Abstract: Systems for storage system rollover and rollback. A data mover agent is installed on a source storage system to capture disaster recovery data and send to a target system. Upon receiving a rollover event signal, a virtualized controller creates one or more replica user virtual machines running on the target system that serve to replicate functions of the user virtual machines from the source storage system. The virtualized controller on the target system converts the target disaster recovery data from a first format to a second format to facilitate use of the target disaster recovery data by the replica user virtual machines. Rollback is initiated when the target system receives a rollback event signal. Differences in the data that have occurred between the rollover event and the rollback signal are calculated and sent to the rollback system. The calculated differences are applied to a registered snapshot on the rollback system.

Abstract: A method for providing real time replication status for a networked virtualization environment for storage management, includes scanning metadata to identify replication status for all virtual disks (vDisks) in the networked virtualization environment, generating replication tasks for vDisks that are identified as under replicated based on the scan, performing the replication tasks, monitoring the progress of the replication tasks and determining the real time replication status of the networked virtualization environment based on the scanned metadata and the monitored progress of the replication tasks.

Abstract: Techniques are provided for more efficiently using the bandwidth of the I/O path between a CPU and volatile memory during the performance of database operation. Relational data from a relational table is stored in volatile memory as column vectors, where each column vector contains values for a particular column of the table. A binary-comparable format may be used to represent each value within a column vector, regardless of the data type associated with the column. The column vectors may be compressed and/or encoded while in volatile memory, and decompressed/decoded on-the-fly within the CPU. Alternatively, the CPU may be designed to perform operations directly on the compressed and/or encoded column vector data. In addition, techniques are described that enable the CPU to perform vector processing operations on the column vector values.

Abstract: A method for maintaining a mapping structure for maintaining metadata for snapshots in a virtualized storage environment, includes taking a snapshot of a virtual disk, generating an entry in a metadata structure for the snapshot, wherein the entry includes metadata for blocks in the snapshot that have been modified since a preceding snapshot and lazily generating an entry in the mapping structure for the snapshot, wherein the entry includes values for each block in the snapshot, wherein a value for a block indicates a presence of metadata in the metadata structure for the block or an absence of metadata in the metadata structure for the block.

Abstract: A method for maintaining a mapping structure for maintaining metadata for snapshots in a virtualized storage environment, includes taking a snapshot of a virtual disk, generating an entry in a metadata structure for the snapshot, wherein the entry includes metadata for blocks in the snapshot that have been modified since a preceding snapshot and lazily generating an entry in the mapping structure for the snapshot, wherein the entry includes values for each block in the snapshot, wherein a value for a block indicates a presence of metadata in the metadata structure for the block or an absence of metadata in the metadata structure for the block.

Abstract: A method for providing real time replication status for a networked virtualization environment for storage management, includes scanning metadata to identify replication status for all virtual disks (vDisks) in the networked virtualization environment, generating replication tasks for vDisks that are identified as under replicated based on the scan, performing the replication tasks, monitoring the progress of the replication tasks and determining the real time replication status of the networked virtualization environment based on the scanned metadata and the monitored progress of the replication tasks.

Abstract: Techniques are described herein for automatically selecting the compression techniques to be used on tabular data. A compression analyzer gives users high-level control over the selection process without requiring the user to know details about the specific compression techniques that are available to the compression analyzer. Users are able to specify, for a given set of data, a “balance point” along the spectrum between “maximum performance” and “maximum compression”. The point thus selected is used by the compression analyzer in a variety of ways. For example, in one embodiment, the compression analyzer uses the user-specified balance point to determine which of the available compression techniques qualify as “candidate techniques” for the given set of data. The compression analyzer selects the compression technique to use on a set of data by actually testing the candidate compression techniques against samples from the set of data.

Abstract: A method for providing real time replication status for a networked virtualization environment for storage management, includes scanning metadata to identify replication status for all virtual disks (vDisks) in the networked virtualization environment, generating replication tasks for vDisks that are identified as under replicated based on the scan, performing the replication tasks, monitoring the progress of the replication tasks and determining the real time replication status of the networked virtualization environment based on the scanned metadata and the monitored progress of the replication tasks.

Abstract: A method for providing real time replication status for a networked virtualization environment for storage management, includes scanning metadata to identify replication status for all virtual disks (vDisks) in the networked virtualization environment, generating replication tasks for vDisks that are identified as under replicated based on the scan, performing the replication tasks, monitoring the progress of the replication tasks and determining the real time replication status of the networked virtualization environment based on the scanned metadata and the monitored progress of the replication tasks.

Abstract: Techniques are provided for more efficiently using the bandwidth of the I/O path between a CPU and volatile memory during the performance of database operation. Relational data from a relational table is stored in volatile memory as column vectors, where each column vector contains values for a particular column of the table. A binary-comparable format may be used to represent each value within a column vector, regardless of the data type associated with the column. The column vectors may be compressed and/or encoded while in volatile memory, and decompressed/decoded on-the-fly within the CPU. Alternatively, the CPU may be designed to perform operations directly on the compressed and/or encoded column vector data. In addition, techniques are described that enable the CPU to perform vector processing operations on the column vector values.

Abstract: Techniques are provided for more efficiently using the bandwidth of the I/O path between a CPU and volatile memory during the performance of database operation. Relational data from a relational table is stored in volatile memory as column vectors, where each column vector contains values for a particular column of the table. A binary-comparable format may be used to represent each value within a column vector, regardless of the data type associated with the column. The column vectors may be compressed and/or encoded while in volatile memory, and decompressed/decoded on-the-fly within the CPU. Alternatively, the CPU may be designed to perform operations directly on the compressed and/or encoded column vector data. In addition, techniques are described that enable the CPU to perform vector processing operations on the column vector values.

