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.
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.
I have a Mongo document which holds an array of elements.
I'd like to reset the .handled attribute of all objects in the array where .profile = XX.
The document is in the following form:
{
"_id": ObjectId("4d2d8deff4e6c1d71fc29a07"),
"user_id": "714638ba-2e08-2168-2b99-00002f3d43c0",
"events": [{
"handled": 1,
"profile": 10,
"data": "....."
} {
"handled": 1,
"profile": 10,
"data": "....."
} {
"handled": 1,
"profile": 20,
"data": "....."
}
...
]
}
so, I tried the following:
.update({"events.profile":10},{$set:{"events.$.handled":0}},false,true)
However it updates only the first matched array element in each document. (That's the defined behaviour for $ - the positional operator.)
How can I update all matched array elements?
With the release of MongoDB 3.6 ( and available in the development branch from MongoDB 3.5.12 ) you can now update multiple array elements in a single request.
This uses the filtered positional $[<identifier>] update operator syntax introduced in this version:
db.collection.update(
{ "events.profile":10 },
{ "$set": { "events.$[elem].handled": 0 } },
{ "arrayFilters": [{ "elem.profile": 10 }], "multi": true }
)
The "arrayFilters" as passed to the options for .update() or even
.updateOne(), .updateMany(), .findOneAndUpdate() or .bulkWrite() method specifies the conditions to match on the identifier given in the update statement. Any elements that match the condition given will be updated.
Noting that the "multi" as given in the context of the question was used in the expectation that this would "update multiple elements" but this was not and still is not the case. It's usage here applies to "multiple documents" as has always been the case or now otherwise specified as the mandatory setting of .updateMany() in modern API versions.
NOTE Somewhat ironically, since this is specified in the "options" argument for .update() and like methods, the syntax is generally compatible with all recent release driver versions.
However this is not true of the mongo shell, since the way the method is implemented there ( "ironically for backward compatibility" ) the arrayFilters argument is not recognized and removed by an internal method that parses the options in order to deliver "backward compatibility" with prior MongoDB server versions and a "legacy" .update() API call syntax.
So if you want to use the command in the mongo shell or other "shell based" products ( notably Robo 3T ) you need a latest version from either the development branch or production release as of 3.6 or greater.
See also positional all $[] which also updates "multiple array elements" but without applying to specified conditions and applies to all elements in the array where that is the desired action.
Also see Updating a Nested Array with MongoDB for how these new positional operators apply to "nested" array structures, where "arrays are within other arrays".
IMPORTANT - Upgraded installations from previous versions "may" have not enabled MongoDB features, which can also cause statements to fail. You should ensure your upgrade procedure is complete with details such as index upgrades and then run
db.adminCommand( { setFeatureCompatibilityVersion: "3.6" } )
Or higher version as is applicable to your installed version. i.e "4.0" for version 4 and onwards at present. This enabled such features as the new positional update operators and others. You can also check with:
db.adminCommand( { getParameter: 1, featureCompatibilityVersion: 1 } )
To return the current setting
UPDATE:
As of Mongo version 3.6, this answer is no longer valid as the mentioned issue was fixed and there are ways to achieve this. Please check other answers.
At this moment it is not possible to use the positional operator to update all items in an array. See JIRA http://jira.mongodb.org/browse/SERVER-1243
As a work around you can:
Update each item individually
(events.0.handled events.1.handled
...) or...
Read the document, do the edits
manually and save it replacing the
older one (check "Update if
Current" if you want to ensure
atomic updates)
What worked for me was this:
db.collection.find({ _id: ObjectId('4d2d8deff4e6c1d71fc29a07') })
.forEach(function (doc) {
doc.events.forEach(function (event) {
if (event.profile === 10) {
event.handled=0;
}
});
db.collection.save(doc);
});
I think it's clearer for mongo newbies and anyone familiar with JQuery & friends.
