Oracle International Corporation

Giridhar Ravipati

Kumar Rajamani

Jaebock Lee

Sanket Jain

Yunrui Li

Margaret Susairaj

Muthu Olagappan

Andre Kruglikov

Jing Zheng

Mahesh Baburao Girkar

Chi Cao Minh

Jonghyun Lee

Hochak Hung

Nitin Karkhanis

Sadhana Kyathappala

Prashanth Shanthaveerappa

David Gagne

Jen-Cheng Huang

Alexander Chen

Qingguang Cui

Frank De Lange

Thomas Baby

Chandrasekharan Iyer

John E McHugh

Philip Yam

Nicolas Michael

Yixiao Shen

Ian Stuart Neall

Churn Don Lee

Ian Neall

Frank Lange

Phillip Yam

Herein is high availability for online transaction processing with redundancy and redo for a federation of pluggable databases and container databases. In an embodiment of a federation of container database management systems that includes a first container database, first redo data of a first pluggable database in a second container database is obtained based on a database dictionary in the first container database. To the first pluggable database in the first container database, the first redo data of the first pluggable database in the second container database is applied. Based on the database dictionary in the first container database, second redo data of a second pluggable database in a third container database is obtained. To the second pluggable database in the first container database, without modifying content of the first pluggable database in the first container database, the second redo data of the second pluggable database in the third container database is applied.

Data guard at PDB (pluggable database) level

Herein is high availability for online transaction processing with redundancy and redo for a federation of pluggable databases and container databases. In a federation of container database management systems that includes a first container database, first redo data of a first pluggable database in a second container database is obtained based on a database dictionary in the first container database. To the first pluggable database in the first container database, the first redo data of the first pluggable database in the second container database is applied. Based on the database dictionary in the first container database, second redo data of a second pluggable database in a third container database is obtained. To the second pluggable database in the first container database, without modifying content of the first pluggable database in the first container database, the second redo data of the second pluggable database in the third container database is applied.

A computer program product, system, and computer implemented method for management of a consolidated database and implementing pluggable database recovery with redo filtering in a consolidated database according to some embodiments. Generally, the process includes ongoing activities that maintain activity logs and summarize the activity for respective activity logs (e.g., in an activity vector maintained in a consolidated database catalog). In some embodiments, event-based activities corresponding to recovery processes are triggered by an administrator or an automated process, completed and then do not occur again until another triggering event. The event-based activities can leverage the summary information to quickly determine which online activity logs are relevant to the type of recovery operation for a particular pluggable database. In this way the approach provided herein enables recovery without requiring that all log activity be analyzed to determine whether it is relevant to a particular pluggable database.

Efficient pluggable database recovery with redo filtering in a consolidated database

Techniques herein make and use a pluggable database archive file (AF). In an embodiment, a source database server of a source container database (SCD) inserts contents into an AF from a source pluggable database (SPD). The contents include data files from the SPD, a listing of the data files, rollback scripts, and a list of patches applied to the SPD. A target database server (TDS) of a target container database (TCD) creates a target pluggable database (TPD) based on the AF. If a patch on the list of patches does not exist in the TCD, the TDS executes the rollback scripts to adjust the TPD. In an embodiment, the TDS receives a request to access a block of a particular data file. The TDS detects, based on the listing of the data files, a position of the block within the AF. The TDS retrieves the block based on the position.

Pluggable database archive

Embodiments minimize downtime involved in moving a PDB between CDBs by allowing read-write access to the PDB through most of the moving operation, and by transparently forwarding connection requests, for the PDB, from the source CDB to the destination CDB. The files of a source PDB are copied from a source CDB to a destination CDB, during which the source PDB may be in read-write mode. The source PDB is then closed to write operations so that changes to the source PDB cease. Another round of recovery is performed on the PDB clone, which applies all changes that have been performed on the source PDB during the copy operation and the PDB clone is opened for read and write operations. Forwarding information is registered with the source location, which information is used to automatically forward connection requests, received at the source location for the moved PDB, to the destination location.

Near-zero downtime relocation of a pluggable database across container databases

Provided herein are data cloud administration techniques that achieve autonomy by using a rules engine that reacts to a database system event by autonomously submitting an asynchronous job to reconfigure a database. In an embodiment, a rules engine receives an event from a DBMS. Based on the event, the rules engine executes a rule to generate a request that indicates configuration details for a database. The rules engine sends the request to a request broker. The request broker dispatches an asynchronous job based on the request. The asynchronous job configures the database based on the configuration details. Thus, databases in a cloud, data grid, or data center may be administered autonomously (without human intervention) base on dynamic conditions that are foreseen and unforeseen.

Rule-based autonomous database cloud service framework

Provided herein are workload management techniques that asynchronously configure pluggable databases within a compute cloud. In an embodiment, the compute cloud receives an administrative request that indicates configuration details for a pluggable database. The compute cloud generates a configuration descriptor that specifies an asynchronous job based on the configuration details of the request. The compute cloud accesses hosting metadata to detect at least one of: a) a current container database that already hosts the pluggable database, b) a target container database that will host the pluggable database, or c) a particular computer that hosts at least one of: the current container database, or the target container database. The compute cloud executes the asynchronous job to configure the pluggable database based on at least one of: the hosting metadata, or the configuration descriptor. Thread pools, lanes, and queues may facilitate load balancing to avoid priority inversion, starvation, and denial of service.

