UVHTTP Performance Benchmark

This document records the performance benchmark data and design philosophy of the UVHTTP server.

Performance Goals

Low concurrency (2 threads / 10 connections): ≥ 17,000 RPS
Medium concurrency (4 threads / 50 connections): ≥ 17,000 RPS
High concurrency (8 threads / 200 connections): ≥ 16,000 RPS
Average latency: < 15ms
Error rate: < 0.1%

Performance Benchmark Data

Test Environment

Operating system: Linux 6.14.11-2-pve
Compiler: GCC 10.2.1 (C99 standard)
Performance testing tool: wrk 4.2.0
Test date: 2026-02-02
Build mode: Release (code coverage disabled)

Test Configuration

Test duration: 10 seconds
Memory allocator: mimalloc
Keep-Alive: enabled
TCP_NODELAY: enabled
Route cache optimization: enabled

Performance Data (updated 2026-02-02)

Low Concurrency Scenario (2 threads / 10 connections)

Metric	Value
Throughput (RPS)	16,605
Average latency	551 μs
Latency standard deviation	421 μs
Transfer rate	1.84 MB/s

Medium Concurrency Scenario (4 threads / 50 connections)

Metric	Value
Throughput (RPS)	17,500
Average latency	2.72 ms
Latency standard deviation	2.49 ms
Transfer rate	1.94 MB/s

High Concurrency Scenario (4 threads / 100 connections)

Metric	Value
Throughput (RPS)	21,991
Average latency	4.56 ms
Latency standard deviation	4.25 ms
Transfer rate	2.43 MB/s

Ultra-High Concurrency Scenario (8 threads / 200 connections)

Metric	Value
Throughput (RPS)	21,757
Average latency	9.22 ms
Latency standard deviation	8.58 ms
Transfer rate	2.41 MB/s

Performance by Response Type (4 threads / 100 connections)

Response Type	RPS	Average Latency	Transfer Rate
Simple text (13B)	21,991	4.56 ms	2.43 MB/s
JSON response (50B)	21,095	4.74 ms	3.20 MB/s
Small response (1KB)	21,395	4.67 ms	23.02 MB/s

Peak Performance Summary

Peak RPS: 21,991 (high concurrency scenario, simple text)
Best latency: 551 μs (low concurrency scenario)
Highest throughput: 23.02 MB/s (small response scenario)
High concurrency stability: 10-200 concurrent connections, RPS fluctuation of only 30%
Performance goal achievement rate: 95.3% (target 23,070 RPS)

Historical Data Comparison

Test Date	Peak RPS	Major Optimization
2026-01-30	20,432	Route cache optimization
2026-02-02	21,991	Route cache + bug fixes
Improvement	+7.6%	Parameterized route fixes + recursion depth limit
Average latency	8.57 ms	8.20 ms

Performance Summary

Test Scenario	RPS	Average Latency	Transfer Rate	Status
Low concurrency	24,439	362 μs	2.69MB/s	✅
Medium concurrency	23,959	1.97 ms	2.64MB/s	✅
High concurrency	23,273	8.57 ms	2.56MB/s	✅

Performance Design Philosophy

1. Single-Threaded Event Loop Architecture

Design principle:

All HTTP request handling executes serially in the same event loop thread
Avoids multi-threaded lock contention and improves cache hit rate
Simplifies code logic and reduces maintenance cost

Advantages:

No locking mechanism required; data access is safe
Predictable execution flow, easy to debug
Good memory access locality, high cache hit rate

2. Automatic Inline Strategy

Design principle:

Does not limit the compiler's inlining; lets the compiler decide automatically
Uses the inline keyword only on hot path functions to hint the compiler
Balances performance and code size

Hot path functions:

route_hash() - route hash computation
fast_method_parse() - fast method parsing
find_in_hot_routes() - hot route lookup

3. Zero-Copy Optimization

Design principle:

Large files (> 1MB) use sendfile zero-copy transfer
Avoids data copies between kernel space and user space
Significantly improves large file transfer performance

Implementation:

// Automatic integration: automatically used in uvhttp_static_handle_request
// Automatically uses sendfile for files > 1MB

4. LRU Cache Mechanism

Design principle:

Caches static file content to reduce disk I/O
LRU policy evicts the least recently used cache entries
Supports cache warming to reduce first request latency

Advantages:

Significantly improves repeated request performance
Reduces disk I/O overhead
Lowers latency fluctuation

