MongoDB：如何查询数据不完整的时间序列？

我将时间序列数据存储在mongoDB集合中，每15分钟存储一个数据点。但有时，由于恶劣条件，一些数据点会丢失。我的数据集如下：

{"device_id": "ABC","temp": 12,"timestamp": 2020-01-04T17:48:09.000+00:00}
{"device_id": "ABC","temp": 10,"timestamp": 2020-01-04T18:03:09.000+00:00}
{"device_id": "ABC","temp": 14,"timestamp": 2020-01-04T18:18:09.000+00:00}
missing frame
missing frame
{"device_id": "ABC","temp": 13,"timestamp": 2020-01-04T19:03:09.000+00:00}
{"device_id": "ABC","temp": 15,"timestamp": 2020-01-04T19:18:09.000+00:00}
missing frame
{"device_id": "ABC","temp": 10,"timestamp": 2020-01-04T19:48:09.000+00:00}
{"device_id": "ABC","temp": 11,"timestamp": 2020-01-04T20:03:09.000+00:00}
...

我不知道如何查询此集合，以便每15分钟生成一个连续的值列表，以便绘制它并显示丢失的消息（如果消息丢失，请更改图形的背景色）。我希望每15分钟有一个结果对齐（将t和t+15分钟之间的值相加），如下所示：

{"timestamp": 2020-01-04T17:45:00.000+00:00, "temp": 12, missing: false}
{"timestamp": 2020-01-04T18:00:00.000+00:00, "temp": 10, missing: false}
{"timestamp": 2020-01-04T18:15:00.000+00:00, "temp": 14, missing: false}
{"timestamp": 2020-01-04T18:30:00.000+00:00, "temp":  0, missing: true}
{"timestamp": 2020-01-04T18:45:00.000+00:00, "temp":  0, missing: true}
{"timestamp": 2020-01-04T19:00:00.000+00:00, "temp": 13, missing: false}
{"timestamp": 2020-01-04T19:15:00.000+00:00, "temp": 15, missing: false}
{"timestamp": 2020-01-04T19:30:00.000+00:00, "temp":  0, missing: true}
{"timestamp": 2020-01-04T19:45:00.000+00:00, "temp": 10, missing: false}
{"timestamp": 2020-01-04T20:00:00.000+00:00, "temp": 11, missing: false}

---

有什么想法吗？提前感谢您的帮助

以下是我在第一次评论中提到的方法的汇总：

db.collection.aggregate( [
  { 
      $sort: { timestamp: 1 } 
  },
  { 
      $group: { 
           _id: null,
           docs: { $push: { timestamp: "$timestamp", device_id: "$device_id", temp: "$temp", missing: false } },
           device_id: { $first: "$device_id" },
           start: { $first: { $toInt: { $divide: [ { "$toLong": "$timestamp" }, 1000 ] } } }, 
           end: { $last: { $toInt: { $divide: [ { "$toLong": "$timestamp" }, 1000 ] } } }
      } 
  },
  { 
      $addFields: {
           docs: {
               $map: {
                    input: { $range: [ { $toInt: "$start" }, { $add: [ { $toInt: "$end" }, 900 ] }, 900 ] }, 
                    as: "ts",
                    in: {
                        ts_exists: { $arrayElemAt: [ 
                                              { $filter: { 
                                                      input: "$docs", as: "d", 
                                                      cond: { $eq: [ { $toInt: { $divide: [ { "$toLong": "$$d.timestamp" }, 1000 ] } },
                                                                      "$$ts"
                                                             ] }
                                               }}, 
                                     0 ] },
                         ts: "$$ts"
                    }
              }
          }
      }
  },
  { 
      $unwind: "$docs" 
  },
  { 
      $addFields: { 
          docs: { 
              $ifNull: [ "$docs.ts_exists", { timestamp: { $toDate: { $multiply: [ "$docs.ts", 1000 ] } }, 
                                              temp: 0, device_id: "$device_id", missing: true 
                                             } 
                       ] 
          }
      }
  },
  { 
      $replaceRoot: { newRoot: "$docs" } 
  }
] ).pretty()

使用以下输入文档：

{"device_id": "ABC","temp": 12,"timestamp": ISODate("2020-01-04T17:45:00.000+00:00") },
{"device_id": "ABC","temp": 10,"timestamp": ISODate("2020-01-04T18:00:00.000+00:00") },
{"device_id": "ABC","temp": 4,"timestamp": ISODate("2020-01-04T18:30:00.000+00:00") },
{"device_id": "ABC","temp": 23,"timestamp": ISODate("2020-01-04T18:45:00.000+00:00") }

{
        "timestamp" : ISODate("2020-01-04T17:45:00Z"),
        "device_id" : "ABC",
        "temp" : 12,
        "missing" : false
}
{
        "timestamp" : ISODate("2020-01-04T18:00:00Z"),
        "device_id" : "ABC",
        "temp" : 10,
        "missing" : false
}
{
        "timestamp" : ISODate("2020-01-04T18:15:00Z"),
        "temp" : 0,
        "device_id" : "ABC",
        "missing" : true
}
{
        "timestamp" : ISODate("2020-01-04T18:30:00Z"),
        "device_id" : "ABC",
        "temp" : 4,
        "missing" : false
}
{
        "timestamp" : ISODate("2020-01-04T18:45:00Z"),
        "device_id" : "ABC",
        "temp" : 23,
        "missing" : false
}

结果：

{"device_id": "ABC","temp": 12,"timestamp": ISODate("2020-01-04T17:45:00.000+00:00") },
{"device_id": "ABC","temp": 10,"timestamp": ISODate("2020-01-04T18:00:00.000+00:00") },
{"device_id": "ABC","temp": 4,"timestamp": ISODate("2020-01-04T18:30:00.000+00:00") },
{"device_id": "ABC","temp": 23,"timestamp": ISODate("2020-01-04T18:45:00.000+00:00") }

{
        "timestamp" : ISODate("2020-01-04T17:45:00Z"),
        "device_id" : "ABC",
        "temp" : 12,
        "missing" : false
}
{
        "timestamp" : ISODate("2020-01-04T18:00:00Z"),
        "device_id" : "ABC",
        "temp" : 10,
        "missing" : false
}
{
        "timestamp" : ISODate("2020-01-04T18:15:00Z"),
        "temp" : 0,
        "device_id" : "ABC",
        "missing" : true
}
{
        "timestamp" : ISODate("2020-01-04T18:30:00Z"),
        "device_id" : "ABC",
        "temp" : 4,
        "missing" : false
}
{
        "timestamp" : ISODate("2020-01-04T18:45:00Z"),
        "device_id" : "ABC",
        "temp" : 23,
        "missing" : false
}

---

您可以通过聚合查询来实现这一点。查询可以输入开始和结束时间戳、时间间隔（即15分钟），并返回您发布的输出。时间戳可以转换为毫秒，在给定的时间戳和间隔范围内，找到所有可能的时间戳，并从可用数据中找到缺失的时间戳。谢谢！是的，我知道这种方法是进行聚合查询。但是我不知道如何在没有找到数据的情况下，在时间戳上加上条件和一个特殊的值。它给出了期望的结果。我可能会对查询做一些改进（稍后更新帖子）。