Autonomous multitenant database cloud service framework

A container is a collection of schemas, objects, and related structures in a multitenant container database (CDB) that appears logically to an application as a separate database. Within a CDB, each container has a unique ID and name. The root database and every PDB is considered a container. PDBs isolate data and operations so that from the perspective of a user or application, each PDB appears as if it were a traditional non-CDB. A database management system that manages a container database is a container database management system (CDBMS). Data and metadata in the root database may include common schemas that make the functionality that users will often use available CDB-wide. To execute a query accessing a common schema, the common schemas may be accessed by sessions of a PDB without switching database contexts.

High-performance implementation of sharing of read-only data in a multi-tenant environment

Techniques are provided for using a sparse file to create a hot archive of a pluggable database of a container database. In an embodiment and while a source pluggable database is in service, a source database server creates a clone of the source pluggable database. Also while the source pluggable database is in service, the source database server creates an archive of the source pluggable database that is based on the clone. Eventually, a need arises to consume the archive. A target database server (which may also be the source database server) creates a target pluggable database based on the archive.

Periodic snapshots of a pluggable database in a container database

Embodiments create a clone of a PDB while the PDB accepts write operations. While the PDB remains in read-write mode, the DBMS copies the data of the PDB and sends the data to a destination location. The DBMS performs data recovery on the PDB clone based on redo entries that record changes made to the source PDB while the DBMS copied the source PDB files. This data recovery makes all changes, to the PDB clone, that occurred to the source PDB during the copy operation. The redo information, on which the data recovery is based, is foreign to the PDB clone since the redo entries were recorded for a different PDB. In order to apply foreign redo information to perform recovery on the PDB clone, a DBMS managing the PDB clone maintains mapping information that maps PDB source reference information to corresponding information for the PDB clone.

Cloning a pluggable database in read-write mode

Embodiments incrementally refresh a clone of a source PDB while the source PDB accepts write operations. Specifically, refreshing the PDB clone incorporates changes made to the source PDB since a refresh reference time stamp, which marks the time at which the PDB clone was created or, if the PDB clone has been previously refreshed, the time at which the PDB clone was last refreshed. A PDB clone is incrementally refreshed by incorporating, into the PDB clone data, those source data blocks that have changed since the refresh reference time stamp. Recovery is performed on the PDB clone, once the blocks are copied, to apply any changes made to the source PDB while the blocks were being copied, which recovery makes the PDB clone files consistent. This recovery is based on redo entries recorded for the source PDB during the time it took to copy the blocks to the PDB clone.

Techniques for keeping a copy of a pluggable database up to date with its source pluggable database in read-write mode

In an approach, a database management system is configured in a manner that allows all pluggable databases within a cluster to be treated, from the perspective of a user, as though stored on a single container database, even when the pluggable databases are distributed across a plurality of container databases. When a command is received by a database server that is to be executed on all or a subset of the pluggable databases within the cluster, the database inspects mapping information replicated on all container databases within the cluster by a cluster synchronization server to determine which container databases store pluggable databases implicated by the command. The command is then forwarded to the database servers responsible for the determined container databases for execution. As a result, the commands issued by the user can be agnostic in regard to the actual location of the pluggable databases within the cluster.

Ability to group multiple container databases as a single container database cluster

Techniques are provided for creating a backup of a source pluggable database (SPD) of a source container database and porting the backup for recovery into a different target container database. In an embodiment, a source database server retrieves metadata that describes backups of the SPD. The source database server inserts, into an unplugged pluggable database of the SPD, the metadata that describes each of the backups. For example, unplugging the SPD may automatically create the unplugged pluggable database. Eventually, the unplugged pluggable database may be plugged into the target container database. A target database server transfers the metadata that describes each of the backups from the unplugged pluggable database and into the target container database. Based on at least one backup and the metadata that describes backups of the SPD, the target database server restores a target pluggable database within the target container database.

Transportable backups for pluggable database relocation

Embodiments provide a migration instruction that effectuates the migration of a pluggable database from a source database server instance to a destination database server instance. Upon receiving the migration instruction, the migrating pluggable database is opened at the destination instance. Connections are terminated at the source instance at a rate that is determined based on statistics maintained for one or more of: the migrating pluggable database, the source instance, the destination instance, a container database, etc. Furthermore, once the migration instruction is received, a certain amount of time is provided before the source instance flushes the dirty buffers for the migrating pluggable database from the buffer cache of the source instance. The delay in flushing dirty buffers from buffer cache allows the source instance to provide data blocks, of the migrating pluggable database, directly to the destination database server instance from the cache.

Growing community of inventors

Foster City, CA, United States of America

Giridhar Ravipati