I want grab all under documents via Id's like the following SQL query:
Select * from table
where subdoc1.id in (1234, 2345, 3456)
or subdoc2.id in (1234, 2345, 3456)
Here is the structure of my document:
{
"id" : new ObjectId("234565"),
"subdocs1" : [ {
"id" : "1234",
... },
{
"id": "2345",
... }],
"subdocs2" : [ {
"id" : "3456",
... },
{
"id": "7890",
... }]
}
How would the query look like in MongoDB?
db.collection.find({
$and : [{"subdocs1._id": {"$in" : ["1234", "2345", "3456"]},
{"subdocs2._id": {"$in" : ["1234", "2345", "3456"]}}]
})
OR
db.collection.find({
$and : [{"subdocs1":{"$elemMatch" : {"$_id" : {"$in" : ["1234", "2345", "3456"]}}}},
{"subdocs2:{"$elemMatch" : {"$_id" : {"$in" : ["1234", "2345", "3456"]}}}}]
})
Here my query, that works:
db.getCollection('documents').find({
$or : [
{ "subdoc1._id" : { "$in" : [ new ObjectId("5807a60afc84c80a5ea86218"), new ObjectId("5808ac5cfc841deda17f827b") ] } },
{ "subdoc2._id" : { "$in" : [ new ObjectId("5807a60afc84c80a5ea86218"), new ObjectId("5808ac5cfc841deda17f827b") ] } },
{ "subdoc3._id" : { "$in" : [ new ObjectId("5807a60afc84c80a5ea86218"), new ObjectId("5808ac5cfc841deda17f827b") ] } },
{ "subdoc4._id" : { "$in" : [ new ObjectId("5807a60afc84c80a5ea86218"), new ObjectId("5808ac5cfc841deda17f827b") ] } }
]})
Thanx!!
Related
I have something like below:
{
"_id" : "1",
"firstArray" : [
{
"_id" : "11",
"secondArray" : [ ]
},
{
"_id" : "12",
"secondArray" : [ ]
},
{
"_id" : "13",
"secondArray" : [ { "type" : "somthing" } ]
}
]
},
{
"_id" : "2",
"firstArray" : [
{
"_id" : "21",
"secondArray" : [ ]
},
{
"_id" : "22",
"secondArray" : [ ]
}
]
}
I need a mongodb query to find documents which ALL of the nested secondArrays are empty? the query should return second document and not the first one.
to solve that, we need to check size of arr2, but to enable that we need first to unwind arr1.
Please find below aggregation framework snippet which solves this problem,
db.pmoubed.aggregate([{
$unwind : "$firstArray"
}, {
$project : {
_id : 1,
firstArray : 1,
isNotEmpty : {
$size : "$firstArray.secondArray"
}
}
}, {
$group : {
_id : "$_id",
isNotEmpty : {
$sum : "$isNotEmpty"
},
firstArray : {
$push : "$firstArray"
}
}
}, {
$match : {
"isNotEmpty" : 0
}
}
])
Any comments welcome
Given this dataset and this mongodb, how to properly convert this aggregation into Mongoose?
I have included, the code using mongoose, which works but I want to know if this is the right way of doing it and that if this aggregation can be improved?
Thanks.
db.cars.aggregate(
//De-normalized the nested array of accounts
{"$unwind": "$accounts"},
//De-normalized the nested array of cars
{"$unwind": "$accounts.cars"},
//match carId to 3C
{"$match": {"accounts.cars.carId" : "3C"}},
//Project the accounts.cars object only
{"$project" : {"accounts.cars" : 1}}
).pretty();
The Mongoose version that I'm trying to improve:
Car.aggregate()
.unwind('accounts')
.unwind('accounts.cars')
.match({'accounts.cars.carId' : "3C"})
.project({"accounts.cars": 1, _id: 0})
.exec(function (err, carsObj) {});
and the dataset (cars):
{
"_id" : ObjectId("56223329b64f07a40ef1c15c"),
"username" : "john",
"email" : "john#john.com",
"accounts" : [
{
"_id" : ObjectId("56322329b61f07a40ef1c15d"),
"cars" : [
{
"carId" : "6A",
"_id" : ObjectId("56323329b64f07a40ef1c15e")
},
{
"carId" : "6B",
"_id" : ObjectId("56323329b64f07a40ef1c15e")
}
]
}
]
},
{
"_id" : ObjectId("56223125b64f07a40ef1c15c"),
"username" : "paul",
"email" : "paul#paul.com",
"accounts" : [
{
"_id" : ObjectId("5154729b61f07a40ef1c15d"),
"cars" : [
{
"carId" : "5B",
"_id" : ObjectId("56323329854f07a40ef1c15e")
}
]
},
{
"_id" : ObjectId("56322117b61f07a40ef1c15d"),
"cars" : [
{
"carId" : "6G",
"_id" : ObjectId("51212929b64f07a40ef1c15e")
},
{
"carId" : "3C",
"_id" : ObjectId("51273329b64f07a40ef1c15e")
},
{
"carId" : "4N",
"_id" : ObjectId("51241279b64f07a40ef1c15e")
}
]
}
]
}
What the aggregation returns is:
[
{ accounts:
{ cars:
{
"carId" : "3C",
"_id" : ObjectId("51273329b64f07a40ef1c15e")
}
}
}
]
I have the collection named companies as below.
