Micron Technology Incorporated

Breakingpoint Systems, Inc.

Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

3com Corporation

Alexander Tomlinson

Steven Moyer

David Boles

John M Groves

Greg A Becker

Rodney S Canion

Nabeel Meeramohideen Mohamed

Gregory Becker

Gaurav Sanjay Ramdasi

Dennis James Cox

Brent Cook

Sudheer Kurichiyath

David Burton

Brian Smith

Jason Dillard Preston

Matthew Laswell

Ryan S Clifton

Deep Datta

Joseph Dempsey

Scott Strentzsch

Stephen Egbert

Terry G Ahnstedt

Tristan Antonio Partin

Jeremy B Moss

An event trigger to flush key-value data from a volatile memory device is detected. In response to detection of the event trigger, a determination is made whether the key-value data comprises uncommitted data for one or more active database transactions. If the key-value data comprises uncommitted data for one or more active database transactions, the uncommitted data is moved to a hold aside buffer in the volatile memory device, and committed data in the key-value data is stored on a non-volatile memory device.

Hold-aside buffer for uncommitted write transactions in a log structured key-value database

Systems and techniques for reducing probabilistic filter query latency are described herein. A query for a probabilistic filter that is stored on a first media may be received from a caller. In response to receiving the query, cached segments of the probabilistic filter stored on a second media may be obtained. Here, the probabilistic filter provides a set membership determination that is conclusive in a determination that an element is not in a set. The query may be executed on the cached segments resulting in a partial query result. Retrieval of remaining data of the probabilistic filter from the first media to the second media may be initiated without intervention from the caller. Here, the remaining data corresponds to the query and data that is not in the cached segment. The partial query results may then be returned to the caller.

Reducing probabilistic filter query latency

In some implementations, a memory device may determine, from a list of key-value pair sets, a key-value pair set. The memory device may identify, from the key-value pair set selected from the list of key-value pair sets, a first key that is included in at least one other key-value pair set from the list of key-value pair sets. The memory device may identify, from the key-value pair set selected from the list of key-value pair sets, a second key that is not included in at least one other key-value pair set from the list of key-value pair sets. The memory device may form a new key-value pair set that excludes the first key and includes the second key. The memory device may replace the key-value pair set selected from the list of key-value pair sets with the new key-value pair set.

Replacing key-value pair sets with new key-value pair sets

Methods, systems, and devices for techniques to balance log structured merge trees are described. A computing system may rebalance a tree structure having an ordered set of leaf nodes by splitting or joining leaf nodes of the tree structure. To split a leaf node, the computing system may select a key to evenly partition key and value data stored in the leaf. The computing system may place each key block of the leaf node having keys less than or equal to the selected key in a first new leaf node, and may place each key block of the leaf node having keys greater than the selected key in a second new leaf node. To join leaf nodes of the tree structure, the computing system may place each key block and each value block of the leaf node and the adjacent leaf node in a new leaf node.

Techniques to balance log structured merge trees

Aspects of the present disclosure provide for operations for a tree data structure that provides order-based node traversal. For some embodiments, the tree data structure stores one or more key-value pairs, implements at least one linked-list data structure, and enables traversal of nodes within the tree data structure based on a key order (e.g., forward or reverse key order).

Data tree with order-based node traversal

Aspects of the present disclosure provide for operations of a key-value tree data structure that merges key-value pair data of a node, in a key-value tree data structure using counter values.

Counter-based compaction of key-value store tree data block

Aspects of the present disclosure provide for operations of a key-value store (KVS) that uses journaling with storage of data (e.g., key data or value data of a key-value pair) in different types of data storage objects (e.g., data storage formats) selected based on one or more criteria.

Key-value store using journaling with selective data storage format

A KVS tree database and operations thereon are described herein. A KVS tree database is a multilevel tree that includes a base level and subsequent levels. The base level includes a heterogeneous kvset in a node, The heterogeneous kvset including entries for multiple KVS trees, such as a first entry for a first KVS tree and a second entry for a second KVS tree, The subsequent level includes a first node including a first homogeneous kvset for the first KVS tree and a second node including a second homogeneous kvset for the second KVS tree. Here, a homogeneous kvset includes nodes from only one KVS tree. The KVS tree database also includes a first determinative mapping of entries between the base level and the subsequent level and a second determinative mapping of entries between subsequent levels.

Heterogenous key-value sets in tree database

Aspects of the present disclosure provide for operations of a key-value tree data structure that: merge key-value sets within a given node by merging and rewriting key blocks of the key-value sets while rewriting or deferring rewrite of value blocks of the merged key-value sets based on whether one or more child nodes of the given node comprise a leaf node; and move one or more portions of the merged key-value set into one or more child nodes of the given node.

Key-value store tree data block spill with compaction

Aspects of the present disclosure provide various embodiments for selective use of a portion of a key, such as a prefix of the key (also referred to as a key prefix of a key), with respect to a key-value store (KVS) tree data structure, such as when storing a key-value pair (associated with the key) within the KVS tree data structure or navigating through the KVS tree data structure. For some embodiments, when navigating a KVS tree based on a key, a first set of levels (e.g., a first series of levels) of the KVS tree is navigated by a first portion (e.g., a prefix) of the key, and a second set of levels (e.g., a second series of levels) of the KVS tree is navigated by a second portion (e.g., an entire portion) of the key.

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Austin, TX, United States of America

Alexander Tomlinson