Nom

nom is a Rust parser combinator framework used to build complex parsers for binary and text data. It functions as an abstract syntax tree generator and a bit-level binary parser, allowing users to construct structured data by combining small, reusable parsing functions.

The framework provides specialized support for zero-copy binary parsing, extracting data as slices from raw byte arrays to avoid memory allocations. It also includes a streaming data parser capable of processing partial input chunks from networks or files and signaling when additional input is required.

The project covers a broad range of parsing capabilities, including lexerless programming language parsing, text format extraction using case-insensitive comparisons, and bit-level logic for binary data processing. It also includes observability features for parsing error mapping, associating error codes with specific input slices to pinpoint failure locations.

Features

Parser Combinators - Functions as a parser combinator framework for building complex binary and text parsers.

Combinator-Based Composition - Enables the construction of complex grammars by chaining small, reusable parsing functions.

Binary Data Parsers - Processes binary formats using bit-level logic to extract specific data fields.

Data Parsing - Extracts structured information from raw byte sequences using bit-level logic.

Streaming Data Processing - Handles partial data chunks and requests more input to complete parsing operations.

Streaming Parsers - Processes partial input chunks incrementally to handle incomplete data streams.

Zero-Copy Data Access - Retrieves subsets of input data as slices to eliminate memory allocation during parsing.

Zero-Copy Deserialization - Extracts data as slices from raw buffers to avoid memory allocations and reduce overhead.

Stream Parsing Utilities - Uses combinators to build complex data structures from binary or text streams.

Bit-Level Binary Parsers - Provides bit-level logic for extracting structured information from binary data streams.

Streaming Input Parsers - Provides a streaming parser that handles partial input chunks from networks or files.

Zero-Copy Parsers - Implements a parser that extracts structured data from raw byte arrays as slices to eliminate memory allocations.

Parser Combinator Frameworks - Provides a complete Rust framework for building complex binary and text parsers using combinators.

Structured Data Extraction - Retrieves organized information from raw byte arrays or text strings using combined parsing functions.

Text Extraction - Extracts organized information from strings using pattern recognition and case-insensitive comparisons.

Data & Text Processing - Processes text data using case-insensitive comparisons and escaped string recognizers.

Abstract Syntax Tree Parsing - Constructs abstract syntax trees for programming languages without requiring a separate lexing phase.

Parse Error Reporters - Provides mechanisms to associate error codes with specific input slices to pinpoint exact line and column failure locations.

Lexerless Parsers - Implements lexerless parsing to construct abstract syntax trees directly from characters.

Language Tooling - Parser combinator framework for Rust.

Gealnom

Features

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