Abstract: Herein are high throughput techniques for incorporating cryptographic blockchains into relational data. In an embodiment, a computer indicates, in a database dictionary in a database, that a relational table is for blockchain storage. The relational table contains application column(s). In response to that indication, the relational table is associated with system column(s) that are unmodifiable by administrators and clients of the database. The system column(s) include a cryptographic hash column. A request to store a particular value in a particular application column is received from a client. In response to receiving the request, a cryptographic hash value is calculated for a new row for the relational table. In the relational table, the computer stores the new row that contains: the particular value in the particular application column, and the cryptographic hash value in the cryptographic hash column.

Abstract: Embodiments allow a primary database system (“primary”) to automatically detect queries directed to the primary database that may be offloaded to a standby database system (“standby”), and then to automatically offload the offload-eligible queries to a standby. The primary identifies offload-eligible queries based on a record of up-to-date database objects within each standby that replicates the primary database. The record of up-to-date standby objects is updated based on heartbeat messages from each standby, and commit timestamps for transactions on the primary. Evaluation of whether a query is offload-eligible is triggered by one or more trigger conditions that include: inclusion of an offload optimizer hint in the query, an estimated run-time of the query, etc. Embodiments further allow an offloaded query over particular changed data in the primary database to access, on a standby database, uncommitted changes made to the particular data in the primary database.

Abstract: Disclosed is a system, method, and computer program product that generates masked data from within a database infrastructure. Instead of requiring an external tool to generate masked data, a database generates masked data using its internal processing mechanisms.

Abstract: Embodiments allow a primary database system (“primary”) to automatically detect queries directed to the primary database that may be offloaded to a standby database system (“standby”), and then to automatically offload the offload-eligible queries to a standby. The primary identifies offload-eligible queries based on a record of up-to-date database objects within each standby that replicates the primary database. The record of up-to-date standby objects is updated based on heartbeat messages from each standby, and commit timestamps for transactions on the primary. Evaluation of whether a query is offload-eligible is triggered by one or more trigger conditions that include: inclusion of an offload optimizer hint in the query, an estimated run-time of the query, etc. Embodiments further allow an offloaded query over particular changed data in the primary database to access, on a standby database, uncommitted changes made to the particular data in the primary database.