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20 रिपॉजिटरी

Awesome GitHub RepositoriesComplex Data Types

Support for non-scalar data structures like maps and unions.

Distinguishing note: Focuses on schema flexibility rather than general data ingestion.

Explore 20 awesome GitHub repositories matching data & databases · Complex Data Types. Refine with filters or upvote what's useful.

Awesome Complex Data Types GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • duckdb/duckdbduckdb का अवतार

    duckdb/duckdb

    38,805GitHub पर देखें↗

    DuckDB is an in-process analytical database engine designed to run directly within an application process. As a zero-dependency, embedded system, it provides enterprise-grade SQL data processing capabilities without the overhead of managing a dedicated database server. It is built to handle complex analytical and aggregation tasks by storing and retrieving information in columns, allowing for high-performance relational data manipulation. The engine distinguishes itself through a columnar vectorized execution model that maximizes CPU cache efficiency during query operations. It employs adapti

    Supports intricate data structures using specialized types for nested or heterogeneous information.

    C++analyticsdatabaseembedded-database
    GitHub पर देखें↗38,805
  • dotnet/coredotnet का अवतार

    dotnet/core

    21,897GitHub पर देखें↗

    This project is a cross-platform development framework and managed runtime environment designed for building high-performance applications. It provides a comprehensive toolkit for constructing web services, cloud-native microservices, and desktop applications, utilizing a unified runtime that handles memory management and execution across diverse operating systems. The framework distinguishes itself through a native ahead-of-time compilation toolchain that transforms source code into optimized, self-contained machine code binaries. This capability enables fast startup times and reduced memory

    Supports complex data structures like union types and collection expressions to simplify data modeling.

    PowerShelldotnetdotnet-core
    GitHub पर देखें↗21,897
  • toml-lang/tomltoml-lang का अवतार

    toml-lang/toml

    20,525GitHub पर देखें↗

    TOML is a configuration file format designed for human readability and unambiguous mapping to hash tables. It serves as a standardized language for structured data, enabling consistent parsing and data exchange across diverse programming environments. The format distinguishes itself through a strict type-system specification that ensures data is interpreted identically regardless of the implementation. It utilizes a line-oriented lexical structure that supports both hierarchical organization through bracketed sections and compact inline embedding for nested objects. This approach allows for t

    Encodes diverse data types including multi-line strings, scientific numbers, and temporal values.

    GitHub पर देखें↗20,525
  • prestodb/prestoprestodb का अवतार

    prestodb/presto

    16,711GitHub पर देखें↗

    Presto is a distributed SQL query engine designed for high-performance analytical processing across heterogeneous data sources. It functions as a data federation platform and massively parallel processing engine, allowing users to execute interactive queries against diverse storage systems without requiring data migration. By mapping remote metadata and structures to a unified relational namespace, it enables seamless cross-platform analysis through a standard SQL interface. The engine distinguishes itself through a pluggable connector architecture and a shared-nothing distributed processing

    Organizes information into arrays, maps, and nested structures to support complex data models within SQL queries.

    Javabig-datadatahadoop
    GitHub पर देखें↗16,711
  • risingwavelabs/risingwaverisingwavelabs का अवतार

    risingwavelabs/risingwave

    9,093GitHub पर देखें↗

    RisingWave is a cloud-native streaming database and real-time analytics engine that uses standard SQL to process continuous data streams. It functions as a streaming data lakehouse, combining the capabilities of a streaming SQL database with a platform that integrates streaming ingestion with open table formats. The system is distinguished by its use of the PostgreSQL wire protocol, allowing it to integrate with existing SQL tools and drivers. It employs a decoupled compute and storage architecture, persisting streaming state and materialized views in cloud object storage to enable independen

    Supports a wide range of standard SQL types, including arbitrary precision decimals and large integers.