I want to query the url corresponding to C1S1category1 . I do not know which companyName it belongs and which catergories it belongs to.
Please can you let me know what query I need to use in Mongoshell to query the document having catergoryname as C1S1category1
{"companyName": "C1",
"url": "www.com1",
"categories" : [
{"SlNo" : 1,
"url" : "www.com1",
"subcategories" : [
{
"CatergoryName":"C1S1category1",
"Url" : "www.com3"
},
{
"CatergoryName":"C1S1category2",
"Url" : "www.com3"
}
]
},
{
"SlNo" : 2,
"url" : "www.com1",
"subcategories" : [
{
"CatergoryName":"C1S2category1",
"Url" : "www.com3"
},
{
"CatergoryName":"C1S2category2",
"Url" : "www.com3"
}
]
}
]
},
{"companyName": "C2",
"url": "www.com21",
"categories" : [
{"SlNo" : 1,
"url" : "www.com22",
"subcategories" : [
{
"CatergoryName":"C2S1category1",
"Url" : "www.com23"
},
{
"CatergoryName":"C2S1category2",
"Url" : "www.com23"
}
]
},
{
"SlNo" : 2,
"url" : "www.com1",
"subcategories" : [
{
"CatergoryName":"C2S2category1",
"Url" : "www.com23"
},
{
"CatergoryName":"C2S2category2",
"Url" : "www.com23"
}
]
}
]
}
You need to use $elemMatch to get required output as following:
db.collection.find({
"categories": {
$elemMatch: {
subcategories: {
$elemMatch: {
CatergoryName: "C1S1category1"
}
}
}
}
},{"categories.$":1,"companyName":1,"url":1}).pretty()
I have the below SQL query,
SELECT *
FROM [TestDB].[dbo].[EmpTbl] WHERE
(EmpDept='IT' and EmpDept='Account')
OR (EmpDept='HR' or EmpDept='Finance')
AND (EmpDept='Sales' or EmpDept='Admin')
How can I write the equivalent query for mongoDB?
As per your question I don't understand your document structure but as per your question I created following documents
{ "_id" : ObjectId("547bfbc78849d6c59671b8e3"), "EmpDept" : "IT" }
{ "_id" : ObjectId("547bfbce8849d6c59671b8e4"), "EmpDept" : "Account" }
{ "_id" : ObjectId("547bfbd48849d6c59671b8e5"), "EmpDept" : "HR" }
{ "_id" : ObjectId("547bfbdb8849d6c59671b8e6"), "EmpDept" : "Finance" }
{ "_id" : ObjectId("547bfbe38849d6c59671b8e7"), "EmpDept" : "Sales" }
{ "_id" : ObjectId("547bfbeb8849d6c59671b8e8"), "EmpDept" : "Admin" }
And query for obtaining result as below
db.collectionName.find({
"$and": [
{
"EmpDept": "IT",
"EmpDept": "Account"
},
{
"$or": [
{
"$or": [
{
"EmpDept": "HR",
"EmpDept": "Finance"
}
]
},
{
"$and": [
{
"EmpDept": "Sales",
"EmpDept": "Admin"
}
]
}
]
}
]
})
abstract document in collection md given:
{
vals : [{
uid : string,
val : string|array
}]
}
the following, partially correct aggregation is given:
db.md.aggregate(
{ $unwind : "$vals" },
{ $match : { "vals.uid" : { $in : ["x", "y"] } } },
{
$group : {
_id : { uid : "$vals.uid" },
vals : { $addToSet : "$vals.val" }
}
}
);
that may lead to the following result:
"result" : [
{
"_id" : {
"uid" : "x"
},
"vals" : [
[
"24ad52bc-c414-4349-8f3a-24fd5520428e",
"e29dec2f-57d2-43dc-818a-1a6a9ec1cc64"
],
[
"5879b7a4-b564-433e-9a3e-49998dd60b67",
"24ad52bc-c414-4349-8f3a-24fd5520428e"
]
]
},
{
"_id" : {
"uid" : "y"
},
"vals" : [
"0da5fcaa-8d7e-428b-8a84-77c375acea2b",
"1721cc92-c4ee-4a19-9b2f-8247aa53cfe1",
"5ac71a9e-70bd-49d7-a596-d317b17e4491"
]
}
]
as x is the result aggregated on documents containing an array rather than a string, the vals in the result is an array of arrays. what i look for in this case is to have a flattened array (like the result for y).
