Abstract: A method for managing a log structured merged (LSM) tree of key value (KV) pairs, the LSM tree is stored in a non-volatile memory, the method may include merging runs of the LSM tree to provide merged runs; writing merged runs to the non-volatile memory; adding new runs to the LSM tree, wherein the adding comprises writing runs to the non-volatile memory; and updating at least one management data structure (MDS) to reflect the merging and the adding; wherein an MDS of the at least one MDS stores a mapping between keys of the KV pairs of the LSM tree, fingerprints associated with the KV pairs of the LSM tree, and compressed run identifiers that identify runs of the LSM tree.

Abstract: A method for updating a log structured merged (LSM) tree, the method includes (a) performing preemptive full merge operations at first LSM tree levels; and (b) performing capacity triggered merge operations at second LSM tree levels while imposing one or more restrictions; wherein the second LSM tree levels comprise a largest LSM tree level and one or more other second LSM tree levels that are larger from each first LSM tree level; wherein files of the one or more other second LSM tree levels are aligned with files of the largest LSM tree level.

Abstract: Embodiments of the invention utilize an improved LSM-tree-based key-value approach to strike the optimal balance between the costs of updates and lookups and storage space. The improved approach involves use of a new merge policy that removes merge operations from all but the largest levels of LSM-tree. In addition, the improved approach may include an improved LSM-tree that allows separate control over the frequency of merge operations for the largest level and for all other levels. By adjusting various parameters, such as the storage capacity of the largest level, the storage capacity of the other smaller levels, and/or the size ratio between adjacent levels in the improved LSM-tree, the improved LSM-tree-based key-value approach may maximize throughput for a particular workload.

Abstract: Embodiments of the invention utilize a “data canopy” that breaks statistical measures down to basic primitives for various data portions and stores the basic aggregates in a library within an in-memory data structure. When a queried statistical measure involves a basic aggregate stored in the library over a data portion that at least partially overlaps the data portion associated with the basic aggregate, the basic aggregate may be reused in the statistical computation of the queried measure.

Abstract: Embodiments of the invention utilize an improved LSM-tree-based key-value approach to strike the optimal balance between the costs of updates and lookups with any given main memory budget. The improved approach involves allocating memory to Bloom filters differently across different levels so as to minimize the sum of the false positive rates associated with the Bloom filters. In addition, the improved approach may predict the impact of the system design parameter(s) and/or environmental parameter(s) on the lookup performance. Subsequently, the improved approach may “self-tune” the system design parameter(s) and/or environment parameter(s) to maximize the throughput.

Abstract: Embodiments of the present invention provide a new multi-level data structure, log-structured merge bush (LSM-bush), to alleviate the performance compromise between LSH-table and LSM-tree data structures. Similar to LSM-tree, LSM-bush may buffer writes in memory, merge the writes as sorted runs across multiple levels in storage, and use in-memory fence pointers and Bloom filters to facilitate lookups. LSM-bush differs from LSM-tree in that it allows newer data to be merged more “lazily” than LSM-tree. This can be achieved by allowing larger numbers of runs to be collected at the smaller levels before merging them.

Abstract: A method for managing a log structured merged (LSM) tree of key value (KV) pairs, the LSM tree is stored in a non-volatile memory, the method may include writing a current run from a buffer to a current run location within the LSM tree, the current run comprises current KV pairs; generating or receiving current fingerprints that are indicative of the current KV pairs; performing a run writing update of a management data structure (MDS) by adding to the MDS, mappings between the current KV pairs, the current fingerprints and a current run identifier; updating the LSM tree by merging at least some runs of the LSM tree; and performing a merge update of the MDS to represent the merging.

