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

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
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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! )
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4. 1.
Observability
Why and What?
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5. “
Monitoring tells you whether a
system is working, observability
lets you ask why it isn’t working.
- Baron Schwartz
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6. Why Observability?
◎ Monoliths to microservices
◎ Microservices create complex interactions.
◎ Failure modes are unpredictable.
◎ Rise of API ecosystem
◎ Monitoring no longer can help us.
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7. 7Source:https://divante.com/blog/10-companies-that-implemented
-the-microservice-architecture-and-paved-the-way-for-others/

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.
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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.
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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.
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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!
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12. 2.
Distributed Tracing
Just a new hype?
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13. 13Source: https://deepsource.io/blog/distributed-tracing/

14. 14Source: https://deepsource.io/blog/distributed-tracing/

15. 15Source: https://deepsource.io/blog/distributed-tracing/

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.
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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
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18. 18Source:https://opentracing.io/docs/

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
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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!
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22. 3.
Hypertrace
An open source distributed tracing &
observability platform
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23. Notable Features
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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.

24. Hypertrace Project
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25. 25
http://bit.ly/hypertrace-community Google slides

26. Thanks!
Any questions?
You can find me at:
- Twitter: @Jayesh_Ahire1
- Mail: jayesh.ahire@traceable.ai
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27. Credits
Special thanks to all the people who made and released
these awesome resources for free:
◎ Presentation template by SlidesCarnival
◎ Photographs by Unsplash
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