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AlisChain Project Optimization Guide

Identified Issues and Optimizations

1. Code Structure Optimization

Current Issues:

  • Redundant monitoring configurations across multiple files
  • Overlapping metrics collection
  • Duplicate dashboard definitions
  • Scattered configuration files

Optimized Structure:

alischain/
├── app/
│   ├── core/
│   │   ├── blockchain/
│   │   │   ├── monitors.py       # Unified blockchain monitoring
│   │   │   └── metrics.py        # Consolidated blockchain metrics
│   │   ├── monitoring/
│   │   │   ├── metrics.py        # Core metrics collection
│   │   │   └── alerts.py         # Centralized alert definitions
│   │   └── analytics/
│   │       ├── reports.py        # Report generation
│   │       └── dashboards.py     # Dashboard templates
│   └── services/
│       ├── websocket_handler.py  # WebSocket management
│       └── incentive_manager.py  # Incentive system
├── config/
│   ├── monitoring.yml           # Unified monitoring config
│   ├── alerts.yml              # Alert rules
│   └── dashboards/             # Dashboard JSON files
└── docs/
    └── technical/              # Consolidated documentation

2. Monitoring Optimization

Consolidated Metrics Collection

from prometheus_client import Counter, Gauge, Histogram
from typing import Dict, Optional

class MetricsCollector:
    """Unified metrics collection for all components"""

    def __init__(self):
        # System Metrics
        self.system_metrics = {
            'cpu_usage': Gauge('system_cpu_usage', 'CPU usage percentage'),
            'memory_usage': Gauge('system_memory_usage', 'Memory usage percentage'),
            'disk_usage': Gauge('system_disk_usage', 'Disk usage percentage')
        }

        # Blockchain Metrics
        self.blockchain_metrics = {
            'transactions': Counter(
                'blockchain_transactions_total',
                'Transaction metrics',
                ['chain', 'type', 'status']
            ),
            'gas_usage': Histogram(
                'blockchain_gas_usage',
                'Gas usage metrics',
                ['chain', 'operation'],
                buckets=[10000, 50000, 100000, 500000]
            )
        }

        # Business Metrics
        self.business_metrics = {
            'claims': Counter(
                'business_claims_total',
                'Claim metrics',
                ['type', 'status']
            ),
            'verifications': Counter(
                'business_verifications_total',
                'Verification metrics',
                ['type', 'outcome']
            )
        }

    def record_metric(self, category: str, name: str, value: float, 
                     labels: Optional[Dict[str, str]] = None):
        """Unified method for recording metrics"""
        metrics_group = getattr(self, f'{category}_metrics')
        metric = metrics_group.get(name)

        if metric is None:
            raise ValueError(f"Unknown metric: {name} in category {category}")

        if labels:
            metric.labels(**labels).inc(value)
        else:
            metric.inc(value)

3. Alert Configuration Optimization

Unified Alert Rules (alerts.yml)

groups:
  - name: critical_alerts
    rules:
      - alert: SystemOverload
        expr: system_cpu_usage > 80 or system_memory_usage > 85
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: System resources critical

      - alert: BlockchainSync
        expr: blockchain_sync_delay > 300
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: Blockchain sync delayed

  - name: business_alerts
    rules:
      - alert: HighFailureRate
        expr: rate(business_verifications_total{outcome="failed"}[5m]) > 0.1
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: High verification failure rate

4. Dashboard Optimization

Unified Dashboard Template

{
  "dashboard": {
    "title": "AlisChain Overview",
    "panels": [
      {
        "title": "System Health",
        "type": "row",
        "panels": [
          {
            "title": "Resource Usage",
            "type": "graph",
            "targets": [
              {
                "expr": "system_cpu_usage",
                "legendFormat": "CPU"
              },
              {
                "expr": "system_memory_usage",
                "legendFormat": "Memory"
              }
            ]
          }
        ]
      },
      {
        "title": "Blockchain Metrics",
        "type": "row",
        "panels": [
          {
            "title": "Transaction Overview",
            "type": "graph",
            "targets": [
              {
                "expr": "rate(blockchain_transactions_total[5m])",
                "legendFormat": "{{chain}} - {{type}}"
              }
            ]
          }
        ]
      }
    ]
  }
}

5. Configuration Management Optimization

Unified Configuration (config.yml)

monitoring:
  metrics:
    collection_interval: 15s
    retention_period: 15d

  alerting:
    evaluation_interval: 1m
    notification_channels:
      - type: slack
        webhook: ${SLACK_WEBHOOK_URL}
      - type: email
        recipients: ["team@alischain.com"]

blockchain:
  networks:
    - name: ethereum
      rpc_url: ${ETH_RPC_URL}
      chain_id: 1
    - name: polygon
      rpc_url: ${POLYGON_RPC_URL}
      chain_id: 137

services:
  websocket:
    host: 0.0.0.0
    port: 5000
    ping_interval: 30

Performance Improvements

  1. Caching Strategy

    from functools import lru_cache
    from typing import Any, Optional
    import redis
    
    class CacheManager:
        def __init__(self, redis_url: str):
            self.redis = redis.from_url(redis_url)
    
        @lru_cache(maxsize=1000)
        def get_cached_data(self, key: str) -> Optional[Any]:
            """Get data from cache with memory and Redis fallback"""
            return self.redis.get(key)
    
        def set_cached_data(self, key: str, value: Any, 
                           expire: int = 3600) -> None:
            """Set data in cache with expiration"""
            self.redis.setex(key, expire, value)
    

  2. Asynchronous Processing

    import asyncio
    from typing import List, Callable
    
    class AsyncTaskManager:
        def __init__(self):
            self.tasks: List[asyncio.Task] = []
    
        async def add_task(self, coro: Callable, *args, **kwargs) -> None:
            """Add a new task to the manager"""
            task = asyncio.create_task(coro(*args, **kwargs))
            self.tasks.append(task)
    
        async def wait_all(self) -> None:
            """Wait for all tasks to complete"""
            if self.tasks:
                await asyncio.gather(*self.tasks)
                self.tasks.clear()
    

Migration Steps

  1. Code Reorganization
  2. Move files to new structure
  3. Update import statements
  4. Consolidate duplicate code

  5. Configuration Updates

  6. Merge configuration files
  7. Update environment variables
  8. Validate all settings

  9. Monitoring Migration

  10. Deploy new metrics collector
  11. Update alert rules
  12. Verify dashboard functionality

  13. Testing

  14. Unit tests for new structure
  15. Integration tests
  16. Performance benchmarks

Recommendations

  1. Immediate Actions
  2. Implement unified metrics collection
  3. Consolidate configuration files
  4. Update deployment scripts

  5. Short-term Improvements

  6. Add caching layer
  7. Implement async task manager
  8. Update documentation

  9. Long-term Goals

  10. Implement automated testing
  11. Set up CI/CD pipeline
  12. Regular performance audits

Last update: 2024-12-08