Simple and high speed serializer for nodejs.
render domhandler DOM nodes to a string
An implementation of the WHATWG URL Standard's URL API and parsing machinery
Get the byte length of an ArrayBuffer, even in engines without a `.byteLength` method.
Robustly get the byte length of a Typed Array
Robustly get the byte offset of a Typed Array
Get the byteLength out of a DataView, robustly.
Get the byteOffset out of a DataView, robustly.
Support for proto3 JSON serialiazation/deserialization for protobuf.js
Utilities for working with htmlparser2's dom
Jest snapshot serializer that beautifies HTML.
The postgres client/server binary protocol, implemented in TypeScript
Get utf8 byte length of string
Reads / writes floats / doubles from / to buffers in both modern and ancient browsers.
Jest utilities for emotion
Fast & forgiving HTML/XML parser
Convert a bytes or octets value (e.g. 34565346) to a human-readable string ('34.6 MB'). Choose between metric or IEC units.
Custom Jest snapshot serializer
A jest serializer for Vue snapshots
A structuredClone polyfill
convert enzyme wrapper to a format compatible with Jest snapshot
HTML parser and serializer.
The Linux x64 binary of Apex AST Serializer
A Node.js framework agnostic library for serializing your data to JSON API
Buffer-oriented text encoding and decoding utilities for Rust
Utility to process gossip routing data from Rapid Gossip Sync Server.
Custom derive macros for the byteable crate.
Shared packet formats and related resources for Deimos DAQ ecosystem
Generates serializers of DataMeta objects to/from byte arrays, which can be used with Hadoop, BigTable and beyond.
Apple uses a system of serialization (I think its called dmap…) where a 4-byte string tells of the information following, both its type and what it represents. Its used in the DAAP (Protocol), QuickTime mov structure and doubtless many other places.
Packing & unpacking various Ruby data to/from a byte string, incl. arrays, hashes and custom data structures
A ruby implementation of the canonical serialization for the Libra network. Canonical serialization guarantees byte consistency when serializing an in-memory data structure. It is useful for situations where two parties want to efficiently compare data structures they independently maintain. It happens in consensus where independent validators need to agree on the state they independently compute. A cryptographic hash of the serialized data structure is what ultimately gets compared. In order for this to work, the serialization of the same data structures must be identical when computed by independent validators potentially running different implementations of the same spec in different languages.
Tokyo Cabinet is a library of routines for managing a database. The database is a simple data file containing records, each is a pair of a key and a value. Every key and value is serial bytes with variable length. Both binary data and character string can be used as a key and a value. There is neither concept of data tables nor data types. Records are organized in hash table, B+ tree, or fixed-length array.
Kyoto Cabinet is a library of routines for managing a database. The database is a simple data file containing records, each is a pair of a key and a value. Every key and value is serial bytes with variable length. Both binary data and character string can be used as a key and a value. Each key must be unique within a database. There is neither concept of data tables nor data types. Records are organized in hash table or B+ tree.
Kyoto Cabinet is a library of routines for managing a database. The database is a simple data file containing records, each is a pair of a key and a value. Every key and value is serial bytes with variable length. Both binary data and character string can be used as a key and a value. Each key must be unique within a database. There is neither concept of data tables nor data types. Records are organized in hash table or B+ tree.
Kyoto Cabinet is a library of routines for managing a database. The database is a simple data file containing records, each is a pair of a key and a value. Every key and value is serial bytes with variable length. Both binary data and character string can be used as a key and a value. Each key must be unique within a database. There is neither concept of data tables nor data types. Records are organized in hash table or B+ tree.
Kyoto Cabinet is a library of routines for managing a database. The database is a simple data file containing records, each is a pair of a key and a value. Every key and value is serial bytes with variable length. Both binary data and character string can be used as a key and a value. Each key must be unique within a database. There is neither concept of data tables nor data types. Records are organized in hash table or B+ tree.
A DataMapper adapter for Amazon's SimpleDB service. Features: * Full set of CRUD operations * Supports all DataMapper query predicates. * Can translate many queries into efficient native SELECT operations. * Migrations * DataMapper identity map support for record caching * Lazy-loaded attributes * DataMapper Serial property support via UUIDs. * Array properties * Basic aggregation support (Model.count("...")) * String "chunking" permits attributes to exceed the 1024-byte limit Note: as of version 1.0.0, this gem supports supports the DataMapper 0.10.* series and breaks backwards compatibility with DataMapper 0.9.*.
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