I have a ~3GB mongo database with several dozen collections. Three of these collections handle ~300 queries per second, while the rest sustain a much lower volume. I expect the traffic to continue to grow quickly.
I'd like to set up a replica set to handle the high-traffic collections. It isn't necessary for this new instance to replicate the rest of the database. Is this possible?
Seems like not possible at the moment by built-in features of mongodb and only way to do is to come up with your own manual replication algorithm or use some other tools written by third parties.
https://github.com/wordnik/wordnik-oss project might help you to achieve this according to the following post.
https://groups.google.com/forum/?fromgroups=#!topic/mongodb-user/Ap9V4ArGuFo
Describes workaround to filter documents in replication.
Replicate only documents where {'public':true} in MongoDB
Or just replicate the data yourself manually which might worth trying.
Good luck.
No that isn't possible now. What you could do is move those collections into another unreplicated database. But this will cause headaches once these collections see higher traffic too, so you would need to move them into your "replication"-db.
But in general Replication isn't the way to go if you need to scale, it's more considered for DR/failover. Replicaset Secondaries can only (optionally) answer read queries but no write queries, this is something you should keep in mind. So if you have high write load this may not cure your problem.
Once you allow your application to read from secondaries you need to live with eventual consistency, meaning that your application isn't guaranteed to see always the latest data. This is caused due to the asynchronous replication to the secondaries.
Indeed you can cure this problem if you configure your writeconcern, so that the write needs to succeeded on all replicas, before it's considered written and your driver returns. But this may slow down your write operations significant.
So for scaling query execution capabilities I would go with Sharding. This is possible on a per collection level, all unsharded collections will remain on a "default-shard".
Not possible but then if the data size is so small and these collections aren't updated, then the only overhead of having them replicated is the small storage size on the secondary. That is a relatively small price to pay, especially since the collections won't grow in size, compared with writing your own replication logic.
Instead of that archive the data, and have only the latest data set on the production server and the rest of the data can archive on the new server.
Related
In the offical MongoDB Documentation it is stated that:
"Clients should never connect to a single shard in order to perform read or write operations." Source
I did try writing some data to a single shard node and it worked, but it is not a big dataset (certainly not one that would require sharding).
Could this lead to other issues, which I am not yet aware of?
Or are clients discouraged from doing this, simply because of performance reasons?
In a sharded cluster, the individual shards contain the data but some of the operation state is stored on mongos nodes. When you communicate with individual shards you bypass mongoses and as such any state management that they would perform.
Depending on the operation, this could be innocuous or could result in unexpected behavior/wrong results.
There isn't a comprehensive list of issues that would arise because the scenario of direct shard access isn't tested (because it's not supported).
Problem is, by default you don't know which data portion is stored on which shard. The balancer is running in background and may change the data distribution at any time.
If you insert some data manually into one shard, then a properly connected client may not find this data.
If you read some data from a single shard then you don't know whether this data is complete or not.
We are currently working on an application using Mongo and we try to evaluate benefits and constraints on each differents architecture choices related to spreading data on multiple databases/collections or using a single shared one.
Is there any performance penalties between one single database with a lot of collections or many databases with less collections per database ?
From what I understand it does not seem to have any impact because sharding is done per collection basis but I would like some confirmations.
Regards
By performance, I guess you mean read/write speed. Using multiple databases with fewer collections would definitely increase your read/write speed since each database can handle more read/write operations on the collections associated with them.
However, spreading data across databases this way I believe can bring about extra complexity to your project, depending on how your codebase is structured, it might introduce complexity to your application logic, things like backup and other admin database operations won't be straight forward, cross collection ad-hoc queries for collection that lives in different databases would be next to impossible.
If the goal of the architecture design is to ensure high read/write speed, you can still go with using a single DB that can be auto-scaled at the deployment level. I don't know much about it but I think Replication is a MongoDB feature that can help you achieve such auto-scaling and if you are in for database-as-a-service, you should check out MongoDB Atlas, auto-scaling is out of the box.
I'm new to mongodb and I'm facing a dilemma regarding my DB Schema design:
Should I create one single collection or put my data into several collections (we could call these categories I suppose).
Now I know many such questions have been asked, but I believe my case is different for 2 reasons:
If I go for many collections, I'll have to create about 120 and that's it. This won't grow in the future.
