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How is Sharding done in MongoDB?
Hashed Sharding involves computing a hash of the shard key field’s value. Each chunk is then assigned a range based on the hashed shard key values. MongoDB automatically computes the hashes when resolving queries using hashed indexes. Applications do not need to compute hashes.
What is data Sharding in MongoDB?
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 sufficient to store the data nor provide an acceptable read and write throughput.
What is Sharding key in MongoDB?
Shard Keys. The shard key is either a single indexed field or multiple fields covered by a compound index that determines the distribution of the collection’s documents among the cluster’s shards.
How should I store data in MongoDB?
In MongoDB, data is stored as documents. These documents are stored in MongoDB in JSON (JavaScript Object Notation) format. JSON documents support embedded fields, so related data and lists of data can be stored with the document instead of an external table.
What is the purpose of MongoDB?
MongoDB can be used as a file system, called GridFS, with load balancing and data replication features over multiple machines for storing files. This function, called grid file system, is included with MongoDB drivers. MongoDB exposes functions for file manipulation and content to developers.
How does MongoDB store data?
MongoDB stores the data on the disk as BSON in your data path directory, which is usually /data/db. There should be two files per collection there, collection.0, which stores the data (and that integer is then incremented as needs be) and collection.ns which stores the namespacing metadata for the collection.
What is MongoDB data synchronization?
In order to maintain up-to-date copies of the shared data set, secondary members of a replica set sync or replicate data from other members. MongoDB uses two forms of data synchronization: initial sync to populate new members with the full data set, and replication to apply ongoing changes to the entire data set.