"Distributed Tracing: New DevOps Foundation" by Jayesh Ahire
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This talk serves as a practical introduction to Distributed Tracing. We will see how we can make best use of open source distributed tracing platforms like Hypertrace with Azure and find the root cause of problems and predict issues in our critical business applications beforehand. Presentation part of Open Source Days on 30 Oct - ossdays.konfhub.com
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"Distributed Tracing: New DevOps Foundation" by Jayesh Ahire
- 1. Distributed Tracing New DevOps Foundation Jayesh Bapu Ahire (@Jayesh_Ahire1)
- 2. Hello! I am Jayesh Bapu Ahire - Founding Engineer, Traceable.ai - AWS ML Hero | Twilio Champion - AI Researcher - Find me at @Jayesh_Ahire1 2
- 3. Agenda ◎ Observability: Why and What? ◎ Telemetry ◎ Metrics, Logs and Traces! ◎ What is Distributed Tracing? ◎ Why do I care? ◎ Demo! (Hoping it won’t fail! ) 3
- 5. “ Monitoring tells you whether a system is working, observability lets you ask why it isn’t working. - Baron Schwartz 5
- 6. Why Observability? ◎ Monoliths to microservices ◎ Microservices create complex interactions. ◎ Failure modes are unpredictable. ◎ Rise of API ecosystem ◎ Monitoring no longer can help us. 6
- 8. What is Observability? ◎ In control theory, observability is defined as a measure of how well internal states of a system can be inferred from knowledge of that system’s external outputs. Simply put, observability is how well you can understand your complex system. ◎ Metrics, events, logs, and traces—or MELT—are at the core of observability. But observability is about a whole lot more than just data. 8
- 9. What is Observability? What characteristics did the queries that timed out at 500ms share in common? Service versions? Browser plugins? - Instrumentation produces data. - Querying data answers our questions. 9
- 10. Telemetry aids observability ◎ Telemetry data isn't observability itself. ◎ Instrumentation code is how we get telemetry. ◎ Telemetry data describes events in the system. All different views into the same underlying truth. 10
- 11. Metrics, Logs and Traces! ◎ Metrics: Aggregated summary statistics. ◎ Logs: Detailed debugging information emitted by processes. ◎ Distributed Tracing: Provides insights into the full lifecycles, aka traces of requests to a system, allowing you to pinpoint failures and performance issues. Structured data can be transmitted into any of these! 11
- 12. 2. Distributed Tracing Just a new hype? 12
- 16. What is Distributed Tracing? ◎ Distributed tracing tracks production requests as they touch different parts of your architecture across the time. ◎ Requests have a unique trace ID, which you can use to lookup a trace diagram, or log entries related to it. ◎ Causal diagrams are easier to understand than scrolling through logs. 16
- 17. Why do I care? ◎ Reduce time in triage by contextualizing errors and delays ◎ Visualize latency like time in my service vs waiting for other services ◎ Understand complex applications like async code or microservices ◎ See your architecture with live dependency diagrams built from traces 17
- 19. Tracing concepts ◎ Span ○ Represents a single unit of work in a system. ○ Typically encapsulates: operation name, a start and finish timestamp, the parent span identifier, the span identifier, and context items. ◎ Trace ○ Defined implicitly by its spans. A trace can be thought of as a directed acyclic graph of spans where the edges between spans are defined as parent/child relationships. ◎ DistributedContext ○ Contains the tracing identifiers, tags, and options that are propagated from parent to child spans 19
- 20. 20
- 21. How about a Demo? We will be using demo apps for generating traces and let’s look at the insights we can get! 21
- 22. 3. Hypertrace An open source distributed tracing & observability platform 22
- 23. Notable Features 23 Feature Description Dashboard UI Global health including most frequently called endpoints, services and backends Service UI Service owners see health and latency overview of their endpoints and dependencies Backend UI Owners of backends like MySQL or Redis can quickly identify slow queries and identify trends Built-in Rosetta Stone Natively understands all major trace data formats like Jaeger and Zipkin Sampling unnecessary Designed to ingest 100% of request traces natively. No need for a sampling collector. Real-time processing Application flow and metrics aggregate automatically, using stream processing.
- 26. Thanks! Any questions? You can find me at: - Twitter: @Jayesh_Ahire1 - Mail: [email protected] 26
- 27. Credits Special thanks to all the people who made and released these awesome resources for free: ◎ Presentation template by SlidesCarnival ◎ Photographs by Unsplash 27