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Instance Configuration

AWS t3.medium Specifications:
  • vCPUs: 2
  • Memory: 4GB RAM
  • Network Performance: Up to 5 Gigabit
Bifrost Configuration:
  • Buffer Size: 15,000
  • Initial Pool Size: 10,000
  • Test Load: 5,000 requests per second (RPS)

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Performance Results

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Overall Performance Metrics

MetricValueNotes
Success Rate100.00%Perfect reliability under high load
Average Request Size0.13 KBLightweight request payload
Average Response Size1.37 KBStandard response size for testing
Average Latency2.12sTotal end-to-end response time
Peak Memory Usage1,312.79 MB~33% of available 4GB RAM

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Detailed Performance Breakdown

OperationLatencyPerformance Notes
Queue Wait Time47.13 µsTime waiting in Bifrost’s internal queue
Key Selection Time16 nsWeighted API key selection
Message Formatting2.19 µsRequest message preparation
Params Preparation436 nsParameter processing
Request Body Preparation2.65 µsHTTP request body assembly
JSON Marshaling63.47 µsJSON serialization time
Request Setup6.59 µsHTTP client configuration
HTTP Request1.56sActual provider API call time
Error Handling189 nsError processing overhead
Response Parsing11.30 msJSON response deserialization
Bifrost’s Total Overhead: 59 µs* *Excludes JSON marshalling and HTTP calls, which are required in any implementation

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Performance Analysis

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Strengths on t3.medium

  1. Perfect Reliability: 100% success rate even at 5,000 RPS
  2. Memory Efficiency: Uses only 33% of available RAM (1,312.79 MB / 4GB)
  3. Minimal Overhead: Just 59 µs of added latency per request
  4. Fast Operations: Sub-microsecond performance for most internal operations

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Resource Utilization

  • Memory Usage: Very efficient at 1,312.79 MB peak usage
  • CPU Performance: Handles 5,000 RPS workload effectively
  • Queue Management: 47.13 µs average wait time indicates good throughput

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Performance Characteristics

Fastest Operations:
  • Key Selection: 16 ns (near-instantaneous)
  • Error Handling: 189 ns
  • Params Preparation: 436 ns
Most Time-Consuming Operations:
  • HTTP Request: 1.56s (external provider call)
  • Response Parsing: 11.30 ms
  • JSON Marshaling: 63.47 µs

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Cost-Effectiveness Analysis

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Value Proposition

The t3.medium instance provides excellent price-to-performance ratio for moderate workloads:
  • Lower infrastructure costs compared to larger instances
  • Efficient resource utilization (33% memory usage)
  • Suitable for most production workloads under 5,000 RPS
  • Room for growth with current resource utilization

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Ideal Use Cases

t3.medium is perfect for:
  • Startups and small teams with budget constraints
  • Development and staging environments
  • Moderate production workloads (< 3,000 RPS sustained)
  • Cost-sensitive deployments where slight latency trade-offs are acceptable

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Configuration Recommendations

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Optimal Settings for t3.medium

Based on test results, these configurations work well: 
{
  "client": {
    "initial_pool_size": 10000,
    "buffer_size": 15000
  }
}

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Tuning Opportunities

For Lower Memory Usage:
  • Reduce initial_pool_size to 7,500-8,000
  • Decrease buffer_size to 12,000-13,000
  • Trade-off: Slightly higher latency
For Better Performance:
  • Increase initial_pool_size to 12,000-13,000
  • Increase buffer_size to 17,000-18,000
  • Trade-off: Higher memory usage (monitor RAM limits)

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Comparison Context

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vs. t3.xlarge Performance

Metrict3.mediumt3.xlargeDifference
Bifrost Overhead59 µs11 µs+81% slower
Queue Wait Time47.13 µs1.67 µs+96% slower
JSON Marshaling63.47 µs26.80 µs+58% slower
Response Parsing11.30 ms2.11 ms+81% slower
Memory Usage1,312.79 MB3,340.44 MB-61% usage
Key Insights:
  • t3.medium uses 61% less memory than t3.xlarge
  • Performance trade-offs are reasonable for cost savings
  • Most operations still complete in microseconds

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Next Steps

When to upgrade to t3.xlarge:
  • Sustained load approaches 4,000+ RPS
  • Queue wait times consistently exceed 75 µs
  • Memory usage approaches 75% of available RAM
  • Run Your Own Benchmarks to test with your specific workload
  • Compare with t3.xlarge for performance scaling analysis