This can also be accomplished with a while loop which checks to see if any documents remain that still have subdocuments that have not been updated. This method preserves the atomicity of your updates (which many of the other solutions here do not).
var query = {
events: {
$elemMatch: {
profile: 10,
handled: { $ne: 0 }
}
}
};
while (db.yourCollection.find(query).count() > 0) {
db.yourCollection.update(
query,
{ $set: { "events.$.handled": 0 } },
{ multi: true }
);
}
The number of times the loop is executed will equal the maximum number of times subdocuments with profile equal to 10 and handled not equal to 0 occur in any of the documents in your collection. So if you have 100 documents in your collection and one of them has three subdocuments that match query and all the other documents have fewer matching subdocuments, the loop will execute three times.
This method avoids the danger of clobbering other data that may be updated by another process while this script executes. It also minimizes the amount of data being transferred between client and server.
This does in fact relate to the long standing issue at http://jira.mongodb.org/browse/SERVER-1243 where there are in fact a number of challenges to a clear syntax that supports "all cases" where mutiple array matches are found. There are in fact methods already in place that "aid" in solutions to this problem, such as Bulk Operations which have been implemented after this original post.
It is still not possible to update more than a single matched array element in a single update statement, so even with a "multi" update all you will ever be able to update is just one mathed element in the array for each document in that single statement.
The best possible solution at present is to find and loop all matched documents and process Bulk updates which will at least allow many operations to be sent in a single request with a singular response. You can optionally use .aggregate() to reduce the array content returned in the search result to just those that match the conditions for the update selection:
db.collection.aggregate([
{ "$match": { "events.handled": 1 } },
{ "$project": {
"events": {
"$setDifference": [
{ "$map": {
"input": "$events",
"as": "event",
"in": {
"$cond": [
{ "$eq": [ "$$event.handled", 1 ] },
"$$el",
false
]
}
}},
[false]
]
}
}}
]).forEach(function(doc) {
doc.events.forEach(function(event) {
bulk.find({ "_id": doc._id, "events.handled": 1 }).updateOne({
"$set": { "events.$.handled": 0 }
});
count++;
if ( count % 1000 == 0 ) {
bulk.execute();
bulk = db.collection.initializeOrderedBulkOp();
}
});
});
if ( count % 1000 != 0 )
bulk.execute();
The .aggregate() portion there will work when there is a "unique" identifier for the array or all content for each element forms a "unique" element itself. This is due to the "set" operator in $setDifference used to filter any false values returned from the $map operation used to process the array for matches.
If your array content does not have unique elements you can try an alternate approach with $redact:
db.collection.aggregate([
{ "$match": { "events.handled": 1 } },
{ "$redact": {
"$cond": {
"if": {
"$eq": [ { "$ifNull": [ "$handled", 1 ] }, 1 ]
},
"then": "$$DESCEND",
"else": "$$PRUNE"
}
}}
])
Where it's limitation is that if "handled" was in fact a field meant to be present at other document levels then you are likely going to get unexepected results, but is fine where that field appears only in one document position and is an equality match.
Future releases ( post 3.1 MongoDB ) as of writing will have a $filter operation that is simpler:
db.collection.aggregate([
{ "$match": { "events.handled": 1 } },
{ "$project": {
"events": {
"$filter": {
"input": "$events",
"as": "event",
"cond": { "$eq": [ "$$event.handled", 1 ] }
}
}
}}
])
And all releases that support .aggregate() can use the following approach with $unwind, but the usage of that operator makes it the least efficient approach due to the array expansion in the pipeline:
db.collection.aggregate([
{ "$match": { "events.handled": 1 } },
{ "$unwind": "$events" },
{ "$match": { "events.handled": 1 } },
{ "$group": {
"_id": "$_id",
"events": { "$push": "$events" }
}}
])
In all cases where the MongoDB version supports a "cursor" from aggregate output, then this is just a matter of choosing an approach and iterating the results with the same block of code shown to process the Bulk update statements. Bulk Operations and "cursors" from aggregate output are introduced in the same version ( MongoDB 2.6 ) and therefore usually work hand in hand for processing.
In even earlier versions then it is probably best to just use .find() to return the cursor, and filter out the execution of statements to just the number of times the array element is matched for the .update() iterations:
db.collection.find({ "events.handled": 1 }).forEach(function(doc){
doc.events.filter(function(event){ return event.handled == 1 }).forEach(function(event){
db.collection.update({ "_id": doc._id },{ "$set": { "events.$.handled": 0 }});
});
});
If you are aboslutely determined to do "multi" updates or deem that to be ultimately more efficient than processing multiple updates for each matched document, then you can always determine the maximum number of possible array matches and just execute a "multi" update that many times, until basically there are no more documents to update.
