Disclosed herein are methods, systems, and processes to distribute and disperse search loads to optimize security event processing in cybersecurity computing environments. A search request that includes a domain specific language (DSL) query directed to a centralized search cluster by an event processing application is intercepted. The event processing application is inhibited from issuing the search request to the centralized search cluster if a structured or semi-structured document matches the DSL query.

A system and method, including determining, by a processor, a data type for each column of a database table; determining, by the processor and based on the determined data type for each column of the database table, an indication of a size of the database table; calculating, by the processor and based on the determined indication of the size of the database table, a start nbit size for a nbit compression process to be used on the database table; specifying, by the processor, the calculated start nbit size for the nbit compression process; and compressing the database table by executing the nbit data compression process using the specified start nbit size.

A method of storing semi-structured data includes receiving user data from a user of a query system where the user data includes semi-structured user data. The method also includes receiving an indication that the semi-structured user data fails to include a fixed schema. In response to the indication that the semi-structured user data fails to include the fixed schema, the method further includes parsing the semi-structured user data into a plurality of data paths and extracting a data type associated with each respective data path of the plurality of data paths. The method additionally includes storing the semi-structured user data as a row entry in a table of a database in communication with the query system wherein each column value associated with the row entry corresponds to a respective one of the plurality of data paths and the data type associated with the respective data path.

A mapping and query service for mapping between object-oriented programming objects and deep key-value data stores. The service to implement a store operation for a mapping and query service that supports the storage of a set of one or more objects having classes and fields written in source code of an object-oriented programming language in a deep key-value data store.

The subject technology receives, by a database system, raw input data from a source table provided by a machine learning development environment, the source table comprising multiple rows where each row includes multiple columns, the raw input data comprising values in a first format, the values comprising input features corresponding to datasets included in the raw input data for machine learning models, the machine learning development environment comprising an external system from the database system and is accessed by a plurality of different users that are external to the database system. The subject technology generates cell data for a feature store table based at least in part on the values from the source table. The subject technology performs at least one database operation to generate the feature store table including at least table metadata, column metadata, and the generated cell data.

Systems, methods, and other embodiments associated with migrating key-value data to a relational database. A method comprises retrieving a namespace from a key-value store and querying the key-value store for all kinds associated with the namespace. For a first kind, retrieving all entities associated with the namespace and the first kind and generating an entity list including an entry for each of the entities retrieved and the corresponding entity data. The entity list is traversed to identify each property name and associated property value. A relational database model is generated including a table based on the namespace and kind. Columns are defined in the table corresponding to each property name from the entity list. Data from each entity is then inserted into a table row and each property value from the entity is mapped to a corresponding column that is defined with the corresponding property name.

The present disclosure involves systems, software, and computer implemented methods for providing default values for fields of data objects in an offline mode. One example method includes receiving, at a client device, a default group mapping that includes a default group identifier and a default value to be used as an initial value for a field. A field mapping can be received that includes a default group identifier and a field identifier. A request can be received while the client device is offline to create an instance of an object. A determination can be made that the field mapping includes a field identifier for a field of the object. A default value can be retrieved, from a local repository on the client device. A field value of the field in a created instance of the data object can be set to be the default value.

The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing hash partitions to determine local densities and distances among users (or among other represented data points) for clustering sparse data into segments. For instance, the disclosed systems can generate hash signatures for users in a sparse dataset and can map users to hash partitions based on the hash signatures. The disclosed systems can further determine local densities and separation distances for particular users (or other represented data points) within the hash partitions. Upon determining local densities and separation distances for datapoints from the dataset, the disclosed systems can select a segment (or cluster of data points) grouped according to a hierarchy of a clustering algorithm, such as a density-peaks-clustering algorithm.

Partial initial construction of a deferred object model. This is done using a map that correlates positions of a hierarchically structured definition and corresponding hierarchical positions within an object model. The map is accessed and used to initially construct a deferred object model that even leaves some of the deferred objects unpopulated. The map is used to determine which parts of the hierarchical object definition no longer need to be parsed in order to construct this initial form of the deferred object model. If a request for an object is detected at some point after the initial construction, and that requested object is not represented even in deferred form in the deferred object model, the system uses the map to find the position of the corresponding object definition in the hierarchical object definition. At that point, the system parses that position, and constructs the object.

A pictogram repository is created of pictograms including expressions that are mapped to at least a portion of source code that is stored in a separate source code repository. A score is recorded for developers for the source code that is stored in the source code repository. A source code search inquiry of at least one pictograms for search query elements is conducted, in which the at least one pictogram for the search query elements are matched to the pictograms in the repository of pictograms that includes expressions that are mapped to at least a portion of source code that is stored in the separate source code repository. Matching source code have the score for their developer checked against a threshold value. Source code meeting the search query elements and having a score for their developer meeting the threshold value are retrieved.