ClickHouse Anomaly Detection with Machine Learning in Spring Boot

December 21, 2025

ClickHouse is powerful for anomaly detection when paired with machine learning. With Spring Boot, you can train models, store data, and detect outliers.

1. Store Data in ClickHouse

Design a table for storing metrics:

CREATE TABLE metrics (
  id UUID DEFAULT generateUUIDv4(),
  source String,
  value Float64,
  timestamp DateTime DEFAULT now()
) ENGINE = MergeTree()
ORDER BY timestamp;

This table holds time-series data for anomaly detection.

2. Fetch Data into Spring Boot

Use JdbcTemplate or JPA to load data for training:

List<Double> values = jdbcTemplate.queryForList(
  "SELECT value FROM metrics WHERE timestamp > now() - INTERVAL 1 DAY",
  Double.class
);

3. Apply a Simple Machine Learning Model

For basic anomaly detection, use Z-score:

double mean = values.stream().mapToDouble(v -> v).average().orElse(0);
double stdDev = Math.sqrt(values.stream()
    .mapToDouble(v -> Math.pow(v - mean, 2)).sum() / values.size());

values.forEach(v -> {
    if (Math.abs((v - mean) / stdDev) > 3) {
        System.out.println("Anomaly detected: " + v);
    }
});

This flags values that deviate significantly from the norm.

4. Automate with Spring Boot Scheduler

Schedule anomaly checks:

@Scheduled(fixedRate = 60000)
public void detectAnomalies() {
    // Fetch values and run detection logic
}

5. Extend with External ML Libraries

Integrate TensorFlow Java or Smile for more advanced anomaly detection models like Isolation Forest or Autoencoders.

6. Alerting and Action

Use Spring Boot’s notification system to log, alert, or trigger actions when anomalies are detected.

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