How to Design a Database Schema in MongoDB? - database

I am Newbie working on a social networking site & using MongoDB as Database. Can you please assist me in What approach should I follow in Designing Database?
My Requirements and Objective for Designing Database
The Most common queries are gonna be related to the user interactions like what topics had user followed, how the user interacted with the post, how many times was the post on the newsfeed of the user and at what time for further showing him that post after some time.
My objective is not to calculate Number of likes, number of comments often but what they query I want to get quickly to keep the performance up is just that Is the user interested in the post by checking did he liked it or not.
So should I:
Make Different collections for storing likes, comments, etc containing the reference field of Posts collection & User Collection for the user who interacted.
Ex:
db.like.find()
{_id: , user_id: "the user_id of the user", post_id: [ALL THE POST_ID's USER LIKED]}
db.comment.find()
{_id: , user_id: "the user_id of the user", comments: [{_id: , post_id: , replied_to: [ LIST OF USERS REPLIED IN THE COMMENT], votes: [ LIST OF ALL THE VOTERS] , timestamp: }] }
Making a List of Embedded Documents in a collection with a user_id, post_id, and any interaction he made with the post.
So one document per user. And one embedded document in the list per post interaction.
Ex:
db.all_the_interactions.find()
{_id: , user: ObjectId("1782..."), username: "Mark Alberto" interactions:[
{post_id: ObjectId("222..."), liked: True, "Comment": ["Hello"], timestamp},
{post_id: ObjectId("672..."), liked: False, "Comment": [], timestamp}
]}
{_id: , user: ObjectId("4355..."), username: "Himesh Reshamiya" interactions:[
{post_id: ObjectId("224..."), liked: True, "Comment": [], timestamp},
{post_id: ObjectId("562..."), liked: False, "Comment": ["Nice"], timestamp}
]}
Making an Entire Collection for a user where all his interactions will be stored.{_id, post_id, liked(if he liked), (list of comments that this particular user made(if any)), time of comment(if any comment made)}.
Ex:
db.user_name.find()
{_id, post_id: 432.., liked: True, Comment: ['StackOverflow']}
{_id, post_id: 433.., liked: True, Comment: []}
Storing all the users who liked the post in the list in the posts-collection itself & a list of embedded documents for comments.
Ex:
db.post.find()
{
"_id" : ObjectId("62f7930758bcf38e4f920e87"),
"username" : "Akshat Bafna",
"fact" : "clone()\r\nCreate a copy of the current queryset.clone()\r\nCreate a copy of the current queryset.",
"background" : "mongodb-logo-vector-2022.png",
"creation_date_time" : "08/13/2022, 17:33:19",
"topic": "MongoDB",
"liked":[ALL THE USER_ID's LIKED THIS POST],
"comments":[{_id: , user_id: ,"replied_to": [ LIST OF USERS REPLIED IN THE COMMENT], votes: [ LIST OF ALL THE VOTERS] , timestamp: } ]
}
Store all the Posts user liked in user collection as a list of posts_id
{ "_id" : ObjectId("62dee9d49974d78430d99af6"),
"user_type" : "initiatives",
"username" : "Anand Mahindra",
"password" : "99fb2f48c6af4761f904fc85f967445665445d40b1f44ec3a9c1fa319", "email" :
"mahindraanand#gmail.co",
"last_login" : "N/A",
"last_logout" : "N/A",
"post_liked": [ALL THE POST_ID's USER LIKED],
"comments": [{_id: , post_id: ,"replied_to": [ LIST OF USERS REPLIED IN THE COMMENT], votes: [ LIST OF ALL THE VOTERS] , timestamp: }] }
As MongoDB is a Dynamic Database what should be the right criteria for designing a database for best performance?

