What is the graph database that can scale out horizontally, handle large volumes of data, and perform low-latency queries? [closed]
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Improve this question 开发者_Python百科Which is the graph database that supports limitless scale out, handles large volumes of data, and can perform low-latency queries? Preferably one that integrates well with Hadoop. I have been reading about couple of products called Neo4J, AllegroGraph, and BigData. Please share your experiences with any of these.
It's 2013 now and this question still shows up in queries, so here are two options for anyone that stumbles by:
Titan: "Titan is a highly scalable graph database optimized for storing and querying massive-scale graphs containing billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users."
GraphLab: "GraphLab is a graph-based, high performance, distributed computation framework written in C++. GraphLab scales to graphs with billions of vertices and edges easily, performing orders of magnitude faster than competing systems. GraphLab combines advances in machine learning algorithms, asynchronous distributed graph computation, prioritized scheduling, and graph placement with optimized low-level system design and efficient data-structures to achieve unmatched performance and scalability in challenging machine learning tasks."
It's 2019. And I'm from Nebula Graph.
- Nebula Graph's data are sharding into multiple servers and the data consistency is implemented by [RAFT protocol2. So it can scale-out horizontally (and handle large volumes of data -- trillions of vertices and edges)
- It's written by C++ (mainly) for OLTP requests (milliseconds of latency)
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