TigerGraph: The parallel graph database explained

How TigerGraph achieves fast data ingest, fast graph traversal, and deep link analytics even for large data sets

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TigerGraph recently introduced a distributed computation mode that significantly improves performance for analytical queries that traverse a large portion of the graph. In distributed query mode, all servers are asked to work on the query; each server’s actual participation is on an as-needed basis. When a traversal path crosses from server A to server B, the minimal amount of information that server B needs to know is passed to it. Since server B already knows about the overall query request, it can easily fit in its contribution.

In a benchmark study, we tested the commonly used PageRank algorithm. This algorithm is a severe test of a graph’s computational and communication speed because it traverses every link, computes a score for every node, and repeats this traverse-and-compute for several iterations. When the graph was distributed across eight servers compared to a single-server, the PageRank query completed five times faster than before.

High performance graph analytics

TigerGraph represents a new era of graph technology that empowers users with true real-time analytics. The technical advantages support more sophisticated, personalized, and accurate analytics, as well as enable organizations to keep up with rapidly changing and expanding data. Specific benefits include:

  • Ability to traverse deep queries with 10-plus hops in subsecond time.
  • Ability to traverse hundreds of million of nodes/links per second per machine.
  • Ability to load 50 to 150 GB of data per hour, per machine.
  • Ability to scale out for size and speed. For example, one enterprise’s production system uses 20 commodity machines to handle 2 billion daily events, in a graph with with more than 1 trillion entities and links.

To learn more about TigerGraph, visit the website here.

Victor Lee is director of product management at TigerGraph. Victor was a circuit designer and technology transfer manager at Rambus before returning to school for his computer science Ph.D., focusing on graph data mining. He received his B.S. in Electrical Engineering and Computer Science from U.C. Berkeley, M.S. in Electrical Engineering from Stanford University, and Ph.D. in Computer Science from Kent State University. He was a visiting professor at John Carroll University before joining TigerGraph.

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