Datascript

Datascript 是一个不可变的、内存中的状态存储和基于模式的三元组存储。它将应用状态作为版本化数据库进行管理，将数据存储为由实体、属性、值和事务组成的不可变事实。

该项目提供了一个用于执行 Datalog 查询的逻辑引擎，支持隐式连接、递归规则和否定。它还具有一个用于检索深度嵌套实体图和相关数据结构的声明式 pull API。

该数据库通过模式驱动的约束和属性类型强制执行数据完整性。它支持原子事务、用于持久化的可插拔存储协议，以及用于优化查找的 B 树属性索引。

该系统专为 Clojure、ClojureScript 和 JavaScript 环境设计。

Features

Triple Stores - Implements a schema-based triple store using an EAVT model for flexible data representation.
Logic-Based Relational Querying - Provides a logic-based relational query engine using Datalog paradigms for Clojure and JavaScript environments.
Triple Stores - Implements a schema-based triple store using an EAVT model to manage immutable facts.
Nested Entity Retrieval - Extracts related data using a pull API that supports recursive exploration of entity graphs.
Application State Management - Provides centralized application state management using an immutable in-memory data store.
Schema-Enforced Triple Stores - Combines a triple store architecture with schema-driven constraints to ensure data integrity and optimize indexing.
Declarative Pull APIs - Provides a declarative pull API for retrieving deeply nested entity graphs and related data.
In-Memory Data Stores - Provides a high-performance in-memory data store for managing ephemeral application state.
In-Memory State Stores - Maintains application state in an immutable in-memory store to ensure low-latency interaction.
Logic Query Engines - Provides a logic query engine using Datalog to resolve complex data relationships through recursive rules and negation.
Entity Graph Fetching - Implements a declarative pull API to retrieve deeply nested entity graphs and related data structures.
Datalog Recursive Rules - Uses a Datalog engine to execute complex lookups including recursive rules and transitive closures.
Complex Search Querying - Supports advanced querying of application state using negation, disjunction, and recursive rules.
Graph Pull APIs - Offers a declarative pull API for extracting deeply nested entity graphs and customized result shapes.
Immutable Data Storage - Treats data as immutable values to create new database versions while preserving older ones.
Datomic-Style Immutable Stores - Implements a Datomic-style immutable store allowing for time-travel queries and consistent state snapshots.
EAVT Triple Stores - Implements a versioned database using an immutable EAVT (Entity-Attribute-Value-Transaction) triple store model.
Transactional State Versioning - Treats all data changes as additive to maintain a history of database snapshots via transactional state versioning.
Schema-Based Data Models - Enforces data integrity and lookup performance via schema-driven attribute types and unique identity constraints.
Atomic Transaction Execution - Supports grouping database updates into atomic transactions to ensure data consistency.
Database Schema Constraints - Enforces data integrity and lookup performance through defined attribute types and unique identity constraints.
Schema Querying - Allows finding entities that match specific attribute configurations by treating the schema definition as a queryable collection.
Database Transactions - Updates database state through atomic transactions supporting upserts and custom functions.
Custom Storage Implementations - Provides a protocol to route database reads and writes to custom external storage backends.
Pluggable Storage Engines - Decouples the in-memory database from the persistence layer through a pluggable storage engine interface.
B-Tree - Implements B-Tree attribute indexing to optimize value lookups and range queries within the state store.
B+ Tree Indexing - Utilizes B-tree indexing to speed up attribute lookups and range queries.
Incremental State Persistence - Minimizes storage overhead by persisting only modified portions of the database to the backend.
JavaScript Interoperability - Integrates with JavaScript engines to allow queries and transactions using standard arrays and objects.
Schema-Driven Validations - Validates data integrity and optimizes indexing by applying attribute types and uniqueness rules during transactions.
State Evolution Tracking - Records database changes to enable rewinding state and maintaining consistent snapshots.
Databases and Persistence - In-memory database and Datalog query engine.
Databases & Data - Immutable in-memory database with Datalog query capabilities.