    Rustapache-icebergdata-engineeringdatabase
    GitHub पर देखें↗9,093
  • redis/redisinsightredis का अवतार

    redis/RedisInsight

    8,556GitHub पर देखें↗

    RedisInsight is a graphical user interface and management tool for browsing, analyzing, and administering Redis databases. It provides a visual environment for exploring key-value data structures, managing database instances, and performing data analysis across different operating systems and deployments. The tool distinguishes itself by providing dedicated visual managers for complex operations, including a vector database manager for configuring embeddings and similarity searches, a query workbench for executing raw commands and Lua scripts, and a performance monitoring dashboard for tracki

    Manages diverse and complex data formats including JSON documents, time series, and probabilistic types.

    TypeScriptdatabase-guiredisredis-gui
    GitHub पर देखें↗8,556
  • magicstack/asyncpgMagicStack का अवतार

    MagicStack/asyncpg

    7,953GitHub पर देखें↗

    asyncpg is an asynchronous database driver and binary protocol client for PostgreSQL. It provides a non-blocking interface for executing SQL statements, streaming result sets, and managing data transfer between an application and a PostgreSQL database. The driver implements the PostgreSQL binary protocol directly to facilitate efficient data transfer and type conversion. It includes a connection pool to maintain and reuse open database connections, reducing the latency associated with repeated handshakes. The project covers a broad range of database integration capabilities, including atomic

    Encodes and decodes composite types, arrays, and custom formats between the database and application.

    Pythonasync-programmingasync-pythonasyncio
    GitHub पर देखें↗7,953
  • msgpack/msgpackmsgpack का अवतार

    msgpack/msgpack

    7,472GitHub पर देखें↗

    MessagePack is a binary object serialization library and a cross-platform data exchange format. It serves as a binary alternative to JSON, converting structured data into a space-efficient binary representation for network transmission and storage. The system provides a standardized format for swapping complex data types across different programming languages and architectures. It allows for the definition of custom data type encoding by pairing application-specific information with specialized serialization markers. The library handles the encoding and decoding of diverse data types, includ

    Defines specialized binary formats for application-specific data structures using extendable serialization markers.

    GitHub पर देखें↗7,472
  • jooq/jooqjOOQ का अवतार

    jOOQ/jOOQ

    6,666GitHub पर देखें↗

    jOOQ is a type-safe SQL query builder for Java that generates code from live database schemas, enabling compile-time validation of SQL syntax and data types. Its core identity is built around a fluent DSL that mirrors SQL structure, a code generator that maps tables, views, and routines to Java objects, and a multi-dialect engine that translates the same DSL into vendor-specific SQL for over 30 databases. The project also includes a SQL parser and transformer for refactoring or dialect conversion, reactive stream integration for non-blocking query execution, and a JDBC proxy diagnostics tool f

    Wraps multiple database columns into a single client-side value object for type-safe composite data handling.

    Javacode-generatordatabasedb2
    GitHub पर देखें↗6,666
  • apache/pinotapache का अवतार

    apache/pinot

    6,098GitHub पर देखें↗

    Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

    Processes and flattens nested JSON or stream document fields to make complex data structures queryable.

    Java
    GitHub पर देखें↗6,098
  • cube2222/octosqlcube2222 का अवतार

    cube2222/octosql

    5,258GitHub पर देखें↗

    Octosql is a federated SQL query engine, data transformer, and streaming SQL processor. It allows users to execute single SQL statements across multiple disparate data sources, including different database types and file formats, to merge and transform results into a unified set. The system distinguishes itself by treating CSV, JSONLines, and Parquet files as virtual tables and utilizing a plugin-based architecture to extend connectivity to external storage engines. It functions as a streaming processor for infinite data streams, using watermarks, retractions, and tumbling windows to maintain

    Utilizes a static type system to manage complex data structures like union types within columns.

