Replication seems to be a lot simpler than sharding, unless I am missing the benefits of what sharding is actually trying to achieve. Don't they both provide horizontal scaling?
In the context of scaling MongoDB:
replication creates additional copies of the data and allows for automatic failover to another node. Replication may help with horizontal scaling of reads if you are OK to read data that potentially isn't the latest.
sharding allows for horizontal scaling of data writes by partitioning data across multiple servers using a shard key. It's important to choose a good shard key. For example, a poor choice of shard key could lead to "hot spots" of data only being written on a single shard.
A sharded environment does add more complexity because MongoDB now has to manage distributing data and requests between shards -- additional configuration and routing processes are added to manage those aspects.
Replication and sharding are typically combined to created a sharded cluster where each shard is supported by a replica set.
From a client application point of view you also have some control in relation to the replication/sharding interaction, in particular:
Read preferences
Write concerns
Consider you have a great music collection on your hard disk, you store the music in logical order based on year of release in different folders.
You are concerned that your collection will be lost if drive fails.
So you get a new disk and occasionally copy the entire collection keeping the same folder structure.
Sharding >> Keeping your music files in different folders
Replication >> Syncing your collection to other drives
Replication is a mostly traditional master/slave setup, data is synced to backup members and if the primary fails one of them can take its place. It is a reasonably simple tool. It's primarily meant for redundancy, although you can scale reads by adding replica set members. That's a little complicated, but works very well for some apps.
Sharding sits on top of replication, usually. "Shards" in MongoDB are just replica sets with something called a "router" in front of them. Your application will connect to the router, issue queries, and it will decide which replica set (shard) to forward things on to. It's significantly more complex than a single replica set because you have the router and config servers to deal with (these keep track of what data is stored where).
If you want to scale Mongo horizontally, you'd shard. 10gen likes to call the router/config server setup auto-sharding. It's possible to do a more ghetto form of sharding where you have the app decide which DB to write to as well.
Sharding
Sharding is a technique of splitting up a large collection amongst multiple servers. When we shard, we deploy multiple mongod servers. And in the front, mongos which is a router. The application talks to this router. This router then talks to various servers, the mongods. The application and the mongos are usually co-located on the same server. We can have multiple mongos services running on the same machine. It's also recommended to keep set of multiple mongods (together called replica set), instead of one single mongod on each server. A replica set keeps the data in sync across several different instances so that if one of them goes down, we won't lose any data. Logically, each replica set can be seen as a shard. It's transparent to the application, the way MongoDB chooses to shard is we choose a shard key.
Assume, for student collection we have stdt_id as the shard key or it could be a compound key. And the mongos server, it's a range based system. So based on the stdt_id that we send as the shard key, it'll send the request to the right mongod instance.
So, what do we need to really know as a developer?
insert must include a shard key, so if it's a multi-parted shard key, we must include the entire shard key
we've to understand what the shard key is on collection itself
for an update, remove, find - if mongos is not given a shard key - then it's going to have to broadcast the request to all the different shards that cover the collection.
for an update - if we don't specify the entire shard key, we have to make it a multi update so that it knows that it needs to broadcast it
Whenever you're thinking about sharding or replication, you need to think in the context of writers/update operations. If you don't need to scale writes then replications, as it fairly simpler, is a good choice for you.
On the other hand, if you workload mostly updates/writes then at some point you'll hit a write bottleneck. If write request comes Mongo blocks other writes request. Those write request blocks until the first request will be done. If you want to scale this writes and want parallelize it then you need to implement sharding.
Just to put this somewhere...
The most basic way to run mongo is as standalone server.
You write a config (file or cli options)
initiate the server using mongod
For this picture, I didn't include the "client". Check the next one.
A replica set is a set of servers initialized exactly as above with a different config file.
To link them, we connect to one of them, and initialize the replica set mode.
They will mirror each other (in the most common configuration). This system guarantees high availability of data.
The initialization of the replica set is represented in the red border box.
Sharding is not about replicating data, but about fragmenting data.
Each fragment of data is called chunk and goes to a different shard. shard = each replica set.
"main" server, running mongos instead of mongod. This is a router for queries from the client.
Obvious: The trade-off is a more complex architecture.
Novelty: configuration server (again, a different config file).
There is much more to add, but apart from the words the pictures hold much the same.
Even mongoDB recommends to study your case carefully before going sharding. Vertical scaling (vs) is probably a good idea at least once before horizontal scaling (hs).
vs is done upgrading hardware (cpu, ram, etc). hs is needs more computers (but could be cheap computers).
