kuzudb/kuzuArchived

Features

• Embedded Database Engines - Operates as an embedded database engine library within the host application process to eliminate network overhead.
• Embedded Graph Database Engines - Provides a high-performance graph database engine that embeds directly into host applications.
• Graph Querying - Retrieves nodes and relationships using pattern matching, filtering, sorting, and pagination to extract specific information.
• Graph Retrieval Augmented Generation - Combines graph traversal and vector search to provide structured context for retrieval-augmented generation.
• Graph Pattern Matching - Finds specific node and relationship structures within the database by defining patterns that represent how data entities connect.
• Post-Query Predicate Filters - Applies boolean predicates to query results to restrict the output to records that satisfy specified conditions.
• ACID Transactional Cores - Provides ACID-compliant transactional integrity to ensure data consistency during concurrent read and write operations.
• Database Transaction Grouping - Groups multiple operations into atomic units that can be committed or rolled back to ensure data consistency.
• Columnar Storage - Utilizes a columnar-based storage architecture to optimize analytical queries and data scanning.
• Advanced Analytics Functions - Executes advanced computations including aggregation, pattern matching, and recursive traversal on graph data.
• Graph Data Enrichers - Integrates large language models and vector indices to augment existing graph datasets with semantic information and external knowledge.
• Database Connection Management - Establishes read-write or read-only access to a database file through connection objects.
• Embedded Database Connections - Facilitates persistent connections to local graph database files for embedded application use.
• Graph Database Queries - Executes graph traversal and analysis queries using standard query languages like Cypher.
• Database Schema Managers - Provides tools for defining and managing graph database table structures and schemas.
• Database Transaction Management - Groups multiple read or write operations into a single atomic unit to ensure data consistency.
• Disk Persistence - Maintains graph data on disk to ensure modifications are saved and accessible after session termination.
• Embedded Database File Persistence - Persists the entire embedded graph database to a single disk file for data durability and portability.
• Full Text Search - Indexes and queries unstructured text data using standard ranking algorithms for efficient information retrieval.
• Graph Data Models - Organizes data into interconnected nodes and edges to facilitate relationship-based querying and schema-defined storage.
• Graph Data Modifiers - Supports standard query syntax for creating, merging, updating, and deleting nodes and relationships to maintain graph state.
• Graph Deletions - Deletes nodes or relationships with optional automatic removal of connected records to ensure referential integrity.
• Graph Schema Definition - Structures graph data into node and relationship tables with strongly typed properties and primary keys.
• Multi-Model Property Graph Stores - Maintains a persistent property graph store to provide context-aware data for retrieval-augmented generation.
• Graph Databases - Establishes persistent connections to graph database instances for query execution.
• Graph Record Creators - Enables the creation of graph nodes and relationships using pattern matching syntax.
• Graph Path Manipulations - Executes complex graph traversals and pattern matching with support for path constraints.
• Graph Query Languages - Finds connections of varying lengths with configurable bounds and internal filters, including specialized syntax for shortest path calculations.
• Graph Entity Representations - Encapsulates graph entities as specialized logical types for storage and traversal.
• Relationship Establishment - Establishes connections between node tables by defining source and target labels with optional properties.
• Hybrid Vector and Full-Text Search - Maintains native indices for both full-text search and vector embeddings to accelerate retrieval within the graph engine.
• Hybrid Vector-Graph Databases - Integrates vector similarity search directly into the graph storage layer to enable semantic retrieval and advanced retrieval-augmented generation workflows.
• Graph Analytics - Executes advanced graph algorithms like PageRank and community detection for deep data analysis.
• Graph Query Filters - Restricts query output using labels, property predicates, or result limits to isolate specific subsets of graph data.
• Table Definitions - Defines node tables with specific properties and primary keys to categorize and store entities.
• Vector Indexing - Integrates vector indices directly into the graph engine to perform high-speed similarity searches alongside pattern matching.
• Vector Similarity Search - Provides native vector similarity search capabilities using distance metrics for semantic retrieval.
• Pre-Filtered Vector Searches - Combines vector similarity search with graph pattern matching by applying metadata filters to searchable graph subsets.
• Recursive SQL Path Traversals - Searches for connections between nodes across a specified range of hops to identify multi-step relationships or recursive associations.
• Cross-Language SDK Integration - Provides native language drivers to integrate database operations into various programming environments.
• Graph Query Parameterizations - Uses parameterized queries to prevent injection attacks and enable efficient reuse of pre-compiled statements.
• Multi-Process Data Stores - Enables simultaneous access to database files by multiple processes while maintaining write integrity.
• Metadata-to-Agent Bridges - Connects databases to large language models and agents to enable data-driven reasoning and tool use.
• Agentic Retrieval Workflows - Orchestrates multi-step workflows combining graph traversal and vector search for automated reasoning.
• Text Embedding Generators - Converts text into numerical vector representations to enable semantic analysis and similarity searches.
