If I have documents with similar structure as below. I am updating them with the results of the computations and I want to know whether the result has already been inserted into a document or not. Let's say for each document I run computation 'c' and computation 'd'. Now I want to display a table of all documents and show whether a computation 'd' has been already carried out. And for this table I do not care about computation 'c'.
{
"_id":1
"a":1,
"resultsOfComputation":{
"c":{large embedded document},
"d":{large embedded document}
}
}
{
"_id":2
"a":1,
"resultsOfComputation":{
"c":{large embedded document}
}
}
I would like to get a result that tells me whether a document contains a specific field. For example, I would like to know whether it contains field "resultsOfComputation.d", no matter what is the value of that field.
An example of the result of the query for "resultsOfComputation.d" would be:
{
"_id":1
"a":1,
"resultsOfComputation":{
"d":true
}
}
{
"_id":2
"resultsOfComputation":{
"d":false
}
}
If "resultsOfComputation.d" is not in the document it can also be undefined, which is also ok:
{
"_id":1
"a":1,
"resultsOfComputation":{
"d":true
}
}
{
"_id":2
"a":1,
"resultsOfComputation":{}
}
In general, the idea is to get all the root elements of the documents, but only true/false/undefined for the selected (one) result of computation, since the result of computation is a large embedded document.
Run the following aggregation pipeline to get the desired results:
db.collection.aggregate([
{
"$project": {
"a": 1,
"resultsOfComputation": {
"d": { "$gt": ["$resultsOfComputation.d", null] }
}
}
}
])
Sample Output
/* 1 */
{
"_id" : 1,
"a" : 1,
"resultsOfComputation" : {
"d" : true
}
}
/* 2 */
{
"_id" : 2,
"a" : 1,
"resultsOfComputation" : {
"d" : false
}
}
Related
In a MongoDB collection, I want to add a new object to an array which may or may not already exist, with the creation date of the object as one of the fields. Here's a barebones version of the structure of my test collection, showing the result that I want to achieve:
{
"_id" : 1,
"parent" : {
"array_1" : [
{ "key" : "value_1"
, "created": ISODate(...)
}
]
}
}
In meteor mongo, I create the parent with the following commands:
use test
db.test.insert({ _id: 1, parent: {} })
I can add a new array with the following command:
db.test.update(
{ _id: 1 }
, { $push: {
"parent.array_1": {
key: "value_1"
}
}
}
, { $currentDate:
{ "parent.array_1.$.created": true }
}
)
However, in the resulting array, the created field is not added. I need to use a second command which refers to the array in the selection query:
db.test.update(
{ _id: 1
, "parent.array_1.key": "value_1"
}
, { $currentDate:
{ "parent.array_1.$.created": true}
}
)
Is there a way to insert the created field at the same time as the array item that it belongs to, in a single command?
You are passing 3 arguments to update. The second argument is supposed to be the update, and the third is supposed to be an options object. If you want to include the create time in the array element, make sure it is in the object that you are pushing on the array. Since you are using javascript, that could be:
db.test.update(
{ _id: 1 }
, { $push: {
"parent.array_1": {
key: "value_1",
created: new Date()
}
}
}
)
I have a collection with documents having the following format
{
name: "A",
details : {
matchA: {
comment: "Hello",
score: 5
},
matchI: {
score: 10
},
lastMatch:{
score: 5
}
}
},
{
name: "B",
details : {
match2: {
score: 5
},
match7: {
score: 10
},
firstMatch:{
score: 5
}
}
}
I don't immediatly know the name of the keys that are children of details, they don't follow a known format, there can be different amounts etc.
I would like to write a query which will update the children in such a manner that any subdocument with a score less than 5, gets a new field added (say lowScore: true).
I've looked around a bit and I found $ and $elemMatch, but those only work on arrays. Is there an equivalent for subdocuments? Is there some way of doing it using the aggregation pipeline?
