TigerGraph Unveils Next Generation Hybrid Search to Power AI at Scale; Also Introduces a Game-Changing Community Edition
TigerGraph Unveils Next Generation Hybrid Search to Power AI at Scale
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Enterprise-Scale
Graph Designed for Speed and Analytics

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What We Do

TigerGraph is delivering the next stage in the evolution of the graph database: the first system capable of real-time analytics on web-scale data. Our Native Parallel Graph™ (NPG) design focuses on both storage and computation, supporting real-time graph updates and offering built-in parallel computation. Our SQL-like graph query language (GSQL) provides for ad-hoc exploration and interactive analysis of Big Data. With GSQL’s expressive capabilities and NPG speed, you’ll be able to perform Deep Link Analytics: uncovering connections that previously were too impractical to reach or too cumbersome to express.

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Fresh Development

Our core system was developed from scratch using C++ and system programming concepts to provide an integrated data technology stack. A native graph storage engine (GSE) was developed to co-locate with the graph processing engine (GPE) for fast and efficient processing of data and algorithms.The GPE is designed to provide built-in parallelism for a MapReduce-based computing model available via APIs. The DB graph is optimally stored both on disk and in-memory, allowing the system to take advantage of the data locality on disk, in-memory and CPU cache.

Efficient Compressed Data

Same data; fewer bytes. TigerGraph uses a proprietary graph format that effectively compresses your data. In benchmarking tests, TigerGraph was the only graph to achieve a storage size smaller than the input data size, usually about half the size. With fewer bytes to store, you get faster computation, faster data transfer, and lower system costs.

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MPP Computational Model

Each vertex and edge in the graph acts as a parallel unit of storage and computation simultaneously.

With this approach, the graph is no longer a static data storage collection; it is a massively parallel computation engine. Vertices can send and receive messages to each other via edges.

A vertex or an edge can store any amount of arbitrary information. The TigerGraph system executes compute functions in parallel on every vertex/edge, taking advantage of multi-core CPU machines and in-memory computing.

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Automatic Partitioning

In a distributed system, the data needs to be spread across the different servers somehow. TigerGraph automatically partitions your data, saving you from the hassle and brittleness of programming a manual sharding method. Purpose-built as a distributed graph, TigerGraph evenly distributes your data in a performant way. Need to grow or shrink a cluster? TigerGraph’s cluster expansion and compression feature will automatically redistribute the data. Don’t settle for manual sharding.

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Property Graph

A property graph is a type of graph model where relationships not only are connections but also carry a name (type) and some properties. A property graph excels at showing connections among data scattered across diverse Data Architectures and data schemas.

TigerGraph is a property graph, which supports the Label Property Model.

A Transformational Technology

The TigerGraph Native Parallel Graph offers a transformational technology, with significant clear advantages over the most well-known graph database solutions on the market. Despite its comprehensive and well-documented graph database functionality, the current leading solution is considerably slower in comparison. In benchmark tests, TigerGraph can load a batch of data in one hour, while the other solution requires a 24-hour day.

Further, by offering parallelism for large scale graph analytics, TigerGraph supports graph parallel algorithms for Very Large Graphs (VLGs) – providing a considerable technological advantage which grows as graphs inevitably grow larger. It works for limited, fast queries that touch anywhere from a small portion of the graph to millions of vertices and edges, as well as more complex analysis that must touch every single vertex in the graph itself. Additionally, real-time incremental graph updates make it suitable for real time graph analytics unlike other solutions.

The TigerGraph advantage lies in the fact that we represent graphs as a computational model. Compute functions can be associated with each vertex and edge in the graph, transforming them into active parallel compute-storage elements, in a behavior identical to what neurons exhibit in human brains.

The TigerGraph advantage lies in the fact that we represent graphs as a computational model. Compute functions can be associated with each vertex and edge in the graph, transforming them into active parallel compute-storage elements, in a behavior identical to what neurons exhibit in human brains.

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Ready to Harness the Power 
of Connected Data?

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