cozodb/cozo

Features

• Logic-Based Relational Querying - Datomic Datalog allows executing relational and recursive queries using logic-based rules to analyze complex data relationships and graph structures.
• Logic Query Engines - Functions as a logic-based database engine using a Datalog-inspired query language for relational and recursive queries.
• Datalog Recursive Rules - Implements Datalog-based recursion for computing transitive closures and complex graph traversals.
• Database State History - Tracks every change to relations over time to enable execution of queries against historical snapshots.
• Historical Data Querying Interfaces - Provides query interfaces to retrieve information exactly as it existed at any given point in time.
• Atomic Transactions - Provides atomic transactions to ensure multiple data operations succeed or fail as a single consistent unit.
• Embedded Database Engines - Integrates directly into application processes as a library, eliminating the need for a separate database server.
• Graph Databases - Provides an embedded library for managing highly connected datasets and executing graph algorithms within a host application.
• Recursive Graph Analysis - Performs deep traversals and pathfinding on highly connected datasets using recursive logic and specialized graph algorithms.
• Relational Data Storage - Provides persistent storage using relational schemas with defined primary keys and named tables.
• Relational Join Engines - Combines records from multiple relations by matching variables across different rule bodies to find common values.
• Vector Search Indexes - Utilizes HNSW indices to perform high-performance proximity and similarity searches on high-dimensional vector embeddings.
• Temporal Versioning - Provides temporal versioning to track historical data states and enable time-travel queries.
• Temporal - Combines high-dimensional vector similarity search with built-in time-travel capabilities for historical state retrieval.
• HNSW Indexes - Implements HNSW indices for high-dimensional vector embeddings to enable fast approximate nearest-neighbor similarity searches.
• Logic-Based - Performs relational, graph, and vector searches across datasets using a logic-based query language.
• Vector Similarity Search - Identifies nearest neighbors in high-dimensional space using distance metrics and proximity indices.
• Embedded Runtime Models - Functions as an embedded library runtime, allowing the database engine to be integrated directly into application processes.
• Graph Algorithms - Applies analytical graph algorithms like PageRank to stored relations through fixed rules.
• Network Analysis - Executes centrality and community detection algorithms to understand relationships between entities in a network.
• Data Upsert Operations - Implements operations that combine insertion and update logic based on defined primary keys.
• Database State Restoration - Retrieves previous versions of data using time-travel capabilities to restore the database to a historical state.
• Probabilistic Duplicate Detection - Identifies near-duplicate strings and lists using MinHash-LSH probabilistic indexing.
• Full Text Search - Indexes and retrieves unstructured text data using tokenization and filtering for keyword-based discovery.
• Full-Text Search Engines - Provides a search system with tokenization and MinHash-LSH indexing for keyword discovery and near-duplicate detection.
• Graph Traversal - Analyzes entity relationships using pathfinding, connectedness, and community detection algorithms.
• Pluggable Storage Backends - Supports swapping between in-memory, local, and distributed storage backends via an architectural abstraction.
• Graph Analysis Frameworks - Provides recursive computations and specialized algorithms to extract insights from highly connected datasets.
• MinHash-LSH Indexing - Uses MinHash-LSH indexing to identify near-duplicate strings and lists via similarity fingerprinting.
• Database Systems - Transactional relational database focused on graph data.

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