How to Architect a Failover and Redundancy Plan for Critical RPCs

High availability (HA) for Remote Procedure Call (RPC) endpoints ensures your dApp or service remains operational even during provider outages, network congestion, or regional disruptions. The core principle is redundancy: deploying multiple, independent RPC nodes across different infrastructure providers and geographic regions. A failover plan automates the switch from a primary, degraded node to a healthy secondary node, minimizing downtime and preserving user experience. For mission-critical applications like DeFi protocols, exchanges, or on-chain analytics, implementing HA is non-negotiable for security and reliability.

Architecting this system begins with defining your requirements. Assess your application's needs for latency, throughput, and data consistency. A trading bot requires sub-second failover, while a dashboard can tolerate brief delays. Next, select and provision your node providers. Avoid single points of failure by using a mix of managed services (like Alchemy, Infura, Chainstack) and self-hosted nodes across cloud providers (AWS, GCP, Azure). Geographic distribution is key to mitigating regional internet issues. Each node should be configured identically to ensure consistent API responses.

The intelligence of your HA system lies in the load balancer or RPC aggregator. This component sits between your application and your node pool, continuously performing health checks. Simple checks ping the node's eth_blockNumber or net_version. Advanced checks validate chain ID correctness and sync status. When a node fails a check, it is automatically removed from the healthy pool. Tools like Nginx with custom Lua scripts, HAProxy, or specialized middleware like Chainscore's RPC Router can manage this logic, routing requests only to operational nodes.

Implement a tiered strategy for node usage. Your primary tier consists of your fastest, most reliable paid nodes. A secondary tier can include reliable public endpoints or slower archival nodes for fallback. A circuit breaker pattern prevents cascading failures by temporarily disabling a node after repeated timeouts. Log all failover events and monitor metrics like error rates, latency percentiles, and successful request volume per endpoint. This data is crucial for optimizing your node selection and identifying chronically unreliable providers.

Finally, rigorous testing is mandatory. Simulate failure scenarios by manually stopping nodes, introducing network latency, or sending malformed requests. Use chaos engineering tools to automate these tests in a staging environment. Validate that your client SDK or application logic correctly handles retries and failover without losing transaction state. Document your architecture, failover procedures, and provider SLAs. A well-architected HA plan transforms RPC infrastructure from a single point of failure into a resilient, self-healing system that powers reliable Web3 applications.

Before designing your failover plan, you must define your Service Level Objectives (SLOs). The most critical metrics are availability (uptime percentage) and latency (P95/P99 response times). For a production-grade service, a common target is 99.9% availability ("three nines") or higher. You'll need monitoring tools like Prometheus, Datadog, or a specialized RPC health dashboard to track these metrics in real-time. Establish baseline performance for your primary RPC endpoint to know when a failover should be triggered.

Your architecture requires at least two distinct RPC provider accounts from different vendors (e.g., Alchemy, Infura, QuickNode, Chainstack). Relying on multiple endpoints from the same provider does not protect against provider-wide outages. You must also provision infrastructure in multiple geographic regions or cloud availability zones to mitigate localized network failures. For Ethereum Mainnet, consider running a self-hosted archive node (requiring ~2TB+ SSD) as a final backup, though this adds significant operational overhead.

The core technical prerequisite is a smart client-side or server-side router. This component continuously health-checks your RPC endpoints (e.g., via eth_blockNumber calls every 5-10 seconds) and routes requests to the fastest, healthiest provider. Libraries like viem and ethers.js support custom RPC providers, allowing you to implement this logic. You will need to code for automatic retries with exponential backoff and implement idempotency keys for write transactions to prevent duplicate submissions during switches.

For handling stateful operations like transaction sending, your system must manage nonce synchronization across failovers. If one RPC provider fails mid-stream, the subsequent provider must be able to accurately query the correct nonce for the sending address. Implement a centralized nonce manager or use a database (like Redis) to track the latest nonce independently of any single RPC. Without this, you risk broadcasting transactions with incorrect nonces, causing them to be stuck or dropped from the mempool.

Finally, establish a clear runbook for manual intervention. Automated failover can handle most issues, but you need procedures for declaring a full incident, communicating with provider support, and executing a manual cutover if automation fails. Document escalation paths, key contacts at your RPC providers, and steps for validating data consistency (e.g., checking finality across nodes) after a failover event. Regular chaos engineering tests, like intentionally disabling your primary endpoint, are essential to validate your plan works under real failure conditions.

