After version 4.0, MongoDB has introduced the multi-document transactions for replica sets. In order to use the new feature, I have converted the testing instance to a replica set, following the official documentation.
The end result was a replica set with a primary node, no secondary nodes and no arbiters.
I would like to know if this architecture has any implications on performance, data integrity, etc... Any help or reference to a similar case is much appreciated
It is valid to have a single member replica set for the purposes of testing or development. This will allow you to use features which require a replica set deployment (for example, transactions in MongoDB 4.0+ and change streams in MongoDB 3.6+).
The main downsides of a single member deployment are that you don't get any of the usual replica set benefits such as data redundancy and fault tolerance, and cannot test more interesting read concerns and write concerns that might be useful in a production deployment. A replica set member has some expected write overhead as compared to a standalone server because it also has to maintain a replication oplog.
A production replica set deployment should have a minimum of three members. See Deploy a replica set in the MongoDB documentation for full details.
I have a problem on Sharded Cluster. I'm testing performance to compare between Sharded and Replica Set.
I have inserted data to Shard 1 directly without mongos and then query it by aggregate query but I cannot found it. I checked in explain plan that shows "SHARDING_FILTER" in stage on Primary shard but doesn't have that in Secondary when I checked explain plan.
What's configuration to control about it?
MongoDB version : 3.0.12
I have inserted data to Shard 1 directly without mongos and then query it by aggregate query but I cannot found it.
It's not entirely clear what your performance comparison is, but irrespective you should always interact with data via mongos for a sharded cluster.
The role of mongos includes keeping track of the sharded cluster metadata (as cached from the config servers), observing data inserts/updates/deletions, and routing requests. Bypassing mongos will lead to potential complications in collection/data visibility (as you have observed) because you are skipping some of the expected data management infrastructure for your sharded deployment.
I checked in explain plan that shows "SHARDING_FILTER" in stage on Primary shard but doesn't have that in Secondary when I checked explain plan.
Secondary reads are eventually consistent, so the state of data on a given secondary may not necessarily match the current sharded cluster metadata. This becomes more problematic with many shards: with a secondary read preference results can potentially be combined from secondaries with significant differences in replication lag.
For consistent queries for a sharded cluster you should always use primary reads (which is the default behaviour) via mongos. Queries against primaries through mongos may include a SHARDING_FILTER stage which filters result documents that are not owned by the current shard (for example, due to migrations in progress where documents need to transiently exist on both a donor and target shard).
As at MongoDB 3.4, secondaries do not have the ability to filter results because they'd need to maintain a separate view of the cluster metadata which matches their eventually consistent state. There's a relevant Jira issue to watch/upvote: SERVER-5931 - Secondary reads in sharded clusters need stronger consistency. I currently would not recommend secondary reads in a sharded cluster (or in general) without careful consideration of the impact of eventual consistency on your use case. For the general case, please read Can I use more replica nodes to scale?.
What's configuration to control about it?
Use the default read preference (primary reads) and always interact with your sharded deployment through mongos.
Obviously, I know why to use a replica set in general.
But, I'm confused about the difference between connecting directly to the PRIMARY mongo instance and connecting to the replica set. Specifically, if I am connecting to Mongo from my node.js app using Mongoose, is there a compelling reason to use connectSet() instead of connect()? I would assume that the failover benefits would still be present with connect(), but perhaps this is where I am wrong...
The reason I ask is that, in mongoose, the connectSet() method seems to be less documented and well-used. Yet, I cannot imagine a scenario where you would NOT want to connect to the set, since it is recommended to always run Mongo on a 3x+ replica set...
If you connect only to the primary then you get failover (that is, if the primary fails, there will be a brief pause until a new master is elected). Replication within the replica set also makes backups easier. A downside is that all writes and reads go to the single primary (a MongoDB replica set only has one primary at a time), so it can be a bottleneck.
Allowing connections to slaves, on the other hand, allows you to scale for reads (not for writes - those still have to go the primary). Your throughput is no longer limited by the spec of the machine running the primary node but can be spread around the slaves. However, you now have a new problem of stale reads; that is, there is a chance that you will read stale data from a slave.
Now think hard about how your application behaves. Is it read-heavy? How much does it need to scale? Can it cope with stale data in some circumstances?
Incidentally, the point of a minimum 3 members in the replica set is to offer resiliency and safe replication, not to provide multiple nodes to connect to. If you have 3 nodes and you lose one, you still have enough nodes to elect a new primary and have replication to a backup node.
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.
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.