Disclosed are systems and methods for monitoring user-defined metrics. A method may include: receiving, from a user device, a metric definition usable to generate queries to obtain data for a metric to be monitored; receiving, from the user device, a monitoring configuration indicative of a manner in which a metric monitoring process associated with the metric definition is to be repeatedly performed; storing the metric definition in a metric definition database; and repeatedly performing the metric monitoring process in accordance with the monitoring configuration. The metric monitoring process may include: retrieving the metric definition from the metric definition database; generating a database query based on the metric definition, the database query including one or more executable database statements defined by the metric definition; executing the database query to obtain query result data, the query result data being data for the metric; and storing the query result data.

Techniques for providing a database performance report in a restricted access environment are disclosed. In some embodiments, a computer system performs a method comprising: receiving, from a computing device of a user, a request to generate a database performance report for a first database that is hosted on a cloud computing platform, the request including a service key; obtaining administrative credentials from a second database using stateless authentication and the service key; obtaining one or more database performance metrics of the first database from the first database using the administrative credentials; and generating the database performance report based on the one or more database performance metrics.

An approach is provided for evaluating a performance of a query. A risk of selecting a low performance access path for a query is determined. The risk is determined to exceed a risk threshold. Based on the risk exceeding the risk threshold and using a machine learning optimizer, first costs of access paths for the query are determined. Using a cost-based database optimizer, second costs of the access paths are determined. Using a strong classifier operating on the first costs and the second costs, a final access path for the query is selected from the access paths.

A method for intelligent query plan cache size management can be implemented. During execution of a plurality of incoming queries in a database management system, the method can measure actual compilation times of generating query execution plans for the plurality of incoming queries. The database management system can have a query execution plan cache which has a size that can store at least some of the query execution plans. The method can monitor differences between the actual compilation times and ideal compilation times of generating query execution plans for the plurality of incoming queries. The ideal compilation times can be estimated by assuming no query execution plan is evicted from the query execution plan cache. The method can adjust the size of the query execution plan cache based on the monitored differences.

A computer-implemented method can measure query locality during execution of a plurality of incoming queries in a database management system. The database management system includes a query execution plan cache which has a size that can store at least some of query execution plans generated for the plurality of incoming queries. Based on the measured query locality, the method can adjust the size of the query execution plan cache.

Techniques are described herein for prediction of an buffer pool size (BPS). Before performing BPS prediction, gathered data are used to determine whether a target workload is in a steady state. Historical utilization data gathered while the workload is in a steady state are used to predict object-specific BPS components for database objects, accessed by the target workload, that are identified for BPS analysis based on shares of the total disk I/O requests, for the workload, that are attributed to the respective objects. Preference of analysis is given to objects that are associated with larger shares of disk I/O activity. An object-specific BPS component is determined based on a coverage function that returns a percentage of the database object size (on disk) that should be available in the buffer pool for that database object. The percentage is determined using either a heuristic-based or a machine learning-based approach.

Methods and systems are disclosed herein for updating a knowledge graph based on a user confirmation. A media guidance application receives a user communication and isolates a term of the user communication. The media guidance application identifies a candidate component of a knowledge graph associated with the term. The media guidance application requests user input directed to confirming whether the term is associated with the candidate component. In response to receiving the user input, the media guidance application modifies a strength of association between the term and the component.

Herein are resource-constrained techniques that plan ahead for resiliently moving pluggable databases between container databases after a failure in a high-availability database cluster. In an embodiment that has a database cluster that hierarchically contains many pluggable databases in many container databases in many virtual machines, a computer identifies many alternative placements that respectively assign each pluggable database instance (PDB) to a respective container database management system (CDBMS). For each alternative placement, a respective placement score is calculated based on the PDBs and the CDBMSs. Based on the placement scores of the alternative placements, a particular placement is selected with a best placement score that indicates optimal resilience for accommodating adversity such as failover and overcrowding.

Each of one or more commit cycles may be associated with a predicted number of updates. A statistics collection time for a database table can be determined by estimating a sum of predicted updates included in one or more commit cycles. Whether the estimated sum of predicted updates is greater than a first threshold may be determined. In addition, a progress point for a first one of the commit cycles can be determined. A time to collect statistics may be selected based on the progress point of the first commit cycle.

Embodiments of the present disclosure describe systems, methods, and computer program products for executing and tuning a machine learning operation within a database. An example method can include receiving a data query referencing an input data set of a database, executing a plurality of machine learning operations to generate, in view of the input data set, a plurality of output data sets each having a respective accuracy value, wherein each of the plurality of machine learning operations is executed by a processing device according to one of a plurality of unique sets of hyperparameters, selecting a first output data set of the plurality of output data sets in view of the accuracy values, and returning the first output data set in response to the data query.