Abstract:
A data management system or “DMS” provides an automated, continuous, real-time, substantially no downtime data protection service to one or more data sources associated with a set of application host servers. To facilitate the data protection service, a host driver embedded in an application server captures real-time data transactions, preferably in the form of an event journal that is provided to other DMS components. The driver functions to translate traditional file/database/block I/O and the like into a continuous, application-aware, output data stream. The host driver includes an event processor that provides the data protection service, preferably by implementing a finite state machine (FSM). In particular, the data protection is provided to a given data source in the host server by taking advantage of the continuous, real-time data that the host driver is capturing and providing to other DMS components. The state of the most current data in DMS matches the state of the data in the host server; as a consequence, the data protection is provided under the control of the finite state machine as a set of interconnected phases or “states.” The otherwise separate processes (initial data upload, continuous backup, blackout and data resynchronization, and recovery) are simply phases of the overall data protection cycle. As implemented by the finite state machine, this data protection cycle preferably loops around indefinitely until, for example, a user terminates the service. A given data protection phase (a given state) changes only as the state of the data and the environment change (a given incident).
Abstract:
The present invention provides a distributed clustering method to allow multiple active instances of consistency management processes that apply the same encoding scheme to be cooperative and function collectively. The techniques described herein facilitate an efficient method to apply an erasure encoding and decoding scheme across dispersed data stores that receive constant updates. The technique can be applied on many forms of distributed persistent data stores to provide failure resiliency and to maintain data consistency and correctness.
Abstract:
A “forward” delta data management technique uses a “sparse” index associated with a delta file to achieve both delta management efficiency and to eliminate read latency while accessing history data. The invention may be implemented advantageously in a data management system that provides real-time data services to data sources associated with a set of application host servers. A host driver embedded in an application server connects an application and its data to a cluster. The host driver captures real-time data transactions, preferably in the form of an event journal that is provided to the data management system. In particular, the driver functions to translate traditional file/database/block I/O into a continuous, application-aware, output data stream. A given application-aware data stream is processed through a multi-stage data reduction process to produce a compact data representation from which an “any point-in-time” reconstruction of the original data can be made.
Abstract:
A data management system (“DMS”) provides an automated, continuous, real-time, substantially no downtime data protection service to one or more data sources. A host driver embedded in an application server captures real-time data transactions, preferably in the form of an event journal. The driver functions to translate traditional file/database/block I/O and the like into a continuous, application-aware, output data stream. The host driver includes an event processor that can perform a recovery operation to an entire data source or a subset of the data source using former point-in-time data in the DMS. The recovery operation may have two phases. First, the structure of the host data in primary storage is recovered to the intended recovering point-in-time. Thereafter, the actual data itself is recovered. The event processor enables such data recovery in an on-demand manner, by allowing recovery to happen simultaneously while an application accesses and updates the recovering data.
Abstract:
A data management method wherein a real-time history of a database system is stored as a logical representation and the logical representation is then used for any point-in-time recovery of the database system. More specifically, a method for capturing transaction data, binary data changes, metadata, and events, and for tracking a real-time history of a database system according to the events. The method enables tracking and storing of consistent checkpoint images of the database system, and also enables tracking of transaction activities between checkpoints. The database system may be recovered to any consistent checkpoint or to any point between two checkpoints.
Abstract:
A data management system (“DMS”) provides an automated, continuous, real-time, substantially no downtime data protection service to one or more data sources. A host driver embedded in an application server captures real-time data transactions, preferably in the form of an event journal. The driver functions to translate traditional file/database/block I/O and the like into a continuous, application-aware, output data stream. The host driver includes an event processor that can perform a recovery operation to an entire data source or a subset of the data source using former point-in-time data in the DMS. The recovery operation may have two phases. First, the structure of the host data in primary storage is recovered to the intended recovering point-in-time. Thereafter, the actual data itself is recovered. The event processor enables such data recovery in an on-demand manner, by allowing recovery to happen simultaneously while an application accesses and updates the recovering data.
Abstract:
A data management system or “DMS” provides an automated, continuous, real-time, substantially no downtime data protection service to one or more data sources associated with a set of application host servers. To facilitate the data protection service, a host driver embedded in an application server captures real-time data transactions, preferably in the form of an event journal that is provided to other DMS components. The driver functions to translate traditional file/database/block I/O and the like into a continuous, application-aware, output data stream. The host driver includes an event processor. When an authorized user determines that a primary copy of the data in the host server has become incorrect or corrupted, the event processor can perform a recovery operation to an entire data source or a subset of the data source using former point-in-time data in the DMS. The recovery operation may have two phases. First, the structure of the host data in primary storage is recovered to the intended recovering point-in-time. Thereafter, the actual data itself is recovered. The event processor enables such data recovery in an on-demand manner, in that it allows recovery to happen simultaneously while an application accesses and updates the recovering data.
Abstract:
A data management system or “DMS” provides a wide range of data services to data sources associated with a set of application host servers. The data management system typically comprises one or more regions, with each region having one or more clusters. A given cluster has one or more nodes that share storage. To facilitate the data service, a host driver embedded in an application server connects an application and its data to a cluster. The host driver provides a method and apparatus for capturing real-time data transactions in the form of an event journal that is provided to the data management system. The driver functions to translate traditional file/database/block I/O into a continuous, application-aware, output data stream. Using the streams generated in this manner, the DMS offers a wide range of data services that include, by way of example only: data protection (and recovery), disaster recovery (data distribution and data replication), data copy, and data query and access.
Abstract:
A “forward” delta data management technique uses a “sparse” index associated with a delta file to achieve both delta management efficiency and to eliminate read latency while accessing history data. The invention may be implemented advantageously in a data management system that provides real-time data services to data sources associated with a set of application host servers. To facilitate a given data service, a host driver embedded in an application server connects an application and its data to a cluster. The host driver captures real-time data transactions, preferably in the form of an event journal that is provided to the data management system. In particular, the driver functions to translate traditional file/database/block I/O into a continuous, application-aware, output data stream. In an illustrative embodiment, a given application aware data stream is processed through a multi-stage data reduction process to produce a compact data representation from which an “any point-in-time” reconstruction of the original data can be made.
Abstract:
A data management system or “DMS” provides an automated, continuous, real-time data protection service to one or more data sources associated with a set of application host servers. To facilitate the service, a host driver embedded in an application server captures real-time data transactions. When a data protection command for a given data source is forwarded to a host driver, an event processor enters into an initial upload state. During this state, the event processor gathers a list of data items to be protected and creates a data list. Then, the event processor moves the data to a DMS core to create initial baseline data. The upload is a stream of application-aware data chunks that are attached to upload events. A resynchronization state is entered when there is a suspicion that the state of the data in the host is out-of-sync with the state of the most current data in the DMS. During upload or upward resynchronization, the application does not have to be shut down.