Failover

The system continuously monitors the health of primary and replica nodes using mechanisms like liveness probes and heartbeats. Failure is declared based on configurable timeouts for network connectivity, process responsiveness, or application-level health checks. This detection must be rapid to minimize the Recovery Time Objective (RTO) but also resilient to transient network blips to avoid unnecessary failovers.

Upon primary failure, a consensus algorithm (e.g., Raft, Paxos) or a designated orchestrator (like Kubernetes) automatically selects and promotes the healthiest standby replica to be the new primary. This process ensures there is only one writable leader at any time, maintaining data consistency. The election must be deterministic to prevent split-brain scenarios where two nodes believe they are primary.

Client connections and in-flight transactions must be gracefully handled during the transition. Features include:

• Connection draining on the old primary.
• Automatic client redirection or retry logic to the new primary endpoint.
• Preservation of idempotent ingestion semantics to ensure data integrity if clients retry writes. This ensures the failover is transparent to the application layer, beyond a brief latency spike.

A robust system maintains near-real-time synchronization between primary and replicas using a Write-Ahead Log (WAL). The Recovery Point Objective (RPO) dictates the replication lag tolerance. The promoted replica must have applied all committed writes from the failed primary. Configurable consistency levels for reads and writes determine how many replicas must acknowledge operations, balancing availability and accuracy during normal and failover states.

After failover, the system automatically reconfigures the cluster topology. This includes:

• Demoting the failed node or marking it offline.
• Reconfiguring replication streams to the new primary.
• Triggering alerts for operator intervention.
• Supporting eventual failback procedures to restore the original node to the cluster as a replica or primary once repaired, often via a rolling restart.

Failover capabilities must be regularly validated through chaos engineering tests (e.g., killing pods, simulating network partitions). Comprehensive vector telemetry provides visibility into the failover event, including metrics for detection time, promotion latency, and any data loss. Slow query logs and performance metrics help assess the impact on search latency post-failover.

The maximum acceptable duration of downtime for a system, defining the target time within which operations must be restored after a failure. For a vector database, this is the key metric that the failover process is designed to meet.

• Directly Drives Failover Design: A low RTO (e.g., seconds) necessitates automated failover with hot standbys. A higher RTO (e.g., minutes/hours) may allow for manual intervention.
• Measured from Failure Detection: The clock starts when the primary node becomes unhealthy, not when the failure initially occurred.
• Business Impact Metric: RTO is determined by the cost of application downtime, not just technical feasibility.

The maximum acceptable amount of data loss measured in time, defining how far back a system must be able to recover. It dictates the replication and durability strategy that works in tandem with failover.

• Governs Replication Strategy: A zero RPO requires synchronous replication where writes are confirmed on a replica before acknowledging the client. A non-zero RPO allows for asynchronous replication.
• Trade-off with Latency: Synchronous replication (for low RPO) increases write latency. The choice directly impacts the consistency level configuration of the database.
• Example: An RPO of 5 minutes means the system can afford to lose up to the last 5 minutes of writes; failover can use a slightly lagging replica.

The process of returning operations to the original primary node after it has been repaired and reintroduced to the cluster following a failover event. It is the counterpart to failover and requires careful orchestration.

• Not Automatic: Often requires manual verification to ensure the original node is fully synchronized and stable.
• Data Synchronization Challenge: The failed primary must catch up on all writes that occurred on the new primary during its downtime, which can be a resource-intensive process.
• Minimizing Disruption: Modern systems may perform a rolling failback or simply designate the old primary as a new standby to avoid a second service interruption.

A dedicated API endpoint (e.g., /health or /ready) that returns the operational status of a service. It is the primary mechanism used by orchestration platforms to trigger automated failover.

• Liveness vs. Readiness:
  • Liveness Probe: Checks if the process is running (e.g., is the container alive?). Failure triggers a restart.
  • Readiness Probe: Checks if the service is ready to accept traffic (e.g., is the index loaded? Are connections pooled?). Failure triggers removal from the load balancer.
• Implementation Depth: A robust health check for a vector database verifies internal components: index accessibility, connection to replicas, memory pressure, and embedding model endpoint connectivity.

A persistent, append-only log where all data modifications are recorded before being applied to the main vector index. It is the foundational technology enabling both durability and the replication required for reliable failover.

• Crash Recovery: After a node crash, the WAL is replayed to restore the index to its last consistent state.
• Replication Vehicle: In leader-based replication, the primary node streams its WAL to follower replicas. This is how replicas stay in sync.
• Point-in-Time Recovery (PITR): By archiving WAL segments, a database can be recovered to any specific moment, which is critical for meeting strict RPOs beyond simple replica failover.

A configurable setting in a distributed database that determines how many replicas must acknowledge a read or write operation before it is considered successful. It defines the trade-off between data accuracy and system latency/availability during failover scenarios.

• Impact on Failover Behavior:
  • Strong Consistency (e.g., ALL): A write must hit all replicas. Guarantees no data loss on failover but can cause writes to fail if a replica is down.
  • Eventual Consistency (e.g., ONE): A write is confirmed by one node. Provides high availability and low latency, but a failed primary may lose recent writes that hadn't propagated.
• Tunable Trade-off: Settings like QUORUM (a majority of replicas) offer a balanced guarantee for most production vector database workloads.