Fairly new to node and mongo. I'm a developer from a relational db background.
I have been asked to write a report to calculate the conversion rate from leads relating to vehicle workshop bookings to invoices. A conversion is where an invoice was produced within 60 days of a lead being generated.
So I have managed with mongodb, mongoose and nodejs to import all of the data from flat files into two collections, leads and invoices. I have 1M leads and about 30M invoices over a 5 year period and the rates are to be produced on a month by month basis. All data has vehicle reg in common.
So my problem is how do I join the data together with mongoose and nodejs?
So far I have attempted for any single lead so find any invoices within a 60 day period in order for the lead to qualify as a conversion. This works but my script stops after about 20 or so successful updates. At this point I think my script which makes individual queries for invoices per lead is too heavy a load on mongodb and I can see that making millions of individual queries is too much for mongodb.
After hours of browsing, I'm not sure what I should be looking for!?
Any help would be greatly appreciated.
Your attempt should be working without a problem. What helps me, though, with large data Mongo DB instances and analysis on them: Run queries directly in Mongo, not through Node. Like that you avoid having to convert Mongo structures (e.g. iterators) into Node structures (e.g. arrays) and generally lose a lot of overhead.
Also, make sure you have correct indices setup. That can be a HUGE difference in terms of performance in big databases.
What I would do then is something like (this should be considered pseudo code):
let converted = 0;
db.leads.find({},{id: 1, date: 1}).forEach(lead => {
const hasInvoices = db.invoices.count({leadId: lead.id, date: {$lt: lead.date + 60}});
converted ++;
});
To speed things up, I'd use the following index for this case:
db.invoices.createIndex({leadId: 1, date: -1});
I have an excel model that is used for running scenario simulation. There is a push to move the analysis to python/javascript for efficiency reasons and eventually move to the web. Below is a snaphot of how the excel model is setup.
The columns represents days which determine working and non working days in a calendar. The rows have variables which quantify how the day is progressing. In other words, the day based var (row) is populated on input. The individual columns then calculate formulas based on the day variable. I have oversimplified to illustrate however there are at least 100 rows with different variables and the 365 days to simulate. Finally, there is an optimization row which is a variable that will be altered to find the best solution.
Now, i need to move this data structure to either Javascript or Python. I understand I need to use 2D arrays to accomplish this task. Any packages and/ or methods that i can utilize to execute this model will be helpful.
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...
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've been pondering moving our current admin system over to a JS framework for a while and I tested out AngularJS today. I really like how powerful it is. I created a demo application (source: https://github.com/andyhmltn/portfolio-viewer) that has a list of 'items' and displays them in a paginated list that you can order/search in realtime.
The problem that I'm having is figuring out how I would replicate this kind of behaviour with a larger data set. Ideally, I want to have a table of items that's sortable/searchable and paginated that's all in realtime.
The part that concerns me is that this table will have 10,000+ records at least. At current, that's no problem as it's a PHP file that limits the query to the current page and appends any search options to the end. The demo above only has about 15-20 records in. I'm wondering how hard it would be to do the same thing with such a large amount of records without pulling all of them into one JSON request at once as it'll be incredibly slow.
Does anyone have any ideas?
I'm used to handle large datasets in JavaScript, and I would suggest you to :
use pagination (either server-sided or client-sided, depending on the actual volume of your data, see below)
use Crossfilter.js to group your records and adopt a several-levels architecture in your GUI (records per month, double click, records per day for the clicked month, etc.)
An indicator I often use is the following :
rowsAmount x columnsAmount x dataManipulationsPerRow
Also, consider the fact that handling large datasets and displaying them are two very differents things.
Indeed pulling so many rows in one request would be a killer. Fortunately Angular has the ng-grid component that can do server-side paging (among many other things). Instructions are provided in the given link.