DevSecOps : Granular Testing and Logging

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Objective: This stage focuses on ensuring that your API endpoints are robust, scalable, and properly instrumented to handle "pandemic-level" traffic. Below is a structured guide based on industry standards and best practices.

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Understanding "Pandemic-Ready" Goals

Definition of Pandemic-Ready Goals

• Scalability: Ensure APIs can handle sudden and extreme traffic spikes without downtime or performance degradation.
• Resilience: APIs should continue to function under failure scenarios, such as partial outages, increased latencies, or high loads.
• Observability: APIs should be instrumented to provide detailed logs and metrics to monitor performance, error rates, and traffic patterns in real-time.
• Accuracy: Ensure APIs return consistent, correct responses even under extreme conditions.

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Steps for Granular Test Sizing for APIs

Step 1: Define Granular Performance Metrics for Each API

The team must establish measurable goals for "pandemic-ready" performance for each API endpoint. This includes:

1. Max Throughput:

   • Requests per second (RPS) each endpoint should handle.
   • Define "normal load," "peak load," and "pandemic-level" load.

2. Latency Targets:

   • Average response time (e.g., <200ms under normal load).
   • Tail-end latency (e.g., 95th percentile <500ms).

3. Error Budget:

   • Allowable error rate (e.g., <0.1% of requests fail under peak load).

4. Concurrent Users:

   • Maximum number of concurrent API consumers the system should support.

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Step 2: Gather Baseline Metrics from Azure

Use Azure Monitor, Application Insights, and Log Analytics to collect real-world data on API performance:

1. Throughput Metrics:

   • Query Azure Log Analytics for request counts across different API endpoints:

     requests
     | summarize RequestCount = count() by name, bin(timestamp, 1m)

2. Latency Metrics:

   • Measure average and tail-end latency:

     requests
     | summarize AvgLatency = avg(duration), P95Latency = percentile(duration, 95) by name

3. Error Rates:

   • Query for failed requests by API endpoint:

     requests
     | where success == false
     | summarize ErrorCount = count() by name, bin(timestamp, 1m)

4. Traffic Patterns:

   • Identify peak traffic times and request distribution patterns.

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Step 3: Simulate Granular Load Tests

Use the collected data to design granular load tests that simulate realistic and extreme conditions.

Tools for Granular Load Testing:

• K6 for API-level load tests:

  import http from 'k6/http';
  
  export let options = {
    stages: [
      { duration: '1m', target: 100 }, // Ramp up to 100 users
      { duration: '5m', target: 500 }, // Sustain 500 users
      { duration: '1m', target: 0 },   // Ramp down
    ],
    thresholds: {
      http_req_duration: ['p(95)<500'], // 95% of requests must complete within 500ms
    },
  };
  
  export default function () {
    http.get('https://your-api-endpoint.com/data');
  }

• Apache JMeter for more complex scenarios involving multiple endpoints and workflows.

• Azure Load Testing for native integration with Azure resources.

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Adding Logging and Instrumentation

Step 1: Define Logging Requirements

1. Standardize Logs:

   • Use a consistent structure for all logs, e.g., JSON format with fields like:

     {
       "timestamp": "2023-01-01T12:00:00Z",
       "level": "INFO",
       "endpoint": "/api/v1/data",
       "method": "GET",
       "responseTime": 120,
       "statusCode": 200,
       "userId": "12345"
     }

2. Log Categories:

   • Access Logs: Log every request, including metadata like request path, HTTP method, response time, and status code.
   • Application Logs: Log internal events, such as database queries or service calls.
   • Error Logs: Log all exceptions and failed requests with detailed error messages.

3. Granular Logging:

   • For APIs processing large payloads, log metadata about the payload size and processing steps.
   • Example for batch processing:

     {
       "timestamp": "2023-01-01T12:00:00Z",
       "level": "INFO",
       "endpoint": "/api/v1/batch-process",
       "batchId": "batch-123",
       "batchSize": 2500,
       "processingTime": 3000
     }

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Step 2: Add Instrumentation for Metrics Collection

1. Use Distributed Tracing:

   • Add distributed tracing headers (e.g., OpenTelemetry) to monitor API calls across services.

2. Add Custom Metrics:

   • Use libraries like Prometheus (for Node.js, Python) or Azure Monitor SDK to capture custom metrics.

   Example for a custom latency metric in Node.js:

   const client = require('applicationinsights');
   
   app.get('/api/data', async (req, res) => {
     const start = Date.now();
     const data = await fetchDataFromDB();
     const latency = Date.now() - start;
   
     client.defaultClient.trackMetric({ name: "API Data Latency", value: latency });
     res.json(data);
   });

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Step 3: Use Azure-Specific Instrumentation

1. Enable Application Insights:

   • Integrate Application Insights into your application to automatically capture logs, traces, and performance metrics.

2. Add Custom Events:

   • Log custom events for specific operations:

     const appInsights = require('applicationinsights');
     appInsights.defaultClient.trackEvent({ name: "BatchProcessed", properties: { batchId: "123", size: 2500 } });

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Aligning on Pandemic-Ready Goals

Step 1: Define API-Specific Goals

1. Set individual goals for each API endpoint:

   • Example:
     • /api/users: 5000 RPS, average latency < 200ms, <1% error rate.
     • /api/orders: 1000 RPS, average latency < 500ms, <0.5% error rate.

2. Group endpoints into categories based on criticality:

   • High Criticality: User authentication, health data submission.
   • Medium Criticality: Analytics, reporting.
   • Low Criticality: Debugging, auxiliary services.

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Step 2: Define Success Metrics

Use Service Level Objectives (SLOs) and Service Level Indicators (SLIs):

• SLO: 99.95% uptime for critical endpoints.
• SLI: Error rate, latency, and throughput metrics for each API.

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Continuous Improvement

A. Real-Time Monitoring

1. Use dashboards in Azure Monitor to track latency, throughput, and error rates in real time.
2. Example for a latency dashboard:

   requests
   | summarize AvgLatency = avg(duration) by bin(timestamp, 1m), name

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B. Feedback Loop

1. Share test results with both development and DevSecOps teams.
2. Iterate on API improvements based on observed bottlenecks and logging insights.

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C. Automate Reporting

1. Generate automated reports on API performance after every test run.
2. Use tools like Power BI or Grafana for visualizing test results.

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Deliverables

1. Granular performance test results for each API endpoint.
2. Enhanced logging and instrumentation with Application Insights integration.
3. Clear "pandemic-ready" performance goals for all APIs.
4. Dashboards and alerts for continuous monitoring of API health.