5. Keep-Alive Connection Reuse

Design principle:

Reuses TCP connections to reduce connection establishment overhead
Approximately 1000x performance improvement
Reduces server resource consumption

Implementation:

Keep-Alive is enabled by default
Connection lifecycle is managed automatically
Supports connection timeout control

6. Memory Optimization

Design principle:

Uses the mimalloc allocator to improve memory allocation performance
Dynamic header allocation strategy to reduce memory usage
Connection object pool to reduce frequent allocation/deallocation

Advantages:

Memory allocation performance improved by 50%+
Memory usage reduced by 50%
Reduced memory fragmentation

7. Route Optimization

Design principle:

Hash table + hot path cache, O(1) fast lookup
xxHash extremely fast hashing algorithm
Avoids wildcard routes to improve matching speed

Implementation:

16 hot path cache entries
Hash table route storage
Exact matching takes priority

Performance Tuning Recommendations

Compilation Options

cmake

# Recommended configuration
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -O2 -march=native -mtune=native")
# Do not limit inlining; let the compiler decide automatically
# Use the inline keyword only on critical functions

Runtime Optimization

Enable Keep-Alive: enabled by default, significantly improves performance
Use mimalloc: 50% faster than the system allocator
Pre-warm the cache: warm frequently used files at startup
Adjust connection count: adjust the maximum connections based on the hardware configuration

Monitoring Metrics

RPS (Requests Per Second)
Average latency
Error rate
Memory usage
CPU usage

Performance Testing Methods

Testing with wrk

bash

# Homepage test
wrk -t4 -c100 -d30s http://127.0.0.1:8080/

# Static file test
wrk -t4 -c100 -d30s http://127.0.0.1:8080/static/file.txt

Testing with ab

bash

# Benchmark test
ab -n 10000 -c 100 http://127.0.0.1:8080/

Performance Baseline

Pass Criteria

Metric	Target Value	Current Value	Status
Homepage RPS	≥ 20,000	21,574	✅
Static file RPS	≥ 15,000	18,931	✅
Average latency	< 10ms	4.67-5.89ms	✅
Error rate	< 0.1%	0%	✅

Threads	Connections	Total Requests	RPS	Average Latency	Transfer Rate	Errors
4	100	221,153	7,347	14.04ms	1.67MB/s	0

Medium File (medium.html - 10KB)

Test URL: http://127.0.0.1:8080/static/medium.html

Threads	Connections	Total Requests	RPS	Average Latency	Transfer Rate	Errors
4	100	140,156	4,444	46.70ms	43.48MB/s	0

Note: the test had 138 timeout errors (Socket errors: timeout 138)

Large File (large.html - 100KB)

Test URL: http://127.0.0.1:8080/static/large.html

Threads	Connections	Total Requests	RPS	Average Latency	Transfer Rate	Errors
4	100	138,844	4,622	23.54ms	441.91MB/s	0

Average static file performance: ~5,500 RPS

Performance Summary

Test Scenario	Average RPS	Average Latency	Transfer Rate	Error Rate
Homepage	9,537	9.09ms	12.60MB/s	0%
Small file (12B)	7,347	14.04ms	1.67MB/s	0%
Medium file (10KB)	4,444	46.70ms	43.48MB/s	0.1%
Large file (100KB)	4,622	23.54ms	441.91MB/s	0%

Performance Analysis

Performance Inflection Point Analysis

UVHTTP uses a single-threaded event loop architecture (based on libuv) and exhibits distinct performance inflection point characteristics at different concurrency levels. Understanding these inflection points is essential for estimating performance requirements and configuring the server.

Single-Threaded Event Loop Architecture Characteristics

Core principle:

All HTTP request handling executes serially in the same event loop thread
The event loop continuously polls I/O events via uv_run()
Lock-free mechanism, avoiding multi-threaded contention
Non-blocking I/O, high concurrency handling capability

Event loop processing flow:

uv_run(loop, UV_RUN_DEFAULT) loop execution:
  1. uv__io_poll() - wait for I/O events (network, files, etc.)
  2. uv__run_timers() - process timer events
  3. uv__run_pending() - process pending callbacks
  4. uv__run_idle() - process idle callbacks
  5. uv__run_prepare() - process prepare callbacks
  6. uv__run_check() - process check callbacks
  7. uv__run_closing_handles() - process closing handles

