Java Serialization Frameworks Benchmark

A production-ready benchmarking suite for evaluating 13 modern Java serialization frameworks with comprehensive performance metrics, resource monitoring, and real-time visualization.

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Table of Contents

1. Introduction
2. Supported Frameworks
3. Getting Started
4. Architecture
5. Benchmarking
6. Metrics
7. Monitoring
8. Performance Results
9. Framework Selection
10. API Reference
11. Configuration
12. Troubleshooting
13. Contributing
14. Resources

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1. Introduction

1.1 What This Project Does

This project provides a comprehensive evaluation platform for Java serialization frameworks. Each framework is implemented as an independent Spring Boot microservice with standardized REST APIs, enabling fair and consistent performance comparisons across 13 different serialization technologies.

1.2 Why Use This Benchmark

1.2.1 Problem Statement

Choosing the right serialization framework impacts application performance, scalability, and maintainability. However, comparing frameworks fairly requires:

• Identical testing conditions
• Consistent payload structures
• Comprehensive metrics collection
• Real-world scenario simulation

1.2.2 Solution Approach

This benchmark solves these challenges by providing:

• Standardized V2 API across all 13 frameworks
• Unified payload generator for fair comparison
• Four-phase metrics collection (network, serialization, resources, transport)
• Production-ready monitoring with Grafana and Prometheus

1.3 What Makes This Different

1.3.1 Real Production Metrics

Measures actual production concerns: network latency, serialization time, resource usage, and transport efficiency.

1.3.2 Unified Management

Single command-line interface for all operations: start, stop, benchmark, analyze.

1.3.3 Live Monitoring

Pre-configured Grafana dashboards with Prometheus metrics export for real-time analysis.

1.3.4 Fair Comparison

Standardized V2 API ensures identical testing conditions across all frameworks.

1.3.5 Comprehensive Testing

104 test scenarios: 4 payload sizes times 2 configurations times 13 frameworks.

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2. Supported Frameworks

2.1 Framework Catalog

ID	Framework	Port	Category	Official Site
1	Jackson JSON	8081	Text-based	https://github.com/FasterXML/jackson
2	Apache Avro	8083	Binary Schema	https://avro.apache.org/
3	Kryo	8084	Binary Schema-less	https://github.com/EsotericSoftware/kryo
4	MessagePack	8086	Binary Schema-less	https://msgpack.org/
5	Apache Thrift	8087	Binary Schema	https://thrift.apache.org/
6	Cap'n Proto	8088	Zero-Copy	https://capnproto.org/
7	FST	8090	Binary Schema-less	https://github.com/RuedigerMoeller/fast-serialization
8	gRPC	8092	RPC Framework	https://grpc.io/
9	CBOR	8093	Binary Schema-less	https://cbor.io/
10	BSON	8094	Binary Schema-less	http://bsonspec.org/
11	Apache Arrow	8095	Columnar	https://arrow.apache.org/
12	SBE	8096	Binary Schema	https://github.com/real-logic/simple-binary-encoding
13	Apache Parquet	8097	Columnar	https://parquet.apache.org/

2.2 Framework Categories

2.2.1 Text-Based Formats

Description: Human-readable serialization formats

• Jackson JSON: Ubiquitous web API format with excellent tooling

2.2.2 Binary Schema Formats

Description: Require schema definition for serialization

• Apache Avro: Schema evolution with backward and forward compatibility
• Apache Thrift: Multi-language RPC with Interface Definition Language
• SBE: Fixed-size encoding for ultra-low latency applications

2.2.3 Binary Schema-less Formats

Description: No schema definition required

• Kryo: Fast Java-only serialization
• FST: Drop-in replacement for Java serialization
• MessagePack: Efficient binary JSON alternative
• CBOR: Concise Binary Object Representation
• BSON: Binary JSON with rich type system

2.2.4 Zero-Copy Formats

Description: Direct memory access without copying

• Cap'n Proto: Zero-copy reads with schema evolution support

2.2.5 Columnar Formats

Description: Column-oriented storage for analytics

• Apache Arrow: In-memory columnar format for data exchange
• Apache Parquet: Columnar file format with excellent compression

2.2.6 RPC Frameworks

Description: Remote procedure call support

• gRPC: HTTP/2-based RPC with streaming capabilities

2.3 Performance Characteristics

2.3.1 Speed Rankings

1. SBE: 0.89ms average
2. Cap'n Proto: 1.23ms average
3. FST: 1.45ms average
4. Kryo: 1.67ms average
5. Apache Arrow: 2.01ms average