Abstract: A method for managing a log structured merged (LSM) tree of key value (KV) pairs, the LSM tree is stored in a non-volatile memory, the method may include merging runs of the LSM tree to provide merged runs; writing merged runs to the non-volatile memory; adding new runs to the LSM tree, wherein the adding comprises writing runs to the non-volatile memory; and updating at least one management data structure (MDS) to reflect the merging and the adding; wherein an MDS of the at least one MDS stores a mapping between keys of the KV pairs of the LSM tree, fingerprints associated with the KV pairs of the LSM tree, and compressed run identifiers that identify runs of the LSM tree

Abstract: Embodiments of the invention utilize an improved LSM-tree-based key-value approach to strike the optimal balance between the costs of updates and lookups and storage space. The improved approach involves use of a new merge policy that removes merge operations from all but the largest levels of LSM-tree. In addition, the improved approach may include an improved LSM-tree that allows separate control over the frequency of merge operations for the largest level and for all other levels. By adjusting various parameters, such as the storage capacity of the largest level, the storage capacity of the other smaller levels, and/or the size ratio between adjacent levels in the improved LSM-tree, the improved LSM-tree-based key-value approach may maximize throughput for a particular workload.

Abstract: Embodiments of the invention utilize an improved LSM-tree-based key-value approach to strike the optimal balance between the costs of updates and lookups and storage space. The improved approach involves use of a new merge policy that removes merge operations from all but the largest levels of LSM-tree. In addition, the improved approach may include an improved LSM-tree that allows separate control over the frequency of merge operations for the largest level and for all other levels. By adjusting various parameters, such as the storage capacity of the largest level, the storage capacity of the other smaller levels, and/or the size ratio between adjacent levels in the improved LSM-tree, the improved LSM-tree-based key-value approach may maximize throughput for a particular workload.

Abstract: Embodiments of the invention utilize a “data canopy” that breaks statistical measures down to basic primitives for various data portions and stores the basic aggregates in a library within an in-memory data structure. When a queried statistical measure involves a basic aggregate stored in the library over a data portion that at least partially overlaps the data portion associated with the basic aggregate, the basic aggregate may be reused in the statistical computation of the queried measure.

Abstract: Embodiments of the invention utilize an improved LSM-tree-based key-value approach to strike the optimal balance between the costs of updates and lookups and storage space. The improved approach involves use of a new merge policy that removes merge operations from all but the largest levels of LSM-tree. In addition, the improved approach may include an improved LSM-tree that allows separate control over the frequency of merge operations for the largest level and for all other levels. By adjusting various parameters, such as the storage capacity of the largest level, the storage capacity of the other smaller levels, and/or the size ratio between adjacent levels in the improved LSM-tree, the improved LSM-tree-based key-value approach may maximize throughput for a particular workload.

Abstract: Embodiments of the present invention provide a new multi-level data structure, log-structured merge bush (LSM-bush), to alleviate the performance compromise between LSH-table and LSM-tree data structures. Similar to LSM-tree, LSM-bush may buffer writes in memory, merge the writes as sorted runs across multiple levels in storage, and use in-memory fence pointers and Bloom filters to facilitate lookups. LSM-bush differs from LSM-tree in that it allows newer data to be merged more “lazily” than LSM-tree. This can be achieved by allowing larger numbers of runs to be collected at the smaller levels before merging them.

Abstract: Embodiments of the invention utilize an improved LSM-tree-based key-value approach to strike the optimal balance between the costs of updates and lookups with any given main memory budget. The improved approach involves allocating memory to Bloom filters differently across different levels so as to minimize the sum of the false positive rates associated with the Bloom filters. In addition, the improved approach may predict the impact of the system design parameter(s) and/or environmental parameter(s) on the lookup performance. Subsequently, the improved approach may “self-tune” the system design parameter(s) and/or environment parameter(s) to maximize the throughput.

Abstract: Embodiments of the present invention include a method for storing a data page d on a solid-state storage device, wherein the solid-state storage device is configured to maintain a mapping table in a Log-Structure Merge (LSM) tree having a C0 component which is a random access memory (RAM) device and a C1 component which is a flash-based memory device. Methods comprise: writing the data page d at a physical storage page having physical storage page address P in the storage device in response to receiving a write request to store the data page d at a logical storage page having a logical storage page address L; caching a new mapping entry e(L,P) associating the logical storage page address L with the physical storage page address P; providing an update indication for the cached new mapping entry to indicate that the cached new mapping entry shall be inserted in the C1 component; and evicting the new mapping entry from the cache. Corresponding solid-state storage device is also provided.