I don't think you can do that using a normal update(). There is a way through the aggregation framework which itself, however, cannot alter any persisted data. So you will need to loop through the results and update your documents individually like e.g. here: Aggregation with update in mongoDB
This is the required query to transform your data into what you need for the subsequent update:
collection.aggregate({
$addFields: {
"details": {
$objectToArray: "$details" // transform "details" into uniform array of key-value pairs
}
}
}, {
$unwind: "$details" // flatten the array created above
}, {
$match: {
"details.v.score": {
$lt: 10 // filter out anything that's not relevant to us
// (please note that I used some other filter than the one you wanted "score less than 5" to get some results using your sample data
},
"details.v.lowScore": { // this filter is not really required but it seems to make sense to check for the presence of the field that you want to create in case you run the query repeatedly
$exists: false
}
}
}, {
$project: {
"fieldsToUpdate": "$details.k" // ...by populating the "details" array again
}
})
Running this query returns:
/* 1 */
{
"_id" : ObjectId("59cc0b6afab2f8c9e1404641"),
"fieldsToUpdate" : "matchA"
}
/* 2 */
{
"_id" : ObjectId("59cc0b6afab2f8c9e1404641"),
"fieldsToUpdate" : "lastMatch"
}
/* 3 */
{
"_id" : ObjectId("59cc0b6afab2f8c9e1404643"),
"fieldsToUpdate" : "match2"
}
/* 4 */
{
"_id" : ObjectId("59cc0b6afab2f8c9e1404643"),
"fieldsToUpdate" : "firstMatch"
}
You could then $set your new field "lowScore" using a cursor as described in the linked answer above.
In my DynamoDB every document has several fields, one of the fields is a document called "engines" that holds several documents (all the engines) that hold several fields, as the picture shows below:
I would like to get all the couples of (engine,definitions) that their definition date is greater than a specific date.
I tried:
cursor=collection.find(
{'engines': { "$elemMatch" :
{ "definitions" :
{'$gt': startdate} } } }
,{'engines':{'$elemMatch':1}},{'engines':{'$elemMatch':{'definitions':1}}} )
but I get:
TypeError: skip must be an instance of int
Can someone help with the query?
You've mixed up the closing } and ended up passing {'engines':{'$elemMatch':{'definitions':1}}} as a skip argument value.
I think you meant:
cursor = collection.find(
{
'engines': {
"$elemMatch": {
"definitions": {
'$gt': startdate
}
}
}
},
{
'engines': {
'$elemMatch': {
'definitions': 1
}
}
}
)
Is there a way to conditionally $addToSet based on a specific key field in a subdocument on an array?
Here's an example of what I mean - given the collection produced by the following sample bootstrap;
cls
db.so.remove();
db.so.insert({
"Name": "fruitBowl",
"pfms" : [
{
"n" : "apples"
}
]
});
n defines a unique document key. I only want one entry with the same n value in the array at any one time. So I want to be able to update the pfms array using n so that I end up with just this;
{
"Name": "fruitBowl",
"pfms" : [
{
"n" : "apples",
"mState": 1111234
}
]
}
Here's where I am at the moment;
db.so.update({
"Name": "fruitBowl",
},{
// not allowed to do this of course
// "$pull": {
// "pfms": { n: "apples" },
// },
"$addToSet": {
"pfms": {
"$each": [
{
"n": "apples",
"mState": 1111234
}
]
}
}
}
)
Unfortunately, this adds another array element;
db.so.find().toArray();
[
{
"Name" : "fruitBowl",
"_id" : ObjectId("53ecfef5baca2b1079b0f97c"),
"pfms" : [
{
"n" : "apples"
},
{
"n" : "apples",
"mState" : 1111234
}
]
}
]
I need to effectively upsert the apples document matching on n as the unique identifier and just set mState whether or not an entry already exists. It's a shame I can't do a $pull and $addToSet in the same document (I tried).
What I really need here is dictionary semantics, but that's not an option right now, nor is breaking out the document - can anyone come up with another way?
FWIW - the existing format is a result of language/driver serialization, I didn't choose it exactly.
further
I've gotten a little further in the case where I know the array element already exists I can do this;
db.so.update({
"Name": "fruitBowl",
"pfms.n": "apples",
},{
$set: {
"pfms.$.mState": 1111234,
},
}
)
But of course that only works;
for a single array element
as long as I know it exists
The first limitation isn't a disaster, but if I can't effectively upsert or combine $addToSet with the previous $set (which of course I can't) then it the only workarounds I can think of for now mean two DB round-trips.
The $addToSet operator of course requires that the "whole" document being "added to the set" is in fact unique, so you cannot change "part" of the document or otherwise consider it to be a "partial match".
You stumbled on to your best approach using $pull to remove any element with the "key" field that would result in "duplicates", but of course you cannot modify the same path in different update operators like that.