The best way to design a schema for that purpose will be using the combination of second and third method if you require all the data that's going to be stored in a document. The document will look like this:
{ "_id" : ObjectId("6323c511a78cf05ddc91d868"), "userReference" : ObjectId("62c1f108449a868937fgg355"), "username" : "Akshat Bafna", "postId" : ObjectId("62f563ed918ea74846d3aa95"), "liked" : false, "lighten" : false, "timesViewed" : 1, "points" : 10, "timeList" : [ "16/09/2022 06:06:33" ] }
{ "_id" : ObjectId("6323c511a78cf05ddc91d869"), "userReference" : ObjectId("62c1f108449a868937edfs7f"), "username" : "Joe Bidden", "postId" : ObjectId("62f7785eb90971094fffe9d8"), "liked" : false, "lighten" : false, "timesViewed" : 1, "points" : 10, "timeList" : [ "16/09/2022 06:06:33" ] }
{ "_id" : ObjectId("6323c511a78cf05ddc91d86a"), "userReference" : ObjectId("62c1f108449a868937fgg355"), "username" : "Akshat Bafna", "postId" : ObjectId("62f7911c4191afb7b90641d6"), "liked" : false, "lighten" : false, "timesViewed" : 1, "points" : 10, "timeList" : [ "16/09/2022 06:06:33" ] }
Inshort Why I prefer this?
Because ever growing arrays with embedded documents will be a performance issue.
Expensive writes & updates.
Document size limit on 16mb in mongodb as ever-growing documents becoz of ever growing arrays will never stop it will be an issue.
Index creation will get expensive.
For more information, see: Why shouldn't I embed large arrays in my documents?.
Also you might have to add some data redundancy for better performance, create ttl indexes with the single user_interactions collection or when using a separate collection for individual user capped collections can be used if it does not violate your requirements.

Related

Firebase Realtime Database orderByChild data structure - ReactJS

I'm having issues querying data with orderByChild. I would like to query all tasks where provided userID matches any approver id. Task ID is autogenerated so I need to flatten this structure somehow.
Each task has two approvers, and each approver has status, timestamp, id.
Any idea on how to flatten it so I can take advantage of orderByChild?
Tasks:{
"df234dgkjsf234" : {
"approvers" : {
"4U1D23dfsdf23e" : {
"id" : "4U1DWf95rJvgfAwDYs7m",
"status" : "pending",
"timestamp" : "pending"
},
"sdf32fdsf34sdg3" : {
"id" : "FXRkK22TjyxKV6z4UkrU",
"status" : "pending",
"timestamp" : "pending"
}
},
"reason" : "test",
"requester" : "4U1DWf95rJvgfAwDYs7m",
"requesterName" : "Dana Mayers",
"status" : "pending",
"tagetValue" : "QT1IkGHS3mmalyXqdCuD",
"taskName" : "Title Change",
"timestamp" : 1644773238,
"value" : "New Title"
},
"d4S34FSAdsf43FM" : {
...
}
}
I want to make ideally one query to get this data as opposed to querying by Approver1 and then by Approver2
Tasks:{
"df234dgkjsf234" : {
"Approver1" : "4U1DWf95rJvgfAwDYs7m",
"Approver1status" : "pending",
"Approver1timestamp" : "pending"
"Approver2" : "FXRkK22TjyxKV6z4UkrU",
"Approver2status" : "pending",
"Approver2timestamp" : "pending",
"reason" : "test",
"requester" : "4U1DWf95rJvgfAwDYs7m",
"requesterName" : "Dana Mayers",
"status" : "pending",
"tagetValue" : "QT1IkGHS3mmalyXqdCuD",
"taskName" : "Title Change",
"timestamp" : 1644773238,
"value" : "New Title"
},
"d4S34FSAdsf43FM" : {
...
}
}
There is no way to do this with one query, since you're looking for two separate values. The closest you can get is by doing two separate queries on (4Approver1 and Approver2 in) the second data structure, and then merging the results in your application code.
