GQL, a property graph query language newly released this year by the ISO, is the second international standard database query language since SQL's debut over 40 years ago. It integrates the strengths of Cypher, SQL, and various general programming languages, making it a powerful tool for analyzing complex graph data.

# Building on Cypher: How GQL Enhances Graph Queries

## Enhanced Graph Pattern Matching

### Powerful Label Matching Syntax

One of the standout features of GQL is its advanced label matching syntax. This allows for more complex queries using logical operators such as `&`, `|`, and `!`.

In Cypher, a typical query might look like this:

```
MATCH (n:Person)
WHERE n.age > 30
RETURN n
```

With GQL, you can perform more sophisticated queries:

```
MATCH (n:Manager & FullTime | !Intern)
WHERE n.age > 30
RETURN n.name AS managerName, n.department AS dept
```

This GQL query finds nodes that are both `Manager` and `FullTime`, or not `Intern`, and have an age greater than 30.

### Regex-like Variable-Length Path Pattern

GQL introduces a regex-like syntax for variable-length patterns, enhancing flexibility in path matching. Examples include `-[e]->{1,3}`, `-[e]->+`, `-[e]->*`, and` -[e]->?`. This allows for more complex path pattern matching compared to Cypher’s syntax.

### Path Restrictors

GQL provides four path restrictors—`WALK`,`TRAIL`,`ACYCLIC`, and `SIMPLE`—to control whether paths can have repeated nodes or edges.

### Path Selectors

GQL offers versatile shortest path queries, including options for finding all shortest paths, any shortest path, and the top k shortest paths. This flexibility allows for detailed and efficient path retrieval in various scenarios.

## Modern Variable Definition

### Local Variable Definition

GQL introduces a dedicated syntax for defining local variables at the beginning of a procedure, similar to early C language practices. These variables are visible throughout the entire procedure scope. The introduction of local variables allows GQL to express nonlinear data flows, enabling data to be reused multiple times.

For instance, when users need to perform multiple analyses on the same dataset, GQL's local variables provide an efficient way to store and reuse intermediate results. This is particularly useful for complex calculations or when the same base data needs to be processed in different ways.

Consider a scenario where we want to analyze user interactions in a social network:

```
TABLE userActivity = {
    MATCH (u:User)-[a:ACTION]->()
    RETURN u.id AS userId, a.timestamp AS actionTime, a.type AS actionType
}
 
VALUE activeUsers = VALUE {
    FOR i IN userActivity
    WHERE i.actionTime > datetime('2024-01-01')
    RETURN collect_list(DISTINCT i. userId)
}
 ​
VALUE uniqueActionTypes = VALUE {
    FOR i IN userActivity
    WHERE i.userId IN activeUsers
    RETURN count(DISTINCT i.actionType) AS uniqueActionTypes
}
```

In this example,` userActivity` captures the base dataset of user actions, which is then reused to identify active users and calculate action statistics. This demonstrates how GQL's local variables enable efficient data reuse and modular query construction.

### The LET Statement: Another Way to Define Variables

The LET statement in GQL defines variables by adding a new column to the current output table, unlike VALUE, which creates procedure-scope visible variables. LET is non-blocking, allowing you to define new variables without interrupting the flow of existing ones, unlike Cypher’s WITH, which requires manual inclusion.

In Cypher, you must explicitly pass all existing variables when defining a new one:

```
MATCH (v)-[e]->(v2)
WITH v, e, v2, 1 AS a
```

Or use `WITH *` to retain all:

```
MATCH (v)-[e]->(v2)
WITH *, 1 AS a
```

In GQL, `LET` simplifies this process:

```
MATCH (v)-[e]->(v2)
LET a = 1
```

Here, `v`, `e`, and `v2` remain accessible, and `a` is seamlessly added. This makes `LET` a more efficient and intuitive choice for variable definition.

## Complex Nested Queries

GQL's `CALL` statement allows you to encapsulate a set of statements into a subquery block, treating them as a single unit that can be referenced by outer statements. This is particularly useful when you need to apply operations on the combined results of multiple queries.

For example, to sort the combined results of two different queries:

```
CALL {
    MATCH (v:Person) RETURN v.name AS name
    UNION
    MATCH (v:City) RETURN v.name AS name
}
ORDER BY name
```

The `CALL` block enables the `ORDER BY` to be applied to the unioned results, rather than just the last query.

