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.
Related
My application is essentially a bunch of microservices deployed across Node.js instances. One service might write some data while a different service will read those updates. (specific example, I'm processing data that is inbound to my solution using a processing pipeline. Stage 1 does something, stage 2 does something else to the same data, etc. It's a fairly common pattern)
So, I have a large data set (~250GB now, and I've read that once a DB gets much larger than this size, it is impossible to introduce sharding to a database, at least, not without some major hoop jumping). I want to have a highly available DB, so I'm planning on a replica set with at least one secondary and an arbiter.
I am still researching my 'sharding' options, but I think that I can shard my data by the 'client' that it belongs to and so I think it makes sense for me to have 3 shards.
First question, if I am correct, if I have 3 shards and my replica set is Primary/Secondary/Arbiter (with Arbiter running on the Primary), I will have 6 instances of MongoDB running. There will be three primaries and three secondaries (with the Arbiter running on each Primary). Is this correct?
Second question. I've read conflicting info about what 'majority' means... If I have a Primary and Secondary and I'm writing using the 'majority' write acknowledgement, what happens when either the Primary or Secondary goes down? If the Arbiter is still there, the election can happen and I'll still have a Primary. But, does Majority refer to members of the replication set? Or to Secondaries? So, if I only have a Primary and I try to write with 'majority' option, will I ever get an acknowledgement? If there is only a Primary, then 'majority' would mean a write to the Primary alone triggers the acknowledgement. Or, would this just block until my timeout was reached and then I would get an error?
Third question... I'm assuming that as long as I do writes with 'majority' acknowledgement and do reads from all the Primaries, I don't need to worry about causally consistent data? I've read that doing reads from 'Secondary' nodes is not worth the effort. If reading from a Secondary, you have to worry about 'eventual consistency' and since writes are getting synchronized, the Secondaries are essentially seeing the same amount of traffic that the Primaries are. So there isn't any benefit to reading from the Secondaries. If that is the case, I can do all reads from the Primaries (using 'majority' read concern) and be sure that I'm always getting consistent data and the sharding I'm doing is giving me some benefits from distributing the load across the shards. Is this correct?
Fourth (and last) question... When are causally consistent sessions worthwhile? If I understand correctly, and I'm not sure that I do, then I think it is when I have a case like a typical web app (not some distributed application, like my current one), where there is just one (or two) nodes doing the reading and writing. In that case, I would use causally consistent sessions and do my writes to the Primary and reads from the Secondary. But, in that case, what would the benefit of reading from the Secondaries be, anyway? What am I missing? What is the use case for causally consistent sessions?
if I have 3 shards and my replica set is Primary/Secondary/Arbiter (with Arbiter running on the Primary), I will have 6 instances of MongoDB running. There will be three primaries and three secondaries (with the Arbiter running on each Primary). Is this correct?
A replica set Arbiter is still an instance of mongod. It's just that an Arbiter does not have a copy of the data and cannot become a Primary. You should have 3 instances per shard, which means 9 instances in total.
Since you mentioned that you would like to have a highly available database deployment, please note that the minimum recommended replica set members for production deployment would be a Primary with two Secondaries.
If I have a Primary and Secondary and I'm writing using the 'majority' write acknowledgement, what happens when either the Primary or Secondary goes down?
When either the Primary or Secondary becomes unavailable, a w:majority writes will either:
Wait indefinitely,
Wait until either nodes is restored, or
Failed with timeout.
This is because an Arbiter carries no data and unable to acknowledge writes but still counted as a voting member. See also Write Concern for Replica sets.
I can do all reads from the Primaries (using 'majority' read concern) and be sure that I'm always getting consistent data and the sharding I'm doing is giving me some benefits from distributing the load across the shards
Correct, MongoDB Sharding is to scale horizontally to distribute load across shards. While MongoDB Replication is to provide high availability.
If you read only from the Primary and also specifies readConcern:majority, the application will read data that has been acknowledged by the majority of the replica set members. This data is durable in the event of partition (i.e. not rolled back). See also Read Concern 'majority'.
What is the use case for causally consistent sessions?
Causal Consistency is used if the application requires an operation to be logically dependent on a preceding operation (causal). For example, a write operation that deletes all documents based on a specified condition and a subsequent read operation that verifies the delete operation have a causal relationship. This is especially important in a sharded cluster environment, where write operations may go to different replica sets.
I'm new to Mongo, have been using Cassandra for a while. I didn't find any clear answers from Mongo's docs. My questions below are all closely related.
1) How to specify the replication factors in Mongo?
In C* you can define replication factors (how many replicas) for the entire database and/or for each table. Mongo's newer replica set concept is similar to C*'s idea. But, I don't see how I can define the replication factors for each table/collection in Mongo. Where to define that in Mongo?
