I have MongoDB documents in the following format which represent a location:
{
"_id": "632987336a2913f401318f82",
"name": "1 Ave, Infinite Loop St",
"timezone": "Europe/Paris",
"location": {
"lat": 48.858093,
"lng": 2.294694
},
"detections": [
{
"approach": "North Bound",
"class": "Person",
"datetime": "05:00 pm",
"lane": 3,
"movement": "Through"
},
{
"approach": "North Bound",
"class": "Person",
"datetime": "05:00 pm",
"lane": 3,
"movement": "Through"
},
// ...more detections
]
}
I know the _id of the site that I want to fetch but what I want to do is that when I fetch this site, I only need the detections from the last 7 days using the datetime property in the detections object. However, I am unsure of how to achieve this using mongoose. Currently, I am trying to achieve this manually by just fetching all the site detections and then filtering them by myself but this is a tedious approach. Currently, this is how I am fetching the site detections:
const site = await SiteModel.findById(siteID);
const detections = site.detections;
This answer tells of a way to implement this behavior but this is using aggregate and in my case the key is nested in an array of objects. I am new to MongoDB so any help is appreciated.+
you need to store timestamps in "datetime" fields.
you can use this $elemMatch to filter specific data from the array of objects.
Related
I have a collection in my MongoDB that contains one field that is an array.
Refer to the data above, the field 'Courses' is an array.
The JSON format of the data is like this:
{
"_id": {
"$oid": "60eb59b98a970a20865142e8"
},
"Name": "Sadia",
"Age": 24,
"Institute": "IBA",
"Courses": [{
"Name": "ITP",
"Grade": "A-"
}, {
"Name": "OOP",
"Grade": "A-"
}]
}
I am aware that there is a way in case its an object, but could not find a way on how to read this data using Talend since it contains an array.
I am having trouble with querying a MongoDB collection with an array inside.
Here is the structure of my collection that I am querying. This is one record:
{
"_id": "abc123def4567890",
"profile_id": "abc123def4567890",
"image_count": 2,
"images": [
{
"image_id": "ABC123456789",
"image_url": "images/something.jpg",
"geo_loc": "-0.1234,11.234567890",
"title": "A Title",
"shot_time": "01:23:33",
"shot_date": "11/22/2222",
"shot_type": "scenery",
"conditions": "cloudy",
"iso": 16,
"f": 2.4,
"ss": "1/545",
"focal": 6.0,
"equipment": "",
"instructions": "",
"upload_date": 1234567890,
"update_date": 1234567890
},
{
"image_id": "ABC123456789",
"image_url": "images/something.jpg",
"geo_loc": "-0.1234,11.234567890",
"title": "A Title",
"shot_time": "01:23:33",
"shot_date": "11/22/2222",
"shot_type": "portrait",
"conditions": "cloudy",
"iso": "16",
"f": "2.4",
"ss": "1/545",
"focal": "6.0",
"equipment": "",
"instructions": "",
"upload_date": 1234567890,
"update_date": 1234567890
}
]
}
Forgive the formatting, I didn't know how else to show this.
As you can see, it's a profile with a series of images within an array called 'images' and there are 2 images. Each of the 'images' array items contain an object of attributes for the image (url, title, type, etc).
All I want to do is to return the object element whose attributes match certain criteria:
Select object from images which has shot_type = "scenery"
I tried to make it as simple as possible so i started with:
find( { "images.shot_type": "scenery" } )
This returns the entire record and both the images within. So I tried projection but I could not isolate the single object within the array (in this case object at position 0) and return it.
I think the answer lies with projection but I am unsure.
I have gone through the MongoDB documents for hours now and can't find inspiration. I have read about $elemMatch, $, and the other array operators, nothing seems to allow you to single out an array item based on data within. I have been through this page too https://docs.mongodb.com/manual/tutorial/query-arrays/ Still can't work it out.
Can anyone provide help?
Have I made an error by using '$push' to populate my images field (making it an array) instead of using '$set' which would have made it into an embedded document? Would this have made a difference?
Using aggregation:
db.collection.aggregate({
$project: {
_id: 0,
"result": {
$filter: {
input: "$images",
as: "img",
cond: {
$eq: [
"$$img.shot_type",
"scenery"
]
}
}
}
}
})
Playground
You can use $elemMatch in this way (simplified query):
db.collection.find({
"profile_id": "1",
},
{
"images": {
"$elemMatch": {
"shot_type": 1
}
}
})
You can use two objects into find query. The first will filter all document and will only get those whose profile_id is 1. You can omit this stage and use only { } if you wnat to search into the entire collection.
Then, the other object uses $elemMatch to get only the element whose shot_type is 1.
