Health Check Endpoint

The HTTP status code is the primary signal. A 200 OK indicates health, while a 5xx series code signals failure. The response body provides granular, machine-readable details beyond the binary up/down state.

Key elements include:

• Overall Status: A top-level status field (e.g., "healthy", "degraded", "unhealthy").
• Component Checks: A nested object detailing the status of subsystems like the vector index, write-ahead log (WAL), connection pool, and disk space.
• Version Information: The software version of the database for deployment validation.

Health endpoints often support distinct checks for liveness and readiness, which are critical for Kubernetes orchestration.

• Liveness Probe: Answers "Is the process running?" A simple, low-cost check (e.g., TCP socket connection) that determines if the container needs restarting.
• Readiness Probe: Answers "Is the service ready to accept traffic?" This is a more comprehensive check verifying that the vector index is loaded, connections to downstream dependencies (like object storage for persisted vectors) are healthy, and the system is not undergoing maintenance or a cold start.

A service can be live but not ready, preventing traffic until fully initialized.

A production-grade health check validates not just the core process, but its critical dependencies. For a vector database, this includes:

• Persistent Storage: Verifying read/write permissions and available space for the vector index and WAL.
• Memory Availability: Ensuring sufficient RAM for caching vectors and performing approximate nearest neighbor (ANN) searches.
• Embedding Model Endpoint: If the database generates embeddings, it may check the latency and availability of the model inference service, often protected by a circuit breaker pattern.
• Downstream Replicas: In a clustered setup, verifying quorum or connectivity to follower nodes.

Beyond binary health, advanced endpoints expose performance telemetry to indicate a degraded state before it becomes a failure.

Common embedded metrics:

• Query Latency: P50, P95, and P99 latency for recent vector similarity searches.
• Cache Hit Ratio: The percentage of vector searches served from memory, indicating cold start recovery status.
• Error Rates: The rate of failed queries or ingestion operations.
• Queue Depth: The number of pending write or search operations.

This data allows orchestration platforms to make smarter decisions, like holding traffic during performance degradation.

While health endpoints must be accessible to orchestration systems, they should not expose sensitive operational data publicly.

Standard practices include:

• Network Isolation: The endpoint is typically only exposed on an internal management or pod network.
• Authentication: Often secured via a simple token, network policy, or mutual TLS, especially in multi-tenant environments.
• Minimal Data Exposure: The response contains only the information necessary for automated systems, avoiding leakage of internal IPs, detailed error messages, or system paths.
• Rate Limiting: To prevent the health check itself from becoming a denial-of-service vector.

Health check behavior is highly configurable to align with specific Service Level Objectives (SLOs).

Configurable parameters:

• Check Depth: The ability to run a "shallow" quick check vs. a "deep" check that performs a synthetic ANN query.
• Timeout and Thresholds: Defining maximum acceptable latency for the health check itself and failure thresholds for metrics like latency or error rate.
• Degraded State Definition: Configuring what combination of high latency, low cache hit ratio, or dependency warnings triggers a "degraded" status instead of "healthy".
• Maintenance Mode: A manual or automated flag to return a "maintenance" status, informing the orchestrator to drain traffic gracefully for a rolling restart or zero-downtime migration.

A Kubernetes mechanism that periodically checks if a containerized application, such as a vector database pod, is running and responsive. It executes a command or HTTP GET request (often to the health check endpoint) inside the container. If the probe fails, the kubelet restarts the container. This ensures basic process availability but does not guarantee the service is ready for work.

• Purpose: Detect and recover from deadlocks or hung processes.
• Common Check: Simple TCP connection or HTTP 200 OK from /health.
• Failure Action: Container restart.

A Kubernetes mechanism that determines if a containerized application is ready to accept traffic. For a vector database, this probe checks if the system is fully initialized: indices are loaded into memory, connections to upstream embedding models are established, and the node has joined the cluster. Unlike a liveness probe, a failed readiness probe does not restart the container; instead, it removes the pod's IP from the service endpoints, preventing traffic from being sent to an unprepared instance.

• Purpose: Prevent requests during startup or temporary degradation.
• Common Check: Validates dependent services (e.g., storage layer, model API).
• Failure Action: Pod marked 'Not Ready'; traffic rerouted.

A stability pattern in distributed systems that temporarily stops calling a failing service after a threshold of failures is reached. In vector database operations, a circuit breaker is often implemented in the client SDK or a sidecar proxy to protect the database from cascading failures. For example, if an embedding model API called during ingestion starts timing out, the circuit breaker 'opens' and fails fast for subsequent calls, allowing the downstream service time to recover. A health check endpoint may be polled to determine when to 'close' the circuit and resume normal operations.

• States: Closed (normal), Open (failing fast), Half-Open (testing recovery).
• Use Case: Protecting vector DB from failing embedding APIs or storage backends.

A target level of reliability for the accuracy of a vector database's similarity search, formally defined as the proportion of true nearest neighbors successfully returned over a measurement period. While a health check endpoint confirms basic uptime, SLOs for recall measure the quality of service. For instance, an SLO might state '99.9% of queries must achieve a recall@10 of 0.95 over a 30-day window.' Violating this SLO consumes the Error Budget, triggering operational reviews. Monitoring this requires synthetic queries with known ground truth, not just endpoint pings.

• Metric: Recall@K (e.g., Recall@10, Recall@100).
• Target: Typically defined as a percentage over a rolling window.

A deployment strategy for vector database clusters where nodes are restarted one at a time in a controlled sequence. This allows the service to remain available with minimal disruption during software upgrades, configuration changes, or security patches. Orchestrators like Kubernetes manage this process by:

1. Draining a node (cordon).
2. Waiting for the readiness probe to fail on the old pod.
3. Terminating the old pod and scheduling a new one on a different node.
4. Waiting for the new pod's readiness probe to succeed.
5. Repeating for the next node. A robust health check endpoint is essential for the orchestrator to accurately determine pod readiness at each step.

A persistent, append-only log in a vector database where all data modifications (inserts, updates, deletes) are recorded before being applied to the main in-memory or on-disk index. The WAL is a core component of durability and crash recovery. A comprehensive health check endpoint should verify the integrity and writability of the WAL. During recovery, the database replays the WAL to restore its state to the point of the last committed transaction. This mechanism enables features like Point-in-Time Recovery (PITR).

• Function: Ensures ACID durability (the 'D').
• Health Check: Verify WAL segment creation and fsync latency.
• Related: Used for replication in distributed systems.