As Sammaye suggested, you need to use the $cond aggregation projection operator to do this: db.Sentiments.aggregate( { $project: { _id: 0, Company: 1, PosSentiment: {$cond: [{$gt: [‘$Sentiment’, 0]}, ‘$Sentiment’, 0]}, NegSentiment: {$cond: [{$lt: [‘$Sentiment’, 0]}, ‘$Sentiment’, 0]} }}, { $group: { _id: “$Company”, SumPosSentiment: {$sum: ‘$PosSentiment’}, SumNegSentiment: {$sum: ‘$NegSentiment’} }});
We can do multiple join conditions with the $lookup aggregation pipeline operator in version 3.6 and newer. We need to assign the fields’s values to variable using the let optional field; you then access those variables in the pipeline field stages where you specify the pipeline to run on the collections. Note that in the … Read more
You can do this by $unwinding the updates array, sorting the resulting docs by date, and then $grouping them back together on _id using the sorted order. db.servers.aggregate( {$unwind: ‘$service.apps.updates’}, {$sort: {‘service.apps.updates.date’: 1}}, {$group: {_id: ‘$_id’, ‘updates’: {$push: ‘$service.apps.updates’}}}, {$project: {‘service.apps.updates’: ‘$updates’}})
This would be the easier way to do it using aggregate: db.contest.aggregate([ {“$group” : {_id:”$province”, count:{$sum:1}}} ])
In the simplest sense this just follows the basic form of “dot notation” as used by MongoDB. That will work regardless of which array member the inner array member is in, as long as it matches a value: db.mycollection.find({ “someArray.someNestedArray.name”: “value” }) That is fine for a “single field” value, for matching multiple-fields you would … Read more
There are a couple of approaches of course depending on your available MongoDB version. These vary from different usages of $lookup through to enabling object manipulation on the .populate() result via .lean(). I do ask that you read the sections carefully, and be aware that all may not be as it seems when considering your … Read more
Your ids variable will be constructed of “strings”, and not ObjectId values. Mongoose “autocasts” string values for ObjectId into their correct type in regular queries, but this does not happen in the aggregation pipeline, as in described in issue #1399. Instead you must do the correct casting to type manually: ids = ids.map(function(el) { return … Read more
TLDR Summary In modern MongoDB releases you can brute force this with $slice just off the basic aggregation result. For “large” results, run parallel queries instead for each grouping ( a demonstration listing is at the end of the answer ), or wait for SERVER-9377 to resolve, which would allow a “limit” to the number … Read more
MongoDB 3.2 now allows one to combine data from multiple collections into one through the $lookup aggregation stage. As a practical example, lets say that you have data about books split into two different collections. First collection, called books, having the following data: { “isbn”: “978-3-16-148410-0”, “title”: “Some cool book”, “author”: “John Doe” } { … Read more
2017 update $lookup can now directly use an array as the local field. $unwind is no longer needed. Old answer The $lookup aggregation pipeline stage will not work directly with an array. The main intent of the design is for a “left join” as a “one to many” type of join ( or really a … Read more