So I'm using mongodb and I'm unsure if I've got the correct / best database collection design for what I'm trying to do.
There can be many items, and a user can create new groups with these items in. Any user may follow any group!
I have not just added the followers and items into the group collection because there could be 5 items in the group, or there could be 10000 (and the same for followers) and from research I believe that you should not use unbound arrays (where the limit is unknown) due to performance issues when the document has to be moved because of its expanding size. (Is there a recommended maximum for array lengths before hitting performance issues anyway?)
I think with the following design a real performance issue could be when I want to get all of the groups that a user is following for a specific item (based off of the user_id and item_id), because then I have to find all of the groups the user is following, and from that find all of the item_groups with the group_id $in and the item id. (but I can't actually see any other way of doing this)
Follower
.find({ user_id: "54c93d61596b62c316134d2e" })
.exec(function (err, following) {
if (err) {throw err;};
var groups = [];
for(var i = 0; i<following.length; i++) {
groups.push(following[i].group_id)
}
item_groups.find({
'group_id': { $in: groups },
'item_id': '54ca9a2a6508ff7c9ecd7810'
})
.exec(function (err, groups) {
if (err) {throw err;};
res.json(groups);
});
})
Are there any better DB patterns for dealing with this type of setup?
UPDATE: Example use case added in comment below.
Any help / advice will be really appreciated.
Many Thanks,
Mac
I agree with the general notion of other answers that this is a borderline relational problem.
The key to MongoDB data models is write-heaviness, but that can be tricky for this use case, mostly because of the bookkeeping that would be required if you wanted to link users to items directly (a change to a group that is followed by lots of users would incur a huge number of writes, and you need some worker to do this).
Let's investigate whether the read-heavy model is inapplicable here, or whether we're doing premature optimization.
The Read Heavy Approach
Your key concern is the following use case:
a real performance issue could be when I want to get all of the groups that a user is following for a specific item [...] because then I have to find all of the groups the user is following, and from that find all of the item_groups with the group_id $in and the item id.
Let's dissect this:
Get all groups that the user is following
That's a simple query: db.followers.find({userId : userId}). We're going to need an index on userId which will make the runtime of this operation O(log n), or blazing fast even for large n.
from that find all of the item_groups with the group_id $in and the item id
Now this the trickier part. Let's assume for a moment that it's unlikely for items to be part of a large number of groups. Then a compound index { itemId, groupId } would work best, because we can reduce the candidate set dramatically through the first criterion - if an item is shared in only 800 groups and the user is following 220 groups, mongodb only needs to find the intersection of these, which is comparatively easy because both sets are small.
We'll need to go deeper than this, though:
The structure of your data is probably that of a complex network. Complex networks come in many flavors, but it makes sense to assume your follower graph is nearly scale-free, which is also pretty much the worst case. In a scale free network, a very small number of nodes (celebrities, super bowl, Wikipedia) attract a whole lot of 'attention' (i.e. have many connections), while a much larger number of nodes have trouble getting the same amount of attention combined.
The small nodes are no reason for concern, the queries above, including round-trips to the database are in the 2ms range on my development machine on a dataset with tens of millions of connections and > 5GB of data. Now that data set isn't huge, but no matter what technology you choose you, will be RAM bound because the indices must be in RAM in any case (data locality and separability in networks is generally poor), and the set intersection size is small by definition. In other words: this regime is dominated by hardware bottlenecks.
What about the supernodes though?
Since that would be guesswork and I'm interested in network models a lot, I took the liberty of implementing a dramatically simplified network tool based on your data model to make some measurements. (Sorry it's in C#, but generating well-structured networks is hard enough in the language I'm most fluent in...).
When querying the supernodes, I get results in the range of 7ms tops (that's on 12M entries in a 1.3GB db, with the largest group having 133,000 items in it and a user that follows 143 groups.)
The assumption in this code is that the number of groups followed by a user isn't huge, but that seems reasonable here. If it's not, I'd go for the write-heavy approach.
Feel free to play with the code. Unfortunately, it will need a bit of optimization if you want to try this with more than a couple of GB of data, because it's simply not optimized and does some very inefficient calculations here and there (especially the beta-weighted random shuffle could be improved).
In other words: I wouldn't worry about the performance of the read-heavy approach yet. The problem is often not so much that the number of users grows, but that users use the system in unexpected ways.
The Write Heavy Approach
The alternative approach is probably to reverse the order of linking:
UserItemLinker
{
userId,
itemId,
groupIds[] // for faster retrieval of the linker. It's unlikely that this grows large
}
This is probably the most scalable data model, but I wouldn't go for it unless we're talking about HUGE amounts of data where sharding is a key requirement. The key difference here is that we can now efficiently compartmentalize the data by using the userId as part of the shard key. That helps to parallelize queries, shard efficiently and improve data locality in multi-datacenter-scenarios.
