There is a lot of content of what kind of relationships should use in a database schema. However, I have not seen anything about mixing both techniques.
The idea is to embed only the necessaries attributes and with them a reference. This way the application have the necessary data for rendering and the reference for the updating methods.
The problem I see here is that the logic for handle any CRUD operations becomes more tricky because its mandatory to update multiples collections however I have all the information in one single read.
Basic schema for a page that only wants the students names of a classroom:
CLASSROOM COLLECTION
{"_id": ObjectID(),
"students": [{"studentId" : ObjectID(),
"name" : "John Doe",
},
...
]
}
STUDENTS COLLECION
{"_id": ObjectID(),
"name" : "John Doe",
"address" : "...",
"age" : "...",
"gender": "..."
}
I use the students' collection in a different page and there I do not want any information about the classroom. That is the reason not to embed the students.
I started to learning mongo a few days ago and I don't know if this kind of schema bring some problems.
You can embed some fields and store other fields in a different collection as you are suggesting.
The issues with such an arrangement in my opinion would be:
What is the authority for a field? For example, what if a field like name is both embedded and stored in the separate collection, and the values differ?
Both updating and querying become awkward as you need to do it differently depending on which field is being worked with. If you make a mistake and go in the wrong place, you create/compound the first issue.
Related
I'm Following this Doc
http://docs.doctrine-project.org/projects/doctrine-mongodb-odm/en/latest/tutorials/getting-started.html
And
http://symfony.com/doc/current/bundles/DoctrineMongoDBBundle/index.html
When I Save My Document, I have two Collection
like this:
{
"_id" : ObjectId("5458e370d16fb63f250041a7"),
"name" : "A Foo Bar",
"price" : 19.99,
"posts" : [
{
"$ref" : "Embedd",
"$id" : ObjectId("5458e370d16fb63f250041a8"),
"$db" : "test_database"
}
]
}
I'd like have
{
"_id" : ObjectId("5458e370d16fb63f250041a7"),
"name" : "A Foo Bar",
"price" : 19.99,
"posts" : [
{
"mycomment" :"dsdsds"
" date" : date
}
]
}
I want denormalization my data. How Can i Do it?
Can I use Methods like $push,$addToSet etc of mongoDb?
Thanks
Doctrine ODM supports both references and embedded documents.
In your first example, you're using references. The main document (let's assume it's called Product) references many Post documents. Those Post documents live in their own collection (for some reason this is named Embedd -- I would suggest renaming that if you keep this schema). By default, ODM uses the DBRef convention for references, so each reference is itself a small embedded document with $ref, $id, and $db fields.
Denormalization can be achieved by using embedded documents (an #EmbedMany mapping in your case). If you were embedding a Post document, the Post class should be mapped as an #EmbeddedDocument. This tells ODM that it's not a first-class document (belonging to its own collection), so it won't have to worry about tracking it by _id and the like (in fact, embedded documents won't even need identifiers unless you want to map one).
My rule of thumb for deciding to embed or references has generally been asking myself, "Will I need this document outside of the context of the parent document?" If a Post will not have an identity outside of the Product record, I'm comfortable embedding it; however, if I find later that my application also wants to show users a list of all of their Posts, or that I need to query by Posts (e.g. a feed of all recent Posts, irrespective of Product), then I may want to reference documents in a Posts collection (or simply duplicate embedded Posts as needed).
Alternatively, you may decide that Posts should exist in both their own collection and be embedded on Product. In that case, you can create an AbstractPost class as a #MappedSuperclass and define common fields there. Then, extend this with both Post and EmbeddedPost sub-classes (mapped accordingly). You'll be responsible for creating some code to generate an EmbeddedPost from a Post document, which will be suitable for embedding in the Product.posts array. Furthermore, you'll need to handle data synchronization between the top-level and embedded Posts (e.g. if someone edits a Post comment, you may want all the corresponding embedded versions updated as well).
On the subject of references: ODM also supports a simple option for reference mappings, in which case it will just store the referenced document's _id instead of the larger DBRef object. In most cases, having DBRef store the collection and database name for each referenced document is quite redundant; however, DBRef is actually useful if you're using single-collection inheritance, as ODM uses the object to store extra discriminator information (i.e. the class of the referenced object).
