Abstract: A system can a divide database into a group of shards distributed among a group of data centers, wherein the group of shards comprises respective leader replicas. The system can determine respective correlation values between pairs of shards of the group of shards. The system can examine the pairs of shards in a descending order of respective correlation values, comprising, in response to determining that a respective pair of shards of the pairs of shards has a first correlation value greater than a predetermined threshold value, and that at least one shard of the respective pair of shards is unlocked, reassigning leader replicas of the respective pair of shards to be stored in a same data center of the group of data centers, and locking the leader replicas of the respective pair of shards from being reassigned to another data center of the group of data centers during the examining.

Abstract: A system can divide a database into a group of shards that are distributed among a group of data centers. The system can train a machine learning model on a group of labeled input data, wherein the group of labeled input data comprises respective requests to operate on the database, and wherein the respective requests are labeled with respective shards of the group of shards used to process the respective requests, and to produce a trained machine learning model. The system can, after training the machine learning model, receive a request. The system can process the 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 leader shards of the group of shards to process the request. The system can send the request to the first data center to be processed.

Abstract: A system can a divide database into a group of shards distributed among a group of data centers, wherein the group of shards comprises respective leader replicas. The system can determine respective correlation values between pairs of shards of the group of shards. The system can examine the pairs of shards in a descending order of respective correlation values, comprising, in response to determining that a respective pair of shards of the pairs of shards has a first correlation value greater than a predetermined threshold value, and that at least one shard of the respective pair of shards is unlocked, reassigning leader replicas of the respective pair of shards to be stored in a same data center of the group of data centers, and locking the leader replicas of the respective pair of shards from being reassigned to another data center of the group of data centers during the examining.

Abstract: A system can generate a neural network, wherein an output of the neural network indicates whether a first test of a computer code will pass given an input of respective results of whether respective tests, of a group of tests of the computer code, pass, and wherein respective weights of the neural network indicate a correlation from a group of correlations comprising a positive correlation between a respective output of a respective node of the neural network and the output of the neural network, a negative correlation between the respective output and the output, and no correlation between the respective output and the output. The system can apply sets of inputs to the neural network, respective inputs of the sets of inputs identifying whether the respective tests pass or fail.