How do I configure Haystack for high availability?

To configure Haystack for high availability, focus on redundancy, load balancing, and failover mechanisms across its components. Haystack’s architecture typically involves document stores (like Elasticsearch), retrieval pipelines, and APIs. Start by ensuring critical services such as your document store and API layer are deployed across multiple nodes or availability zones. For example, run Elasticsearch in a cluster with at least three nodes configured with shard replication. This ensures data remains accessible even if a node fails. Similarly, deploy Haystack’s REST API or pipelines using container orchestration tools like Kubernetes, which can automatically restart failed instances and distribute traffic.

Next, implement load balancing and health checks. Use a load balancer (e.g., NGINX or cloud-based solutions) to distribute incoming requests across API instances. Configure health checks to detect unresponsive nodes and reroute traffic. For document stores like Elasticsearch, use client-side load balancing libraries (e.g., the Elasticsearch client’s built-in node rotation) to spread queries across available nodes. If using Haystack’s pipelines with multiple retrievers (e.g., combining Elasticsearch and a dense retriever), design fallback logic to switch to a backup retriever if the primary fails. For instance, a custom Pipeline class could catch exceptions from one component and reroute requests to another.

Finally, automate monitoring and recovery. Set up alerts for metrics like node health, latency spikes, or error rates using tools like Prometheus and Grafana. For document stores, enable automatic snapshotting (e.g., Elasticsearch’s snapshot lifecycle management) to recover data during outages. Test failure scenarios: simulate node crashes or network partitions to validate failover behavior. For example, kill an Elasticsearch node and ensure queries continue via replicas. Regularly update configurations—such as adjusting Kubernetes pod anti-affinity rules to prevent co-locating critical services—to minimize single points of failure. These steps create a system that tolerates failures without downtime, aligning with high-availability best practices.