学习如何建立企业级监控和维护体系，确保 Claude Code 在生产环境中的稳定运行和持续优化。
34.3.1 监控体系概述#
监控的重要性#
企业级监控对于 Claude Code 部署至关重要，它可以帮助：
- 确保可用性：及时发现和解决服务中断
- 优化性能：识别性能瓶颈并优化资源使用
- 安全防护：检测异常行为和安全威胁
- 成本控制：监控使用情况和资源消耗
- 合规审计：满足企业合规要求
监控维度#
企业级监控维度
MONITORING_DIMENSIONS = { "可用性监控": { "指标": ["服务状态", "响应时间", "错误率"], "目标": "99.9% 可用性" }, "性能监控": { "指标": ["API 延迟", "令牌使用", "并发连接"], "目标": "P95 延迟 < 2s" }, "资源监控": { "指标": ["CPU 使用率", "内存使用", "磁盘 I/O", "网络带宽"], "目标": "资源利用率 < 80%" }, "安全监控": { "指标": ["异常访问", "权限违规", "数据泄露"], "目标": "零安全事件" }, "成本监控": { "指标": ["API 调用成本", "令牌成本", "基础设施成本"], "目标": "成本控制在预算内" } }
34.3.2 指标收集#
Prometheus 配置#
bashyaml # prometheus.yml global: scrape_interval: 15s evaluation_interval: 15s scrape_configs: # Claude Code API 监控 - job_name: 'claude-code-api' static_configs: - targets: ['localhost:8080'] metrics_path: '/metrics' scrape_interval: 10s # LLM 网关监控 - job_name: 'llm-gateway' static_configs: - targets: ['localhost:4000'] metrics_path: '/metrics' scrape_interval: 10s # 开发容器监控 - job_name: 'dev-containers' static_configs: - targets: ['localhost:9323'] metrics_path: '/metrics' scrape_interval: 30s # 沙箱监控 - job_name: 'sandbox' static_configs: - targets: ['localhost:9100'] metrics_path: '/metrics' scrape_interval: 15s alerting: alertmanagers: - static_configs: - targets: ['localhost:9093'] ### 自定义指标导出器 # claude_code_exporter.py from prometheus_client import start_http_server, Gauge, Counter, Histogram import time import json import requests from datetime import datetime # 定义指标 api_requests_total = Counter( 'claude_code_api_requests_total', 'Total API requests', ['endpoint', 'status'] ) api_latency = Histogram( 'claude_code_api_latency_seconds', 'API request latency', ['endpoint'] ) active_sessions = Gauge( 'claude_code_active_sessions', 'Number of active sessions' ) tokens_used = Counter( 'claude_code_tokens_used_total', 'Total tokens used', ['model', 'type'] ) cost_incurred = Gauge( 'claude_code_cost_usd', 'Total cost incurred in USD' ) class ClaudeCodeMetricsCollector: def __init__(self, api_base_url='http://localhost:8080'): self.api_base_url = api_base_url self.start_time = datetime.now() def collect_api_metrics(self): """收集 API 指标""" try: # 获取 API 状态 response = requests.get(f'{self.api_base_url}/health') if response.status_code == 200: data = response.json() # 更新活跃会话数 active_sessions.set(data.get('active_sessions', 0)) # 更新令牌使用 tokens = data.get('tokens_used', {}) for model, count in tokens.items(): tokens_used.labels(model=model, type='input').inc(count.get('input', 0)) tokens_used.labels(model=model, type='output').inc(count.get('output', 0)) # 更新成本 cost_incurred.set(data.get('total_cost', 0.0)) except Exception as e: print(f"Error collecting API metrics: {e}") def collect_performance_metrics(self): """收集性能指标""" try: # 测试 API 延迟 start_time = time.time() response = requests.get(f'{self.api_base0_url}/health') latency = time.time() - start_time # 记录延迟 api_latency.labels(endpoint='/health').observe(latency) # 记录请求 api_requests_total.labels( endpoint='/health', status=response.status_code ).inc() except Exception as e: print(f"Error collecting performance metrics: {e}") def collect_sandbox_metrics(self): """收集沙箱指标""" try: response = requests.get(f'{self.api_base_url}/sandbox/status') if response.status_code == 200: data = response.json() # 沙箱违规计数 violations = data.get('violations', 0) # 可以添加更多沙箱相关指标 except Exception as e: print(f"Error collecting sandbox metrics: {e}") def run(self, interval=10): """运行指标收集器""" start_http_server(9100) print("Metrics server started on port 9100") while True: self.collect_api_metrics() self.collect_performance_metrics() self.collect_sandbox_metrics() time.sleep(interval) if __name__ == '__main__': collector = ClaudeCodeMetricsCollector() collector.run()