    Go
    GitHub पर देखें↗5,258
  • datawhalechina/joyful-pandasdatawhalechina का अवतार

    datawhalechina/joyful-pandas

    5,164GitHub पर देखें↗

    यह प्रोजेक्ट pandas डेटा विश्लेषण का एक व्यापक ट्यूटोरियल और निर्देशिका है, जिसे डेटा मैनिपुलेशन और विश्लेषण सीखने के लिए डिज़ाइन किया गया है। यह टैबुलर डेटा प्रोसेसिंग गाइड और टाइम सीरीज़ विश्लेषण के लिए एक मैनुअल के रूप में कार्य करता है, जो डेटासेट को क्लीन, मर्ज और ट्रांसफॉर्म करने के लिए एक स्ट्रक्चर्ड दृष्टिकोण प्रदान करता है। यह रिपॉजिटरी एक डेटा फीचर इंजीनियरिंग कोर्स के रूप में काम करती है, जो मशीन लर्निंग मॉडल के प्रदर्शन को बेहतर बनाने के लिए डेटासेट फीचर्स के निर्माण और चयन पर ट्यूटोरियल प्रदान करती है। इसमें एलिमेंट-वाइज गणितीय गणनाओं और मैट्रिक्स मैनिपुलेशन के लिए एक वेक्टराइज्ड डेटा ऑपरेशन्स गाइड भी शामिल है। यह सामग्री डेटा क्लीनिंग वर्कफ़्लो, डेटा इंटीग्रेशन कार्यों और टैबुलर डेटा विश्लेषण सहित क्षमताओं की एक विस्तृत श्रृंखला को कवर करती है। यह टेक्स्ट संबंधी जानकारी को प्रोसेस करने, कैटेगोरिकल डेटा को संभालने और बड़े डेटासेट के लिए निष्पादन गति को अनुकूलित करने के लिए मार्गदर्शन प्रदान करती है। यह प्रोजेक्ट Jupyter Notebooks की एक श्रृंखला के रूप में है जिसमें व्यावहारिक अभ्यास और लक्षित अभ्यास समस्याएं शामिल हैं।

    Provides specialized techniques for managing timestamps, date offsets, and categorical variables.

    Jupyter Notebookpandas
    GitHub पर देखें↗5,164
  • microsoft/typescript-handbookmicrosoft का अवतार

    microsoft/TypeScript-Handbook

    4,855GitHub पर देखें↗

    यह प्रोजेक्ट TypeScript भाषा के लिए एक व्यापक गाइड और शैक्षिक संसाधन है। यह भाषा के मूलभूत सिद्धांतों को कवर करता है, जिसमें इसका स्ट्रक्चरल टाइप सिस्टम, स्टेटिक टाइप विश्लेषण और टाइप्ड सोर्स फाइलों को JavaScript में ट्रांसपाइल करने की प्रक्रिया शामिल है। सामग्री विस्तार से बताती है कि जेनेरिक्स, कंडीशनल टाइप्स और मैप्ड टाइप्स का उपयोग करके जटिल डेटा और पुन: प्रयोज्य टाइप लॉजिक को कैसे मॉडल किया जाए। यह बाहरी JavaScript लाइब्रेरी के लिए टाइप सेफ्टी प्रदान करने के लिए डिक्लेरेशन फाइलों के उपयोग और JSDoc एनोटेशन के माध्यम से मौजूदा JavaScript प्रोजेक्ट्स में टाइप चेकिंग के एकीकरण की भी व्याख्या करती है। सामग्री का दायरा ऑब्जेक्ट-ओरिएंटेड प्रोग्रामिंग पैटर्न, DOM हेरफेर और कंपाइलर व्यवहार के कॉन्फ़िगरेशन तक फैला हुआ है। इसमें मॉड्यूल इंटरऑपरेबिलिटी को प्रबंधित करने, बिल्ड पाइपलाइन सेट करने और बेहतर डेवलपर उत्पादकता के लिए एडिटर इंटेलिजेंस का उपयोग करने पर मार्गदर्शन शामिल है।

    Provides techniques for creating reusable structures and shorthand aliases to model complex data shapes.

