NashTech Insights

Implementing Jaeger Best Practices for Success

Rahul Miglani
Rahul Miglani
Table of Contents

Deploying a distributed tracing system like Jaege in a production environment requires careful planning and adherence to best practices. Jaeger’s insights into your application’s performance and interactions can be invaluable, but ensuring its seamless operation amidst the complexities of a production setting is paramount. In this blog, we’ll guide you through the implementation of Jaeger best practices for production, helping you achieve optimal trace collection, analysis, and utilization.

1. Start with a Clear Strategy

Before deploying Jaeger, define your goals. Identify what metrics and insights you aim to capture and how they align with your performance optimization and troubleshooting strategies.

2. Proper Infrastructure Planning

Ensure your infrastructure is adequately prepared for Jaeger deployment:

  • Resource Allocation: Allocate sufficient resources for Jaeger components, especially the collector and storage backend.
  • Scaling Considerations: Design your deployment to scale horizontally as your application load increases.

3. Choose the Right Storage Backend

Jaeger supports multiple storage backends like Elasticsearch, Cassandra, and more. Choose a backend that suits your organization’s infrastructure, familiarity, and scaling requirements.

4. Monitoring and Alerting

Implement robust monitoring and alerting for Jaeger and its components. Set up alerts for key metrics like storage capacity, latency thresholds, and error rates.

5. Security Considerations

Ensure proper security measures:

  • Authentication and Authorization: Secure access to Jaeger components and data.
  • Network Segmentation: Segment Jaeger components from public networks to prevent unauthorized access.
  • Encryption: Encrypt communication between Jaeger components and storage backends.

6. Optimal Sampling Strategies

Choose the right sampling strategy to balance data volume and performance impact. Sampling is crucial to prevent overwhelming your system with trace data.

7. Distributed Context Propagation

Ensure trace context is propagated across service boundaries consistently. This maintains trace continuity and helps in correlating spans across services.

8. Maintain Retention Policies

Set retention policies for trace data to ensure you retain relevant data without overwhelming your storage backend.

9. Backup and Disaster Recovery

Implement backup and disaster recovery plans to safeguard your trace data. Regularly back up trace information to prevent data loss.

10. Regular Maintenance and Updates

Stay updated with the latest Jaeger releases and updates. Regularly maintain and update Jaeger components to benefit from bug fixes and new features.

11. Testing and Load Simulation

Test Jaeger in a controlled environment before deploying it in production. Simulate production-level load to gauge its performance impact and resource usage.


Implementing Jaeger best practices for production goes beyond just deploying a tracing system – it’s about ensuring that your application’s performance insights remain accurate, actionable, and resilient in a dynamic environment. By following these recommendations, you’re poised to harness Jaeger’s power to its fullest potential, optimizing performance, streamlining troubleshooting, and enhancing your application’s overall health. As you navigate through the complexities of a production environment, Jaeger becomes your ally, providing a clear lens into your microservices interactions and aiding in creating a more robust and efficient system.

Rahul Miglani

Rahul Miglani

Rahul Miglani is Vice President at NashTech and Heads the DevOps Competency and also Heads the Cloud Engineering Practice. He is a DevOps evangelist with a keen focus to build deep relationships with senior technical individuals as well as pre-sales from customers all over the globe to enable them to be DevOps and cloud advocates and help them achieve their automation journey. He also acts as a technical liaison between customers, service engineering teams, and the DevOps community as a whole. Rahul works with customers with the goal of making them solid references on the Cloud container services platforms and also participates as a thought leader in the docker, Kubernetes, container, cloud, and DevOps community. His proficiency includes rich experience in highly optimized, highly available architectural decision-making with an inclination towards logging, monitoring, security, governance, and visualization.

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