We want to design a scalable database. If we have N users with 1 Billion user responses, from the 2 options below which will be a good design? We would want to query based on userID as well as Reponse ID.
Having 2 Collections one for the user information and another to store the responses along with user ID. Each response is stored as a document so we will have 1 billion documents.
User Collection
{
"userid" : "userid1",
"password" : "xyz",
,
"City" : "New York",
},
{
"userid" : "userid2",
"password" : "abc",
,
"City" : "New York",
}
responses Collection
{
"userid": "userid1",
"responseID": "responseID1",
"response" : "xyz"
},
{
"userid": "userid1",
"responseID": "responseID2",
"response" : "abc"
},
{
"userid": "userid2",
"responseID": "responseID3",
"response" : "mno"
}
Having 1 Collection to store both the information as below. Each response is represented by a new key (responseIDX).
{
"userid" : "userid1",
"responseID1" : "xyz",
"responseID2" : "abc",
,
"responseN"; "mno",
"city" : "New York"
}
If you use your first options, I'd use a relational database (like MySQL) opposed to MongoDB. If you're heartfelt on MongoDB, use it to your advantage.
{
"userId": n,
"city": "foo"
"responses": {
"responseId1": "response message 1",
"responseId2": "response message 2"
}
}
As for which would render a better performance, run a few benchmark tests.
Between the two options you've listed - I would think using a separate collection would scale better - or possibly a combination of a separate collection and still using embedded documents.
Embedded documents can be a boon to your schema design - but do not work as well when you have an endlessly growing set of embedded documents (responses, in your case). This is because of document growth - as the document grows - and outgrows the allocated amount of space for it on disk, MongoDB must move that document to a new location to accommodate the new document size. That can be expensive and have severe performance penalties when it happens often or in high concurrency environments.
Also, querying on those embedded documents can become troublesome when you are looking to selectively return only a subset of responses, especially across users. As in - you can not return only the matching embedded documents. Using the positional operator, it is possible to get the first matching embedded document however.
So, I would recommend using a separate collection for the responses.
Though, as mentioned above, I would also suggest experimenting with other ways to group those responses in that collection. A document per day, per user, per ...whatever other dimensions you might have, etc.
Group them in ways that allow multiple embedded documents and compliments how you would query for them. If you can find the sweet spot between still using embedded documents in that collection and minimizing document growth, you'll have fewer overall documents and smaller index sizes. Obviously this requires benchmarking and testing, as the same caveats listed above can apply.
Lastly (and optionally), with that type of data set, consider using increment counters where you can on the front end to supply any type of aggregated reporting you might need down the road. Though the Aggregation Framework in MongoDB is great - having, say, the total response count for a user pre-aggregated is far more convenient then trying to get a count by running a aggregate query on the full dataset.
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I am creating a project that requires data storage and I am considering useing MongoDB but am having trouble finding the logical / optimal way of organising the data
My simplified data needs to store Place information like so:
{place_city : "London",
place_owner: "Tim",
place_name: "Big Ben"}
{place_city : "Paris",
place_owner: "Tim",
place_name: "Eifel Tower"}
{place_city : "Paris",
place_owner: "Ben",
place_name: "The Louvre"}
And here are the main operations I need
Retrieve all my places
Retrieve all my friends places
Retrieve all my friends cities
If I use mongoDB a collection document max size is 16meg right? If that is correct then I can't store all the information in a PLACES similar to my example above right?
I would probably need to create a "OWNER" collection? like so:
{
owner: "Tim",
cities: [ {
name: "London",
places:[ {name:"Big Ben"}]
},
{
name: "Paris",
places:[ {name:"Eifel Tower"}, {name: "The Louvre"}]
}
]
}
but the problem now is that Retrieving my friends places becomes cumbersome and my friends cities even more so....
any advice from a cunning DB architect woudl be much appreciated.
The size limit of 16MB is per document, not per collection.
{place_city : "London", place_owner: "Tim", place_name: "Big Ben"}
is a very little document, so don't worry. The design of your collections depends heavily on how you query your data.
The data size limitation is per document and not per collection. Collections can easily become several GB (or even TB) large.
