I have contact documents which contain an embedded document "lead". So the data would look like this:
{
"_id" : ObjectId("54f8fa496d6163ad64010000"),
"name" : "teretyrrtuyytiuyi",
"email" : "rertytruyy#fdgjioj.com",
"fax" : "",
"birth_date" : null,
"phone" : "dfgdfhfgjg",
"phone_2" :
"hgjhkhjkljlj",
"lead" : { "_id" : ObjectId("54f8fa496d6163ad64020000"), "appointment status" : "dfhgfgjghjk" }
}
When there are many contacts, there will be many leads. I want to retrieve all the leads in the collection, without retrieving the owning contact. I tried the following but none seem to work:
db.lead.find()
db.contacts.find({ 'lead.$' : 1})
Any way to do this?
If that query makes sense for you, you should have probably used a different data structure. If your embedded document has an id, it is almost certainly supposed to be a first-level citizen instead.
You can work around this using the aggregation framework, but I'd consider that a hack that probably works around some more profound problem with your data model.
It's also not very elegant:
>
> db.contacts.aggregate({ $project : {
"appointment_status" : "$lead."appointment_status",
"lead_id" : "$lead.id", ... } });
>
That way, it'll look as if leads was a collection of its own, but it's not and this is just a bad hack around it.
Note that there's no wildcard operator, so if you want to have all fields projected to the root level, you'll have to do it manually. It'd be much easier to simply read the regular documents - if that's not what you need, correct your schema design.
Related
First let me explain schema of my collections.
I have 3 collections
company,deal,price
I want to use information from all three collection and make a single reactive,responsive table. Here is the image
Now the schema for price collection is like this
{
"_id" : "kSqH7QydFnPFHQmQH",
"timestamp" : ISODate("2015-10-11T11:49:50.241Z"),
"dealId" : "X5zTJ2y675PjmaLMx",
"deal" : "Games",
"price" : [{
"type" : "worth",
"value" : "Bat"
}, {
"type" : "Persons",
"value" : 4
}, {
"type" : "Cost",
"value" : 5
}],
"company" : "Company1"
}
Schema for company collection is
{
"_id" : "da2da"
"name" : "Company1"
}
Schema for deal collection is
{
"_id" : "X5zTJ2y675PjmaLMx",
"name" : "Games"
}
For each company there will be 3 columns added in table(worth,persons,cost)
For each deal there will be a new row in table.
As the information is coming from 3 collections into a single table. First I want to ask is it wise to make a table from 3 different collections? If yes how could I do that in blaze?
If no. Then I will have to make table from price collection only . What should be schema of this collection in best way.
P.S in both cases I want to make table reactive.
Firstly, I recommend reywood:publish-composite for publishing related collections.
Secondly there is no intrinsic problem in setting up a table like this, you'll first figure out which collection to loop over with your {{#each}} in spacebars and then you'll define helpers that return the values from the related collections to your templates.
As far as your schema design, the choice as to whether to use nesting within a collection vs. using an entirely separate collection is typically driven by size. If the related object is overall "small" then nesting can work well. You automatically get the nested object when you publish and query that collection. If otoh it's going to be "large" and/or you want to avoid having to update every document when something in the related object changes then a separate collection can be better.
If you do separate your collections then you'll want to refer to objects from the other collection by _id and not by name since names can easily change. For example in your price collection you'd want to use companyId: "da2da" instead of company: "Company1"
I have two MongoDB collections user and customer which are in one-to-one relationship. I'm new to MongoDB and I'm trying to insert documents manually although I have Mongoose installed. I'm not sure which is the correct way of storing document reference in MongoDB.
I'm using normalized data model and here is my Mongoose schema snapshot for customer:
/** Parent user object */
user: {
type: Schema.Types.ObjectId,
ref: "User",
required: true
}
user
{
"_id" : ObjectId("547d5c1b1e42bd0423a75781"),
"name" : "john",
"email" : "test#localhost.com",
"phone" : "01022223333",
}
I want to make a reference to this user document from the customer document. Which of the following is correct - (A) or (B)?
customer (A)
{
"_id" : ObjectId("547d916a660729dd531f145d"),
"birthday" : "1983-06-28",
"zipcode" : "12345",
"address" : "1, Main Street",
"user" : ObjectId("547d5c1b1e42bd0423a75781")
}
customer (B)
{
"_id" : ObjectId("547d916a660729dd531f145d"),
"birthday" : "1983-06-28",
"zipcode" : "12345",
"address" : "1, Main Street",
"user" : {
"_id" : ObjectId("547d5c1b1e42bd0423a75781")
}
}
Remember these things
Embedding is better for...
