Related
I encountered weird thing when using C# MongoDB CountDocumentAsync function. I enabled query logging on MongoDB and this is what I got:
{
"op" : "command",
"ns" : "somenamespace",
"command" : {
"aggregate" : "reservations",
"pipeline" : [
{
"some_query_key": "query_value"
},
{
"$group" : {
"_id" : null,
"n" : {
"$sum" : 1
}
}
}
],
"cursor" : {}
},
"keyUpdates" : 0,
"writeConflicts" : 0,
"numYield" : 9,
"locks" : {
"Global" : {
"acquireCount" : {
"r" : NumberLong(24)
}
},
"Database" : {
"acquireCount" : {
"r" : NumberLong(12)
}
},
"Collection" : {
"acquireCount" : {
"r" : NumberLong(12)
}
}
},
"responseLength" : 138,
"protocol" : "op_query",
"millis" : 2,
"execStats" : {},
"ts" : ISODate("2018-09-27T14:08:48.099Z"),
"client" : "172.17.0.1",
"allUsers" : [ ],
"user" : ""
}
simple count is converted into an aggregate.
More interestingly when I use CountAsync function (which btw is marked obsolete with remark I should be using CountDocumentsAsync) it produces:
{
"op" : "command",
"ns" : "somenamespace",
"command" : {
"count" : "reservations",
"query" : {
"query_key": "query_value"
}
},
"keyUpdates" : 0,
"writeConflicts" : 0,
"numYield" : 9,
"locks" : {
"Global" : {
"acquireCount" : {
"r" : NumberLong(20)
}
},
"Database" : {
"acquireCount" : {
"r" : NumberLong(10)
}
},
"Collection" : {
"acquireCount" : {
"r" : NumberLong(10)
}
}
},
"responseLength" : 62,
"protocol" : "op_query",
"millis" : 2,
"execStats" : {
},
"ts" : ISODate("2018-09-27T13:58:27.758Z"),
"client" : "172.17.0.1",
"allUsers" : [ ],
"user" : ""
}
which is what I would expect. Does anyone know what might be a reason for this behavior? I browsed documentation but didn't find anything interesting regarding it.
This is the documented behaviour for drivers supporting 4.0 features. The reason for the change is to remove confusion and make it clear when an estimate is used and when it is not.
When counting based on a query filter (rather than just counting the entire collection) both methods will cause the server to iterate over matching documents to count them and therefore have similar performance.
From MongoDb docs: db.collection.count()
NOTE:
MongoDB drivers compatible with the 4.0 features deprecate their
respective cursor and collection count() APIs in favor of new APIs for
countDocuments() and estimatedDocumentCount(). For the specific API
names for a given driver, see the driver documentation.
From MongoDb docs: db.collection.countDocuments()
db.collection.countDocuments(query, options)
New in version 4.0.3.
Returns the count of documents that match the query for a collection
or view. The method wraps the $group aggregation stage with a $sum
expression to perform the count and is available for use in
Transactions.
A more detailed explanation for this change in API can be found on the MongoDb JIRA site:
Drivers supporting MongoDB 4.0 must deprecate the count() helper and
add two new helpers - estimatedDocumentCount() and countDocuments().
Both helpers are supported with MongoDB 2.6+.
The names of the new helpers were chosen to make it clear how they
behave and exactly what they do. The estimatedDocumentCount helper
returns an estimate of the count of documents in the collection using
collection metadata, rather than counting the documents or consulting
an index. The countDocuments helper counts the documents that match
the provided query filter using an aggregation pipeline.
The count() helper is deprecated. It has always been implemented using
the count command. The behavior of the count command differs depending
on the options passed to it and the topology in use and may or may not
provide an accurate count. When no query filter is provided the count
command provides an estimate using collection metadata. Even when
provided with a query filter the count command can return inaccurate
results with a sharded cluster if orphaned documents exist or if a
chunk migration is in progress. The countDocuments helper avoids these
sharded cluster problems entirely when used with MongoDB 3.6+, and
when using Primary read preference with older sharded clusters.
I have a document with a field containing a very long string. I need to concatenate another string to the end of the string already contained in the field.
The way I do it now is that, from Java, I fetch the document, extract the string in the field, append the string to the end and finally update the document with the new string.
The problem: The string contained in the field is very long, which means that it takes time and resources to retrieve and work with this string in Java. Furthermore, this is an operation that is done several times per second.
