Oracle International Corporation

Binoy Sukumaran

Sumanta Kumar Chatterjee

Ravi Shankar Thammaiah

Nagarajan Muthukrishnan

Garret Frederick Swart

Santosh Narayan Shilimkar

Joydip Kundu

Prasanna Venkatesh Ramamurthi

Krishna Chander

Jeffrey Wright

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

Collaborative analysis system for analyzing logs

Generating a hierarchical data structure that represents a log

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 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

Disclosed is an improved approach to implement a mechanism to provide customer control over access to cloud infrastructure by the cloud provider's operator employees. This mechanism allow customer controlled access to any cloud infrastructure that belongs to or is otherwise allocated to the customer.

Framework for customer control and auditing of operator access to infrastructure in a cloud service

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.

Data structure generation system for analyzing logs

Techniques are described herein for isolating a network stack in a multitenant container DBMS. A first and second pluggable database are established within a container database. A first network stack is assigned to the first pluggable database and a second network stack is assigned to the second pluggable database. A request is received to connect to the first pluggable database and a connection is established to the first pluggable database using the first network stack. A request is received to connect to the second pluggable database and a connection is established to the second pluggable database using the second network stack.

Isolating a network stack for pluggable databases

Techniques are described herein for isolating runtime environments in a multitenant container DBMS. A CDB may contain a database dictionary that defines a plurality of pluggable databases and a respective database nest for each pluggable database. Each respective database nest of said each pluggable database is associated with different operating system privileges. When database sessions are established on a container DBMS, each database session is given access to a pluggable database by attaching the respective database nest of the pluggable database to each database session.

Growing community of inventors

Foster City, CA, United States of America

Binoy Sukumaran