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143 Repos

Awesome GitHub RepositoriesData Type Validation

Checks that a value matches a specific type or instance constructor to ensure data conforms to expected structures.

Distinct from Data Schema Validation: Focuses on runtime type checking against schema definitions, distinct from general testing.

Explore 143 awesome GitHub repositories matching software engineering & architecture · Data Type Validation. Refine with filters or upvote what's useful.

Awesome Data Type Validation GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • chalarangelo/30-seconds-of-codeAvatar von Chalarangelo

    Chalarangelo/30-seconds-of-code

    128,121Auf GitHub ansehen↗

    30-seconds-of-code is a comprehensive knowledge base and programming snippet library designed to support software engineering education and professional development. It provides a curated collection of reusable code units and technical guides that help developers master core language mechanics, design patterns, and architectural philosophies. The project distinguishes itself by offering a wide-ranging library of algorithmic solutions and web development patterns that are organized into modular, independently testable units. It emphasizes functional programming paradigms and declarative logic,

    Provides standardized type-checking patterns to ensure data integrity and prevent runtime errors.

    JavaScriptastroawesome-listcss
    Auf GitHub ansehen↗128,121
  • lodash/lodashAvatar von lodash

    lodash/lodash

    61,235Auf GitHub ansehen↗

    Lodash is a JavaScript utility library and data manipulation toolkit. It provides a collection of modular functions for transforming, filtering, and validating arrays, objects, strings, and numbers. The project functions as a functional programming toolkit, offering capabilities for function composition, currying, and lazy evaluation. It includes mechanisms for execution control, such as debouncing and throttling, to manage the timing and frequency of function invocations. The library covers a broad surface of data operations, including deep cloning and merging of complex nested structures,

    Checks if values match specific types such as arrays, objects, dates, or booleans to ensure data consistency.

    JavaScriptjavascriptlodashmodules
    Auf GitHub ansehen↗61,235
  • hasura/graphql-engineAvatar von hasura

    hasura/graphql-engine

    32,064Auf GitHub ansehen↗

    graphql-engine is an automated GraphQL API engine that transforms database tables and relationships into a queryable GraphQL schema. It functions as a federation gateway and mapper, instantly generating APIs with built-in filtering, pagination, and mutations from existing databases and remote schemas. The project distinguishes itself through a fine-grained access control layer that enforces row-level and field-level permissions. It further provides a real-time data subscription server that converts standard queries into live streams and a system for triggering event-driven webhooks and notifi

    Parses and validates metadata types at runtime using generated conversion classes to ensure data integrity.

    TypeScriptaccess-controlapiautomatic-api
    Auf GitHub ansehen↗32,064
  • samuelcolvin/pydanticAvatar von samuelcolvin

    samuelcolvin/pydantic

    28,043Auf GitHub ansehen↗

    Pydantic ist eine Datenvalidierungsbibliothek und ein Parsing-Framework für Python. Es fungiert als typbasierter Schema-Validator, der Standard-Python-Typannotationen verwendet, um sicherzustellen, dass Eingabedaten vordefinierten strukturellen Schemata entsprechen. Das Projekt bietet Funktionen zum Parsen von Rohdaten in typisierte Objekte durch automatische Typkonvertierung und Validierung. Dies umfasst die Serialisierung von Daten und die Validierung von Datenstrukturen zur Durchsetzung der Korrektheit. Das Framework deckt verschiedene Anwendungsbereiche ab, einschließlich der Verifizierung von API-Anfragen und der Verwaltung von Anwendungskonfigurationen. Es ermöglicht die Transformation von Rohformaten wie JSON in strukturierte Python-Objekte.

    Ensures input data conforms to defined schemas by checking values against Python type constructors.

    Python
    Auf GitHub ansehen↗28,043
  • jashkenas/underscoreAvatar von jashkenas

    jashkenas/underscore

    27,345Auf GitHub ansehen↗

    Underscore is a JavaScript utility library providing a suite of functional programming and data manipulation helpers. It serves as a framework for transforming data collections, composing functions, managing objects, and performing precise data type validation without modifying core language prototypes. The project includes a functional programming toolkit designed to control function execution timing and behavior through techniques such as debouncing, throttling, and partial application. It also provides a dedicated object manipulation utility for cloning, merging, picking, and omitting prop

    Offers a system to validate if values are arrays, functions, dates, or other specific JavaScript types.

