实时数据分析案例
# 实时数据分析案例
# 案例概述
本案例展示如何使用StreamSQL进行实时数据分析，包括实时指标计算、异常检测和趋势分析。
# 案例1：实时销售指标分析
# 业务场景
电商平台需要实时监控销售指标，包括实时销售额、订单量、平均客单价等。
# 数据输入
{
"order_id": "ORD001",
"user_id": "U001",
"product_id": "P001",
"amount": 299.99,
"quantity": 2,
"timestamp": "2024-01-15T10:30:00Z",
"category": "electronics"
}
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# 期望输出
{
"window_start": "2024-01-15T10:00:00Z",
"window_end": "2024-01-15T11:00:00Z",
"total_sales": 15999.50,
"order_count": 45,
"avg_order_value": 355.54,
"top_category": "electronics",
"growth_rate": 12.5
}
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# 案例2：用户行为实时分析
# 业务场景
分析用户实时行为模式，识别活跃用户和异常行为。
# 数据输入
{
"user_id": "U001",
"action": "page_view",
"page": "/product/123",
"session_id": "S001",
"timestamp": "2024-01-15T10:30:00Z",
"device": "mobile",
"duration": 45
}
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# 期望输出
{
"user_id": "U001",
"session_duration": 1800,
"page_views": 15,
"bounce_rate": 0.2,
"is_active_user": true,
"risk_score": 0.1,
"last_activity": "2024-01-15T10:30:00Z"
}
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# 案例3：系统性能实时监控
# 业务场景
实时监控系统性能指标，及时发现性能瓶颈和异常。
# 数据输入
{
"server_id": "SRV001",
"cpu_usage": 75.5,
"memory_usage": 68.2,
"disk_usage": 45.8,
"network_in": 1024000,
"network_out": 512000,
"timestamp": "2024-01-15T10:30:00Z"
}
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# 期望输出
{
"server_id": "SRV001",
"avg_cpu_5min": 72.3,
"max_memory_5min": 78.9,
"total_network_5min": 15360000,
"cpu_trend": "increasing",
"alert_level": "warning",
"predicted_capacity": "2024-01-15T12:00:00Z"
}
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# 案例4：金融交易实时风控
# 业务场景
实时分析金融交易，识别可疑交易和风险模式。
# 数据输入
{
"transaction_id": "TXN001",
"user_id": "U001",
"amount": 5000.00,
"currency": "USD",
"merchant": "Amazon",
"location": "New York",
"timestamp": "2024-01-15T10:30:00Z",
"card_type": "credit"
}
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# 期望输出
{
"transaction_id": "TXN001",
"risk_score": 0.85,
"risk_factors": ["high_amount", "unusual_location"],
"user_daily_total": 8500.00,
"merchant_fraud_rate": 0.02,
"decision": "review",
"confidence": 0.92
}
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# 案例5：物联网设备状态分析
# 业务场景
实时分析物联网设备状态，预测设备故障和维护需求。
# 数据输入
{
"device_id": "DEV001",
"temperature": 68.5,
"humidity": 45.2,
"vibration": 2.1,
"power_consumption": 150.5,
"timestamp": "2024-01-15T10:30:00Z",
"location": "Factory_A"
}
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# 期望输出
{
"device_id": "DEV001",
"health_score": 0.92,
"anomaly_detected": false,
"predicted_failure_time": null,
"maintenance_priority": "low",
"efficiency_rating": 0.88,
"trend_analysis": "stable"
}
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# 分析特点
# 实时性要求
- 低延迟: 毫秒级响应时间
- 高吞吐: 支持大量并发数据流
- 准确性: 保证分析结果的准确性
# 技术优势
- 流式计算: 无需等待批处理
- 增量更新: 高效的状态维护
- 弹性扩展: 根据负载自动调整
# 应用价值
- 业务洞察: 实时了解业务状况
- 风险控制: 及时发现和处理风险
- 运营优化: 基于实时数据优化运营
# 📚 相关文档
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上次更新: 2025/07/27, 15:17:27