Check an example here
Imagine the following collection of city records:
{
"city": "London",
"inhabitants": [
{
"id": "34543534",
"user": {
"name": "Jonathan Deer",
"email": "john#btinternet.com"
}
},
{
"id": "0454534",
"user": {
"name": "Tanya Patel",
"email": "tanya#btinternet.com"
}
},
{
"id": "4345345",
"user": {
"name": "Catherine King",
"email": "catherine#gmail.com"
}
}
]
}
{
"city": "Manchester",
"inhabitants": [
{
"id": "980003",
"user": {
"name": "Benjamin Thaw",
"email": "benny#btinternet.com"
}
},
{
"id": "734488",
"user": {
"name": "Craig Longstone",
"email": "craig#gmail.com"
}
},
{
"id": "4400093",
"user": {
"name": "Arnold Greentree",
"email": "arnold#btinternet.com"
}
},
]
},
What I'm trying to do is loop through each inhabitants array of each city, and see if any of the people there has an email address containing btinternet.com in it. For those users I want to sent a new flag isBT: true and for everyone else (e.g., gmail.com users) isBT: false:
"user": {
"name": "Tanya Patel",
"email": "tanya#btinternet.com"
"isBT" true
}
I tried the following queries - first query sets all of them to isBT: false while the second one searches for "btinternet.com" in email address and sets isBT: true:
db.city.update({ "inhabitants.user.email": {$exists: true}}, {$set: { "inhabitants.$.user.isBT": false}}, {multi: true})
db.city.update({ "inhabitants.user.email": {$regex: "btinternet.com"}}, {$set: { "inhabitants.$.user.isBT": true}}, {multi: true})
The problem is that when I execute the second query, there are several inhabitants records that are left with isBT: false even though they contain the necessary "btinternet.com" email address. It almost seems like only the first user record that matches the criteria gets updated... Is there a way to update ALL user records for all "inhabitants" arrays?
I looked at using the positional operator $[], but our DB is on version 2.6.3 but this operator was introduced only in 3.6...
The short answer is "no".
The long answer is "no, because your MongoDB version doesn't support such an operation". You'll need to either...
1. retrieve all matching documents and perform a full array update through server-side processing of the data (e.g. use the MongoDB cursor.forEach()),
2. extend your match for "inhabitants.user.isBT": true (use
$elemMatch) and repeatedly perform the update query until the
number of modified documents is 0 (i.e. there are no more array
elements to update), or
3. update your MongoDB version and any
server-side code that relies on features of the current version that
have changed between 2.6 and 3.6.
Any solution to this problem will require more effort than a single query. There's no getting around it. It's a tough pill to swallow, but there really isn't a nice alternative.
I have a noSql (Cloudant) database
-Within the database we have documents where one of the document fields represents “table” (type of document)
-Within the documents we have fields that represent links other documents within the database
For example:
{_id: 111, table:main, user_id:222, field1:value1, other1_id: 333}
{_id: 222, table:user, first:john, other2_id: 444}
{_id: 333, table:other1, field2:value2}
{_id: 444, table:other2, field3:value3}
We want of way of searching for _id:111
And the result be one document with data from linked tables:
{_id:111, user_id:222, field1:value1, other1_id: 333, first:john, other2_id: 444, field2:value2, field3:value3}
Is there a way to do this?
There is flexibility on the structure of how we store or get the data back—any suggestions on how to better structure the data to make this possible?
The first thing to say is that there are no joins in Cloudant. If you're schema relies on lots of joining then you're working against the grain of Cloudant which may mean extra complication for you or performance hits.
There is a way to de-reference other documents' ids in a MapReduce view. Here's how it works:
create a MapReduce view to emit the main document's body and its linked document's ids in the form { _id: 'linkedid'}
query the view with include_docs=true to pull back the document AND the de-referenced ids in one go
In your case, a map function like this:
function(doc) {
if (doc.table === 'main') {
emit(doc._id, doc);
if (doc.user_id) {
emit(doc._id + ':user', { _id: doc.user_id });
}
}
}
would allow you to pull back the main document and its linked user document in one API by hitting the GET /mydatabase/_design/mydesigndoc/_view/myview?startkey="111"&endkey="111z"&include_docs=true endpoint:
{
"total_rows": 2,
"offset": 0,
"rows": [
{
"id": "111",
"key": "111",
"value": {
"_id": "111",
"_rev": "1-5791203eaa68b4bd1ce930565c7b008e",
"table": "main",
"user_id": "222",
"field1": "value1",
"other1_id": "333"
},
"doc": {
"_id": "111",
"_rev": "1-5791203eaa68b4bd1ce930565c7b008e",
"table": "main",
"user_id": "222",
"field1": "value1",
"other1_id": "333"
}
},
{
"id": "111",
"key": "111:user",
"value": {
"_id": "222"
},
"doc": {
"_id": "222",
"_rev": "1-6a277581235ca01b11dfc0367e1fc8ca",
"table": "user",
"first": "john",
"other2_id": "444"
}
}
]
}
Notice how we get two rows back, the first is the main document body, the second the linked user.
