Techniques are described for querying on hybrid formats and storages by a DBMS to leverage faster mirror format (MF) data and completeness of persistent format (PF) data. In an embodiment, the DBMS receives a query that specifies both in-memory and disk-only columns. The DBMS identifies that a scan-based operation is referencing an in-memory column stored within both MF and PF data. The DBMS may initiate performing the operation on the in-memory column on one of the formats of data. While doing so, the DBMS may determine that the actual improvement metric for the operation on the selected format data is not achieving the desired improvement. If so, the DBMS may dynamically switch the performing of the scan-based operation to the other format of the same data.

Various examples are directed to systems and methods for tuning a database service in a cloud platform. A tuning service may access a neural network model trained to classify workload points to one of classes. The tuning service may execute the neural network model with a first source workload point as input to return a first class as output, where the first source workload describing a source database. The tuning service may select a target workload for the first source workload point from a plurality of reference workloads. Selecting the target workload may be based at least in part on the first class returned by the neural network model. The tuning service may generate a recommended knob configuration for the source database using the target workload.

A method of processing a database configuration parameter, performed by a computer device, is provided. The method includes: (a) obtaining a current configuration parameter of a database; (b) determining a current database state indicator value corresponding to the current configuration parameter; (c) generating, by using a parameter adjustment model, parameter adjustment data based on the current database state indicator value; (d) adjusting the current configuration parameter based on the parameter adjustment data, to obtain a new configuration parameter; (e) updating the current configuration parameter to the new configuration parameter; and (f) repeating operations (b)-(e) until an adjustment termination condition is met, to obtain the updated current configuration parameter as a recommended configuration parameter upon adjustment termination.

A determination is made that a database system is resource bound resulting in a resource bound condition. Signals for the resources being bound in the database system are identified. Events associated with the signals are extracted. Events are correlated temporally to identify a time interval for which an arrival rate meter (ARM) is helpful. Database system segments are selected that effect key performance indicators associated with the identified time interval. Parameters for the selected database system segments to be deferred by the database system are estimated. The estimated parameters are incorporated into an arrival rate meter (ARM). The ARM is put into effect.

An apparatus, method and computer program product for physical database design and tuning in relational database management systems. A relational database management system executes in a computer system, wherein the relational database management system manages a relational database comprised of one or more tables storing data. A Deep Reinforcement Learning based feedback loop process also executes in the computer system for recommending one or more tuning actions for the physical database design and tuning of the relational database management system, wherein the Deep Reinforcement Learning based feedback loop process uses a neural network framework to select the tuning actions based on one or more query workloads performed by the relational database management system.

A method for data compression may include scanning input data, performing, based on the scanning, a compression operation to generate compressed data using the input data, finding, based on the scanning, a delimiter in the input data, and generating, based on a position of the delimiter in the input data, a portion of data using the compressed data. The input data may include a record, the delimiter indicates a boundary of the record, and the portion of data may include the record. The generating may include generating the portion of data based on a portion size. The portion size may be a default portion size. The portion size may be based on a default portion size and a length of a match in the input data.

Content is captured and archived at an archive center (AC) and, depending upon records management (RM) policy, is managed by the AC or under RM control by a content server (CS). Both the AC and CS may be part of an enterprise content management system. The AC provides a user-friendly interface through which retention zones may be defined, and functionality for applying RM policy. The functionality can be triggered via a specific content property or through a retention zone under RM control. The RM control can be turned on or off from within the AC using the user-friendly interface. Archived content is not moved or duplicated. Rather, metadata and a link to the storage location are sent to the CS which, in turn, creates a content server document that is linked to the archived content. Only a portion of archived content is exposed to the CS through the AC.

A system, for example, a multi-tenant system performs upgrades of database management systems deployed on a cloud platform. The database management system is stored on the cloud platform in a data storage unit for storing data of the database, and an instructions storage unit for storing executable instructions. A cloud platform image comprising instructions for the database management system is received. A cloud platform image is deployed on a new instructions storage unit. An upgraded database management system is built by providing the new instructions storage unit with access to the data storage unit. In an embodiment, the database management system is used by a multi-tenant system and stores a multi-tenant schema. The structure of the multi-tenant schema is defined using a multi-tenant schema template that is included in the instructions storage unit.

Despite the increase of memory capacity and CPU computing power, memory performance remains the bottleneck of in-memory database management systems due to ever-increasing data volumes and application demands. Because the scale of data workloads has out-paced traditional CPU caches and memory bandwidth, one can improve data movement from memory to computing units to improve performance in in-memory database scenarios. A near-memory database accelerator framework offloads data-intensive database operations via or to a near-memory computation engine. The database accelerator's system architecture can include a database accelerator software module/driver and a memory module with a database accelerator engine. An application programming interface (API) can be provided to support database accelerator functionality. Memory of the database accelerator can be directly accessible by the CPU.

In an embodiment, the disclosed technologies include identifying a content item of a first digital data source as a candidate for linking with a target entity of a second digital data source by matching a candidate entity mentioned in the content item to the target entity in accordance with semantic similarity data computed between the candidate entity and the target entity; inputting at least one feature of the content item and at least one feature of the target entity to a set of digital models that analyze the at least one feature of the content item and the at least one feature of the target entity and determine and output qualitative data; based on the qualitative data, determining link risk data; based on the link risk data and the semantic similarity data, and determining whether to generate a link between the content item and the target entity.