So the closest thing you will get is issuing separate operations but also doing that with the "Bulk Operations API" which is introduced with MongoDB 2.6. This allows both to be sent to the server at the same time for the closest thing to a "contiguous" operations list you will get:
var bulk = db.so.initializeOrderedBulkOp();
bulk.find({ "Name": "fruitBowl", "pfms.n": "apples": }).updateOne({
"$pull": { "pfms": { "n": "apples" } }
});
bulk.find({ "Name": "fruitBowl" }).updateOne({
"$push": { "pfms": { "n": "apples", "state": 1111234 } }
})
bulk.execute();
That pretty much is your best approach if it is not possible or practical to move the elements to another collection and rely on "upserts" and $set in order to have the same functionality but on a collection rather than array.
I have faced the exact same scenario. I was inserting and removing likes from a post.
What I did is, using mongoose findOneAndUpdate function (which is similar to update or findAndModify function in mongodb).
The key concept is
Insert when the field is not present
Delete when the field is present
The insert is
findOneAndUpdate({ _id: theId, 'likes.userId': { $ne: theUserId }},
{ $push: { likes: { userId: theUserId, createdAt: new Date() }}},
{ 'new': true }, function(err, post) { // do the needful });
The delete is
findOneAndUpdate({ _id: theId, 'likes.userId': theUserId},
{ $pull: { likes: { userId: theUserId }}},
{ 'new': true }, function(err, post) { // do the needful });
This makes the whole operation atomic and there are no duplicates with respect to the userId field.
I hope this helpes. If you have any query, feel free to ask.
As far as I know MongoDB now (from v 4.2) allows to use aggregation pipelines for updates.
More or less elegant way to make it work (according to the question) looks like the following:
db.runCommand({
update: "your-collection-name",
updates: [
{
q: {},
u: {
$set: {
"pfms.$[elem]": {
"n":"apples",
"mState": NumberInt(1111234)
}
}
},
arrayFilters: [
{
"elem.n": {
$eq: "apples"
}
}
],
multi: true
}
]
})
In my scenario, The data need to be init when not existed, and update the field If existed, and the data will not be deleted. If the datas have these states, you might want to try the following method.
// Mongoose, but mostly same as mongodb
// Update the tag to user, If there existed one.
const user = await UserModel.findOneAndUpdate(
{
user: userId,
'tags.name': tag_name,
},
{
$set: {
'tags.$.description': tag_description,
},
}
)
.lean()
.exec();
// Add a default tag to user
if (user == null) {
await UserModel.findOneAndUpdate(
{
user: userId,
},
{
$push: {
tags: new Tag({
name: tag_name,
description: tag_description,
}),
},
}
);
}
This is the most clean and fast method in the scenario.
As a business analyst , I had the same problem and hopefully I have a solution to this after hours of investigation.
// The customer document:
{
"id" : "1212",
"customerCodes" : [
{
"code" : "I"
},
{
"code" : "YK"
}
]
}
// The problem : I want to insert dateField "01.01.2016" to customer documents where customerCodes subdocument has a document with code "YK" but does not have dateField. The final document must be as follows :
{
"id" : "1212",
"customerCodes" : [
{
"code" : "I"
},
{
"code" : "YK" ,
"dateField" : "01.01.2016"
}
]
}
// The solution : the solution code is in three steps :
// PART 1 - Find the customers with customerCodes "YK" but without dateField
// PART 2 - Find the index of the subdocument with "YK" in customerCodes list.
// PART 3 - Insert the value into the document
// Here is the code
// PART 1
var myCursor = db.customers.find({ customerCodes:{$elemMatch:{code:"YK", dateField:{ $exists:false} }}});
// PART 2
myCursor.forEach(function(customer){
if(customer.customerCodes != null )
{
var size = customer.customerCodes.length;
if( size > 0 )
{
var iFoundTheIndexOfSubDocument= -1;
var index = 0;
customer.customerCodes.forEach( function(clazz)
{
if( clazz.code == "YK" && clazz.changeDate == null )
{
iFoundTheIndexOfSubDocument = index;
}
index++;
})
// PART 3
// What happens here is : If i found the indice of the
// "YK" subdocument, I create "updates" document which
// corresponds to the new data to be inserted`
//
if( iFoundTheIndexOfSubDocument != -1 )
{
var toSet = "customerCodes."+ iFoundTheIndexOfSubDocument +".dateField";
var updates = {};
updates[toSet] = "01.01.2016";
db.customers.update({ "id" : customer.id } , { $set: updates });
// This statement is actually interpreted like this :
// db.customers.update({ "id" : "1212" } ,{ $set: customerCodes.0.dateField : "01.01.2016" });
}
}
}
});
Have a nice day !