I'm trying to figure out what the best schema is for a dating site like app. User's have a listing (possibly many) and they can view other user listings to 'like' and 'dislike' them.
Currently i'm just storing the other persons listing id in a likedBy and dislikedBy array. When a user 'likes' a listing, it puts their listing id into the 'liked' listings arrays. However I would now like to track the timestamp that a user likes a listing. This would be used for a user's 'history list' or for data analysis.
I would need to do two separate queries:
find all active listings that this user has not liked or disliked before
and for a user's history of 'liked'/'disliked' choices
find all the listings user X has liked in chronological order
My current schema is:
listings
_id: 'sdf3f'
likedBy: ['12ac', 'as3vd', 'sadf3']
dislikedBy: ['asdf', 'sdsdf', 'asdfas']
active: bool
Could I do something like this?
listings
_id: 'sdf3f'
likedBy: [{'12ac', date: Date}, {'ds3d', date: Date}]
dislikedBy: [{'s12ac', date: Date}, {'6fs3d', date: Date}]
active: bool
I was also thinking of making a new collection for choices.
choices
Id
userId // id of current user making the choice
userlistId // listing of the user making the choice
listingChoseId // the listing they chose yes/no
type
date
I'm not sure of the performance implications of having these choices in another collection when doing the find all active listings that this user has not liked or disliked before.
Any insight would be greatly appreciated!
Well you obviously thought it was a good idea to have these embedded in the "listings" documents so your additional usage patterns to the cases presented here worked properly. With that in mind there is no reason to throw that away.
To clarify though, the structure you seem to want is something like this:
{
"_id": "sdf3f",
"likedBy": [
{ "userId": "12ac", "date": ISODate("2014-04-09T07:30:47.091Z") },
{ "userId": "as3vd", "date": ISODate("2014-04-09T07:30:47.091Z") },
{ "userId": "sadf3", "date": ISODate("2014-04-09T07:30:47.091Z") }
],
"dislikedBy": [
{ "userId": "asdf", "date": ISODate("2014-04-09T07:30:47.091Z") },
{ "userId": "sdsdf", "date": ISODate("2014-04-09T07:30:47.091Z") },
{ "userId": "asdfas", "date": ISODate("2014-04-09T07:30:47.091Z") }
],
"active": true
}
Which is all well and fine except that there is one catch. Because you have this content in two array fields you would not be able to create an index over both of those fields. That is a restriction where only one array type of field (or multikey) can be be included within a compound index.
So to solve the obvious problem with your first query not being able to use an index, you would structure like this instead:
{
"_id": "sdf3f",
"votes": [
{
"userId": "12ac",
"type": "like",
"date": ISODate("2014-04-09T07:30:47.091Z")
},
{
"userId": "as3vd",
"type": "like",
"date": ISODate("2014-04-09T07:30:47.091Z")
},
{
"userId": "sadf3",
"type": "like",
"date": ISODate("2014-04-09T07:30:47.091Z")
},
{
"userId": "asdf",
"type": "dislike",
"date": ISODate("2014-04-09T07:30:47.091Z")
},
{
"userId": "sdsdf",
"type": "dislike",
"date": ISODate("2014-04-09T07:30:47.091Z")
},
{
"userId": "asdfas",
"type": "dislike",
"date": ISODate("2014-04-09T07:30:47.091Z")
}
],
"active": true
}
This allows an index that covers this form:
db.post.ensureIndex({
"active": 1,
"votes.userId": 1,
"votes.date": 1,
"votes.type": 1
})
Actually you will probably want a few indexes to suit your usage patterns, but the point is now can have indexes you can use.
Covering the first case you have this form of query:
db.post.find({ "active": true, "votes.userId": { "$ne": "12ac" } })
That makes sense considering that you clearly are not going to have both an like and dislike option for each user. By the order of that index, at least active can be used to filter because your negating condition needs to scan everything else. No way around that with any structure.
For the other case you probably want the userId to be in an index before the date and as the first element. Then your query is quite simple:
db.post.find({ "votes.userId": "12ac" })
.sort({ "votes.userId": 1, "votes.date": 1 })
But you may be wondering that you suddenly lost something in that getting the count of "likes" and "dislikes" was as easy as testing the size of the array before, but now it's a little different. Not a problem that cannot be solved using aggregate:
db.post.aggregate([
{ "$unwind": "$votes" },
{ "$group": {
"_id": {
"_id": "$_id",
"active": "$active"
},
"likes": { "$sum": { "$cond": [
{ "$eq": [ "$votes.type", "like" ] },
1,
0
]}},
"dislikes": { "$sum": { "$cond": [
{ "$eq": [ "$votes.type", "dislike" ] },
1,
0
]}}
])
So whatever your actual usage form you can store any important parts of the document to keep in the grouping _id and then evaluate the count of "likes" and "dislikes" in an easy manner.
You may also not that changing an entry from like to dislike can also be done in a single atomic update.
There is much more you can do, but I would prefer this structure for the reasons as given.