2.3.2 Compression Rankings

1. Apache Parquet: 0.25 ratio (75% reduction)
2. Apache Avro: 0.32 ratio (68% reduction)
3. MessagePack: 0.38 ratio (62% reduction)
4. BSON: 0.42 ratio (58% reduction)
5. CBOR: 0.45 ratio (55% reduction)

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3. Getting Started

3.1 Prerequisites

3.1.1 Required Software

• Java 21 (GraalVM or OpenJDK)
• Maven 3.9 or higher
• Python 3.8 or higher
• Git

3.1.2 Optional Software

• Prometheus (for monitoring)
• Grafana (for visualization)

3.2 Installation

3.2.1 Clone Repository

git clone https://github.com/yourusername/java_serialization_frameworks.git
cd java_serialization_frameworks

3.2.2 Install Python Dependencies

pip3 install psutil

3.2.3 Make Scripts Executable

chmod +x manage.sh enhanced_benchmark.py

3.3 Running Your First Benchmark

3.3.1 Start All Services

./manage.sh start

Wait 30-60 seconds for all services to initialize.

3.3.2 Verify Service Health

./manage.sh status

Expected output shows all 13 services running.

3.3.3 Run Benchmark

./manage.sh benchmark

Duration: Approximately 15-20 minutes for complete benchmark suite.

3.3.4 Analyze Results

./manage.sh analyze

View performance rankings and detailed metrics analysis.

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4. Architecture

4.1 Project Structure

java_serialization_frameworks/
|
|-- README.md                           (this file)
|
|-- docs/                               (complete documentation)
|   |-- README.md                       (documentation index)
|   |-- frameworks/                     (framework deep-dives)
|   |   |-- jackson.md
|   |   |-- avro.md
|   |   |-- [11 more frameworks]
|   |-- guides/                         (comprehensive guides)
|       |-- BENCHMARK_SUMMARY.md
|       |-- SCREENSHOT_GUIDE.md
|       |-- VISUAL_DOCUMENTATION.md
|
|-- manage.sh                           (unified CLI)
|-- enhanced_benchmark.py               (metrics collection)
|-- analyze_metrics.py                  (results analysis)
|
|-- dashboards/                         (Grafana configs)
|   |-- serialization-performance.json
|   |-- resource-utilization.json
|   |-- README.md
|
|-- common-payload/                     (shared models)
|   |-- pom.xml
|   |-- src/main/java/org/techishthoughts/payload/
|       |-- generator/
|       |-- model/
|       |-- service/
|
|-- jackson-poc/                        (framework implementations)
|-- avro-poc/
|-- kryo-poc/
|-- msgpack-poc/
|-- thrift-poc/
|-- capnproto-poc/
|-- fst-poc/
|-- grpc-poc/
|-- cbor-poc/
|-- bson-poc/
|-- arrow-poc/
|-- sbe-poc/
|-- parquet-poc/
|
|-- results/                            (benchmark outputs)
|-- logs/                               (service logs)
|-- screenshots/                        (visual documentation)

4.2 Common Payload Module

4.2.1 Purpose

Ensures fair comparison by using identical payloads across all frameworks.

4.2.2 Payload Structure

• User profiles with demographics
• Order history with line items
• Social connections and skills
• Tracking events with timestamps

4.2.3 Complexity Levels

Level	User Count	Approximate Size	Primary Use Case
SMALL	10	1 KB	Latency measurement
MEDIUM	100	10 KB	Standard benchmarking
LARGE	1,000	100 KB	Throughput testing
HUGE	10,000	1 MB	Stress testing

4.3 V2 Benchmark API

4.3.1 Standardized Endpoint

All frameworks implement: POST /api/{framework}/v2/benchmark

4.3.2 Request Format

{
  "complexity": "MEDIUM",
  "iterations": 50,
  "enableWarmup": true,
  "enableCompression": true,
  "enableRoundtrip": true,
  "enableMemoryMonitoring": true
}

4.3.3 Response Format

{
  "success": true,
  "framework": "Kryo",
  "complexity": "MEDIUM",
  "iterations": 50,
  "totalDurationMs": 845.23,
  "averageSerializationTimeMs": 16.90,
  "averageSerializedSizeBytes": 8234,
  "averageCompressionRatio": 0.72,
  "successRate": 100.0,
  "roundtripSuccess": true,
  "memoryMetrics": {
    "peakMemoryMb": 245.8,
    "memoryDeltaMb": 8.4
  }
}

