Displaying 1 to 10 from 10 results

A Concurrent Hashtable

A hashtable implementation that allows simultaneous reads and writes from multiple threads. Also offering a concurrent Dictionary and WeakDictionary as hashtable specializations.

Convert Hashtable Rows into DataTable Columns in C#

Simplest way to convert a Hashtable into a DataTable with all the Hashtable rows converted into DataTable columns.

L4 - L4 (Lock-Free on Read) Hashtable is a C++ library that implements hash table with arbitray byte stream keys/values

L4 (Lock-Free on Read) Hashtable is a C++ library that provides a fixed-size hashtable, where keys and values are arbitrary bytes.L4 HashTable is optimized for lookup operations. It uses Epoch Queue (deterministic garbage collector) to achieve lock-free lookup operations.

diskhash - Diskbased (persistent) hashtable

A simple disk-based hash table (i.e., persistent hash table). It is a hashtable implemented on memory-mapped disk, so that it can be loaded with a single mmap() system call and used in memory directly (being as fast as an in-memory hashtable once it is loaded from disk).

libdict - C library of key-value data structures.

All data structures in this library support insert, search, and remove, and have bidirectional iterators. The sorted data structures (everything but hash tables) support near-search operations: searching for the key greater or equal to, strictly greater than, lesser or equal to, or strictly less than, a given key. The tree data structures also support the selecting the nth element, which generally takes linear time, but only takes logarithmic time in path-reduction and weight-balanced trees. The API is designed with efficiency as a primary concern. For example, an insert call returns a boolean indicating whether or not the key was already present in the dictionary (i.e. whether there was an insertion or a collision), and a pointer to the location of the associated data. Thus, an insert-or-update operation can be supported with a single traversal of the data structure. In addition, the code is written to be very efficient, and almost all recursive algorithms have been rewritten to use iteration instead.

data-structures - Go datastructures.

Copyright 2013, Licensed under the GPL version 2. Please reach out to me directly if you require another licensing option. I am willing to work with you. To collect many important data structures for usage in go programs. Golang's standard library lacks many useful and important structures. This library attempts to fill the gap. I have implemented data-structure's as I have needed them. If there is a missing structure or even just a missing (or incorrect) method open an issue, send a pull request, or send an email patch.

js-hashtable - Javascript hashtables. Use *anything* as a key, not just strings.

An easy way to use anything you need/want as a key to a hash. The npm module name used to be hashtable, but I renamed it because someone else requested to use it and I haven't updated this project in a couple years.

node-hashtable - Native hashtable interface for when V8 objects can't take the heat

Sometimes you need to store so much data in memory that V8 can get a bit clogged up. This Node.js module provides an interface to a native hashmap data structure that exists outside of V8's memory constraints. V8 is great, but was never really meant for driving large software systems. Try adding a few million non-integer keys to an object and you'll start to see things bog down. This module is not intended to be a general replacement for javascript objects (that would be silly). Instead, it is meant to be used when you need maps larger than V8's virtual machine can handle.

HashMap - An open addressing linear probing hash table, tuned for delete heavy workloads

A hash table mostly compatible with the C++11 std::unordered_map interface, but with much higher performance for many workloads. This hash table uses open addressing with linear probing and backshift deletion. Open addressing and linear probing minimizes memory allocations and achives high cache effiency. Backshift deletion keeps performance high for delete heavy workloads by not clobbering the hash table with tombestones.