The alternative is to add a secondary data structure, where you map from the user ID back to the tasks they're allowed to approve:
UserTasks: {
"4U1D23dfsdf23e": {
"df234dgkjsf234": true,
"d4S34FSAdsf43FM": true
},
"sdf32fdsf34sdg3": {
"df234dgkjsf234": true
}
}
With such an additional structure you can easily find the tasks for a specific UID, and then load the task details for each.
For more on this, I recommend also reading:
Many to Many relationship in Firebase
Firebase query if child of child contains a value
Firebase Query Double Nested

MongoDB - how do I pull the record based on the first element in array

Here are 2 documents in the attached file. I need to pull the title field based on the filter (Harrison Ford is the 1st element in the actor field). So I need to pull the 2nd document. Thanks for your help.
{
"_id" : ObjectId("5e66e96a2f86fd04deaa59c5"),
"title" : "Star Trek Into Darkness",
"actors" : [
"Chris Pine",
"Zachary Quinto",
"Harrison Ford",
"Karl Urban"
]
}
{
"_id" : ObjectId("5e66e96a2f86fd04deaa59c6"),
"title" : "Raiders of the lost ark",
"actors" : [
"Harrison Ford",
"Jonathan Frakes",
"Brent Spiner",
"LeVar Burton"
]
}
so you want to get the title of a movie that has Harrison Ford as the first actor right?
if so, give this a try:
db.collection.find(
{ 'actors.0': 'Harrison Ford' },
{ title: 1, _id: 0 }
)
https://mongoplayground.net/p/f4-o13NjYNc
btw, when you say pull it may confuse people because there's a $pull operator in mongodb.
db.collection.update({_id:'Your match id'}, {$pull:{actors:'Jonathan Frakes'}});
you can pull record with $pull and then update that record. Please check below link
https://docs.mongodb.com/manual/reference/operator/update/pull/

Aggregation. I want to count record in mongo and I then want to multiply that count by a field in a different collection [duplicate]

How do I perform the SQL Join equivalent in MongoDB?
For example say you have two collections (users and comments) and I want to pull all the comments with pid=444 along with the user info for each.
comments
{ uid:12345, pid:444, comment="blah" }
{ uid:12345, pid:888, comment="asdf" }
{ uid:99999, pid:444, comment="qwer" }
users
{ uid:12345, name:"john" }
{ uid:99999, name:"mia" }
Is there a way to pull all the comments with a certain field (eg. ...find({pid:444}) ) and the user information associated with each comment in one go?
At the moment, I am first getting the comments which match my criteria, then figuring out all the uid's in that result set, getting the user objects, and merging them with the comment's results. Seems like I am doing it wrong.
As of Mongo 3.2 the answers to this question are mostly no longer correct. The new $lookup operator added to the aggregation pipeline is essentially identical to a left outer join:
https://docs.mongodb.org/master/reference/operator/aggregation/lookup/#pipe._S_lookup
From the docs:
{
$lookup:
{
from: <collection to join>,
localField: <field from the input documents>,
foreignField: <field from the documents of the "from" collection>,
as: <output array field>
}
}
Of course Mongo is not a relational database, and the devs are being careful to recommend specific use cases for $lookup, but at least as of 3.2 doing join is now possible with MongoDB.
We can merge/join all data inside only one collection with a easy function in few lines using the mongodb client console, and now we could be able of perform the desired query.
