In my project, I have servers that will send ping request to websites, measuring their response time and store it every minute.
I'm going to use Mongodb and i'm searching for best data model.
which data model is better?
1- have a collection for each website and each request as a document.
(1000 collection)
or
2- have a collection for all websites and each website as a document and each request as sub-document.
Both solutions should face of one certain limitation of mongodb. With the first one, that you said each website a collection, the limitation is in the number of the collections while each one will have a namespace entry and the namespace size is 16MB so around 16.000 entries can fit in. (the size of the namespace can be increased) In my opinion this is a much better solution while you said 1000 collections are expected and it can be handled. (Should be considered that indexes has their own namespace entries and count in the 16.000). In this case you can store the entries as documents you can handle them after generally much easier than with the embedded array.
Embedded array limitation. This limitation in the second case is a hard one. Your documents cannot grow bigger than 16MB. This one is BSON size and it can store quite many things inside documents but if you use huge documents which varies in size , and change size in time your storage will get fragmented. The reason is that will be clear if you watch this webinar . Basically this is the worth what you can do in terms of storage usage.
If you likely to use aggregation framework for further analysis it will be also harder with the embedded array concept.
You could do either, but I think you will have to factor in periodic growth in database for either case. During the expansion of datafiles database will be slow/unresponsive. (There might be a setting so this happens in the background - I forget ).
A related question - MongoDB performance with growing data structure, specifically the "Padding Factor"
With first approach, there is an upper limit to number of websites you can store imposed by max number of collections. You can do the calculations based on http://docs.mongodb.org/manual/reference/limits/.
In second approach, while #of collection don't matter as much, but growth of database is something you will want to consider.
One approach is to initialize it with empty data, so it takes lasts longer before expanding.
For instance.
{
website: name,
responses: [{
time: Jan 1, 2013, 0:1, ...
},
{
time: Jan 1, 2013, 0:2, ...
}
... and so for each minute/interval you expect.
]
}
The downside is, it might take you longer to initialize but you will have to worry about this later.
Either ways, it is a cost you will have to pay. The only question is when? Now? or later?
Consider reading their usecases, particularly - http://docs.mongodb.org/manual/use-cases/hierarchical-aggregation/
Related
I am using mongoDB with mongoose, and I need to create a schema "advertisement" which has a field called "views" in which will be and array of
{userId: String, date: Date}.
I want to know if this is a good practice, since although I know now how much it will grow (until 1500 and then reseted) in the future I will not. I want to know if for example it would seriously affect the performance of the application if that array could be 50000 or 100000 or whatever. (It is is an unbounded array) In this cases what would be the best practice. I thought just storing an increasing number, but business decision is to know by who and when the ad was seen.
I know that there is a limit only for the document (16mb), but not for the fields themselves. But my questions is more related to performance rather than document limit.
Thank you!
Edit => In the end definitely it is not a good idea to let an array grow unbounded. I checked at the answer they provided first..and it is a good approach. However since I will be querying the whole document with the array property quite often I didn't want to split it. So, since I don't want to store data longer than 3 days in the array..I will pull all elements that have 3 days or more, and I hope this keeps the array clean.
I know that there is a limit only for the document (16mb), but not for
the fields themselves.
Fields and their values are parts of the document, so they make direct impact on the document size.
Beside that, having such a big arrays usually is not the best approach. It decreases performance and complicates queries.
In your case, it is much better to have a separate views collection of the documents which are referencing to the advertisements by their _id.
Also, if you expect advertisement.views to be queried pretty often or, for example, you often need to show the last 10 or 20 views, then the Outlier pattern may also work for you.
First of all, I am using MongoDB 3.0 with the new WiredTiger storage engine. Also using snappy for compression.
The use case I am trying to understand and optimize for from a technical point of view is the following;
I have a fairly large collection, with about 500 million documents that takes about 180 GB including indexes.
Example document:
{
_id: 123234,
type: "Car",
color: "Blue",
description: "bla bla"
}
Queries consist of finding documents with a specific field value. Like so;
thing.find( { type: "Car" } )
In this example the type field should obviously be indexed. So far so good. However the access pattern for this data will be completely random. At a given time I have no idea what range of documents will be accessed. I only know that they will be queried on indexed fields, returning at the most 100000 documents at a time.
What this means in my mind is that the caching in MongoDB/WiredTiger is pretty much useless. The only thing that needs to fit in the cache are the indexes. An estimation of the working set is hard if not impossible?
What I am looking for is mostly tips on what kinds of indexes to use and how to configure MongoDB for this kind of use case. Would other databases work better?
Currently I find MongoDB to work quite well on somewhat limited hardware (16 GB RAM, non SSD disc). Queries return in decent time and obviously instantly if the result set is already in the cache. But as already stated this will most likely not be the typical case. It is not critical that the queries are lightning fast, more so that they are dependable and that the database will run in a stable manner.
