Oracle International Corporation

Ravi Shankar Thammaiah

Nagarajan Muthukrishnan

Sumanta Kumar Chatterjee

Binoy Sukumaran

Jonathan Giloni

Kevin S Neel

Santosh Narayan Shilimkar

Garret Frederick Swart

Carol L Colrain

Douglas N Surber

Tong Zhou

Arturo Ismael Quinto

Rohan Ankalikar

Raunak Rungta

Sandeep Kumar Samudrala

Juan R Loaiza

Lakshminarayanan Luxi Chidambaran

Srinath Krishnaswamy

Krishna Mohan Itikarlapalli

Sreekumar K Seshadri

Roshni Chatterjee

William Henry Benjamin Habeck

Vijayendra Rao Lakkundi

Sue-kyoung Lee

Scot McKinley

Manoj Dhanapal

Abhishek Chitre

Vijayendra Lakkundi

William Habeck

Mohit Singal

Rajagopalan Duraisamy

Sajithkumar Santhamma Sasikumar

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

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

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

Techniques are disclosed herein for identifying, recording and restoring the state of a database session and various aspects thereof. A session template data structure is generated that includes session attribute values describing various aspects of the session that is established between a client system and a database management system (DBMS and enables the client system to issue to the DBMS commands for execution. Based on the session attribute values, DBMS may generate a template identifier corresponding to the session template data structure. The template identifier may be stored in an association with the session state that it partially (or in whole) represents. In an embodiment, when another state of a session is captured, if the template identifier for the state is the same, then rather than storing the attribute-value pairs for the other state, the template identifier is further associated with the other state. In an embodiment, a request boundary is detected where the session is known to be at a recoverable point. If recovery of the session is needed, the session state is restored, and replay of commands start from this point. Each command replayed is verified to produce the same session state as it produced by the original execution. If the session is determined to be a safe point, then all the commands recorded for replay prior to the safe point may be deleted. In an embodiment, the template is used to set the initial state when borrowing from a session pool. The state tracking is also used to identify that the session can be failed over safely during planned operation as the session is unlikely to drain by itself even when not used.

Session state tracking

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.

Data structure generation system for analyzing logs

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.

Fine grained memory and heap management for sharable entities across coordinating participants in database environment

Growing community of inventors

Redwood Shores, CA, United States of America

Ravi Shankar Thammaiah