The foundation of any failover plan is redundancy. You must integrate multiple RPC providers, ideally from different infrastructure companies, to eliminate single points of failure. For Ethereum, this means configuring endpoints from services like Alchemy, Infura, and Chainstack, or combining a managed service with your own Erigon or Geth node. The key metric is provider diversity, not just node count. A common pitfall is routing all traffic through a single provider's load balancer, which can become a centralized choke point during network-wide issues.

Intelligent traffic routing and health checks determine the effectiveness of your redundancy. A simple pattern uses a client-side or proxy layer to ping each endpoint for latency and block height synchronicity before sending requests. More advanced systems employ weighted routing based on historical performance metrics. For critical reads, you can implement a primary-failover model, while for writes (e.g., sending transactions), a retry logic with exponential backoff across different providers is essential to overcome temporary broadcast failures.

Implementing these patterns requires careful state management. Your application must handle the consistency boundary when switching providers. Cached data like nonces or recent block hashes might differ slightly between nodes. A robust client will re-fetch this state from the new primary endpoint after a failover event. For WebSocket subscriptions, your architecture must include reconnection logic that can seamlessly re-subscribe to event streams on a backup provider if the primary connection drops.

Monitoring is the final critical component. Beyond basic uptime, track chain-specific health indicators: peer count, finalization lag (for PoS chains), and gas price accuracy. Tools like the Chainscore API provide standardized health scores across multiple providers, simplifying this monitoring layer. Set up alerts for degraded performance, not just total failure, to trigger proactive failover before your users experience issues. Your failover plan is only as good as the observability that supports it.

A resilient RPC infrastructure is non-negotiable for production dApps. Global load balancing distributes user requests across multiple RPC providers and geographic regions, preventing any single point of failure from taking your application offline. This architecture is critical for handling traffic spikes, provider outages, and regional network issues. The core mechanism enabling this is health checks, which continuously monitor endpoint latency, error rates, and block height to route traffic only to healthy nodes.

To implement this, you need a load balancer that supports active health probes. Services like Cloudflare Load Balancing, AWS Global Accelerator, or a self-hosted solution like Traefik or HAProxy can be configured. A basic health check for an Ethereum RPC might send a eth_blockNumber request every 10 seconds. The endpoint is marked unhealthy if it fails to respond within 2 seconds, returns a 5xx HTTP error, or lags behind the network's latest block by more than 5 blocks. This real-time monitoring is the foundation of automatic failover.

Your failover plan defines the routing logic when health checks fail. A common strategy is active-passive failover, where all traffic goes to a primary provider until it fails, then switches to a backup. For higher availability, use active-active load balancing with weighted routing (e.g., 70% to Provider A, 30% to Provider B). This spreads risk and can be adjusted based on performance metrics. The failover should be automatic and near-instantaneous to prevent transaction failures for end-users.

Here's a conceptual code snippet for a health check service using Node.js and the ethers library. It checks block height synchronicity and response time, logging the result to a monitoring system.

javascriptCopy
const { ethers } = require('ethers');
async function checkRpcHealth(rpcUrl, expectedLag = 5) {
  const provider = new ethers.JsonRpcProvider(rpcUrl);
  const start = Date.now();
  try {
    const blockNumber = await provider.getBlockNumber();
    const latency = Date.now() - start;
    // Fetch a reference block from a trusted source
    const isSynced = (referenceBlock - blockNumber) <= expectedLag;
    return { healthy: latency < 2000 && isSynced, blockNumber, latency };
  } catch (error) {
    return { healthy: false, error: error.message };
  }
}

For a complete architecture, integrate these health checks with a load balancer's API to dynamically update a pool of backend targets. Combine this with geographic routing (GeoDNS) to direct users to the lowest-latency healthy endpoint. Always maintain a diverse provider portfolio—using services from Alchemy, Infura, Chainstack, and a self-hosted node—to mitigate correlated risks. Document your failover runbooks and test the switchover process regularly during low-traffic periods to ensure reliability.

Monitoring and alerting are the final pillars. Use dashboards (e.g., Grafana) to visualize key metrics: request latency per endpoint, error rate, success rate, and failover events. Set up alerts for when a provider is marked unhealthy or when traffic is routed to your tertiary backup, signaling a significant incident. This proactive approach, combining automated health checks, strategic failover, and vigilant monitoring, creates a robust RPC layer capable of supporting mission-critical DeFi, NFT, and gaming applications.

