Sap Se

Lohit Kumar A P

Rajendra Kumar

Venkatesh R

Pankaj Kumar Routray

Heinz Wolf

Kirubakaran Paramasivan

A method for machine learning based database management is provided. The method may include training a machine learning model to detect an anomaly that is present and/or developing in a database system. The anomaly in the database system may be detected by at least processing, with a trained machine learning model, one or more performance metrics for the database system. In response to detecting the presence of the anomaly at the database system, one or more remedial actions may be determined for correcting and/or preventing the anomaly at the database system. The one or more remedial actions may further be sent to a database management system associated with the database system. Related systems and articles of manufacture are also provided.

Machine learning based database anomaly prediction

Methods and apparatus are disclosed to update software on a legacy system without disruption of live applications. In a database server environment, a nameserver restart can utilize a pre-existing hook facility to detect a newly introduced script and execute an initialization function of the script, leading to activation or launch of the script. In a use case of a high availability database server, the script can cause a copy of a replication status to be stored at a remote location. Upon failure of the database server, retrieval and verification of the replication status from the remote location enables failover to a replica server to be performed safely and automatically.

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Bengaluru, India

Lohit Kumar A P