Abstract: A bulk delete operation with reduced latency allows for retrieval of deleted data. Each database object holds a Delete SCN indicating when a bulk delete operation was last performed. Each row in the database object holds a Row Modification SCN indicating when the row was last updated. A bulk delete is performed by writing the old value of the Delete SCN to the undo tablespace and updating the Delete SCN. No undo information is stored for the rows. A write is performed by finding a deleted row, storing undo information for the deleted row and writing over the deleted row. To read from the database object, a rollback operation is performed, if necessary. Those rows are then retrieved for which the Row Modification SCN is higher than the Delete SCN and is less than or equal to the timestamp for the requested data.

Abstract: A database server stores compressed units in data blocks of a database. A table (or data from a plurality of rows thereof) is first compressed into a “compression unit” using any of a wide variety of compression techniques. The compression unit is then stored in one or more data block rows across one or more data blocks. As a result, a single data block row may comprise compressed data for a plurality of table rows, as encoded within the compression unit. Storage of compression units in data blocks maintains compatibility with existing data block-based databases, thus allowing the use of compression units in preexisting databases without modification to the underlying format of the database. The compression units may, for example, co-exist with uncompressed tables. Various techniques allow a database server to optimize access to data in the compression unit, so that the compression is virtually transparent to the user.

Abstract: A database server stores compressed units in data blocks of a database. A table (or data from a plurality of rows thereof) is first compressed into a “compression unit” using any of a wide variety of compression techniques. The compression unit is then stored in one or more data block rows across one or more data blocks. As a result, a single data block row may comprise compressed data for a plurality of table rows, as encoded within the compression unit. Storage of compression units in data blocks maintains compatibility with existing data block-based databases, thus allowing the use of compression units in preexisting databases without modification to the underlying format of the database. The compression units may, for example, co-exist with uncompressed tables. Various techniques allow a database server to optimize access to data in the compression unit, so that the compression is virtually transparent to the user.

Abstract: Techniques are provided for more efficiently using the bandwidth of the I/O path between a CPU and volatile memory during the performance of database operation. Relational data from a relational table is stored in volatile memory as column vectors, where each column vector contains values for a particular column of the table. A binary-comparable format may be used to represent each value within a column vector, regardless of the data type associated with the column. The column vectors may be compressed and/or encoded while in volatile memory, and decompressed/decoded on-the-fly within the CPU. Alternatively, the CPU may be designed to perform operations directly on the compressed and/or encoded column vector data. In addition, techniques are described that enable the CPU to perform vector processing operations on the column vector values.

Abstract: Techniques are provided for more efficiently using the bandwidth of the I/O path between a CPU and volatile memory during the performance of database operation. Relational data from a relational table is stored in volatile memory as column vectors, where each column vector contains values for a particular column of the table. A binary-comparable format may be used to represent each value within a column vector, regardless of the data type associated with the column. The column vectors may be compressed and/or encoded while in volatile memory, and decompressed/decoded on-the-fly within the CPU. Alternatively, the CPU may be designed to perform operations directly on the compressed and/or encoded column vector data. In addition, techniques are described that enable the CPU to perform vector processing operations on the column vector values.

Abstract: Techniques are provided for more efficiently using the bandwidth of the I/O path between a CPU and volatile memory during the performance of database operation. Relational data from a relational table is stored in volatile memory as column vectors, where each column vector contains values for a particular column of the table. A binary-comparable format may be used to represent each value within a column vector, regardless of the data type associated with the column. The column vectors may be compressed and/or encoded while in volatile memory, and decompressed/decoded on-the-fly within the CPU. Alternatively, the CPU may be designed to perform operations directly on the compressed and/or encoded column vector data. In addition, techniques are described that enable the CPU to perform vector processing operations on the column vector values.

Abstract: Techniques are described herein for automatically selecting the compression techniques to be used on tabular data. A compression analyzer gives users high-level control over the selection process without requiring the user to know details about the specific compression techniques that are available to the compression analyzer. Users are able to specify, for a given set of data, a “balance point” along the spectrum between “maximum performance” and “maximum compression”. The point thus selected is used by the compression analyzer in a variety of ways. For example, in one embodiment, the compression analyzer uses the user-specified balance point to determine which of the available compression techniques qualify as “candidate techniques” for the given set of data. The compression analyzer selects the compression technique to use on a set of data by actually testing the candidate compression techniques against samples from the set of data.

Abstract: Described herein are compression and processing optimizations by using data transformation techniques. In example embodiments, a byte-wise differential transformation is applied to columnar data represented as a list of length-value pairs to determine a list of delta pairs that is subsequently compressed and stored on persistent storage. A length separation transformation is applied to separate a list of length-value pairs into a length array and a corresponding data value array, where these two arrays are subsequently compressed and stored separately on persistent storage. A native number transformation is applied to a set of number values to remove the lengths stored in the number values, where the transformed set is stored on persistent storage instead of the original set of number values.

Abstract: Embodiments of the present invention that pertain to methods and systems for the calculation of resource requirements for a composite application are described. In one embodiment, information describing what component applications are associated with the composite application is received. Information describing metrics that are to be measured for the component applications is received. Information describing interrelationships between the component applications associated with the composite application is received. Information describing rules for calculating the resource requirements for the component applications is received. The rules are based on the interrelationships between the component applications and the metrics that are to be measured.