Related
I got a question about modeling wishlists using mongodb and mongoose. The idea is I need a user beeing able to have many different wishlists which contain many wishes, each wish making a reference to a single article
I was thinking about it and because a wishlist only belong to a single user I thought using embedded document for that.
Same for the wish beeing embedded to a wishlist.
So I got something like that
var UserSchema = new Schema({
...
wishlists: [wishlistSchema]
...
})
var WishlistSchema = new Schema({
...
wishes: [wishSchema]
...
})
but my question is what to do with the article ? should I use a reference or should I copy the article's data in an embedded document.
If I use embedded document I got an update problem. When the article's price change, to update every wish referencing this article become a struggle. But to access those wishes's article is a piece of cake.
If I use reference, The update is not a problem anymore but I got a probleme when I filter the wish depending on their article criteria ( when I filter the wishes depending on a price, category etc .. ).
I think the second way is probably the best but I don't know how if it's possible to build a query to filter the wish depending on the article's field. I tried a lot of things using population but nothing works very well when you need to populate depending on a nested object field. ( for exemple getting wishes where their article respond to certain conditions ).
Is this kind of query doable ?
Sry for the loooong question and for my bad English :/ but any advice would be great !
In my experience in dealing with NoSQL database (mongo, mainly), when designing a collection, do not think of the relations. Instead, think of how you would display, page, and retrieve the documents.
I would prefer embedding and updating multiple schema when there's a change, as opposed to doing a ref, for multiple reasons.
Get would be fast and easy and filter is not a problem (like you've said)
Retrieve operations usually happen a lot more often than updates and with proper indexing, you wouldn't really have to bother about performance.
It leverages on NoSQL's schema-less nature and you'll be less prone restructuring due to requirement changes (new sorting, new filters, etc)
Paging would be a lot less of a hassle, and UI would not be restricted with it's design with paging and limit.
Joining could become expensive. Redundant data might be a hassle to update but it's always better than not being able to display a data in a particular way because your schema is normalized and joining is difficult.
I'd say that the rule of thumb is that only split them when you do not need to display them together. It is not impossible to join them back if you do, but definitely more troublesome.
I want to design a question structure with some comments. Which relationship should I use for comments: embed or reference?
A question with some comments, like stackoverflow, would have a structure like this:
Question
title = 'aaa'
content = 'bbb'
comments = ???
At first, I thought of using embedded comments (I think embed is recommended in MongoDB), like this:
Question
title = 'aaa'
content = 'bbb'
comments = [ { content = 'xxx', createdAt = 'yyy'},
{ content = 'xxx', createdAt = 'yyy'},
{ content = 'xxx', createdAt = 'yyy'} ]
It is clear, but I'm worried about this case: If I want to edit a specified comment, how do I get its content and its question? There is no _id to let me find one, nor question_ref to let me find its question. (Is there perhaps a way to do this without _id and question_ref?)
Do I have to use ref rather than embed? Do I then have to create a new collection for comments?
This is more an art than a science. The Mongo Documentation on Schemas is a good reference, but here are some things to consider:
Put as much in as possible
The joy of a Document database is that it eliminates lots of Joins. Your first instinct should be to place as much in a single document as you can. Because MongoDB documents have structure, and because you can efficiently query within that structure (this means that you can take the part of the document that you need, so document size shouldn't worry you much) there is no immediate need to normalize data like you would in SQL. In particular any data that is not useful apart from its parent document should be part of the same document.
Separate data that can be referred to from multiple places into its own collection.
This is not so much a "storage space" issue as it is a "data consistency" issue. If many records will refer to the same data it is more efficient and less error prone to update a single record and keep references to it in other places.
Document size considerations
MongoDB imposes a 4MB (16MB with 1.8) size limit on a single document. In a world of GB of data this sounds small, but it is also 30 thousand tweets or 250 typical Stack Overflow answers or 20 flicker photos. On the other hand, this is far more information than one might want to present at one time on a typical web page. First consider what will make your queries easier. In many cases concern about document sizes will be premature optimization.
Complex data structures:
MongoDB can store arbitrary deep nested data structures, but cannot search them efficiently. If your data forms a tree, forest or graph, you effectively need to store each node and its edges in a separate document. (Note that there are data stores specifically designed for this type of data that one should consider as well)
It has also been pointed out than it is impossible to return a subset of elements in a document. If you need to pick-and-choose a few bits of each document, it will be easier to separate them out.
