Table 0.1: |
The Ever Accelerating Trend toward Faster, Cheaper, Lower Risk Delivery of Software |
Figure 0.1: |
Deployments per Day vs. Number of Developers |
Figure 1.1: |
Lead Time vs. Process Time of a Deployment Operation |
Figure 1.2: |
A Technology Value Stream with a Deployment Lead Time of Three Months |
Figure 1.3: |
A Technology Value Stream with a Lead Time of Minutes |
Figure 1.4: |
The Three Ways |
Figure 1.5: |
American Airlines’ DevOps Transformation Journey |
Figure 2.1: |
An Example Kanban Board Spanning Requirements, Dev, Test, Staging, and In Production |
Figure 2.2: |
Simulation of “Envelope Game” |
Figure 3.1: |
Feedback Cycle Times |
Figure 3.2: |
Cycle Time vs. Andon Pulls at Excella |
Table 4.1: |
The Westrum Organizational Typology Model |
Figure 5.1: |
The Technology Adoption Curve |
Figure 5.2: |
American Airlines’ Delivery Transformation |
Table 5.1: |
American Airlines’ New Vocabulary |
Figure 6.1: |
An Example Value Stream Map |
Figure 6.2: |
Invest 20% of Capacity in those Who Create Positive, User-Invisible Value |
Figure 7.1: |
Functional vs. Market Orientation |
Table 7.1: |
Specialists vs. Generalists vs. “E-shaped” Staff |
Figure 8.1: |
Functional Teams in Silos vs. Long-Lived, Multi-Skilled Teams |
Figure 10.1: |
The Deployment Pipeline |
Figure 10.2: |
The Ideal and Non-Ideal Automated Testing Pyramids |
Figure 10.3: |
Running Automated and Manual Tests in Parallel |
Figure 12.1: |
Number of Developers Deploying per Week at Facebook |
Figure 12.2: |
Daily Deployments at CSG International |
Figure 12.3: |
Elite and High Performers Achieve Faster Deployment Lead Times and MTTR (2019) |
Figure 12.4: |
The Deployinator Console at Etsy |
Figure 12.5: |
Blue-Green Deployment Patterns |
Figure 12.6: |
The Canary Release Pattern |
Figure 12.7: |
How Structure Influences Behavior and Quality |
Figure 12.8: |
From Siloed Approach to Cross-Functional Teams |
Figure 12.9: |
Conventional vs. Cross-Functional Structure |
Figure 13.1: |
Google Cloud Datastore |
Table 13.1: |
Architectural Archetypes |
Figure 13.2: |
Blackboard Learn Code Repository: Before Building Blocks |
Figure 13.3: |
Blackboard Learn Code Repository: After Building Blocks |
Figure 14.1: |
Incident Resolution Time for Elite, High, Medium, and Low Performers (2019) |
Figure 14.2: |
Monitoring Framework |
Figure 14.3: |
One Line of Code to Generate Telemetry using StatsD and Graphite at Etsy |
Figure 14.4: |
User Excitement of New Features in User Forum Posts after Deployments |
Figure 15.1: |
Standard Deviations (σ)Mean (μ) with Gaussian Distribution |
Figure 15.2: |
Downloads per Minute: Over-Alerting when Using “Three Standard Deviation” Rule |
Figure 15.3: |
Downloads per Minute: Histogram of Data Showing Non-Gaussian Distribution |
Figure 15.4: |
Netflix Customer Viewing Demand for Five Days |
Figure 15.5: |
Netflix Scryer Forecasting Customer Traffic and the Resulting AWS Schedule of Computer Resources |
Figure 15.6: |
Autodesk Share Price and Thirty-Day Moving Average Filter |
Figure 15.7: |
Transaction Volume: Under-Alerting Using “Three Standard Deviation” Rule |
Figure 15.8: |
Transaction Volume: Using Kolmogorov-Smirnov Test to Alert on Anomalies |
Figure 16.1: |
Deployment to Etsy.com Causes PHP Run-Time Warnings and Is Quickly Fixed |
Figure 16.2: |
The “Service Handback” at Google |
Figure 16.3: |
The LRR and HRR at Google |
Figure 18.1: |
Comments and Suggestions on a GitHub Pull Request |
Figure 18.2: |
Size of Change vs. Lead Time for Reviews at Google |
Figure 21.1: |
The ASREDS Learning Loop |
Figure 22.1: |
Jenkins Running Automated Security Testing |
Figure 22.2: |
Number of Brakeman Security Vulnerabilities Detected |
Figure 22.3: |
Time to Remediate vs. Time to Update Dependencies (TTU) |
Figure 22.4: |
Five Behavioral Clusters for Open-Source Projects |
Figure 22.5: |
Developers See SQL Injection Attempts in Graphite at Etsy |
Figure AF.1: |
Average Development Window by Day of Week per User |
Figure A.1: |
The Core, Chronic Conflict Facing Every IT Organization |
Table A.1: |
The Downward Spiral |
Figure A.2: |
Queue Size and Wait Times as Function of Percent Utilization |
Table A.2: |
Two Stories |
Figure A.3: |
The Toyota Andon Cord |