I have two Schema defined as below:
var WorksnapsTimeEntry = BaseSchema.extend({
student: {
type: Schema.ObjectId,
ref: \'Student\'
},
timeEntries:
You don't want .populate()
here but instead you want two queries, where the first matches the Student
objects to get the _id
values, and the second will use $in to match the respective WorksnapsTimeEntry
items for those "students".
Using async.waterfall just to avoid some indentation creep:
async.waterfall(
[
function(callback) {
Student.find({ "status": "student" },{ "_id": 1 },callback);
},
function(students,callback) {
WorksnapsTimeEntry.find({
"student": { "$in": students.map(function(el) {
return el._id
})
},callback);
}
],
function(err,results) {
if (err) {
// do something
} else {
// results are the matching entries
}
}
)
If you really must, then you can .populate("student")
on the second query to get populated items from the other table.
The reverse case is to query on WorksnapsTimeEntry
and return "everything", then filter out any null
results from .populate()
with a "match" query option:
WorksnapsTimeEntry.find().populate({
"path": "student",
"match": { "status": "student" }
}).exec(function(err,entries) {
// Now client side filter un-matched results
entries = entries.filter(function(entry) {
return entry.student != null;
});
// Anything not populated by the query condition is now removed
});
So that is not a desirable action, since the "database" is not filtering what is likely the bulk of results.
Unless you have a good reason not to do so, then you probably "should" be "embedding" the data instead. That way the properties like "status
" are already available on the collection and additional queries are not required.
If you are using a NoSQL solution like MongoDB you should be embracing it's concepts, rather than sticking to relational design principles. If you are consistently modelling relationally, then you might as well use a relational database, since you won't be getting any benefit from the solution that has other ways to handle that.