Data Consistency
MongoDB makes a trade off between efficiency and consistency. The rule is changes to a single document are always atomic, while updates to multiple documents should never be assumed to be atomic. There is also no way to "lock" a record on the server (you can build this into the client's logic using for example a "lock" field). When you design your schema consider how you will keep your data consistent. Generally, the more that you keep in a document the better.
For what you are describing, I would embed the comments, and give each comment an id field with an ObjectID. The ObjectID has a time stamp embedded in it so you can use that instead of created at if you like.
In general, embed is good if you have one-to-one or one-to-many relationships between entities, and reference is good if you have many-to-many relationships.
Well, I'm a bit late but still would like to share my way of schema creation.
I have schemas for everything that can be described by a word, like you would do it in the classical OOP.
E.G.
Comment
Account
User
Blogpost
...
Every schema can be saved as a Document or Subdocument, so I declare this for each schema.
Document:
Can be used as a reference. (E.g. the user made a comment -> comment has a "made by" reference to user)
Is a "Root" in you application. (E.g. the blogpost -> there is a page about the blogpost)
Subdocument:
Can only be used once / is never a reference. (E.g. Comment is saved in the blogpost)
Is never a "Root" in you application. (The comment just shows up in the blogpost page but the page is still about the blogpost)
I came across this small presentation while researching this question on my own. I was surprised at how well it was laid out, both the info and the presentation of it.
http://openmymind.net/Multiple-Collections-Versus-Embedded-Documents
It summarized:
As a general rule, if you have a lot of [child documents] or if they are large, a separate collection might be best.
Smaller and/or fewer documents tend to be a natural fit for embedding.
Actually, I'm quite curious why nobody spoke about the UML specifications. A rule of thumb is that if you have an aggregation, then you should use references. But if it is a composition, then the coupling is stronger, and you should use embedded documents.
And you will quickly understand why it is logical. If an object can exist independently of the parent, then you will want to access it even if the parent doesn't exist. As you just can't embed it in a non-existing parent, you have to make it live in it's own data structure. And if a parent exist, just link them together by adding a ref of the object in the parent.
Don't really know what is the difference between the two relationships ?
Here is a link explaining them:
Aggregation vs Composition in UML
If I want to edit a specified comment, how to get its content and its question?
You can query by sub-document: db.question.find({'comments.content' : 'xxx'}).
This will return the whole Question document. To edit the specified comment, you then have to find the comment on the client, make the edit and save that back to the DB.
In general, if your document contains an array of objects, you'll find that those sub-objects will need to be modified client side.
Yes, we can use the reference in the document. To populate another document just like SQL i joins. In MongoDB, they don't have joins to map one to many relationship documents. Instead that we can use populate to fulfil our scenario.
var mongoose = require('mongoose')
, Schema = mongoose.Schema
var personSchema = Schema({
_id : Number,
name : String,
age : Number,
stories : [{ type: Schema.Types.ObjectId, ref: 'Story' }]
});
var storySchema = Schema({
_creator : { type: Number, ref: 'Person' },
title : String,
fans : [{ type: Number, ref: 'Person' }]
});
The population is the process of automatically replacing the specified paths in the document with the document(s) from other collection(s). We may populate a single document, multiple documents, plain objects, multiple plain objects, or all objects returned from a query. Let's look at some examples.
Better you can get more information please visit: http://mongoosejs.com/docs/populate.html
I know this is quite old but if you are looking for the answer to the OP's question on how to return only specified comment, you can use the $ (query) operator like this:
db.question.update({'comments.content': 'xxx'}, {'comments.$': true})
MongoDB gives freedom to be schema-less and this feature can result in pain in the long term if not thought or planned well,
There are 2 options either Embed or Reference. I will not go through definitions as the above answers have well defined them.
When embedding you should answer one question is your embedded document going to grow, if yes then how much (remember there is a limit of 16 MB per document) So if you have something like a comment on a post, what is the limit of comment count, if that post goes viral and people start adding comments. In such cases, reference could be a better option (but even reference can grow and reach 16 MB limit).
So how to balance it, the answer is a combination of different patterns, check these links, and create your own mix and match based on your use case.
https://www.mongodb.com/blog/post/building-with-patterns-a-summary
https://www.mongodb.com/blog/post/6-rules-of-thumb-for-mongodb-schema-design-part-1
If I want to edit a specified comment, how do I get its content and
its question?
If you had kept track of the number of comments and the index of the comment you wanted to alter, you could use the dot operator (SO example).