Below a complete example,
.- Authors:
db.authors.insert([
{
_id: 'a1',
name: { first: 'orlando', last: 'becerra' },
age: 27
},
{
_id: 'a2',
name: { first: 'mayra', last: 'sanchez' },
age: 21
}
]);
.- Categories:
db.categories.insert([
{
_id: 'c1',
name: 'sci-fi'
},
{
_id: 'c2',
name: 'romance'
}
]);
.- Books
db.books.insert([
{
_id: 'b1',
name: 'Groovy Book',
category: 'c1',
authors: ['a1']
},
{
_id: 'b2',
name: 'Java Book',
category: 'c2',
authors: ['a1','a2']
},
]);
.- Book lending
db.lendings.insert([
{
_id: 'l1',
book: 'b1',
date: new Date('01/01/11'),
lendingBy: 'jose'
},
{
_id: 'l2',
book: 'b1',
date: new Date('02/02/12'),
lendingBy: 'maria'
}
]);
.- The magic:
db.books.find().forEach(
function (newBook) {
newBook.category = db.categories.findOne( { "_id": newBook.category } );
newBook.lendings = db.lendings.find( { "book": newBook._id } ).toArray();
newBook.authors = db.authors.find( { "_id": { $in: newBook.authors } } ).toArray();
db.booksReloaded.insert(newBook);
}
);
.- Get the new collection data:
db.booksReloaded.find().pretty()
.- Response :)
{
"_id" : "b1",
"name" : "Groovy Book",
"category" : {
"_id" : "c1",
"name" : "sci-fi"
},
"authors" : [
{
"_id" : "a1",
"name" : {
"first" : "orlando",
"last" : "becerra"
},
"age" : 27
}
],
"lendings" : [
{
"_id" : "l1",
"book" : "b1",
"date" : ISODate("2011-01-01T00:00:00Z"),
"lendingBy" : "jose"
},
{
"_id" : "l2",
"book" : "b1",
"date" : ISODate("2012-02-02T00:00:00Z"),
"lendingBy" : "maria"
}
]
}
{
"_id" : "b2",
"name" : "Java Book",
"category" : {
"_id" : "c2",
"name" : "romance"
},
"authors" : [
{
"_id" : "a1",
"name" : {
"first" : "orlando",
"last" : "becerra"
},
"age" : 27
},
{
"_id" : "a2",
"name" : {
"first" : "mayra",
"last" : "sanchez"
},
"age" : 21
}
],
"lendings" : [ ]
}
I hope this lines can help you.
This page on the official mongodb site addresses exactly this question:
https://mongodb-documentation.readthedocs.io/en/latest/ecosystem/tutorial/model-data-for-ruby-on-rails.html
When we display our list of stories, we'll need to show the name of the user who posted the story. If we were using a relational database, we could perform a join on users and stores, and get all our objects in a single query. But MongoDB does not support joins and so, at times, requires bit of denormalization. Here, this means caching the 'username' attribute.
Relational purists may be feeling uneasy already, as if we were violating some universal law. But let’s bear in mind that MongoDB collections are not equivalent to relational tables; each serves a unique design objective. A normalized table provides an atomic, isolated chunk of data. A document, however, more closely represents an object as a whole. In the case of a social news site, it can be argued that a username is intrinsic to the story being posted.
You have to do it the way you described. MongoDB is a non-relational database and doesn't support joins.
With right combination of $lookup, $project and $match, you can join mutiple tables on multiple parameters. This is because they can be chained multiple times.
Suppose we want to do following (reference)
SELECT S.* FROM LeftTable S
LEFT JOIN RightTable R ON S.ID = R.ID AND S.MID = R.MID
WHERE R.TIM > 0 AND S.MOB IS NOT NULL
Step 1: Link all tables
you can $lookup as many tables as you want.
$lookup - one for each table in query
$unwind - correctly denormalises data , else it'd be wrapped in arrays
Python code..
db.LeftTable.aggregate([
# connect all tables
{"$lookup": {
"from": "RightTable",
"localField": "ID",
"foreignField": "ID",
"as": "R"
}},
{"$unwind": "R"}
])
Step 2: Define all conditionals
$project : define all conditional statements here, plus all the variables you'd like to select.
Python Code..
db.LeftTable.aggregate([
# connect all tables
{"$lookup": {
"from": "RightTable",
"localField": "ID",
"foreignField": "ID",
"as": "R"
}},
{"$unwind": "R"},
# define conditionals + variables
{"$project": {
"midEq": {"$eq": ["$MID", "$R.MID"]},
"ID": 1, "MOB": 1, "MID": 1
}}
])
Step 3: Join all the conditionals
$match - join all conditions using OR or AND etc. There can be multiples of these.
