Oracle International Corporation

Nagarajan Muthukrishnan

Ravi Shankar Thammaiah

Sumanta Kumar Chatterjee

Binoy Sukumaran

Santosh Narayan Shilimkar

Garret Frederick Swart

Rohan Ankalikar

Sandeep Kumar Samudrala

Prasanna Venkatesh Ramamurthi

William Henry Benjamin Habeck

Roshni Chatterjee

Vijayendra Rao Lakkundi

Sue-kyoung Lee

Srikanth Nagandla

Yu Li

Paul Hsu

Harikishan Sivaramakrishnan

Amit Kendrekar

Ranjit Murali

Sajithkumar Santhamma Sasikumar

Rajagopalan Duraisamy

Mohit Singal

Abhishek Chitre

Vijayendra Lakkundi

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.

Models for detecting and managing excessive log patterns

A computer system performs tasks in an access restricted environment. Data is logged in diagnostic files about logical resources in use by the computer system as the computer system attempts to perform the tasks. Occasionally, a problem may prevent the computer system from correctly performing a task. A machine authenticates a user in the access-restricted environment and receives error metadata to initiate an automated process for generating a troubleshooting signature. The automated process involves selecting a metadata extraction policy based on a category of error and using the metadata extraction policy to extract metadata from a diagnostic file. The extracted metadata is analyzed to determine troubleshooting components including the problem, a source of the problem, and/or a version of software that encountered the problem. These troubleshooting components are combined in the troubleshooting signature, which is consumed in a diagnostic tool environment that is separate from the access-restricted environment.

Extracting diagnostic metadata to identify problems in access-restricted computing environments

Techniques are provided for maintaining snapshots of state information for a plurality of resource entities, within a distributed cloud service. A first snapshot of state information for the plurality of resource entities is maintained at an endpoint in persistent storage. A request to modify resources allocated to resource entities is received. A virtual lock on the state information reflected in the first snapshot is obtained. Upon obtaining the virtual lock, a service determines, based on the first snapshot, that there are available resources to perform the request to modify resources. A second snapshot of the state information, reflecting the modification of resources allocated to resource entities, is then generated and stored at the endpoint in persistent storage. The virtual lock on the state information is released and the resources allocated to the resource entities are modified according to the request.

Representation and management of infrastructure state of a distributed database cloud service

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.

Generating log timeline data structures based on the hierarchical data structure

Herein are hierarchical and non-intrusive techniques to detect and diagnose incidental contention between database tenants. In an embodiment, a computer hosts a database server that operates a container database. The database server monitors level one performance metrics that characterize the performance of at least a first pluggable database (PDB) in the container database. The database server detects, in the level one performance metrics, a performance degradation of the first PDB. Responsively, the database server dynamically configures collection of level two performance metrics that characterize the performance of at least the first PDB and a second PDB in the container database. The database server detects, in the level two performance metrics, that the performance degradation is caused by the second PDB. The database server generates an alert that identifies the second PDB. The alert contains a particular metric of the level two performance metrics, and the particular metric characterizes the performance of the second PDB.

Proactive performance supervision of multitenant cloud DB using hierarchical triage and comparative approach maintaining the data privacy and isolation

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. The configurations are specified in and adjusted using one or more corresponding templates, where the template comprise a set of rules that may have various interdependencies and specify how to determine what value a particular configuration setting should be to automatically configuration and reconfigure CDB instances and open PDBs.

Scalable specification and self-governance for autonomous databases, cluster databases and multi-tenant databases in cloud and on-prem environment

Collaborative analysis system for analyzing logs

Generating a hierarchical data structure that represents a log

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.

Cardinal method for hierarchical phased secure access to system entities in isolated multi-tenant database for autonomous cloud environments

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.

Dual personality memory for autonomous multi-tenant cloud environment

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.

Dynamic hierarchical placement of consolidated and pluggable databases in autonomous environments

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. The CDBMS contains an intelligent scheduler that concurrently executes the second and third administrative tasks and defers execution of the first administrative task until after completion of the second administrative task. For example, the administrative tasks may be distributed to different databases for parallelism or instead sequenced for phased execution based on dependencies between administrative tasks or phases.

Declarative method of grouping, migrating and executing units of work for autonomous hierarchical database systems

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.

Resilience based database placement in clustered environment

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.

Growing community of inventors

Foster City, CA, United States of America

Nagarajan Muthukrishnan