A method, when executed by data processing hardware, causes the data processing hardware to perform operations including receiving, from a user device, a hyperparameter optimization request requesting optimization of one or more hyperparameters of a machine learning model. The operations include obtaining training data for training the machine learning model and determining a set of hyperparameter permutations of the one or more hyperparameters. For each respective hyperparameter permutation in the set of hyperparameter permutations, the operations include training a unique machine learning model using the training data and the respective hyperparameter permutation and determining a performance of the trained model. The operations include selecting, based on the performance of each of the trained unique machine learning models of the user device, one of the trained unique machine learning models. The operations include generating one or more predictions using the selected one of the trained unique machine learning models.

A data processing system that provides access to operator state information includes a plurality of operators that are configured to perform a computation with respect to data received from data sources. State information is associated with at least one of the plurality of operators. The data processing system also includes an object graph that comprises a representation of the computation, and that may dynamically change at runtime. The data processing system also includes an interface that provides access to the state information via the object graph. The data processing system also includes a query manager that is executable to process a graph query to retrieve the state information by traversing a plurality of nodes within the object graph. Temporal navigation is also supported. Thus, processing a graph query may involve navigating to a node in the object graph at a certain point in time.

Aspects of the present disclosure address systems, methods, and devices for tracking object dependencies in a cloud database system. An object dependency created between a referencing object and a referenced object is detected. Based on detecting the object dependency, a dependency record is generated. The dependency record includes dependency information describing the object dependency between the reference object and the referenced object. The dependency record is stored in a database of dependency records.

A method includes generating a plurality of parity blocks from a plurality of lines of data blocks. The plurality of lines of data blocks are stored in data sections of memory of a cluster of computing devices of the computing system by distributing storage of individual data blocks of the plurality of lines of data blocks among unique data sections of the cluster of computing devices. The plurality of parity blocks are stored in parity sections of memory of the cluster of computing devices by distributing storage of parity blocks of the plurality of parity blocks among unique parity sections of the cluster of computing devices.

A computer-implemented method includes receiving a query specifying an operation to perform on a first table of a plurality of data blocks stored. Each data block in the first table includes a respective reference count indicating a number of tables referencing the data block. The method also includes determining that the operation specified by the query includes copying the plurality of data blocks in the first table into a second table and, in response, for each data block of the plurality of data blocks in the first table copied into the second table, incrementing, the respective reference count associated with the data block in the first table, appending, by the data processing hardware, into metadata of the second table, a reference of the corresponding data block copied into the second table.

A robotic device management service obtains, from a client device operating in a first network, a request to obtain data from a robotic device operating in a second network. In response to the request, the robotic device management service issues a token to the client device that can be provided in future queries to obtain the data. The robotic device management service provides parameters of the request to the robotic device to cause the robotic device to generate and provide the data to the robotic device management service. In response to another request to obtain the data, where the other request includes the token, the robotic device management service queries a database to determine whether the data is available from a storage location of the service. If the data is available, the service provides the data to the client device to fulfill the other request.

Systems and methods may generate or modify a pattern, to search text, utilizing a hierarchical structure. The system and method may receive instructions for generating or modifying the pattern. The system and methods may identify or generate a hierarchical structure containing one or more levels each of which includes one or more objects that store values. The system and method may define a pattern by assigning values to the hierarchical structure when the instructions are for generating the pattern; or may modify one or more values in the hierarchical structure when the instructions are for modifying the pattern. The system and method may receive pattern matching instructions. The system and method may identify, based on the pattern matching instructions and utilizing the hierarchical structure, one or more portions of the program that includes the generated or modified pattern and implement one or more pattern matching functions to provide results.

A computer-implemented method can comprise establishing programmatic connections to a digitally stored first database comprising over one million records, each of the records comprising columns; reading a configuration file that specifies tables in the database; for each particular table, forming and submitting a plurality of queries to the database, each of the queries specifying data aggregation operations, and in response thereto, receiving result sets of records of the database; calculating metadata metrics that characterize columns of the records in the result sets and storing the metadata metrics in tables for string column statistics, numeric column statistics, date column statistics, based upon a particular data type among different data types of the columns; generating presentation instructions which when rendered cause displaying one or more graphical visualizations in a graphical user interface.

A configuration associated with locating personally identifiable information stored in a database is received. An alternate metadata source separate from the database is identified. The separate alternate metadata source is searched using the configuration to identify locations in the database that store personally identifiable information.

Multi-database subsetting includes receiving a set of source tables. It further includes sorting the set of source tables based at least in part on dependency relationships among tables in the set of source tables. It further includes determining a traversal order based at least in part on the sorting of the set of source tables. It further includes executing the traversal order. Executing the traversal order includes visiting a table in the source set of tables according to the determined traversal order and issuing a query to extract a subset of data from the table being visited.