$project: undefine all conditionals
Complete Python Code..
db.LeftTable.aggregate([
# connect all tables
{"$lookup": {
"from": "RightTable",
"localField": "ID",
"foreignField": "ID",
"as": "R"
}},
{"$unwind": "$R"},
# define conditionals + variables
{"$project": {
"midEq": {"$eq": ["$MID", "$R.MID"]},
"ID": 1, "MOB": 1, "MID": 1
}},
# join all conditionals
{"$match": {
"$and": [
{"R.TIM": {"$gt": 0}},
{"MOB": {"$exists": True}},
{"midEq": {"$eq": True}}
]}},
# undefine conditionals
{"$project": {
"midEq": 0
}}
])
Pretty much any combination of tables, conditionals and joins can be done in this manner.
You can join two collection in Mongo by using lookup which is offered in 3.2 version. In your case the query would be
db.comments.aggregate({
$lookup:{
from:"users",
localField:"uid",
foreignField:"uid",
as:"users_comments"
}
})
or you can also join with respect to users then there will be a little change as given below.
db.users.aggregate({
$lookup:{
from:"comments",
localField:"uid",
foreignField:"uid",
as:"users_comments"
}
})
It will work just same as left and right join in SQL.
As others have pointed out you are trying to create a relational database from none relational database which you really don't want to do but anyways, if you have a case that you have to do this here is a solution you can use. We first do a foreach find on collection A( or in your case users) and then we get each item as an object then we use object property (in your case uid) to lookup in our second collection (in your case comments) if we can find it then we have a match and we can print or do something with it.
Hope this helps you and good luck :)
db.users.find().forEach(
function (object) {
var commonInBoth=db.comments.findOne({ "uid": object.uid} );
if (commonInBoth != null) {
printjson(commonInBoth) ;
printjson(object) ;
}else {
// did not match so we don't care in this case
}
});
Here's an example of a "join" * Actors and Movies collections:
https://github.com/mongodb/cookbook/blob/master/content/patterns/pivot.txt
It makes use of .mapReduce() method
* join - an alternative to join in document-oriented databases
$lookup (aggregation)
Performs a left outer join to an unsharded collection in the same database to filter in documents from the “joined” collection for processing. To each input document, the $lookup stage adds a new array field whose elements are the matching documents from the “joined” collection. The $lookup stage passes these reshaped documents to the next stage.
The $lookup stage has the following syntaxes:
Equality Match
To perform an equality match between a field from the input documents with a field from the documents of the “joined” collection, the $lookup stage has the following syntax:
{
$lookup:
{
from: <collection to join>,
localField: <field from the input documents>,
foreignField: <field from the documents of the "from" collection>,
as: <output array field>
}
}
The operation would correspond to the following pseudo-SQL statement:
SELECT *, <output array field>
FROM collection
WHERE <output array field> IN (SELECT <documents as determined from the pipeline>
FROM <collection to join>
WHERE <pipeline> );
Mongo URL
It depends on what you're trying to do.
You currently have it set up as a normalized database, which is fine, and the way you are doing it is appropriate.
However, there are other ways of doing it.
You could have a posts collection that has imbedded comments for each post with references to the users that you can iteratively query to get. You could store the user's name with the comments, you could store them all in one document.
The thing with NoSQL is it's designed for flexible schemas and very fast reading and writing. In a typical Big Data farm the database is the biggest bottleneck, you have fewer database engines than you do application and front end servers...they're more expensive but more powerful, also hard drive space is very cheap comparatively. Normalization comes from the concept of trying to save space, but it comes with a cost at making your databases perform complicated Joins and verifying the integrity of relationships, performing cascading operations. All of which saves the developers some headaches if they designed the database properly.
