Search Results for "data structures c"

Fast JSON is a high-performance JSON parser and generator written in Go, designed for speed and efficiency in handling large volumes of structured data. It avoids unnecessary memory allocations and reflection, enabling significantly faster parsing compared to standard libraries. The project provides a low-level API that allows developers to work directly with JSON structures without converting them into intermediate representations. Its design prioritizes minimal overhead and maximum throughput, making it suitable for performance-critical applications such as APIs, data pipelines, and real-time systems. fastjson also supports both parsing and serialization, offering flexibility in data handling. ...

HCL is a toolkit for creating structured configuration languages that are both human- and machine-friendly, for use with command-line tools. Although intended to be generally useful, it is primarily targeted toward DevOps tools, servers, etc. HCL has both a native syntax, intended to be pleasant to read and write for humans, and a JSON-based variant that is easier for machines to generate and parse. It includes an expression syntax that allows basic inline computation and, with support from...

Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data, think XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. Protocol buffers currently support generated code in Java, Python, Objective-C, and C++. ...

...Just as GJSON allows fast retrieval, SJSON allows fast mutation: you provide a JSON document, a dot-notation path (including array indices or the special “-1” index for appending), and a new value, and SJSON produces the updated JSON. It supports many data types (numbers, strings, bools, arrays, maps), raw byte paths, and options for in-place replacement. Because it avoids full unmarshalling into structs, it is very efficient for many use-cases where you just need to tweak a JSON document quickly. Paths support appending to arrays, creating intermediate structures if missing, and deleting (via the Delete function). ...

jsonapi provides helpers and reference code for working with the JSON:API specification, focusing on predictable serialization, deserialization, and linkage of related resources. It enforces the spec’s conventions—data, attributes, relationships, included—so clients and servers exchange data in a consistent, cacheable way. By centralizing how resource identifiers, links, and pagination metadata are emitted, it reduces subtle incompatibilities between services. The library favors explicit schemas and type-safe bindings where possible, which helps large teams avoid drift in field names and relationship structures. ...