如果没有文档，则MongoDB的汇总返回计数为0

本文介绍了如果没有文档，则MongoDB的汇总返回计数为0的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有一个MongoDB查询，该查询根据日期对5分钟的窗口进行分组并返回计数(这是使用count: { $sum: 1 }在该5分钟的窗口中的文档总数).

I have a MongoDB query that groups by 5min windows based on date and returns count (which is the total number of documents in that 5min window using count: { $sum: 1 }).

如果该组中不存在任何文档，我希望该查询还为特定的5分钟窗口返回0计数.但是，目前看来，仅返回具有正计数的组.

I'd like to have the query also return a count of 0 for a particular 5min window if no documents exist in that group. However currently, looks like only groups with a positive count are returned.

当前查询:

const cursor = await collection.aggregate([ { $sort : { time : 1 } }, { $match: { $and: [ {selector: string }, {time: {$gte: timestamp }} ] } }, { $group: { _id: { $subtract: [ { $subtract: [ "$time", 0 ] }, { $mod: [ { $subtract: [ "$time", 0 ] }, 1000 * 60 * 5 ]} ], }, count: { $sum: 1 } } } ])

预期的响应:带有文档总数(包括总和0)的时间戳记

Expected response: timestamp with count of documents including sum 0

{ _id: 1525162000000, count: 314 } { _id: 1523144100000, count: 0 } { _id: 1512155500000, count: 54 }

提前谢谢！

推荐答案

免责声明:我不建议在服务器端(在MongoDB内部)这样做，而是在客户端处理这种情况.

Disclaimer: I do not recommend doing this on the server side (so inside MongoDB) but rather handle that case on the client side.

也就是说，这是您问题的通用解决方案，应该可以轻松地适应您的具体情况.

That said, here is a generic solution to your problem which should be easily adaptable to your specific case.

假设您有以下文档(或示例中的聚合管道输出):

Imagine you have the following documents (or output from an aggregation pipeline as in your example):

{ "category" : 1 } { "category" : 1 } // note the missing { category: 2 } document here { "category" : 3 }

下面的管道将创建空的存储桶(因此，在category字段中的值范围(本例中为2)中缺失的间隙"值的计数为0的文档):

The following pipeline will create empty buckets (so documents with a count of 0 for the "gap" values that are missing from the range of values in the category field - in this case the number 2):

var bucketSize = 1; db.getCollection('test').aggregate({ $group: { _id: null, // throw all documents into the same bucket "min": { $min: "$category" }, // just to calculate the lowest "max": { $max: "$category" }, // and the highest "category" value "docs": { $push: "$$ROOT" } // and also keep the root documents } }, { $addFields: { "docs": { // modify the existing docs array - created in the previous stage $concatArrays: [ // by concatenating "$docs", // the existing docs array { $map: { // with some other array that will be generated input: { $range: [ "$min", "$max", bucketSize ] // based on the min and max values and the bucket size }, as: "this", in: { // but represented not as a plain number but as a document that effectively creates a bogus document "category": "$$this", // the bogus category will be set to the respective value "bogus": 1 // marker that allows us not to count this document in the next stage and still get a bucket from $group } } } ] } } }, { $unwind: "$docs" // flatten the "docs" array which will now contain the bogus documents, too }, { $group: { _id: "$docs.category", // group by category "count": { // this is the result we are interested in $sum: { // which will be aggregated by calculating the sum for each document of $cond: [ // either 0 or 1 per document { $eq: [ "$docs.bogus", 1 ] }, // depending on whether the document should count as a result or not 0, 1 ] } } } })

上面查询的输出将是:

{ "_id" : 2, "count" : 0.0 // this is what we wanted to achieve } { "_id" : 3, "count" : 1.0 // correct number of matches } { "_id" : 1, "count" : 2.0 // correct number of matches }