    JavaScriptdocumentationlearntypescript
    GitHub पर देखें↗4,855
  • h2database/h2databaseh2database का अवतार

    h2database/h2database

    4,607GitHub पर देखें↗

    H2 Java में लिखा गया एक JDBC-अनुपालन रिलेशनल डेटाबेस मैनेजमेंट सिस्टम है। यह एक एम्बेड करने योग्य SQL डेटाबेस के रूप में कार्य करता है जो नेटवर्क लेटेंसी को हटाने के लिए सीधे एप्लिकेशन प्रोसेस के भीतर चल सकता है, या उच्च-प्रदर्शन वाले वोलेटाइल स्टोरेज के लिए इन-मेमोरी डेटाबेस के रूप में कार्य कर सकता है। इसमें SQL कमांड निष्पादित करने और स्कीमा प्रबंधित करने के लिए एक वेब-आधारित कंसोल भी शामिल है। सिस्टम को इसके लचीले डिप्लॉयमेंट मोड द्वारा पहचाना जाता है, जिसमें रिमोट TCP/IP एक्सेस के लिए स्टैंडअलोन सर्वर मोड और स्थानीय व रिमोट कनेक्टिविटी के लिए मिक्स्ड मोड शामिल है। इसमें एक डायलेक्ट एमुलेशन लेयर और कम्पैटिबिलिटी मोड हैं जो इसे अन्य डेटाबेस सिस्टम के व्यवहार और सिंटैक्स की नकल करने की अनुमति देते हैं। इंजन ACID ट्रांजेक्शन (मल्टी-वर्जन कॉनकरेंसी कंट्रोल के साथ), जियोस्पेशियल और JSON डेटा सपोर्ट, और उन्नत विश्लेषणात्मक विंडो फंक्शन्स जैसी व्यापक क्षमताएं प्रदान करता है। इसमें डेटा संरक्षण के लिए कंप्रेस्ड बैकअप, SQL स्क्रिप्ट रिस्टोरेशन और बड़े डेटासेट को संभालने के लिए ऑफ-हीप मेमोरी प्रबंधन के टूल शामिल हैं।

    Supports non-scalar data structures including JSON, UUIDs, and enumerated types.

    Javadatabasejavajdbc
    GitHub पर देखें↗4,607
  • isar/hiveisar का अवतार

    isar/hive

    4,390GitHub पर देखें↗

    Hive is a lightweight NoSQL key-value database written in pure Dart for local data persistence. It functions as a type-safe document store that allows for the saving and retrieval of complex data structures and custom objects. The system distinguishes itself through the use of custom adapters for object serialization and symmetric-key encryption to secure data at rest. For web environments, it provides a persistence layer that wraps IndexedDB and utilizes web workers. The project covers broad capability areas including container management, atomic transactional writes, and indexed data retri

    Supports storing non-scalar data structures such as lists and maps while maintaining data integrity.

    Dartdartdatabaseencryption
    GitHub पर देखें↗4,390
  • kuzudb/kuzukuzudb का अवतार

    kuzudb/kuzu

    3,965GitHub पर देखें↗

    Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data management. It operates as a library within the host application process, utilizing a columnar-based storage architecture and just-in-time query compilation to execute complex graph traversals and pattern matching efficiently. By mapping database files directly into system memory, it ensures data durability and high-speed access while maintaining ACID-compliant transactional integrity. The engine distinguishes itself by integrating vector similarity search and full-text search di

    Organizes data using nested structures, maps, and variant types.

