| CPC G06F 16/2282 (2019.01) [G06F 16/278 (2019.01)] | 20 Claims |
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1. A computer-implemented method, comprising
training, by a processor set, a machine learning model using a machine learning classification algorithm which uses an existing predicted split percentage and an existing predicted split ratio of a tablespace, wherein the machine learning model is continuously trained after a split to determine a predicted split percentage and a predicated split ratio for a next split event;
providing, by the processor set, the tablespace comprising an original partition, the original partition having a lower boundary L, an upper boundary U, a split percentage P, and a split ratio R using the trained machine learning model;
monitoring, by the processor set, a capacity of partitions in the tablespace; and
in response to the original partition reaching a fullness of P based on the monitored capacity of partitions in the tablespace, automatically splitting, by the processor set, the original partition to a first progeny partition and a second progeny partition,
wherein the first progeny partition has a lower boundary L1 that is the same as the lower boundary L of the original partition and an upper boundary U1 that is determined based on whether an insert is a sequential insert or a random insert, and
wherein the second progeny partition has an upper boundary U2 that is the same as the upper boundary U of the original partition and a lower boundary L2 that is determined based on whether the insert is the sequential insert or the random insert,
the machine learning model further comprises a support vector machine (SVM),
the machine learning model is trained using partition size, history of insert volume, and frequency of insert for determining and outputting the predicted split percentage, and
the machine learning model is trained using history of insert distribution and current key distribution for determining and outputting the predicted split ratio.
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