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Mohammad Waseem
Mohammad Waseem

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Optimizing Slow Database Queries in Legacy Codebases with Kubernetes

#kubernetes #devops #performance

Introduction

Legacy applications often face performance bottlenecks due to inefficient database queries, which can severely impact system responsiveness and user experience. As a DevOps specialist, leveraging modern container orchestration tools like Kubernetes can help troubleshoot and optimize these bottlenecks effectively.

This article explores a systematic approach to identifying and resolving slow queries in a legacy codebase, employing Kubernetes for deployment agility and observability.

Assessing the Problem

Initially, pinpoint the slow queries by enabling detailed database profiling. Use EXPLAIN ANALYZE in your database to gather execution plans and identify bottlenecks.

For example:

EXPLAIN ANALYZE SELECT * FROM users WHERE email = 'example@email.com';
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Look for sequential scans or missing indexes that contribute to latency.

Containerizing the Legacy Application

First, ensure your legacy application runs in a containerized environment. Create a Dockerfile tailored to your stack, for instance:

FROM openjdk:8-jdk
WORKDIR /app
COPY . /app
RUN ./gradlew build
CMD ["java", "-jar", "build/libs/legacy-app.jar"]
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Build and push your image:

docker build -t myorg/legacy-app:latest .
docker push myorg/legacy-app:latest
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Deploy on Kubernetes using a Deployment manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: legacy-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: legacy
  template:
    metadata:
      labels:
        app: legacy
    spec:
      containers:
      - name: legacy
        image: myorg/legacy-app:latest
        ports:
        - containerPort: 8080
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Apply with:

kubectl apply -f deployment.yaml
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Enhancing Observability

Leverage Kubernetes-native tools for monitoring:

  • Prometheus for metrics collection
  • Grafana for visualization
  • Kube-state-metrics for resource metrics

Instrument your app with metrics for database query durations, for example using Micrometer in Java:

MeterRegistry registry = ...;
Timer queryTimer = Timer.builder("db.query.duration")
  .register(registry);

// Wrap query execution
queryTimer.record(() -> {
    // execute query
});
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This data will be crucial for identifying query performance issues.

Applying Query Optimization Techniques

Based on profiling, prioritize query rewriting, indexing, or caching. For example, if an index on email in users table improves latency:

CREATE INDEX idx_users_email ON users(email);
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Test performance improvements with EXPLAIN ANALYZE to confirm reduced execution time.

Automating and Scaling with Kubernetes

Scale replicas dynamically to handle load spikes:

kubectl scale deployment legacy-app --replicas=5
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Use Horizontal Pod Autoscaler for real-time scaling based on CPU or custom metrics:

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: legacy-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: legacy-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50
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Apply with:

kubectl apply -f hpa.yaml
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Continuous Monitoring & Iteration

Integrate your observability stack with CI/CD pipelines to continually monitor query performance post-deployments. Regularly review metrics, update indexes, and adjust caching strategies to maintain optimal database responsiveness.

Conclusion

Kubernetes provides a robust platform for deploying, monitoring, and tuning legacy applications. Combining container orchestration with targeted database optimization techniques enables DevOps teams to significantly improve query performance, extend the lifespan of legacy systems, and deliver better user experiences.

By following these practices, you can systematically identify bottlenecks, validate improvements, and automate scaling, ensuring your legacy codebases evolve without compromising performance.

🛠️ QA Tip

To test this safely without using real user data, I use TempoMail USA.

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