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How to Migrate Data From Neo4j to Nebula Graph

This article mainly introduces how to migrate your data from Neo4j to Nebula Graph with Nebula Graph Exchange (or Exchange for short), a data migration tool backed by the Nebula Graph team. Before introducing how to import data, let’s first take a look at how data migration is implemented inside Nebula Graph.

Data Processing in Nebula Graph Exchange

The name of our data migration tool is Nebula Graph Exchange. It uses Spark as the import platform to support huge dataset import and ensure performance. The DataFrame, a distributed collection of data organized into named columns, provided by Spark supports a wide array of data sources. With DataFrame, to add new data source, you only need to provide the code for the configuration file to read and the Reader type returned by the DataFrame.

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Performance Comparison: Neo4j vs Nebula Graph vs JanusGraph

This article describes how the Tencent (HKG.0700) Cloud team compares Nebula Graph with two other popular graph databases on the market from several perspectives.

By their nature of dealing with interconnections, graph databases are perfect for fraud detection and building knowledge graphs in the security field. To better serve the Tencent Cloud business scenarios, the Tencent Cloud Security team has to select a highly performant graph database which fits the business development well, which is how this performance comparison comes into play.

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Analyzing Relationships in Game of Thrones With NetworkX, Gephi, and Nebula Graph (Part Two)

In the last post, we showed the character relationship for the Game of Thrones by using NetworkX and Gephi. In this post, we will show you how to access data in Nebula Graph by using NetworkX.

NetworkX

NetworkX is a modeling tool for the graph theory and complex networks written by Python. With its rich, easy-to-use built-in graphs and analysis algorithms, it’s easy to perform complex network analysis and simulation modeling.

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Data Migration From JanusGraph to Nebula Graph – Practice at 360 Finance

Speaking of graph data processing, we have had experience in using various graph databases. In the beginning, we used the stand-alone edition of AgensGraph. Later, due to its performance limitations, we switched to JanusGraph, a distributed graph database. I introduced details on how to migrate data in my article “Migrate tens of billions of graph data into JanusGraph (only in Chinese)”. As the data size and the number of business calls grew, a new problem appeared: Each query consumed too much time. In some business scenarios, a single query took up to 10 seconds, and with increase of the data size, a more complicated single query needed two or three seconds. These problems had seriously affected the performance of the entire business process and the development of related businesses.

The architecture design of JanusGraph determines that a single query is time-consuming. The core reason is that its storage depends on the external storage, and JanusGraph cannot control the external storage well. In our production environment, an HBase cluster is used, which makes it impossible for all queries to be pushed down to the storage layer for processing. Instead, data can only be queried from HBase to the JanusGraph Server memory and then filtered accordingly.

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Access Control in Nebula Graph: Design, Code, and Operations

Access Control List (ACL) is not alien to database users and it is a significant part of data security. Like other database vendors, Nebula Graph takes data security seriously and now supports role-based access control.

In this article, we will detail user management with roles and privileges of Nebula Graph.

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Nebula Graph Architecture — A Bird’s Eye View

Nebula Graph is an open-source distributed graph database solution. As an open-source project, we would like those who are interested in graph databases to know as much as possible about it. This includes how Nebula Graph is designed and why it is a highly performant database.

As a result, we have developed a series of architecture articles. In them, we are going to cover three areas. First, we will provide a high-level overview of the architecture of Nebula Graph – the focus of this article. Next, we will cover how Nebula Graph’s storage engine works. And finally, we will illustrate how Nebula Graph’s query engine works, including how we have designed nQGL, our graph query language.