Dynamically predict optimal parallel apply algorithms

A system having one or more computer processors, the system configured to:

• receive source workload data and replication environment data, wherein the source workload data comprises at least a stream of changes to a target database management system;
• analyze characteristics of the source workload data and the replication environment data;
• input, as input variables, the characteristics of the source workload data and the replication environment data into a machine learning algorithm;
• identify a plurality of parallel apply algorithms;
• obtain, from the machine learning algorithm, an optimal parallel apply algorithm from the plurality of parallel apply algorithms;
• and
• apply the optimal parallel apply algorithm to the target database management system.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a server to cause the server to perform a method, the method comprising:

• receiving source workload data and replication environment data, wherein the source workload data comprises at least a stream of changes to a target database management system;
• analyzing characteristics of the source workload data and the replication environment data;
• inputting, as input variables, the characteristics of the source workload data and the replication environment data into a machine learning algorithm;
• identifying a plurality of parallel apply algorithms;
• obtaining, from the machine learning algorithm, an optimal parallel apply algorithm from the plurality of parallel apply algorithms;
• and
• applying the optimal parallel apply algorithm to the target database management system.