Abstract: One example method includes receiving a write request that includes a data structure version to be written, wherein the data structure version is associated with a unique identifier, storing the data structure version in association with the unique identifier, receiving a read request for a most recent version of the data structure and, when the stored data structure version is not the most recent version of the data structure, examining respective unique identifiers of each of a group of other stored data structure versions to determine which stored data structure version is the most recent. Finally, the example method includes returning the most recent data structure version, notwithstanding that one or more other data structure versions existed at the time that the read request was received.

Abstract: One example method includes receiving a write request that includes a data structure version to be written, wherein the data structure version is associated with a unique identifier, storing the data structure version in association with the unique identifier, receiving a read request for a most recent version of the data structure and, when the stored data structure version is not the most recent version of the data structure, examining respective unique identifiers of each of a group of other stored data structure versions to determine which stored data structure version is the most recent. Finally, the example method includes returning the most recent data structure version, notwithstanding that one or more other data structure versions existed at the time that the read request was received.

Abstract: One example method includes receiving a write request that includes a data structure version to be written, wherein the data structure version is associated with a unique identifier, storing the data structure version in association with the unique identifier, receiving a read request for a most recent version of the data structure and, when the stored data structure version is not the most recent version of the data structure, examining respective unique identifiers of each of a group of other stored data structure versions to determine which stored data structure version is the most recent. Finally, the example method includes returning the most recent data structure version, notwithstanding that one or more other data structure versions existed at the time that the read request was received.

Abstract: One example method includes receiving a write request that includes a data structure version to be written, wherein the data structure version is associated with a unique identifier, storing the data structure version in association with the unique identifier, receiving a read request for a most recent version of the data structure and, when the stored data structure version is not the most recent version of the data structure, examining respective unique identifiers of each of a group of other stored data structure versions to determine which stored data structure version is the most recent. Finally, the example method includes returning the most recent data structure version, notwithstanding that one or more other data structure versions existed at the time that the read request was received.

Abstract: A deduplicated microservices-based storage system and method running in a cloud includes frontend and backend microservice nodes that process requests from a plurality of clients and interface with a master node and one or more worker nodes connected as a network for deduplicating, storing and retrieving data from object storage. A custom metric compares demand on system resources to thresholds and dynamically scales resources to add or eliminate resources according to demand. The master node partitions the range of values identifying data similarity groups across the worker nodes, reassigns ranges of values using consistent hashing as worker nodes are scaled, and maintains a mapping of ranges of values to worker IP addresses.

Abstract: One example method includes receiving a write request that includes a data structure version to be written, wherein the data structure version is associated with a unique identifier, storing the data structure version in association with the unique identifier, receiving a read request for a most recent version of the data structure and, when the stored data structure version is not the most recent version of the data structure, examining respective unique identifiers of each of a group of other stored data structure versions to determine which stored data structure version is the most recent. Finally, the example method includes returning the most recent data structure version, notwithstanding that one or more other data structure versions existed at the time that the read request was received.

Abstract: A deduplicated microservices-based storage system and method running in a cloud includes frontend and backend microservice nodes that process requests from a plurality of clients and interface with a master node and one or more worker nodes connected as a network for deduplicating, storing and retrieving data from object storage. A custom metric compares demand on system resources to thresholds and dynamically scales resources to add or eliminate resources according to demand. The master node partitions the range of values identifying data similarity groups across the worker nodes, reassigns ranges of values using consistent hashing as worker nodes are scaled, and maintains a mapping of ranges of values to worker IP addresses.

Abstract: Write performance is accelerated in a microservices deduplication system having a master node and a worker node, where the worker node receives and writes updates to data similarity groups to a cache and to a primary write-ahead log. Periodically, the worker writes updated similarity groups from cache to object storage, and flushes the contents of the primary write-ahead log. During flushing of the primary write-ahead log, the worker writes similarity group updates to a secondary write-ahead log which is swapped for the primary write-ahead log and which becomes a new primary write-ahead log for updates during flushing and during a subsequent time period. The previous primary write-ahead log becomes the new secondary write-ahead log, and the log swapping is repeated during subsequent time periods.

Abstract: One example method includes receiving a write request that includes a data structure version to be written, wherein the data structure version is associated with a unique identifier, storing the data structure version in association with the unique identifier, receiving a read request for a most recent version of the data structure and, when the stored data structure version is not the most recent version of the data structure, examining respective unique identifiers of each of a group of other stored data structure versions to determine which stored data structure version is the most recent. Finally, the example method includes returning the most recent data structure version, notwithstanding that one or more other data structure versions existed at the time that the read request was received.

Abstract: In a method of processing data similarity groups, a combined read/write cache comprises an in-memory data structure having a fixed allocation of physical memory that includes a write portion comprising memory allocated to write entries and a read portion comprising memory allocated to read entries. Similarity group entries are written into the cache using keys based upon an identifier of a similarity group, and similarity group entries are read from the cache using keys based upon both similarity group and subgroup identifiers. The sizes of memory allocated to the write portion and to the read portion are dynamically varied within the fixed memory allocation based upon demand while maintaining the fixed memory allocation constant.

Abstract: In a deduplicated microservices-based storage system and method running in a cloud includes frontend and backend microservice nodes that process requests from a plurality of clients and interface with a master node and one or more worker nodes connected as a network for deduplicating, storing and retrieving data from object storage, a worker node receives queries and fingerprints for data segments of a data similarity group to be deduplicated, obtains and returns the similarity group from its cache or from object storage using a key. The system compares the received fingerprints to stored fingerprints to determine non-duplicates for deduplication. The worker node updates similarity groups in its cache and in a write-ahead log with a reference to a compression region of non-duplicate segments, and periodically writes modified objects from its cache to object storage.

Abstract: In a deduplicated microservices-based storage system and method running in a cloud includes frontend and backend microservice nodes that process requests from a plurality of clients and interface with a master node and one or more worker nodes connected as a network for deduplicating, storing and retrieving data from object storage, a worker node receives queries and fingerprints for data segments of a data similarity group to be deduplicated, obtains and returns the similarity group from its cache or from object storage using a key. The system compares the received fingerprints to stored fingerprints to determine non-duplicates for deduplication. The worker node updates similarity groups in its cache and in a write-ahead log with a reference to a compression region of non-duplicate segments, and periodically writes modified objects from its cache to object storage.