2) Replica sets and replication factors?
It seems you can define multiple replica sets in a Mongo cluster for your databases. A replica set can have up to 7 voting members. Does a 5-member replica set means the data is replicated 5 times? Or replica sets are only for voting the primary?
3) Replica sets for what collections?
The replica set configuration doc didn't mention anything about databases or collections. Is there a way to specify what collections a replica set is intended for?
4) Replica sets definition?
If I wanted to create a 15-node Mongo cluster and keeping 3 copies of each record, how to partition the nodes into multiple replica sets?
Thanks.
Mongo replication works by replicating the entire instance. It is not done at the individual database or collection level. All replicas contain a copy of the data except for arbiters. Arbiters do not hold any data and only participate in elections of a new primary. They are usually deployed to create enough of a majority that if an instance goes down a new instance can be elected as the primary.
Its pretty well explained here https://docs.mongodb.com/manual/replication/
Replication is referred to the process of ensuring that the same data is available on more than one Mongo DB Server. This is sometimes required for the purpose of increasing data availability.
Because if your main MongoDB Server goes down for any reason, there will be no access to the data. But if you had the data replicated to another server at regular intervals, you will be able to access the data from another server even if the primary server fails.
Another purpose of replication is the possibility of load balancing. If there are many users connecting to the system, instead of having everyone connect to one system, users can be connected to multiple servers so that there is an equal distribution of the load.
In MongoDB, multiple MongDB Servers are grouped in sets called Replica sets. The Replica set will have a primary server which will accept all the write operation from clients. All other instances added to the set after this will be called the secondary instances which can be used primarily for all read operations.
So I have a production sharded MongoDB cluster that has 8 shards (replica sets) managed by mongos. Let's say I have 20 servers which are running my application and each of the servers runs a mongos process that manages the 8 shards.
Given this setup, when I check the number of ops on each of my mongos on the 20 servers, I can see that my number of inserts and deletes are in proportion - which is in accordance with my application logic. However, when I run mongostat --discover on the individual shards, I see that deletes are nearly 4x the number of inserts which violates both my application logic as well as the 1:1 ratio indicated by mongos. Straightforward intuition supports that mongos would write to only one shard and so the average ratio of inserts and deletes across individual shards should be the same as that on mongos (which the application directly writes to) unless mongos does something different internally with the shards.
Could anyone point me to any relevant info on why this would happen or let me know if something could possibly wrong with my infra?
Thanks
The reason for this is that I was running the remove() queries to mongos without specifying my shard key. In that case, mongos does not know which shard to direct the query to and thus broadcasts the query to all the shards effectively performing more deletes than a targeted query.
Check documentation for more information.
When I put shard and Replica Set together, I am confused.
Why does the reference say that the shards are replica sets?
Do replica sets contains shards?
Can someone give me a conceptual explanation?
Replica Set is a cluster of MongoDB servers which implements Master - slave implementation. So, basically same data is shared between multiple replica i.e Master and Slave(s). Master is also termed as primary node and Slave(s) is/are considered as Secondary nodes.
It replicates your data on multiple mongo instances to solve/avoid fail overs. MongoDB also perform election of Primary node between secondary nodes automatically whenever Primary node goes down.
Sharding is used to store large data set between multiple machines. So basically, if you simply wants to compare Sharded nodes doesnt/may not contain same data where as Relicated nodes contains same data.
Sharding has different purpose,large data set is spread accross multiple machines.
Now, this large data set's subset can also be replicated to multiple nodes as primary and secondary to overcome failovers. So basically a shard can have multiple replica-set. These replica set of a shard contains subset of data for a large data set.
So, multiple shards can complete the whole large data set which are separated in the form of chunks. These chunks can be replicated within a Shard using Replica set.
You can also get more details related to this in MongoDB manual.
Sharding happens one level above replication.
When you use both sharding and replication, your cluster consists of many replica-sets and one replica-set consists of many mongod instances.
However, it is also possible to create a cluster of stand-alone mongod instances which are not replicated or have only some shards implemented as replica-sets and some shards implemented as stand-alone mongod instances.
Each shard is a replica set, not the shards are replica sets.
This is a language barrier, in English to say such a thing really means the same as "each shard is a replica set" in this context.
So to explain, say you have a collection of names a-z. Shard 1 holds a-b. This shard is also a replica set which means it has automated failover and replication of that range as well. So sharding in this sense is a top level term that comes above replica sets.
Shards are used to break a collection and store parts of it in different places. It is not necessary that a shard be a replica set, it can be a single server, but to achieve reliability and avoid loss of data, a replica set can be used as a shard instead of a single server. So, if one of the servers in the replica set goes down, the others will still hold the data.
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.