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Question: How does partitioning work in MongoDB?


Partitioning in MongoDB is usually referred to as 'sharding'. Sharding is the process of storing data records across multiple machines and it is MongoDB’s approach to meeting the demands of data growth. As the size of the data increases, a single machine may not be able to store the data or provide an acceptable read and write throughput. Sharding solves this problem by distributing data across multiple MongoDB instances.

How Sharding Works

Sharding involves dividing the data set into smaller chunks called 'shards'. Each shard is held on a separate database server instance, thus distributing the load. MongoDB uses the sharding key to distribute the data among the shards. The choice of a good sharding key is crucial for evenly distributing data and ensuring balance among the shards.

Components of MongoDB Sharding

  • Shard: A shard is a single MongoDB instance that holds a portion of the sharded data.
  • Config Servers: Config servers store metadata about the cluster, such as the cluster’s configuration details and the mapping between the shards and the data.
  • Query Routers (mongos): The query routers direct the operations to the appropriate shard(s) and aggregate the results. Applications do not connect directly to the shard instances but instead, connect through a mongos instance.

Setting Up Sharding in MongoDB

  1. Initialize the Config Server:

First, you start one or more config servers. If deploying a production sharded cluster, deploy three config servers for redundancy.

mongod --configsvr --dbpath /data/configdb --port 27019
  1. Start the Shard Servers:

Each shard is a mongod instance. Start each shard on its own server.

mongod --shardsvr --dbpath /data/sharddb1 --port 27018
  1. Start the Mongos Instances:

The mongos instances route queries from your application to the correct shard(s).

mongos --configdb configServer1:27019 --port 27017
  1. Add Shards to the Cluster:

Connect to one of the mongos instances and use the sh.addShard() command to add each shard to the cluster.


Choosing a Sharding Key

  • It's important to choose a sharding key that will distribute your data evenly across shards.
  • A poor choice can lead to 'shard key hotspots', where a significant portion of queries target a single shard, creating bottlenecks.


  • Scalability: Sharding provides horizontal scalability.
  • Complexity: Managing a sharded cluster is more complex than handling a single instance.
  • Balance: MongoDB automatically balances data across shards, but initial sharding key selection is crucial.

By properly setting up and managing sharded clusters, MongoDB can scale out to accommodate large data sizes and high throughput operations, making it suitable for large-scale applications.

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