data structures. You'll need to model a DetectedMEVEvent object that includes fields such as strategyType, profitAmount, profitToken, victimAddresses, exploiterAddress, blockNumber, and transactionHash. This object will be the standardized output of your detection modules. For initial development, focus on a single, well-defined strategy like Uniswap V3 sandwich attacks, which have clear on-chain signatures involving a victim's swap transaction flanked by the attacker's own swaps.

Implement your first detector as a function that analyzes a block's transactions. Using the Ethers.js or Viem library, fetch transaction receipts and their logs. For a sandwich attack, the logic scans for three related swaps in the same block: an initial Swap event from the attacker (front-run), the victim's Swap event, and a final Swap event from the attacker (back-run). The attacker's swaps must use the same token pair as the victim and originate from the same address. Calculate the attacker's profit by comparing input/output amounts from their swap logs.

To scale and maintain your system, abstract the detection logic into a modular architecture. Create a base MEVDetector class or interface, with specific strategy classes like SandwichDetector and LiquidationDetector extending it. Each detector implements a scanBlock(blockNumber) method. Use a detector registry to manage and run all active modules. This pattern allows you to easily add new strategies, such as JIT liquidity detection for Uniswap V3, by creating a new class without modifying the core scanning engine.

Finally, integrate real-time mempool monitoring for proactive detection. By subscribing to pending transactions via eth_subscribe, you can attempt to identify MEV bundles before they are mined. This requires parsing transaction calldata and simulating potential state changes using tools like Tenderly or a local EVM simulation. While more complex, this allows your dashboard to provide alerts, offering protocols a chance to react—for example, by adjusting slippage tolerances or canceling a vulnerable transaction.

After capturing raw data from the mempool and finalized blocks, you need a persistent storage layer. A time-series database like TimescaleDB (a PostgreSQL extension) or InfluxDB is ideal for storing sequential data points such as transaction timestamps, gas prices, and bundle details. For structured relational data—like known searcher addresses, protocol metadata, or validator information—a standard PostgreSQL database works well. This separation allows for efficient querying; time-series data is optimized for aggregations over time windows, while relational tables handle entity relationships.

The aggregation layer processes raw data into meaningful metrics. Common aggregations for an MEV dashboard include: total_value_extracted (sum of profit across bundles), avg_priority_fee by block, searcher_activity (transactions per address), and dominance of specific MEV strategies (e.g., arbitrage vs. liquidations). You can implement these aggregations using batch jobs (e.g., with Apache Airflow or Prefect) that run on a schedule, or with streaming pipelines using Apache Kafka or Apache Flink for near-real-time metrics. Storing pre-computed aggregates in a cache like Redis speeds up dashboard load times.

Here is a simplified example of a PostgreSQL schema for storing captured MEV bundle data and a query to aggregate daily searcher profit. This structure enables tracking the lifecycle of a bundle from mempool to chain inclusion.

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CREATE TABLE mev_bundles (
    id SERIAL PRIMARY KEY,
    bundle_hash VARCHAR(66) UNIQUE,
    searcher_address CHAR(42),
    block_number INTEGER,
    included_at TIMESTAMPTZ,
    total_value_extracted_eth DECIMAL(30, 18),
    gas_used BIGINT,
    gas_price_wei NUMERIC
);

-- Query: Total profit by searcher for the last 7 days
SELECT
    searcher_address,
    SUM(total_value_extracted_eth) as total_profit_eth,
    DATE(included_at) as day
FROM mev_bundles
WHERE included_at > NOW() - INTERVAL '7 days'
GROUP BY searcher_address, DATE(included_at)
ORDER BY total_profit_eth DESC;

For effective monitoring, define Key Performance Indicators (KPIs) that align with your protocol's risk exposure. Critical KPIs include: MEV tax as a percentage of swap volume on your DEX, latency between transaction submission and inclusion, and the concentration risk of MEV revenue among a few searchers. Visualizing these KPIs requires connecting your aggregated data to a frontend library like Chart.js, D3.js, or a dashboard framework like Grafana. Ensure your data pipeline updates these visualizations with low latency to provide actionable insights for protocol developers and governance participants.