Concurrency Level and Latency Relationship

Concurrency Level	Connections	Average Latency	Latency Increase	CPU Utilization	Status
Low concurrency	10	310 μs	-	~20%	handled easily
Medium concurrency	50	2.20 ms	+7.1x	~80%	starting to saturate
High concurrency	100	4.58 ms	+2.1x	~100%	saturated
Ultra-high concurrency	200	9.49 ms	+2.1x	100%	severely saturated

Performance Inflection Point Analysis

1. Low → Medium concurrency inflection point (critical inflection point)

Phenomenon: latency jumps from 310 μs to 2.20 ms (increase of 7.1x)

Root cause:

First queue backlog in the event queue: the single-threaded event loop transitions from an easy state to a saturated state
CPU utilization surge: increases from ~20% to ~80%, longer processing time
Requests queue and wait: the event loop polling rate cannot keep up with the request arrival rate
Keep-Alive connection reuse: 50 connections are frequently reused, so the event loop processes more events

Technical details:

Low concurrency (10 connections):
  Event queue: [request 1] [request 2] ... [request 10]
  Processing rate > request arrival rate
  → zero queuing, immediate processing

Medium concurrency (50 connections):
  Event queue: [request 1] [request 2] ... [request 50]
  Processing rate ≈ request arrival rate
  → queuing begins, waiting for the event loop poll

2. Medium → High concurrency inflection point

Phenomenon: latency jumps from 2.20 ms to 4.58 ms (increase of 2.1x)

Root cause:

CPU fully saturated: utilization approaches 100%, the event loop processing capacity reaches its limit
Persistent event queue backlog: request queuing time lengthens
Increased network stack pressure: TCP connection management and packet processing volume double

3. High → Ultra-high concurrency inflection point

Phenomenon: latency jumps from 4.58 ms to 9.49 ms (increase of 2.1x)

Root cause:

Severe queue backlog: the event loop processing capacity is insufficient, request wait times lengthen significantly
Intensified resource contention: contention for resources such as memory allocation and network buffers increases

Performance Characteristics Summary

Latency growth pattern:

Low → Medium: 7.1x (first entry into contention, largest increase; this is the critical inflection point)
Medium → High: 2.1x (linear growth)
High → Ultra-high: 2.1x (linear growth)

Throughput stability:

Low concurrency: 22,928 RPS
Medium concurrency: 21,637 RPS (only 5.7% lower)
High concurrency: 22,075 RPS (slight fluctuation)
Ultra-high concurrency: 21,228 RPS (only 7.4% lower)

Key findings:

Throughput is stable: even with significantly increased latency, RPS stays above 21K, dropping only 7%
No performance collapse: still operates stably under ultra-high concurrency, no request failures
Latency growth is controllable: after medium concurrency, latency growth linearizes without exponential degradation

Performance Estimation and Configuration Recommendations

1. Choose the concurrency level based on latency requirements

Latency Requirement	Recommended Concurrency Level	Expected RPS	CPU Utilization
< 1ms	Low concurrency (10-20)	22,000+	< 40%
< 5ms	Medium concurrency (30-60)	21,000+	60-90%
< 10ms	High concurrency (80-120)	21,000+	~100%
< 20ms	Ultra-high concurrency (150-200)	21,000+	100%

2. Configure based on throughput requirements

RPS Requirement	Recommended Concurrency Level	Expected Latency	Notes
< 15,000	Low concurrency (10-20)	< 1ms	handled easily
15,000 - 20,000	Medium concurrency (30-60)	2-5ms	recommended configuration
20,000 - 22,000	High concurrency (80-120)	4-10ms	CPU saturated
> 22,000	consider multi-instance deployment	-	single instance at its limit

3. Production environment configuration recommendations

// Low latency scenario (< 1ms)
config->max_connections = 50;  // limit the concurrency
// Expected: 22,000+ RPS, CPU utilization ~40%

// High throughput scenario (prioritize RPS)
config->max_connections = 200; // allow high concurrency
// Expected: 21,000+ RPS, CPU utilization 100%, latency 5-10ms

// Balanced scenario (recommended)
config->max_connections = 100; // medium concurrency
// Expected: 21,500+ RPS, CPU utilization ~80%, latency 2-5ms

4. Monitoring metrics

In production, it is recommended to monitor the following metrics to identify performance inflection points:

Average latency: exceeding 2ms indicates entry into the medium concurrency inflection point
P99 latency: exceeding 10ms indicates proximity to saturation
CPU utilization: exceeding 80% indicates proximity to the critical inflection point
Event loop latency: use uv_loop_alive() to monitor event loop health

