Abstract: A blockchain-based data snapshot method, performed by a consensus node in a blockchain network, includes: obtaining a snapshot trigger instruction and a trigger moment of the snapshot trigger instruction; performing snapshot processing on one or more transaction blocks in a ledger at the trigger moment, to obtain a snapshot account state of a transaction account, the snapshot account state being account data related to transaction data in the transaction blocks; obtaining a write-ahead logging (WAL) log of a target block, the target block being a block with a highest block height in the transaction blocks, the WAL log including a log of account data corresponding to the target block; and correcting dirty data in the snapshot account state according to the WAL log, to obtain a corrected snapshot account state, the dirty data being generated based on incomplete transaction data in the target block included in the ledger.

Abstract: Methods, non-transitory computer readable media, and storage management devices that ingest artifacts from applications. The artifacts are stored on storage tiers using storage providers each configured to interface with one of the storage tiers. Metadata for each of the artifacts is generated and stored in a metadata store. The metadata includes a unique identifier for each of the artifacts associated with a storage location for each of the artifacts on the storage tiers. Configurable expressions of a storage policy are periodically and automatically applied to the metadata to determine when one or more of the artifacts should be migrated. The one or more of the artifacts are migrated from a first storage tier to a second storage tier using first and second storage providers configured to interface with the first and second storage tiers, respectively, when the determination indicates that the one or more of the artifacts should be migrated.

Abstract: A decentralized two-phase commit in a datastore or database includes determining pre-validating a transaction, writing first metadata to a leader partition, the first metadata including a state indicating preparing and mutations of the transaction, and writing second metadata of a follower partition to indicate the follower partition is locked by the leader partition. The decentralized two-phase commit further includes updating the state of the first metadata to indicate committed and performing the transaction on the leader partition, performing the transaction on the follower partition and unlocking the follower partition, and updating the first metadata to remove the mutations of the transaction and updating the state of the first metadata to indicate the transaction is completed. The decentralized two-phase commit may further include selecting a leader partition key and updating the state of the first metadata to indicate committed unapplied.

Abstract: A first archive of a first snapshot of a source storage is caused to be stored to a remote storage. At least a portion of content of the first archive is stored in data chunks stored in a first chunk object of the remote storage and the first archive is associated with a first data policy. A second archive of a second snapshot of the source storage is caused to be stored to the remote storage. At least a portion of content of the second archive is referenced from data chunks stored in the first chunk object and the second archive is associated with a second data policy. Policy compliance of the chunk object storing data chunks referenced by the first archive and the second archive that are different is automatically managed based on the first data policy and the second data policy that are different.

Abstract: The present teaching relates to a method, system and programming for recovering a data storage. In response to a failure of the data storage, a last checkpointed version is retrieved from a checkpoint file created previously for the failure of the data storage. A recovery table is updated to indicate the last checkpointed version and an incarnation number indicating a number of previous recovery of the data storage. The data storage is recovered based on the recovery table. A version number of the last checkpointed version is reset to zero upon a successful recovery of the data storage. The incarnation number is incremented.

Abstract: Creating point-in-time versions of files for applications at a storage system includes maintaining at the storage system a data structure correlating each of a plurality of applications with files for each of the applications and correlating each of the files with portions of the data storage system, using the data structure to determine specific files for a particular one of the applications for which a point-in-time version is being created, using the data structure to determine specific portions of the storage system corresponding to the specific files, suspending writes to the specific portions, completing previous writes to the specific portions following suspending writes, and performing a snapshot of the specific files following completing previous writes. The portions of the data storage may be extents. A host may provide information to the storage system to correlate applications with files and to correlate files with portions of the data storage system.

Abstract: Implementations of the present disclosure include receiving, by a parser, a view source file and an extension source file, the view source file defining a view on data stored in a database, the extension source file defining an extension to the view, parsing, by the parser, the view source file to provide a view abstract syntax tree (AST) and the extension source file to provide an extension AST, providing, by the parser, a merged AST based on the view AST and the extension AST, generating a mixed runtime data object using the merged AST, and providing the mixed runtime data object for consumption by at least one runtime component.

Abstract: A source node creates a file and a grouping tag for the file. The source node stores the grouping tag in a location in metadata for the file. When preparing for replication, the source node retrieves the grouping tag from the location in the metadata. If the legacy location in the metadata includes any value which was stored after the most recent replication involving the file, and the size of the legacy location in the metadata was changed after the most recent replication involving the file, then the system enables a destination node to store the grouping tag in the legacy location in the metadata by sending records comprising a request to replace a record stored at the legacy location in the metadata with a larger record, and combining the grouping tag with any value which changed the size of the metadata after a most recent replication involving the file.

