How does sharding work in mongodb

The primary shard has no relation to the primary in a replica set. The mongos selects the primary shard when creating a new database by picking the shard in the cluster that has the least amount of data.

To change the primary shard for a database, use the movePrimary command. The process of migrating the primary shard may take significant time to complete, and you should not access the collections associated to the database until it completes. Depending on the amount of data being migrated, the migration may affect overall cluster operations.

Consider the impact to cluster operations and network load before attempting to change the primary shard. When you deploy a new sharded cluster with shards that were previously used as replica sets, all existing databases continue to reside on their original replica sets.

Databases created subsequently may reside on any shard in the cluster. Sharding is a concept in MongoDB, which splits large data sets into small data sets across multiple MongoDB instances. Sometimes the data within MongoDB will be so huge, that queries against such big data sets can cause a lot of CPU utilization on the server. To tackle this situation, MongoDB has a concept of Sharding, which is basically the splitting of data sets across multiple MongoDB instances.

The collection which could be large in size is actually split across multiple collections or Shards as they are called. Logically all the shards work as one collection. Step 2 Start the mongodb instance in configuration mode. Suppose if we have a server named Server D which would be our configuration server, we would need to run the below command to configure the server as a configuration server. Step 5 If you have Server A and Server B which needs to be added to the cluster, issue the below commands.

This e-book is a general overview of MongoDB, providing a basic understanding of the database. These postings are my own and do not necessarily represent BMC's position, strategies, or opinion. See an error or have a suggestion? Please let us know by emailing blogs bmc. Shanika Wickramasinghe is a software engineer by profession and a graduate in Information Technology. Her specialties are Web and Mobile Development.

Shanika considers writing the best medium to learn and share her knowledge. She is passionate about everything she does, loves to travel, and enjoys nature whenever she takes a break from her busy work schedule. You can connect with her on LinkedIn. November 11, 9 minute read.

This comprehensive article explores sharding in MongoDB. What is sharding? How sharding works When dealing with high throughput applications or very large databases, the underlying hardware becomes the main limitation.

To mitigate this problem, there are two types of scaling methods. Vertical scaling Vertical scaling is the traditional way of increasing the hardware capabilities of a single server.

Horizontal scaling This method divides the dataset into multiple servers and distributes the database load among each server instance. That means sharded clusters consist of three main components: The shard Mongos Config servers Shard A shard is a single MongoDB instance that holds a subset of the sharded data. Mongos Mongos act as the query router providing a stable interface between the application and the sharded cluster.

Config Servers Configuration servers store the metadata and the configuration settings for the whole cluster. Components illustrated The following diagram from the official MongoDB docs explains the relationship between each component: The application communicates with the routers mongos about the query to be executed. The mongos instance consults the config servers to check which shard contains the required data set to send the query to that shard.

Finally, the result of the query will be returned to the application. To mitigate this, before sharding the collection, the shard key must be created based on: The schema of the data set How the data set is queried Chunks Chunks are subsets of shared data. However, as sharding utilizes shards with replica sets, all queries are distributed among all the nodes in the cluster.

Replication requires vertical scaling when handling large data sets. This requirement can lead to hardware limitations and prohibitive costs compared to the horizontal scaling approach.

But, because MongoDB utilizes horizontal scaling, the workload is distributed. When the need arises, additional servers can be added to a cluster.

In sharding, both read and write performance directly correlates to the number of server nodes in the cluster. A sharded cluster can continue to operate even if a single or multiple shards are unavailable. While the data on those shards are unavailable, the client application can still access all the other available shards within the cluster without any downtime.

In production environments, all individual shards deploy as replica sets, further increasing the availability of the cluster. Limitations Sharding requires careful planning and maintenance to maintain a sharded cluster—because of the complexity involved. When you shard a MongoDB collection, there is no way to unshard the sharded collection. The shard key directly impacts the overall performance of the underlying cluster, as it is used to identify all the documents within the collections. There are some operational restrictions within a MongoDB sharded environment.

For example, the geoSearch command is not supported within a sharded environment. In an instance where a shard key or a prefix of compound shard key is not present, Mongo will perform a broadcast operation that queries all the shards in the cluster, which can result in long-running query tasks.