You could do f.ex.
db.questions.update(
{
"title": "aaa"
},
{
"comments.0.contents": "new text"
}
)
(as another way to edit the comments inside the question)
I have an app in which customers can partially define their schema by adding custom field to various domain objects we have. We are looking at doing trending data for these custom fields. I've been thinking about storing the data in a format which has the the changes listed on the object.
{
_id: "id",
custom1: 2,
changes: {
'2011-3-25': { custom1: 1 }
}
}
This would obviously have to be less than the max document size (16mb) which I think is well within the amount of changes we'd have.
Another alternative would be have multiple records for every object change:
{
_id: "id",
custom1: 1,
changeTime: '2011-3-25'
}
This doesn't have the document restriction, but there would be more records, requiring more work to get the full change set for a record.
Which would you choose?
I think I'd be looking to go down the single document route if it will remain within the 16MB limit. That way, it's just a single read to load a record and all of it's changes which should be pretty darn quick. Having the changes listed within the document feels like a natural way to model the data.
In situations like this, especially if it's not something I've done before, I'd try to knock up a benchmark to test the approaches - that way, the pros/cons/performance of each approach presents itself to you and (hopefully) gives you confidence in the approach you choose.
I want to design a question structure with some comments. Which relationship should I use for comments: embed or reference?
A question with some comments, like stackoverflow, would have a structure like this:
Question
title = 'aaa'
content = 'bbb'
comments = ???
At first, I thought of using embedded comments (I think embed is recommended in MongoDB), like this:
Question
title = 'aaa'
content = 'bbb'
comments = [ { content = 'xxx', createdAt = 'yyy'},
{ content = 'xxx', createdAt = 'yyy'},
{ content = 'xxx', createdAt = 'yyy'} ]
It is clear, but I'm worried about this case: If I want to edit a specified comment, how do I get its content and its question? There is no _id to let me find one, nor question_ref to let me find its question. (Is there perhaps a way to do this without _id and question_ref?)
Do I have to use ref rather than embed? Do I then have to create a new collection for comments?
This is more an art than a science. The Mongo Documentation on Schemas is a good reference, but here are some things to consider:
Put as much in as possible
The joy of a Document database is that it eliminates lots of Joins. Your first instinct should be to place as much in a single document as you can. Because MongoDB documents have structure, and because you can efficiently query within that structure (this means that you can take the part of the document that you need, so document size shouldn't worry you much) there is no immediate need to normalize data like you would in SQL. In particular any data that is not useful apart from its parent document should be part of the same document.
Separate data that can be referred to from multiple places into its own collection.
This is not so much a "storage space" issue as it is a "data consistency" issue. If many records will refer to the same data it is more efficient and less error prone to update a single record and keep references to it in other places.
Document size considerations
MongoDB imposes a 4MB (16MB with 1.8) size limit on a single document. In a world of GB of data this sounds small, but it is also 30 thousand tweets or 250 typical Stack Overflow answers or 20 flicker photos. On the other hand, this is far more information than one might want to present at one time on a typical web page. First consider what will make your queries easier. In many cases concern about document sizes will be premature optimization.
Complex data structures:
MongoDB can store arbitrary deep nested data structures, but cannot search them efficiently. If your data forms a tree, forest or graph, you effectively need to store each node and its edges in a separate document. (Note that there are data stores specifically designed for this type of data that one should consider as well)
It has also been pointed out than it is impossible to return a subset of elements in a document. If you need to pick-and-choose a few bits of each document, it will be easier to separate them out.
Data Consistency
MongoDB makes a trade off between efficiency and consistency. The rule is changes to a single document are always atomic, while updates to multiple documents should never be assumed to be atomic. There is also no way to "lock" a record on the server (you can build this into the client's logic using for example a "lock" field). When you design your schema consider how you will keep your data consistent. Generally, the more that you keep in a document the better.
For what you are describing, I would embed the comments, and give each comment an id field with an ObjectID. The ObjectID has a time stamp embedded in it so you can use that instead of created at if you like.
In general, embed is good if you have one-to-one or one-to-many relationships between entities, and reference is good if you have many-to-many relationships.
Well, I'm a bit late but still would like to share my way of schema creation.
I have schemas for everything that can be described by a word, like you would do it in the classical OOP.
E.G.
Comment
Account
User
Blogpost
...
Every schema can be saved as a Document or Subdocument, so I declare this for each schema.