EDIT:
Guess I left out some important things. The database will be mostly for archival purposes. As such, data arrives from another source in bulk, say once a day. Updates will be very rare.
The example I used was a bit contrived but in essence that is what queries look like. When I mentioned multiple indexes I meant the type and color fields in that example. So documents will be queried on using these fields. As it is now, we only care about returning all documents that have a specific type, color etc. Naturally, the plan we have is to only query on fields that we have an index for. So ad-hoc queries are off the table.
Right now the index sizes are quite manageable. For the 500 million documents each of these indexes are about 2.5GB and fit easily in RAM.
Regarding average data size of an operation, I can only speculate at this point. As far as I know, typical operations return about 20k documents, with an average object size in the range of 1200 bytes. This is the stat reported by db.stats() so I guess it is for the compressed data on disc, and not how much it actually takes once in RAM.
Hope this bit of extra info helped!
Basically, if you have a consistent rate of reads that are uniformly at random over type (which is what I'm taking
I have no idea what range of documents will be accessed
to mean), then you will see stable performance from the database. It will be doing some stable proportion of reads from cache, just by good luck, and another stable proportion by reading from disk, especially if the number and size of documents are about the same between different type values. I don't think there's a special index or anything to help you besides just better hardware. Indexes should remain in RAM because they'll constantly be being used.
I suppose more information would help, as you mention only one simple query on type but then talk about having multiple indexes to worry about keeping in RAM. How much data does the average operation return? Do you ever care to return a subset of docs of certain type or only all of them? What do inserts and updates to this collection look like?
Also, if the documents being read are truly completely random over the dataset, then the working set is all of the data.
There is a set of registrators, say 100k. Every registrator 24 times a day gives value smth like 23.123. I need to save this value and time. Then I need to calculate how value changes for some period, e.g. 4jun2014 - 19jul2014: In order to do this I have to find last value of 3jun2014 and last value of 19jul2014.
First I am trying to estimate size of data stored by one registrator. Time+value must be lower than 100 bytes. 1 year is < 100*24*365 = 720kB of data, so I can easily store 10 years of data (since 7.2M < 16M limit) at my document. I decided not to store registered data at registeredData collection but to store registrator data embedded in registrator object as a tree timedata->year->month->day:
{
code: '3443-12',
timedata: {
2013: {
6: {
13: [
{t:1391345679, d:213.12},
{t:1391349679, d:213.14},
]
}
}
}
}
So it is easy to get values of the day: just get find({code: "3443-12"})[0].timedata[2013][6][13].
When I get new data, I just push it into array of existing document and it eventually grows from zero to 7Mb.
Questions
What is the stored size of {t:1391345679, d:213.12} line, is it less than 100bytes?
Is it right way to organize database for such purposes?
100k documents with 5Mb size = 500G. Does MongoDB deal fast with database size much more than RAM size?
Update
I decided to store time not as a timestamp but as time in seconds from the start of a day: 0 - 86399: {t: 86123, d: 213.12}.
Regarding your last question, " Does MongoDB deal fast with database size much more than RAM size?" the answer is it can, but it depends on a number of factors.
MongoDB works best when the working set fits within the memory available to MongoDB. When it does not you tend to see rather rapid performance declines. How big that working set is a function of database schema, indexes built and your data access patterns.
Let's say you have a years worth of data in your database, but regularly only touch the last few days of data. Then your working set is likely to be composed of the memory required to keep the last few days of data in memory, plus enough of the indexes in memory for you to properly update and read from them.
Alternatively, if you are randomly accessing data across a year and have high and update volume you may have a significantly larger working set to deal with.
As a point of comparison, I've got a production MongoDB instance that has around 500M documents in it, taking up around 2 TB of disk storage. Total memory on the primary of the replica set is 128GB (1/16th the total storage) and we're not experiencing any performance problems.
The key for all of it though is how much data do you access over time. The killer for MongoDB performance is memory contention, when you are paging out data to service a new request only to re-page that old data right back in. And it gets far worse if you cannot keep your indexes in memory.
I've tested it and it is less than 100 B, in deed, it is 48 B:
var num=100000;
for(i=0;i<num;i++){
db.foo.insert({t:1391345679, d:213.12})
};
db.foo.stats().avgObjSize // => Outputs 48
It looks like what you are doing is kind of a hack to avoid normalising your data (m.b. for transaction purposes?) and sooner or later you may run into problems (e.g. requirements change, size of your data changes, new fields are introduced etc.) I do not know your schema and domain, but if you go with denomarmalized model as you are doing you must be sure that documents will not exceed the size limit of 16MB. That being said, I would recommend schema design article.
Answers:
The previous answer gives a hint about the document size. You can use it as a starting point.
