I'm starting a new website project and i would like to use DocumentDB as database instead of traditional RDBMS.
I will need two kind of documents to store:
User documents, they will hold all the user data.
Survey documents, that will hold all data about survays.
May i put both kind in a single collection or should i create one collection for each?
How you do this is totally up to you - it's a fairly broad question, and there are good reasons for combining, and good reasons for separating. But objectively, you'll have some specific things to consider:
Each collection has its own cost footprint (starting around $24 per collection).
Each collection has its own performance (RU capacity) and storage limit.
Documents within a collection do not have to be homogeneous - each document can have whatever properties you want. You'll likely want some type of identification property that you can query on, to differentiate document types, should you store them all in a single collection.
Transactions are collection-scoped. So, for example, if you're building server-side stored procedures and need to modify content across your User and Survey documents, you need to keep this in mind.
Related
Im using MongoDB for storing information for a nodeJS application and a doubt came to my mind, after finding that it is possible to use ObjectID to ref another document. As it is known, MongoDB is a no-SQL db, so there is no need for consistency whatsoever and information can be repeated.
So, lets say, I have a collection for users and one of their field values is 'friends', which is an array of this user friends (another users). What is the best practice, saving all the user info there (thus repeating the same thing over and over again throughout the DB) or saving only the ObjectID of the friendUser (makes way more sense to me, but it sounds kinda SQL mindset). I'm not really getting when should I use each of the options, so a professional opinion would be very appreciated.
To model relationships between connected data, you can reference a document or embed it in another document as a subdocument.
Referencing a document does not create a “real” relationship between these two documents as does with a relational database.
Referencing documents is also known as normalization. It is good for data consistency but creates more queries in your system.
Embedding documents is also known as denormalization.
The benefit of Embedding approach is getting all the data you need about a document and it’s sub-document(s) with a single query. Therefore, this approach is very fast. The drawback is that data may not stay as consistent in the database.
Important
If one document is to be used by many documents then better create a referenced doc.
i. Will Save Space.
ii. if any change required, we will have to update only the referenced doc
instead of updating many docs.
Create sub doc(embedded)
i. If another document is not dependent on the subdocument.
Source: https://vegibit.com/mongoose-relationships-tutorial/
Recommended reading:
MongoDB Applied Design Patterns by Rick Copeland
To Embed or Reference
I have to design a schema in such a way that I can store user id and their order which can be multiple products like bread, butter plus in addition to that I want to store the quantity of product ordered, please guide.
It is difficult to provide you with a real solution to your problem as designing a NoSQL DB structure depends on how you want to access your data. You can keep orders as nested/embedded documents in the User model or store them in a separate collection. In the first case, you will have all the data in one requests, but you will not be able to query and receive orders, that match certain criteria as you will get all orders including those that match. And then you would need to filter them out. Or you could use aggregation to get exactly what you need.
However, there is a limitation to keep in mind. MongoDB document has a size limitation - 16 megabytes. Since users may have very many orders, you can reach the document size limit for some users for sure. Aggregation also has a limitation - Pipeline stages have a limit of 100 megabytes of RAMe but you can override it.
Having orders in a separate collection would require you to separately load them for users. While it is one more request, it will give you more flexibility in terms of how you query them.
Then, of course, create/update operations are also done differently for both cases.
My advice would be that you carefully design your application first - what data you need and where you will show it, how you create/update it. It will give you a better idea and chances are that relational DB will be a better choice for what you need (though absolutely not necessary).
I am new to Azure Cosmos DB using the DocumentDB API. I plan to model my data so that one document references another document. This is pretty straight forward, as described in Modeling document data. However, I also would like to separate the related documents into different collections (this decision is related to how the data are partitioned).
Edit 7/24/2017: In response to a comment wondering why I chose to use separate collections: The reasoning for a separate collections mainly comes down to partition keys and read/write priorities. Since a certain partition key is required to be present in ALL documents in the collection, it then makes sense to separate documents that the chosen partition key doesn't belong. After much weighing of options, the partition key that I settled on was one that would optimize write speeds and evenly distribute my data across shards - but unfortunately it did not logically belong in my "Metadata" documents. Since there is a one to gazillion relationship between metadata and measurements, I chose to use a reference to the metadata in the measurements instead of embedding. And because metadata would rarely (or never) be appended to each measurement, I considered the expense of an additional round-trip to the DB a very low concern.
Since the reference is a "weak link" that is not verified by the database, is it possible and wise to store additional information, such as the collection name? That is, instead of having just a string id, we may use a kind of path?
Metadata document in collection "Metadata":
{
"id": "metadata1",
...
}
Measurement document in collection "Measurements":
{
"id": "measurement1",
"metadata-id" : "../Metadata/metadata1",
...
}
Then, when I parse the data in my application/script I know what collection and document to query.
Finally, I assume there are other/better ways to go about this and I welcome your suggests (e.g. underscores, not slashes; use a symbol to represent a collection, like $Metadata; etc). Or, is my use of relations spanning collections a code smell?
Thank you!
Edit: To the downvoter, can you please explain your reasoning? Is my question uninformed, unclear, or not useful? Why?
You're thinking about this the wrong way and incurring significantly more cost for an "optimization" that isn't necessary since you're billed at the per collection level. What you should be doing is picking a more generic partition key. Something like key or partitionKey. The tradeoff here is that you'll need to ensure in your client application that you populate this property on all of your documents (it may lead to a duplicated value, but ultimately that's okay). They you can continue to use the value of whatever you chose originally for your Measurements document and set something different for your Metadata documents.
I've written about this extensively in some other answers here and I believe it's one of the biggest misunderstandings about using Cosmos effectively and at scale. It doesn't help that in many Cosmos examples they talk about picking a partitionKey like deviceId or postal code which implies that you're dealing with homogeneous documents.