This could be tested with a more elaborate version of the testbed, but I didn't find the time yet, and frankly, I think it's overkill for most applications.
I read your comment/use-case. So I update my answer.
I suggest to change the design as per this article: MongoDB Many-To-Many
The design approach is different and you might want to remodel your approach to this. I'll try to give you an idea to start with.
I make the assumption that a User and a Follower are basically the same entities here.
I think the point you might find interesting is that in MongoDB you can store array fields and this is what I will use to simplify/correct your design for MongoDB.
The two entities I would omit are: Followers and ItemGroups
Followers: It is simply a User who can follow Groups. I would add an
array of group ids to have a list of Groups that the User follows. So instead of having an entity Follower, I would only have User with an array field that has a list of Group Ids.
ItemGroups: I would remove this entity too. Instead I would use an array of Item Ids in the Group entity and an array of Group Ids in the Item entity.
This is basically it. You will be able to do what you described in your use case. The design is simpler and more accurate in the sense that it reflects the design decisions of a document based database.
Notes:
You can define indexes on array fields in MongoDB. See Multikey Indexes for example.
Be wary about using indexes on array fields though. You need to understand your use case in order to decide whether it is reasonable or not. See this article. Since you only reference ObjectIds I thought you could try it, but there might be other cases where it is better to change the design.
Also note that the ID field _id is a MongoDB
specific field type of ObjectID used as primary key. To access the ids you can refer to it e.g. as user.id, group.id, etc. You can use an index to ensure uniqueness as per this question.
Your schema design could look like this:
As to your other question/concerns
Is there a recommended maximum for array lengths before hitting performance issues anyway?
the answer is in MongoDB the document size is limited to 16 MB and there is now way you can work around that. However 16 MB is considered to be sufficient; if you hit the 16 MB then your design has to be improved. See here for info, section Document Size Limit.
I think with the following design a real performance issue could be when I want to get all of the groups that a user is following for a specific item (based off of the user_id and item_id)...
I would do this way. Note how "easier" it sounds when using MongoDB.
get the item of the user
get groups that reference that item
I would be rather concerned if the arrays get very large and you are using indexes on them. This could overall slow down write operations on the respective document(s). Maybe not so much in your case, but not entirely sure.
You're on the right track to creating a performant NoSQL schema design, and I think you're asking the right questions as to how to properly lay things out.
Here's my understanding of your application:
It looks like Groups can both have many Followers (mapping users to groups) and many Items, but Items may not necessarily be in many Groups (although it is possible). And from your given use-case example, it sounds like retrieving all the Groups an Item is in and all the Items in a Group will be some common read operations.
In your current schema design, you've implemented a model between mapping users to groups as followers and items to groups as item_groups. This works alright until you mention the problem with more complex queries:
I think with the following design a real performance issue could be when I want to get all of the groups that a user is following for a specific item (based off of the user_id and item_id)
I think a few things could help you out in this situation:
Take advantage of MongoDB's powerful indexing capabilities. In particular, I think you should consider creating compound indexes on your Follower objects covering your Group and User, and your Item_Groups on Item and Group, respectively. You'll also want to make sure this kind of relationship is unique, in that a user can only follow a group once and an item can only be added to a group once. This would best be achieved in some pre-save hooks defined in your schema, or using a plugin to check for validity.
FollowerSchema.index({ group: 1, user: 1 }, { unique: true });
Item_GroupsSchema.index({ group: 1, item: 1 }, { unique: true });
Using an index on these fields will create some overhead when writing to the collection, but it sounds like reading from the collection will be a more common interaction so it'll be worth it (I'd suggest reading more up on index performance).
Since a User probably won't be following thousands of groups, I think it'd be worthwhile to include in the user model an array of groups the user is following. This will help you out with that complex query when you want to find all instances of an item in groups that a user is currently following, since you'll have the list of groups right there. You'll still have the implementation where your using $in: groups, but it'll be with one less query to the collection.
As I mentioned before, it seems like items may not necessarily be in that many groups (just like users won't necessarily be following thousands of groups). If the case may commonly be that an item is in maybe a couple hundred groups, I'd consider just adding an array to the item model for each group that it gets added to. This would increase your performance when reading all the groups an item is in, a query you mentioned would be a common one. Note: You'd still use the Item_Groups model to retrieve all the items in a group by querying on the (now indexed) group_id.
Unfortunately NoSQL databases aren't eligible in this case. Your data model seems exact relational. According to MongoDB documentation we can do only these and can perform only these.