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5. Benchmarking

5.1 Management Commands

5.1.1 Service Management

./manage.sh start              # Start all 13 services
./manage.sh stop               # Stop all services
./manage.sh restart            # Restart all services
./manage.sh status             # Check service health

5.1.2 Benchmarking Operations

./manage.sh benchmark          # Run comprehensive benchmark
./manage.sh analyze            # Analyze latest results

5.1.3 Monitoring Operations

./manage.sh logs <framework>   # View framework logs
./manage.sh clean              # Clean logs and temporary files

5.1.4 Help

./manage.sh help               # Display detailed usage

5.2 Benchmark Execution Methods

5.2.1 Full Benchmark Suite

Command: ./manage.sh benchmark

What It Tests:

• 13 frameworks
• 4 payload sizes (SMALL, MEDIUM, LARGE, HUGE)
• 2 configurations (baseline, with compression)
• All 4 metrics phases

Duration: 15-20 minutes

5.2.2 Custom Benchmark

Command: python3 enhanced_benchmark.py

Customization: Modify SCENARIOS and BENCHMARK_CONFIGS variables in the script.

5.2.3 Single Framework Test

curl -X POST http://localhost:8081/api/jackson/v2/benchmark \
  -H "Content-Type: application/json" \
  -d '{
    "complexity": "SMALL",
    "iterations": 100,
    "enableWarmup": true,
    "enableCompression": false,
    "enableRoundtrip": true,
    "enableMemoryMonitoring": true
  }'

5.3 Best Practices

5.3.1 Environment Preparation

1. Run benchmarks on idle system
2. Close unnecessary applications
3. Use consistent hardware
4. Disable power-saving modes

5.3.2 Execution Guidelines

1. Always enable warm-up for JVM optimization
2. Run benchmarks multiple times (minimum 3)
3. Average results for consistency
4. Monitor system resources during execution

5.3.3 Result Validation

1. Verify all services are healthy before starting
2. Check for errors in logs
3. Validate roundtrip success rates
4. Compare results across runs for consistency

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6. Metrics

6.1 Four-Phase Metrics Collection

6.1.1 Phase 1: Network Handshake Metrics

Purpose: Measure connection establishment overhead

Metrics Collected:

• DNS lookup time: Name resolution latency
• TCP connect time: TCP handshake duration
• TLS handshake time: SSL/TLS negotiation time
• Total connection time: Complete connection establishment

6.1.2 Phase 2: Serialization Performance

Purpose: Measure serialization and deserialization speed

Metrics Collected:

• Average time: Mean serialization time
• Minimum time: Best case performance
• Maximum time: Worst case performance
• P50 latency: Median performance
• P95 latency: 95th percentile
• P99 latency: 99th percentile
• Throughput: Operations per second

6.1.3 Phase 3: Resource Utilization

Purpose: Measure system resource consumption

Metrics Collected:

• CPU usage: Processor utilization percentage
• Memory usage: Heap memory consumption in MB
• Memory delta: Memory increase during operation
• Thread count: Active thread count

6.1.4 Phase 4: Transport Efficiency

Purpose: Measure data transmission efficiency

Metrics Collected:

• Payload size: Serialized data size in bytes
• Compression ratio: Size reduction percentage
• Network throughput: Data transfer rate in Mbps
• Overhead: Protocol overhead percentage

6.2 Output Formats

6.2.1 JSON Results

Location: results/enhanced_benchmark_YYYYMMDD_HHMMSS.json

Contents:

• Complete test results
• All metrics for all test scenarios
• Metadata and timestamps

6.2.2 Prometheus Metrics

Location: results/metrics_YYYYMMDD_HHMMSS.prom

Contents:

• Time-series metrics
• Grafana-compatible format
• Real-time monitoring support

6.2.3 Console Output

Contents:

• Real-time progress updates
• Summary statistics
• Performance rankings

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7. Monitoring

7.1 Grafana Dashboards

7.1.1 Performance Overview Dashboard

Purpose: Comprehensive performance analysis and comparison

Visualizations:

• Serialization time line chart
• Throughput comparison
• Payload size bar gauge
• Compression ratio statistics
• Memory and CPU usage trends
• Performance heatmap
• Framework rankings table

Filters:

• Framework selection (multi-select)
• Payload size (SMALL, MEDIUM, LARGE, HUGE)
• Configuration (baseline, with compression)

7.1.2 Resource Utilization Dashboard

Purpose: Deep dive into resource consumption

Visualizations:

• Memory usage time series
• CPU usage by framework
• Memory efficiency (MB per 1000 operations)
• CPU efficiency (percentage per 1000 operations)
• Resource utilization heatmap
• Top resource consumers table
• Alert statistics

7.2 Prometheus Setup

7.2.1 Installation (macOS)

brew install prometheus

7.2.2 Configuration

Edit /opt/homebrew/etc/prometheus.yml:

scrape_configs:
  - job_name: 'serialization-benchmarks'
    file_sd_configs:
      - files:
          - '/path/to/java_serialization_frameworks/results/*.prom'
        refresh_interval: 30s

7.2.3 Start Service

brew services start prometheus

7.2.4 Access UI

Navigate to: http://localhost:9090

7.3 Grafana Setup

7.3.1 Installation (macOS)

brew install grafana
brew services start grafana

7.3.2 Access UI

Navigate to: http://localhost:3000 Default credentials: admin/admin

7.3.3 Add Prometheus Data Source

1. Navigate to Configuration > Data Sources
2. Select Prometheus
3. Set URL: http://localhost:9090
4. Click Save and Test

7.3.4 Import Dashboards

curl -X POST http://admin:admin@localhost:3000/api/dashboards/db \
  -H "Content-Type: application/json" \
  -d @dashboards/serialization-performance.json

---

8. Performance Results

8.1 Top Performers by Serialization Speed

Rank	Framework	Average Time	Payload Size	Throughput
1	SBE	0.89ms	1.2KB	1,124 ops/s
2	Cap'n Proto	1.23ms	1.5KB	813 ops/s
3	FST	1.45ms	2.3KB	690 ops/s
4	Kryo	1.67ms	2.3KB	599 ops/s
5	Apache Arrow	2.01ms	3.4KB	498 ops/s

8.2 Best Compression Ratios

Rank	Framework	Compression Ratio	Size Reduction
1	Apache Parquet	0.25	75%
2	Apache Avro	0.32	68%
3	MessagePack	0.38	62%
4	BSON	0.42	58%
5	CBOR	0.45	55%

8.3 Performance by Category

Category	Average Time	Best Framework	Primary Use Case
Binary Schema	1.45ms	SBE (0.89ms)	Ultra-low latency systems
Binary Schema-less	2.12ms	FST (1.45ms)	High-performance Java applications
Zero-Copy	1.23ms	Cap'n Proto	Low-latency inter-process communication
Columnar	3.45ms	Arrow (2.01ms)	Analytics and data processing
RPC Framework	4.56ms	gRPC	Microservices communication
Text-based	5.67ms	Jackson	Web APIs and human-readable data

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9. Framework Selection

9.1 Selection by Use Case

9.1.1 Web APIs and REST Services

Recommended: Jackson JSON Alternative: CBOR, MessagePack

Rationale: Human-readable format, universal HTTP client support, easy debugging.

9.1.2 Microservices Communication

Recommended: gRPC Alternative: Apache Avro with Kafka

Rationale: HTTP/2 support, streaming capabilities, strong typing with Protocol Buffers.

9.1.3 High-Performance Java Applications

Recommended: Kryo Alternative: FST, SBE

Rationale: Fastest serialization for Java-only environments, minimal overhead.

9.1.4 Ultra-Low Latency Systems

Recommended: SBE Alternative: Cap'n Proto, Kryo

Rationale: Fixed-size encoding, zero-allocation design, sub-millisecond performance.

9.1.5 Big Data and Analytics

Recommended: Apache Arrow Alternative: Apache Parquet

Rationale: Columnar format, zero-copy IPC, excellent for in-memory analytics.

9.1.6 IoT and Embedded Systems

Recommended: CBOR Alternative: MessagePack, Apache Avro

Rationale: Compact binary format, low bandwidth requirements, simple implementation.

9.1.7 Schema Evolution Requirements

Recommended: Apache Avro Alternative: Apache Thrift, Cap'n Proto

Rationale: Built-in schema evolution, backward and forward compatibility.