I have recorded changes from an information system in a mongo database. Every time a set of values are set or changed, a record is saved in the mongo database.
The change collection is in the following form:
{ "user_id": 1, "timestamp": { "date" : "2010-09-22 09:28:02", "timezone_type" : 3, "timezone" : "Europe/Paris" } }, "changes: { "fieldA": "valueA", "fieldB": "valueB", "fieldC": "valueC" } }
{ "user_id": 1, "timestamp": { "date" : "2010-09-24 19:01:52", "timezone_type" : 3, "timezone" : "Europe/Paris" } }, "changes: { "fieldA": "new_valueA", "fieldB": null, "fieldD": "valueD" } }
{ "user_id": 1, "timestamp": { "date" : "2010-10-01 11:11:02", "timezone_type" : 3, "timezone" : "Europe/Paris" } }, "changes: { "fieldD": "new_valueD" } }
Of course there are thousands of records per user with different attributes which represent millions of records. What I want to do is to see a user status at a given time. By example, the user_id 1 at 2010-09-30 would be
fieldA: new_valueA
fieldC: valueC
fieldD: valueD
This means I need to flatten all the changes prior to a given date for a given user into a single record. Can I do that directly in mongo ?
Edit: I am using the 2.0 version of mongodb hence cannot benefit from the aggregation framework.
Edit: It sounds I have found the answer to my question.
var mapTimeAndChangesByUserId = function() {
var key = this.user_id;
var value = { timestamp: this.timestamp.date, changes: this.changes };
emit(key, value);
}
var reduceMergeChanges = function(user_id, changeset) {
var mergeFunction = function(a, b) { for (var attr in b) a[attr] = b[attr]; };
var result = {};
changeset.forEach(function(e) { mergeFunction(result, e.changes); });
return { timestamp: changeset.pop().timestamp, changes: result };
}
The reduce function merges the changes in the order they come and returns the result.
db.user_change.mapReduce(
mapTimeAndChangesByUserId,
reduceMergeChanges,
{
out: { inline: 1 },
query: { user_id: 1, "timestamp.date": { $lt: "2010-09-30" } },
sort: { "timestamp.date": 1 }
});
'results' : [
"_id": 1,
"value": {
"timestamp": "2010-09-24 19:01:52",
"changes": {
"fieldA": "new_valueA",
"fieldB": null,
"fieldC": "valueC",
"fieldD": "valueD"
}
}
]
Which is fine to me.
You could write a MR to do this.
Since the fields are a lot like tags you can modify a nice cookbook example of counting tags here: http://cookbook.mongodb.org/patterns/count_tags/ of course instead of counting you want the latest value applied (assumption since this is not clear in your question) for that field.
So lets get our map function:
map = function() {
if (!this.changes) {
// If there were not changes for some reason lets bail this record
return;
}
// We iterate the changes
for (index in this.changes) {
emit(index /* We emit the field name */, this.changes[index] /* We emit the field value */);
}
}
And now for our reduce:
reduce = function(values){
// This part is dependant upon your input query. If you add a sort of
// date (ts) DESC then you will prolly want the first index (0) not the last as
// gathered here by values.length
return values[values.length];
}
And this will output a single document per field change of the type:
{
_id: your_field_ie_fieldA,
value: whoop
}
You can then iterate the end of the (most likely) in line output and, bam, you have your changes.
This is of course one way of dong it and is not designed to be run completely in line to your app, however that all depends on the size of the data your working on; it could be run very close.
I am unsure whether the group and distinct can run on this but it looks like it might: http://docs.mongodb.org/manual/reference/method/db.collection.group/#db-collection-group however I should note that group is basically a MR wrapper but you could do something like (untested just like the MR above):
db.col.group( {
key: { 'changes.fieldA': 1, // the rest of the fields },
cond: { 'timestamp.date': { $gt: new Date( '01/01/2012' ) } },
reduce: function ( curr, result ) { },
initial: { }
} )
But it does require you to define the keys instead of just iterating them programmably (maybe a better way).