Real-Time Anomaly Detection at Scale

hardPROSystem ArchitectureML System DesignML System Design 2

Design a system that detects anomalies across millions of time series metrics for infrastructure monitoring.

Scenario: A cloud platform monitors 5 million time series metrics (CPU, memory, network, latency, error rates) across 100,000 servers. The system must detect anomalies within 60 seconds, distinguish real incidents from noise, correlate related anomalies into incidents, and minimize alert fatigue from false alarms.

Your Task: Design the anomaly detection and alerting pipeline for this scale.

Your design should address:

Processing millions of time series in real-time with bounded latency
Detecting anomalies in diverse metric shapes (spiky, periodic, trending)
Reducing false positives through contextual analysis and correlation
Grouping related anomalies into incidents for efficient triage
Adapting to changing baselines (deployments, traffic growth)

Think about: Seasonal patterns (daily, weekly), metric interdependencies, cold-start for new metrics, alert routing and escalation, and the cost of missed alerts vs false alarms.

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Concept primerPREVIEW

Background Knowledge

The problem of real-time anomaly detection at scale involves identifying unusual patterns in large volumes of time series data. This requires a deep understanding of time series analysis, which encompasses techniques for handling data that varies over time. Key concepts include trend, seasonality, and noise, which can be modeled using various statistical and machine learning techniques. For instance, autoregressive integrated moving average (ARIMA) models can be used to forecast future values based on past patterns, while exponential smoothing (ES) methods can help to reduce the impact of noise.

In the context of anomaly detection, it's essential to distinguish between different types of anomalies, such as point anomalies (single data points that are far from the norm), collective anomalies (a group of data points that are anomalous together), and contextual anomalies (data points that are anomalous given specific conditions). To address the diverse metric shapes mentioned in the problem, techniques like seasonal decomposition and trend analysis can be employed to separate the time series into its constituent parts. Furthermore, metric interdependencies can be modeled using graph-based methods or multivariate analysis to capture relationships between different metrics.

To minimize false positives and false negatives, it's crucial to consider the cost of missed alerts versus false alarms. This trade-off can be addressed using receiver operating characteristic (ROC) curves, which plot the true positive rate against the false positive rate at different thresholds. Additionally, alert routing and escalation strategies can be designed to ensure that alerts are properly prioritized and handled. The cold-start problem for new metrics can be mitigated using techniques like transfer learning or online learning, which enable the system to adapt quickly to new data.

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Your design approach

Describe your system design approach. Consider components, data flow, and key decisions.

Design questions(Select all that apply)

Q1What approach handles the diverse anomaly types across millions of time series?

Q2How should the system reduce false positive alerts?

Q3What streaming architecture supports real-time anomaly detection at scale?

Q4How should the system handle concept drift in metric behavior?

0 of 4 questions answered