A valid approach for MongoDB 2.4 and 2.2 versions could also use .aggregate() to find this value:
var result = db.collection.aggregate([
{ "$match": { "events.handled": 1 } },
{ "$unwind": "$events" },
{ "$match": { "events.handled": 1 } },
{ "$group": {
"_id": "$_id",
"count": { "$sum": 1 }
}},
{ "$group": {
"_id": null,
"count": { "$max": "$count" }
}}
]);
var max = result.result[0].count;
while ( max-- ) {
db.collection.update({ "events.handled": 1},{ "$set": { "events.$.handled": 0 }},{ "multi": true })
}
Whatever the case, there are certain things you do not want to do within the update:
Do not "one shot" update the array: Where if you think it might be more efficient to update the whole array content in code and then just $set the whole array in each document. This might seem faster to process, but there is no guarantee that the array content has not changed since it was read and the update is performed. Though $set is still an atomic operator, it will only update the array with what it "thinks" is the correct data, and thus is likely to overwrite any changes occurring between read and write.
Do not calculate index values to update: Where similar to the "one shot" approach you just work out that position 0 and position 2 ( and so on ) are the elements to update and code these in with and eventual statement like:
{ "$set": {
"events.0.handled": 0,
"events.2.handled": 0
}}
Again the problem here is the "presumption" that those index values found when the document was read are the same index values in th array at the time of update. If new items are added to the array in a way that changes the order then those positions are not longer valid and the wrong items are in fact updated.
So until there is a reasonable syntax determined for allowing multiple matched array elements to be processed in single update statement then the basic approach is to either update each matched array element in an indvidual statement ( ideally in Bulk ) or essentially work out the maximum array elements to update or keep updating until no more modified results are returned. At any rate, you should "always" be processing positional $ updates on the matched array element, even if that is only updating one element per statement.
Bulk Operations are in fact the "generalized" solution to processing any operations that work out to be "multiple operations", and since there are more applications for this than merely updating mutiple array elements with the same value, then it has of course been implemented already, and it is presently the best approach to solve this problem.
First: your code did not work because you were using the positional operator $ which only identifies an element to update in an array but does not even explicitly specify its position in the array.
What you need is the filtered positional operator $[<identifier>]. It would update all elements that match an array filter condition.
Solution:
db.collection.update({"events.profile":10}, { $set: { "events.$[elem].handled" : 0 } },
{
multi: true,
arrayFilters: [ { "elem.profile": 10 } ]
})
Visit mongodb doc here
What the code does:
{"events.profile":10} filters your collection and return the documents matching the filter
The $set update operator: modifies matching fields of documents it acts on.
{multi:true} It makes .update() modifies all documents matching the filter hence behaving like updateMany()
{ "events.$[elem].handled" : 0 } and arrayFilters: [ { "elem.profile": 10 } ]
This technique involves the use of the filtered positional array with arrayFilters. the filtered positional array here $[elem] acts as a placeholder for all elements in the array fields that match the conditions specified in the array filter.
Array filters
You can update all elements in MongoDB
db.collectioname.updateOne(
{ "key": /vikas/i },
{ $set: {
"arr.$[].status" : "completed"
} }
)
It will update all the "status" value to "completed" in the "arr" Array
If Only one document
db.collectioname.updateOne(
{ key:"someunique", "arr.key": "myuniq" },
{ $set: {
"arr.$.status" : "completed",
"arr.$.msgs": {
"result" : ""
}
} }
)
But if not one and also you don't want all the documents in the array to update then you need to loop through the element and inside the if block
db.collectioname.find({findCriteria })
.forEach(function (doc) {
doc.arr.forEach(function (singlearr) {
if (singlearr check) {
singlearr.handled =0
}
});
db.collection.save(doc);
});
I'm amazed this still hasn't been addressed in mongo. Overall mongo doesn't seem to be great when dealing with sub-arrays. You can't count sub-arrays simply for example.