A critical RPC endpoint requires a multi-layered redundancy strategy to mitigate downtime from server failures, network issues, or regional outages. DNS failover is a fundamental layer, allowing you to redirect traffic from a failed primary endpoint to a healthy secondary endpoint. This is achieved by configuring your domain's DNS records with multiple A/AAAA records or using a managed DNS service like Amazon Route 53, Cloudflare, or Google Cloud DNS. These services perform health checks on your endpoints and automatically update DNS responses when an endpoint is deemed unhealthy, a process with a Time-To-Live (TTL)-dependent propagation delay.

For Web3 RPCs, a simple active-passive DNS failover is often insufficient due to the stateful nature of some requests and the need for low-latency responses. An active-active architecture, where traffic is distributed across multiple healthy endpoints, provides better performance and resilience. This can be implemented using DNS-based load balancing (round-robin) or, more effectively, with a Global Server Load Balancer (GSLB). A GSLB uses health checks and geographic routing policies to direct users to the closest, healthiest endpoint, minimizing latency and maximizing uptime for global users.

To architect a robust plan, start by deploying redundant RPC nodes across multiple cloud providers (e.g., AWS, GCP) and geographic regions. Use infrastructure-as-code tools like Terraform or Pulumi to ensure identical, reproducible deployments. Each node should be behind its own load balancer and health check endpoint (e.g., a /health API that checks chain synchronicity). Your primary DNS provider should be configured to monitor these health endpoints. A typical configuration includes a primary endpoint in us-east-1 and a failover in eu-west-1, with a health check interval of 30 seconds and a failure threshold of 2.

Implementing intelligent traffic management goes beyond basic health. Use latency-based routing to direct users to the region with the lowest network delay. For specialized RPC needs, you can implement weighted routing to send a percentage of traffic to nodes optimized for specific tasks—like 70% to a general-purpose node and 30% to an archive node for historical queries. For blockchain RPCs, it's critical that your health check validates the node is synced within a certain block height (e.g., within 5 blocks of a reference node) to avoid serving stale data.

Finally, establish clear operational procedures. Monitor key metrics: endpoint health status, request latency, error rates (5xx responses), and block height delta. Set up alerts for health check failures. Document the failover and fallback process, including estimated recovery time objectives (RTO). Test your failover regularly by simulating a zone outage. Remember, the effectiveness of DNS failover is limited by DNS caching; setting a low TTL (e.g., 60 seconds) on your records is essential for quick recovery, albeit with a slight increase in DNS query load.

A failover plan for Remote Procedure Call (RPC) endpoints ensures your application remains operational when a primary blockchain node fails. The core challenge is managing stateful connections, particularly for WebSocket subscriptions to events like new blocks or pending transactions. A simple DNS-based failover is insufficient because it breaks active eth_subscribe sessions. Effective architecture requires a load balancer or proxy layer that intelligently routes traffic and replicates connection state between healthy nodes in your cluster.

The foundation is a multi-node RPC cluster. Deploy identical, synchronized nodes (e.g., Geth, Erigon, Besu) across multiple availability zones or cloud regions. Use infrastructure-as-code tools like Terraform or Pulumi for consistent deployment. Each node should connect to the same blockchain network and maintain a recent state. Implement health checks that probe for syncing status, peer count, and latency. A common pattern is to run a lightweight HTTP service on each node that returns a 200 status only when the node is fully synced and healthy, which your load balancer can query.

For HTTP/HTTPS JSON-RPC traffic, a layer-7 load balancer (e.g., NGINX, HAProxy, or cloud-native solutions like AWS ALB) can manage failover. Configure it with active health checks against a method like eth_blockNumber. When the primary node fails the health check, the balancer automatically reroutes requests to the next healthy backend. To maintain consistency for read-heavy applications, consider implementing session affinity (sticky sessions) based on user ID or API key, ensuring a user's requests consistently hit the same node during a session, which can prevent nonce mismanagement issues.

WebSocket persistence is more complex. A dropped WS connection means all active subscriptions are lost. To solve this, you need a session-aware proxy. Solutions include Socket.io with a Redis-backed adapter or specialized proxies like Juggernaut or Pushpin. These tools maintain the WebSocket connection to the client while managing the backend connection to the RPC node. If the backend node fails, the proxy can silently reconnect to a standby node and, if possible, re-establish the same subscriptions using a locally cached state, making the failover nearly transparent to the end-user application.

