Cloud Native Monitoring: Practical Challenges and Solutions for Modern Architecture

✍ Scribed by Kenichi Shibata; Rob Skillington; Martin Mao

• Publisher: O'Reilly Media, Incorporated
• Year: 2022
• Tongue: English
• Leaves: 65
• Category: Library

✦ Synopsis

By using distributed architectures, the cloud native ecosystem enables organizations to build scalable, resilient, and novel software architectures. But the ever-changing nature of distributed systems means that previous approaches to monitoring can no longer keep up. Cloud native systems require a new approach to monitoring, one that is open source compatible, scalable, reliable, and able to control for massive data growth. But cloud native monitoring can't exist in a vacuum: it needs to be part of a broader observability strategy. In this report, authors Kenichi Shibata, Martin Mao, and Rob Skillington introduce the three phases of observability, a pragmatic, goal-driven approach to cloud native monitoring that emphasizes remediating problems. With this method, you'll collect, aggregate, and analyze metrics that focus on the outcomes you want to achieve: to rectify or prevent issues in your system so you can focus on improving business outcomes.

✦ Table of Contents

Cover
Chronosphere
Copyright
Table of Contents
Chapter 1. The Cloud Native Impact on Observability
Challenges of Cloud Native Observability
Deep Dive into Observability Data
Observability Data Is Growing in Scale
Understanding Cardinality and Dimensionality
Cloud Native Systems Are Flexible and Ephemeral
The Goldilocks Zone of Cloud Native Observability
Cloud Native Environments Emit Exponentially More Data Than Traditional Environments
Delivering Reduced Business Outcomes
Observability Practitioners Lose Focus
Increasing Cost of Observability Data
The Cloud Native Impact
Slower Troubleshooting
Tools Become Unreliable
Use Context to Troubleshoot Faster
The Three Phases of Observability: An Outcome-Focused Approach
Remediating at Any Phase, with Any Signal
Conclusion
Chapter 2. Cloud Native Challenges in the Real World
Impact of Uncontrolled Data Growth on System Performance
Controlling Cost
Case Study 1: Improving Performance While Gaining Huge Cost Savings
The Challenge
Approach
Impact of Uncontrolled Data Growth on Observability Reliability
Poor Developer Experience Caused by Poor Observability Data
Case Study 2: Increased Observability Reliability and Improved Developer Experience
The Challenge
Approach
Making Way for Fast-Paced Innovation
Regulatory Requirements
Case Study 3: Navigating Observability Challenges in Balancing Rapid Fintech Growth and SLA Compliance
The Challenge
Approach
Conclusion
Chapter 3. Strategies for Controlling Observability Data Growth and Complexity
Emerging Solution Using a Repeatable Framework
Using FinOps as an Inspiration
Observability Data Optimization Cycle
Step 0: Centralized Governance
Autonomy and Allocations to Increase Responsibility and Improve Responsiveness
Usable Capacity by Allocation to Optimize Use Cases
Using Observability Team as Consultants Instead of as Bottlenecks
Framework Components
Step 1: Analyze
Traffic Analysis
Usage Analysis
Combining Traffic and Usage Analysis to Make Decisions
Output of Analyze Step
Step 2: Refine
Dropping
Retention
Resolution
Downsampling
Aggregation
Output of Refine Step
Step 3: Operate
Expanding Visibility and Coverage
Freeing Up More of the Observability Team’s Time to Tackle Strategic Projects
Conclusion
Chapter 4. Open Source Telemetry Standards: Prometheus, OpenTelemetry, and Beyond
Instrumentation Before Prometheus and OTel
Data Collection Is Controlled by Users
Prometheus
Interoperability Between Different Observability Tools
Standardization to Prometheus
Prometheus Reliability
Prometheus: The Good
Prometheus: The Not-So-Good
OpenTelemetry
What Is OTel?
The OTel Specification
OTel: The Promise
OTel: The Reality
Where to Start with OTel
Implications of OTel’s Approach
Fluent Bit
Conclusion
About the Authors