日志收集配置#
bashyaml # filebeat.yml filebeat.inputs: - type: log enabled: true paths: - /var/log/claude-code/*.log fields: service: claude-code environment: production fields_under_root: true - type: log enabled: true paths: - /var/log/llm-gateway/*.log fields: service: llm-gateway environment: production fields_under_root: true - type: log enabled: true paths: - /var/log/claude-sandbox/*.log fields: service: claude-sandbox environment: production fields_under_root: true output.elasticsearch: hosts: ["elasticsearch:9200"] index: "claude-code-%{+yyyy.MM.dd}" setup.kibana: host: "kibana:5601" processors: - add_host_metadata: ~ - add_cloud_metadata: ~ ## 34.3.3 告警配置 ### Prometheus 告警规则 # alert_rules.yml groups: - name: claude_code_alerts interval: 30s rules: # 服务可用性告警 - alert: ClaudeCodeServiceDown expr: up{job="claude-code-api"} == 0 for: 1m labels: severity: critical annotations: summary: "Claude Code 服务不可用" description: "Claude Code API 服务已宕机超过 1 分钟" # API 错误率告警 - alert: HighAPIErrorRate expr: | rate(claude_code_api_requests_total{status=~"5.."}[5m]) / rate(claude_code_api_requests_total[5m]) > 0.05 for: 5m labels: severity: warning annotations: summary: "API 错误率过高" description: "API 错误率超过 5% (当前: {{ $value }})" # API 延迟告警 - alert: HighAPILatency expr: | histogram_quantile(0.95, rate(claude_code_api_latency_seconds_bucket[5m]) ) > 2 for: 5m labels: severity: warning annotations: summary: "API 延迟过高" description: "API P95 延迟超过 2 秒 (当前: {{ $value }}s)" # 令牌使用告警 - alert: HighTokenUsage expr: | rate(claude_code_tokens_used_total[1h]) > 100000 for: 10m labels: severity: warning annotations: summary: "令牌使用率过高" description: "令牌使用率超过 100,000/小时 (当前: {{ $value }})" # 成本告警 - alert: HighCostIncurred expr: claude_code_cost_usd > 1000 for: 1h labels: severity: warning annotations: summary: "成本超过阈值" description: "累计成本超过 $1000 (当前: ${{ $value }})" # 沙箱违规告警 - alert: SandboxViolations expr: | rate(claude_sandbox_violations_total[5m]) > 10 for: 5m labels: severity: critical annotations: summary: "沙箱违规频繁" description: "沙箱违规率超过 10/分钟 (当前: {{ $value }})" # 资源使用告警 - alert: HighCPUUsage expr: | rate(process_cpu_seconds_total{job="claude-code-api"}[5m]) > 0.8 for: 10m labels: severity: warning annotations: summary: "CPU 使用率过高" description: "CPU 使用率超过 80% (当前: {{ $value }})" - alert: HighMemoryUsage expr: | process_resident_memory_bytes{job="claude-code-api"} / node_memory_MemTotal_bytes > 0.8 for: 10m labels: severity: warning annotations: summary: "内存使用率过高" description: "内存使用率超过 80% (当前: {{ $value }})"
Alertmanager 配置#
bashyaml # alertmanager.yml global: resolve_timeout: 5m route: group_by: ['alertname', 'severity'] group_wait: 10s group_interval: 10s repeat_interval: 12h receiver: 'default' routes: - match: severity: critical receiver: 'critical-alerts' continue: false - match: severity: warning receiver: 'warning-alerts' continue: false receivers: - name: 'default' email_configs: - to: 'team@company.com' from: 'alerts@company.com' smarthost: 'smtp.company.com:587' auth_username: 'alerts@company.com' auth_password: 'password' - name: 'critical-alerts' email_configs: - to: 'oncall@company.com' from: 'alerts@company.com' smarthost: 'smtp.company.com:587' auth_username: 'alerts@company.com' auth_password: 'password' slack_configs: - api_url: 'https://hooks.slack.com/services/XXX/YYY/ZZZ' channel: '#critical-alerts' title: 'Claude Code Critical Alert' text: '{{ range .Alerts }}{{ .Annotations.description }}{{ end }}' - name: 'warning-alerts' email_configs: - to: 'dev-team@company.com' from: 'alerts@company.com' smarthost: 'smtp.company.com:587' auth_username: 'alerts@company.com' auth_password: 'password' slack_configs: - api_url: 'https://hooks.slack.com/services/XXX/YYY/ZZZ' channel: '#warnings' title: 'Claude Code Warning' text: '{{ range .Alerts }}{{ .Annotations.description }}{{ end }}' inhibit_rules: - source_match: severity: 'critical' target_match: severity: 'warning' equal: ['alertname'] ## 34.3.4 可视化仪表板 ### Grafana 仪表板配置 { "dashboard": { "title": "Claude Code Enterprise Dashboard", "panels": [ { "title": "API 请求速率", "targets": [ { "expr": "rate(claude_code_api_requests_total[5m])", "legendFormat": "{{ endpoint }}" } ], "type": "graph" }, { "title": "API 延迟 (P95)", "targets": [ { "expr": "histogram_quantile(0.95, rate(claude_code_api_latency_seconds_bucket[5m]))", "legendFormat": "P95" } ], "type": "graph" }, { "title": "活跃会话数", "targets": [ { "expr": "claude_code_active_sessions", "legendFormat": "Sessions" } ], "type": "stat" }, { "title": "令牌使用率", "targets": [ { "expr": "rate(claude_code_tokens_used_total[1h])", "legendFormat": "{{ model }} - {{ type }}" } ], "type": "graph" }, { "title": "累计成本", "targets": [ { "expr": "claude_code_cost_usd", "legendFormat": "Cost (USD)" } ], "type": "stat" }, { "title": "API 错误率", "targets": [ { "expr": "rate(claude_code_api_requests_total{status=~\"5..\"}[5m]) / rate(claude_code_api_requests_total[5m])", "legendFormat": "Error Rate" } ], "type": "graph" }, { "title": "沙箱违规", "targets": [ { "expr": "rate(claude_sandbox_violations_total[5m])", "legendFormat": "Violations/min" } ], "type": "graph" }, { "title": "资源使用", "targets": [ { "expr": "rate(process_cpu_seconds_total{job=\"claude-code-api\"}[5m])", "legendFormat": "CPU" }, { "expr": "process_resident_memory_bytes{job=\"claude-code-api\"} / 1024 / 1024 / 1024", "legendFormat": "Memory (GB)" } ], "type": "graph" } ] } }
34.3.5 日志分析#
ELK Stack 配置#
bashpython # log_analyzer.py import elasticsearch from elasticsearch import Elasticsearch from datetime import datetime, timedelta import json class ClaudeCodeLogAnalyzer: def __init__(self, es_host='http://localhost:9200'): self.es = Elasticsearch([es_host]) self.index_pattern = 'claude-code-*' def search_errors(self, hours=24): """搜索错误日志""" query = { "query": { "bool": { "must": [ {"match": {"level": "ERROR"}}, {"range": { "@timestamp": { "gte": (datetime.now() - timedelta(hours=hours)).isoformat() } }} ] } } } response = self.es.search(index=self.index_pattern, body=query) return response['hits']['hits'] def search_slow_requests(self, threshold_seconds=2, hours=24): """搜索慢请求""" query = { "query": { "bool": { "must": [ {"range": { "latency": { "gte": threshold_seconds } }}, {"range": { "@timestamp": { "gte": (datetime.now() - timedelta(hours=hours)).isoformat() } }} ] } } } response = self.es.search(index=self.index_pattern, body=query) return response['hits']['hits'] def analyze_user_activity(self, user_id, days=7): """分析用户活动""" query = { "query": { "bool": { "must": [ {"match": {"user_id": user_id}}, {"range": { "@timestamp": { "gte": (datetime.now() - timedelta(days=days)).isoformat() } }} ] } }, "aggs": { "daily_requests": { "date_histogram": { "field": "@timestamp", "calendar_interval": "day" }, "aggs": { "total_tokens": { "sum": { "field": "tokens_used" } } } } } } response = self.es.search(index=self.index_pattern, body=query) return