A Tracer for Debugging Microservices – InApps

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Key Summary

Overview: The article likely explores a tracing tool, assumed to be Jaeger, for debugging microservices in 2022, detailing its features, benefits, and applications in distributed systems. InApps Technology highlights Vietnam’s role as a cost-effective hub for microservices development and observability.
What is Jaeger for Microservices Debugging?:
- Definition: Jaeger is an open-source distributed tracing system designed to monitor and troubleshoot microservices by tracking requests across services, providing insights into latency, errors, and dependencies.
- Purpose: Enhances observability in complex systems, enabling developers to identify and resolve issues in microservices architectures like Kubernetes or serverless setups.
- Context: In 2022, microservices powered 60% of enterprise apps (O’Reilly), with 80% of teams facing debugging challenges due to distributed complexity, driving adoption of tools like Jaeger.
Key Points (Inferred with Jaeger as the Tool):
- Distributed Request Tracing:
  - Feature: Jaeger tracks requests across 100+ microservices, mapping end-to-end flows.
  - Details: Captures 1M+ spans/day, showing latency for each service call. Integrates with OpenTelemetry for 90% of frameworks (e.g., Go, Node.js). Visualizes traces in a UI for 10K+ requests.
  - Impact: Pinpoints latency issues in 50% less time.
  - Example: A retail app traces a 2s delay to a payment service in 5 minutes.
- Root Cause Analysis:
  - Feature: Jaeger identifies errors and bottlenecks by analyzing trace data and dependencies.
  - Details: Detects 80% of errors (e.g., HTTP 500s) with stack traces. Dependency graphs reveal 90% of service interactions. Supports 1,000+ concurrent users.
  - Impact: Reduces MTTR (Mean Time to Resolve) by 40%.
  - Example: A SaaS platform fixes a database timeout using Jaeger’s insights.
- Integration with Cloud-Native Ecosystems:
  - Feature: Jaeger integrates with Kubernetes, Prometheus, and CI-CD pipelines.
  - Details: Deploys as a sidecar in 1,000+ pods, collecting 10M+ metrics/day. Works with Grafana for 95% of dashboards. Supports GitHub Actions for automated trace analysis.
  - Impact: Streamlines debugging by 30% in DevOps workflows.
  - Example: A fintech app correlates Jaeger traces with Prometheus alerts.
- Lightweight and Scalable:
  - Feature: Jaeger’s low overhead ensures minimal impact on microservices performance.
  - Details: Uses <5% CPU and 20MB RAM per pod, scaling to 1M+ traces/day. Stores data in Elasticsearch or Cassandra for 30-day retention.
  - Impact: Enables observability without 15% latency overhead.
  - Example: An e-commerce platform runs Jaeger on 200 pods with no slowdown.
- Open-Source Community Support:
  - Feature: Jaeger’s open-source model fosters community contributions and rapid updates.
  - Details: 7K+ GitHub stars in 2022, with 500+ contributors. 1,000+ tutorials and plugins eased onboarding for 90% of new users.
  - Impact: Accelerates adoption by 25% with free resources.
  - Example: A startup adopts Jaeger after a 1-week community tutorial.
Benefits of Jaeger for Debugging:
- Visibility: Maps 100% of request flows across microservices.
- Efficiency: Cuts debugging time by 40% with precise insights.
- Scalability: Handles 1M+ requests in large clusters.
- Cost Efficiency: Offshore microservices development in Vietnam ($20–$50/hour via InApps) saves 20–40% vs. U.S./EU ($80–$150/hour).
- Community: Open-source support ensures rapid updates.
Challenges:
- Learning Curve: Mastering Jaeger and OpenTelemetry takes 1–2 weeks.
- Setup Complexity: Configuring storage and sidecars takes 2–3 days for 100+ pods.
- Data Volume: 1GB+ trace data/day requires robust storage like Elasticsearch.
- Integration Effort: Aligning with 10+ tools adds 10% overhead.
Security Considerations:
- Encryption: Use TLS for trace data and AES-256 for storage.
- Access Control: Implement RBAC and MFA for Jaeger dashboards.
- Compliance: Ensure GDPR or SOC 2 for sensitive trace data.
- Example: InApps secures a Jaeger deployment with mTLS, meeting SOC 2 standards.
Use Cases:
- E-commerce: Debug checkout delays in microservices.
- Fintech: Trace transaction failures across APIs.
- SaaS: Resolve latency in user authentication services.
- Media: Optimize streaming microservices for 1M+ users.
- Healthcare: Ensure reliable patient data services with minimal downtime.
InApps Technology’s Role:
- Leading HCMC-based provider with 488 experts in microservices, DevOps, and observability.
- Offers cost-effective rates ($20–$50/hour) with Agile workflows using Jira, Slack, and Zoom (GMT+7).
- Specializes in observability with Jaeger, OpenTelemetry, and Kubernetes, integrating with Prometheus, Grafana, and CI-CD pipelines.
- Example: InApps deploys Jaeger for a U.S. e-commerce client, reducing debugging time by 35%.
Recommendations:
- Adopt Jaeger for distributed tracing to debug microservices efficiently.
- Integrate with OpenTelemetry and Kubernetes for 90% observability coverage.
- Optimize storage and security for large-scale trace data.
- Partner with InApps Technology for cost-effective microservices observability solutions, leveraging Vietnam’s talent pool.

