TigerGraph, a graph database born to roar
Graph is a data model that has long lingered on the fringe of mainstream adoption. But that is changing, as graph lends itself well to representing many real world problems, and the technology is evolving.
When you see IBM and Oracle moving in this space, trying to beef up their offerings, publishing reports and mentioning how they use graph for cases such as anti-fraud, you know there’s something going on.
It’s not the IBMs and Oracles of the world that are leading in this space however. The leaders in terms of market share are Neo4j and Titan, the latter recently acquired by DataStax and is now the basis of DataStax Enterprise Graph.
Either way, none of those were able to deal with graph data at Twitter scale when Yu Hu needed that. Social graphs are a prime example of utilizing the graph model, Hu was working at Twitter till 2011, and the graph databases that were around at the time could not cope.
Hu has a Ph.D. Computer Science from UCSD, 26 patents in distributed systems & databases, led Teradata’s big data initiatives, and worked on Twitter‘s distributed data infrastructure. So when faced with that problem, Hu saw an opportunity and went off to create a solution. Hu founded GraphSQL in 2012 and has been working with a team of 30 engineers since.
Today GraphSQL is officially entering a new stage in its development, including a new name: TigerGraph. The product is now generally available, a series A founding round of US$33 million is announced and a hosted version of TigerGraph based on Amazon EC2 is launching
Good for TigerGraph, but why should you care? So far that sounds like a typical startup coming out of stealth announcement. The thing however is that graph analytics and the platforms that support them will be increasingly important going forward, and TigerGraph is an important new entry in this space
The 5 years that elapsed since Hu started out with GraphSQL is a long time, but it seems a lot has been accomplished during this period. TigerGraph boasts a new parallel architecture for native graph storage and processing that puts it ahead of the competition, and has the benchmarks and the use cases to back this up.
TigerGraph positions itself as a solution for real-time graph analytics for extremely big data. For doing things such as fraud prevention, recommendations and network management for clients like Alipay, Visa, Wish and the State Grid Corporation of China.
These are massive clients with massive data, so how come they chose to go with a vendor you have probably never heard of?
One thing that may have helped in getting for example what TigerGraph says is the largest transaction graph in production in the world at Alipay, with more than 100 billion vertices, 600 billion edges and 2 billion daily real time updates, is TigerGraph’s backing.
TigerGraph is backed by a mix of Chinese and US investors (Qiming VC, Baidu, Ant Financial, AME Cloud, Morado Ventures, Zod Nazem, Danhua Capital, DCVC) and has its HQ in Silicon Valley and a branch office in Shanghai. And the US$33 million TigerGraph scored in series A is the #2 in funding after Neo4j for graph startups.
That helps, but not if the performance is not there. Taking a look at TigerGraph’s benchmark comparing its performance to competitors Neo4j and Titan substantiates this.
According to TigerGraph’s benchmark, TigerGraph runs queries from 4 to almost 500 times faster than the competition, loads data from 2 to 25 times faster, and uses about 80 percent less space to store that data.
Benchmarks are like opinions – everybody has one – but that does sound too good to be true.