Both replication and sharding can be used (individually or together) for horizontal scaling of a MongoDB installation.
Sharding is MongoDB's solution for meeting the demands of data growth. Sharding stores data records across multiple servers to provide faster throughput on read and write queries, particularly for very large data sets.
Any of the servers in the sharded cluster can respond to a read or write operation, which greatly speeds up query responses.
Replication is MongoDB's solution for providing stability, backup, and disaster recovery to a MongoDB installation. This process copies and synchronizes the replica data set across multiple servers. This prevents downtime if one server goes offline.
Any of the secondary servers can respond to read queries, but only the primary server will perform write operations. The results of the write operation will then be propagated out to the secondary servers.
Scenario 1: Fault-Tolerance
In this scenario, the user is storing billing data in a MongoDB installation. This data is mission-critical to the user's business, and needs to be available 24/7, even if a server crashes or is taken offline.
MongoDB replication is the best solution for this user. With replication, the entire data set is mirrored on multiple servers. If a server fails or is taken offline, the other servers in the cluster take over.
Scenario 2: High Performance
In this scenario, the user is running a social networking site which is run from a MongoDB database. As the social network grows, the MongoDB data set has grown along with it. The user is seeing query times and page loads increase beyond an acceptable point. It is critical that the user's MongoDB installation receives a major performance boost.
Setting up a sharded MongoDB cluster is the best solution for this user. The sharded cluster will break up the user's data set and store parts of it on separate secondary servers. Each secondary server can respond to read or write queries on its portion of the data, which greatly increases the installation's response time
MongoDB Atlas is a Database as a service in could. It support three major cloud providers such as Azure , AWS and GCP. In cloud environment , we usually talk about high availability and scalability. In Atlas “clusters”, can be either a replica set or a sharded cluster.
These two address high availability and scalability features of our cloud environment.
In general Cluster is a group of servers used to achieve a specific task. So sharded clusters are used to store data in across multiple machines to meet the demand of data growth. As the size of the data increases, a single machine may not be sufficient to store the data nor provide an acceptable read and write throughput. Sharded clusters supports the horizontal scalability of the underling cloud environment.
A replica set in MongoDB is a group of mongod processes that maintain the same data set. Replica sets provide redundancy and high availability, and are the basis for all production deployments.In a replica, one node is a primary node that receives all write operations. All other instances, such as secondaries, apply operations from the primary so that they have the same data set. Replica set mainly focus on the availability of data.
Please check the documentation
Thank You.
Related
In replica set cluster of MongoDB how can i ensure quick response for a concurent users when my primary is busy in serving another request?
Do i need to use load balancer, or the mongodb itself route the query to available Secondary?
Thanks
You don't need to use a load balancer, or to route queries to secondary nodes; the primary node can handle concurrent queries by itself:
MongoDB supports concurrent queries, both reads and writes, using a granular locking system
It is not advised to use secondaries to provide extra read capacity, as replication design makes this inefficient and unreliable for most use cases
If your primary is taking a long time serving a single request, in such a way that it locks out other requests, that should be addressed by redesigning an inefficient query or adding suitable indexes.
If your server is struggling to serve multiple users despite the queries being optimised, look at whether your hardware is insufficient for the job
If you still find that you need to scale out your reads and writes, the recommended way to do that is by sharding, not by using other nodes of a replica set.
Normally writes are handled by master and reads should be send to secondaries by setting read preference. Although it might take some negligible time to get data propagated to secondaries, as secondaries use oplog copy for data replication.
You do not need any load balancer, Mongo is capable of doing these things.
Read more about it here -
https://docs.mongodb.com/manual/replication/
We're using amazon web service for a business application which is using node.js server and mongodb as database. Currently the node.js server is runing on a EC2 medium instance. And we're keeping our mongodb database in a separate micro instance. Now we want to deploy replica set in our mongodb database, so that if the mongodb gets locked or unavailble, we still can run our database and get data from it.
So we're trying to keep each member of the replica set in separate instances, so that we can get data from the database even if the instance of the primary memeber shuts down.
Now, I want to add load balancer in the database, so that the database works fine even in huge traffic load at a time. In that case I can read balance the database by adding slaveOK config in the replicaSet. But it'll not load balance the database if there is huge traffic load for write operation in the database.
To solve this problem I got two options till now.
