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Description
HugeGraph is a high-performance and scalable graph database capable of managing billions of vertices and edges efficiently due to its robust OLTP capabilities. This database allows for seamless storage and querying, making it an excellent choice for complex data relationships. It adheres to the Apache TinkerPop 3 framework, enabling users to execute sophisticated graph queries using Gremlin, a versatile graph traversal language. Key features include Schema Metadata Management, which encompasses VertexLabel, EdgeLabel, PropertyKey, and IndexLabel, providing comprehensive control over graph structures. Additionally, it supports Multi-type Indexes that facilitate exact queries, range queries, and complex conditional queries. The platform also boasts a Plug-in Backend Store Driver Framework that currently supports various databases like RocksDB, Cassandra, ScyllaDB, HBase, and MySQL, while also allowing for easy integration of additional backend drivers as necessary. Moreover, HugeGraph integrates smoothly with Hadoop and Spark, enhancing its data processing capabilities. By drawing on the storage structure of Titan and the schema definitions from DataStax, HugeGraph offers a solid foundation for effective graph database management. This combination of features positions HugeGraph as a versatile and powerful solution for handling complex graph data scenarios.
Description
Sparksee, which was previously referred to as DEX, optimizes both space and performance while maintaining a compact design that enables swift analysis of extensive networks. It supports a wide range of programming languages including .Net, C++, Python, Objective-C, and Java, making it versatile across various operating systems. The graph data is efficiently organized using bitmap data structures, achieving significant compression ratios. These bitmaps are divided into chunks that align with disk pages, enhancing input/output locality for better performance. By leveraging bitmaps, computations are executed using binary logic instructions that facilitate efficient processing in pipelined architectures. The system features complete native indexing, which ensures rapid access to all graph data structures. Node connections are also encoded as bitmaps, further reducing their storage footprint. Advanced I/O strategies are implemented to minimize the frequency of data pages being loaded into memory, ensuring optimal resource usage. Each unique value in the database is stored only once, effectively eliminating unnecessary redundancy, and contributing to overall efficiency. This combination of features makes Sparksee a powerful tool for handling large-scale graph data analyses.
API Access
Has API
API Access
Has API
Integrations
Hadoop
Spark
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
HugeGraph
Founded
2018
Website
github.com/hugegraph/hugegraph
Vendor Details
Company Name
Sparsity Technologies
Founded
2010
Country
Spain
Website
sparsity-technologies.com
