{
"name": "Grieg And Sibelius Songs",
"status": 1,
"lastupdate": 1306294550,
"intro": "",
"artist": [
"ar9341cd00668311e0a45217d9fa59cf02",
"ar9341cd00668311e0a45217d9fa59cf0x"
],
"publisher": "(Warner Music)",
"release_date": "2006-05-08"
}
I want to find the the data that artist column "ar9341cd00668311e0a45217d9fa59cf02" in array("ar9341cd00668311e0a45217d9fa59cf02","ar9341cd00668311e0a45217d9fa59cf0d","ar9341cd00668311e0a45217d9fa59cf0r") what should I do?
You can use $exists to check the presence of the item in array,
db.yourcollectionname.find({artist: {$exists:'ar9341cd00668311e0a45217d9fa59cf02'}})
Related
I had to change one of the fields of my collection in mongoDB from an object to array of objects containing a lot of data. New documents get inserted without any problem, but when attempted to get old data, it never maps to the original DTO correctly and runs into errors.
subject is the field that was changed in Students collection.
I was wondering is there any way to update all the records so they all have the same data type, without losing any data.
The old version of Student:
{
"_id": "5fb2ae251373a76ae58945df",
"isActive": true,
"details": {
"picture": "http://placehold.it/32x32",
"age": 17,
"eyeColor": "green",
"name": "Vasquez Sparks",
"gender": "male",
"email": "vasquezsparks#orbalix.com",
"phone": "+1 (962) 512-3196",
"address": "619 Emerald Street, Nutrioso, Georgia, 6576"
},
"subject":
{
"id": 0,
"name": "math",
"module": {
"name": "Advanced",
"semester": "second"
}
}
}
This needs to be updated to the new version like this:
{
"_id": "5fb2ae251373a76ae58945df",
"isActive": true,
"details": {
"picture": "http://placehold.it/32x32",
"age": 17,
"eyeColor": "green",
"name": "Vasquez Sparks",
"gender": "male",
"email": "vasquezsparks#orbalix.com",
"phone": "+1 (962) 512-3196",
"address": "619 Emerald Street, Nutrioso, Georgia, 6576"
},
"subject": [
{
"id": 0,
"name": "math",
"module": {
"name": "Advanced",
"semester": "second"
}
},
{
"id": 1,
"name": "history",
"module": {
"name": "Basic",
"semester": "first"
}
},
{
"id": 2,
"name": "English",
"module": {
"name": "Basic",
"semester": "second"
}
}
]
}
I understand there might be a way to rename old collection, create new and insert data based on old one in to new one. I was wondering for some direct way.
The goal is to turn subject into an array of 1 if it is not already an array, otherwise leave it alone. This will do the trick:
update args are (predicate, actions, options).
db.foo.update(
// Match only those docs where subject is an object (i.e. not turned into array):
{$expr: {$eq:[{$type:"$subject"},"object"]}},
// Actions: set subject to be an array containing $subject. You MUST use the pipeline version
// of the update actions to correctly substitute $subject in the expression!
[ {$set: {subject: ["$subject"] }} ],
// Do this for ALL matches, not just first:
{multi:true});
You can run this converter over and over because it will ignore converted docs.
If the goal is to convert and add some new subjects, preserving the first one, then we can set up the additional subjects and concatenate them into one array as follows:
var mmm = [ {id:8, name:"CORN"}, {id:9, name:"DOG"} ];
rc = db.foo.update({$expr: {$eq:[{$type:"$subject"},"object"]}},
[ {$set: {subject: {$concatArrays: [["$subject"], mmm]} }} ],
{multi:true});
I have a series of deeply nested json strings in a pyspark dataframe column. I need to explode and filter based on the contents of these strings and would like to add them as columns. I've tried defining the StructTypes but each time it continues to return an empty DF.
Tried using json_tuples to parse but there are no common keys to rejoin the dataframes and the row numbers dont match up? I think it might have to do with some null fields
The sub field can be nullable
Sample JSON
{
"TIME": "datatime",
"SID": "yjhrtr",
"ID": {
"Source": "Person",
"AuthIFO": {
"Prov": "Abc",
"IOI": "123",
"DETAILS": {
"Id": "12345",
"SId": "ABCDE"
}
}
},
"Content": {
"User1": "AB878A",
"UserInfo": "False",
"D": "ghgf64G",
"T": "yjuyjtyfrZ6",
"Tname": "WE ARE THE WORLD",
"ST": null,
"TID": "BPV 1431: 1",
"src": "test",
"OT": "test2",
"OA": "test3",
"OP": "test34
},
"Test": false
}
I have a big issue, i don't know what to do...
What I wanna is to find all objects with Object2 name. I have Object 2 with name element.
What I wanna is to find all objects with the value X in the element name inside Object2. in the example is the value name is ="IWANTALLOBJECTSWITHTHISNAME"
the Json structure.
"objects": [
{
"_id": "5c69a62cf9acf00d00dbc02d",
"date": "2222-02-24T00:00:00.000Z",
"description": "22",
"Object1": {
"_id": "5c69a62cf9acf00d00dbc02b",
"date": "2222-02-24T00:00:00.000Z",
"user": "5c30fd5890bbd24a1c46c7ee",
"positionsObject1": [
{
"id": 1,
"Object2": {
"_id":"5c69a62cf9acf00d00dbc02c",
"name": "IWANTALLOBJECTSWITHTHISNAME"
},
"description": "22",
"value": 22
}
],
"id": 13,
"__v": 0
},
"user": "5c30fd5890bbd24a1c46c7ee",
"id": 7,
"__v": 0
}
]
I'm new in mongoDB and this query is really really hard. I tried everything. Thank very much for the help.