• Graph Data Ingestion - Provides high-performance bulk ingestion of data into graph structures using query-based transformations.
• Database Access - Provides programmatic access to databases via command-line tools and client libraries.
• JSON Property Support - Models and persists properties as native JSON types within graph nodes, allowing for direct querying and manipulation.
• Concurrent Read-Write Transactions - Handles multiple simultaneous read and write operations safely using an internal transaction manager to maintain data integrity.
• Graph Community Detection - Groups nodes into clusters by maximizing modularity to identify dense sub-networks.
• Vector Nearest Neighbor Search - Queries vector indexes to retrieve nearest neighbors with configurable search accuracy and performance trade-offs.
• Visual Data Explorers - Provides a web-based graphical interface for visual data exploration.
• Complex Data Types - Organizes data using nested structures, maps, and variant types.
• Conditional Cell Counting - Counts occurrences of values based on boolean or numeric conditions for targeted statistical analysis.
• Cross-Platform Database Runtimes - Runs natively on major operating systems and architectures for broad compatibility.
• Database APIs - Provides native language bindings to manage schemas and execute database operations directly from application code.
• Memory-Mapped File Access - Maps database files directly into system memory to provide high-performance disk access and data durability.
• Statistical Aggregators - Calculates summary metrics like averages, counts, and sums across graph data during query execution.
• Cross-Dataset Analysis - Enables cross-platform analysis by querying external formats like Parquet, CSV, and Delta Lake directly.
• Data Persistence Management - Controls automatic checkpointing and disk-spilling to ensure data integrity and manage memory usage.
• Graph Data Enrichers - Augments graph data with semantic context using large language models and vector search.
• Data Import and Export - Transfers datasets between the database and external formats or storage systems like Parquet and Arrow to facilitate integration with analytics and machine learning workflows.
• Data Schema Management - Provides tools to manage database schemas and table structures through a graphical interface.
• Query Result Transformations - Aggregates multiple individual values from a query result into a single list for further processing or structured output.
• Database Connectivity - Establishes direct connections to local database files in read-write or read-only modes.
• Database Extenders - Loads modular components at runtime to add specialized capabilities like graph algorithms.
• Database Management Environments - Provides a browser-based environment for managing database operations, schemas, and imports.
• Table Schemas - Updates table structures by adding, removing, or renaming columns and tables.
• Execution Plan Analysis - Displays query execution plans to help developers optimize performance.
• Nodejs Query Interfaces - Enables graph database operations within server-side JavaScript environments.
• Record Updates - Modifies existing database records using key-value pairs or maps.
• Database Schema Visualizers - Visualizes database schemas as graphs to help users understand data structures and connections.
• Delta Lake Table Management - Reads data directly from Delta Lake storage formats using standard query clauses without requiring a prior import process.
• Remote Delta Table Access - Provides direct integration with Delta Lake datasets for cross-platform analytical querying.
• Dynamic Extension Loading - Supports a modular extension architecture, enabling users to load specialized analytical components at runtime.
• Graph Subquery Filters - Performs nested lookups using existence checks or count operations to filter or aggregate data based on related graph patterns.
• Expression-Based Data Querying - Processes complex data using mathematical, logical, and temporal operators to transform graph information.
• External Data Importers - Reads and imports data directly from tabular structures like Parquet and CSV into the database.
• External Database Attachments - Allows linking external relational databases to the graph environment for seamless data access.
• External Data Integrations - Connects to remote storage and databases to ingest data for cross-platform analysis.
• File-Based Data Import - Loads node and relationship data into the database from CSV or Parquet files using a graphical interface to define or update table structures.
• Direct Object Store Querying - Reads and writes data directly from S3-compatible object storage using standard query syntax for seamless cloud integration.
• Graph Analytics Algorithms - Calculate the k-core degree for each node in a graph to identify dense sub-structures where nodes maintain minimum connections.
• Bulk Data Importers - Provides high-performance batch processing for loading large datasets from various external file formats into graph tables.
• CSV Bulk Import Utilities - Provides high-performance bulk ingestion of CSV data into graph node and relationship tables.
• Structured Graph Schemas - Provides schema-defined node and relationship tables to enable efficient, high-performance vectorized operations on complex datasets.
• External Graph Database Attachments - Supports querying multiple graph database files simultaneously within a single session.
• Graph Element Upserting - Adds new nodes or relationships to existing tables or updates them if they already exist based on specified property values.
• Pattern-Based Merges - Matches a specified node or relationship pattern and creates the entire structure if it does not already exist.
• Natural Language Querying - Translates natural language prompts into executable graph queries to simplify data retrieval.
• Group-By Aggregations - Calculates summary statistics across graph entities by grouping results based on shared properties.
• In-Browser Database Engines - Executes graph database operations directly in web browsers using low-level compilation targets.
• In-Memory Databases - Initializes temporary graph database instances in system memory for high-speed, transient data processing.