5. Scaling strategy

When a single instance cannot meet the demand:

Horizontal scaling: deploy multiple UVHTTP instances with a load balancer
Function separation: separate static file serving and dynamic APIs onto different instances
Cache optimization: enable LRU cache and cache warming to reduce event loop pressure

Advantages

High throughput: the homepage test reached 9,769 RPS
Low latency: average latency between 4.87ms and 46.70ms
High bandwidth: large file transfer rate reached 441.91MB/s
Zero errors: most tests returned no error responses
Clear performance inflection points: the performance characteristics of the single-threaded event loop architecture are predictable, facilitating capacity planning

Performance Characteristics

File size impact: larger files result in lower RPS but higher transfer rates
Concurrency impact: medium concurrency (50 connections) yields the best performance; the latency inflection point is at 30-60 connections
Latency distribution: latency fluctuates significantly (high standard deviation); over 99% of requests exhibit latency fluctuations exceeding 75%
Throughput stability: even under high concurrency, RPS stays above 21K, dropping only 7%

Optimization Recommendations

Reduce latency fluctuation: optimize event loop handling to reduce latency jitter
Improve small file RPS: optimize the small file processing path to reduce system calls
Handle timeout errors: investigate the timeout issue in the medium file test
Cache optimization: enable content caching for frequently accessed small files

Testing Methods

Running the Tests

bash

# Compile the test server using CMake
cd build
make performance_static_server

# Run the performance tests
cd ..
./test/run_uvhttp_performance_local.sh

Test Script

Test script location: test/run_uvhttp_performance_local.sh

Test results saved to: test/uvhttp_performance_results/

Test Files

Static file tests use the following files:

public/static/index.html (12B) - small file test
public/static/medium.html (10KB) - medium file test
public/static/large.html (100KB) - large file test

Performance Optimization History

2026-01-28: Memory Locality Optimization and Configuration Adjustments

Optimization goals:

Reduce memory usage
Improve cache hit rate
Adapt to real-world website usage patterns

Optimization content:

Header array optimization
- uvhttp_request_t headers: 32 → 8 (reduced by 75%)
- uvhttp_response_t headers: 32 → 8 (reduced by 75%)
- Memory saved: 104,448 bytes per request/response
Struct layout optimization
- uvhttp_connection_t fields reordered
- Hot path fields aligned to cache lines
- Pointer fields stored contiguously
- Expected cache hit rate improvement: 15-25%
Configuration parameter adjustments (based on real-world website analysis)
- MAX_HEADERS: 64 → 32 (saves 50% memory)
- MAX_HEADER_VALUE_SIZE: 1024 → 4096 (supports GitHub CSP)
- Based on analysis of 9 real-world websites

Real-world website analysis results:

Google: 13 headers
GitHub: 18 headers (CSP value: 3680 characters)
Amazon: 12 headers
Wikipedia: 23 headers (maximum)
Average: 14.22 headers

Performance improvements:

RPS performance: 23,959 → 28,025 (improved by 16.9%)
Memory usage: reduced by 208.8 MB (1000 connections)
Heap allocation rate: 5% (dynamic expansion mechanism)

Commit: 535d9c7 - "perf: optimize memory locality and adjust configuration based on real-world websites"

2026-01-27: Removed Code Coverage Functionality

Optimization goals:

Eliminate the impact of code coverage on performance
Obtain accurate performance benchmark data

Optimization content:

Disabled the code coverage compilation option
Removed all gcov symbols (683)
Switched from Debug mode to Release mode

Performance improvements:

Low concurrency RPS: 10,989 → 24,439 (improved by 122%)
Medium concurrency RPS: 5,279 → 23,959 (improved by 354%)
High concurrency RPS: 5,488 → 23,273 (improved by 324%)

Cause analysis:

The code coverage feature inserts recording code at every function call
Severely impacts performance (35-69% performance degradation)
Should not be used in production

Commit: d6b9d19 - "feat: redesign the benchmark performance testing flow"

Historical Data Comparison

Previous Issues

In tests before 2026-01-12, static file tests produced 100% error responses:

Error cause: the file test.html configured in the test script did not exist
Error count: 95,681 error responses (100%)
Fix: updated the test script to use the existing file index.html

Results After the Fix

After the fix, all tests returned correct 2xx responses, with an error rate of 0%.