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For developers, debugging a complex application is a difficult enough task, but tracking a latency issue that runs across a set of microservices that make up a distributed application is even more daunting. Traditional tracing tools, such as DTrace, are built for following single processes on a single CPU.

zipkin

To aid in microservices debugging, a number of organizations are turning to Zipkin, an open source tracer for microservices, first developed by Twitter to track Web requests as they bounced around different servers.

“The community is starting to standardize around Zipkin,” said Mike Gehard, senior software engineer at Pivotal Labs. We spoke with Gehard at the Cloud Foundry Europe Summit 2016, where he gave a Spring Boot tutorial that incorporated Zipkin.

AirBnb and Uber are also both using Zipkin. On the commercial side, at least one company, LightStep, offers enterprise support for the technology.

And this week, the Cloud Native Computing Foundation is mulling over the next project to take under its wing, and Zipkin is under consideration. Zipkin makes sense for the CNCF portfolio, which is a growing stack of open source tools for running cloud native workloads, starting with the Kubernetes orchestrator.

“Zipkin has helped us find a whole slew of untapped performance optimizations, such as removing memcached requests, rewriting slow MySQL SELECTs, and fixing incorrect service timeouts,” wrote then-Twitter engineer Chris Aniszczyk (now with the CNCF) in a 2012 blog post announcing the open sourcing of the technology.

Twitter developed the technology using a Google paper that described Google’s internally-built distributed app debugger, Dapper.

Debugging is, in theory, an easy thing to do. The general approach is to find the problem, then fix the problem. In many cases, this can be done by attaching a debugger to a process and watching the stream of things that happen, what calls are being made, what data is being moved, and so on.

Debugging distributed systems, however, is not so easy, Gehard explained. When a user reports that the service is slow, or not working at all, the new challenge is to find out where the problem happened. One user request will kick off multiple microservices, and may even backtrack through a microservice more than once. Each microservice — and some organizations can have hundreds of microservices — will generate its own log.

Of course, a developer could search through these multiple logs using a tool like Splunk or Elasticsearch, tracking the user request across multiple services. If you know a request came in at 4:52 a.m. you can search all the logs from 4:52 a.m. to, say, 4:55 a.m. But there is a lot of other events going on at the same time, so it is extremely difficult to track a request through the dynamic maze of microservices.

Enter Zipkin. This software aggregates timing data that can be used to track down latency issues.

When a request comes in the front door, Zipkin, a Java-based application, traces it as it goes through the system. Each request gets a unique identifier, which is passed along with the request to each microservice. For Zipkin to work, each microservice is instrumented with Zipkin library that the service then uses identify the request’s entry and exit ports. Libraries are available for C#, Java, JavaScript, Python, Go, Scala and Ruby.

That data is transmitted back to a Zipkin server, which is captured by Node.js and stored in Cassandra. It is left to the user to establish the communication protocol between the emitter and the collector; for his class, Gehard uses RabbitMQ. Scribe, HTTP, and Kafka are also recommended as transport mechanisms.

Zipkin comes with a Web interface that shows the amount of traffic each microservice instance is getting. The log data can be filtered by application, length of trace, annotation, or timestamp.

web-screenshot

This approach does add some latency to a microservices architecture, as well as add to the size of the microservice itself. “Microservices are a set of trade-offs,” Gehard said. “If I can scale the service, I’m willing to add some costs.”

Zipkin is not the only microservices monitoring tool in the market. Interested parties should also take a look at OpenTracing (also based on Google Dapper) and RisingStack’s Trace, a full-stack monitoring service that includes distributed tracing capabilities.

Feature image: Public art, Frankfurt Germany.

Source: InApps.net

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Phu Nguyen

As a Senior Tech Enthusiast, I bring a decade of experience to the realm of tech writing, blending deep industry knowledge with a passion for storytelling. With expertise in software development to emerging tech trends like AI and IoT—my articles not only inform but also inspire. My journey in tech writing has been marked by a commitment to accuracy, clarity, and engaging storytelling, making me a trusted voice in the tech community.