With NoSQL, if you accept that redundancy and storage space aren't issues because of their cost (both in processor time required to do updates and hard drive costs to store extra data), denormalizing isn't an issue (for embedded arrays that become hundreds of thousands of items it can be a performance issue, but most of the time that's not a problem). Additionally you'll have several application and front end servers for every database cluster. Have them do the heavy lifting of the joins and let the database servers stick to reading and writing.
TL;DR: What you're doing is fine, and there are other ways of doing it. Check out the mongodb documentation's data model patterns for some great examples. http://docs.mongodb.org/manual/data-modeling/
There is a specification that a lot of drivers support that's called DBRef.
DBRef is a more formal specification for creating references between documents. DBRefs (generally) include a collection name as well as an object id. Most developers only use DBRefs if the collection can change from one document to the next. If your referenced collection will always be the same, the manual references outlined above are more efficient.
Taken from MongoDB Documentation: Data Models > Data Model Reference >
Database References
Before 3.2.6, Mongodb does not support join query as like mysql. below solution which works for you.
db.getCollection('comments').aggregate([
{$match : {pid : 444}},
{$lookup: {from: "users",localField: "uid",foreignField: "uid",as: "userData"}},
])
You can run SQL queries including join on MongoDB with mongo_fdw from Postgres.
MongoDB does not allow joins, but you can use plugins to handle that. Check the mongo-join plugin. It's the best and I have already used it. You can install it using npm directly like this npm install mongo-join. You can check out the full documentation with examples.
(++) really helpful tool when we need to join (N) collections
(--) we can apply conditions just on the top level of the query
Example
var Join = require('mongo-join').Join, mongodb = require('mongodb'), Db = mongodb.Db, Server = mongodb.Server;
db.open(function (err, Database) {
Database.collection('Appoint', function (err, Appoints) {
/* we can put conditions just on the top level */
Appoints.find({_id_Doctor: id_doctor ,full_date :{ $gte: start_date },
full_date :{ $lte: end_date }}, function (err, cursor) {
var join = new Join(Database).on({
field: '_id_Doctor', // <- field in Appoints document
to: '_id', // <- field in User doc. treated as ObjectID automatically.
from: 'User' // <- collection name for User doc
}).on({
field: '_id_Patient', // <- field in Appoints doc
to: '_id', // <- field in User doc. treated as ObjectID automatically.
from: 'User' // <- collection name for User doc
})
join.toArray(cursor, function (err, joinedDocs) {
/* do what ever you want here */
/* you can fetch the table and apply your own conditions */
.....
.....
.....
resp.status(200);
resp.json({
"status": 200,
"message": "success",
"Appoints_Range": joinedDocs,
});
return resp;
});
});
You can do it using the aggregation pipeline, but it's a pain to write it yourself.
You can use mongo-join-query to create the aggregation pipeline automatically from your query.
This is how your query would look like:
const mongoose = require("mongoose");
const joinQuery = require("mongo-join-query");
joinQuery(
mongoose.models.Comment,
{
find: { pid:444 },
populate: ["uid"]
},
(err, res) => (err ? console.log("Error:", err) : console.log("Success:", res.results))
);
Your result would have the user object in the uid field and you can link as many levels deep as you want. You can populate the reference to the user, which makes reference to a Team, which makes reference to something else, etc..
Disclaimer: I wrote mongo-join-query to tackle this exact problem.
playORM can do it for you using S-SQL(Scalable SQL) which just adds partitioning such that you can do joins within partitions.
Nope, it doesn't seem like you're doing it wrong. MongoDB joins are "client side". Pretty much like you said:
At the moment, I am first getting the comments which match my criteria, then figuring out all the uid's in that result set, getting the user objects, and merging them with the comment's results. Seems like I am doing it wrong.
1) Select from the collection you're interested in.
2) From that collection pull out ID's you need
3) Select from other collections
4) Decorate your original results.
It's not a "real" join, but it's actually alot more useful than a SQL join because you don't have to deal with duplicate rows for "many" sided joins, instead your decorating the originally selected set.