Document:
Can be used as a reference. (E.g. the user made a comment -> comment has a "made by" reference to user)
Is a "Root" in you application. (E.g. the blogpost -> there is a page about the blogpost)
Subdocument:
Can only be used once / is never a reference. (E.g. Comment is saved in the blogpost)
Is never a "Root" in you application. (The comment just shows up in the blogpost page but the page is still about the blogpost)
I came across this small presentation while researching this question on my own. I was surprised at how well it was laid out, both the info and the presentation of it.
http://openmymind.net/Multiple-Collections-Versus-Embedded-Documents
It summarized:
As a general rule, if you have a lot of [child documents] or if they are large, a separate collection might be best.
Smaller and/or fewer documents tend to be a natural fit for embedding.
Actually, I'm quite curious why nobody spoke about the UML specifications. A rule of thumb is that if you have an aggregation, then you should use references. But if it is a composition, then the coupling is stronger, and you should use embedded documents.
And you will quickly understand why it is logical. If an object can exist independently of the parent, then you will want to access it even if the parent doesn't exist. As you just can't embed it in a non-existing parent, you have to make it live in it's own data structure. And if a parent exist, just link them together by adding a ref of the object in the parent.
Don't really know what is the difference between the two relationships ?
Here is a link explaining them:
Aggregation vs Composition in UML
If I want to edit a specified comment, how to get its content and its question?
You can query by sub-document: db.question.find({'comments.content' : 'xxx'}).
This will return the whole Question document. To edit the specified comment, you then have to find the comment on the client, make the edit and save that back to the DB.
In general, if your document contains an array of objects, you'll find that those sub-objects will need to be modified client side.
Yes, we can use the reference in the document. To populate another document just like SQL i joins. In MongoDB, they don't have joins to map one to many relationship documents. Instead that we can use populate to fulfil our scenario.
var mongoose = require('mongoose')
, Schema = mongoose.Schema
var personSchema = Schema({
_id : Number,
name : String,
age : Number,
stories : [{ type: Schema.Types.ObjectId, ref: 'Story' }]
});
var storySchema = Schema({
_creator : { type: Number, ref: 'Person' },
title : String,
fans : [{ type: Number, ref: 'Person' }]
});
The population is the process of automatically replacing the specified paths in the document with the document(s) from other collection(s). We may populate a single document, multiple documents, plain objects, multiple plain objects, or all objects returned from a query. Let's look at some examples.
Better you can get more information please visit: http://mongoosejs.com/docs/populate.html
I know this is quite old but if you are looking for the answer to the OP's question on how to return only specified comment, you can use the $ (query) operator like this:
db.question.update({'comments.content': 'xxx'}, {'comments.$': true})
MongoDB gives freedom to be schema-less and this feature can result in pain in the long term if not thought or planned well,
There are 2 options either Embed or Reference. I will not go through definitions as the above answers have well defined them.
When embedding you should answer one question is your embedded document going to grow, if yes then how much (remember there is a limit of 16 MB per document) So if you have something like a comment on a post, what is the limit of comment count, if that post goes viral and people start adding comments. In such cases, reference could be a better option (but even reference can grow and reach 16 MB limit).
So how to balance it, the answer is a combination of different patterns, check these links, and create your own mix and match based on your use case.
https://www.mongodb.com/blog/post/building-with-patterns-a-summary
https://www.mongodb.com/blog/post/6-rules-of-thumb-for-mongodb-schema-design-part-1
If I want to edit a specified comment, how do I get its content and
its question?
If you had kept track of the number of comments and the index of the comment you wanted to alter, you could use the dot operator (SO example).
You could do f.ex.
db.questions.update(
{
"title": "aaa"
},
{
"comments.0.contents": "new text"
}
)
(as another way to edit the comments inside the question)
I am trying to figure out the equivalent of foreign keys and indexes in NoSQL KVP or Document databases. Since there are no pivotal tables (to add keys marking a relation between two objects) I am really stumped as to how you would be able to retrieve data in a way that would be useful for normal web pages.
Say I have a user, and this user leaves many comments all over the site. The only way I can think of to keep track of that users comments is to
Embed them in the user object (which seems quite useless)
Create and maintain a user_id:comments value that contains a list of each comment's key [comment:34, comment:197, etc...] so that that I can fetch them as needed.
However, taking the second example you will soon hit a brick wall when you use it for tracking other things like a key called "active_comments" which might contain 30 million ids in it making it cost a TON to query each page just to know some recent active comments. It also would be very prone to race-conditions as many pages might try to update it at the same time.
How can I track relations like the following in a NoSQL database?
All of a user's comments
All active comments
All posts tagged with [keyword]
All students in a club - or all clubs a student is in
Or am I thinking about this incorrectly?
All the answers for how to store many-to-many associations in the "NoSQL way" reduce to the same thing: storing data redundantly.