Small subdocuments
Data that does not change regularly
When eventual consistency is acceptable
Documents that grow by a small amount
Data that you’ll often need to perform a second query to fetch Fast reads
References are better for...
Large subdocuments
Volatile data
When immediate consistency is necessary
Documents that grow a large amount
Data that you’ll often exclude from the results
Fast writes
Variant A is Better.
you can use also populate with Mongoose
Use variant A. As long as you don't want to denormalize any other data (like the user's name), there's no need to create a child object.
This also avoids unexpected complexities with the index, because indexing an object might not behave like you expect.
Even if you were to embed an object, _id would be a weird name - _id is only a reserved name for a first-class database document.
One to one relations
1 to 1 relations are relations where each item corresponds to exactly one other item. e.g.:
an employee have a resume and vice versa
a building have and floor plan and vice versa
a patient have a medical history and vice versa
//employee
{
_id : '25',
name: 'john doe',
resume: 30
}
//resume
{
_id : '30',
jobs: [....],
education: [...],
employee: 25
}
We can model the employee-resume relation by having a collection of employees and a collection of resumes and having the employee point to the resume through linking, where we have an ID that corresponds to an ID in th resume collection. Or if we prefer, we can link in another direction, where we have an employee key inside the resume collection, and it may point to the employee itself. Or if we want, we can embed. So we could take this entire resume document and we could embed it right inside the employee collection or vice versa.
This embedding depends upon how the data is being accessed by the application and how frequently the data is being accessed. We need to consider:
frequency of access
the size of the items - what is growing all the time and what is not growing. So every time we add something to the document, there is a point beyond which the document need to be moved in the collection. If the document size goes beyond 16MB, which is mostly unlikely.
atomicity of data - there're no transactions in MongoDB, there're atomic operations on individual documents. So if we knew that we couldn't withstand any inconsistency and that we wanted to be able to update the entire employee plus the resume all the time, we may decide to put them into the same document and embed them one way or the other so that we can update it all at once.
In mongodb its very recommended to embedding document as possible as you can, especially in your case that you have 1-to-1 relations.
Why? you cant use atomic-join-operations (even it is not your main concern) in your queries (not the main reason). But the best reason is each join-op (theoretically) need a hard-seek that take about 20-ms. embedding your sub-document just need 1 hard-seek.
I believe the best db-schema for you is using just an id for all of your entities
{
_id : ObjectId("547d5c1b1e42bd0423a75781"),
userInfo :
{
"name" : "john",
"email" : "test#localhost.com",
"phone" : "01022223333",
},
customerInfo :
{
"birthday" : "1983-06-28",
"zipcode" : "12345",
"address" : "1, Main Street",
},
staffInfo :
{
........
}
}
Now if you just want the userinfo you can use
db.users.findOne({_id : ObjectId("547d5c1b1e42bd0423a75781")},{userInfo : 1}).userInfo;
it will give you just the userInfo:
/* 0 */
{
"name" : "john",
"email" : "test#localhost.com",
"phone" : "01022223333"
}
And if you just want the **customerInfo ** you can use
db.users.findOne({_id : ObjectId("547d5c1b1e42bd0423a75781")},{customerInfo : 1}).customerInfo;
it will give you just the customerInfo :
/* 0 */
{
"birthday" : "1983-06-28",
"zipcode" : "12345",
"address" : "1, Main Street"
}
and so on.
This schema has the minimum hard round-trip and actually you are using mongodb document-based feature with best performance you can achive.
I know this has been covered quite a lot on here, however, i'm very new to MongoDB and am struggling with applying answers i've found to my situation.
In short, I have two collections 'total_by_country_and_isrc' which is the output from a MapReduce function and 'asset_report' which contains an asset_id not present in the 'total_by_country_and_isrc' collection or the original raw data collection this was MapReduced from.
