Abstract: A computer program product, system, and computer implemented method for scalable specification and self-governance for autonomous databases, cluster databases, and multi-tenant databases in cloud and on-prem environments. The approach disclosed herein enables management of a consolidated databases using a template-based process that allows for the consolidated databases (CDBs) and pluggable databases (PBDs) to be reconfigured automatically. In some embodiments, the approach instantiates one or more monitoring modules and one or more CDB/PDB configuration managers. These elements can detect relevant changes in the conditions in which CDB instances and open PDBs operate and adjust the configurations thereof in response.

Abstract: A computing device is configured to allocate memory for exclusive use of an execution entity from both a shared memory area and a private memory area of the device. Specifically, the shared memory area is configured with a united memory pool (UMP) component. The UMP component is configured to provide portions of huge page-based memory to execution entities for exclusive use of the execution entities. Memory granules that are allocated to the UMP component are divided into smaller memory chunks (which are smaller than a huge page), each of which can be allocated for exclusive use of an execution entity. These memory chunks are mapped to virtual address spaces of the assigned execution entities. Because memory granules can be allocated to, and deallocated from, the UMP component at run-time, the amount of memory that is available for private data generated by execution entities is able to be dynamically adjusted.

Abstract: Techniques described herein include frameworks and models for identifying, analyzing, and addressing hangs within distributed and heterogenous computing environments. A hang detection framework may model a distributed computing environment as a complex forest of interrelated requests. The hang detection framework may generate hang graphs based upon requests that are being processed and/or waited upon within the distributed environment. For example, a node within an acyclic graph may represent an execution entity that is currently processing one or more requests. Directed edges that connect one node to another may represent requests upon which an execution entity is waiting for another execution node to fulfill. The model may be used to isolate and address the root cause of hangs within the computing environment.

Abstract: New data structures for analyzing a log are generated. A hierarchical data structure includes a plurality of hierarchical nodes. Each node is associated with data and metadata. Each node may also be associated with analysis data. Information (data, metadata, and/or analysis data) of an ancestor node is imputed to a descendant node; a descendant node inherits the information of an ancestor node. When determining analysis data for a particular hierarchical node, information from any ancestor node to the particular hierarchical node may be used; however, information from non-ancestor nodes is not necessarily used. A timeline data structure includes a reference to a hierarchical node within a hierarchical data structure and a reference to an event type. The timeline data structure is thereby associated with the information of the referenced hierarchical node and information of any ancestor nodes to the referenced hierarchical node.

Abstract: Plugins that are independently written are executed in a collaborative manner to analysis a log. A plugin executing with respect to a particular node of a hierarchical data structure determines values for a set of keys based on information of the particular node and/or any ancestor nodes, and information stored in a shared repository. The plugin stores the values for the keys as additional information of the particular hierarchical node and/or into the shared repository. The plugin does not access information of non-ancestor nodes when executing with respect to the particular hierarchical node. Each plugin writes into and retrieves from the shared repository using the shared naming convention, thereby sharing information. The sequence of execution of the plugins is not dependent on dependencies amongst the plugins. If a dependent plugin requiring an output from a requisite plugin is first executed, the dependent plugin is flagged as pending and subsequently re-executed.

Abstract: Plugins that are independently written are executed in a collaborative manner to analysis a log. A plugin executing with respect to a particular node of a hierarchical data structure determines values for a set of keys based on information of the particular node and/or any ancestor nodes, and information stored in a shared repository. The plugin stores the values for the keys as additional information of the particular hierarchical node and/or into the shared repository. The plugin does not access information of non-ancestor nodes when executing with respect to the particular hierarchical node. Each plugin writes into and retrieves from the shared repository using the shared naming convention, thereby sharing information. The sequence of execution of the plugins is not dependent on dependencies amongst the plugins. If a dependent plugin requiring an output from a requisite plugin is first executed, the dependent plugin is flagged as pending and subsequently re-executed.

Abstract: Plugins that are independently written are executed in a collaborative manner to analysis a log. A plugin executing with respect to a particular node of a hierarchical data structure determines values for a set of keys based on information of the particular node and/or any ancestor nodes, and information stored in a shared repository. The plugin stores the values for the keys as additional information of the particular hierarchical node and/or into the shared repository. The plugin does not access information of non-ancestor nodes when executing with respect to the particular hierarchical node. Each plugin writes into and retrieves from the shared repository using the shared naming convention, thereby sharing information. The sequence of execution of the plugins is not dependent on dependencies amongst the plugins. If a dependent plugin requiring an output from a requisite plugin is first executed, the dependent plugin is flagged as pending and subsequently re-executed.