response def detect_anomalies(self, hours=1): """检测异常""" # 计算平均请求速率 avg_query = { "query": { "range": { "@timestamp": { "gte": (datetime.now() - timedelta(hours=hours*2)).isoformat(), "lt": (datetime.now() - timedelta(hours=hours)).isoformat() } } }, "aggs": { "avg_rate": { "avg": { "script": { "source": "doc['request_count'].value" } } } } } avg_response = self.es.search(index=self.index_pattern, body=avg_query) avg_rate = avg_response['aggregations']['avg_rate']['value'] # 检查当前速率是否异常 current_query = { "query": { "range": { "@timestamp": { "gte": (datetime.now() - timedelta(hours=hours)).isoformat() } } }, "aggs": { "current_rate": { "avg": { "script": { "source": "doc['request_count'].value" } } } } } current_response = self.es.search(index=self.index_pattern, body=current_query) current_rate = current_response['aggregations']['current_rate']['value'] # 如果当前速率超过平均值的 2 倍，视为异常 if current_rate > avg_rate * 2: return { "anomaly": True, "avg_rate": avg_rate, "current_rate": current_rate, "threshold": avg_rate * 2 } return {"anomaly": False} # 使用示例 analyzer = ClaudeCodeLogAnalyzer() # 搜索错误 errors = analyzer.search_errors(hours=24) print(f"发现 {len(errors)} 个错误") # 搜索慢请求 slow_requests = analyzer.search_slow_requests(threshold_seconds=2, hours=24) print(f"发现 {len(slow_requests)} 个慢请求") # 分析用户活动 user_activity = analyzer.analyze_user_activity(user_id="user123", days=7) # 检测异常 anomalies = analyzer.detect_anomalies(hours=1) if anomalies['anomaly']: print(f"检测到异常！当前速率: {anomalies['current_rate']}, 阈值: {anomalies['threshold']}") ## 34.3.6 维护策略 ### 定期维护任务 #!/bin/bash # maintenance.sh set -e LOG_DIR="/var/log/claude-code" BACKUP_DIR="/backup/claude-code" DATE=$(date +%Y-%m-%d) echo "=== Claude Code 维护脚本 - $DATE ===" # 1. 日志轮转 echo "执行日志轮转..." logrotate -f /etc/logrotate.d/claude-code # 2. 清理旧日志 echo "清理 30 天前的日志..." find $LOG_DIR -name "*.log" -mtime +30 -delete # 3. 备份配置 echo "备份配置文件..." mkdir -p $BACKUP_DIR/$DATE cp -r /etc/claude-code $BACKUP_DIR/$DATE/ # 4. 清理缓存 echo "清理缓存..." rm -rf /tmp/claude-code-cache/* # 5. 数据库维护（如果使用） echo "执行数据库维护..."
psql -U claude -d claude_code -c "VACUUM ANALYZE;"
6. 生成维护报告
echo "生成维护报告..." cat > $BACKUP_DIR/$DATE/maintenance-report.txt << EOF Claude Code 维护报告 日期: $DATE 日志轮转: 完成 旧日志清理: 完成 配置备份: 完成 缓存清理: 完成 数据库维护: 完成 磁盘使用情况: $(df -h /var/log/claude-code) 服务状态: $(systemctl status claude-code --no-pager) EOF echo "维护完成！报告已保存到 $BACKUP_DIR/$DATE/maintenance-report.txt"
bash### 健康检查脚本
python
health_check.py
import requests import json import sys from datetime import datetime
class ClaudeCodeHealthChecker: def init(self, api_base_url='http://localhost:8080'): self.api_base_url = api_base_url self.checks = []
bashdef check_api_health(self): """检查 API 健康状态""" try: response = requests.get(f'{self.api_base_url}/health', timeout=5) if response.status_code == 200: data = response.json() self.checks.append({ "name": "API Health", "status": "healthy", "details": data }) return True else: self.checks.append({ "name": "API Health", "status": "unhealthy", "details": f"Status code: {response.status_code}" }) return False except Exception as e: self.checks.append({ "name": "API Health", "status": "unhealthy", "details": str(e) }) return False def check_llm_gateway(self): """检查 LLM 网关""" try: response = requests.get('http://localhost:4000/health', timeout=5) if response.status_code == 200: self.checks.append({ "name": "LLM Gateway", "status": "healthy", "details": response.json() }) return True else: self.checks.append({ "name": "LLM Gateway", "status": "unhealthy", "details": f"Status code: {response.status_code}" }) return False except Exception as e: self.checks.append({
