Oracle International Corporation

Sybase, Inc.

Sumanta Kumar Chatterjee

Juan R Loaiza

Margaret Susairaj

Ravi Shankar Thammaiah

Nagarajan Muthukrishnan

Binoy Sukumaran

Ann Rhee

Alexander Tsukerman

Richard L Frank

Bjorn Dag Johnsen

Kothanda Umamageswaran

Richard P Mousseau

Vadim Makhervaks

Kant C Patel

Feroz Alam Khan

Avneesh Pant

Bhaskar Mathur

Sridhar Subramaniam

Akshay Shah

Sudeep Vatsanath Reguna

Sue K Lee

Vivekananda C Kolla

Garret Frederick Swart

Jia Shi

Jean De Lavarene

Santosh Narayan Shilimkar

Vijay Sridharan

Sue-Kyoung Lee

Kesavan P Srinivasan

Bang Nguyen

Tirthankar Lahiri

Kumar Rajamani

Hakan Jakobsson

Bjørn Dag Johnsen

Sanjay Kaluskar

Dmitry Mikhailovich Potapov

Ashok M Joshi

Jonathan Giloni

Arun Kaimalettu

Varun Malhotra

Sue-kyoung Lee

Hanuma Kodavalla

Raunak Rungta

Waleed Ojeil

William Henry Benjamin Habeck

Roshni Chatterjee

Joseph Francis Therrattil Koonen

Bruce McCready

Ashok Madhukar Joshi

Abhishek Chitre

Mohit Singal

Jose Antonio Lasa Gutierrez

Ranjit Mario Noronha

Akshay D Shah

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

Collaborative analysis system for analyzing logs

Generating a hierarchical data structure that represents a log

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

Disclosed is an improved approach to access the contents of database file system. The contents of the database file system can be acquired and stored in a directory buffer. A parallel tree structure is used to retrieve directory contents quickly and in a consistent fashion.

Method and system to implement directory reads for a database file system

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

Techniques are provided for block-level fail atomicity on byte-level non-volatile media. In one technique, an offset table and application data that stores content of a file are stored for a file. The offset table includes multiple entries, each entry being associated with a different offset value and storing a logical block address (LBA) that references a location in the application data. In response to receiving a request, that includes an input buffer and an offset value, to update the file: (a) an entry, in the offset table, that corresponds to the offset value and comprises a first LBA is identified; (b) a second LBA that is considered free is identified; (c) the second LBA is replaced with the first LBA; (d) the input buffer is written to a location, in the application data, that the second LBA references; and (e) the second LBA is added in the entry.

High-performance, block-level fail atomicity on byte-level non-volatile media

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.

Isolated hierarchical runtime environments for multi-tenant databases

Techniques are described herein for cloning a database. According to some embodiments, a database server receives a request to clone a source database. In response to receiving the request, the database server retrieves a set of one or more storage credentials for a set of one or more respective storage systems on which a set of files of the source database are stored. The set of storage credentials grant permission to the database server to create snapshot copies on the set of storage systems. The database server generates, for a target database using the set of storage credentials, a snapshot copy of each respective file in the set of files of the source database. The snapshot copy of the respective file points to the same set of one or more data blocks as the respective file until at least one of the data blocks is modified.

Storage integrated snapshot cloning for database

A method and apparatus for intelligent network resource manager for distributed computing systems is provided. A first priority is assigned to a first virtual channel set that includes at least two virtual channels of a plurality of virtual channels associated with a physical communication channel. A second priority is assigned to a second virtual channel set that includes at least one virtual channel of the plurality of virtual channels. The first virtual channel set has more virtual channels than the second virtual channel set. Outbound messages of the first priority are directed to virtual channels of the first virtual channel set. Outbound messages of the second priority are directed to virtual channels of the second virtual channel set. The virtual channels are processed in a round-robin order, where processing includes sending the outbound messages over the physical communication channel.

Intelligent network resource manager

Growing community of inventors

Menlo Park, CA, United States of America

Sumanta Kumar Chatterjee