Simple tool for storing/retrieving objects events based hierarchical keypaths.
Takes binding declarations and returns key-tree-store of functions that can be used to apply those bindings.
Better localStorage
TypeScript definitions for use-sync-external-store
TypeScript definitions for store
Azure Key Vault Secrets
A [W3C HTML JSON forms spec](http://www.w3.org/TR/html-json-forms/) compliant field appender (for lack of a better name). Useful for people implementing `application/x-www-form-urlencoded` and `multipart/form-data` parsers.
Simple key-value storage with support for multiple backends
Takes binding declarations and returns key-tree-store of functions that can be used to apply those bindings.
Utilities for managing WordPress preferences.
The App Store Server Library
Different binary search tree implementations, including a self-balancing one (AVL)
A mock store for testing your redux async action creators and middleware
An Azure Storage Blob solution to store checkpoints when using Event Hubs.
NPM module for storing persistent data
Library of associative containers; it implements TreeMap, TreeSet, TreeMultiMap and TreeMultiSet classes
Interval search tree with TypeScript support
Simple key-value storage with support for multiple backends
datastore interface
TypeScript definitions for redux-mock-store
Crazy fast http radix based router
Bindings for the Watchman file watching service
ember-cli addon tree cache key builder
A WebdriverIO service to exchange data across processes
Provides a generic tree data structure with ability to store keyed node elements in the tree. The implementation mixes in the Enumerable module.
lithos is a self-contained embedded key-value store written from scratch as a native extension — no external database dependency. It uses a log-structured merge (LSM) tree: a write-ahead log makes every write durable, an in-memory sorted memtable flushes to immutable SSTables (with bloom filters), and compaction merges them. Keys and values are arbitrary binary strings; keys are kept in sorted order so you get ordered iteration and range scans, plus crash recovery via WAL replay. Windows MSVC (mswin) Ruby only.
treestore stores two different types of data: 1) values, which are stored according to their SHA-1 hashcode 2) trees, which are sets of values and/or other trees, stored via a SHA-1 hashcode In addition, there are references that allow you to 'bookmark' a SHA-1 hashcode for easier lookup. If you think of the core git, but on any key-value backend store (like the included Redis one), you've got the right idea.
Rack::Config::Flexible is an alternative to Rack::Config, offering much greater flexibility. Configuration options are stored as key-value pairs in _sections_, partitioned by _environments_. For example: + environment + section key -> value pairs A simple DSL is provided and can be used either within a passed configuration block (to ::new), or to the #configuration method. Facilities are also provided to load whole environments, and sections from either a single YAML file structured like, or from a directory tree. See the README file or RDoc documentation for more info.
EncodeM v3.0 brings complete M language (MUMPS) subscript encoding to Ruby, supporting numbers, strings, and composite keys with perfect sort order. Build hierarchical database keys like M("users", 42, "email") that sort correctly as raw bytes. This 40-year production-tested algorithm from YottaDB/GT.M powers Epic (70% of US hospitals) and VistA. Perfect for B-tree indexes, key-value stores, and any system requiring sortable hierarchical keys. All types maintain correct ordering when compared as byte strings - no decoding needed.
In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. It provides near-constant-time operations (bounded by the inverse Ackermann function) to add new sets, to merge existing sets, and to determine whether elements are in the same set. In addition to many other uses (see the Applications section), disjoint-sets play a key role in Kruskal's algorithm for finding the minimum spanning tree of a graph. A disjoint-set forest consists of a number of elements each of which stores an id, a parent pointer, and, in efficient algorithms, a value called the "rank". The parent pointers of elements are arranged to form one or more trees, each representing a set. If an element's parent pointer points to no other element, then the element is the root of a tree and is the representative member of its set. A set may consist of only a single element. However, if the element has a parent, the element is part of whatever set is identified by following the chain of parents upwards until a representative element (one without a parent) is reached at the root of the tree. Forests can be represented compactly in memory as arrays in which parents are indicated by their array index. Disjoint-set data structures model the partitioning of a set, for example to keep track of the connected components of an undirected graph. This model can then be used to determine whether two vertices belong to the same component, or whether adding an edge between them would result in a cycle. The Union–Find algorithm is used in high-performance implementations of unification. This data structure is used by the Boost Graph Library to implement its Incremental Connected Components functionality. It is also a key component in implementing Kruskal's algorithm to find the minimum spanning tree of a graph. Note that the implementation as disjoint-set forests doesn't allow the deletion of edges, even without path compression or the rank heuristic. Sharir and Agarwal report connections between the worst-case behavior of disjoint-sets and the length of Davenport–Schinzel sequences, a combinatorial structure from computational geometry.
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