Finally, consider data retention and cost. Raw mempool data is voluminous. Implement a tiered storage strategy: keep high-resolution data for a short, actionable period (e.g., 7 days), roll up older data into hourly or daily aggregates, and archive cold data to cheaper object storage like AWS S3 Glacier. Tools like TimescaleDB's continuous aggregates or InfluxDB's downsampling tasks can automate this process. This ensures your dashboard remains performant and cost-effective while maintaining a historical record for long-term trend analysis.

The frontend serves as the primary interface for visualizing MEV threats. A modern framework like Next.js or Vite with React is recommended for its component-based architecture and ecosystem. Use a library such as Recharts or Chart.js to render time-series data for metrics like sandwich attack volume, arbitrage profit, and gas price spikes. The core dashboard should display key protocol health indicators: total value extracted by MEV bots, the frequency of harmful transactions, and the success rate of user transactions versus bots. Integrate Wagmi or Ethers.js to connect user wallets, enabling personalized views of their transaction history and potential exposure.

Real-time data is critical for MEV monitoring. Implement WebSocket connections to your backend API to push live alerts to the UI without requiring page refreshes. When a new harmful transaction is detected—such as a sandwich attack targeting a specific liquidity pool—the frontend should immediately display a notification card. This card should include the transaction hash (linked to a block explorer like Etherscan), the affected protocol (e.g., Uniswap V3), the estimated extracted value in USD, and the involved token pair. Consider using a state management library like Zustand or TanStack Query to efficiently manage this streaming data and UI state across components.

The alert system extends beyond the browser. Implement a server-side notification service that can dispatch alerts via Telegram Bot API, Discord webhooks, or email (using services like Resend or SendGrid). When your backend's detection logic triggers an alert, it should publish an event to a queue (e.g., using Redis or RabbitMQ). A separate worker process consumes these events and formats them into human-readable messages sent to subscribed users. For protocol teams, you can configure alerts for specific severity thresholds, such as "Alert me when sandwich attack volume on our pool exceeds $10,000 in a 1-hour window."

To make the dashboard actionable, build a Transaction Simulator component. This tool allows users or developers to input a pending transaction payload. The frontend sends this to a backend endpoint that simulates its execution against the current mempool state using a tool like Ethereum Tracer or Geth's debug_traceCall. The UI then displays a risk assessment, showing if the transaction is likely to be front-run, sandwiched, or if its gas price is insufficient. This practical feature directly helps users mitigate MEV risks before submitting transactions.

Finally, ensure the application is secure and performant. Use environment variables for all API keys and service URLs. Implement proper CORS policies on your backend API. For the frontend, consider static site generation (SSG) or incremental static regeneration (ISR) with Next.js for fast load times. The complete system—comprising the React frontend, WebSocket server, and alert dispatcher—transforms raw blockchain data into a real-time monitoring and defense tool against maximal extractable value.

Your dashboard is now ready to provide real-time visibility into MEV activity affecting your protocol. Before deploying to production, ensure you have robust error handling for your data pipeline—consider implementing retry logic for RPC calls and setting up alerts for data feed failures. For public deployment, secure your API endpoints and implement rate limiting to prevent abuse. Tools like pm2 or Docker containers can help manage your backend service reliably. Remember to monitor the dashboard's own performance, as parsing large transaction traces can be resource-intensive.

To extend your dashboard's utility, consider integrating with an alerting system like PagerDuty or a Discord webhook to notify your team of critical events, such as a sudden spike in sandwich attacks or a profitable arbitrage opportunity exceeding a threshold. You can also add historical analysis by storing aggregated data in a time-series database like TimescaleDB. This allows for tracking trends, such as the weekly volume of MEV extracted from your protocol or the average profit per block for searchers, providing deeper strategic insights.

For more advanced MEV research, connect your dashboard to the Flashbots Protect RPC to see how transactions are being routed through private channels. You could also implement a simulation engine using frameworks like Tenderly or Foundry's forge to replay suspicious transactions and estimate their exact financial impact. Exploring the mev-share protocol or SUAVE can provide insights into future, more transparent MEV ecosystems. Continue to iterate based on the specific risks your protocol faces, whether in DeFi, NFTs, or other verticals.