    C++cypherdatabaseembeddable
    GitHub पर देखें↗3,965
  • msgspec/msgspecmsgspec का अवतार

    msgspec/msgspec

    3,821GitHub पर देखें↗

    msgspec is a high-performance data modeling, serialization, and schema validation toolkit for Python. It serves as a type-safe serialization framework that integrates schema enforcement and data parsing into a single pass, functioning as both a data serialization library and a schema validation system based on standard Python type annotations. The project distinguishes itself through high-performance structural primitives, including compilation-based routine generation and zero-copy buffer parsing. It optimizes memory usage via garbage collection-aware layouts and reduces processing overhead

    Supports encoding and decoding of non-scalar types like UUIDs, decimals, and datetimes using type annotations.

    Pythondeserializationjsonjson-schema
    GitHub पर देखें↗3,821
  • google/fuzzinggoogle का अवतार

    google/fuzzing

    3,772GitHub पर देखें↗

    This project is a comprehensive software fuzzing knowledge base and technical guide designed for discovering software bugs and vulnerabilities. It serves as a resource for implementing coverage-guided, structure-aware, and hybrid fuzzing across various targets, including compiled binaries and hardware kernels. The resource provides specialized guidance on using grammars and defined data formats to generate syntactically valid inputs for complex APIs. It also details methods for combining grey-box fuzzing with symbolic execution to reach deep execution paths and utilizes binary instrumentation

    Explains how to split a single data stream into multiple inputs for APIs requiring complex parameter sets.

    C++
    GitHub पर देखें↗3,772
  • solnic/virtussolnic का अवतार

    solnic/virtus

    3,746GitHub पर देखें↗

    Virtus is a Ruby attribute management and data coercion library used to define object schemas with typed attributes. It functions as a tool for transforming nested JSON structures and complex input formats into structured internal Ruby data types. The project provides a framework for creating value objects that are compared by their attribute values rather than memory identity. It allows for the mapping of complex external data into domain objects and supports the implementation of custom coercion logic to ensure data consistency. The library covers data modeling through schema-driven attrib

    Converts input data into structured formats like typed arrays, hashes, or nested objects.

    Ruby
    GitHub पर देखें↗3,746
  • software-mansion/typegpusoftware-mansion का अवतार

    software-mansion/TypeGPU

    2,564GitHub पर देखें↗

    TypeGPU is a tool for type-safe WebGPU development that enables writing shaders in TypeScript. It translates high-level TypeScript function definitions and structures into WebGPU Shading Language source code to automate shader generation and validate logic using a type system. The project provides a mechanism for cross-library GPU interoperability by sharing typed buffers without copying data to system memory. It also integrates the Model Context Protocol to allow AI agents to inspect generated shader code and diagnose runtime errors. The system manages WebGPU resource mapping through typed

    Translates complex data structures into typed binary formats to ensure correct memory alignment during CPU-to-GPU transfer.

    TypeScriptgpgpugpugpu-computing
    GitHub पर देखें↗2,564
  1. Home
  2. Data & Databases
  3. Complex Data Types

सब-टैग एक्सप्लोर करें

  • Complex Type CoercionAutomatically coerces input data into structured formats like typed arrays and nested objects. **Distinct from Complex Data Types:** Focuses on the automatic conversion process into complex types, not just the storage of such types.
  • Composite Type Encoders1 सब-टैगEncoders for translating complex, non-scalar database types into application-level formats. **Distinct from Complex Data Types:** Specifically handles the encoding/decoding process of composite types, whereas the parent defines the types themselves.
  • Input Stream SplittingTechniques for dividing a single byte stream into multiple structured integers or options for a target. **Distinct from Complex Data Types:** Focuses on the operational splitting of a fuzzer's data stream rather than static schema definitions
  • Temporal and Categorical Data HandlingSpecialized processing for non-scalar types including timestamps, date offsets, and categorical variables. **Distinct from Complex Data Types:** Focuses on pandas-specific handling of temporal and categorical types rather than general schema flexibility
  • Type-Based Data ModelingThe use of union types, intersections, and aliases to standardize complex data shapes in a type system. **Distinct from Complex Data Types:** Focuses on the type-system modeling of data shapes rather than the storage of complex data in databases.