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Site Reliability Engineer Design Document

1. Introduction

Purpose

The purpose of this document is to establish a robust Site Reliability Engineering (SRE) framework for SafeBank’s operational needs. This framework ensures the seamless operation of critical banking services by prioritizing reliability, availability, and performance whilst at the same time maintaining operational efficiency and controlling costs. It provides a strategic blueprint for automating incident response, optimizing monitoring practices, and aligning reliability objectives with customer expectations and SLAs.

This document also seeks to emphasize a proactive approach to system reliability. It aims to minimize downtime, prevent incidents, and enhance cross-team collaboration. The goal is to build resilient systems capable of meeting the demands of a highly regulated and customer-centric banking environment.

Scope

This design applies to all SafeBank environments, development, staging, and production. Ultimately ensuring consistent reliability practices across the entire service lifecycle. It focuses on the following core operational areas:

1. Critical Banking Services: Emphasis is placed on maintaining uptime and seamless functionality for essential customer-facing services, including payments, transfers, and account management.

2. Transaction Pipelines: Real-time processing and scalability for transaction workflows to meet stringent SLAs (e.g., <2-second completion time), minimizing the risk of bottlenecks during peak loads.

3. Database Operations: Strategies to reduce latency, ensure high availability, and prevent data loss through proactive monitoring, query optimization, and redundancy planning.

4. API Ecosystem: Reliability mechanisms for API integrations that connect internal systems and external partners, including monitoring error rates and enforcing service limits for consistent performance.

5. End-to-End Monitoring and Alerts: Implementation of monitoring tools and alerting thresholds to track uptime, transaction speeds, error rates, and load conditions across all layers of the system. Alerts are configured to route to appropriate teams (e.g., Slack channel #security-concerns) for timely action.

6. Incident Response: Design and automation of workflows to ensure fast, effective incident resolution while meeting high-severity incident response targets (<30 minutes).

2. Monitoring Design and Implementation

Monitoring Tools

• Azure Monitor: To track infrastructure-level metrics such as uptime, VM health, and network latency
• Application Insights: For application-level monitoring, including request rates, response times, and exceptions
• Log Analytics: Centralized log collection and query platform for troubleshooting
• Workbooks: Custom dashboards for visualizing SLA adherence and alert trends

Monitored Metrics (SLI/SLO Alignment)

1. Uptime: Target 99% uptime monitored through Azure Monitor
2. Transaction Speed: Monitored for completion within 2 seconds using Application Insights
3. API Errors: Error rate threshold set to 1%, tracked via Application Insights
4. Data Retrieval: Query response times tracked with thresholds of 3 seconds
5. System Load: Resource utilization during peak load tracked with Azure Monitor

3. Incident Response

Alerting Strategy

All alerts are sent to the #security-concerns Slack channel via an integrated Logic App.

1. Uptime Alerts: Triggered if uptime drops below 99%. Notification sent to the Infrastructure Team
2. Transaction Speed Alerts: Triggered if transactions exceed 2 seconds, notifying the Application Development Team
3. API Error Alerts: Triggered if API errors exceed 1%. Notifications are sent to the Backend Development Team
4. Data Retrieval Alerts: Triggered for query times exceeding 3 seconds. Notification sent to Database Administrators
5. Load Alerts: Triggered when performance degradation occurs during peak load, notifying the Infrastructure Team

Incident Workflow

1. Detection: Alerts generated and routed via Logic Apps to the Slack channel and relevant teams
2. Triage: On-call team reviews the incident severity and categorizes it (high, medium, low)
3. Response: Teams act based on runbooks and automated workflows to mitigate the issue
4. Post-Incident Review: Documented in Confluence, including root cause analysis and corrective actions

Response Times

• High-severity: <30 minutes response time
• Medium-severity: <1 hour response time
• Low-severity: <8 hours response time

4. Capacity Design

Scalability Strategies

• Auto-scaling: Enabled for Azure App Services and Databases based on CPU utilization and request rates
• Redundancy: Multi-region deployment for critical services to ensure failover capabilities
• Traffic Distribution: Azure Front Door used for load balancing and geo-distribution of traffic

Capacity Planning

• Forecasting is based on historical traffic patterns using Azure Monitor
• Monthly reviews to adjust reserved capacity for predictable workloads

5. Performance Efficiency

Performance Goals

• Transaction response times <2 seconds
• API error rates <1%
• Query response times <3 seconds

Optimizations

• Database indexes and query tuning performed quarterly
• Application Insights used for code profiling to identify bottlenecks
• CDN integration for static content delivery to reduce latency

6. Cost Optimization

Strategies

• Reserved capacity for VMs and databases
• Log Analytics data retention set to 30 days for cost control
• Automated scripts to identify underutilized resources and recommend scaling down

7. Operational Excellence and Release Engineering

Deployment Strategy

• Blue-green deployment for production to ensure zero-downtime releases
• CI/CD pipelines using Azure DevOps to automate build, test, and deployment workflows

Audits and Training

• Monthly audits of monitoring dashboards and alerts
• Bi-annual training sessions for the technical team on new tools and workflows

8. Reliability Design

Availability Mechanisms

• Azure Traffic Manager for failover across regions
• Backup and restore policies for databases with daily snapshots
• 99% SLA supported by multi-region redundancy and automated recovery scripts

Disaster Recovery

• Recovery time objective (RTO): <1 hour
• Recovery point objective (RPO): <15 minutes
• Periodic failover drills to test recovery readiness

9. Continuous Improvement

• Incident data reviewed weekly to improve SLI/SLO alignment
• Quarterly retrospectives with cross-functional teams to refine processes and tools
• A/B testing of new optimizations before full-scale rollouts

10. Conclusion

This Site Reliability Engineering design was developed to ensure that SafeBank’s vital services abide within clearly defined SLA and SLO targets while going beyond customer expectations regarding availability, performance, and reliability. Having a strong monitoring and alerting design is important in doing so because it enables teams to foresee and fix problems proactively before they happen, minimizing downtime and always maintaining customer experience.

The integration of automation for incident response, together with the detailed escalation procedures and communication workflows, enhance the organization’s ability to respond effectively to high-severity incidents within defined timelines. This approach reduces the Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR), which ultimately strengthens the overall operational resilience.

This design incorporates scalability to accommodate business growth and includes cost savings in resource use that will also enhance the observability of the system performance. Taken as a whole, these strategies ensure that SafeBank can adjust to the different requirements placed upon it and earn customers’ confidence in the competitive world of finance.

By prioritizing reliability, scalability, and customer satisfaction, this SRE design fosters an environment of continuous improvement and operational excellence, safeguarding SafeBank’s position as a trusted and innovative financial institution.