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Documents (objects) map nicely to programming language data types.
Embedded documents and arrays reduce need for joins.
Dynamic schema makes polymorphism easier.
Embedding makes reads and writes fast.
Indexes can include keys from embedded documents and arrays.
Optional streaming writes (no acknowledgments).
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Replicated servers with automatic master failover.
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Automatic sharding distributes collection data across machines.
Eventually-consistent reads can be distributed over replicated servers.
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Any field in a MongoDB document can be indexed (indices in MongoDB are conceptually similar to those in RDBMSes). Secondary indices are also available.
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MongoDB supports master-slave replication. A master can perform reads and writes. A slave copies data from the master and can only be used for reads or backup (not writes). The slaves have the ability to select a new master if the current one goes down.
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MongoDB scales horizontally using sharding. The developer chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.)
MongoDB can run over multiple servers, balancing the load and/or duplicating data to keep the system up and running in case of hardware failure. Automatic configuration is easy to deploy, and new machines can be added to a running database.
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MongoDB could be used as a file system, taking advantage of load balancing and data replication features over multiple machines for storing files.
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MapReduce can be used for batch processing of data and aggregation operations. The aggregation framework enables users to obtain the kind of results for which the SQL GROUP BY clause is used.