There are some practices. MongoDB advises to us using Followers collection to get which user follows which group and vice versa with good performance. You may find the closest case to your situation on this page on slide 14th. But I think the slides can eligible if you want to get each result on different page. For instance; You are a twitter user and when you click the followers button you'll see the all your followers. And then you click on a follower name you'll see the follower's messages and whatever you can see. As we can see all of those work step-by-step. No needed a relational query.
I believe that you should not use unbound arrays (where the limit is unknown) due to performance issues when the document has to be moved because of its expanding size. (Is there a recommended maximum for array lengths before hitting performance issues anyway?)
Yes, you're right. http://askasya.com/post/largeembeddedarrays .
But if you have about a hundred items in your array there is no problem.
If you have fixed size some data thousands you may embed them into your relational collections as array. And you can query your indexed embedded document fields rapidly.
In my humble opinion, you should create hundreds of thousands test data and check performances of using embedded documents and arrays eligible to your case. Don't forget creating indexes appropriate your queries. You may try to using document references on your tests. After tests if you like performance of results go ahead..
You had tried to find group_id records that are followed by a specific user and then you've tried to find a specific item with those group_id. Would it be possible Item_Groups and Followers collections have a many-to-many relation?
If so, many-to-many relation isn't supported by NoSQL databases.
Is there any chance you can change your database to MySQL?
If so you should check this out.
briefly MongoDB pros against to MySQL;
- Better writing performance
briefly MongoDB cons against to MySQL;
- Worse reading performance
If you work on Node.js you may check https://www.npmjs.com/package/mysql and https://github.com/felixge/node-mysql/
Good luck...
Related
I have a use case where I need to do complicated string matching on records of which there are about 5.1 Million of. When I say complicated string matching, I mean using library to do fuzzy string matching. (http://blog.bripkens.de/fuzzy.js/demo/)
The database we use at work is SAP Hana which is excellent for retrieving and querying because it's in memory so I would like to avoid pulling data out of there and re-populating it in memory on the application layer but at the same time I cannot take advantages of the libraries (there is an API for fuzzy matching in the DB but it's not comprehensive enough for us).
What is the middle ground here? If I do pre-processing and associate words in the DB with certain keywords the user might search for I can cut down the overhead but are there any best practises that are employed when It comes to this ?
If it matters. The list is a list of Billing Descriptors (that show up on CC statements) therefore, the user will search these descriptors to find out which companies the descriptor belongs too.
Assuming your "billing descriptor" is a single column, probably of type (N)VARCHAR I would start with a very simple SAP HANA fuzzy search, e.g.:
SELECT top 100 SCORE() AS score, <more fields>
FROM <billing_documents>
WHERE CONTAINS(<bill_descr_col>, <user_input>, FUZZY(0.7))
ORDER BY score DESC;
Maybe this is already good enough when you want to apply your js library on the result set. If not, I would start to experiment with the similarCalculationMode option, like 'similarcalculationmode=substringsearch' etc. And I would always have a look at the response times, they can be higher when using some of the options.
Only if response times are to high, or many active concurrent users are using your query, I would try to create a fuzzy search index on your search column. If you need more search options, you can also create a fullext index.
But that all really depends on you use case, the values you want to compare etc.
There is a very comprehensive set of features and options for different use cases, check help.sap.com/hana/SAP_HANA_Search_Developer_Guide_en.pdf.
In a project we did a free style search on several address columns (name, surname, company name, post code, street) and we got response times of 100-200ms on ca 6 Mio records WITHOUT using any special indexes.
This question has been driving me crazy and I can't get my head around it. I come from a MySQL relational background and have been using Meteorjs and Mongo. For the purposes of this question take the example of posts and authors. One Author to Many Posts. I have come up with two ways in which to do this:
Have a single collection of posts - Each post has the author information embedded into the document. This of course leads to denormalization and issues such as if the author name changes how do you keep the data correct.
Have two collections: posts and authors - Each post has an author ID which references the authors collection. I then attempt to do a "join" on a non relational database while trying to maintain reactivity.
It seems to me with MongoDB degrees of denormalization is acceptable and I am tempted to embed as implementing joins really does feel like going against the ideals of Mongo.
Can anyone shed any light on what is the right approach especially in terms of wanting my app data to scale well and be manageable?
Thanks
Denormalisation is useful when you're scaling your application and you notice that some queries are taking too much time to complete. I also noticed that most Mongodb developers tend to forget about data normalisation but that's another topic.
Some developers say things like: "Don't use observe and observeChanges because it's slow". We're building real-time applications so that a normal thing to happen, it's a CPU intensive app design.