Abstract: A computer program product, system, and computer implemented method for scalable specification and self-governance for autonomous databases, cluster databases, and multi-tenant databases in cloud and on-prem environments. The approach disclosed herein enables management of a consolidated databases using a template-based process that allows for the consolidated databases (CDBs) and pluggable databases (PBDs) to be reconfigured automatically. In some embodiments, the approach instantiates one or more monitoring modules and one or more CDB/PDB configuration managers. These elements can detect relevant changes in the conditions in which CDB instances and open PDBs operate and adjust the configurations thereof in response.

Abstract: Plugins that are independently written are executed in a collaborative manner to analysis a log. A plugin executing with respect to a particular node of a hierarchical data structure determines values for a set of keys based on information of the particular node and/or any ancestor nodes, and information stored in a shared repository. The plugin stores the values for the keys as additional information of the particular hierarchical node and/or into the shared repository. The plugin does not access information of non-ancestor nodes when executing with respect to the particular hierarchical node. Each plugin writes into and retrieves from the shared repository using the shared naming convention, thereby sharing information. The sequence of execution of the plugins is not dependent on dependencies amongst the plugins. If a dependent plugin requiring an output from a requisite plugin is first executed, the dependent plugin is flagged as pending and subsequently re-executed.

Abstract: Embodiments described herein include a log management system that detects and addresses excessive log patterns at runtime. In some embodiments, the log management system tracks cumulative attributes associated with patterns that repeat within a set monitoring files, such as a set of log or trace records. The cumulative attributes that are monitored may include the cumulative storage size and/or cumulative count of patterns detected in the monitoring files. The log management system may determine whether a triggering condition is satisfied for initiating a responsive action to reduce the amount of log data that is transmitted and/or stored. If the triggering condition is satisfied, then the log management system may remove one or more instances of the pattern from the monitoring files and/or perform other actions to reduce the overhead of logging operations.

Abstract: Plugins that are independently written are executed in a collaborative manner to analysis a log. A plugin executing with respect to a particular node of a hierarchical data structure determines values for a set of keys based on information of the particular node and/or any ancestor nodes, and information stored in a shared repository. The plugin stores the values for the keys as additional information of the particular hierarchical node and/or into the shared repository. The plugin does not access information of non-ancestor nodes when executing with respect to the particular hierarchical node. Each plugin writes into and retrieves from the shared repository using the shared naming convention, thereby sharing information. The sequence of execution of the plugins is not dependent on dependencies amongst the plugins. If a dependent plugin requiring an output from a requisite plugin is first executed, the dependent plugin is flagged as pending and subsequently re-executed.

Abstract: New data structures for analyzing a log are generated. A hierarchical data structure includes a plurality of hierarchical nodes. Each node is associated with data and metadata. Each node may also be associated with analysis data. Information (data, metadata, and/or analysis data) of an ancestor node is imputed to a descendant node; a descendant node inherits the information of an ancestor node. When determining analysis data for a particular hierarchical node, information from any ancestor node to the particular hierarchical node may be used; however, information from non-ancestor nodes is not necessarily used. A timeline data structure includes a reference to a hierarchical node within a hierarchical data structure and a reference to an event type. The timeline data structure is thereby associated with the information of the referenced hierarchical node and information of any ancestor nodes to the referenced hierarchical node.

Abstract: Herein is database administration workflow automation with source annotations and intelligent scheduling techniques for applying a hierarchy of interdependent administrative tasks to distributed and/or nested databases. In an embodiment, a source language compiler analyzes annotations to identify a hierarchy of administrative tasks that administers pluggable databases in container databases. From the annotations, a runtime codebase is generated that implements and invokes the administrative task hierarchy. At runtime, a container database management system (CDBMS) autonomously identifies and instantiates the administrative tasks, including identifying a dependency of a first administrative task on a second administrative task and a lack of dependency of a third administrative task on the second administrative task.

