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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The GSQL Query Language is the Choice for Fast and Scalable Graph Operations and Analytics

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A Modern Graph Query Language
The GSQL® query language is the choice for fast and scalable graph operations and analytics, for the cloud or on-premises. GSQL’s similarity to SQL, High-level syntax, Turing completeness, and built-in parallelism brings faster performance, faster development and the ability to describe any algorithm.

Features and Benefits

Schema-Based

Schema-Based

Built-in High Performance Parallelism

Achieves the fastest results.

SQL-Like

Familiar to over 1 million users.

Conventional Control Flow

(FOR, WHILE, IF/ELSE) Makes it easy to implement conventional algorithms.

Procedural Queries Calling Queries

Parameterized queries are flexible and can be used to build more complex queries.

Transactional Graph Updates

HTAP - Hybrid Transactional / Analytical Processing, with real-time data updates.
Illustration of a laptop displaying an SQL query: SELECT C.cust_name, C.cust_contact FROM Customers C. Surrounding icons indicate data processing steps. The background features connecting lines and a light blue gradient.

SQL Friendly

Building on SQL is important for many reasons. It is the best-known database language, period. Based on a solid foundation of relational algebra, SQL has proven itself to be robust and enterprise-ready. This is why GSQL features an SQL-like syntax that reduces the learning curve for SQL programmers.

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A New Standard

Enterprises are using a variety of query languages today. With the widespread adoption of graph databases, it’s time for a standard graph query language. GSQL takes the best features of previous relational and graph query languages, to set the new standard.

Read TigerGraph’s vision for extending its GSQL query language to support simultaneous querying of graph and relational data in a fashion that brings out the commonalities between the two models