Option 1: I've to shard the database and keep each shard in separate instance. And under each shard there will be a reaplica set in the same instance. But there is a problem, as the shard divides the database in multiple parts, so each shard will not keep same data within it. So if one instance shuts down, we'll not be able to access the data from the shard within that instance.
To solve this problem I'm trying to divide the database in shards and each shard will have a replicaSet in separate instances. So even if one instance shuts down, we'll not face any problem. But if we've 2 shards and each shard has 3 members in the replicaSet then I need 6 aws instances. So I think it's not the optimal solution.
Option 2: We can create a master-master configuration in the mongodb, that means all the database will be primary and all will have read/write access, but I would also like them to auto-sync with each other every so often, so they all end up being clones of each other. And all these primary databases will be in separate instance. But I don't know whether mongodb supports this structure or not.
I've not got any mongodb doc/ blog for this situation. So, please suggest me what should be the best solution for this problem.
This won't be a complete answer by far, there is too many details and I could write an entire essay about this question as could many others however, since I don't have that kind of time to spare, I will add some commentary about what I see.
Now, I want to add load balancer in the database, so that the database works fine even in huge traffic load at a time.
Replica sets are not designed to work like that. If you wish to load balance you might in fact be looking for sharding which will allow you to do this.
Replication is for automatic failover.
In that case I can read balance the database by adding slaveOK config in the replicaSet.
Since, to stay up to date, your members will be getting just as many ops as the primary it seems like this might not help too much.
In reality instead of having one server with many connections queued you have many connections on many servers queueing for stale data since member consistency is eventual, not immediate unlike ACID technologies, however, that being said they are only eventually consistent by 32-odd ms which means they are not lagging enough to give decent throughput if the primary is loaded.
Since reads ARE concurrent you will get the same speed whether you are reading from the primary or secondary. I suppose you could delay a slave to create a pause of OPs but that would bring back massively stale data in return.
Not to mention that MongoDB is not multi-master as such you can only write to one node a time makes slaveOK not the most useful setting in the world any more and I have seen numerous times where 10gen themselves recommend you use sharding over this setting.
Option 2: We can create a master-master configuration in the mongodb,
This would require you own coding. At which point you may want to consider actually using a database that supports http://en.wikipedia.org/wiki/Multi-master_replication
This is since the speed you are looking for is most likely in fact in writes not reads as I discussed above.
Option 1: I've to shard the database and keep each shard in separate instance.
This is the recommended way but you have found the caveat with it. This is unfortunately something that remains unsolved that multi-master replication is supposed to solve, however, multi-master replication does add its own ship of plague rats to Europe itself and I would strongly recommend you do some serious research before you think as to whether MongoDB cannot currently service your needs.
You might be worrying about nothing really since the fsync queue is designed to deal with the IO bottleneck slowing down your writes as it would in SQL and reads are concurrent so if you plan your schema and working set right you should be able to get a massive amount of OPs.
There is in fact a linked question around here from a 10gen employee that is very good to read: https://stackoverflow.com/a/17459488/383478 and it shows just how much throughput MongoDB can achieve under load.
It will grow soon with the new document level locking that is already in dev branch.
Option 1 is the recommended way as pointed out by #Sammaye but you would not need 6 instances and can manage it with 4 instances.
Assuming you need below configuration.
2 shards (S1, S2)
1 copy for each shard (Replica set secondary) (RS1, RS2)
1 Arbiter for each shard (RA1, RA2)
You could then divide your server configuration like below.
Instance 1 : Runs : S1 (Primary Node)
Instance 2 : Runs : S2 (Primary Node)
Instance 3 : Runs : RS1 (Secondary Node S1) and RA2 (Arbiter Node S2)
Instance 4 : Runs : RS2 (Secondary Node S2) and RA1 (Arbiter Node S1)
You could run arbiter nodes along with your secondary nodes which would help you in election during fail-overs.
I'm looking for a hosting service to host my mongodb database, such as MongoLab-MongoHQ-Heroku-AWS EBS, etc.
What I need is to find one of this services (or another) that provides auto-scaling my storage size.
Is there a way (service) to auto-scale mongodb? How?
There are many hosting providers for MongoDB that provide scaling solutions.
Storage size is only one dimension to scaling; there are other resources to monitor like memory, CPU, and I/O throughput.
There are several different approaches you can use to scale your MongoDB storage size:
1) Deploy a MongoDB replica set and scale vertically
The minimum footprint you would need is two data bearing nodes (a primary and a secondary) plus a third node which is either another data node (secondary) or a voting-only node without data (arbiter).