bash"name": "LLM Gateway", "status": "unhealthy", "details": str(e) }) return False def check_sandbox(self): """检查沙箱状态""" try: response = requests.get(f'{self.api_base_url}/sandbox/status', timeout=5) if response.status_code == 200: data = response.json() self.checks.append({ "name": "Sandbox", "status": "healthy", "details": data }) return True else: self.checks.append({ "name": "Sandbox", "status": "unhealthy", "details": f"Status code: {response.status_code}" }) return False except Exception as e: self.checks.append({ "name": "Sandbox", "status": "unhealthy", "details": str(e) }) return False def check_disk_space(self, threshold=90): """检查磁盘空间""" import shutil usage = shutil.disk_usage('/') percent = (usage.used / usage.total) * 100 if percent < threshold: self.checks.append({ "name": "Disk Space", "status": "healthy", "details": f"Usage: {percent:.1f}%" }) return True else: self.checks.append({ "name": "Disk Space", "status": "unhealthy", "details": f"Usage: {percent:.1f}% (Threshold: {threshold}%)" }) return False def check_memory(self, threshold=90): """检查内存使用""" import psutil percent = psutil.virtual_memory().percent if percent < threshold: self.checks.append({ "name": "Memory", "status": "healthy", "details": f"Usage: {percent:.1f}%" }) return True else: self.checks.append({ "name": "Memory", "status": "unhealthy", "details": f"Usage: {percent:.1f}% (Threshold: {threshold}%)" }) return False def run_all_checks(self): """运行所有检查""" self.check_api_health() self.check_llm_gateway() self.check_sandbox() self.check_disk_space() self.check_memory() return self.checks def generate_report(self): """生成健康检查报告""" report = { "timestamp": datetime.now().isoformat(), "overall_status": "healthy", "checks": self.checks } # 确定整体状态 for check in self.checks: if check['status'] == 'unhealthy': report['overall_status'] = 'unhealthy' break return report def print_report(self): """打印报告""" report = self.generate_report() print("=" * 50) print(f"Claude Code 健康检查报告") print(f"时间: {report['timestamp']}") print(f"整体状态: {report['overall_status'].upper()}") print("=" * 50) for check in report['checks']: status_icon = "✓" if check['status'] == 'healthy' else "✗" print(f"{status_icon} {check['name']}: {check['status']}") print(f" 详情: {check['details']}") print() return report['overall_status'] == 'healthy'
if name == 'main': checker = ClaudeCodeHealthChecker() checker.run_all_checks() is_healthy = checker.print_report()
bashsys.exit(0 if is_healthy else 1)
34.3.7 灾难恢复#
备份策略#
#!/bin/bash
backup.sh
set -e BACKUP_DIR="/backup/claude-code" DATE=$(date +%Y-%m-%d_%H-%M-%S) BACKUP_PATH="$BACKUP_DIR/$DATE" echo "=== Claude Code 备份脚本 - $DATE ==="
创建备份目录
mkdir -p $BACKUP_PATH
1. 备份配置文件
echo "备份配置文件..." tar -czf $BACKUP_PATH/config.tar.gz /etc/claude-code
2. 备份数据库
echo "备份数据库..."
bash# pg_dump -U claude claude_code > $BACKUP_PATH/database.sql # 3. 备份日志 echo "备份日志..." tar -czf $BACKUP_PATH/logs.tar.gz /var/log/claude-code # 4. 备份用户数据 echo "备份用户数据..." tar -czf $BACKUP_PATH/user-data.tar.gz /var/lib/claude-code # 5. 生成备份清单 echo "生成备份清单..." cat > $BACKUP_PATH/manifest.txt << EOF 备份清单 日期: $DATE 配置文件: config.tar.gz 数据库: database.sql 日志: logs.tar.gz 用户数据: user-data.tar.gz 文件大小: $(du -sh $BACKUP_PATH/*) EOF # 6. 上传到远程存储（可选） echo "上传到远程存储..." # aws s3 cp $BACKUP_PATH s3://company-backups/claude-code/$DATE --recursive # 7. 清理旧备份（保留最近 30 天） echo "清理旧备份..." find $BACKUP_DIR -type d -mtime +30 -exec rm -rf {} \; echo "备份完成！备份位置: $BACKUP_PATH"