I would suggest you keep your data as simple as you have, like:
{place_city : "London",
place_owner: "Tim",
place_name: "Big Ben"}
{place_city : "Paris",
place_owner: "Tim",
place_name: "Eifel Tower"}
{place_city : "Paris",
place_owner: "Ben",
place_name: "The Louvre"}
I am thinking that friends are stored like this:
{
username: "Ben",
friends: [ "Tim", "Bob" ]
}
Then your three queries can be done as:
All your places: db.places.find( { place_owner: "Ben" } );
All your friends' places with two queries (pseudo code):
friends = db.friends.find( { username: "Ben" } );
// friends = [ "Tim", "Bob" ], you do need to do some code to make this change
db.places.find( { place_owner: { $in: [ "Tim", "Bob" ] } } );
All your friends' cities with two queries (pseudo code):
friends = db.friends.find( { username: "Ben" } );
db.so.distinct( 'name', { place_owner: { $in: [ "Tim", "Bob" ] } } );
Even with millions of documents, this should work fine, providing you have an index on the fields that you query for: { place_owner: 1 } and { username: 1 }.
I love MongoDB, but this data is not a good candidate for MongoDB. MongoDB does not support relationships, and that is basically all you are tracking here. Use a relational database to store the relationships.
Think of it like this: under the skin of the DBMS, MongoDB or SQL, an index is an index, a table is a table (basically). You get more performance from MongoDB not because it does the same things faster, but because you are able to use it to get your DB server to do less. (E.g. pull an entire document containing nested arrays and subdocs instead of joining a bunch of tables together). There are some fundamental differences in the way MongoDB handles updates, but for querying simple data sets most systems are going to be relatively similar. One big difference between the two rooted in the way MongoDB works is that it cannot use data in a collection as parameters for a another query, which is basically the whole point of a relational database. Since 2 of your use cases require "joins" (to "all my friends"), you need two queries.
So what you're doing with two queries is the same thing as a join, except relational databases are optimized to do this extremely efficiently; I promise you it will be slower to do this join manually, plus you're sending all data (friends' IDs) over the wire and making an extra DB connection. Now, if you could store all your friends' cities and places in a single document, MongoDB will probably be (slightly) faster than joining, but now you've got a new problem, because you now have to manage all this, anytime anyone adds a city or place all their friends have to be modified--this is unrealistic.
But there is even more to the story than that, because SQL DBMS are extremely mature applications with lots of features to improve query performance. They let you do things like create "materialize views" that store all your friends cities and places in memory and update themselves automatically any time one of their source tables is updated so you don't have to do anything special, you'd just query and you'd get your data without actually executing any joins. (A materialized table is not the right fit here, but just as an example, it is possible if you needed it.)
ALSO, when you are using MongoDB, a guideline I've found helpful is this, anytime you are asking yourself whether your document will be large enough to store all the data you will EVENTUALLY have, you probably have a design problem on your hands. If a document's growth is unbound, it should probably be enumerated within a collection instead. Or put another way, your collections should grow as your application is used, not your document's size (much).
If breaking apart your schema like this means that for primary operations you are doing a lot of manual joins, it is worth considering the question of whether or not you should be using a relational database instead.
I have seen many references pointing to the use of Lucene or Solr as a NoSQL data store, not just the indexing engine：
NoSQL (MongoDB) vs Lucene (or Solr) as your database
http://searchhub.org/2010/04/29/for-the-guardian-solr-is-the-new-database/
However, because Lucene only provides a "flat" document structure, where each field can be multi-value (scalar), I can't seem to fully understand how people are mapping complex structured data into Lucene for index and store. For example:
{
"firstName": "Joe",
"lastName": "Smith",
"addresses" ： [
{
"type" : "home",
"line1" : "1 Main Street",
"city" : "New York",
},
{
"type" : "office",
"line1" : "P.O. Box 1234",
"zip：“10000”
}
]
}
Things can obviously get more complex. I.e. what if the object has two collections: addresses and phone numbers? what if address itself has a collection?
I can think of two ways to map this two lucene "document":
Create a stored but not indexed field to store a JSON/BSON version of the object, and then create other index but don't store fields for indexing/searching.
Find a smart way to somehow fit the object into Lucene way of storing data. I.e. use dot notation to flat the fields, use multi-value fields to store individual collection value and then somehow recreate the object on its way back...
I wonder if people have dealt with similar problems before and what solution have you used?
Take a look at my Stupid Lucene Tricks: Hierarchies for one approach.