Abstract: Herein are multitenant database security techniques for fine-grained and dynamic access control of protected resources according to phases and roles. In an embodiment, a computer associates a process with a particular pluggable database of many pluggable databases in a container database in a database management system (DBMS). From the process, a request is received to access a protected resource that the particular pluggable database does not contain. Based on the protected resource and the particular pluggable database, the DBMS dynamically decides, in response to receiving the request, whether to permit access to the protected resource. Based on authorization by the DBMS, the protected resource is accessed to fulfil the request. In an embodiment, centrally managed policy decisions for restricted access are dynamically employed across a fleet of databases through a global policy manager.

Abstract: Many computer applications comprise multiple threads of executions. Some client application requests are fulfilled by multiple cooperating processes. Techniques are disclosed for creating and managing memory namespaces that may be shared among a group of cooperating processes in which the memory namespaces are not accessible to processes outside of the group. The processes sharing the memory each have a handle that references the namespace. A process having the handle may invite another process to share the memory by providing the handle. A process sharing the private memory may change the private memory or the processes sharing the private memory according to a set of access rights assigned to the process. The private shared memory may be further protected from non-sharing processes by tagging memory segments allocated to the shared memory with protection key and/or an encryption key used to encrypt/decrypt data stored in the memory segments.

Abstract: Herein are resource-constrained techniques that plan ahead for resiliently moving pluggable databases between container databases after a failure in a high-availability database cluster. In an embodiment that has a database cluster that hierarchically contains many pluggable databases in many container databases in many virtual machines, a computer identifies many alternative placements that respectively assign each pluggable database instance (PDB) to a respective container database management system (CDBMS). For each alternative placement, a respective placement score is calculated based on the PDBs and the CDBMSs. Based on the placement scores of the alternative placements, a particular placement is selected with a best placement score that indicates optimal resilience for accommodating adversity such as failover and overcrowding.

Abstract: A computer program product, system, and computer implemented method for scalable specification and self-governance for autonomous databases, cluster databases, and multi-tenant databases in cloud and on-prem environments. The approach disclosed herein enables management of a consolidated databases using a template-based process that allows for the consolidated databases (CDBs) and pluggable databases (PBDs) to be reconfigured automatically. In some embodiments, the approach instantiates one or more monitoring modules and one or more CDB/PDB configuration managers. These elements can detect relevant changes in the conditions in which CDB instances and open PDBs operate and adjust the configurations thereof in response.

Abstract: Herein are resource-constrained techniques that plan ahead for resiliently moving pluggable databases between container databases after a failure in a high-availability database cluster. In an embodiment, a computer identifies many alternative placements that respectively assign each pluggable database to a respective container database. For each alternative placement, a respective resilience score is calculated for each pluggable database that is based on the container database of the pluggable database. Based on the resilience scores of the pluggable databases for the alternative placements, a particular placement is selected as an optimal placement that would maximize utilization of computer resources, minimize database latencies, maximize system throughput, and maximize the ability of the database cluster to avoid a service outage.

Abstract: Many computer applications comprise multiple threads of executions. Some client application requests are fulfilled by multiple cooperating processes. Techniques are disclosed for creating and managing memory namespaces that may be shared among a group of cooperating processes in which the memory namespaces are not accessible to processes outside of the group. The processes sharing the memory each have a handle that references the namespace. A process having the handle may invite another process to share the memory by providing the handle. A process sharing the private memory may change the private memory or the processes sharing the private memory according to a set of access rights assigned to the process. The private shared memory may be further protected from non-sharing processes by tagging memory segments allocated to the shared memory with protection key and/or an encryption key used to encrypt/decrypt data stored in the memory segments.

Abstract: Many computer applications comprise multiple threads of executions. Some client application requests are fulfilled by multiple cooperating processes. Techniques are disclosed for creating and managing memory namespaces that may be shared among a group of cooperating processes in which the memory namespaces are not accessible to processes outside of the group. The processes sharing the memory each have a handle that references the namespace. A process having the handle may invite another process to share the memory by providing the handle. A process sharing the private memory may change the private memory or the processes sharing the private memory according to a set of access rights assigned to the process. The private shared memory may be further protected from non-sharing processes by tagging memory segments allocated to the shared memory with protection key and/or an encryption key used to encrypt/decrypt data stored in the memory segments.