In NoSQL, you don't design your database based on the relationships between data entities. You design your database based on the queries you will run against it. Use the same criteria you would use to denormalize a relational database: if it's more important for data to have cohesion (think of values in a comma-separated list instead of a normalized table), then do it that way.
But this inevitably optimizes for one type of query (e.g. comments by any user for a given article) at the expense of other types of queries (comments for any article by a given user). If your application has the need for both types of queries to be equally optimized, you should not denormalize. And likewise, you should not use a NoSQL solution if you need to use the data in a relational way.
There is a risk with denormalization and redundancy that redundant sets of data will get out of sync with one another. This is called an anomaly. When you use a normalized relational database, the RDBMS can prevent anomalies. In a denormalized database or in NoSQL, it becomes your responsibility to write application code to prevent anomalies.
One might think that it'd be great for a NoSQL database to do the hard work of preventing anomalies for you. There is a paradigm that can do this -- the relational paradigm.
The couchDB approach suggest to emit proper classes of stuff in map phase and summarize it in reduce.. So you could map all comments and emit 1 for the given user and later print out only ones. It would require however lots of disk storage to build persistent views of all trackable data in couchDB. btw they have also this wiki page about relationships: http://wiki.apache.org/couchdb/EntityRelationship.
Riak on the other hand has tool to build relations. It is link. You can input address of a linked (here comment) document to the 'root' document (here user document). It has one trick. If it is distributed it may be modified at one time in many locations. It will cause conflicts and as a result huge vector clock tree :/ ..not so bad, not so good.
Riak has also yet another 'mechanism'. It has 2-layer key name space, so called bucket and key. So, for student example, If we have club A, B and C and student StudentX, StudentY you could maintain following convention:
{ Key = {ClubA, StudentX}, Value = true },
{ Key = {ClubB, StudentX}, Value = true },
{ Key = {ClubA, StudentY}, Value = true }
and to read relation just list keys in given buckets. Whats wrong with that? It is damn slow. Listing buckets was never priority for riak. It is getting better and better tho. btw. you do not waste memory because this example {true} can be linked to single full profile of StudentX or Y (here conflicts are not possible).
As you see it NoSQL != NoSQL. You need to look at specific implementation and test it for yourself.
Mentioned before Column stores look like good fit for relations.. but it all depends on your A and C and P needs;) If you do not need A and you have less than Peta bytes just leave it, go ahead with MySql or Postgres.
good luck
user:userid:comments is a reasonable approach - think of it as the equivalent of a column index in SQL, with the added requirement that you cannot query on unindexed columns.
This is where you need to think about your requirements. A list with 30 million items is not unreasonable because it is slow, but because it is impractical to ever do anything with it. If your real requirement is to display some recent comments you are better off keeping a very short list that gets updated whenever a comment is added - remember that NoSQL has no normalization requirement. Race conditions are an issue with lists in a basic key value store but generally either your platform supports lists properly, you can do something with locks, or you don't actually care about failed updates.
Same as for user comments - create an index keyword:posts
More of the same - probably a list of clubs as a property of student and an index on that field to get all members of a club
You have
"user": {
"userid": "unique value",
"category": "student",
"metainfo": "yada yada yada",
"clubs": ["archery", "kendo"]
}
"comments": {
"commentid": "unique value",
"pageid": "unique value",
"post-time": "ISO Date",
"userid": "OP id -> THIS IS IMPORTANT"
}
"page": {
"pageid": "unique value",
"post-time": "ISO Date",
"op-id": "user id",
"tag": ["abc", "zxcv", "qwer"]
}
Well in a relational database the normal thing to do would be in a one-to-many relation is to normalize the data. That is the same thing you would do in a NoSQL database as well. Simply index the fields which you will be fetching the information with.
For example, the important indexes for you are
Comment.UserID
Comment.PageID
Comment.PostTime
Page.Tag[]
If you are using NosDB (A .NET based NoSQL Database with SQL support) your queries will be like
SELECT * FROM Comments WHERE userid = ‘That user’;
SELECT * FROM Comments WHERE pageid = ‘That user’;
SELECT * FROM Comments WHERE post-time > DateTime('2016, 1, 1');
SELECT * FROM Page WHERE tag = 'kendo'
Check all the supported query types from their SQL cheat sheet or documentation.
Although, it is best to use RDBMS in such cases instead of NoSQL, yet one possible solution is to maintain additional nodes or collections to manage mapping and indexes. It may have additional cost in form of extra collections/nodes and processing, but it will give an solution easy to maintain and avoid data redundancy.