# Comparison between GQL and SQL/PGQ

SQL/PGQ extends its graph query capabilities based on SQL, and it shares a read-only graph pattern matching syntax with GQL. However, compared to SQL/PGQ, GQL provides a complete set of read-write syntax, which can better meet the various operation requirements of graph databases.

In addition, although SQL is powerful, its syntax design has some well-known issues. For example, the clause order of SQL is not intuitive and difficult to combine, and users often need to use workarounds to achieve complex requirements. Google recently published an article titled "SQL Has Problems. We Can Fix Them: Pipe Syntax In SQL," pointing out these syntax design issues of SQL and proposing a special pipeline operator aimed at making clauses more linear and combinable, thereby enhancing the flexibility and intuitiveness of SQL.

GQL, on the other hand, is inherently linear and composable. It features orthogonal statements like `MATCH` for graph pattern matching, `FILTER ` for filtering results, `FOR ` for iterating over lists, `LET` for variable definitions, and CALL for complex nested queries.

In addition, GQL includes `INSERT`, `SET` and other DML statements, which can be also elegantly combined in a linear manner with DQL statements.

For example, you can match nodes and then insert an edge between them:

```
MATCH (a:player{id:1, name:"Tim"}(b:player{id:2, name:"Jerry"})
INSERT (a)-[:follow{followness:90}]->(b)
```

The following two figures intuitively demonstrate the differences in syntax between SQL and GQL.

**Figure 1: SQL's Rigid Clause Structure**

![](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/74ac57c6f7a292190fc6b1ff2b10139f.png)

**Figure 2: GQL's Linear Statement Composition**

![](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/c5ab0435a24e95b58f87f65bde9d2103.png)

# Looking Forward: The Road Ahead for GQL

The first version of GQL has already included numerous features, showcasing the outstanding contributions of the standard committee members. Personally, I look forward to the possibility of future developments such as subgraph extraction, diverse table construction methods, window functions, and advanced programming constructs like conditional statements, loop statements, and lambda expressions. These enhancements will further solidify GQL's role as a powerful and user-friendly query language。

As a modern, advanced data query language, GQL excels in functionality and ease of use, making it essential for industries managing vast networks, such as telecommunications and social media. Its capability for real-time analytics provides immediate insights, which is crucial for decision-making in dynamic environments. Looking ahead, GQL's ongoing development promises to shape how we interact with data, paving the way for innovations in AI, healthcare, logistics, and beyond.

In conclusion, GQL is set to transform data management, and we eagerly anticipate its future advancements.


GQL, ISO's new graph query language, boosts complex data analysis with advanced pattern matching and versatile queries, vital for network-intensive industries.

Unveiling ISO-GQL: The Future of Graph Query Languages

In today's rapidly evolving business landscape, modern enterprises are constantly seeking innovative solutions to stay competitive and drive success. One of the most significant advancements in data management and analytics this year has been the release of [the first ISO-GQL standard on April 12](https://www.nebula-graph.io/posts/nebulagraph_enterprise_5.0_gql_support). This groundbreaking development in the graph database industry introduces a standardized graph query language, unifying the querying of graph databases and offering immense potential for enterprises.

# Why ISO-GQL is Important to Modern Enterprises

ISO-GQL's release is particularly relevant as it directly addresses several critical factors that underpin the success of modern enterprises. By providing a universal querying language, ISO-GQL ensures consistency and interoperability across different graph database platforms, which is crucial for data-driven decision-making. The efficiency of ISO-GQL in traversing and querying complex networks supports agility and innovation, allowing businesses to quickly adapt to market changes and innovate continuously.

ISO-GQL provides a standardized graph query language that unifies the querying of graph databases, which is crucial for several reasons:

- Standardization: ISO-GQL offers a universal querying language that ensures consistency and interoperability across different graph database platforms.
- Efficiency: Designed specifically for graph data, ISO-GQL enables efficient traversal and querying of complex networks, which is essential for applications requiring deep link analysis.
- Flexibility: ISO-GQL supports flexible data modeling, allowing enterprises to represent and query intricate relationships without the constraints of traditional relational databases.
- Scalability: Graph databases using ISO-GQL can scale horizontally, handling large volumes of interconnected data with ease.
Future-Proofing: Adopting ISO-GQL positions enterprises to stay ahead of technological advancements and industry standards, ensuring long-term compatibility and support.