I know I'll never need to query or insert into multiple collections. I will always have to query only one, since a document in collection X is not related to any document stored in the other collections. Documents may hold references to other parts of the DB though (like userId etc).
So my question is: could the 120 collections improve query performance? Is this a useful optimization in my case?
Or should I just go for single collection + sharding?
Each collection is expected hold millions of documents. If use only one, it will store billions of docs.
Thanks in advance!
------- Edit:
Thanks for the great answers.
In fact the 120 collections is only a self made limit, it's not really optimal:
The data in the collections is related to web publishers. There could be millions of these (any web site can join).
I guess the ideal situation would be if I could create a collection for each publisher (to hold their data only). But obviously, this is not possible due to mongo limitations.
So I came up with the idea of a fixed number of collections to at least distribute the data somehow. Like: collection "A_XX" would hold XX Platform related data for publishers whose names start with "A".. etc. We'll only support a few of these platforms, so 120 collections should be more than enough.
On another website someone suggested using many databases instead of many collections. But this means overhead and then I would have to use / manage many different connections.
What do you think about this? Is there a better solution?
Sorry for not being specific enough in my original question.
Thanks in advance
Single Sharded Collection
The edited version of the question makes the actual requirement clearer: you have a collection that can potentially grow very large and you want an approach to partition the data. The artificial collection limit is your own planned partitioning scheme.
In that case, I think you would be best off using a single collection and taking advantage of MongoDB's auto-sharding feature to distribute the data and workload to multiple servers as required. Multiple collections is still a valid approach, but unnecessarily complicates your application code & deployment versus leveraging core MongoDB features. Assuming you choose a good shard key, your data will be automatically balanced across your shards.
You can do not have to shard immediately; you can defer the decision until you see your workload actually requiring more write scale (but knowing the option is there when you need it). You have other options before deciding to shard as well, such as upgrading your servers (disks and memory in particular) to better support your workload. Conversely, you don't want to wait until your system is crushed by workload before sharding so you definitely need to monitor the growth. I would suggest using the free MongoDB Monitoring Service (MMS) provided by 10gen.
On another website someone suggested using many databases instead of many collections. But this means overhead and then I would have to use / manage many different connections.
Multiple databases will add significantly more administrative overhead, and would likely be overkill and possibly detrimental for your use case. Storage is allocated at the database level, so 120 databases would be consuming much more space than a single database with 120 collections.
Fixed number of collections (original answer)
If you can plan for a fixed number of collections (120 as per your original question description), I think it makes more sense to take this approach rather than using a monolithic collection.
NOTE: the design considerations below still apply, but since the question was updated to clarify that multiple collections are an attempted partitioning scheme, sharding a single collection would be a much more straightforward approach.
The motivations for using separate collections would be:
Your documents for a single large collection will likely have to include some indication of the collection subtype, which may need to be added to multiple indexes and could significantly increase index sizes. With separate collections the subtype is already implicit in the collection namespace.
Sharding is enabled at the collection level. A single large collection only gives you an "all or nothing" approach, whereas individual collections allow you to control which subset(s) of data need to be sharded and choose more appropriate shard keys.
You can use the compact to command to defragment individual collections. Note: compact is a blocking operation, so the normal recommendation for a HA production environment would be to deploy a replica set and use rolling maintenance (i.e. compact the secondaries first, then step down and compact the primary).
MongoDB 2.4 (and 2.2) currently have database-level write lock granularity. In practice this has not proven a problem for the vast majority of use cases, however multiple collections would allow you to more easily move high activity collections into separate databases if needed.
Further to the previous point .. if you have your data in separate collections, these will be able to take advantage of future improvements in collection-level locking (see SERVER-1240 in the MongoDB Jira issue tracker).
The main problem here is that you will gain very little performance in the current MongoDB versions if you separate out collections into the same database. To get any sort of extra performance over a single collection setup you would need to move the collections out into separate databases, then you will have operational overhead for judging what database you should query etc.
So yes, you could go for 120 collections easily however, you won't really gain anything currently due to: https://jira.mongodb.org/browse/SERVER-1240 not being implemented (anytime soon).
Housing billions of documents in a single collection isn't too bad. I presume that even if you was to house this in separate collections it probably would not be on a single server either, just like sharding a single collection, so any speed reduction due to multi server setup will also not matter in this case.
In my personal opinion, using a single collection is easier on everything.
