Database replication error recovery based on supervised learning

A computer-implemented method comprising:

• training a predictive model using a set of training data;
• performing database replication processing in a cloud computing environment such that one or more log files are generated, the one or more log files including text generated during the database replication processing;
• obtaining the text from the one or more log files;
• transforming the text into a plurality of vectors, each vector representing an instance of an error that occurred during database replication processing, wherein transforming the text includes:
• segmenting the text into sentences;
• segmenting the sentences into tokens such that segmented sentences are generated;
• tagging the tokens of the segmented sentences as parts of speech such that tagged sentences are generated;
• detecting entities in the tagged sentences;
• detecting relations among the entities;
• and
• converting the relations into vectors;
• classifying, using the predictive model, one or more errors based on the vectors as a recoverable error such that one or more recoverable errors are identified;
• determining, for the one or more recoverable errors based, at least in part, on corresponding error sub-types, one or more recovery jobs associated with database replication processing in the cloud computing environment;
• and
• automatically invoking the one or more recovery jobs, the one or more recovery jobs including re-executing at least a portion of the database replication processing.

A tangible, non-transitory computer-readable storage medium having instructions stored thereon which, when executed by a processing device, cause the processing device to:

• train a predictive model using a set of training data;
• perform database replication processing in a cloud computing environment such that one or more log files are generated, the one or more log files including text generated during the database replication processing;
• obtain the text from the one or more log files;
• transform the text into a plurality of vectors, each vector representing an instance of an error that occurred during database replication processing, wherein transforming the text includes:
• segmenting the text into sentences;
• segmenting the sentences into tokens such that segmented sentences are generated;
• tagging the tokens of the segmented sentences as parts of speech such that tagged sentences are generated;
• detecting entities in the tagged sentences;
• detecting relations among the entities;
• and
• converting the relations into vectors;
• classify, using the predictive model, one or more errors based on the vectors as a recoverable error such that one or more recoverable errors are identified;
• determine, for the one or more recoverable errors based, at least in part, on corresponding error sub-types, one or more recovery jobs associated with database replication processing in the cloud computing environment;
• and
• automatically invoke the one or more recovery jobs, the one or more recovery jobs including re-executing at least a portion of the database replication processing.