# Top 10 Use Cases for GQL in Modern Enterprises
## 1. Property Graph Model
ISO-GQL defines a property graph model where nodes (vertices) and edges (relationships) can have properties (key-value pairs) associated with them. In social network analysis, platforms use property graphs to represent users (nodes) and their interactions (edges), with properties like user demographics and interaction types, facilitating detailed analysis of user behavior and network dynamics. LinkedIn uses property graphs to model its professional network, representing users as nodes and connections as edges with properties like job titles, companies, and skills. This allows LinkedIn to efficiently manage and query vast amounts of user data, providing personalized job recommendations and networking suggestions. This capability enables detailed and scalable modeling of complex relationships, enhancing data-driven decision-making and personalized user experiences.

## 2. Graph Pattern Matching (GPM)
ISO-GQL supports complex graph pattern matching, enabling users to specify intricate patterns to search within the graph data. In fraud detection, financial institutions can use GPM to identify suspicious transaction patterns, such as circular payment schemes or unusual connections between accounts, helping to uncover fraudulent activities. For example, JPMorgan Chase uses graph pattern matching to detect fraud by identifying suspicious transaction patterns and relationships between accounts. By analyzing these patterns, the bank can proactively prevent fraudulent activities. This enhances security and compliance by enabling the detection of complex fraud patterns and anomalies in real-time.
## 3. Query Performance and Optimization (QPP)

ISO-GQL includes mechanisms for query performance and optimization, ensuring efficient execution of complex queries. In e-commerce, platforms can optimize recommendation queries to provide real-time, personalized product suggestions to users, enhancing the shopping experience and increasing sales. Amazon optimizes product recommendation queries to provide real-time, personalized suggestions to users based on their browsing and purchase history. This improves customer satisfaction and drives sales by delivering fast, relevant recommendations and insights.

## 4. Data Manipulation

ISO-GQL allows for the creation, updating, and deletion of graph data, enabling dynamic and real-time data management. In IT infrastructure management, companies use data manipulation capabilities to continuously update the status and relationships of IT components, ensuring an accurate and current view of the infrastructure. Netflix uses data manipulation to continuously update its content library and user preferences, ensuring that recommendations are always current and relevant. This supports dynamic and agile data management, allowing enterprises to respond quickly to changing data and user needs.
## 5. Multi-Graph Joint Query
ISO-GQL supports querying across multiple graphs, allowing for the integration and analysis of data from different sources. In healthcare data integration, providers can perform joint queries across patient records, treatment data, and research databases to derive comprehensive insights for personalized patient care. Pfizer performs joint queries across clinical trial data, patient records, and research databases to derive comprehensive insights for drug development and personalized medicine. This facilitates comprehensive data integration and analysis, leading to better-informed decisions and innovative solutions.
## 6. Undirected Graph Support
ISO-GQL supports undirected graphs, which are useful for scenarios where the direction of relationships is not significant. In telecommunications network optimization, undirected graphs can represent symmetrical relationships between network nodes, helping to optimize network performance and reliability. This enhances network optimization and reliability, ensuring robust and efficient communication infrastructures.
## 7. Path Queries
ISO-GQL allows querying for paths between nodes, including shortest paths, all paths, and paths with specific constraints. In supply chain management, companies, such as DHL, use path queries to find the most efficient routes for transporting goods, considering factors like distance, cost, and time, thereby optimizing logistics and reducing costs.
## 8. Schema Flexibility
ISO-GQL supports flexible schema definitions, allowing for dynamic and evolving data models without rigid schemas. In enterprise search, organizations can continuously update and integrate new types of data from various sources, ensuring a comprehensive and up-to-date information retrieval system. Google uses schema flexibility to continuously update and integrate new types of data from various sources into its knowledge graph, enhancing search accuracy and relevance. This supports agile and adaptive data management, enabling enterprises to evolve their data models as needed.
## 9. Declarative Syntax
ISO-GQL provides a declarative query language, making it easier for users to specify what they want to retrieve without detailing how to retrieve it. In enterprise search, employees can use simple and intuitive queries to search across integrated knowledge graphs, retrieving relevant information quickly and accurately without needing deep technical knowledge. Salesforce uses declarative syntax to allow users to easily query customer relationship data, generating insights and reports without needing deep technical expertise. This empowers business users to access and analyze data independently, improving productivity and decision-making.
## 10. ACID Compliance
ISO-GQL ensures ACID (Atomicity, Consistency, Isolation, Durability) compliance, providing reliable transaction processing. In financial services, banks rely on ACID compliance to ensure the integrity and consistency of transactions, even in the event of system failures, thus maintaining trust and reliability in their operations.