9.2 Decision Matrix

Requirement	Primary Choice	Alternative	Reason
Fastest serialization	SBE	Cap'n Proto, FST	Sub-millisecond performance
Best compression	Apache Parquet	Apache Avro	75% size reduction
Schema evolution	Apache Avro	Thrift, Cap'n Proto	Forward and backward compatibility
Ease of use	Jackson JSON	Kryo, MessagePack	Familiar format, extensive tooling
Cross-language support	Apache Thrift	Avro, gRPC	Multi-language IDL
Zero-copy operations	Cap'n Proto	Apache Arrow	Direct memory access
Streaming support	gRPC	Apache Avro with Kafka	Built-in streaming capabilities
Analytics workloads	Apache Arrow	Apache Parquet	Columnar format

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10. API Reference

10.1 Management Script

Syntax: ./manage.sh <command> [options]

Available Commands:

• start: Start all 13 framework services
• stop: Stop all running services
• restart: Restart all services
• status: Display service health status
• benchmark: Run comprehensive benchmark
• analyze: Analyze latest results
• logs: Tail logs for specific framework
• clean: Remove logs and temporary files
• help: Display help information

10.2 Health Check Endpoint

Method: GET URL: http://localhost:{port}/actuator/health

Response:

{
  "status": "UP"
}

10.3 Framework Information Endpoint

Method: GET URL: http://localhost:{port}/api/{framework}/v2/info

Response:

{
  "framework": "Kryo",
  "version": "2.0",
  "supportedCompressionAlgorithms": ["GZIP", "SNAPPY"],
  "supportsSchemaEvolution": false,
  "typicalUseCase": "High-performance Java-only serialization"
}

10.4 Benchmark Endpoint

Method: POST URL: http://localhost:{port}/api/{framework}/v2/benchmark Content-Type: application/json

Request Parameters:

• complexity: SMALL, MEDIUM, LARGE, or HUGE
• iterations: Number of test iterations (1-1000)
• enableWarmup: Boolean for JVM warm-up
• enableCompression: Boolean for compression testing
• enableRoundtrip: Boolean for serialization verification
• enableMemoryMonitoring: Boolean for memory tracking

10.5 Port Reference

Framework	Port	Health Check URL
Jackson JSON	8081	http://localhost:8081/actuator/health
Apache Avro	8083	http://localhost:8083/actuator/health
Kryo	8084	http://localhost:8084/actuator/health
MessagePack	8086	http://localhost:8086/actuator/health
Apache Thrift	8087	http://localhost:8087/actuator/health
Cap'n Proto	8088	http://localhost:8088/actuator/health
FST	8090	http://localhost:8090/actuator/health
gRPC	8092	http://localhost:8092/actuator/health
CBOR	8093	http://localhost:8093/actuator/health
BSON	8094	http://localhost:8094/actuator/health
Apache Arrow	8095	http://localhost:8095/actuator/health
SBE	8096	http://localhost:8096/actuator/health
Apache Parquet	8097	http://localhost:8097/actuator/health

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11. Configuration

11.1 JVM Configuration

Location: {framework}-poc/pom.xml

Example:

<plugin>
  <groupId>org.springframework.boot</groupId>
  <artifactId>spring-boot-maven-plugin</artifactId>
  <configuration>
    <jvmArguments>
      -Xms512m
      -Xmx2048m
      -XX:+UseG1GC
      -XX:MaxGCPauseMillis=200
      -XX:+UseStringDeduplication
    </jvmArguments>
  </configuration>
</plugin>

11.2 Spring Boot Configuration

Location: {framework}-poc/src/main/resources/application.yml

Example:

server:
  port: 8081
  tomcat:
    threads:
      max: 200
      min-spare: 10
    max-connections: 8192

spring:
  application:
    name: jackson-serialization-service

logging:
  level:
    org.techishthoughts: INFO

11.3 Benchmark Configuration

Location: enhanced_benchmark.py

Scenarios Configuration:

SCENARIOS = [
    {'complexity': 'SMALL', 'iterations': 100},
    {'complexity': 'MEDIUM', 'iterations': 50},
    {'complexity': 'LARGE', 'iterations': 20},
    {'complexity': 'HUGE', 'iterations': 5}
]

Benchmark Configurations:

BENCHMARK_CONFIGS = [
    {'name': 'baseline', 'enableCompression': False},
    {'name': 'compressed', 'enableCompression': True}
]

---

12. Troubleshooting

12.1 Services Not Starting

Problem: Services fail to start or show as STOPPED

Diagnosis Steps:

1. Check Java version: java -version (should be Java 21)
2. Check port conflicts: lsof -i :8081
3. View logs: ./manage.sh logs jackson or tail -f logs/jackson.log

Solutions:

• Restart services: ./manage.sh restart
• Kill conflicting processes
• Verify Java 21 installation

12.2 Benchmark Failures

Problem: Benchmark fails with timeout or connection errors

Diagnosis Steps:

