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Monitoring with container insights & anomaly
detection
Greg Eppel
Global Solutions Architect
CloudWatch & X-Ray
AWS
S V C 3 0 8
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Topics
Monitoring
Observability
Container insights
Anomaly detection
Demo
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Reactive monitoring
Blissful ignorance
Confusion
Stress
False hope
Desperation
Enlightenment
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Monitoring must evolve
Monolithic to
microservice
Short-lived
resources
^Devices
^Data
Faster release
velocity
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Metrics
What: Numeric representation of data measured over
intervals of time
Why: Useful for identifying trends, mathematical
modeling, and prediction
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Logs
What: Immutable, timestamped record of discrete
events that happened over time
Why: Useful for uncovering emergent and
unpredictable behavior
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Traces
What: Representation of a series of related distributed
events that encode the end-to-end request flow
through a distributed system
Why: Provides visibility into both the path traversed by
a request as well as the structure of a request
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Observability
This is the goal
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Observability is the goal
These are the tools
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Amazon CloudWatch
Container Insights (Preview)
Enables you to monitor, isolate, and diagnose your containerized
applications and microservices environments
1. Fully managed automated summary/analysis of compute capacity
2. Ad hoc analytics capability
3. Application tracing observability
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Amazon CloudWatch
Anomaly Detection (Preview)
Enables developers, operators and systems engineers to
automate, accelerate, and improve the detection and alerting of
abnormal system and application behavior.
1. Self-learning metric behavior
2. Aware of seasonality
3. Dynamic alarming
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Container Insights: Takeaways
1. Fully managed automated summary/analysis of compute capacity
2. Ad hoc analytics capability
3. Application tracing observability
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Anomaly detection: Takeaways
1. Self-learning metric behavior
2. Aware of seasonality
3. Dynamic alarming
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http://aws.amazon.com/cloudwatch/getting-started
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Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Greg Eppel
geppel@amazon.com