MongoDB中基于对象键的日期范围过滤与子文档返回需求

To retrieve documents where the analytics.days keys fall within a specified date range (and retain only those matching days in the result), you’ll use MongoDB’s aggregation framework. Here’s a practical, step-by-step solution:

Step 1: Define Your Date Range #

First, convert your start/end date strings into proper Date objects to enable accurate range comparisons. For example:

const startDate = new Date("2019-01-15");
const endDate = new Date("2019-01-25");
endDate.setHours(23, 59, 59, 999); // Ensure we include the full end day

Step 2: Aggregation Pipeline #

Use this pipeline to filter qualifying documents and keep only the days within your range:

db.yourCollectionName.aggregate([
  // Convert the days object into an array of key-value pairs for processing
  {
    $addFields: {
      "analytics.daysArray": { $objectToArray: "$analytics.days" }
    }
  },
  // Parse each date string (DD-MM-YYYY) into a Date object
  {
    $addFields: {
      "analytics.daysArray": {
        $map: {
          input: "$analytics.daysArray",
          as: "day",
          in: {
            k: "$$day.k",
            v: "$$day.v",
            parsedDate: {
              $dateFromString: {
                dateString: "$$day.k",
                format: "%d-%m-%Y" // Critical for matching your date format
              }
            }
          }
        }
      }
    }
  },
  // Keep only documents with at least one day in the target range
  {
    $match: {
      "analytics.daysArray.parsedDate": {
        $gte: startDate,
        $lte: endDate
      }
    }
  },
  // Filter the days array to retain only matches, then convert back to an object
  {
    $addFields: {
      "analytics.days": {
        $arrayToObject: {
          $filter: {
            input: "$analytics.daysArray",
            cond: {
              $and: [
                { $gte: ["$$this.parsedDate", startDate] },
                { $lte: ["$$this.parsedDate", endDate] }
              ]
            }
          }
        }
      }
    }
  },
  // Clean up the temporary array field we added
  {
    $project: {
      "analytics.daysArray": 0
    }
  }
])

How It Works #

Let’s break down each stage to understand the logic:

1. $objectToArray: Converts the dynamic analytics.days object into an array of {k: dateString, v: dayData} pairs, making it easy to process individual dates.
2. $map + $dateFromString: Parses each date string into a proper Date object—this is essential because we can’t compare string dates reliably (e.g., "28-12-2018" vs "20-01-2019" would be out of order as strings).
3. $match: Filters out any documents that don’t have at least one day within your specified range.
4. $filter + $arrayToObject: Keeps only the days that fall within the range, then converts the filtered array back into an object to replace the original analytics.days.
5. $project: Removes the temporary daysArray field to clean up the final result.

Example Output #

For your sample input and date range (15-01-2019 to 25-01-2019), the result will be exactly what you requested:

{
  "_id": ObjectId("5c51b3e2bb564f3f1ccf84c2"),
  "createdAt": 1548858338324,
  "updatedAt": 1548858843028,
  "teamId": "ef08f13",
  "analytics": {
    "aggregate": {
      "averageViewTime": 5.6000000000000005,
      "averageClicks": 2.5,
      "totalViews": 2,
      "totalVisits": 4
    },
    "days": {
      "20-01-2019": {
        "dayAvgViewTime": 5.6000000000000005,
        "dayAvgClicks": 2.5,
        "dayViews": 2,
        "dayVisits": 4
      }
    }
  }
}

内容的提问来源于stack exchange，提问作者Aman kumar