High Availability and Disaster Recovery Patterns

Ensuring your feature store not only performs well but also remains consistently available and resilient to failures is fundamental for production machine learning systems. Downtime or data loss in a feature store can halt model inference, disrupt training pipelines, and ultimately impact business operations. This section details patterns for achieving high availability (HA) and implementing effective disaster recovery (DR) strategies tailored to the unique components of a feature store.

High Availability refers to the system's ability to remain operational despite component failures, typically within a single data center or availability zone (AZ), or across multiple AZs within a region. Disaster Recovery, conversely, focuses on recovering operations after a major event incapacitates an entire data center or region.

High Availability for Feature Store Components

Different parts of the feature store have distinct availability requirements and failure modes.

Online Store HA

The online store typically has the most stringent HA requirements because it directly serves features for real-time predictions. Latency and availability are significant here.

• Replication: Most online store technologies (NoSQL databases, in-memory caches) support replication.
  • Leader-Follower: Writes go to a leader node, which replicates changes to follower nodes. Reads can often be served by followers. Failover involves promoting a follower to leader. This is a common pattern offering a good balance of consistency and performance.
  • Multi-Leader: Multiple nodes accept writes, replicating to each other. This can improve write availability but introduces complexity in conflict resolution.
  • Quorum-Based: Writes and reads require acknowledgment from a minimum number (a quorum) of replicas, offering tunable consistency guarantees.
• Managed Services: Cloud provider databases (e.g., AWS DynamoDB Global Tables, Google Cloud Spanner, Azure Cosmos DB) often provide built-in multi-AZ replication and automated failover, simplifying HA management.
• Load Balancing: Distribute read/write requests across multiple healthy instances or replicas of the online store using load balancers. Health checks are essential for automatically removing failed instances from rotation.

A typical high-availability setup for an online store using leader-follower replication across two Availability Zones within a single region, fronted by a load balancer.

Offline Store HA

The offline store (e.g., data lake storage like S3, GCS, ADLS, or data warehouses) generally has less critical immediate availability needs than the online store, but data durability and accessibility for training/batch jobs are important.

• Cloud Storage Durability: Major cloud storage services are designed for high durability (e.g., 99.999999999% or higher) by automatically replicating data across multiple devices and facilities within a region. This often covers typical HA requirements for the stored data itself.
• Compute Redundancy: Ensure batch feature computation jobs (e.g., using Spark or Flink) run on clusters with fault tolerance. Cluster managers like YARN or Kubernetes handle node failures, and jobs can be configured to retry failed tasks. Running compute across multiple AZs adds another layer of resilience.

Serving API and Metadata Store HA

The API serving feature vectors and the metadata store managing feature definitions also require high availability.

• Stateless API Instances: Design the serving API to be stateless. This allows you to run multiple instances behind a load balancer. If one instance fails, traffic is redirected to healthy ones without loss of session information. Auto-scaling groups can automatically adjust the number of instances based on load and health checks.
• Metadata Store Replication: The metadata store (often a relational database like PostgreSQL or MySQL) should use standard database HA techniques, such as primary/standby replication across AZs, automated backups, and potentially read replicas if read load is significant.

Disaster Recovery Patterns

DR plans address major failures, aiming to restore service within defined Recovery Time Objectives (RTO - how quickly service must be restored) and Recovery Point Objectives (RPO - how much data loss is acceptable).

• Backup and Restore:
  • Concept: Regularly back up the offline store, online store, and metadata store to a durable location, preferably in a different geographic region.
  • Process: In a disaster, provision new infrastructure in a recovery region and restore data from backups.
  • Trade-offs: Typically offers the highest RTO and potentially higher RPO (depending on backup frequency) but is the least expensive option. Suitable for less critical systems or where some downtime/data loss is tolerable.
• Pilot Light:
  • Concept: Maintain minimal core infrastructure (e.g., database schemas, basic compute configuration) in the DR region. Data (offline, online, metadata) is replicated asynchronously to the DR region.
  • Process: In a disaster, scale up the infrastructure (compute instances, database sizes) in the DR region, attach the replicated data, and switch traffic.
  • Trade-offs: Faster RTO than backup/restore, lower cost than warm/hot standby. Requires data replication mechanisms.
• Warm Standby:
  • Concept: Maintain a scaled-down but fully functional version of the feature store in the DR region. Data is actively replicated.
  • Process: In a disaster, scale up the resources in the DR region to handle full production load and redirect traffic.
  • Trade-offs: Faster RTO than pilot light, higher cost due to running idle resources. Data replication consistency is significant.
• Hot Standby (Multi-Region Active-Active/Active-Passive):
  • Concept: Run full-scale, independent deployments of the feature store in multiple regions.
    • Active-Passive: One region handles all traffic; the other is ready for immediate failover.
    • Active-Active: Both regions serve traffic simultaneously (often geographically routed).
  • Process: Failover is often near-instantaneous, potentially automated via DNS or global load balancers.
  • Trade-offs: Lowest RTO and RPO, highest availability. However, significantly more complex and expensive to implement and operate, especially concerning cross-region data consistency and synchronization for both online and offline stores. Requires careful architectural design (see Chapter 1 discussion on multi-region considerations).

Comparison of Disaster Recovery strategies showing increasing infrastructure readiness and cost from Backup/Restore to Hot Standby in the DR region.

Cross-Region Data Replication

Implementing DR strategies requires replicating data between regions.

• Offline Store: Cloud storage often provides cross-region replication features (e.g., S3 CRR). These are typically asynchronous.
• Online Store: Many databases offer cross-region read replicas or fully managed global tables (e.g., DynamoDB Global Tables, Cosmos DB multi-region writes). Be mindful of the consistency guarantees (often eventual consistency) and latency implications. Custom asynchronous replication pipelines might be needed for specific requirements or unsupported databases.
• Metadata Store: Database replication (asynchronous or synchronous, depending on RPO/RTO needs) is the standard approach.

Testing and Validation

A DR plan is only useful if it works. Regular testing is non-negotiable:

• Simulate Failures: Conduct drills simulating various failure scenarios (AZ failure, region failure, database corruption).
• Test Recovery Procedures: Validate the steps needed to bring up the DR environment and restore service.
• Measure RTO/RPO: Verify that actual recovery times and data loss meet the defined objectives.
• Automate: Automate failover and failback procedures as much as possible to reduce manual errors and speed up recovery.

Choosing the right HA/DR strategy involves balancing availability requirements, acceptable downtime (RTO), acceptable data loss (RPO), complexity, and cost. For critical ML applications relying heavily on fresh features, investing in more sophisticated patterns like Warm or Hot Standby, despite their complexity, is often necessary to ensure business continuity.