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TigerGraph Upgrades GSQL

July 24, 2020

GSQL brings modern aggregation support to graph analytics, and is a property graph query language designed for SQL users. The company’s current advances with GSQL make this innovative query language even more similar to SQL, the standard language for storing, manipulating, and retrieving data within a relational database. The result: A lower barrier to entry and easier adoption for SQL users as they chart their advanced graph analytics course. TigerGraph’s work with GSQL has garnered industry recognition from SIGMOD, the premier international database conference.

“GSQL’s innovation, speed and scalability boost its ease of use within the SQL community, delivering a SQL-like experience with graph analytics,” said Dr. Yu Xu, CEO, and founder, TigerGraph. “Simultaneously, we’re working to help enterprises – from banks to healthcare companies – uncover meaningful, actionable, real-time insights from connected data that can improve people’s lives. GSQL’s acknowledgment by SIGMOD, the renowned data management academic conference, validates its influence on the graph database industry as well as the database community at large. Our work in advanced graph analytics is ongoing and ever-evolving, and we remain dedicated to supporting inventive customer applications while innovating our product offerings.”

Database experts have recognized GSQL’s power for efficient expression and execution of graph algorithms and graph analytics. The GSQL-related advancements complement TigerGraph 3.0, which delivers several “no code” advanced graph analytics features. These “graph for all” features allow data scientists and business users to move data from the relational database to graph database and build advanced analytics patterns by drawing -- without the need to write complex queries. TigerGraph 3.0 extends GSQL even further with rich pattern matching with analytics and additional support for distributed GSQL.

TigerGraph’s GSQL builds on SQL functionality and extends it with a unique construct called an “accumulator.” Accumulators allow users to perform complex computations faster on connected datasets. The Visual Query Builder feature in TigerGraph 3.0 allows users to employ accumulators to aggregate without writing code. TigerGraph's accumulators are more powerful and richer than traditional aggregation functions in SQL; as dynamic data objects, with parallel processing like MapReduce and Spark, they are an integral part of modern graph analytics.

TigerGraph’s Chief Scientist and distinguished researcher, Dr. Alin Deutsch, presented the GSQL language paper at SIGMOD 2020, outlining the massive benefits for analyzing connected datasets with graph analytics. TigerGraph’s GSQL will support GQL, the upcoming ISO language for property graph querying currently being created by the same international working group that looks after the SQL standard. TigerGraph’s language standard team -- which also includes VP of Engineering Dr. Mingxi Wu and Head of Product Strategy and Developer Relations Dr. Victor Lee -- is actively involved in the project. The team will work to ensure that GQL and TigerGraph’s unique analytical capabilities are compatible.

The research paper presents the following GSQL-related design features, which are unique in the industry:

GSQL includes three features which synergize to form a modern graph analytical capability and address an important class of iterative graph algorithms – algorithms that are left out by other current and proposed languages. These three features are default shortest-path semantics of pattern matching, accumulators as vertex-attached run-time attributes and global query states, and control flow primitives.

GSQL is unique in the industry due to its adoption of shortest-path semantics as the default pattern matching semantics. Shortest-path semantics is currently being discussed in the standard body.

GSQL’s aggregation support -- based on the concept of accumulators -- subsumes SQL-style aggregation and allows single-pass traversals that compute in parallel multiple aggregations, even on disjoint criteria. The paper quantifies experimentally the significant speedup of accumulator-based vs. SQL-style aggregation for graph traversal queries.

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