FalkorDB
neo4j
Memgraph
TigerGraph DB
JanusGraph
Amazon Neptune
NetworkX
ArangoDB
MySQL
PostgreSQL
Microsoft SQL
MongoDB
SQLite
Oracle DBaaS
Oracle Database 12c
Redis
FalkorDB delivers an accurate, multi-tenant RAG solution powered by a low-latency, scalable graph database technology. Our solution is purpose-built for development teams working with complex, interconnected data—whether structured or unstructured—in real-time or interactive user environments.
FalkorDBFalkorDB's answer
C, Rust, Next.js
FalkorDB's answer
An ultra-low latency Graph Database
FalkorDB's answer
x100 faster than the leading solutions
FalkorDB's answer
Developers, Architects, Data scientists, CTOs
FalkorDB's answer
An ultra-low latency Graph Database that perfects the Knowledge Graph for KG-RAG. Effectively overcoming the existing limitations of RAG for Large Language Models (LLM).
FalkorDB is the first queryable Property Graph database to use sparse matrices to represent the adjacency matrix in graphs and linear algebra to query the graph.
MySQL might be a bit more popular than FalkorDB. We know about 4 links to it since March 2021 and only 3 links to FalkorDB. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Use a low-latency graph database: Integrate FalkorDB for its sparse matrix representation and optimized linear algebra-based traversals. Queries execute in milliseconds—critical for real-time AI interactions. - Source: dev.to / about 2 months ago
In vector databases, data is stored as high-dimensional vector embeddings, which are numerical representations generated by machine learning models to capture the features of data. When querying, the input is converted into a vector embedding, and similarity searches are performed between the query vector and stored embeddings using distance metrics like cosine similarity or Euclidean distance to retrieve the most... - Source: dev.to / about 2 months ago
For AI architects, integrating graph-native storage with LLMs isn’t optional—it’s imperative for building systems capable of robust, multi-hop reasoning at scale. - Source: dev.to / 2 months ago
So, I did a quick read through the mysql reference and found a bunch of flush related commands. I tried:. Source: almost 2 years ago
MySQL: Any SQL or DB knock-off, really... mysql.com - mariadb.org - sqlite.org. Source: over 2 years ago
15 years and five strokes ago. I was a Unix sysadmin. ALthough I was never an actual programmer, I did maintenance/light enhancement for the organization's website, in php. Now, as self-administered cognative therapy, I'm going back to it. This is an evil HR application that uses the mysql.com employees sample database. The module below enables the evil HR end user to generate a list of the oldest workers so... Source: almost 4 years ago
I always use the packages from mysql.com, that way I don't have to deal with strange configuration stuff along those lines, but anyway, I'm afraid I'm out of ideas. Surely someone else would have run in to the same issue here though. Source: almost 4 years ago
neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.
PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.
Memgraph - Memgraph is an open source graph database built for real-time streaming and compatible with Neo4j. Whether you're a developer or a data scientist with interconnected data, Memgraph will get you the immediate actionable insights fast.
Microsoft SQL - Microsoft SQL is a best in class relational database management software that facilitates the database server to provide you a primary function to store and retrieve data.
TigerGraph DB - Application and Data, Data Stores, and Graph Database as a Service
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
