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It also previously supported a local, client-side storage abstraction named Dojo Storage. Dojo Storage allows web applications to store data on the client-side, persistently and securely and with a user's permission. When included in a web page, Dojo Storage determines the best method for persistently storing information.
Web storage offers two different storage areas—local storage and session storage—which differ in scope and lifetime. Data placed in local storage is per origin—the combination of protocol, host name, and port number as defined in the same-origin policy. The data is available to all scripts loaded from pages from the same origin that ...
ArangoDB is a transactional native multi-model database supporting two major NoSQL data models (graph and document [1]) with one query language. Written in C++ and optimized for in-memory computing. In addition ArangoDB integrated RocksDB for persistent storage. ArangoDB supports Java, JavaScript, Python, PHP, NodeJS, C++ and Elixir.
In computer programming, thread-local storage (TLS) is a memory management method that uses static or global memory local to a thread. The concept allows storage of data that appears to be global in a system with separate threads. Many systems impose restrictions on the size of the thread-local memory block, in fact often rather tight limits.
JavaScript is the server-side language used to develop services for the Opera Unite feature of the Opera browser. This is a server built into the browser. The JavaScript API includes local file access to a virtual sandboxed file-system and persistent storage via persistent global variables. PostgreSQL: V8: Embedded language PLV8 [7]
Data storage that is directly attached to a compute device; A network-attached storage device accessed via a local network, rather than a wide area network; Local storage, a JavaScript web storage facility allowing websites to store data on a user's device; Local shared object (or Flash cookie), Adobe Flash data stored on a user's computer
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It is a fork of Google's LevelDB optimized to exploit multi-core processors (CPUs), and make efficient use of fast storage, such as solid-state drives (SSD), for input/output (I/O) bound workloads. It is based on a log-structured merge-tree (LSM tree) data structure. It is written in C++ and provides official language bindings for C++, C, and Java.