# Conclusion
The technical characteristics of ISO-GQL, such as the Property Graph Model, Graph Pattern Matching, Query Performance and Optimization, and others, provide powerful capabilities for managing and querying graph data. These features enable modern enterprises to address complex data challenges across various domains, from fraud detection and social network analysis to supply chain management and IT infrastructure monitoring. By adopting ISO-GQL, enterprises can leverage the full potential of their graph databases, driving innovation, efficiency, and competitive advantage.

[NebulaGraph, as the first distributed graph database with native GQL support, empowers enterprises to unlock the full potential of their data and stay ahead in a competitive landscape.](https://www.nebula-graph.io/posts/enterprise_5.0_GA_announcement) If you’re interested in taking a trial, feel free to [contact us](https://www.nebula-graph.io/contact).


The ISO-GQL release introduces a standardized graph query language, unifying the querying of graph databases and offering immense potential for enterprises.

Top 10 Use Cases for GQL in Modern Enterprises


# What is GQL?

Graph Query Language (GQL) is a specialized language designed for graph databases, paralleling the role that SQL plays in relational databases. 



## Background 

GQL is the second language in the entire database field, having undergone approximately five years from the initial establishment of the project to its final release. Initially, scholar Alastair Green found that the graph database field was fragmented, with various query languages such as  Cypher, GSQL and PGQL. Recognizing the redundancy and need for standardization, Green advocated for a unified, standardized language for querying graph databases. His proposal garnered widespread support, with over 4,000 organizations and individuals participating in a vote, resulting in a 95% approval rate for the creation of a standard language for graph database queries.In May 2024, ISO officially released ISO/IEC GQL.


## Comprehensiveness 

GQL is a comprehensive language that includes Data Manipulation Language (DML), Data Definition Language (DDL), and Data Query Language (DQL), effectively covering all necessary aspects of database operations. This capability allows GQL to support the creation, deletion, modification, and querying of graph data, as well as the definition and modification of the structure of graph databases. GQL aims to provide a unified standard for querying and managing graph databases, thereby enhancing interoperability and reducing dependency on specific graph database vendors.


# What impact will GQL have?



### Enhancing the Importance of Graph Database Technology

The establishment of GQL as a standard underscores the significance of graph database technology. Since 2013, graph databases have been at the forefront of database technology trends.According to Gartner, 80% of data and analytics innovations will use graph technologies by 2025.


### Reducing Learning and Migration Costs

The market is replete with graph database vendors, each with their own query language, leading to high learning and migration costs. GQL standardization mitigates these costs by providing a unified language, facilitating easier transitions and switches between different graph database products.


### Promoting the Application and Development of Graph Databases

Graph databases have evolved through various stages, from initial confusion about the concept to widespread recognition of their problem-solving capabilities. Today, they have proven their value in applications such as public security, recommendation systems, data selection, and supply chain manufacturing. The standardization of GQL is expected to further promote their application and development.


### Encouraging "Thinking in Graph" 

Nowadays, graph databases have passed the capability verification phase and have arrived at a critical time where they are used to think about and solve problems. GQL standardization is crucial for promoting the concept of "Thinking in Graph" , enhancing efficiency and enthusiasm for new database products and languages.


### Active Engagement from Education and Academia

Currently, numerous universities both domestically and internationally are looking to collaborate with NebulaGraph to offer courses related to GQL, preparing students who are about to enter the workforce to understand and utilize this new database language.