    JavaScript
    Auf GitHub ansehen↗27,345
  • jquense/yupAvatar von jquense

    jquense/yup

    23,673Auf GitHub ansehen↗

    Yup is a JavaScript schema validation library used to define data shapes and validate runtime values. It functions as an object schema validator and a data coercion engine, allowing developers to transform raw input values into desired types before performing validation checks. The library is distinguished by its support for dynamic schema validation, where rules can be adjusted at runtime based on sibling field values or external context. It also enables recursive data structuring for polymorphic fields and provides a system for extracting static TypeScript interfaces from runtime schema def

    Performs runtime type checking against schema definitions to ensure data conforms to expected structures.

    TypeScript
    Auf GitHub ansehen↗23,673
  • basarat/typescript-bookAvatar von basarat

    basarat/typescript-book

    21,533Auf GitHub ansehen↗

    This project is a comprehensive educational resource and programming guide for the TypeScript language. It serves as a manual for the type system and a reference for the language's core syntax, focusing on writing type-safe code and building scalable applications. The content provides detailed instruction on the TypeScript type system, covering interfaces, generics, and structural typing. It further acts as a compiler reference, analyzing how source code is transformed into an abstract syntax tree through scanning and parsing. The guide also covers software architecture design, detailing pro

    Instructs on verifying that objects match required member shapes to ensure compatibility between data structures.

    TypeScriptfreeopen-sourcetypescript
    Auf GitHub ansehen↗21,533
  • hapijs/joiAvatar von hapijs

    hapijs/joi

    21,192Auf GitHub ansehen↗

    Joi is a JavaScript data validation library used to define schemas that validate, cast, and sanitize data objects. It functions as an object schema validator and parser, ensuring that input data matches specific types and formats before it is processed by an application. The library features a conditional validation engine capable of dynamic schema enforcement, where validation logic and dependencies change based on the values of other keys within an object. It also serves as a data casting and sanitization tool, transforming input values into target types and removing sensitive keys from the

    Enforces that values match specific primitive or complex types according to a defined schema.

    JavaScripthapijavascriptschema
    Auf GitHub ansehen↗21,192
  • sideway/joiAvatar von sideway

    sideway/joi

    21,192Auf GitHub ansehen↗

    Joi is a JavaScript data validation library used to define schemas that ensure the structure and data types of objects remain consistent. It functions as a schema-based validator and object schema definition tool, preventing invalid information from entering an application by checking data against predefined constraints and rules. The library employs a chainable fluent interface and a constraint-based validation engine to build complex validation pipelines. It utilizes recursive tree traversal to validate nested data structures and a type-coercion pipeline to transform input values into the t

    Provides a validation engine that checks input data against a predefined tree of type requirements and logic rules.

    JavaScript
    Auf GitHub ansehen↗21,192
  • digitalocean/netboxAvatar von digitalocean

    digitalocean/netbox

    20,870Auf GitHub ansehen↗

    NetBox is a network infrastructure source of truth, acting as a centralized database for modeling and documenting the intended state of network devices, IP addresses, and cabling. It functions as a data center infrastructure management tool, an IP address management system, and a network automation platform. The system provides a programmable interface and a validated data model designed to drive external network configuration and provisioning tools. It enables the maintenance of records for network circuits, devices, and logical connections with full change auditing. Capabilities include ne

    Enforces data integrity rules and property requirements on infrastructure objects to prevent configuration errors.

    Python
    Auf GitHub ansehen↗20,870
  • graphql/graphql-jsAvatar von graphql

    graphql/graphql-js

    20,397Auf GitHub ansehen↗

    This project is the JavaScript reference implementation of the GraphQL specification. It provides a query engine and schema parser designed to parse, validate, and execute queries to retrieve or mutate data based on a defined schema. The implementation includes a framework for mapping codebase structures to a strongly typed system and a tool for converting query strings into abstract syntax trees for programmatic analysis. The library covers the full surface of GraphQL API implementation, including schema definition, language parsing, and query validation. It provides the necessary infrastru

    Validates that query semantics and types conform to the predefined schema definitions.