An example of the data in 'total_by_country_and_isrc' is:
{ "_id" : { "custom_id" : 4748532, "isrc" : "GBCEJ0100080",
"country" : "AE" }, "value" : 0 }
And an example of the data in the 'asset_report' is:
{ "_id" : ObjectId("51824ef016f3edbb14ef5eae"), "Asset ID" :
"A836656134476364", "Asset Type" : "Web", "Metadata Origination" :
"Unknown", "Custom ID" : "4748532", "ISRC" : "", }
I'd like to end up with the following ('total_by_country_and_isrc_with_asset_id'):
{ "_id" : { "Asset ID" : "A836656134476364", "custom_id" : 4748532,
"isrc" : "GBCEJ0100080", "country" : "AE" }, "value" : 0 }
I know how I would approach with in a relational database but I really want to try and get this working in Mongo as i'm dealing with some pretty large collections and feel Mongo is the right tool for the job.
Can anyone offer some guidance here?
I think you want to use the "reduce" output action: Output to a Collection with an Action. You'll need to regenerate total_by_country_and_isrc, because it doesn't look like asset_report has the fields it needs to generate the keys you already have in total_by_country_and_isrc – so "joining" the data is impossible.
First, write a map method that is capable of generating the same keys from the original collection (used to generate total_by_country_and_isrc) and also from the asset_report collection. Think of these keys as the "join" fields.
Next, map and reduce your original collection to create total_by_country_and_isrc with the correct keys.
Finally, map asset_report with the same method you used to generate total_by_country_and_isrc, but use a reduce function that can be used to reduce the intersection (by key) of this mapped data from asset_report and the data in total_by_country_and_isrc.
Gurus - I'm stuck in a situation that I can't figure out how I can query from the following collection "spouse", which has embedded document "surname" and check for equality with "surname" of this document:
{
"_id" : ObjectId("50bd2bb4fcfc6066b7ef090d"),
"name" : "Gwendolyn",
"surname" : "Davis",
"birthyear" : 1978,
"spouse" : {
"name" : "Dennis",
"surname" : "Evans",
"birthyear" : 1969
},
I need to query:
Output data for all spouses with the same surnames (if the surname of
one of the spouses is not specified, assume that it coincides with the
name of another)
I tried something like this:
db.task.find( {"surname" : { "spouse.surname" : 1 }} )
but it failed)
PLEASE PLEASE Guide me how I can achieve this any example/sample? based on this will be really helpful :-)
Thanks a lot!
You have three options.
Use $where modifier:
db.task.find({$where: 'this.spouse.surname === this.surname'})
Update all your documents and add special flag. After that you will be able to query documents by this flag. It's faster then $where, but requires altering your data.
Use MapReduce. It's quite complicated, but it allows you to do nearly anything.
I need help incrementing value of all keys in participants without having to know name of the keys inside of it.
> db.conversations.findOne()
{
"_id" : ObjectId("4faf74b238ba278704000000"),
"participants" : {
"4f81eab338ba27c011000001" : NumberLong(2),
"4f78497938ba27bf11000002" : NumberLong(2)
}
}
I've tried with something like
$mongodb->conversations->update(array('_id' => new \MongoId($objectId)), array('$inc' => array('participants' => 1)));
to no avail...
You need to redesign your schema. It is never a good idea to have "random key names". Even though MongoDB is schemaless, it still means you need to have defined key names. You should change your schema to:
{
"_id" : ObjectId("4faf74b238ba278704000000"),
"participants" : [
{ _id: "4f81eab338ba27c011000001", count: NumberLong(2) },
{ _id: "4f78497938ba27bf11000002", count: NumberLong(2) }
]
}
Sadly, even with that, you can't update all embedded counts in one command. There is currently an open feature request for that: https://jira.mongodb.org/browse/SERVER-1243
In order to still update everything, you should:
query the document
update all the counts on the client side
store the document again
In order to prevent race conditions with that, have a look at "Compare and Swap" and following paragraphs.
It is not possible to update all nested elements in one single move in current version of MongoDB. So I can advice to use "foreach {}".
Read realted topic: How to Update Multiple Array Elements in mongodb
I hope this feature will be implemented in next version.