Reading through Rosenberg & Mateos' The Cloud at Your Service I stumbled upon the concept of database sharding.

Authors point out to several advantages of such approach including high (online) availability, faster queries, handling higher user loads and better parallelization (they also warn of potential pitfalls!)

Is sharding used in geospatial world? What are the applications? Advantages? Disatvantages?


MongoDB is a Database that supports Sharding

Sharding offers:

Advantages: Automatic balancing for changes in load and data distribution

Easy addition of new machines Scaling out to one thousand nodes

No single points of failure

Automatic failover

Disadvantages: Sharding must be ran in trusted security mode, without explicit security.

Shard keys are immutable in the current version

All (non-multi)updates, upserts, and inserts must include the current shard key. This may cause issues for anyone using a mapping library since you don't have full control of updates.




I don't have enough reputation to comment on the accepted answer, so here is just some updated information about sharding geopstial data in MongoDB.

Current with version 3.2.9 (Sept 2016) you cannot use a geospatial index as a shard key

A shard key index cannot be an index that specifies a multikey index, a text index or a geospatial index on the shard key fields.

source: https://docs.mongodb.com/manual/reference/limits/#limits-shard-keys

I hope this will change.

Additionally, the stated disadvantage about requiring "trusted" security mode is not true in the current version. Since version 2.0 authentication with a sharded cluster has been implemented (https://docs.mongodb.com/v2.6/tutorial/enable-authentication-in-sharded-cluster/)

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