DevSecOps : Identify Current Max Batch Concurrency

[emvaldes]

Objective:

Determine the maximum number of concurrent batches (senders) the system can handle without performance degradation, given different batch sizes.

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Deliverables

1. Concurrency Test Results: Metrics for each batch size and concurrency level.
2. Visualizations: Charts showing concurrency levels vs. latency, error rates, and resource usage.
3. Concurrency Thresholds: Documented maximum concurrency levels for different batch sizes.
4. Pilot Report: Insights from testing recommended concurrency levels in production.

[emvaldes]

Analyze Current Concurrency Limits

Goal: Understand the system's existing concurrency behavior and identify potential bottlenecks.

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Tasks:

1. Review Historical Logs and Metrics

   • Sub-Tasks:
     1. Query Azure Log Analytics for historical concurrency metrics.
     2. Identify peak concurrency scenarios and their associated performance impacts.
     3. Analyze error rates during high-concurrency periods.

2. Identify Concurrency Constraints

   • Sub-Tasks:
     1. Document system components that impact concurrency, such as:
        • Database connection limits.
        • Thread pool sizes in APIs or batch processors.
        • Queue throughput (e.g., Azure Service Bus or Event Hub).
     2. Verify any existing concurrency throttling mechanisms.

3. Baseline Performance Metrics

   • Sub-Tasks:
     1. Define acceptable thresholds for concurrency testing:
        • Maximum latency (e.g., <500ms per batch).
        • Error rates (e.g., <1% failures).
        • Resource utilization (e.g., CPU/memory usage <80%).
     2. Collect baseline metrics for the current concurrency setup.

[emvaldes]

Design and Execute Concurrency Tests

Goal: Measure system performance while increasing the number of concurrent senders for different batch sizes.

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Tasks:

1. Prepare the Test Environment

   • Sub-Tasks:
     1. Ensure a high-fidelity staging environment that mirrors production.
     2. Configure monitoring tools (Azure Monitor, Application Insights) to capture concurrency metrics.

2. Define Test Scenarios

   • Sub-Tasks:
     1. Test concurrency levels with batch sizes of 500, 1000, 2500, and 5000.
     2. Increment concurrency levels gradually:
        • Example: Start with 1 sender and increase to 5, 10, 20, 50, and 100 senders.
     3. Include failure scenarios (e.g., one sender failing mid-batch).

3. Execute Concurrency Tests

   • Sub-Tasks:
     1. Use tools like K6, Locust, or JMeter to simulate multiple senders.
     2. Monitor performance metrics for each concurrency level:
        • Latency (average, 95th percentile).
        • Error rates (e.g., failed batches).
        • Resource usage (CPU, memory, disk I/O).
     3. Record system behavior at each concurrency level to identify thresholds.

4. Monitor During Testing

   • Sub-Tasks:
     1. Use Azure Monitor to track resource utilization (CPU, memory, disk IOPS).
     2. Use Application Insights to monitor latency and errors in real time.

[emvaldes]

Analyze Results and Determine Concurrency Thresholds

Goal: Evaluate the performance metrics to identify the maximum concurrency levels for different batch sizes.

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Tasks:

1. Aggregate Test Results

   • Sub-Tasks:
     1. Consolidate metrics for each batch size and concurrency level.
     2. Plot concurrency levels vs. latency, error rates, and resource usage.

2. Identify Concurrency Limits

   • Sub-Tasks:
     1. Determine the concurrency level at which:
        • Latency begins to spike.
        • Error rates exceed acceptable thresholds (<1%).
        • Resource utilization exceeds safe levels (>80% CPU or memory usage).
     2. Document the maximum concurrency for each batch size tested.

3. Document Bottlenecks

   • Sub-Tasks:
     1. Identify components that limit concurrency (e.g., database, queue, API thread pools).
     2. Provide recommendations for addressing these bottlenecks (e.g., increasing database connection limits, optimizing queue throughput).

[emvaldes]

Validate Findings in Production

Goal: Test the identified concurrency thresholds in a controlled production environment.

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Tasks:

1. Pilot Concurrency Levels

   • Sub-Tasks:
     1. Select a subset of production workloads to test concurrency thresholds.
     2. Monitor performance metrics closely during pilot runs.

2. Compare with Historical Metrics

   • Sub-Tasks:
     1. Analyze how the system performs compared to historical high-concurrency events.
     2. Document any deviations or unexpected behaviors.

3. Finalize Concurrency Recommendations

   • Sub-Tasks:
     1. Prepare a detailed report summarizing findings and the recommended concurrency limits.
     2. Present results to stakeholders for validation and approval.

[emvaldes]

Monitoring Batch Concurrency Metrics

1. Metrics to Monitor

1. Batch Processing Metrics:
   • Number of concurrent batches processed.
   • Average processing time per batch.
   • Error rates during concurrent processing.
2. Infrastructure Metrics:
   • CPU, memory, and disk IOPS utilization during concurrency tests.
3. Queue Metrics (if applicable):
   • Queue depth (number of unprocessed messages).
   • Message processing latency.

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2. KQL Queries for Monitoring Concurrency

1. Batch Processing Latency (Grouped by Sender):

   customEvents
   | where name == "BatchProcessed"
   | summarize AvgLatency = avg(todouble(customDimensions['duration'])), P95Latency = percentile(todouble(customDimensions['duration']), 95) by customDimensions['senderId'], bin(timestamp, 1m)

2. Error Rate by Sender:

   customEvents
   | where name == "BatchError"
   | summarize ErrorCount = count() by customDimensions['senderId'], bin(timestamp, 1m)

3. Resource Utilization During Concurrency Testing:

   Perf
   | where ObjectName == "Processor" and CounterName == "% Processor Time"
   | summarize AvgCPU = avg(CounterValue) by bin(TimeGenerated, 1m)

4. Throughput (Batches Processed Per Second):

   customEvents
   | where name == "BatchProcessed"
   | summarize Throughput = count() by bin(timestamp, 1s)

[emvaldes]

Visualizing Concurrency Metrics

1. Line Charts:
   • Plot concurrency levels vs. latency and error rates.
   • Example: A chart showing how latency increases as concurrency increases.
2. Bar Charts:
   • Show the number of errors per sender at different concurrency levels.
3. Heatmaps:
   • Visualize resource utilization (CPU, memory) at different concurrency levels.