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It Is Time for A Modern Graph Query Language

  • Yu Xu
  • May 22, 2018
  • About TigerGraph, blog, GQL, Graph Database Market, GSQL
  • Blog >
  • It Is Time for A Modern Graph Query Language

The time is ripe for an international standard graph query language. Industry vendors including Neo4j have called this out, and we at TigerGraph wholeheartedly agree. As graphs continue to see widespread adoption, we have certainly reached a tipping point for our industry.

Still, it is important to note that an older solution with more marketing spend behind it doesn’t necessarily lend itself as the best option. Performance, expressiveness and ease-of-use are key for determining the right query language fit to help graph cross the chasm to the mainstream.

Graph technology continues to serve as an important driver of digital transformation initiatives for organizations across the board. Unlike previous solutions, graphs offer the unique and very valuable ability to unleash new insights and business outcomes from interconnected data.

An international standard will help solve pervasive challenges in the graph market. These include the difficulty of finding proficient graph developers and the need to speed up enterprise ramp-up and adoption. My colleague and VP of Engineering Mingxi Wu discusses more in his blog. However, when it comes to evaluating a standard graph language, there are key things to consider, as Wu continues to outline and point out in detail.

Yes, enterprises are successfully using a range of query languages such as Cypher, Gremlin, GSQL and more. However as data and use cases grow and become more complex, organizations need both the ability to scale up and out, and the ability to generate the material performance impact needed for big data, for complex analytics (machine learning and AI), and for real-time operations.

While legacy graph products and query languages offer an incremental change, they are unable to entirely fulfill these needs and therefore drive true digital business transformation. Neo4j’s call for Cypher as the query language standard in the “Age of Graph” misses the mark.

The next phase of the graph database evolution needs the right query language. Industry requires a dramatic step away from inadequate legacy models to achieve digital transformation at scale. More than anything, what the market needs is a modern graph query language, along with a super fast and scalable system to support it.

GSQL is that language.

What is GSQL? It is the best modern graph query language on the market. It is user-friendly, highly expressive and Turing-complete. Unlike other options, GSQL supports the real-life business requirements organizations already experience.

I founded TigerGraph six years ago with the goal to deliver a graph database offering what enterprises need: real-time speed, big-data scale and ease-of-use. Our database team – who are experts in Massively Parallel Programming (MPP) – seriously considered each and every query language options available, including Cypher and Gremlin.

Our conclusion? Every query language we came across was inadequate in addressing real enterprise needs. Each missed the mark in delivering the features enterprises need in an age of real-time big data. These include ease of use, familiarity of SQL-like syntax and expressive power for solving real-world complex business problems, all with the performance of an MPP engine.

And so, we spent years to create a language that did. This was GSQL. Along with the TigerGraph MPP graph database, GSQL was engineered from the ground up to support parallel loading, querying and updates for real-time transactions and ultra-fast analytics.

Since publicly launching late last year, customer feedback on TigerGraph has been amazing. Time and time again we hear from customers who are delighted to gain data insights they previously thought were impossible given the limitations of legacy graph database solutions and query languages. In other words, by using GSQL they have made the impossible, possible.

Our customers also report GSQL’s ease of use for creating queries over their big data. Speed has been another benefit: queries using GSQL deliver results in seconds, compared to hours using Neo4j or DataStax, if the query is even possible at all. GSQL is in use by Alipay, the world’s largest graph deployment, with over 2 billion transactions per day (reads and writes). TigerGraph and GSQL are used in production to support multi-hop queries spanning 3-10+ hops, all in  a graph with 100+ billion nodes and nearly a trillion relationships (see our benchmark report).

GSQL’s advantages over other graph query languages are well documented. The numbers prove it and I invite you to see the comparisons with Cypher and Gremlin for yourself here.

For those looking to migrate quickly and easily to GSQL from Cypher or Gremlin, contact us at [email protected] to learn more our Query Language Migration Tool Kit.

Lastly, stay tuned for the release of our upcoming TigerGraph Developer Edition. We invite you to check out the free trial of our enterprise version in the meantime.  

It’s time for a powerful and user-friendly graph query language. GSQL has what matters most: performance, expressiveness and ease-of-use.

As the discussion continues, I’d love to hear your input. Contact me at [email protected].

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    Dr. Jay Yu

    Dr. Jay Yu | VP of Product and Innovation

    Dr. Jay Yu is the VP of Product and Innovation at TigerGraph, responsible for driving product strategy and roadmap, as well as fostering innovation in graph database engine and graph solutions. He is a proven hands-on full-stack innovator, strategic thinker, leader, and evangelist for new technology and product, with 25+ years of industry experience ranging from highly scalable distributed database engine company (Teradata), B2B e-commerce services startup, to consumer-facing financial applications company (Intuit). He received his PhD from the University of Wisconsin - Madison, where he specialized in large scale parallel database systems

    Todd Blaschka | COO

    Todd Blaschka is a veteran in the enterprise software industry. He is passionate about creating entirely new segments in data, analytics and AI, with the distinction of establishing graph analytics as a Gartner Top 10 Data & Analytics trend two years in a row. By fervently focusing on critical industry and customer challenges, the companies under Todd's leadership have delivered significant quantifiable results to the largest brands in the world through channel and solution sales approach. Prior to TigerGraph, Todd led go to market and customer experience functions at Clustrix (acquired by MariaDB), Dataguise and IBM.