Monitoring

Abstract Conventional monitoring and alerting frameworks in distributed systems often rely on threshold-based metrics, which can produce excessive false positives and fail to capture complex dependencies between signals. This paper proposes an applied framework for using information-theoretic measures—specifically mutual information and the broader notion of entropy covariance—to detect anomalies and support governance in event-driven infrastructure. The approach shifts focus from isolated metrics to the relationships between uncertainties: when two signals that normally exhibit dependency diverge, the system can treat this as an indicator of instability. Leveraging existing mathematical foundations from information theory, this work evaluates the operational value of dependency-aware monitoring on real event streams (e.g., Kafka topics, MongoDB change data capture). Rolling measures of mutual information and covariance are integrated into monitoring pipelines, and their effectiveness is compared against conventional thresholds. ...

The concept of covariance of entropies can be understood as a way of quantifying how uncertainties in different signals vary together. Rather than monitoring each metric independently, the focus shifts to the relationships between sources of uncertainty. When signals that typically exhibit aligned behavior diverge, this can provide an early indicator of system anomalies. While this terminology is uncommon, the underlying idea overlaps strongly with established constructs in information theory, particularly mutual information. Mutual information measures the reduction in uncertainty about one random variable given knowledge of another, and has been widely applied to anomaly detection and monitoring tasks. ...

Many systems already know a lot about themselves — you just have to listen. MongoDB change streams (CDC) emit a continuous feed of inserts, updates, and deletes. With a little routing into a fast analytical database like ClickHouse, you can let the system “discover itself”: jobs, runs, schedules, dependencies, and even the fingerprints of human intervention. 1. Capture the Raw Feed First, set up a connector: MongoDB → Kafka → ClickHouse In ClickHouse, land the JSON envelopes losslessly: ...

Once your MongoDB change streams are flowing through Kafka and landing in ClickHouse, you’ve got a live, queryable event history for every state change in your systems. The obvious next step: start using it immediately — even before you build full-blown dashboards or machine learning models. 1. Detect Missing or Late Events One of the fastest wins is catching when something doesn’t happen. If you know a collection normally sees certain events every day, you can query for absences: ...