NashTech Insights

Navigating Microservices Performance: Analyzing Distributed Traces with Kiali

Rahul Miglani
Rahul Miglani
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In the intricate landscape of microservices, understanding the journey of a request as it traverses through various components is paramount for ensuring optimal performance. Distributed tracing has emerged as a powerful technique to gain insights into these intricate interactions. When combined with Kiali, an observability tool designed for Istio, you unlock a realm of capabilities to analyze distributed traces and unveil performance bottlenecks within your microservices architecture. In this guide, we’ll walk you through the process of effectively using Kiali to analyze distributed traces and enhance the performance of your microservices.

The Power of Distributed Tracing

Distributed tracing provides a detailed view of how requests flow through the different services that constitute your application. It captures timing data and dependencies, allowing you to pinpoint where bottlenecks and delays occur. This insight is invaluable for troubleshooting and optimizing your microservices architecture.

Unleashing Kiali’s Distributed Tracing Capabilities

Step 1: Access Kiali Dashboard

  1. Navigate to your Kiali dashboard using the provided URL.
  2. Log in with the necessary credentials.

Step 2: Navigate to Distributed Tracing

  1. In the Kiali dashboard, locate the Distributed Tracing tab and click on it.
  2. You’ll be presented with a view that displays the traces of requests as they move through the service mesh.

Step 3: Filter and Visualize Traces

  1. Utilize filters to narrow down the traces you want to analyze. You can filter by service, operation, response code, and more.
  2. Once filtered, Kiali displays a visual representation of traces, showing how requests flow across different services.

Step 4: Analyze Trace Details

  1. Click on a specific trace to delve deeper into its details.
  2. Kiali’s trace view shows you the sequence of services involved, the time taken at each step, and any potential errors encountered.

Step 5: Identify Bottlenecks

  1. Look for services that consistently show higher response times or increased error rates.
  2. Pinpoint services that might be causing delays or failures in the request flow.

Step 6: Utilize Metrics and Insights

  1. Kiali integrates with metrics and health indicators. Use these insights to correlate trace data with service health and performance metrics.
  2. This helps in identifying patterns and root causes of performance issues.

Best Practices for Effective Analysis

  • Segment Traces: Break down traces into smaller segments to analyze specific stages of a request’s journey.
  • Correlate Metrics: Combine trace data with performance metrics to identify patterns and make informed decisions.
  • Collaborate: Share trace data and findings with relevant teams to expedite problem resolution.
  • Regular Checks: Make analyzing distributed traces a regular practice to catch performance degradation early.


Analyzing distributed traces with Kiali provides a powerful window into the intricate world of microservices interactions. By visualizing the path of requests, identifying bottlenecks, and correlating data with metrics, you gain a comprehensive understanding of your application’s behavior. Kiali’s integrated features empower you to pinpoint performance issues and streamline troubleshooting efforts. Embrace the insights obtained from distributed traces to optimize your microservices architecture, enhance user experience, and elevate the overall performance of your applications. With Kiali at your disposal, you’re equipped to master the art of microservices performance analysis.

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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