Please refer to this question that I answered regarding homogeneous vs heterogeneous in documentdb. The biggest argument for this pattern is the new addition of Graph APIs in Cosmos which necessitate having many different types of entities in a single collection and supports exactly the use case you're describing minus the extra collections. Obviously when dealing with heterogeneous types there isn't going to be a single property present on all documents that is appropriate for a partition key which is why you need to go generic.
What you're trying to do is feasible. The convention you use is not particularly important, as long as you can figure out the reference. Keep in mind though, that using this type of "relations" will be rather slow, because you need to fetch all documents from one collection and then fetch the related documents in a separate query. It can have a serious impact on your application.
Another possibility is to optimise your data for reading: you can embed the metadata document inside the other document. Your data will be duplicated, so if you update those documents, you will have to update them in both collections, but you'll probably write less often than you read (probably, if that's not the case, this setup would be worse).
Your documents would look like this:
Metadata document in collection "Metadata":
{
"id": "metadata1",
...
}
Measurement document in collection "Measurements":
{
"id": "measurement1",
"metadata" : {
"id": "metadata1",
...
},
...
}
I am using Azure DocumentDB and all my experience in NoSql has been in MongoDb. I looked at the pricing model and the cost is per collection. In MongoDb I would have created 3 collections for what I was using: Users, Firms, and Emails. I noted that this approach would cost $24 per collection per month.
I was told by the people I work with that I'm doing it wrong. I should have all three of those things stored in a single collection with a field to describe what the data type is. That each collection should be related by date or geographic area so one part of the world has a smaller portion to search.
and to:
"Combine different types of documents into a single collection and add
a field across all to separate them in searching like a type field or
something"
I would never have dreamed of doing that in Mongo, as it would make indexing, shard keys, and other things hard to get right.
There might not be may fields that overlap between the objects (example: Email and firm objects)
I can do it this way, but I can't seem to find a single example of anyone else doing it that way - which indicates to me that maybe it isn't right. Now, I don't need an example, but can someone point me to some location that describes which is the 'right' way to do it? Or, if you do create a single collection for all data - other than Azure's pricing model, what are the advantages / disadvantages in doing that?
Any good articles on DocumentDb schema design?
Yes. In order to leverage CosmosDb to it's full potential need to think of a Collection is an entire Database system and not as a "table" designed to hold only one type of object.
Sharding in Cosmos is exceedingly simply. You just specify a field that all of your documents will populate and select that as your partition key. If you just select a generic value such as key or partitionKey you can easily separate the storage of your inbound emails, from users, from anything else by picking appropriate values.
class InboundEmail
{
public string Key {get; set;} = "EmailsPartition";
// other properties
}
class User
{
public string Key {get; set;} = "UsersPartition";
// other properties
}
What I'm showing is still only an example though. In reality your partition key values should be even more dynamic. It's important to understand that queries against a known partition are extremely quick. As soon as you need to scan across multiple partitions you'll see much slower and more costly results.
So, in an app that ingests a lot of user data. Keeping a single user's activity together in one partition might make sense for that particular entity.
If you want evidence that this is the appropriate way to use CosmosDb, consider the addition of the new Gremlin Graph APIs. Graphs are inherently heterogenous as they contain many different entities and entity types as well as the relationships between them. The query boundary of Cosmos is at the collection level so if you tried putting your entities all in different collections none of the Graph API or queries would work.
EDIT:
I noticed in the comments you made this statement And you would have an index on every field in both objects. CosmosDb does automatically index every field of every document. They use a special proprietary path based indexing mechanism that ensures every path of your JSON tree has indices on it. You have to specifically opt out of this auto indexing feature.
I'm planning to implement this schema in MongoDB, I have been doing some readings about schema design, and the notion was whenever you structure your data like a relational database you must be doing something wrong.
My questions:
what should I do when collection size gets larger than 16MB limit?
app_log in server_log collections gets might in some cases grow larger than 16MB depending how busy the server is.
I'm aware of the cap feature that I could use, but the requirement is store all logs for 90 days.
Do you see any potential issues with my design?
Is it a good practice to have the application check collection size and create new collection by day / hour ..etc to accommodate log size growth?
Thanks
Your collection size is not restricted to 16MB, as one of the comments pointed out, you can check in the MongoDB manual that it is the largest document size. So there is no need to separate the same class of data between different collections, in fact it would be a major headache for you to do so :) One user collection, one for your servers and one for your server_logs. You can then create references from one collection to the next by using the id field.
Whether this is a good design or not will depend on your queries. In general, you want to avoid using joins in Mongo (they're still possible, but if you're doing a bunch of joins, you're using it wrong, and really should use a relational DB :-)
For example, if most of your queries are on the server_log collection and only use the fields in that collection, then you'll be fine. OTOH, if your server_log queries always need to pull in data from the server collection as well (say for example the name and userId fields), then it might be worth selectively denormalizing that data. That's a fancy way of saying, you may wish to copy the name and userId fields into your server_log documents, so that your queries can avoid having to join with the server collection. Of course, every time you denormalize, you add complexity to your application which must now ensure that the data is consistent across multiple collections (e.g., when you change the server name, you have to make sure you change it in the server_logs, too).
You may wish to make a list of the queries you expect to perform, and see if they can be done with a minimum of joins with your current schema. If not, see if a little denormalization will help. If you're getting to the point where either you need to do a bunch of joins or a lot of manual management of denormalized data in order to satisfy your queries, then you may need to rethink your schema or even your choice of DB.
what should I do when collection size gets larger than 16MB limit
In Mongodb there is no limit for collection size. Limit is exist for each document. Each document should not exceed the size of 16 MB.
Do you see any potential issues with my design?
No issue with above design