My question: Is there a way to concatenate a string to an existing field, without having to fetch (db.<doc>.find()) the contents of the field first? In reality all I want is (field.contents += new_string).
I already made this work using Javascript and eval, but as I found out, MongoDB locks the database when it executes javascript, which makes the overall application even slower.
Starting Mongo 4.2, db.collection.updateMany() can accept an aggregation pipeline, finally allowing the update of a field based on its current value:
// { a: "Hello" }
db.collection.updateMany(
{},
[{ $set: { a: { $concat: [ "$a", "World" ] } } }]
)
// { a: "HelloWorld" }
The first part {} is the match query, filtering which documents to update (in this case all documents).
The second part [{ $set: { a: { $concat: [ "$a", "World" ] } } }] is the update aggregation pipeline (note the squared brackets signifying the use of an aggregation pipeline). $set (alias of $addFields) is a new aggregation operator which in this case replaces the field's value (by concatenating a itself with the suffix "World"). Note how a is modified directly based on its own value ($a).
For example (it's append to the start, the same story ):
before
{ "_id" : ObjectId("56993251e843bb7e0447829d"), "name" : "London
City", "city" : "London" }
db.airports
.find( { $text: { $search: "City" } })
.forEach(
function(e, i){
e.name='Big ' + e.name;
db.airports.save(e);
}
)
after:
{ "_id" : ObjectId("56993251e843bb7e0447829d"), "name" : "Big London
City", "city" : "London" }
Old topic but i had the same problem.
Since mongo 2.4, you can use $concat from aggregation framework.
Example
Consider these documents :
{
"_id" : ObjectId("5941003d5e785b5c0b2ac78d"),
"title" : "cov"
}
{
"_id" : ObjectId("594109b45e785b5c0b2ac97d"),
"title" : "fefe"
}
Append fefe to title field :
db.getCollection('test_append_string').aggregate(
[
{ $project: { title: { $concat: [ "$title", "fefe"] } } }
]
)
The result of aggregation will be :
{
"_id" : ObjectId("5941003d5e785b5c0b2ac78d"),
"title" : "covfefe"
}
{
"_id" : ObjectId("594109b45e785b5c0b2ac97d"),
"title" : "fefefefe"
}
You can then save the results with a bulk, see this answer for that.
this is a sample of one document i have :
{
"_id" : 1,
"s" : 1,
"ser" : 2,
"p" : "9919871172",
"d" : ISODate("2018-05-30T05:00:38.057Z"),
"per" : "10"
}
to append a string to any feild you can run a forEach loop throught all documents and then update desired field:
db.getCollection('jafar').find({}).forEach(function(el){
db.getCollection('jafar').update(
{p:el.p},
{$set:{p:'98'+el.p}})
})
This would not be possible.
One optimization you can do is create batches of updates.
i.e. fetch 10K documents, append relevant strings to each of their keys,
and then save them as single batch.
Most mongodb drivers support batch operations.
db.getCollection('<collection>').update(
// query
{},
// update
{
$set: {<field>:this.<field>+"<new string>"}
},
// options
{
"multi" : true, // update only one document
"upsert" : false // insert a new document, if no existing document match the query
});
Given a document structure:
[{
_id: ObjectId("someId"),
Property: {
CollectionProperty: [{
ItemKey: 28,
StringArray: [
"ItemA",
"ItemB"
]
}]
}
}]
Performing the following operation in the mongo shell does what I want:
db.getCollection('collection').updateOne(
{
"_id" : ObjectId("someId"),
"Property.CollectionProperty.ItemKey" : 28
},
{ $push :
{
"Property.CollectionProperty.$.StringArray" : {
$each : [ "AItemA" ],
$sort : 1
}
}
});
However, I cannot find in the documentation or anywhere on the net how to reproduce THAT $sort command in C# code. I can reproduce if it had fields by using "field":1, but not a non-document array like that.
The mongodb documentation that describes that sort is here:
MongoDB Sort Elements that are not documents
Here's what I have in C# so far:
Builders<DataType>.Update.PushEach("Property.CollectionProperty.$.StringArray",
new[] { "AItemA" }, null, null, ???)));
The ??? is what I don't know how to do... does anyone have any suggestions?
TIA
This is apparently currently unsupported, but planned for a future release in the C# driver, as documented here:
https://jira.mongodb.org/browse/CSHARP-1271
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.
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.