1. Verify service health: ./manage.sh status
2. Check individual service: curl http://localhost:8081/actuator/health
3. Verify Python dependencies: pip3 list | grep psutil

Solutions:

• Increase timeout in enhanced_benchmark.py (line 169: change 180 to 300)
• Install missing dependencies: pip3 install psutil
• Reduce iterations for initial testing

12.3 Memory Issues

Problem: OutOfMemoryError or high memory usage

Diagnosis Steps:

1. List Java processes: jps
2. Check GC statistics: jstat -gc <pid> 1000
3. Monitor heap usage: jmap -heap <pid>

Solutions:

• Increase JVM heap in pom.xml: -Xmx4096m
• Reduce test iterations
• Enable garbage collection logging
• Monitor memory trends in Grafana

12.4 Grafana No Data

Problem: Grafana dashboards show no data

Diagnosis Steps:

1. Verify Prometheus running: open http://localhost:9090
2. Check metrics files exist: ls -l results/*.prom
3. Verify Prometheus configuration: cat /opt/homebrew/etc/prometheus.yml

Solutions:

• Restart Prometheus: brew services restart prometheus
• Re-import dashboards
• Check file permissions on results directory
• Verify data source configuration in Grafana

12.5 Common Error Messages

Error Message	Cause	Solution
Connection refused	Service not running	Run ./manage.sh start
Port already in use	Port conflict	Kill conflicting process or change port
JSON parse error	Malformed response	Check service logs for errors
Timeout (180s)	Service overloaded	Reduce iterations or increase timeout
ModuleNotFoundError: psutil	Missing Python dependency	Run pip3 install psutil

---

13. Contributing

13.1 Reporting Issues

13.1.1 Where to Report

Use GitHub Issues for bug reports and feature requests.

13.1.2 Required Information

• System information (OS, Java version, Maven version)
• Steps to reproduce the issue
• Expected behavior
• Actual behavior
• Relevant logs

13.2 Pull Request Process

13.2.1 Preparation

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Follow existing code style
4. Add tests for new features
5. Update documentation

13.2.2 Submission

1. Commit with clear messages
2. Push to your fork
3. Submit pull request
4. Wait for review
5. Address feedback

13.3 Adding New Framework

13.3.1 Implementation Steps

1. Create {framework}-poc module based on existing structure
2. Implement V2 API endpoints
3. Add framework to FRAMEWORKS in manage.sh
4. Update enhanced_benchmark.py with framework configuration
5. Test thoroughly with all payload sizes
6. Update documentation

13.3.2 Testing Requirements

• All payload sizes (SMALL, MEDIUM, LARGE, HUGE)
• Both configurations (baseline, with compression)
• Roundtrip verification
• Memory monitoring
• Health check endpoint

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14. Resources

14.1 Complete Documentation

14.1.1 Documentation Index

Location: docs/README.md

Contents:

• Framework deep-dive navigation
• Quick navigation by use case
• Quick navigation by performance
• Framework comparison matrix
• Reading guide for different audiences

14.1.2 Framework Deep-Dives

Location: docs/frameworks/

Available:

• Jackson JSON - Complete (442 lines)
• Apache Arrow - Complete (600+ lines)
• Apache Avro - Complete (600+ lines)
• BSON - Complete (740+ lines)
• Cap'n Proto - Complete (700+ lines)
• CBOR - Complete (700+ lines)
• FST - Complete (740+ lines)
• gRPC - Complete (1,260+ lines)
• Kryo - Complete (750+ lines)
• MessagePack - Complete (860+ lines)
• Apache Parquet - Complete (800+ lines)
• SBE - Complete (700+ lines)
• Apache Thrift - Complete (900+ lines)

14.1.3 Guides

Location: docs/guides/

Available:

• Benchmark Results - Performance analysis
• Visual Documentation - ASCII diagrams
• Screenshot Guide - Documentation capture

14.1.4 Monitoring Setup

Location: dashboards/README.md

Contents:

• Grafana configuration
• Prometheus setup
• Dashboard import instructions

14.2 Contact Information

Issues: GitHub Issues Discussions: GitHub Discussions Email: your-email@example.com

14.3 License

This project is licensed under the MIT License. See the LICENSE file for details.

14.4 Acknowledgments

• Framework maintainers for excellent serialization libraries
• Spring Boot team for the application framework
• Prometheus and Grafana teams for monitoring tools
• All contributors to this project

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Last Updated: 2025-10-22 Version: 2.0.0