Are there computed fields in MongoDB? [duplicate] - mongodb

Question

This question already has answers here:
MongoDB - The argument to $size must be an Array, but was of type: EOO / missing
(3 answers)
Closed 5 years ago.
Are there computed fields in MongoDB?
In SQL I can do:
SELECT A+B AS C FROM MYTABLE WHERE C>10
Can I do something similar in MongoDB?
UPDATE
I did with projection:
db.segments.aggregate(
[
{
$project: {
"_id": 1,
numberOfRestrictions: { $size: "$Speed Restrictions" }
}
}
]
)
and it works.
Unfortunately, further pipelining does not:
db.segments.aggregate(
[
{
$project: {
"_id": 1,
numberOfRestrictions: { $size: "$Speed Restrictions" }
}
},
{
$match: {
"numberOfRestrictions": {
"$gt": 1
}
}
}
]
)
Latter causes error
The argument to $size must be an Array, but was of type: EOO

Answer 1

Yes. It is called aggregation pipelines. Specifically, you need to use a $project stage to create the C field, and then use a $match stage to find all documents which match the criterion.
Example
Let's create some documents first:
for( var i = 1; i <=10; i++){
db.agg.insert({a:i,b:i})
}
Which results in a collection looking like this:
> db.agg.find()
{ "_id" : ObjectId("56c1b5561a3b578f37a99d4d"), "a" : 1, "b" : 1 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d4e"), "a" : 2, "b" : 2 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d4f"), "a" : 3, "b" : 3 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d50"), "a" : 4, "b" : 4 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d51"), "a" : 5, "b" : 5 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d52"), "a" : 6, "b" : 6 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d53"), "a" : 7, "b" : 7 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d54"), "a" : 8, "b" : 8 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d55"), "a" : 9, "b" : 9 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d56"), "a" : 10, "b" : 10 }
Finding all documents for which C > 10
db.agg.aggregate([
// You need to include all fields you want to have
// in the resulting document within the $project stage
{ "$project":{ a:1, b:1, c:{ "$add": ["$a","$b"] }}},
{ "$match":{ c:{ "$gt":10 }}}
])
Returns the following result:
{ "_id" : ObjectId("56c1b5561a3b578f37a99d52"), "a" : 6, "b" : 6, "c" : 12 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d53"), "a" : 7, "b" : 7, "c" : 14 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d54"), "a" : 8, "b" : 8, "c" : 16 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d55"), "a" : 9, "b" : 9, "c" : 18 }
{ "_id" : ObjectId("56c1b5561a3b578f37a99d56"), "a" : 10, "b" : 10, "c" : 20 }

Answer 2

There is an operator called $expr that enables you to use aggregation framework operator inside the find() query.
For instance, the SQL query
SELECT A+B AS C FROM MYTABLE WHERE C>10
can be translated to a mongo query as
db.segments.find({
"$expr": {
"$gt": [
{ "$add": [ "$A", "$B" ] },
10
]
}
})
And for checking an array length it's similar
db.segments.find({
"$expr": {
"$gt": [
{ "$size": "$SpeedRestrictions" },
10
]
}
})
With the aggregation framework it's also possible to use $expr within a $match pipeline step:
db.segments.aggregate([
{ "$match": {
"$expr": {
{ "$gt": [
{ "$size": "$SpeedRestrictions" },
10
] }
}
} }
])
And if the $expr operator is not available, for backwards compatibility one can use $redact as
db.segments.aggregate([
{ "$redact": {
"$cond": [
{ "$gt": [
{ "$size": "$SpeedRestrictions" },
10
] },
"$$KEEP",
"$$PRUNE"
]
} }
])
The other approach is to use the $addFields pipeline operator for creating the computed fields and the $match operator for filtering documents based on that computed field:
db.collection.aggregate([
{ "$addFields": { "C": { "$add": [ "$A", "$B" ] } } },
{ "$match": { "C": { "$gt": 10 } } }
])