📈 Monitoring & Alerting in Distributed Systems
Welcome back to The Code Hut Distributed Systems series! In this post, we’ll cover how to monitor your distributed systems and set up alerts to catch issues before they impact users.
Why Monitoring Matters
Distributed systems are complex and failures can propagate quickly. Effective monitoring helps you:
- Detect anomalies and failures early
- Understand system behavior
- Improve reliability and performance
1. Key Metrics to Monitor
- Latency: Measure response times across services
- Throughput: Requests processed per second
- Error rates: Count of failed requests or exceptions
- Resource usage: CPU, memory, disk, network
- Queue depth: For messaging systems like Kafka
2. Tools for Monitoring
- Prometheus: Metrics collection and storage
- Grafana: Dashboards for visualizing metrics
- ELK Stack (Elasticsearch, Logstash, Kibana): Centralized logging and analysis
- Jaeger / Zipkin: Distributed tracing for request flows
3. Example: Monitoring Kafka Consumers in Java
// Kafka consumer metrics example
KafkaConsumer consumer = new KafkaConsumer<>(props);
consumer.subscribe(Collections.singletonList("orders-topic"));
// Access Kafka metrics
Map metrics = consumer.metrics();
metrics.forEach((name, metric) -> {
System.out.println(name.name() + ": " + metric.metricValue());
});
4. Setting Up Alerts
Alerts notify you when metrics exceed thresholds. Examples:
- High latency → trigger PagerDuty or Slack alert
- Consumer lag in Kafka → alert to ops team
- CPU/memory usage > 80% → auto-scale or investigate
5. Best Practices
- Define meaningful SLOs/SLAs
- Monitor both infrastructure and application-level metrics
- Use dashboards and automated alerts together
- Test your alerting rules regularly
Next in the Series
In the next post, we’ll explore Microservices Scaling Patterns to handle increased load and grow your distributed system efficiently.
Label for this post: Distributed Systems
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