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tonskydatascript

Name: tonsky/datascript
Author: tonsky

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Datascript

该数据库通过模式驱动的约束和属性类型强制执行数据完整性。它支持原子事务、用于持久化的可插拔存储协议，以及用于优化查找的 B 树属性索引。

该系统专为 Clojure、ClojureScript 和 JavaScript 环境设计。

Features

Triple Stores - Implements a schema-based triple store using an EAVT model for flexible data representation.
Logic-Based Relational Querying - Provides a logic-based relational query engine using Datalog paradigms for Clojure and JavaScript environments.
Triple Stores - Implements a schema-based triple store using an EAVT model to manage immutable facts.
Nested Entity Retrieval - Extracts related data using a pull API that supports recursive exploration of entity graphs.
Application State Management - Provides centralized application state management using an immutable in-memory data store.
Schema-Enforced Triple Stores - Combines a triple store architecture with schema-driven constraints to ensure data integrity and optimize indexing.
Declarative Pull APIs - Provides a declarative pull API for retrieving deeply nested entity graphs and related data.
In-Memory Data Stores - Provides a high-performance in-memory data store for managing ephemeral application state.
In-Memory State Stores - Maintains application state in an immutable in-memory store to ensure low-latency interaction.
Logic Query Engines - Provides a logic query engine using Datalog to resolve complex data relationships through recursive rules and negation.
Entity Graph Fetching - Implements a declarative pull API to retrieve deeply nested entity graphs and related data structures.
Datalog Recursive Rules - Uses a Datalog engine to execute complex lookups including recursive rules and transitive closures.
Complex Search Querying - Supports advanced querying of application state using negation, disjunction, and recursive rules.
Graph Pull APIs - Offers a declarative pull API for extracting deeply nested entity graphs and customized result shapes.
Immutable Data Storage - Treats data as immutable values to create new database versions while preserving older ones.
Datomic-Style Immutable Stores - Implements a Datomic-style immutable store allowing for time-travel queries and consistent state snapshots.
EAVT Triple Stores - Implements a versioned database using an immutable EAVT (Entity-Attribute-Value-Transaction) triple store model.
Transactional State Versioning - Treats all data changes as additive to maintain a history of database snapshots via transactional state versioning.
Schema-Based Data Models - Enforces data integrity and lookup performance via schema-driven attribute types and unique identity constraints.
Atomic Transaction Execution - Supports grouping database updates into atomic transactions to ensure data consistency.
Database Schema Constraints - Enforces data integrity and lookup performance through defined attribute types and unique identity constraints.
Schema Querying - Allows finding entities that match specific attribute configurations by treating the schema definition as a queryable collection.
Database Transactions - Updates database state through atomic transactions supporting upserts and custom functions.
Custom Storage Implementations - Provides a protocol to route database reads and writes to custom external storage backends.
Pluggable Storage Engines - Decouples the in-memory database from the persistence layer through a pluggable storage engine interface.
B-Tree - Implements B-Tree attribute indexing to optimize value lookups and range queries within the state store.
B+ Tree Indexing - Utilizes B-tree indexing to speed up attribute lookups and range queries.
Incremental State Persistence - Minimizes storage overhead by persisting only modified portions of the database to the backend.
JavaScript Interoperability - Integrates with JavaScript engines to allow queries and transactions using standard arrays and objects.
Schema-Driven Validations - Validates data integrity and optimizes indexing by applying attribute types and uniqueness rules during transactions.
State Evolution Tracking - Records database changes to enable rewinding state and maintaining consistent snapshots.
Databases and Persistence - In-memory database and Datalog query engine.
Databases & Data - Immutable in-memory database with Datalog query capabilities.

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查看 Datascript 的所有 30 个替代方案

常见问题解答

tonsky/datascript 是做什么的？

tonsky/datascript 的主要功能有哪些？

tonsky/datascript 的主要功能包括：Triple Stores, Logic-Based Relational Querying, Nested Entity Retrieval, Application State Management, Schema-Enforced Triple Stores, Declarative Pull APIs, In-Memory Data Stores, In-Memory State Stores。

tonsky/datascript 有哪些开源替代品？

tonsky/datascript 的开源替代品包括： cozodb/cozo — Cozo is a logic-based database engine that functions as a relational data store, an embedded graph database, and a… tporadowski/redis — Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL… tinyplex/tinybase — TinyBase is a reactive data store and in-memory relational database designed for client-side state persistence. It… pubkey/rxdb — This project is a reactive, offline-first NoSQL database engine designed for JavaScript applications. It provides a… mystenlabs/sui — Sui is a blockchain platform featuring an object-centric state model and resource-oriented smart contracts. It… typedb/typedb — TypeDB is a strongly-typed graph database and knowledge graph management system. It serves as a multi-model data store…