    TypeScript
    Auf GitHub ansehen↗20,397
  • oneuijs/you-dont-need-jqueryAvatar von oneuijs

    oneuijs/You-Dont-Need-jQuery

    20,161Auf GitHub ansehen↗

    This project is a reference guide and collection of implementation patterns for replacing legacy libraries with native JavaScript. It provides a vanilla JavaScript reference guide, a modern web API cookbook, and a DOM manipulation cheat sheet to help migrate frontend dependencies to standard browser APIs. The project focuses on mapping library functions to native interfaces for DOM manipulation, network requests, and event handling. It includes a utility pattern library for common tasks such as string cleaning, type checking, and element styling. The covered capabilities include DOM element

    Offers a set of native language checks to validate if values are arrays, functions, or objects.

    JavaScript
    Auf GitHub ansehen↗20,161
  • sindresorhus/type-festAvatar von sindresorhus

    sindresorhus/type-fest

    17,233Auf GitHub ansehen↗

    type-fest is a library of reusable utility types for performing complex transformations and validations on objects, arrays, strings, and numeric ranges in TypeScript. It provides a collection of type definitions designed to handle advanced structural changes and constraints. The project distinguishes itself by offering specialized logic for string literal processing, such as casing transformations and pattern-based modifications, and type-level arithmetic for calculating numeric ranges and absolute values. It also includes utilities for enforcing deep immutability, ensuring union mutual exclu

    Determines if a type contains optional properties to allow conditional logic based on field presence.

    TypeScriptnpm-packagetypestypescript
    Auf GitHub ansehen↗17,233
  • ajv-validator/ajvAvatar von ajv-validator

    ajv-validator/ajv

    14,733Auf GitHub ansehen↗

    Ajv is a high-performance data validation framework that compiles JSON schemas into optimized, standalone JavaScript functions. By transforming declarative schema definitions into executable code, it eliminates runtime interpretation overhead and provides a secure, efficient way to enforce data integrity across both browser and server environments. The library distinguishes itself through its focus on performance and type safety. It employs advanced compilation techniques, including abstract syntax tree optimization and function caching, to ensure rapid validation. Beyond standard checks, it

    Checks that a value matches a specific type or instance constructor to ensure data conforms to expected structures.

    TypeScriptajvjson-schemavalidator
    Auf GitHub ansehen↗14,733
  • microsoft/rusttrainingAvatar von microsoft

    microsoft/RustTraining

    14,636Auf GitHub ansehen↗

    This project is a structured Rust programming curriculum and systems programming course designed to take learners from beginner to expert levels. It provides a comprehensive set of training materials focused on mastering the core syntax, idioms, and technical foundations of the Rust language. The project features a specialized language transition framework that maps concepts from C++, managed languages, and dynamic typing to Rust idioms. This allows developers from different ecosystems to translate architectural patterns and memory models into idiomatic Rust. The training covers a broad rang

    Instructs on using type system constraints to make invalid program states unrepresentable at compile time.

    Rust
    Auf GitHub ansehen↗14,636
  • graphql/graphql-specAvatar von graphql

    graphql/graphql-spec

    14,573Auf GitHub ansehen↗

    This repository contains the official technical specification for GraphQL. It serves as the formal standard defining the query language, the execution engine, and the schema definition rules required to maintain consistency across different API implementations. The specification establishes a language-agnostic standard for query syntax and semantics, alongside a formal protocol for introspection. It provides the documented algorithms and logic requirements necessary for implementing a consistent server, ensuring that metadata about types and fields can be discovered by automated tools. The p

    Checks request structure and semantics against the schema before execution to ensure type safety.

