Sharded database load distributor

A method, comprising:

• training, by a system comprising at least one processor, a machine learning model based on a group of input data, the group of input data comprising respective requests to operate on a data store that are labeled with respective shards of a group of shards that are distributed among a group of data centers, wherein the respective shards are used to process the respective requests, and to produce a trained machine learning model;
• processing, by the system, a request with the trained machine learning model to predict that a data center of the group of data centers is threshold likely to have a largest number of shards of the group of shards to process the request;
• and
• sending, by the system, the request to the data center.

A non-transitory computer-readable medium comprising instructions that, in response to execution, cause a system comprising at least one processor to perform operations, comprising:

• dividing a data store into a group of shards that are distributed among a group of data centers;
• training a machine learning model on a group of input data, wherein the group of input data comprises respective requests to operate on the data store that are labeled with respective shards of the group of shards used to process the respective requests, and to produce a trained machine learning model;
• processing a request with the trained machine learning model to predict that a data center of the group of data centers will have a largest number of shards of the group of shards to process the request;
• and
• sending the request to the data center.