恢复脚本#
bashbash #!/bin/bash # restore.sh set -e if [ -z "$1" ]; then echo "用法: $0 <备份目录>" exit 1 fi BACKUP_PATH="$1" echo "=== Claude Code 恢复脚本 ===" echo "备份目录: $BACKUP_PATH" # 1. 停止服务 echo "停止服务..." systemctl stop claude-code # 2. 恢复配置文件 echo "恢复配置文件..." tar -xzf $BACKUP_PATH/config.tar.gz -C / # 3. 恢复数据库 echo "恢复数据库..." # psql -U claude -d claude_code < $BACKUP_PATH/database.sql # 4. 恢复用户数据 echo "恢复用户数据..." tar -xzf $BACKUP_PATH/user-data.tar.gz -C / # 5. 启动服务 echo "启动服务..." systemctl start claude-code # 6. 验证恢复 echo "验证恢复..." sleep 5 if systemctl is-active --quiet claude-code; then echo "服务启动成功！" else echo "服务启动失败！" exit 1 fi echo "恢复完成！" ## 34.3.8 性能优化 ### 缓存策略 # cache_manager.py import redis import json from datetime import timedelta class CacheManager: def __init__(self, redis_host='localhost', redis_port=6379): self.redis = redis.Redis(host=redis_host, port=redis_port, decode_responses=True) def cache_api_response(self, key, response, ttl=3600): """缓存 API 响应""" self.redis.setex(key, ttl, json.dumps(response)) def get_cached_response(self, key): """获取缓存的响应""" cached = self.redis.get(key) if cached: return json.loads(cached) return None def cache_token_count(self, user_id, count, ttl=86400): """缓存令牌计数""" key = f"tokens:{user_id}:{datetime.now().strftime('%Y-%m-%d')}" self.redis.incrby(key, count) self.redis.expire(key, ttl) def get_token_count(self, user_id): """获取令牌计数""" key = f"tokens:{user_id}:{datetime.now().strftime('%Y-%m-%d')}" count = self.redis.get(key) return int(count) if count else 0 def cache_model_response(self, model, prompt_hash, response, ttl=7200): """缓存模型响应""" key = f"model:{model}:{prompt_hash}" self.redis.setex(key, ttl, json.dumps(response)) def get_cached_model_response(self, model, prompt_hash): """获取缓存的模型响应""" key = f"model:{model}:{prompt_hash}" cached = self.redis.get(key) if cached: return json.loads(cached) return None # 使用示例 cache = CacheManager() # 缓存 API 响应 cache.cache_api_response("api:user:123:profile", {"name": "John"}, ttl=3600) # 获取缓存的响应 cached = cache.get_cached_response("api:user:123:profile")
负载均衡配置#
bashyaml # nginx.conf upstream claude_code_backend { least_conn; server claude-code-1:8080 weight=3; server claude-code-2:8080 weight=2; server claude-code-3:8080 weight=1; keepalive 32; } server { listen 80; server_name claude-code.company.com; # 重定向到 HTTPS return 301 https://$server_name$request_uri; } server { listen 443 ssl http2; server_name claude-code.company.com; ssl_certificate /etc/nginx/ssl/claude-code.crt; ssl_certificate_key /etc/nginx/ssl/claude-code.key; # SSL 配置 ssl_protocols TLSv1.2 TLSv1.3; ssl_ciphers HIGH:!aNULL:!MD5; ssl_prefer_server_ciphers on; # 日志 access_log /var/log/nginx/claude-code-access.log; error_log /var/log/nginx/claude-code-error.log; # 代理配置 location / { proxy_pass http://claude_code_backend; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Forwarded-Proto $scheme; # 超时配置 proxy_connect_timeout 60s; proxy_send_timeout 60s; proxy_read_timeout 60s; # 缓冲配置 proxy_buffering on; proxy_buffer_size 4k; proxy_buffers 8 4k; proxy_busy_buffers_size 8k; # 健康检查 health_check interval=10s fails=3 passes=2; } # 健康检查端点 location /health { proxy_pass http://claude_code_backend/health; access_log off; } }
34.3.9 小结#
本节介绍了企业级监控和维护的各个方面，包括：
- 监控体系概述和监控维度
- 指标收集（Prometheus、自定义导出器）
- 告警配置（Prometheus、Alertmanager）
- 可视化仪表板（Grafana）
- 日志分析（ELK Stack）
- 维护策略（定期维护、健康检查）
- 灾难恢复（备份和恢复）
- 性能优化（缓存、负载均衡）
通过建立完善的监控和维护体系，企业可以确保 Claude Code 在生产环境中的稳定运行，及时发现和解决问题，优化性能和成本控制。