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Distributed Tracing: New DevOps Foundation

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Distributed Tracing
New DevOps
Foundation
Jayesh Bapu Ahire
(@Jayesh_Ahire1)

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Hello!
I am Jayesh Bapu Ahire
- Founding Engineer, Traceable.ai
- AWS ML Hero | Twilio Champion
- AI Researcher
- Find me ...

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Agenda
◎ Observability: Why and What?
◎ Telemetry
◎ Metrics, Logs and Traces!
◎ What is Distributed Tracing?
◎ Why do I ca...

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Distributed Tracing: New DevOps Foundation

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This talk will serve 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.

This talk will serve 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.

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Distributed Tracing: New DevOps Foundation

  1. 1. Distributed Tracing New DevOps Foundation Jayesh Bapu Ahire (@Jayesh_Ahire1)
  2. 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. 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
  4. 4. 1. Observability Why and What? 4
  5. 5. “ Monitoring tells you whether a system is working, observability lets you ask why it isn’t working. - Baron Schwartz 5
  6. 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
  7. 7. 7 Source:https://divante.com/blog/10-companies-that- implemented-the-microservice-architecture-and-paved-the- way-for-others/
  8. 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. 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. 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. 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. 12. 2. Distributed Tracing Just a new hype? 12
  13. 13. 13Source: https://deepsource.io/blog/distributed- tracing/
  14. 14. 14Source: https://deepsource.io/blog/distributed- tracing/
  15. 15. 15Source: https://deepsource.io/blog/distributed- tracing/
  16. 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. 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
  18. 18. 18Source:https://opentracing.io/docs/
  19. 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. 20
  21. 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. 22. 3. Hypertrace An open source distributed tracing & observability platform 22
  23. 23. Why Hypertrace? Topology View the topology of all services and backends in real-time. Scalable for 100s of them. Dashboards Pre-canned dashboards for Services, APIs and Backends Extensibility Write your own enrichers to enrich trace data for your business needs and create views around your business needs. Interoperable Works OOB with all open source tracing formats like Zipkin, OTel, Jaeger. GraphQL APIs Traces, spans and entities exposed through GraphQL APIs. Build the next creative use case API & Trace Analytics Powerful slice & dice of all the data powered by Apache Pinot 23
  24. 24. Hypertrace Project 24
  25. 25. 25 http://bit.ly/hypertrace- community Google slides
  26. 26. Thanks! Any questions? You can find me at: - Twitter: @Jayesh_Ahire1 - Mail: jayesh.ahire@traceable.ai 26
  27. 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

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