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Distributed Tracing
New DevOps
Foundation
Jayesh Bapu Ahire
(@Jayesh_Ahire1)
Hello!
I am Jayesh Bapu Ahire
- Founding Engineer, Traceable.ai
- AWS ML Hero | Twilio Champion
- AI Researcher
- Find me at @Jayesh_Ahire1
2
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
1.
Observability
Why and What?
4
“
Monitoring tells you whether a
system is working, observability
lets you ask why it isn’t working.
- Baron Schwartz
5
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
Source:https://divante.com/blog/10-companies-that-
implemented-the-microservice-architecture-and-paved-the-
way-for-others/
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
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
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
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
2.
Distributed Tracing
Just a new hype?
12
13Source: https://deepsource.io/blog/distributed-
tracing/
14Source: https://deepsource.io/blog/distributed-
tracing/
15Source: https://deepsource.io/blog/distributed-
tracing/
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
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
18Source:https://opentracing.io/docs/
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
How about a Demo?
We will be using demo
apps for generating
traces and let’s look at
the insights we can
get!
21
3.
Hypertrace
An open source distributed tracing &
observability platform
22
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
Hypertrace Project
24
25
http://bit.ly/hypertrace-
community
Google slides
Thanks!
Any questions?
You can find me at:
- Twitter: @Jayesh_Ahire1
- Mail: jayesh.ahire@traceable.ai
26
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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Distributed Tracing: New DevOps Foundation

  • 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
  • 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. 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
  • 26. Thanks! Any questions? You can find me at: - Twitter: @Jayesh_Ahire1 - Mail: jayesh.ahire@traceable.ai 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