You can specify the path using dot notation:
db.col.find({ "objects.Object1.positionsObject1.Object2.name": "IWANTALLOBJECTSWITHTHISNAME" })
I'm trying to execute a query like:
{array.0.property: {$ne: null}}.
It return nothing even if all documents have this property different from null.
After some tests i noticed that it work using $elemMatch, but i need to query only for the first element of the array.
The first element is to be considered as "Master" where all query should search.
I can't change document "schema".
Anyone know ho to solve this problem?
I'm using Mongodb 3.6.8.
Thanks in advice.
Example query:
db.getCollection('tasks').find({'details.0.code': {$ne: null}});
Example documents:
{
"name": "test",
"date": 2018-07-17 06:30:00.000Z,
.....,
"details": [
{
"code": '123',
"description": 'something',
"resolutionYear": 2018
},
{
"code": null,
"description": 'secondary',
"resolutionYear": 2019
}
]
},
{
"name": "exam",
"date": 2018-09-20 09:00:00.000Z,
.....,
"details": [
{
"code": null,
"description": 'exam',
"resolutionYear": null
}
]
}
I have some documents in the "company" collection structured this way :
[
{
"company_name": "Company 1",
"contacts": {
"main": {
"email": "main#company1.com",
"name": "Mainuser"
},
"store1": {
"email": "store1#company1.com",
"name": "Store1 user"
},
"store2": {
"email": "store2#company1.com",
"name": "Store2 user"
}
}
},
{
"company_name": "Company 2",
"contacts": {
"main": {
"email": "main#company2.com",
"name": "Mainuser"
},
"store1": {
"email": "store1#company2.com",
"name": "Store1 user"
},
"store2": {
"email": "store2#company2.com",
"name": "Store2 user"
}
}
}
]
I'm trying to retrieve the doc that have store1#company2.com as a contact but cannot find how to query a specific value of a specific propertie of an "indexed" list of objects.
My feeling is that the contacts lists should not not be indexed resulting in the following structure :
{
"company_name": "Company 1",
"contacts": [
{
"email": "main#company1.com",
"name": "Mainuser",
"label": "main"
},
{
"email": "store1#company1.com",
"name": "Store1 user",
"label": "store1"
},
{
"email": "store2#company1.com",
"name": "Store2 user",
"label": "store2"
}
]
}
This way I can retrieve matching documents through the following request :
db.company.find({"contacts.email":"main#company1.com"})
But is there anyway to do a similar request on document using the previous structure ?
Thanks a lot for your answers!
P.S. : same question for documents structured this way :
{
"company_name": "Company 1",
"contacts": {
"0": {
"email": "main#company1.com",
"name": "Mainuser"
},
"4": {
"email": "store1#company1.com",
"name": "Store1 user"
},
"1": {
"email": "store2#company1.com",
"name": "Store2 user"
}
}
}
Short answer: yes, they can be queried but it's probably not what you want and it's not going to be really efficient.
The document structure in the first and third block is basically the same - you have an embedded document. The only difference between are the name of the keys in the contacts object.
To query document with that kind of structure you will have to do a query like this:
db.company.find({ $or : [
{"contacts.main.email":"main#company1.com"},
{"contacts.store1.email":"main#company1.com"},
{"contacts.store2.email":"main#company1.com"}
]});
This query will not be efficient, especially if you have a lot of keys in the contacts object. Also, creating a query will be unnecessarily difficult and error prone.
The second document structure, with an array of embedded objects, is optimal. You can create a multikey index on the contacts array which will make your query faster. The bonus is that you can use a short and simple query.
I think the easiest is really to shape your document using the structure describe in your 2nd example : (I have not fixed the JSON)
{
"company_name": "Company 1",
"contacts":{[
{"email":"main#company1.com","name":"Mainuser", "label": "main", ...}
{"email":"store1#company1.com","name":"Store1 user", "label": "store1",...}
{"email":"store2#company1.com","name":"Store2 user", "label": "store2",...}
]}
}
like that you can easily query on email independently of the "label".
So if you really want to use the other structure, (but you need to fix the JSON too) you will have to write more complex code/aggregation pipeline, since we do not know the name and number of attributes when querying the system. Theses structures are also probably hard to use by the developers independently of MongoDB queries.
Since it was not clear let me show what I have in mind
db.company.save(
{
"company_name": "Company 1",
"contacts":[
{"email":"main#company1.com","name":"Mainuser", "label": "main"},
{"email":"store1#company1.com","name":"Store1 user", "label": "store1"},
{"email":"store2#company1.com","name":"Store2 user", "label": "store2"}
]
}
);
db.company.save(
{
"company_name": "Company 2",
"contacts":[
{"email":"main#company2.com","name":"Mainuser", "label": "main"},
{"email":"store1#company2.com","name":"Store1 user", "label": "store1"},
{"email":"store2#company2.com","name":"Store2 user", "label": "store2"}
]
}
);
db.company.ensureIndex( { "contacts.email" : 1 } );
db.company.find( { "contacts.email" : "store1#company2.com" } );
This allows you to store many emails, and query with an index.