# What challenges will GQL face?


Despite its potential, the promotion and application of GQL will face several key challenges:



### Adaptability Challenge for Database Vendors


Database vendors each have their own unique languages and architectural systems. Faced with the implementation of the new GQL language, how will these vendors respond? Some may choose to directly adapt, converting GQL into their own proprietary languages and implementing a conversion mechanism. However, this approach could lead to a significant decrease in performance, and as GQL continues to evolve, this adaptation method may gradually fail to meet new demands. Therefore, for database vendors, aligning their graph database products with GQL is an extremely challenging task. 

But for NebulaGraph, this is merely following a trend of technological integration, as NebulaGraph has been fully committed to supporting GQL for two years. From the very beginning of GQL's proposal and formulation, NebulaGraph has been deeply involved, frequently obtaining the latest information and continuously optimizing its underlying data architecture to ensure outstanding performance when executing GQL.


### Transformation Challenge at the Application Layer

For the application layer, retrofitting existing database products to accommodate GQL presents a challenge. Graph databases are typically used in online applications that require handling a large number of real-time queries, thus demanding extremely high stability. The design logic of most application vendors is to make as few changes as possible to the existing system. Consequently, asking them to replace all query languages with GQL involves a substantial amount of work.


### Talent Reserve Issue

GQL was just released on April 12th of this year, and currently, most people have limited understanding of GQL. Learning a new language and applying it in a real-world work environment requires a considerable amount of time. Therefore, building a talent pool becomes another challenge in promoting GQL.


### Inadequacies within GQL Itself

The V1 version of GQL still has room for improvement in areas such as modifying graph types, indexes, and primary keys, and it is anticipated that future versions will address these issues. We need to allow GQL ample time to become more refined and robust, and in the meantime, we also gain more time to adapt to the requirements of GQL.


# Conclusion


In summary, GQL as the standardized language for graph databases will not only enhance the importance of graph database technology and reduce learning and migration costs but also promote the application and development of graph databases, encourage "Thinking in Graph" and gain active participation from the education and academic sectors. 

Despite the many challenges GQL will face, overall, the future of GQL is full of hope and excitement. Because when we embrace GQL, we are not just adopting a new standard; we are stepping into a future where the possibilities are as vast as the connections within a graph. We must rise to challenges and harness the collective power of innovation to unleash the full potential of GQL.

GQL is setting a new standard for graph databases, unifying query languages and simplifying operations. It promotes "Thinking in Graph," enhancing efficiency and fostering innovation. Despite challenges, GQL's future is bright, offering a dynamic path for data management.

Graph Databases Revolutionized: Why GQL is the New Standard

Since the recent release of the [GQL (Graph Query Language) standard by ISO](https://www.iso.org/standard/76120.html), there have been many discussions among graph database vendors and research institutions on how it will influence the industry. Apparently, its prevalence is backed by the wide applications of graph databases across diverse sectors- from recommendation engines to supply chains- a standard unified language for querying and managing graph databases is needed.

The significance of GQL lies in its ability to replace multiple database-specific query languages with a single, standardized one. This facilitates the interoperability between graph databases and calls the end of dependance of certain graph database vendors. Moreover, beyond the query language GQL defines, what a graph database should be and what key characteristics it should own are finally standardized, laying a far-reaching influential foundation to the development of the graph database industry. In this article, I will walk you through some important terms of GQL and explore its transformative potential for the industry.

# Key Terms and Definitions of GQL
GQL aims to establish a unified, declarative graph database query language that is both compatible with modern data types and can intuitively express the complex logic of a graph. It defines a comprehensive and robust framework for interacting with property graph databases, including DQL, DML, DDL, providing a modern and flexible approach to graph data management and analysis. Below are some key definitions of GQL that developers or users of graph databases should be aware of.

## Property Graph Data Model
GQL operates on a data model including nodes (vertices) and edges (relationships), which allows for pattern-based analysis and flexible data addition. The data model is specifically tailored for Property Graph Databases in that GQL is based on relatively mature graph query languages with wide applications, absorbing its advantages and settling the new standards.

Resource Description Framework (RDF), once another type of graph data model, is not included in GQL as a standard graph data model. With GQL definition, it is apparent that property graph data model is the de facto standard.

