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Berkeley DB (BDB) is a high-performance, embedded database library with bindings in C, C++, Java, Perl, Python, Tcl, Smalltalk and many other programming languages. BDB stores arbitrary key/data pairs as byte arrays, and supports multiple data items for a single key. BDB can support thousands of
Modula-2 collections library. The library provides an API for collection ADTs, in particular: stacks, queues, double ended queues, priority queues, balanced binary search trees, patricia tries, dynamic arrays and hash tables to store arbitrary data. The hash table ADT allows data to be stored and
LevelDB is a library that implements a fast key-value store. Features * Keys and values are arbitrary byte arrays. * Data is stored sorted by key. * Callers can provide a custom comparison function to override the sort order. * The basic operations are Put(key,value)
Tokyo Cabinet is the successor of QDBM, a high performance database library similar to the DBM family. It also supports hash and B-tree databases and does not require any server process. The overall speed is improved compared to QDBM.
Credis is a client library written in plain C for communicating with Redis servers. Redis is a high performance key-value database, refer to Redis project page for more information. Credis aims to be fast and minimalistic with respect to memory usage. It runs on Linux and should run on most posix
Lightning DB (aka MDB) is an ultra-high performance database using memory-mapped data. It is fully transactional with ACID guarantees and uses Multi-Version Concurrency Control and scales perfectly across large numbers of CPUs. It is also extremely compact, at only 35KB of object code for the
Stash is a persistence engine for .NET. It eschews relational models and object/relational mapping and instead follows the No-Sql paradigm of storing serialised graphs or ‘documents’. It is effectively a Key/value store with access via Indexes. Stash currently uses BerkeleyDB as its
C library for key-value storage. The library provides an API to create and use dynamic associative arrays for arbitrary data. Data may be stored and retrieved by copy or by reference. Stored data is reference counted to prevent dangling pointers. Optimised for fast lookups and large data sets.
A collection of Python dictionaries supporting attribute-style access. Includes defaultdict, OrderedDict, restricted, and frozen implementations.
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