Methods and system are presented for monitoring database processes to generate machine learning predictions. A plurality of database processes executed on database implementations can be monitored, wherein the monitoring includes determining a start time, an end time, and a number of rows impacted by portions of the database processes, and the monitored database processes generate instances of machine learning data including at least the number of rows impacted and an associated duration of time. Using a machine learning component and the machine learning data, a duration of time can be predicted for a candidate database process for execution on a database implementation.

Techniques are described for enabling real-time database performance measurement and tuning in a service provider network. To efficiently test one or more proposed configuration changes to a database in a service provider network, a database service is able to create a replicated copy of the database in an environment that mirrors that of the primary database. The database service then automatically causes database traffic destined for the primary database to be routed to both the primary database and the test database. Once the test database is created and traffic is routed to both databases, the database service obtains performance data by monitoring performance of both the primary database and the test database over a period of time. Based on the obtained performance database, the database service can automatically determine which of the primary database and the test database is exhibiting better performance.

A system and method includes using a queue with reduce operations. A method can include, responsive to generation of one or more markers by a first node or a second node, causing a submission of one or more markers to a queue associated with a computing cluster. Additionally, responsive to a determination that the first node has completed a first reduce operation, directing the first node to perform a first copy operation to copy first data identified by a first marker of the one or more markers in the queue, where the first copy operation is performed concurrently with the second reduce operation.

What is disclosed is an improved approach to perform automatic partitioning, without requiring any expertise on the part of the user. A three stage processing pipeline is provided to generate candidate partition schemes, to evaluate the candidate using real table structures that are empty, and to then implement a selected scheme with production data for evaluation. In addition, an improved approach is described to perform automatic interval partitioning, where the inventive concept implements interval partitioning that does not impose these implicit constraints on the partition key column.

A data store system may include at least one storage device to store a plurality of data and at least one processor with access to the storage device. The at least one processor may receive a plurality of features associated with an environment. The at least one processor may further generate a state representation of the environment based on the plurality of features. The at least one processor may further generate a plurality of predicted future states of the environment based on the state representation. The at least one processor may further generate at least one action to be performed by the environment based on the plurality of predicted future states. The at least one processor may provide the at least one action to the environment to be performed. A method and computer-readable medium are also disclosed.

Techniques are disclosed for tuning external invocations utilizing weight-based parameter resampling. In one example, a computer system determines a plurality of samples, each sample being associated with a parameter value of a plurality of potential parameter values of a particular parameter. The computer system assigns weights to each of the parameter values, and then selects a first sample for processing via a first external invocation based on a weight of the parameter value of the first sample. The computer system then determines feedback data associated with a level of performance of the first external invocation. The computer system adjusts the weights of the parameter values of the particular parameter based on the feedback data. The computer system then selects a second sample of the plurality of samples to be processed via execution of a second external invocation based on the adjustment of weights of the parameter values.

An example operation may include one or more of creating, by a blockchain user of a blockchain network, a world state checkpoint transaction requesting world state validation, endorsing, by one or more endorser nodes or peers, the world state checkpoint transaction, transferring endorsements to the blockchain user, recording, by an orderer node or peer, the endorsed world state checkpoint transaction into a block, validating and committing all transactions in the block, calculating and signing a hash of a current world state, by all blockchain nodes or peers of the blockchain network, and verifying, by the blockchain user, world state integrity from the calculated and signed hashes of the current world state.

A computer identifies, within a hierarchical database, data elements associated with a selected function associated with the database, comprising. The computer identifies at least one function associated with a hierarchical database containing data elements. The computer, in response to identifying the function, identifies within a list of indica, at least one reference indicia corresponding to the at least one function. The computer identifies within a monolithic application relevant code elements associated with the reference indicia. The computer generates an activity log associated with execution of the relevant code elements. The computer identifies, within the activity log, a group of data elements associated with the execution of the relevant code elements. The computer generates a group data element clusters using a Machine Learning algorithm. The computer identifies at least one of the group of data element clusters as relevant to the at least one function.

Automated batch generation and subsequent submission and monitoring of batches processed by a system is disclosed. A plurality of records to be submitted to a system for processing is accessed. A plurality of record groups is generated, each record group corresponding to a different subset of records. A plurality of batch transaction records is generated. Each batch transaction record corresponds to one of the plurality of record groups, and includes a subset identifier that references a subset of records of a record group to which the batch transaction record corresponds, and a status field. A first batch transaction record that identifies a first subset of records is accessed. The status field of the first batch transaction record is set to a submitted value, and instructions are sent to the system to process the first subset of records.

A system for performing test data management is disclosed. The system includes a data library having one or more databases, the one or more databases undergo a onetime data transfer to a data store, a performing test data management non-transitory storage media residing on the one or more databases, the performing test data management non-transitory storage media includes a means for performing artificial intelligence that resides on the performing test data management non-transitory storage media, a processor system, and a plurality of components and functions residing on the performing test data management non-transitory storage media, the components and functions include a first data transfer function, a data explorer component, a second data transfer function, a data modeler component, a third data transfer function, a data validation component, a fourth data transfer function, a main transfer function, and a data generator component. The system includes a corresponding method as well.