## Graph Pattern Matching (GPM)
GPM language defined by GQL enables users to write simple queries for complex data analysis. While traditional graph database query languages support single pattern matching, GQL further facilitates complex pattern matching across multiple patterns. For example, GQL supports path aggregation, grouping variables and nested pattern matching with optional filtering, offering expressive capabilities to handle more sophisticated business logic.

## GQL Schema
GQL allows for both schema-free graphs, which accept any data, and mandatory schema graphs, which are constrained by a predefined graph type specified in a “GQL-schema”. 
This dual approach of GQL supporting two types of schema caters to a wide range of data management needs, from the flexibility of schema-free graphs to the precision of schema-constrained ones.

Schema-free graphs allow adding new attributes to nodes or relationships at any time without modifying the data model. This adaptability is beneficial when dealing with complex and changing data, but from another perspective, schema-free graphs shifts the burden of data management complexities, such as handling data consistency and data quality, onto developers.

On the contrary, the mandatory schema graph offers a rigid framework that guarantees data consistency and integrity. The deterministic data structure within a mandatory schema makes any data changes clear and manageable. Furthermore, the predefined data structure enhances the comprehensibility and usability of data, which brings optimized query processes for both users and systems. While the mandatory schema graphs may sacrifice some flexibility, the trade-off is often justified in production environments where data structures are well-defined and the output data exhibits regular patterns.

## Graph Types
Graph types are templates that restrict the contents of a graph to specific node and edge types, offering a certain level of data control and structure. Under GQL definition, a graph type can be applied to multiple graphs, which means that the same graph structure type can be shared in different applications, making it more flexible. For example, data of a business might differ in different departments, among various regions and the data permissions may be isolated from each other. Under this situation, using the same graph type can facilitate business management as multiple graphs with the same graph type enable permission management and data privacy compliance regulations.

# Notable Advancements of GQL
## Separation of GQL-catalog and GQL-data
GQL defines a persistent and extensible catalog initialization runtime environment with reference to SQL: GQL-catalog. GQL lists its stored data objects, including various metadata, such as graph, graph type, procedure, function, etc. GQL-catalog can be independently maintained or upgraded from data itself, which allows for flexible permission management and a unified, standardized approach to catalog management.

![image](https://www-cdn.nebula-graph.io/blogs/GQL_catalog.png)

## Multi-graph Joint Query
GQL enables multi-graph joint queries. By using different graph expressions in the query process, users can perform operations such as union, conditional rules, and join can be performed on different graphs. This capability benefits scenarios such as anti-fraud investigations and the integration of public and private knowledge graphs, where cross-referencing public and private data sets is crucial. These scenarios require both data isolation and integrated data analysis due to data compliance, maintenance and other reasons. Therefore, the data needs to be split into multiple graphs, but they need to be combined to complete a certain business requirement.
![image](https://www-cdn.nebula-graph.io/blogs/multi-graph.png)

## Supporting Undirected Graph
Different from the previous definition of graph databases where relationships always have a direction, GQL allows undirected graphs. In some scenarios, there is naturally no direction of relationships between vertices, such as friendships. While these relationships could be modeled as directed, doing so would necessitate two separate edges, making the modeling and querying process complicated. 

# Conclusion
In summary, the standardization of GQL is a significant step forward for the graph database industry. Not only providing simplified user experience, GQL regulates what property graph databases are and what features they should own, referring to real-world use cases. It boosts the transformative potential of graph databases for all industries where they are leveraged. 

[NebulaGraph Enterprise](https://www.nebula-graph.io/posts/nebulagraph_enterprise_5.0_gql_support) is the first distributed graph database to offer native GQL support in that it is built on GQL and designed for GQL. For those eager to experience the future of graph database querying, a free trial awaits. [Reach out](https://www.nebula-graph.io/contact) today to discover the power of GQL for yourself.



The article discusses the transformative potential of the Graph Query Language and delves into its details, including its key definitions and notable advancements such as multi-graph joint queries and the support of undirected graphs.

Beyond a Query Language: How GQL is Shaping the Future of Graph Databases

The International Organization for Standardization (ISO) has recently published a new standard for a property graph database language called [GQL (ISO/IEC 39075)](https://www.iso.org/standard/76120.html). Developed by the same committee responsible for SQL, GQL is designed to parallel SQL and is the first new database query language released by ISO in over 35 years. GQL is significant for its ability to handle property graph databases, which are gaining prominence in big data and NoSQL technologies.