Automated failover requires continuous monitoring. Implement alerts for node health, latency spikes, and error rates using Prometheus and Grafana or a commercial APM tool. The failover process itself should be automated via your orchestration platform (Kubernetes, Nomad) or load balancer configuration. Practice chaos engineering by deliberately failing nodes in a staging environment to test your redundancy plan. Document the recovery process, including steps for reintegrating a repaired node into the cluster without causing service disruption.

The foundation of any failover plan is redundancy. You must deploy your RPC nodes across multiple, geographically distributed cloud providers and availability zones. For example, run nodes on both AWS us-east-1 and Google Cloud europe-west1. This protects against regional outages and provider-specific failures. Use infrastructure-as-code tools like Terraform or Pulumi to manage these deployments identically, ensuring configuration parity and eliminating manual setup errors that could cause drift between environments.

Intelligent traffic routing is the mechanism that activates your redundancy. Implement a load balancer or API gateway (e.g., NGINX, HAProxy, or a cloud-native solution like AWS Global Accelerator) in front of your node cluster. Configure health checks that probe critical endpoints like /eth_blockNumber for latency and success rate. The routing layer must automatically detect a failing node and divert traffic to healthy endpoints within seconds. For blockchain-specific checks, monitor chain syncing status and peer count to catch degradations before they cause RPC failures.

Your monitoring stack must provide the observability needed to make routing decisions and post-mortem analysis. Instrument each node with Prometheus metrics for system (CPU, memory, disk I/O) and application-level data (request latency, error rates, gas estimation accuracy). Use Grafana dashboards to visualize health across the entire fleet. Centralized logging with Loki or Elasticsearch is crucial for aggregating node logs, enabling you to trace a user's failed transaction across potential failover events and identify root causes.

Alerting should be proactive and actionable. Set up alerts in Prometheus Alertmanager or Datadog for key thresholds: - Request failure rate exceeding 2% - P95 latency above 1 second - Node falling behind the chain head by more than 50 blocks - Health check failures from the load balancer. These alerts must trigger notifications to an on-call engineer via PagerDuty or Opsgenie and, where possible, automatically execute runbooks to restart services or isolate nodes.

Document and practice your incident response procedures. Maintain a runbook that details steps for common failure scenarios: a node crash, a cloud zone outage, or a consensus client bug. Conduct regular failover drills by intentionally taking down a primary node in a staging environment to validate that traffic reroutes correctly and alerts fire as expected. This practice builds team muscle memory and exposes flaws in your automation or documentation before a real incident occurs.

Finally, continuously measure and improve your system's resilience. Track key Service Level Objectives (SLOs) like availability (target 99.9% uptime) and latency (P95 under 500ms). Use the error budget from these SLOs to guide infrastructure investments. Analyze every incident and failover event to identify single points of failure, such as a shared database for your node manager, and work to eliminate them. A failover plan is a living system that evolves with your application's needs and the changing blockchain landscape.

Architecting for RPC redundancy requires a layered approach. You must first identify critical dependencies like your primary provider's API endpoints, blockchain nodes, and load balancers. Next, implement automated health checks that monitor latency, error rates, and block height synchronization. The core of the system is the failover logic, which uses these health metrics to automatically reroute traffic to a pre-configured backup, such as a secondary provider like Chainscore or a self-hosted node cluster. This logic should be embedded in your application's RPC client configuration or a dedicated proxy layer.

To move from theory to practice, start by auditing your current setup. Document every RPC call your dApp makes and categorize them by priority. For high-priority calls, establish a redundancy budget and select at least one backup provider. Implement a simple, client-side failover using a library like web3.js or ethers.js, which allows you to pass an array of provider URLs. Test the failover by temporarily disabling your primary endpoint and verifying your application gracefully switches. Use tools like the Chainscore Dashboard to monitor performance across providers in real-time.

Your next steps should focus on hardening the system. Simulate failure scenarios regularly, including high latency, corrupted responses, and chain reorganizations. Integrate circuit breakers to prevent cascading failures and alerting via Slack or PagerDuty for manual intervention. Finally, treat your configuration as code. Use infrastructure-as-code tools (e.g., Terraform, Ansible) to manage provider API keys and endpoint URLs, ensuring your failover environment can be recreated instantly. This proactive, automated approach transforms RPC reliability from a hope into a guaranteed feature of your application's architecture.