awesome-repositories.comBlog
© 2026 Bringes Technology SRL·VAT RO45896025·hello@bringes.io
MCPBlogSitemapPrivacyTerms
Jq | Awesome Repository
← All repositories

jqlang/jq

0
View on GitHub↗
33,686 stars·1,701 forks·C·other·0 viewsjqlang.org↗

Jq

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Let's find more awesome repositories

Features

  • Data Transformation Languages - Provides a declarative environment for immutable data transformation through functional composition.
  • Command-Line Data Processors - Provides a lightweight tool for parsing, filtering, and transforming structured data streams directly from the terminal.
  • Stream Filters - Applies granular filters to individual values within a continuous JSON stream.
  • Stream Processing Engines - Processes JSON data as continuous streams to maintain a low memory footprint.
  • Data Transformation Functions - Performs arithmetic, logical, and structural transformations on JSON values.
  • Large Data Streamers - Parses massive JSON documents by streaming path-value pairs to minimize memory usage.
  • Tree Traversal Engines - Implements recursive path-based selection and transformation for deeply nested hierarchical data.
  • Functional Pipelines - Composes data processing stages as a series of discrete, functional operations.
  • Data Processing Pipelines - Consumes input records sequentially to perform real-time filtering, mapping, and reduction.
  • Object Property Accessors - Retrieves object properties and array elements using safe dot notation and optional chaining.
  • Stack-Based Virtual Machines - Executes compiled filter expressions using a custom stack-based bytecode interpreter.
  • Pattern Matching Engines - Matches and transforms data nodes based on structural patterns and predicates.
  • Conditional Data Filters - Processes input data by applying a conditional predicate to pass through only those items that satisfy specified criteria.
  • Path Extractors - Extracts specific elements or values from JSON structures using path-based filters.
  • Collection Iterators - Processes every element of an array or object by producing each item as an individual result for subsequent operations.
  • Data Mappers - Transforms elements within an array or object by applying a function to each value and returning the resulting modified collection.
  • Data Path Mappers - Generates comprehensive lists of paths to elements within nested JSON structures.
  • Data Pipeline Transformers - Cleans and reformats raw data outputs from APIs or databases to match required schemas.
  • Data Reducers - Aggregates multiple values into a single result by iteratively applying an update expression to an initial value across a collection.
  • JSON Construction Utilities - Constructs complex JSON arrays and objects using specialized literal syntax.
  • Path Filters - Identifies paths to elements based on node types and specific value criteria.
  • Lazy Evaluation Engines - Defers expression evaluation to efficiently handle large or infinite data structures.
  • Regular Expression Engines - Executes pattern matching and scanning tasks to extract or modify text content.

    • This project is a command-line processor designed for the parsing, filtering, and transformation of structured data streams. It functions as a declarative programming environment that treats data as immutable streams, allowing users to perform complex structural modifications through the composition of small, reusable functions. By utilizing a recursive tree traversal engine, the system enables the navigation, inspection, and modification of deeply nested hierarchical data structures.

    The engine distinguishes itself through a stream-oriented architecture that processes input records one by one, maintaining a low memory footprint even when handling massive documents. It employs a custom stack-based virtual machine to execute compiled filter expressions efficiently, while its lazy evaluation semantics ensure that expressions are only computed when required by the pipeline. This combination of functional pipeline composition and pattern-matching capabilities allows for sophisticated data manipulation directly from the terminal.

    Beyond its core processing model, the system provides a comprehensive suite of tools for data navigation, arithmetic and logical operations, and collection management. It supports advanced logic control, including variable assignment and iterative structures, alongside robust text manipulation through regular expression processing. These features facilitate a wide range of tasks, from automated log analysis and configuration file manipulation to complex data pipeline transformations.