This article draws on insights from the highlights of the full GQL documents [(39075 Database Language GQL)](https://www.linkedin.com/posts/jtc1news_gql-database-language-standard-isoiec-39075-activity-7186398287963279360-E6rV?utm_source=share&utm_medium=member_desktop) and presents the fundamental GQL definitions with examples.

# Use Cases of Property Graph Databases
The applications of Property Graph Databases span across various industries and scenarios. They have played a crucial role in fraud detection in the financial sector, driven the creation of real-time recommendation engines in customer-centric systems, and have been a cornerstone in the development of knowledge graphs within large-scale organizations. The strength of property graphs lies in their ability to efficiently handle immense volumes of data and the intricate relationships that exist within this data. This unique capability has redefined the way people understand and interact with data, opening up new opportunities and solutions across different fields.
			
Focusing on the realm of real-time recommendations, the unique architecture of property graph databases allows for the representation of complex relationships between users and items, such as products on an e-commerce platform or movies on a streaming service. The support for highly connected data structures and relationships within graph databases facilitates quick and accurate predictions, empowering businesses with immediate, actionable insights. The ability to instantly assess the current business context, match user profiles, capture new behaviors, evaluate alternatives, and generate recommendations is a testament to the power and potential of graph databases.

# GQL Definition with examples
GQL defines a comprehensive set of operations for querying, updating, and managing schema of graph databases. It supports complex pattern matching, data manipulation, and a variety of graph models. Key concepts include nodes (or vertices), relationships (or edges), properties, and labels. Understanding these core elements is crucial to mastering GQL.The following examples offer a concise overview of GQL capabilities through illustrations.

## Graph Pattern Matching (GPM)
GQL uses a powerful GPM language to simplify complex data analysis. For instance, to find all nodes with a one-hop relationship to a node named 'Avery', the query would be:

```
MATCH (a {firstname: 'Avery'})-[b]->(c) RETURN a, b, c
```
This query allows the user to retrieve relationships without needing to specify them.

## Quantified Path Patterns (QPP)
GQL supports complex queries like QPP, which can find paths of varying lengths. An example query to find paths up to five hops long is:

```
MATCH ((a)-[r]->(b)){1,5} RETURN a, r, b
```

## Data Manipulation
Nodes and edges can be added using the INSERT statement. For example, to insert a node labeled as 'Pet' with properties:

```
INSERT (:Pet {name: 'Unique', pettype: 'Dog'})
```

To insert two nodes and an edge between them, the query could be:

```
INSERT (:Person {firstname: 'Avery', lastname: 'Stare', joined: date("2022-08-23")})
-[:LivesIn {since: date("2022-07-15")}]->
(:City {name: 'Granville', state: 'OH', country: 'USA'})
```

## Modifying and Deleting Data
GQL allows modification of data by setting or removing properties. For example:

```
MATCH (d:Pet) WHERE d.name="Unique" SET d.weight_kg=26
MATCH (e {lastname: 'Stare'}) REMOVE e.joined
```

Deletion is done by detaching and then removing nodes and relationships:

```
MATCH (a {firstname: 'Avery'})-[b]->(c) DETACH DELETE a, c
```
## Transactions
GQL supports serializable transactions with explicit or implicit `START TRANSACTION` statements, and can be terminated with `COMMIT` or `ROLLBACK`.

## Schema-free and Fixed-schema Graphs
GQL supports both schema-free graphs, which accept any data, and fixed-schema graphs, which are constrained by a predefined graph type. An example of creating a graph type and then a graph using that type is:

```
CREATE GRAPH TYPE /MyFolder/control/fraud_TYPE
// ... (graph type definition)
CREATE GRAPH /MyFolder/control/fraud TYPED /MyFolder/control/fraud_TYPE
```

## Comparing Nodes and Edges to Tables
In GQL, nodes with specific labels are akin to tables, and edges describe relationships between nodes without the need for the query writer to understand the relationships beforehand.

# How to Start GQL Journey
There are several resources available to start learning GQL. The official ISO website provides extensive documentation and tutorials. Additionally, practicing with a graph database that supports GQL, like the upcoming [NebulaGraph Enterprise v5.0](https://www.nebula-graph.io/posts/nebulagraph_enterprise_5.0_gql_support), can offer hands-on experience.