As your storage needs change, you can adjust the storage on your secondaries by temporarily stopping the mongod service and doing the O/S changes to adjust storage provisioning on your dbpath. After you adjust each secondary, you would restart mongod and allow that secondary to catch up on replication sync before changing the next secondary. Lastly, you would step down and upgrade the primary.
2) Deploy a MongoDB sharded cluster and scale horizontally.
A sharded cluster allows you to partition databases across a number of shards, which are typically backed by replica sets (although technically a shard can be a standalone mongod). In this case you can add (or remove) shards based on your requirements. Changing the number of shards can have a notable performance impact due to chunk migration across shards, so this isn't something you'd do too reactively (i.e. far more likely on a daily or weekly basis rather than hourly).
3) Let a hosted database-as-a-service provider take care of the details for you :)
Replica sets and sharding are the basic building blocks, but hosted providers can often take care of the details for you. Armed with this terminology you should be able to approach hosting providers and ask them about available plan options and costing.
I have a Mongo cluster that backs an application that I use in production. It's very important to my business and clustered across a number of boxes to optimize for speed and redundancy. I'd like to make the data in said cluster available for running analytical queries and enqueued tasks, but I definitely don't want these to harm production performance. Is it possible to just mirror all of my data against a single box I throw into the cluster with some special tag that I can then use for analytics? It's fine if it's slow. I just want it to be cheap and not to affect production read/write speeds.
Since you're talking about redundancy, I assume you have a replica set.
In that case you can use a hidden replica set member to perform the calculations you need.
Just keep in mind that the member count must be odd. If you add a node you might need to also add an arbiter. Or maybe you can just hide one of the already existing members.
If you are looking for a way to increase querying speed having a lot of data, you have to look might look into sharding with mongodb. Basically what it does is dividing your big amount of data into small shards and stores them on different machines.
If you are looking to increase redundancy (in order to make backup or to be able to do offline processing without touching primary servers) you have to look into replication with mongodb. If you are doing replication, keep in mind that the data on the replicas will be always lagging behind a primary (nothing to worry about, but just need to know this fact to decide can you allow read from the replicas). As it was pointed by Rafa, hidden replica sets are well suited for backup and offline data processing. They will still be able to get all the data from primary (with small lag), but are invisible to secondary reads and can not become primary.
There is a nice mongodb course which is talking in depth about replication and sharding, so may be it is worth listening and trying it.
I have decided to start developing a little web application in my spare time so I can learn about MongoDB. I was planning to get an Amazon AWS micro instance and start the development and the alpha stage there. However, I stumbled across a question here on Stack Overflow that concerned me:
But for durability, you need to use at least 2 mongodb server
instances as master/slave. Otherwise you can lose the last minute of
your data.
Is that true? Can't I just have my box with everything installed on it (Apache, PHP, MongoDB) and rely on the data being correctly stored? At least, there must be a config option in MongoDB to make it behave reliably even if installed on a single box - isn't there?
The information you have on master/slave setups is outdated. Running single-server MongoDB with journaling is a durable data store, so for use cases where you don't need replica sets or if you're in development stage, then journaling will work well.
However if you're in production, we recommend using replica sets. For the bare minimum set up, you would ideally run three (or more) instances of mongod, a 'primary' which receives reads and writes, a 'secondary' to which the writes from the primary are replicated, and an arbiter, a single instance of mongod that allows a vote to take place should the primary become unavailable. This 'automatic failover' means that, should your primary be unable to receive writes from your application at a given time, the secondary will become the primary and take over receiving data from your app.
You can read more about journaling here and replication here, and you should definitely familiarize yourself with the documentation in general in order to get a better sense of what MongoDB is all about.
Replication provides redundancy and increases data availability. With multiple copies of data on different database servers, replication protects a database from the loss of a single server. Replication also allows you to recover from hardware failure and service interruptions. With additional copies of the data, you can dedicate one to disaster recovery, reporting, or backup.
In some cases, you can use replication to increase read capacity. Clients have the ability to send read and write operations to different servers. You can also maintain copies in different data centers to increase the locality and availability of data for distributed applications.
Replication in MongoDB
A replica set is a group of mongod instances that host the same data set. One mongod, the primary, receives all write operations. All other instances, secondaries, apply operations from the primary so that they have the same data set.
The primary accepts all write operations from clients. Replica set can have only one primary. Because only one member can accept write operations, replica sets provide strict consistency. To support replication, the primary logs all changes to its data sets in its oplog. See primary for more information.