Currently, our system is not entirely normalized, and we use meteor-publish-composite to obtain the normalized data in mongodb. Some models have very few dependencies, but others have arrays of objects (i.e. sub-documents) with few foreign keys that we are subscribing to when fetching each model.
An example would be a Post containing a list of Comment sub-documents, where each comment has a userId field.
My question is, while I know it would be faster to use collection hooks and update the collection with data denormalization, how does Meteor handle multiple subscriptions on the same collection?
Is a hundred subscriptions on the same collection affect the application speed (significantly)? What about a thousand? etc.
This may not fully answer your question, however after spending countless hours tuning the performance of a large meteor app, I thought I would share some of the things that I have learned.
In Meteor, when you define a publication, you are setting up a reactive query that continues to push data to subscribed clients when changes to the underlying mongo data causes the result of the query to change. In other words, it sets up a query that will continually push data to clients as the data is inserted, updated, or removed. The mechanism by which it does this is by creating an observer on the query.
When an observer is initialized (e.g. when publication is subscribed to), it will query mongodb for the initial dataset to send down and then use the oplog to detect changes going forward. Fortunately, meteor is able to re-use an existing observer for a new subscription if the query is for the same collection, same selectors, and same options.
This means that you could create hundreds of subscriptions against many different publications, but if they are hitting against the same collection and using the same query selectors then you effectively only have 1 observe in play. For more details, I highly recommend reading this article from kadira.io (from which I acquired the information I used in this answer).
In addition to this, Meteor is also able to deal with multiple publications publishing the same document, and when this occurs, the documents will be merged into one. See this for more detail.
Lastly, because of Meteor's MergeBox component, it will minimize the data being sent over the wire across all your subscriptions by keeping track of what data changed vs. what is already on the client.
Therefore, in your specific example, it sounds like you will be running several different subscriptions on effectively the same query (since you are just trying to de-normalize your data) and dataset. Because of all the optimizations that I described above, I would guess that you won't be plagued by performance issues by taking this approach.
I have done similar things in one of my apps and have never had an issue.
Related
I'm currently experimenting with a test collection on a LAN-accessible MongoDB server and data in a Meteor (v1.6) application. View layer of choice is React and right now I'm using the createContainer to bind the subscriptions to props.
The data that gets put in the MongoDB storage is updated on a daily basis and consists of a big set of data from several SQL databases, netting up to about 60000 lines of JSON per day. The data has been ever-so-slightly reshaped to be turned into a usable format whilst remaining as RAW as I'd like it to be.
The working solution right now is fetching all this data and doing further manipulations client-side to prepare the data for visualization. The issue should seem obvious: each client is fetching a set of documents that grows every day and repeats a lot of work on earlier entries before being ready to display. I want to do this manipulation on the server, through MongoDB's Aggregation Framework.
My initial idea is to do the aggregations on the server and to create new Collections containing smaller, more specific datasets without compromising the RAWness of the original Collection. That would mean the "reduced" Collections can still be reactive, as I've been able to confirm through testing in a Remote Desktop, subscribing to an aggregated Collection which I can update through Robo3T.
I don't know if this would be ideal. As far as storage goes, there's plenty of room for the extra Collections. But I have no idea how to set up an automated aggregation script on said server. And regarding Meteor, I've tried using meteorhacks:aggregate and jcbernack:reactive-aggregate but couldn't figure out how to deal with either one of them. If anyone is dealing, or has dealt with, something similar; I'd love to hear ideas / suggestions.
I'd like to use MongoDB to store chat messages as part of a chat application. The database will be used to display chat history to users joining a channel.
I am trying to determine the best way to model this data in the database. The application is a simple chat app which contains numerous channels that users can chat in. Here are a few options I've considered:
A Messages Collection containing a document for every message. This is easy to implement, however with any significant usage many documents would be created.
A Channels Collection containing a document for every channel. This would result in fewer documents. Messages would be stored as an array on a channel document.
Which of these options is preferred, and why? Is there a better option not listed here?