Abstract: A search request may define a minimum value and a maximum value for a numerical parameter of the search. A plurality of binary searches may identify a minimum entry and a maximum entry, each binary search comprising selecting a single entry and using at least one analytical model to generate a score for the single entry, wherein the score of the minimum entry corresponds with the minimum value and the score of the maximum entry corresponds with the maximum value. The at least one analytical model may be used to generate a score for each of a set of entries between the minimum entry and the maximum entry while ignoring entries outside of the set of entries between the minimum entry and the maximum entry. Entries responsive to the request may be identified within the scored set of entries.

Abstract: One example method includes performing data protection operations including backup operations. Backups of an availability group include a full backup, transaction log backups, and differential backups. Each differential backup includes transactions since the full backup. The differential backups are performed based on a trigger and, during a restore operation, the differential backups reduce the number of restores that need to be performed and can be restored instead of restoring the corresponding transactional log backups.

Abstract: Role-based data access, including: assigning, to a storage volume of a storage system, a volume-level access policy; and determining whether to allow access to the storage volume based on the volume-level access policy and one or more attributes of a request for the access, including allowing the access responsive to the one or more attributes meeting the volume-level access policy or denying the access responsive to the one or more attributes failing to meet the volume-level access policy.

Abstract: Disclosed herein are embodiments of systems and methods for partition-based scanning of external tables for query processing. In an example embodiment, a database platform receives a query that includes one or more predicates, where the query is directed at least to data in an external table that is stored in an external storage platform that is external to the database platform. The database platform identifies, based on metadata that summarizes the data in the external table, one or more partitions of the external table that potentially include data that satisfies the one or more predicates. The database platform also identifies, from the one or more identified partitions, data that satisfies the one or more predicates. The database platform sends a response to the query to the client, the response comprising the data satisfying the one or more predicates.

Abstract: Techniques relating to managing “bad” or “imperfect” data being imported into a database system are described herein. A lifecycle technology solution helps receive data from a variety of different data sources of a variety of known and/or unknown formats, standardize it, fit it to a known taxonomy through model-assisted classification, store it to a database in a manner that is consistent with the taxonomy, and allow it to be queried for a variety of different usages. Auto-classification, enrichment, clustering model and model stacks, and/or other disclosed techniques, may be used in these and/or other regards.

Abstract: An illustrative embodiment disclosed herein is an apparatus including a processor having programmed instructions to receive, from a client, a request to create a catalog item corresponding to a file, determine an identifier of a cluster manager associated with a cluster having available capacity, send, to the cluster manager, a source location of the catalog item, and cause the cluster manager to store the catalog item from the source location in the cluster.

Abstract: A server in a cloud-based environment interfaces with storage devices that store shared content accessible by two or more users. Individual items within the shared content are associated with respective object metadata that is also stored in the cloud-based environment. Download requests initiate downloads of instances of a virtual file system module to two or more user devices associated with two or more users. The downloaded virtual file system modules capture local metadata that pertains to local object operations directed by the users over the shared content. Changed object metadata attributes are delivered to the server and to other user devices that are accessing the shared content. Peer-to-peer connections can be established between the two or more user devices. Object can be divided into smaller portions such that processing the individual smaller portions of a larger object reduces the likelihood of a conflict between user operations over the shared content.

Abstract: An apparatus and method are described herein for training a similarity model that is used to predict similarity between item pairs. In one example, the apparatus is configured to train (build) the similarity model by using machine learning (e.g., a multivariate multiple liner regression process) that utilizes an independent variable including metadata (e.g., title, genre, writer, plot keywords) associated with training items, and two dependent variables including user contributed similarity scores for training item pairs, and collaborative filtering similarity scores for the training item pairs. Then, the apparatus uses the trained model to predict similarity between items.

Abstract: A database replication using heterogenous encoding is provided. Aspects include obtaining a database and analyzing a data pattern of data in the database. Aspects also include identifying a plurality of candidate encoding formats and evaluating a computing cost for encoding the database for each of the plurality of candidate encoding formats. Aspects further include selecting an encoding format from the plurality of candidate encoding formats based at least in part on the computing cost and storing a backup copy of the database using the encoding format.

Abstract: The disclosed technology relates to a system configured to receive operations data from a content management system, wherein the operations data comprises a log of operations, execute the log of operations, and update, based on execution of the log of operations, a remote tree representing a server state for content items stored on the content management system.

Abstract: The disclosure describes temporal optimization of data operations using distributed search and server management, including configuring one or more host groups, determining one or more stripes associated with one or more shards distributed among the one or more host groups, receiving a query to retrieve data, evaluating the query to identify a time characteristic associated with the data, identifying a location from which to retrieve the data, and rewriting the query to run on at least one of the one or more host groups at the location using a distributed search platform, the another query being targeted at a host group associated with the class.

Abstract: A uniform model for distinct types of data replication, including receiving, at a source data repository, an update to a dataset; generating, based on the update to the dataset, both metadata describing the update to the dataset and also a metadata representation of the dataset; and initiating, based on the same metadata describing the update to the dataset and also based on the same metadata representation of the dataset, either a first type of data replication or a second type of data replication from among a plurality of types of data replication.