In my opinion, you should always aim for a normalised database design and then you have to decide, try and test which fields, that duplicated/denormalised, could improve your app's performance. Example: You remove 1 query per user. The UI need an extra field and it's fast to duplicated it, etc.
With the denormalisation you've an extra price to pay. You've to update the denormalised fields according to the main collection.
Example:
Let's say that you Authors and Articles collections. On each article you have the author name. The author might change his name. With a normalised scenario, it works fine. With a denormalised scenario you have to update the Author document name AND every single article, owned by this author, with the new name.
Keeping a normalised design makes you life easier but denormalisation, eventually, becomes necessary.
From a MeteorJs perspective: With the normalised scenario you're sending data from 2 Collections to the client. With the denormalised scenario, you only send 1 collection. You can also reactively join on the server and send 1 collection to the client, although it increases the RAM usage because of MergeBox on the server.
Denormalisation is something that it's very specify for you application needs. You can use Kadira to find ways of making your application faster. The database design is only 1 factor out of many that you play with when trying to improve performance.
I am working with a database that was handed down to me. It has approximately 25 tables, and a very buggy query system that hasn't worked correctly for a while. I figured, instead of trying to bug test the existing code, I'd just start over from scratch. I want to say before I get into it, "I'm not asking anyone to build the code for me". I'm not that lazy, all I want to know is, what would be the best way to lay out the code? The existing query uses "JOIN" to combine the results of all the tables in one variable, and spits it into the query. I have been told in other questions displaying this code, that it's just too much, and far too many bugs to try to single out what is causing the break.
What would be the most efficient way to query these tables that reference each other?
Example: Person chooses car year, make, model. PHP then gathers that information, and queries the SQL database to find what parts have matching year, vehicle id's, and parts compatible. It then uses those results to pull parts that have matching car model id's, OR vehicle id's(because the database was built very sloppily, and compares all the different tables to produce: Parts, descriptions, prices, part number, sku number, any retailer notes, wheelbase, drive-train compatibility, etc.
I've been working on this for two weeks, and I'm approaching my deadline with little to no progress. I'm about to scrap their database, and just do data entry for a week, and rebuild their mess if it would be easier, but if I can use the existing pile of crap they've given me, and save some time, I would prefer it.
Would it be easier to do a couple queries and compare the results, then use those results to query for more results, and do it step by step like that, or is one huge query comparing everything at once more efficient?
Should I use JOIN and pull all the tables at once and compare, or pass the input into individual variables, and pass the PHP into javascript on the client side to save server load? Would it be simpler to break the code up so I can identify the breaking points, or would using one long string decrease query time, and server loads? This is a very complex question, but I just want to make sure there aren't too many responses asking for clarification on trivial areas. I'm mainly seeking the best advice possible on how to handle this complicated situation.
Rebuild the database then make a php import to bring over the data.
I am trying to fetch documents from couchdb based on certain specific filters through javascript. For example i need to get the list of employees from a db where the key can be either city, age, state, gross income, gender or a combination of two or more such keys.
The problem i am facing is as the number of possible keys increase the number of views i need to write also increases drastically. I want to avoid writing so many views. So is it possible to do so ??
In addition to checking out Matt's suggestion about couchdb-lucene, you might also look into list functions: they're quite useful when you have a small set of basic view queries that will reduce the number of records fetched to a manageable level and you want to do a bunch of ad-hoc queries that further filter those records.
well, i am creating a network that allows users creating posts and like them.
Asking on stackoverflow i've understood how to structure my database:
A collection which includes a document for each post.
A collection which includes a document for each like, in each of these documents there is a reference to post is referenced to.
When i want to get ALL likes about a post i can query the like collection looking for the reference to that post.
And till here i am ok. But assuming i'll have millions documents in like collection, i wondered how could i query and search among them in not too long time.
And i was advised of ensureIndex, in this case, i have to ensureindex of the field which contains reference to a post.
But when do i have to create this index? is enough to create it once (for example when i set up my database) and it will be as default in mongodb or do i have to do it during application life-time? thank you
But assuming i'll have millions documents in like collection, i wondered how could i query and search among them in not too long time.
I assume you would most likely want to do a count on the likes as an example?
You can't, instead you use optimizations to combat this. A count on millions of rows might get a bit slow.
A typical scenario are counters in SQL techs that you use to amend the parent row with a sum figure of its children.
Same applies to MongoDB.
You would aggregate important data to the top.
If you require to actually query the likes to show some who have liked it then you limit those likes. Google+ and other networks tend to limit the amount of likes they show to about 1,000.
And i was advised of ensureIndex,
Adding indexes to a database does help with actually searching for documents.
But when do i have to create this index? is enough to create it once
Yes, MongoDB will manage the index itself. You only need to ensure it once.