•Tim Knapik, Mandie Liu, David Schnurr, Sisil Mehta, Anando Sen•8 min read•intermediate•
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•View OriginalOverview
The article discusses Uber's implementation of an automated feature rollout system that utilizes robust regression analysis to ensure safe deployments across its mobile applications. It highlights the importance of monitoring and quickly addressing regressions to enhance user experience and streamline the development process.
What You'll Learn
1
How to implement automated feature rollouts using regression analysis
2
Why monitoring app state events is crucial for regression detection
3
When to rollback a feature deployment based on regression metrics
Prerequisites & Requirements
- Understanding of regression analysis and feature deployment processes
- Familiarity with analytics dashboards and monitoring tools(optional)
Key Questions Answered
How does Uber's Auto Rollouts system enhance feature deployment?
Uber's Auto Rollouts system automates the feature deployment process by monitoring app state events and regression metrics. It allows for quick rollbacks if regressions are detected, thereby ensuring a smoother user experience and reducing developer workload.
What metrics are used to detect regressions in Uber's system?
The system monitors app state events, crash events, and application-not-responding (ANR) events to detect regressions. By correlating these metrics with feature deployment events, it can identify issues and trigger rollbacks if necessary.
What are the benefits of using an automated rollout system?
The automated rollout system increases deployment speed, enhances stability by monitoring multiple metrics, and allows for quick attribution of regressions to specific features or owners. This leads to a more efficient development process and improved user experience.
When should a feature rollback be initiated?
A feature rollback should be initiated when regression metrics indicate a significant negative impact on app performance, such as increased crash rates or application-not-responding events. The system automatically handles this process during deployment.
Key Statistics & Figures
Increase in feature rollout speed
Doubled
The automation of the rollout process has significantly increased the speed at which new features can be deployed.
User sample size needed for a safe rollout
~18,000 users
To achieve a Minimum Detectable Effect (MDE
Technologies & Tools
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Backend
Go
Used for the configuration layer of the Auto Rollouts system.
Backend
Java
Part of the analytics engine for processing metrics.
Backend
Scala
Also part of the analytics engine for processing metrics.
Frontend
React
Used for the user interface layer of the Auto Rollouts system.
Data Processing
Hadoop
Utilized in the data lake for ETL processes.
Data Processing
Presto
Used in conjunction with Hadoop for querying data.
Key Actionable Insights
1Implement automated monitoring for feature rollouts to quickly identify regressions.By automating the monitoring process, teams can reduce the time spent on manual checks and focus on developing new features, ultimately improving productivity.
2Use analytics dashboards to visualize the impact of new features on app performance.Dashboards can provide real-time insights into how features affect user experience, allowing for timely adjustments and enhancements.
3Establish clear rollback procedures based on regression metrics.Having predefined criteria for rollbacks ensures that issues are addressed promptly, minimizing user disruption and maintaining app reliability.
Common Pitfalls
1
Failing to monitor regression metrics can lead to undetected issues during feature rollouts.
Without proper monitoring, regressions may negatively impact user experience before they are identified and addressed, leading to potential customer dissatisfaction.
2
Overlooking the importance of rollback procedures can slow down the response to regressions.
If teams do not have clear rollback criteria, they may hesitate to revert features, allowing issues to persist longer than necessary.
Related Concepts
Feature Deployment Strategies
Regression Analysis Techniques
Automated Monitoring Systems