There is alot of nonsense and FUD on this page. Turns out 5 years later MongoDB is still a thing.
I think, if You need normalized data tables - You need to try some other database solutions.
But I've foun that sollution for MOngo on Git
By the way, in inserts code - it has movie's name, but noi movie's ID.
Problem
You have a collection of Actors with an array of the Movies they've done.
You want to generate a collection of Movies with an array of Actors in each.
Some sample data
db.actors.insert( { actor: "Richard Gere", movies: ['Pretty Woman', 'Runaway Bride', 'Chicago'] });
db.actors.insert( { actor: "Julia Roberts", movies: ['Pretty Woman', 'Runaway Bride', 'Erin Brockovich'] });
Solution
We need to loop through each movie in the Actor document and emit each Movie individually.
The catch here is in the reduce phase. We cannot emit an array from the reduce phase, so we must build an Actors array inside of the "value" document that is returned.
The code
map = function() {
for(var i in this.movies){
key = { movie: this.movies[i] };
value = { actors: [ this.actor ] };
emit(key, value);
}
}
reduce = function(key, values) {
actor_list = { actors: [] };
for(var i in values) {
actor_list.actors = values[i].actors.concat(actor_list.actors);
}
return actor_list;
}
Notice how actor_list is actually a javascript object that contains an array. Also notice that map emits the same structure.
Run the following to execute the map / reduce, output it to the "pivot" collection and print the result:
printjson(db.actors.mapReduce(map, reduce, "pivot"));
db.pivot.find().forEach(printjson);
Here is the sample output, note that "Pretty Woman" and "Runaway Bride" have both "Richard Gere" and "Julia Roberts".
{ "_id" : { "movie" : "Chicago" }, "value" : { "actors" : [ "Richard Gere" ] } }
{ "_id" : { "movie" : "Erin Brockovich" }, "value" : { "actors" : [ "Julia Roberts" ] } }
{ "_id" : { "movie" : "Pretty Woman" }, "value" : { "actors" : [ "Richard Gere", "Julia Roberts" ] } }
{ "_id" : { "movie" : "Runaway Bride" }, "value" : { "actors" : [ "Richard Gere", "Julia Roberts" ] } }
We can merge two collection by using mongoDB sub query. Here is example,
Commentss--
`db.commentss.insert([
{ uid:12345, pid:444, comment:"blah" },
{ uid:12345, pid:888, comment:"asdf" },
{ uid:99999, pid:444, comment:"qwer" }])`
Userss--
db.userss.insert([
{ uid:12345, name:"john" },
{ uid:99999, name:"mia" }])
MongoDB sub query for JOIN--
`db.commentss.find().forEach(
function (newComments) {
newComments.userss = db.userss.find( { "uid": newComments.uid } ).toArray();
db.newCommentUsers.insert(newComments);
}
);`
Get result from newly generated Collection--
db.newCommentUsers.find().pretty()
Result--
`{
"_id" : ObjectId("5511236e29709afa03f226ef"),
"uid" : 12345,
"pid" : 444,
"comment" : "blah",
"userss" : [
{
"_id" : ObjectId("5511238129709afa03f226f2"),
"uid" : 12345,
"name" : "john"
}
]
}
{
"_id" : ObjectId("5511236e29709afa03f226f0"),
"uid" : 12345,
"pid" : 888,
"comment" : "asdf",
"userss" : [
{
"_id" : ObjectId("5511238129709afa03f226f2"),
"uid" : 12345,
"name" : "john"
}
]
}
{
"_id" : ObjectId("5511236e29709afa03f226f1"),
"uid" : 99999,
"pid" : 444,
"comment" : "qwer",
"userss" : [
{
"_id" : ObjectId("5511238129709afa03f226f3"),
"uid" : 99999,
"name" : "mia"
}
]
}`
Hope so this will help.