for me it seems like that what i want to achieve by one aggegration call only, is currently not supported by any given operation as e.g. a type conversion cannot be done or unwind expectes in every case an array as input type.
is map reduce the only option i have? if not ... any hints?
thanks!
You can use the aggregation to do the computation you want without changing your schema (though you might consider changing your schema simply to make queries and aggregations of this field easier to write).
I broke up the pipeline into multiple steps for readability. I also simplified your document slightly, again for readability.
Sample input:
> db.md.find().pretty()
{
"_id" : ObjectId("512f65c6a31a92aae2a214a3"),
"uid" : "x",
"val" : "string"
}
{
"_id" : ObjectId("512f65c6a31a92aae2a214a4"),
"uid" : "x",
"val" : "string"
}
{
"_id" : ObjectId("512f65c6a31a92aae2a214a5"),
"uid" : "y",
"val" : "string2"
}
{
"_id" : ObjectId("512f65e8a31a92aae2a214a6"),
"uid" : "y",
"val" : [
"string3",
"string4"
]
}
{
"_id" : ObjectId("512f65e8a31a92aae2a214a7"),
"uid" : "z",
"val" : [
"string"
]
}
{
"_id" : ObjectId("512f65e8a31a92aae2a214a8"),
"uid" : "y",
"val" : [
"string1",
"string2"
]
}
Pipeline stages:
> project1 = {
"$project" : {
"uid" : 1,
"val" : 1,
"isArray" : {
"$cond" : [
{
"$eq" : [
"$val.0",
[ ]
]
},
true,
false
]
}
}
}
> project2 = {
"$project" : {
"uid" : 1,
"valA" : {
"$cond" : [
"$isArray",
"$val",
[
null
]
]
},
"valS" : {
"$cond" : [
"$isArray",
null,
"$val"
]
},
"isArray" : 1
}
}
> unwind = { "$unwind" : "$valA" }
> project3 = {
"$project" : {
"_id" : 0,
"uid" : 1,
"val" : {
"$cond" : [
"$isArray",
"$valA",
"$valS"
]
}
}
}
Final aggregation:
> db.md.aggregate(project1, project2, unwind, project3, group)
{
"result" : [
{
"_id" : "z",
"vals" : [
"string"
]
},
{
"_id" : "y",
"vals" : [
"string1",
"string4",
"string3",
"string2"
]
},
{
"_id" : "x",
"vals" : [
"string"
]
}
],
"ok" : 1
}
If you modify your schema using always "vals.val" field as an array field (even when the record contains only one element) you can do it easily as follows:
db.test_col.insert({
vals : [
{
uid : "uuid1",
val : ["value1"]
},
{
uid : "uuid2",
val : ["value2", "value3"]
}]
});
db.test_col.insert(
{
vals : [{
uid : "uuid2",
val : ["value4", "value5"]
}]
});
Using this approach you only need to use two $unwind operations: one unwinds the "parent" array and the second unwinds every "vals.val" value. So, querying like
db.test_col.aggregate(
{ $unwind : "$vals" },
{ $unwind : "$vals.val" },
{
$group : {
_id : { uid : "$vals.uid" },
vals : { $addToSet : "$vals.val" }
}
}
);
You can obtain your expected value:
{
"result" : [
{
"_id" : {
"uid" : "uuid2"
},
"vals" : [
"value5",
"value4",
"value3",
"value2"
]
},
{
"_id" : {
"uid" : "uuid1"
},
"vals" : [
"value1"
]
}
],
"ok" : 1
}
And no, you can't execute this query using your current schema, since $unwind fails when the field isn't an array field.