There are many, many ways to go about modeling something like this. There is no generic "best way," as it really depends on how you plan on using the data, how the app is going to function, etc. However, there are a few things to consider with your approach.
First, having a lot of documents is not an issue. That's what Mongo does - it's great at storing lots of documents. I am a strong advocate of modularity, as it makes things more flexible. I reflect that mindset in my database by separating data as much as possible, and then using references to populate as needed.
This means you have to do more population, but in the end it prevents you from pidgeon holing yourself into having to do things a certain way.
So for your example in particular, a good way would be to combine what you've mentioned above: Have a Messages collection which creates a document for every message. Then have a Channels collection which stores an array of Message IDs (not the message itself).
Why is this useful? I'm assuming you will want to load a Channel, but not all 2,000 messages that are in it. You probably want to load the first 50, and then load more via infinite scroll or something.
This allows you to fetch a Channel document and then populate the first 50 messages. Then you can incrementally fetch 50 more messages at a time if needed.
If you store all of the messages in that array, your Channel document is going to get very, very large.
This also allows a user to edit their Message without editing the Channel document in any way - this is very important!
Having a separate Message schema also allows you to do things like fetch all the messages from a single user. You'll probably want to have a reference in the Message to a User ID.
There is a lot to consider when modeling data like this, but the important things to think about are "How am I going to need to fetch this data?" and "How will I need to modify this data?" Then figure out if your current format makes one of those things difficult.
I am trying to pick MongoDB as my preferred database. I need help on the design of my table.
App background - analytics app where contacts push their own events and related custom data. Contact can have many events. Eg: contact did this, did that etc.
event_type, custom_data (json), epoch_time
eg:
event 1: event_type: page_visited, custom-data: {url: pricing, referrer: google}, current_time
event 2: event_type: video_watched, custom-data: {url: video_link}, current_time
event 3: event_type: paid, custom_data: {plan:lite, price:35}
These events are custom and are defined by the user. Scalability is a concern.
These are the common use cases:
give me a list of users who have come to pricing page in the last 7 days
give me a list of users who watched the video and paid more than 50
give me a list of users who have visited pricing, watched video but NOT paid at least 20
What's the best way to design my table?
Is it a good idea to use embedded events in this case?
In Mongo they are called collections and not tables, since the data is not rows/columns :)
(1) I'd make an Event collection and a Users collections
(2) I'd do 1 document per Event which has a userId in it.
(3) If you need realtime data you will want an index on what you want to query by (i.e. never do a scan over the whole collection).
(4) if there are things which are needed for reporting only, I'd recommend making a reporting node (i.e. a different mongo instance) and using replication to copy data to that mongo instance. You can put additional indexes for reporting on that node. That way the additional indexes and any expensive queries will not affect production performance.
Notes on sharding
If your events collection is going to become large - you may need to consider sharding. Perhaps sharding by user Id. However, I'd recommend that may be a longer term solution and not to dive into that until you need it.
One thing to note, is that mongo has currently (2.6) a database level write locking implementation. Which means you can only perform 1 write at a time. It allows many reads. Which means that if you want a high write system AND have a lot of users, you will need to look into sharding at some point. However, in my experience so far, administratively 1 primary node with a secondary (and reporting node) is easier to setup. We currently can handle around 10,000 operations per second with that setup.
However, we have had issues with spikes in users coming to the system. You'll want to make sure you have enough memory for your indexes. And SSD's would be recommended to. as a surge in users can result in cache misses (i.e. index not in memory) which causes it to be read off the hard disk.
One final note - there are a lot of NoSQL DB's and they all have their pros and cons. I personally found that high write, low read, and realtime anaysis of lots of data is not really mongo's strength. So it does depend on what you are doing. It sounds like you are still learning the fundamentals. It might be worth a read of all the available types to pick the right tool for the right job.
Let's say I have a collection called Articles. If I were to insert a new document into that collection without providing a value for the _id field, MongoDB will generate one for me that is specific to the machine and the time of the operation (e.g. sdf4sd89fds78hj).
However, I do have the ability to pass a value for MongoDB to use as the value of the _id key (e.g. 1).
