Continuous Queries and Alerting

In this lesson, you will learn how to bridge the gap between passive data storage and active system monitoring by implementing continuous queries that trigger alerts the moment thresholds are breached.

The Concept of Continuous Queries

In traditional databases, you execute a query, receive a result set, and the interaction ends. However, when monitoring high-frequency time-series data—such as server CPU usage or sensor throughput—you need the database to act as an active observer. A continuous query is a specialized, long-running query that executes automatically as new data enters the system. Rather than polling for data, the engine evaluates the state of your metrics in near real-time.

Triggering Real-Time Alerts

When a continuous query detects that a metric has moved outside an acceptable range, it triggers an alert. This process follows a three-step cycle:

1. Ingestion & Windowing: Data arrives and is processed through a moving time window (e.g., "last 5 minutes").
2. Evaluation: The continuous query performs an aggregation (like AVG() or MAX()) on this window and compares it against a predefined threshold.
3. Notification: If the threshold is violated, the system pushes a signal to an external notification handler.

Consider this logic representing a continuous check for high memory consumption:

-- Conceptual logic for a continuous alert
CREATE CONTINUOUS QUERY high_memory_alert ON sensors
BEGIN
  SELECT mean(usage_pct) INTO threshold_violations
  FROM memory_metrics
  GROUP BY time(1m)
  HAVING mean(usage_pct) > 90
END;

In this example, the database does not wait for a user request. As soon as the mean value for the 1-minute window exceeds 90%, the database records the violation in a separate table or triggers a webhook.

By offloading this logic to the database engine, you reduce the latency between a system failure and human notification. This approach is superior to external scripts because the database engine optimizes the aggregation process, ensuring that alerting logic scales efficiently even as the volume of time-series data grows.

Key Takeaway: Continuous queries transform your database from a passive repository into an active monitoring system by evaluating thresholds against streaming data windows in real-time.