High Availability

The core principle of eliminating single points of failure (SPOF) by duplicating critical components. This includes:

• Node Redundancy: Running multiple validator or RPC nodes in parallel.
• Geographic Distribution: Deploying infrastructure across multiple data centers or cloud regions.
• Data Replication: Ensuring blockchain state and transaction data are mirrored across redundant systems to prevent data loss.

The process where a system automatically detects a component failure and switches to a standby system without human intervention. Key mechanisms include:

• Health Checks: Continuous monitoring of node liveness and sync status.
• Load Balancers: Intelligently routing requests away from unhealthy endpoints to healthy ones.
• Consensus Mechanism Switches: In validator setups, a backup node can automatically take over proposal duties if the primary fails.

Distributing network or computational workloads across multiple nodes to prevent any single resource from becoming a bottleneck. This ensures:

• Scalability: The system can handle increased transaction volume.
• Reliability: Traffic is routed to available, healthy nodes.
• Performance: Reduced latency for end-users by connecting them to the nearest or least busy endpoint. Techniques include round-robin, latency-based, and health-check-weighted routing.

A system's ability to continue operating correctly in the event of a partial failure. This goes beyond simple redundancy to include:

• Graceful Degradation: Maintaining core functions even if some features are unavailable.
• Automated Recovery: Systems that can restart failed processes, provision new nodes, or reconfigure themselves automatically (e.g., using container orchestration like Kubernetes).
• Byzantine Fault Tolerance (BFT): A property of consensus algorithms that allows a network to reach agreement even if some nodes act maliciously or arbitrarily.

Comprehensive visibility into system health, performance, and logs to proactively identify and diagnose issues. Essential components are:

• Metrics: Tracking uptime, latency, error rates, and resource utilization (CPU, memory, disk I/O).
• Alerting: Automated notifications sent to engineers when thresholds are breached.
• Logging & Tracing: Detailed records of system events and the journey of individual requests (RPC calls) to pinpoint failures.

A set of policies and procedures for restoring critical systems after a catastrophic event, such as a data center outage. This involves:

• Backup Strategies: Regular, tested backups of node state, keys, and configuration.
• Recovery Point Objective (RPO): The maximum acceptable amount of data loss measured in time.
• Recovery Time Objective (RTO): The target time within which a service must be restored after a disaster. A robust DR plan ensures business continuity.

A failover pattern where one node (active) handles all traffic while one or more identical nodes (passive) remain on standby, synchronized and ready to take over. The passive node becomes active only upon detection of a failure in the primary system.

• Key Mechanism: Uses a heartbeat or health-check system for failure detection.
• Example: A primary database server with a replica that can be manually or automatically promoted.
• Trade-off: Higher cost for idle resources, but provides a clean, simple failover state.

A pattern where multiple nodes are active simultaneously, sharing the incoming workload via a load balancer. If one node fails, traffic is automatically redistributed to the remaining healthy nodes.

• Key Mechanism: Requires stateless application design or shared, synchronized state (e.g., a central database).
• Example: A web farm of application servers behind a load balancer like AWS Elastic Load Balancer or NGINX.
• Benefit: Maximizes resource utilization and scales horizontally while providing inherent fault tolerance.

A tightly coupled pattern where multiple servers (nodes) work together as a single, logical system. Clusters manage shared state and provide automatic failover and load distribution.

• Key Mechanism: Uses cluster management software (e.g., Kubernetes, Apache ZooKeeper, Corosync/Pacemaker) for node coordination.
• Types: Failover clusters (for high availability) and load-balancing clusters (for high performance).
• Example: A Kubernetes cluster where pods are automatically rescheduled on healthy nodes if a failure occurs.

A pattern that distributes system components across multiple geographic regions or data centers to protect against large-scale outages like natural disasters or regional network failures.

• Key Mechanism: Uses DNS-based failover (e.g., Route 53) or global load balancers to direct traffic to the healthy region.
• Strategies: Can be implemented as active-passive (hot standby in another region) or active-active (serving global traffic).
• Challenge: Increased complexity due to data replication latency and consistency models.

A core data-layer pattern for HA, where data is copied from a primary database to one or more replica databases. Replicas can serve read traffic and be promoted to primary if needed.

• Replication Modes: Synchronous (strong consistency, higher latency) and Asynchronous (higher performance, potential data loss).
• Example: PostgreSQL streaming replication or Amazon RDS Multi-AZ deployments.
• Purpose: Ensures data durability and provides read scalability alongside availability.

A software design pattern that prevents a system from repeatedly trying to execute an operation that's likely to fail. It acts as a proxy for operations, monitoring for failures and "opening the circuit" to fail fast.

• States: Closed (normal operation), Open (failing fast, no calls), Half-Open (testing recovery).
• Benefit: Prevents cascading failures and allows failing services time to recover.
• Implementation: Libraries like Netflix Hystrix or Resilience4j provide this functionality for microservices.

Disaster Recovery comprises the policies, tools, and procedures to restore critical systems and data after a catastrophic event. It is a broader operational discipline that complements high availability.

• Relation to HA: While HA aims to prevent downtime, DR focuses on recovering from it. The combination is often expressed as objectives: Recovery Time Objective (RTO) and Recovery Point Objective (RPO).
• In Web3: Protocols may have governance-activated emergency shutdowns or upgradeable contract logic as part of a DR plan.

A Service Level Agreement is a formal commitment between a service provider and a client, specifying measurable metrics like uptime, often expressed as a percentage of availability.

• Standard Metrics: "Five nines" (99.999%) availability allows ~5 minutes of downtime per year. "Four nines" (99.99%) allows ~53 minutes per year.
• Enforcement: SLAs typically include financial penalties or service credits for breaches. In decentralized systems, staking slashing can be an analogous enforcement mechanism for validator performance.