Mongodb Query Multiple Levels Deep

I have the following document:
{
"_id" : ObjectId("5a202aa0728bac010a8d2467"),
"tickers" : {
"information_technology" : [
"ACN",
"ATVI",
"ADBE",
"AMD",
],
"misc" : [
"AA",
"GE",
"AAPL",
"PFE",
]
},
"name" : "S&P500"
}
I want to query the document by name ("S&P500") and return a list within the "tickers" field.
I tried db.collection.find_one("$and": [{'name': 'S&P500'}, {'tickers': 'misc'}]) but no documents were returned.
I am new to mongodb so I may have missed something in the documentation.
Thanks for any help.
The API for Collection.find_one is similar with Collection.find except that limit is ignored and a single document returned for a match or None if there is no match.
find(filter=None, projection=None, skip=0, limit=0, no_cursor_timeout=False, cursor_type=CursorType.NON_TAILABLE, sort=None, allow_partial_results=False, oplog_replay=False, modifiers=None, manipulate=True) Docs
An appropriate filter is {'name': 'S&P500'} when looking to match documents with name equals S&P500.
Also, an appropriate projection is {'tickers.misc': True} when projecting only tickers.misc.
db.collection.find_one({'name': 'S&P500'}, {'tickers.misc': True})

How to find documents in MongoDb matching a field and subdocument field that are in an array

The document structure is as follows:
{
"_id" : "V001-99999999",
"vendor_number" : "V001",
"created_time" : ISODate("2016-04-26T22:15:34Z"),
"updated_time" : ISODate("2016-06-07T21:45:46.413Z"),
"items" : [
{
"sku" : "99999999-1",
"status" : "ACTIVE",
"listing_status" : "LIVE",
"inventory" : 10,
"created_time" : ISODate("2016-05-14T22:15:34Z"),
"updated_time" : ISODate("2016-05-14T20:42:21.753Z"),
},
{
"sku" : "99999999-2",
"status" : "INACTIVE",
"listing_status" : "LIVE",
"inventory" : 10,
"created_time" : ISODate("2016-04-26T22:15:34Z"),
"updated_time" : ISODate("2016-06-06T20:42:21.753Z"),
}
]
}
I want to obtain the sku from the item, the conditions are:
1) "vendor_number" = "XXX"
2) items.status = "ACTIVE" AND items.updated_time < [given_date]
Result example:
"sku" : "99999999-2"
or csv:
"sku","99999999-2"
Thank you for your support.
This should be what you want. Although I'm assuming you wanted "status": "active"?
db.getCollection('collection').aggregate([
{ $match: { "vendor_number": "XXXX" } },
{ $project: {
"items": {
$filter: {
input: "$items",
as: "item",
cond: { $eq: ["$$item.status", "ACTIVE"] } // or maybe ["$$item.listing_status", "LIVE"] ?
}
}
}
},
{ $project: { "items.sku": true } }
])
I love using aggregation to manipulate stuff. It's great all the things you can do with it. So here's what's going on:
The first part is simple. The $match step in the aggregation pipeline says just give me documents where vendor_number is "XXXX".
The next part is a bit hairy. The first projection step creates a new field, called "items", I could have called it "results" or "bob" if I wanted to. The $filter specifies which items should go into this new field. The new "items" field will be an array that will have all the results from the previous items field, hence the input: "$items", where you're using the keyword "item" to represent each input item that comes into the filter. Next, the condition says, for each item, only put it in my new "items" array if the item's status is "ACTIVE". You can change it to ["$$items.listing_status", "LIVE"] if that's what you needed. All of this will pretty much give you you're result.
The last project just get's rid of all other fields except for items.sku in each element in the new "items" array.
Hope this help. Play around with it and see what else you can do with the collection and aggregation. Let me know if you need any more clarification. If you haven't used aggregation before, take a look at the aggregation docs and the list of pipeline operators you can use with aggregation. Pretty handy tool.

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