Choosing an effective data models depends on your application needs. The main question is the decision to denormalize or use linking. Note, generally with denormalized data you achieve better performance for read operations, as well as the ability to request and retrieve related data in a single database operation. Embedding makes it possible to update a document in a single atomic write operation (transactionally). So, when to use embedded (denormalized):
you have “contains” relationships between entities. See Model
One-to-One Relationships with Embedded Documents.
you have one-to-many relationships between entities. In these relationships the “many” or
child documents always appear with or are viewed in the context of the
“one” or parent documents. See Model One-to-Many Relationships with
Embedded Documents.
In your situation your documents will grow after creation which can impact write performance and lead to data fragmentation. You can control this with padding factor.
- About the performance: it depends on how you create your indexes. More importantly, on your access patterns. For each query executed often, check out the output from explain() to see how many documents have been checked.
Is it a good idea to create per day collections for data on a given day (we could start with per day and then move to per hour if there is too much data). Is there a limit on the number of collections we can create in mongodb, or does it result in performance loss (is it an overhead for mongodb to maintain so many collections). Does a large number of collections have any adverse effect on performance?
To give you more context, the data will be more like facebook feeds, and only the latest data (say last one week or month) is more important to us. Making per day collections keeps the number of documents low, and probably would result in fast access. Even if we need old data, we can fall back to older collections. Does this make sense, or am I heading in the wrong direction?
what you actually need is to archive the old data. I would suggest you to take a look at this thread at the mongodb mailing list:
https://groups.google.com/forum/#!topic/mongodb-user/rsjQyF9Y2J4
Last post there from Michael Dirolf (10gen)says:
"The OS will handle LRUing out data, so if all of your queries are
touching the same portion of data that should stay in memory
independently of the total size of the collection."
so I guess you can stay with single collection and good indexes will do the work.
anyhow, if the collection goes too big you can always run manual archive process.
Yes, there is a limit to the number of collections you can make. From the Mongo documentation Abhishek referenced:
The limitation on the number of namespaces is the size of the namespace file divided by 628.
A 16 megabyte namespace file can support approximately 24,000 namespaces. Each index also counts as a namespace.
Indexes etc. are included in the namespaces, but even still, it would take something like 60 years to hit that limit.
However! Have you considered what happens when you want data that spans collections? In other words, if you wanted to know how many users have feeds updated in a week, you're in a bit of a tight spot. It's not easy/trivial to query across collections.
I would recommend instead making one collection to store the data and simply move data out periodically as Tamir recommended. You can easily write a job to move data out of the collection every week or every month.
Creating a collection is not much overhead, but it the overhead is larger than creating a new document inside a collections.
There is a limitation on the no of collections that you can create: " http://docs.mongodb.org/manual/reference/limits/#Number of Namespaces "
Making new collections to me, won't be having any performance difference because in RAM you cache only those data that you actually query. In your case it will be recent feeds etc.
But having per day/hour collection will help you in achieving old data very easily.
This is related to my last question.
We have an app where we are storing large amounts of data per user. Because of the nature of data, previously we decided to create a new database for each user. This would have required a large no. of databases (probably millions) -- and as someone pointed out in a comment, that this indicated wrong design.
So we changed the design and now we are thinking about storing each user's entire information in one collection. This means one collection exactly maps to one user. Since there are 12,000 collections available per database, we can store 12,000 users per DB (and this limit could be increased).
But, now my question is -- is there any limit on the no. of documents a collection can have. Because of the way we need to store data per user, we expect to have a huge (tens of millions in extreme cases) no. of document per documents. Is that OK for MongoDB and design-wise?
EDIT
Thanks for the answers. I guess then it's OK to use large no of documents per collection.
The app is a specialized inventory control system. Each user has a large no. of little pieces of information related to them. Each piece of information has a category and some related stuff under that category. Moreover, no two collections need to see each other's data -- hence an index that touch more than one collection is not needed.
To adjust the number of collections/indexes you can have (~24k is the limit--~12k is what they say for collections because you have the _id index by default, but keep in mind, if you have more indexes on the collections, that will use namespace up as well), you can use the --nssize option when you start up mongod.
There are plenty of implementations around with billions of documents in a collection (and I'm sure there are several with trillions), so "tens of millions" should be fine. There are some numbers such as counts returned that have constraints of 64 bits, so after you hit 2^64 documents you might find some issues.
What sort of query and update load are you going to be looking at?
Your design still doesn't make much sense. Why store each user in a separate collection?
What indexes do you have on the data? If you are indexing by some field that has content that's common across all the users you'll get a significant saving in total index size by having a single collection with one index.
Index size is often the limiting factor not total database size when it comes to performance.
Why do you have so many documents per user? How large are they?
Craigslist put 2+ billion documents in MongoDB so that shouldn't be an issue if you have the hardware to support it and aren't being inefficient with your indexes.
If you posted more of your schema here you'd probably get better advice.