Real-Time Anomaly Detection Pipelines with ClickHouse and Spring Boot

December 22, 2025

Real-time anomaly detection is essential in modern applications that rely on fast data insights. By combining ClickHouse’s high-performance analytics engine with Spring Boot, you can build pipelines that detect unusual patterns as data streams in. This approach helps you quickly identify problems, fraud, or unexpected behavior without adding complex infrastructure.

Building the Pipeline

The pipeline starts with data ingestion. ClickHouse is optimized for handling large volumes of time-series or event data. In your Spring Boot application, you can create services that receive events and write them directly to ClickHouse using JDBC or a repository layer.

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

This schema is optimized for time-based queries, which are common in anomaly detection.

Collecting Data in Real Time

Spring Boot can expose REST or messaging endpoints that ingest data from different producers. Using JdbcTemplate, the application inserts incoming records into ClickHouse efficiently. Batch inserts are recommended for higher throughput.

jdbcTemplate.batchUpdate("INSERT INTO metrics (source, value) VALUES (?, ?)", batchParams);

Detecting Anomalies

Once data is flowing, anomaly detection logic can be applied. Start with simple statistical methods such as moving averages, standard deviations, or Z-scores. For example, query the last minute of data and calculate anomalies:

SELECT source, avg(value) AS mean, stddevPop(value) AS std
FROM metrics
WHERE timestamp > now() - INTERVAL 1 MINUTE
GROUP BY source;

The result provides thresholds to detect abnormal spikes or drops.

Integrating with Spring Boot Scheduler

Anomaly detection checks can run on a fixed schedule using Spring Boot’s @Scheduled tasks. Every few seconds or minutes, queries run against ClickHouse and the results are evaluated. When anomalies are found, actions such as logging, alerting, or triggering external APIs can be executed immediately.

@Scheduled(fixedRate = 60000)
public void detectAnomalies() {
    // Query ClickHouse and evaluate thresholds
}

Scaling with Advanced Models

For more sophisticated detection, you can integrate external libraries such as Smile or TensorFlow. Data is fetched from ClickHouse into Spring Boot, processed with machine learning models like Isolation Forest or Autoencoders, and flagged anomalies can be written back into ClickHouse for auditing.

Alerting and Visualization

Detected anomalies should not only be logged but also monitored visually. You can store alerts in a separate ClickHouse table and connect it to visualization tools like Grafana. Spring Boot can also integrate with email or messaging systems to send alerts in real time.

Ads go here

#ads