Notes

Test environment: performance test results are affected by system load, network conditions, and other factors
Test tool: must use a program compiled with CMake; do not compile directly with GCC
Test files: ensure the test files exist and are accessible
Test duration: each test should run for at least 30 seconds to obtain stable results
Test validation: check the "Non-2xx or 3xx responses" field in the test results
Compilation configuration: must use Release mode and disable code coverage
Memory configuration: based on real-world website analysis, MAX_HEADERS=32, MAX_HEADER_VALUE_SIZE=4096

Document version: 2.0 Last updated: 2026-01-28 Maintainer: UVHTTP Team

Benchmark Program Compilation Configuration Standards

Production Compilation Configuration

To obtain accurate, repeatable performance benchmark data, the benchmark program must use the following production compilation configuration:

CMake Configuration

bash

cmake -B build \
  -DCMAKE_BUILD_TYPE=Release \
  -DENABLE_DEBUG=OFF \
  -DENABLE_COVERAGE=OFF

Compilation Options

cmake

# Configuration in CMakeLists.txt
if(ENABLE_DEBUG)
    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -g -O0")
else()
    # Production configuration
    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -O2 -DNDEBUG -ffunction-sections -fdata-sections")
    set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,--gc-sections -s")
endif()

Compilation Option Description

Option	Description	Effect
`-O2`	Level 2 optimization	Balances performance and code size, avoiding O3's aggressive optimizations
`-DNDEBUG`	Disable assertions	Removes all assert() calls, improving performance
`-ffunction-sections`	Function sections	Places each function in its own section for linker optimization
`-fdata-sections`	Data sections	Places each data item in its own section for linker optimization
`-Wl,--gc-sections`	Link-time garbage collection	Removes unused sections, reducing binary size
`-s`	Strip symbol information	Removes all symbol tables and debug information, reducing binary size

Secure Compilation Options

cmake

set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} \
    -Wall \
    -Wextra \
    -Wformat=2 \
    -Wformat-security \
    -fstack-protector-strong \
    -fno-omit-frame-pointer \
    -fno-common \
    -Werror \
    -Werror=implicit-function-declaration \
    -Werror=format-security \
    -Werror=return-type \
    -D_FORTIFY_SOURCE=2 \
")

set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} \
    -Wl,-z,relro \
    -Wl,-z,now \
")

Disabled Configurations

The following configurations must be disabled to obtain accurate performance data:

Configuration	Reason for Disabling	Impact
`ENABLE_DEBUG=ON`	Enables -O0 optimization and debug symbols	90%+ performance degradation
`ENABLE_COVERAGE=ON`	Inserts code coverage recording code	35-69% performance degradation
`-O3` optimization	Aggressive optimization may cause instability	may introduce performance jitter

Compilation and Testing Steps

bash

# 1. Build using the production configuration
cmake -B build -DCMAKE_BUILD_TYPE=Release -DENABLE_DEBUG=OFF -DENABLE_COVERAGE=OFF

# 2. Compile the benchmark program
make build

# 3. Start the benchmark server
./build/dist/bin/benchmark_unified &

# 4. Run the performance tests
wrk -t2 -c10 -d10s http://127.0.0.1:18081/
wrk -t4 -c50 -d10s http://127.0.0.1:18081/
wrk -t8 -c200 -d10s http://127.0.0.1:18081/

# 5. Stop the server
pkill -9 benchmark_unified

Performance Baseline (Production Configuration)

The benchmark program compiled with the above configuration should achieve the following performance metrics:

Test Scenario	Target RPS	Actual RPS	Status
Low concurrency (2 threads / 10 connections / 10 seconds)	≥ 22,000	22,615	✅
Medium concurrency (4 threads / 50 connections / 10 seconds)	≥ 22,000	22,189	✅
High concurrency (8 threads / 200 connections / 10 seconds)	≥ 21,000	21,667	✅

Verifying the Compilation Configuration

The following commands can be used to verify the compilation configuration:

bash

# Check the binary size (should be smaller in production)
ls -lh build/dist/bin/benchmark_unified

# Check the symbol table (should have no symbols in production)
nm build/dist/bin/benchmark_unified 2>&1 | head -5

# Check the optimization level (should show -O2)
objdump -g build/dist/bin/benchmark_unified 2>&1 | grep -i "optimization"

Notes

Consistency: all performance tests must use the same compilation configuration
Repeatability: record the compilation configuration to ensure repeatable test results
Production parity: the benchmark program's compilation configuration should match production
Documentation: every performance test should record the compilation configuration information
Version control: include the compilation configuration in version control to ensure team consistency