• Persistence Mode Configurators - Allows balancing data durability and performance by configuring on-disk or in-memory storage modes.
• Indexing and Search - Executes specialized graph algorithms, full-text searches, and vector similarity operations to uncover complex relationships.
• Interactive Graph Visualizers - Renders complex graph structures as interactive diagrams for visual analysis.
• Join Order Selections - Allows users to define specific join sequences and algorithms to override automatic query optimization.
• Graph Property Indexing - Builds searchable indexes on node string properties to enable efficient full-text retrieval with configurable filtering.
• First Non-Null Value Selectors - Returns the first non-null value from inputs or conditionally returns null during query execution.
• Null Value Filtering in Queries - Evaluates data fields for null values to include or exclude records from query results.
• Property Extraction - Collect specific property values from a list of nodes or relationships within a path into a single result set.
• Path Cost Calculations - Computes the length of a path or the total cost of a weighted path to analyze graph traversal results.
• Memory-Spilling Engines - Offloads intermediate query results to temporary disk storage when memory limits are reached to ensure processing stability.
• Prepared Statements - Runs parameterized queries to safely separate query logic from input values.
• Query Performance Analyzers - Analyzes query performance by examining execution plans and profiles.
• Relationship Management - Manages valid connection paths between node tables within existing relationship structures.
• Full Text Indexing - Searches indexed string properties using the BM25 algorithm to return relevant results based on keyword matching.
• Graph Analytics - Executes specialized graph algorithms, full-text searches, and vector similarity searches to derive insights from interconnected datasets.
• External Database Linkers - Allows linking multiple external databases to a single session for cross-platform analysis.
• Interactive Graph Explorers - Provides an interactive interface for exploring graph data and filtering results without manual query writing.
• Table Deletions - Deletes node or relationship tables and their associated data while ensuring referential integrity.
• Temporal Data Handling - Provides dedicated types for dates, timestamps, and intervals to manage temporal information.
• User-Defined Functions - Allows registration of custom functions to extend database query logic.
• HNSW Indexes - Implements HNSW vector indices to enable efficient similarity searches and nearest neighbor lookups.
• Virtual Graph Projections - Lists or removes temporary graph projections to maintain workspace organization and free up system resources.
• External Table Querying - Executes read-only queries against attached databases and files to process remote records.
• External Data Source Attachments - Enables querying external data formats and databases directly without requiring a full import process.
• Table Format Integrations - Queries data directly from table formats like Iceberg and Delta Lake without permanent import.
• Logical Expression Combiners - Combines multiple conditions using logical operators to filter data based on complex criteria.
• Language Bindings - Provides native language bindings and drivers to facilitate integration into various programming environments.
• Dataframe Importers - Integrates external tabular data into existing graph structures.
• Connected Component Analysis - Groups nodes into clusters based on their connectivity, assigning a unique identifier to each set of nodes that can reach one another.
• Graph Visualization Libraries - Renders query results as interactive node-link diagrams for visual analysis.
• Graph Analysis Algorithms - Runs analytical graph computations like PageRank or community detection directly on stored data using standard query syntax.
• Native Graph Algorithm Runtimes - Runs built-in graph analysis routines directly on stored data using a dedicated extension to process complex relationships efficiently.
• Subgraph Projections - Create temporary, filtered views of specific nodes and relationships to isolate relevant data for graph algorithm execution.
• Shortest Path Algorithms - Identifies the most direct connection between two nodes in a graph by evaluating path length or cumulative edge weights.
• Database Query Chaining - Supports chaining query methods to enable multi-step data processing and complex analytical workflows.
• PageRank Algorithms - Computes PageRank scores for nodes in a graph by iteratively distributing rank across connections.
• Strongly Connected Components Algorithms - Groups nodes into clusters where every node is mutually reachable from every other node using parallel or single-threaded graph algorithms.
• Concurrent Query Execution - Supports concurrent database operations using blocking or non-blocking interfaces.
• Graph Schema Enforcers - Ensures consistent graph structure by enforcing predefined node and relationship table schemas.
• Just-in-Time Compilers - Translates high-level graph queries into optimized machine code at runtime to minimize data retrieval overhead.
• Database Extension Modules - Download and load external modules from a central repository to extend core database functionality with additional features.
• Modular Extension Architectures - Supports a modular extension architecture for loading specialized analytical components at runtime.
• Graph Path Validators - Check recursive paths for repeated nodes or relationships to identify acyclic paths or trails within the graph.
• Graph Path Components - Retrieve lists of nodes or relationships from a recursive path to allow for further inspection or data processing.
• Multi-Operator String Matching - Matches strings against regular expression patterns or substrings based on defined criteria.
• Database Connectivity Drivers - Provides official language drivers to facilitate seamless integration into various programming environments.
• Database Systems - Embeddable property graph database with Cypher support.
• Databases - Embeddable property graph database.

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