    JavaScript
    Auf GitHub ansehen↗14,573
  • rstacruz/cheatsheetsAvatar von rstacruz

    rstacruz/cheatsheets

    14,429Auf GitHub ansehen↗

    This project is a comprehensive collection of web development reference guides and technical cheat sheets. It provides a curated set of markdown-based documentation designed to help developers quickly locate syntax patterns and API examples for common web technologies and programming languages. The repository serves as a specialized reference library covering several distinct technical domains. It includes extensive guides for CSS, focusing on selectors, Flexbox, Grid, and responsive layout properties, as well as a DevOps command reference for Docker, Kubernetes, AWS, Ansible, and general she

    Offers reference patterns for validating data values using assertion styles in tests.

    SCSS
    Auf GitHub ansehen↗14,429
  • jbranchaud/tilAvatar von jbranchaud

    jbranchaud/til

    14,116Auf GitHub ansehen↗

    This project is a markdown knowledge base used to maintain a curated collection of concise technical notes and write-ups across various programming languages and tools. It serves as a searchable personal reference library for documenting technical discoveries and software development patterns. The system implements a learning in public workflow, transforming markdown-based content storage into a static site. It utilizes directory-based routing to map folder structures to URL paths and employs schema-driven type generation to ensure data consistency across the knowledge base. The codebase cov

    Generates TypeScript definitions from content schemas to ensure data consistency across the knowledge base.

    Vim Script
    Auf GitHub ansehen↗14,116
  • hkuds/openharnessAvatar von HKUDS

    HKUDS/OpenHarness

    14,084Auf GitHub ansehen↗

    OpenHarness is a framework for building and orchestrating AI agents that utilize tools and plugins to execute complex tasks. It provides an orchestration system for managing language model lifecycles and a multi-agent coordination system for delegating workloads across teams of specialized subagents. The project features an agent gateway that bridges language model agents to external chat platforms and communication channels. It includes a tool integration engine for executing shell, file, and web operations, supported by a memory and skill manager that handles persistent user preferences and

    Ensures language model outputs match expected schemas through strict type validation before tool execution.

    Python
    Auf GitHub ansehen↗14,084
  • phpstan/phpstanAvatar von phpstan

    phpstan/phpstan

    13,999Auf GitHub ansehen↗

    This project is a static analysis engine and type checker designed for PHP codebases. It evaluates source code structure and type annotations to identify potential bugs, type mismatches, and logic errors without executing the application. By parsing code into an abstract syntax tree and applying a rule-based validation framework, it enforces code quality and safety standards across a project. What distinguishes this tool is its sophisticated type inference engine, which models dynamic language features, magic methods, and conditional types to maintain accuracy even in unconventional code. It

    Enforces specific property requirements on objects to ensure they contain the expected data fields and types.

    PHPphpphp7phpstan
    Auf GitHub ansehen↗13,999
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  1. Home
  2. Software Engineering & Architecture
  3. Data Schema Validation
  4. Data Type Validation