It depends what the usage is.
If you only need them for display, you can the complex value (addresses) as a JSON string and store it as multiple value field, if you need to use them as index, you can choose following struture:
"addresses_type"： [
"home",
"office"
],
"addresses_line1"： [
"1 Main Street",
"P.O. Box 1234"
],
"addresses_city"： [
"New York",
""
],
"addresses_zip"： [
"",
"10000"
]
Let's say I have a collection of documents such as:
{ "_id" : 0 , "owner":0 "name":"Doc1"},{ "_id" : 1 , "owner":1, "name":"Doc1"}, etc
And, on the other hand the owners are represented as a separate collection:
{ "_id" : 0 , "username":"John"}, { "_id" : 1 , "username":"Sam"}
How can I make sure that, when I insert a document it references the user in a correct way. In old-school RDBMS this could easily be done using a Foreign Key.
I know that I can check the correctness of insertion from my business code, BUT what if an attacker tampers with my request to the server and puts "owner" : 100, and Mongo doesn't throw any exception back.
I would like to know how this situation should be handled in a real-word application.
Thank you in advance!
MongoDB doesn't have foreign keys (as you have presumably noticed). Fundamentally the answer is therefore, "Don't let users tamper with the requests. Only let the application insert data that follows your referential integrity rules."
MongoDB is great in lots of ways... but if you find that you need foreign keys, then it's probably not the correct solution to your problem.
To answer your specific question - while MongoDB encourages handling foreign-key relationships on the client side, they also provide the idea of "Database References" - See this help page.
That said, I don't recommend using a DBRef. Either let your client code manage the associations or (better yet) link the documents together from the start. You may want to consider embedding the owner's "documents" inside the owner object itself. Assemble your documents to match your usage patterns and MongoDB will shine.
This is a one-to-one to relationship. It's better to embed one document in another, instead of maintaining separate collections. Check here on how to model them in mongodb and their advantages.
Although its not explicitly mentioned in the docs, embedding gives you the same effect as foreign key constraints. Just want to make this idea clear. When you have two collections like that:
C1:
{ "_id" : 0 , "owner":0 "name":"Doc1"},{ "_id" : 1 , "owner":1, "name":"Doc1"}, etc
C2:
{ "_id" : 0 , "username":"John"}, { "_id" : 1 , "username":"Sam"}
And if you were to declare foreign key constraint on C2._id to reference C1._id (assuming MongoDB allows it), it would mean that you cannot insert a document into C2 where C2._id is non-existent in C1. Compare this with an embedded document:
{
"_id" : 0 ,
"owner" : 0,
"name" : "Doc1",
"owner_details" : {
"username" : "John"
}
}
Now the owner_details field represents the data from the C2 collection, and the remaining fields represent the data from C1. You can't add an owner_details field to a non-existent document. You're essentially achieving the same effect.
This questions was originally answered in 2011, so I decided to post an update here.
Starting from version MongoDB 4.0 (released in June 2018), it started supporting multi-document ACID transactions.
Relations now can be modeled in two approaches:
Embedded
Referenced (NEW!)
You can model referenced relationship like so:
{
"_id":ObjectId("52ffc33cd85242f436000001"),
"contact": "987654321",
"dob": "01-01-1991",
"name": "Tom Benzamin",
"address_ids": [
ObjectId("52ffc4a5d85242602e000000")
]
}
Where the sample document structure of address document:
{
"_id":ObjectId("52ffc4a5d85242602e000000"),
"building": "22 A, Indiana Apt",
"pincode": 123456,
"city": "Los Angeles",
"state": "California"
}
If someone really wants to enforce the Foreign keys in the Project/WebApp. Then you should with a MixSQL approach i.e. SQL + NoSQL
I would prefer that the Bulky data which doesn't have that much references then it can be stored in NoSQL database Store. Like : Hotels or Places type of data.
But if there is some serious things like OAuth modules Tables, TokenStore and UserDetails and UserRole (Mapping Table) etc.... then you can go with SQL.
I would also reccommend that if username's are unique, then use them as the _id. You will save on an index. In the document being stored, set the value of 'owner' in the application as the value of 'username' when the document is created and never let any other piece of code update it.
If there are requirements to change the owner, then provide appropirate API's with business rules implemented.
There woudln't be any need of foreign keys.