# Conclusion
GQL is a powerful new standard for property graph databases, offering a rich set of features for data manipulation and analysis. Its power and potential are evident in its ability to redefine data interaction, providing immediate, actionable insights, and opening new avenues across different fields. As this language continues to evolve, it is expected to play a crucial role in our understanding and interaction with data. 

With the upcoming NebulaGraph Enterprise, we're thrilled to bring native GQL support to our users. We believe that the adoption of GQL is a significant step forward in simplifying graph database usage and spurring innovation in the field. Stay tuned for more updates as we continue to explore the exciting possibilities GQL offers. If you're interested in NebulaGraph Enterprise, feel free to [reach out](https://www.nebula-graph.io/contact) to us.


This article draws on insights from the highlights of the full GQL documents and presents the fundamental GQL definitions with examples.

Understanding GQL: A Comprehensive Overview of the Standard Graph Query Language

ISO/IEC released the [international standard of Graph Query Language (GQL)](https://www.iso.org/standard/76120.html) on April 12th, 2024. This publication establishes the foundations for property graphs, covering their creation, maintenance, and control, along with the data they comprise. It also standardizes the data management language for outlining and modifying the structure of these graphs and their collections.

# Why the International Standard of GQL Matters
The standardization of a language plays an instrumental role in its adoption and application. The standardization of SQL in the 1980s led to its global acceptance by relational databases, thereby propelling the growth of relational databases.

Similarly, the standardization of GQL will have far-reaching impacts on the industry. GQL standards will ensure data portability and manipulation across GQL implementations, and compatibility with programming languages and database tools. It will foster a dynamic graph database ecosystem and lower the entry barrier for this technology, enabling more enterprises to effectively utilize graph databases for complex relational data.

# NebulaGraph Enterprise Offers Native GQL Support Since v5.0
As a member of Linked Data Benchmark Council, Vesoft (NebulaGraph) actively participates in the formulation and promotion of GQL standards and announces its GQL native support in the latest version of NebulaGraph Enterprise.

In addition to the outperforming horizontal scalability, high availability and HPA capability, NebulaGraph Enterprise v5.0 stands as the first distributed graph database to natively support GQL. Unlike systems that are just compatible or adapted to GQL, NebulaGraph Enterprise v5.0 has been redesigned to support GQL at the overall architecture level: it is built on and designed for GQL. This enhancement offers superior data compatibility and interoperability, thereby amplifying the business value of graph databases across various scenarios.

# Why Enterprises Should Adopt the Graph Database with Native Support for GQL

Choosing a graph database with native support for GQL means that enterprises can directly take the following advantages of GQL benefits.

- Enhanced Interoperability

Adopting a graph database with native support for GQL enables seamless data migration between different systems and platforms. This freedom reduces dependence on a single database provider, making it easy to switch between solutions and enabling simultaneous use of multiple data systems.

- Improved Stability

Enterprises embracing GQL will no longer rely on independent tools or software layers to be compatible with GQL statements. Therefore, they can enjoy the simplified system with less likelihood of errors and ease of operation and maintenance.

- Enhanced Security

GQL has a robust security framework that offers mature security control. Systems that do not natively support GQL may require additional development to achieve similar security features, potentially leading to risks such as data leaks and non-compliance with regulations.

- Cost-Efficient Maintenance

Adopting graph databases with native support for GQL enables companies to quickly adapt to market changes, which can directly reduce the complexity and expense of future system upgrades and maintenance for enterprises.

In conclusion, the release of GQL marks a significant milestone of graph database technology, fostering innovation and widespread adoption of graph databases as well as the overall growth of the industry. As the first distributed graph database that natively supports GQL, NebulaGraph Enterprise v5.0 aligns with GQL standards and will continue to refine its performance and stability to empower various industries, such as financial services, technology, e-commerce and manufacturing, to unlock the value of complex relational data.

If you’re interested in embracing GQL with NebulaGraph Enterprise v5.0, feel free to [reach out](https://www.nebula-graph.io/contact) for a free trial.

The release of GQL marks a significant milestone of graph database technology and  NebulaGraph Enterprise v5.0 announces native GQL support. 

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