I used Javier's first solution. Read the array into events then loop through and build the set exp:
var set = {}, i, l;
for(i=0,l=events.length;i<l;i++) {
if(events[i].profile == 10) {
set['events.' + i + '.handled'] = 0;
}
}
.update(objId, {$set:set});
This can be abstracted into a function using a callback for the conditional test
The thread is very old, but I came looking for answer here hence providing new solution.
With MongoDB version 3.6+, it is now possible to use the positional operator to update all items in an array. See official documentation here.
Following query would work for the question asked here. I have also verified with Java-MongoDB driver and it works successfully.
.update( // or updateMany directly, removing the flag for 'multi'
{"events.profile":10},
{$set:{"events.$[].handled":0}}, // notice the empty brackets after '$' opearor
false,
true
)
Hope this helps someone like me.
I've been looking for a solution to this using the newest driver for C# 3.6 and here's the fix I eventually settled on. The key here is using "$[]" which according to MongoDB is new as of version 3.6. See https://docs.mongodb.com/manual/reference/operator/update/positional-all/#up.S[] for more information.
Here's the code:
{
var filter = Builders<Scene>.Filter.Where(i => i.ID != null);
var update = Builders<Scene>.Update.Unset("area.$[].discoveredBy");
var result = collection.UpdateMany(filter, update, new UpdateOptions { IsUpsert = true});
}
For more context see my original post here:
Remove array element from ALL documents using MongoDB C# driver
$[] operator selects all nested array ..You can update all array items with '$[]'
.update({"events.profile":10},{$set:{"events.$[].handled":0}},false,true)
Reference
Please be aware that some answers in this thread suggesting use $[] is WRONG.
db.collection.update(
{"events.profile":10},
{$set:{"events.$[].handled":0}},
{multi:true}
)
The above code will update "handled" to 0 for all elements in "events" array, regardless of its "profile" value. The query {"events.profile":10} is only to filter the whole document, not the documents in the array. In this situation it is a must to use $[elem] with arrayFilters to specify the condition of array items so Neil Lunn's answer is correct.
Actually, The save command is only on instance of Document class.
That have a lot of methods and attribute. So you can use lean() function to reduce work load.
Refer here. https://hashnode.com/post/why-are-mongoose-mongodb-odm-lean-queries-faster-than-normal-queries-cillvawhq0062kj53asxoyn7j
Another problem with save function, that will make conflict data in with multi-save at a same time.
Model.Update will make data consistently.
So to update multi items in array of document. Use your familiar programming language and try something like this, I use mongoose in that:
User.findOne({'_id': '4d2d8deff4e6c1d71fc29a07'}).lean().exec()
.then(usr =>{
if(!usr) return
usr.events.forEach( e => {
if(e && e.profile==10 ) e.handled = 0
})
User.findOneAndUpdate(
{'_id': '4d2d8deff4e6c1d71fc29a07'},
{$set: {events: usr.events}},
{new: true}
).lean().exec().then(updatedUsr => console.log(updatedUsr))
})
Update array field in multiple documents in mongo db.
Use $pull or $push with update many query to update array elements in mongoDb.
Notification.updateMany(
{ "_id": { $in: req.body.notificationIds } },
{
$pull: { "receiversId": req.body.userId }
}, function (err) {
if (err) {
res.status(500).json({ "msg": err });
} else {
res.status(200).json({
"msg": "Notification Deleted Successfully."
});
}
});
if you want to update array inside array
await Booking.updateOne(
{
userId: req.currentUser?.id,
cart: {
$elemMatch: {
id: cartId,
date: date,
//timeSlots: {
//$elemMatch: {
//id: timeSlotId,
//},
//},
},
},
},
{
$set: {
version: booking.version + 1,
'cart.$[i].timeSlots.$[j].spots': spots,
},
},
{
arrayFilters: [
{
'i.id': cartId,
},
{
'j.id': timeSlotId,
},
],
new: true,
}
);
I tried the following and its working fine.
.update({'events.profile': 10}, { '$set': {'events.$.handled': 0 }},{ safe: true, multi:true }, callback function);
// callback function in case of nodejs