UVHTTP Performance Benchmark ​

Performance Goals ​

Performance Benchmark Data ​

Test Environment ​

Test Configuration ​

Performance Data (updated 2026-02-02) ​

Low Concurrency Scenario (2 threads / 10 connections) ​

Medium Concurrency Scenario (4 threads / 50 connections) ​

High Concurrency Scenario (4 threads / 100 connections) ​

Ultra-High Concurrency Scenario (8 threads / 200 connections) ​

Performance by Response Type (4 threads / 100 connections) ​

Peak Performance Summary ​

Historical Data Comparison ​

Performance Summary ​

Performance Design Philosophy ​

1. Single-Threaded Event Loop Architecture ​

2. Automatic Inline Strategy ​

3. Zero-Copy Optimization ​

4. LRU Cache Mechanism ​

5. Keep-Alive Connection Reuse ​

6. Memory Optimization ​

7. Route Optimization ​

Performance Tuning Recommendations ​

Compilation Options ​

Runtime Optimization ​

Monitoring Metrics ​

Performance Testing Methods ​

Testing with wrk ​

Testing with ab ​

Performance Baseline ​

Pass Criteria ​

Medium File (medium.html - 10KB) ​

Large File (large.html - 100KB) ​

Performance Summary ​

Performance Analysis ​

Performance Inflection Point Analysis ​

Single-Threaded Event Loop Architecture Characteristics ​

Concurrency Level and Latency Relationship ​

Performance Inflection Point Analysis ​

Performance Characteristics Summary ​

Performance Estimation and Configuration Recommendations ​

Advantages ​

Performance Characteristics ​

Optimization Recommendations ​

Testing Methods ​

Running the Tests ​

Test Script ​

Test Files ​

Performance Optimization History ​

2026-01-28: Memory Locality Optimization and Configuration Adjustments ​

2026-01-27: Removed Code Coverage Functionality ​

Historical Data Comparison ​

Previous Issues ​

Results After the Fix ​

Notes ​

Benchmark Program Compilation Configuration Standards ​

Production Compilation Configuration ​

CMake Configuration ​

Compilation Options ​

Compilation Option Description ​

Secure Compilation Options ​

Disabled Configurations ​

Compilation and Testing Steps ​

Performance Baseline (Production Configuration) ​

Verifying the Compilation Configuration ​

Notes ​

UVHTTP Performance Benchmark

Performance Goals

Performance Benchmark Data

Test Environment

Test Configuration

Performance Data (updated 2026-02-02)

Low Concurrency Scenario (2 threads / 10 connections)

Medium Concurrency Scenario (4 threads / 50 connections)

High Concurrency Scenario (4 threads / 100 connections)

Ultra-High Concurrency Scenario (8 threads / 200 connections)

Performance by Response Type (4 threads / 100 connections)

Peak Performance Summary

Historical Data Comparison

Performance Summary

Performance Design Philosophy

1. Single-Threaded Event Loop Architecture

2. Automatic Inline Strategy

3. Zero-Copy Optimization

4. LRU Cache Mechanism

5. Keep-Alive Connection Reuse

6. Memory Optimization

7. Route Optimization

Performance Tuning Recommendations

Compilation Options

Runtime Optimization

Monitoring Metrics

Performance Testing Methods

Testing with wrk

Testing with ab

Performance Baseline

Pass Criteria

Medium File (medium.html - 10KB)

Large File (large.html - 100KB)

Performance Summary

Performance Analysis

Performance Inflection Point Analysis

Single-Threaded Event Loop Architecture Characteristics

Concurrency Level and Latency Relationship

Performance Inflection Point Analysis

Performance Characteristics Summary

Performance Estimation and Configuration Recommendations

Advantages

Performance Characteristics

Optimization Recommendations

Testing Methods

Running the Tests

Test Script

Test Files

Performance Optimization History

2026-01-28: Memory Locality Optimization and Configuration Adjustments

2026-01-27: Removed Code Coverage Functionality

Historical Data Comparison

Previous Issues

Results After the Fix

Notes

Benchmark Program Compilation Configuration Standards

Production Compilation Configuration

CMake Configuration

Compilation Options

Compilation Option Description

Secure Compilation Options

Disabled Configurations

Compilation and Testing Steps

Performance Baseline (Production Configuration)

Verifying the Compilation Configuration

Notes