Unter-Tags erkunden

  • Algebraic Data Type Validations1 Sub-TagValidating runtime data against algebraic data type schemas using Either, Option, and Validation to enforce domain constraints and accumulate errors. **Distinct from Data Type Validation:** Distinct from Data Type Validation: focuses on validating against algebraic data types (sum/product types) with error accumulation, not just checking a value against a primitive type.
  • Assertion-Based ValidationsChecks that values match expected types or truthiness using testing assertions. **Distinct from Data Type Validation:** Focuses on test assertions for equality and membership, whereas the parent focuses on structural schema validation.
  • Binary Data Validation1 Sub-TagVerifies that a value is a binary blob and checks its associated MIME type and size. **Distinct from Data Type Validation:** Specifically focuses on binary blobs and MIME types rather than generic instance constructors or type checks.
  • Collection Type Validation4 Sub-TagsValidation of specialized data collections such as Sets, Maps, and Arrays to ensure element-level type safety. **Distinct from Data Type Validation:** Specializes in validating collection structures and their contents, whereas Data Type Validation is a general check for any specific type.
  • Custom Schema Definitions1 Sub-TagCapabilities for implementing user-defined schema types and custom validation logic. **Distinct from Data Type Validation:** Distinct from Data Type Validation: focuses on the creation of new schema types using a plain-object pattern rather than just verifying a value.
  • Domain-Driven Type ConstraintsUsing custom types to model valid real-world value sets to prevent invalid data states at the type level. **Distinct from Data Type Validation:** Distinct from general Data Type Validation: focuses on the design of types to make invalid states unrepresentable.
  • Index Range ValidatorsValidates that types represent valid index ranges for parallel iteration. **Distinct from Data Type Validation:** Focuses on index range validation for parallel iteration, distinct from general data type schema validation.
  • Loose Tuple ValidationArray validation that enforces specific types for initial elements while preserving trailing unknown items. **Distinct from Data Type Validation:** Distinct from Data Type Validation: specifically targets the tuple-like behavior of arrays with loose trailing elements.
  • Nested Object Validation2 Sub-TagsRecursive validation of properties that are instances of other decorated classes or complex types. **Distinct from Data Type Validation:** Focuses on recursive class instance validation rather than simple runtime type checking.
  • Object Shape Validation10 Sub-TagsEnforces specific property requirements on objects to ensure they contain the expected data fields and types. **Distinct from Data Type Validation:** Focuses on static analysis of object properties rather than runtime type validation.
  • Parameter Type Enforcement1 Sub-TagUsing type hints and data models to validate and instantiate parameters before pipeline execution. **Distinct from Data Type Validation:** Distinct from general data type validation: specifically targets the validation of pipeline parameters using type hints.
  • Strict Tuple ValidationArray validation that enforces a fixed length and specific types for all elements, rejecting any extras. **Distinct from Loose Tuple Validation:** Unlike Loose Tuple Validation, it forbids trailing unknown items and requires an exact length.
  • Strongly-Typed Query ValidationChecking request structure and semantics against a schema to ensure type safety. **Distinct from Data Type Validation:** Specifically targets the validation of a query language's request against its schema, not general data type checking.
  • Strongly-Typed Schema ValidationValidation of database operations against a declarative structural blueprint to ensure logical consistency. **Distinct from Data Type Validation:** Focuses on database schema validation for read/write operations rather than generic application-level data type checking.
  • Strongly-Typed Validators2 Sub-TagsValidation tools that leverage language type systems to ensure data integrity at compile and runtime. **Distinct from Data Type Validation:** Distinct from Data Type Validation: focuses on the validator implementation and rule set rather than just checking a value against a type.
  • Structural Integrity ValidatorsTools for verifying that data structures conform to expected schemas or previous states. **Distinct from Data Type Validation:** Distinct from Data Type Validation: focuses on structural integrity and schema comparison rather than simple type checking.
  • Transformation Validators1 Sub-TagEnforces schema and type integrity during the compilation of data transformation pipelines. **Distinct from Data Type Validation:** Distinct from Data Type Validation: focuses specifically on compile-time validation of transformation logic rather than runtime schema checking.
  • Type-Annotation Validation2 Sub-TagsValidation logic that uses language-level type hints to verify data structures at runtime. **Distinct from Data Type Validation:** Specifically uses Python type annotations for runtime validation rather than general data type checks.
  • Type-Safe Schema ValidatorsValidators that synchronize runtime data checks with static type definitions to ensure consistency. **Distinct from Data Type Validation:** Distinct from Data Type Validation: focuses on the synchronization between static TS types and runtime validation logic.
  • Uniform Record ValidationValidation for objects where all keys and values must conform to a single, consistent schema. **Distinct from Object Key Validation:** Distinct from Object Shape Validation as it enforces a uniform rule for all entries rather than specific per-key definitions.
  • Union Type Validation1 Sub-TagValidation that checks a value against multiple possible schemas and succeeds if any one matches. **Distinct from Union Validation Optimizers:** Unlike Union Validation Optimizers, this is the core mechanism for checking multiple possible types rather than an optimization for it.
  • Value Type Conversions4 Sub-TagsTransforms data from one primitive or complex type to another during the validation pipeline. **Distinct from Data Type Validation:** Distinct from Data Type Validation: focuses on the actual conversion (transformation) of the value, not just checking its type.