I have been reading about tag aware sharding..These are the links I referred:
http://www.mongodb.org/display/DOCS/Tag+Aware+Sharding
http://www.kchodorow.com/blog/2012/07/25/controlling-collection-distribution/
Kristina has explained the concept in a very lucid manner and one thing is for sure: this enhancement is going to make MongoDB more developer-friendly.
But my question is.. It looks like tagging/retagging is meant to easily migrate chunks around..get all writes on to a preferred data center etc..But how does this fit into the old system of range partitioning and the way Mongo learns key-distributions for balancing? It is said that the shard-key cannot be changed, and that's because the data is assumed to be distributed across shards and changing the shard-key would disturb this. Isn't applying a tag essentially doing the same? So is tag-aware sharding meant to handle this problem?
EDIT:
And any idea how are the indexes affected by such huge migrations?
Aafreen,
You are correct. At this stage shard-tagging performs many of the same functions as balancing with the shard key. The one thing it does not do is perform any level of distribution beyond that of tagging. So it is probably more correct to say that the tagging architecture lives on top of the existing sharding architecture.
You must keep in mind that tagging only governs:
a) where tagged data will go, untagged data will use the shard-key
b) that tagged data shared amongst a number of tagged servers will still need to be distributed
You can most certainly use the tag aware sharding to manually control data distribution in the same manner that the balancer does now, by making granular enough tags so that data is put where you want it and distributed evenly.
The use case however, is more like the Documentation you linked. Where you have a large number of shards broken up into a smaller subset. In this example you would be tagging each object and then the tag would push it to the correct geographic location (for lower latency retrieval) and once within the correct geography the original sharding architecture would take over and distribute amongst the tagged shards.
As for indexes, they are heavily affected by migrations, as they need to be repointed. But the level of load is the same for that of a large number of chunk migrations - like adding a new shard to a cluster.
I need to load 6.6 billion bigrams into a collection but I can't find any information on the best way to do this.
Loading that many documents onto a single primary key index would take forever but as far as I'm aware mongo doesn't support the equivalent of partitioning?
Would sharding help? Should I try and split the data set over many collections and build that logic into my application?
It's hard to say what the optimal bulk insert is -- this partly depends on the size of the objects you're inserting and other immeasurable factors. You could try a few ranges and see what gives you the best performance. As an alternative, some people like using mongoimport, which is pretty fast, but your import data needs to be json or csv. There's obviously mongodrestore, if the data is in BSON format.
Mongo can easily handle billions of documents and can have billions of documents in the one collection but remember that the maximum document size is 16mb. There are many folk with billions of documents in MongoDB and there's lots of discussions about it on the MongoDB Google User Group. Here's a document on using a large number of collections that you may like to read, if you change your mind and want to have multiple collections instead. The more collections you have, the more indexes you will have also, which probably isn't what you want.
Here's a presentation from Craigslist on inserting billions of documents into MongoDB and the guy's blogpost.
It does look like sharding would be a good solution for you but typically sharding is used for scaling across multiple servers and a lot of folk do it because they want to scale their writes or they are unable to keep their working set (data and indexes) in RAM. It is perfectly reasonable to start off with a single server and then move to a shard or replica-set as your data grows or you need extra redundancy and resilience.
However, there are other users use multiple mongods to get around locking limits of a single mongod with lots of writes. It's obvious but still worth saying but a multi-mongod setup is more complex to manage than a single server. If your IO or cpu isn't maxed out here, your working set is smaller than RAM and your data is easy to keep balanced (pretty randomly distributed), you should see improvement (with sharding on a single server). As a FYI, there is potential for memory and IO contention. With 2.2 having improved concurrency with db locking, I suspect that there will be much less of a reason for such a deployment.
You need to plan your move to sharding properly, i.e. think carefully about choosing your shard key. If you go this way then it's best to pre-split and turn off the balancer. It will be counter-productive to be moving data around to keep things balanced which means you will need to decide up front how to split it. Additionally, it is sometimes important to design your documents with the idea that some field will be useful for sharding on, or as a primary key.
Here's some good links -
Choosing a Shard Key
Blog post on shard keys
Overview presentation on sharding
Presentation on Sharding Best Practices
You can absolutely shard data in MongoDB (which partitions across N servers on the shard key). In fact, that's one of it's core strengths. There is no need to do that in your application.
For most use cases, I would strongly recommend doing that for 6.6 billion documents. In my experience, MongoDB performs better with a number of mid-range servers rather than one large one.