My question is, are there any advantages to using my own custom _ids, or is it best to just let Mongo do its thing? In what scenarios would I need to assign a custom _id?
Update
For anyone else that may find this. The general idea (as I understand it) is that there's nothing wrong with assigning your own _ids, but it forces you to maintain unique values within your application layer, which is a PITA, and requires an extra query before every insert to make sure you don't accidentally duplicate a value.
Sammaye provides an excellent answer here:
Is it bad to change _id type in MongoDB to integer?
Advantages with generating your own _ids:
You can make them more human-friendly, by assigning incrementing numbers: 1, 2, 3, ...
Or you can make them more human-friendly, using random strings: t3oSKd9q
(That doesn't take up too much space on screen, could be picked out from a list, and could potentially be copied manually if needed. However you do need to make it long enough to prevent collisions.)
If you use randomly generated strings they will have an approximately even sharding distribution, unlike the standard mongo ObjectIds, which tends to group records created around the same time onto the same shard. (Whether that is helpful or not really depends on your sharding strategy.)
Or you may like to generate your own custom _ids that will group related objects onto one shard, e.g. by owner, or geographical region, or a combination. (Again, whether that is desirable or not depends on how you intend to query the data, and/or how rapidly you are producing and storing it. You can also do this by specifying a shard key, rather than the _id itself. See the discussion below.)
Advantages to using ObjectIds:
ObjectIds are very good at avoiding collisions. If you generate your own _ids randomly or concurrently, then you need to manage the collision risk yourself.
ObjectIds contain their creation time within them. That can be a cheap and easy way to retain the creation date of a document, and to sort documents chronologically. (On the other hand, if you don't want to expose/leak the creation date of a document, then you must not expose its ObjectId!)
The nanoid module can help you to generate short random ids. They also provide a calculator which can help you choose a good id length, depending on how many documents/ids you are generating each hour.
Alternatively, I wrote mongoose-generate-unique-key for generating very short random ids (provided you are using the mongoose library).
Sharding strategies
Note: Sharding is only needed if you have a huge number of documents (or very heavy documents) that cannot be managed by one server. It takes quite a bit of effort to set up, so I would not recommend worrying about it until you are sure you actually need it.
I won't claim to be an expert on how best to shard data, but here are some situations we might consider:
An astronomical observatory or particle accelerator handles gigabytes of data per second. When an interesting event is detected, they may want to store a huge amount of data in only a few seconds. In this case, they probably want an even distribution of documents across the shards, so that each shard will be working equally hard to store the data, and no one shard will be overwhelmed.
You have a huge amount of data and you sometimes need to process all of it at once. In this case (but depending on the algorithm) an even distribution might again be desirable, so that all shards can work equally hard on processing their chunk of the data, before combining the results at the end. (Although in this scenario, we may be able to rely on MongoDB's balancer, rather than our shard key, for the even distribution. The balancer runs in the background after data has been stored. After collecting a lot of data, you may need to leave it to redistribute the chunks overnight.)
You have a social media app with a large amount of data, but this time many different users are making many light queries related mainly to their own data, or their specific friends or topics. In this case, it doesn't make sense to involve every shard whenever a user makes a little query. It might make sense to shard by userId (or by topic or by geographical region) so that all documents belonging to one user will be stored on one shard, and when that user makes a query, only one shard needs to do work. This should leave the other shards free to process queries for other users, so many users can be served at once.
Sharding documents by creation time (which the default ObjectIds will give you) might be desirable if you have lots of light queries looking at data for similar time periods. For example many different users querying different historical charts.
But it might not be so desirable if most of your users are querying only the most recent documents (a common situation on social media platforms) because that would mean one or two shards would be getting most of the work. Distributing by topic or perhaps by region might provide a flatter overall distribution, whilst also allowing related documents to clump together on a single shard.
You may like to read the official docs on this subject:
https://docs.mongodb.com/manual/sharding/#shard-key-strategy
https://docs.mongodb.com/manual/core/sharding-choose-a-shard-key/
I can think of one good reason to generate your own ID up front. That is for idempotency. For example so that it is possible to tell if something worked or not after a crash. This method works well when using re-try logic.
Let me explain. The reason people might consider re-try logic:
Inter-app communication can sometimes fail for different reasons, (especially in a microservice architecture). The app would be more resilient and self-healing by codifying the app to re-try and not give up right away. This rides over odd blips that might occur without the consumer ever being affected.
For example when dealing with mongo, a request is sent to the DB to store some object, the DB saves it, but just as it is trying to respond to the client to say everything worked fine, there is a network blip for whatever reason and the “OK” is never received. The app assumes it didn't work and so the app may end up re-trying the same data and storing it twice, or worse it just blows up.
Creating the ID up front is an easy, low overhead way to help deal with re-try logic. Of course one could think of other schemes too.
Although this sort of resiliency may be overkill in some types of projects, it really just depends.
I have used custom ids a couple of times and it was quite useful.
In particular I had a collection where I would store stats by date, so the _id was actually a date in a specific format. I did that mostly because I would always query by date. Keep in mind that using this approach can simplify your indexes as no extra index is needed, the basic cursor is sufficient.
Sometimes the ID is something more meaningful than a randomly generated one. For example, a user collection may use the email address as the _id instead. In my project I generate IDs that are much shorter than the ones Mongodb uses so that the ID shown in the URL is much shorter.
I'll use an example , i created a property management tool and it had multiple collections. For simplicity some fields would be duplicated for example the payment. And when i needed to update these record it had to happen simultaneously across all collections it appeared in so i would assign them a custom payment id so when the delete/query action is performed it changes all instances of it database wide
I have this schema for support of in-site messaging:
When I send a message to another member, the message is saved to Message table; a record is added to MessageSent table and a record per recipient is added to MessageInbox table. MessageCount is being used to keep track of number of messages in the inbox/send folders and is filled using insert/delete triggers on MessageInbox/MessageSent - this way I can always know how many messages a member has without making an expensive "select count(*)" query.
Also, when I query member's messages, I join to Member table to get member's FirstName/LastName.
Now, I will be moving the application to MongoDB, and I'm not quite sure what should be the collection schema. Because there are no joins available in MongoDB, I have to completely denormalize it, so I woudl have MessageInbox, MessageDraft and MessageSent collections with full message information, right?
Then I'm not sure about following:
What if a user changes his First/LastName? It will be stored denormalized as sender in some messages, as a part of Recipients in other messages - how do I update it in optimal ways?
How do I get message counts? There will be tons of requests at the same time, so it has to be performing well.
Any ideas, comments and suggestions are highly appreciated!
I can offer you some insight as to what I have done to simulate JOINs in MongoDB.
In cases like this, I store the ID of a corresponding user (or multiple users) in a given object, such as your message object in the messages collection.
(Im not suggesting this be your schema, just using it as an example of my approach)
{
_id: "msg1234",
from: "user1234",
to: "user5678",
subject: "This is the subject",
body: "This is the body"
}
I would query the database to get all the messages I need then in my application I would iterate the results and build an array of user IDs. I would filter this array to be unique and then query the database a second time using the $in operator to find any user in the given array.
Then in my application, I would join the results back to the object.
It requires two queries to the database (or potentially more if you want to join other collections) but this illustrates something that many people have been advocating for a long time: Do your JOINs in your application layer. Let the database spend its time querying data, not processing it. You can probably scale your application servers quicker and cheaper than your database anyway.
I am using this pattern to create real time activity feeds in my application and it works flawlessly and fast. I prefer this to denormalizing things that could change like user information because when writing to the database, MongoDB may need to re-write the entire object if the new data doesnt fit in the old data's place. If I needed to rewrite hundreds (or thousands) of activity items in my database, then it would be a disaster.
Additionally, writes on MongoDB are blocking so if a scenario like I've just described were to happen, all reads and writes would be blocked until the write operation is complete. I believe this is scheduled to be addressed in some capacity for the 2.x series but its still not going to be perfect.
Indexed queries, on the other hand, are super fast, even if you need to do two of them to get the data.