Imagine asking ChatGPT, “How did the 2023 global chip shortage impact Tesla’s suppliers in Shenzhen through three degrees of separation?” Despite its eloquence, the LLM would likely falter—not because it lacks data, but because it lacks a way to map hidden, dynamic relationships. This is where graph databases like NebulaGraph become essential for the next generation of AI and LLMs.

As AI shifts from statistical pattern recognition to knowledge-driven reasoning, traditional databases (SQL, NoSQL) hit a wall. Their rigid tables and documents can’t model the interconnectedness of real-world data—supply chains, fraud networks, or molecular interactions. This blog explores how graph technology is rewriting the rules of intelligent systems.

# LLM Hallucinations: When “Confidently Wrong” Becomes Dangerous
LLMs hallucinate because they infer answers from patterns, not facts. Without grounding in verified knowledge, they risk generating plausible but dangerous outputs. Hallucinations aren’t just errors—they’re liabilities because they lack access to verified, structured knowledge. A medical chatbot might invent a drug interaction that doesn’t exist, risking patient safety.

# The Limitations of Traditional Databases
One of the biggest limitations of traditional databases is that they treat data points as isolated entities. Relational databases force data into tables, losing nuanced connections. For example, mapping a 10-layer corporate ownership structure requires 50+ JOINs in SQL, slowing queries to minutes. Vector databases retrieve semantically similar data but miss contextual relationships.

AI often struggles with the isolated data because so much of its power comes from understanding relationships. Graph databases, by contrast, are designed to store and manage data as interconnected entities, allowing AI to make sense of the links between seemingly disparate elements.

# Knowledge Graphs for Intelligent AI Systems
Graph databases are the backbone of such knowledge graphs, providing a powerful means of organizing and traversing complex webs of information. They allow AI systems to dynamically explore and connect various pieces of data, enhancing both the depth and the accuracy of the answers provided. Knowledge graphs are already being used in domains such as finance, healthcare, and education, where the complexity of the data requires more than a surface-level understanding.

Consider a fraud detection system for a major bank. Instead of treating each transaction as an isolated data point, a graph-powered AI can look at an entire network of transactions—cross-referencing user relationships, transaction frequency, locations, and any outliers that indicate something suspicious. It’s this networked understanding that enables real-time decision-making, stopping fraud as it happens rather than analyzing it after the damage is done.

# Graph RAG: Minimize Hallucinations of Traditional RAG
Graph RAG (Retrieval-Augmented Generation) is an advanced evolution of traditional RAG that integrates knowledge graphs into the retrieval process, significantly enhancing contextual understanding and multi-hop reasoning capabilities. Graph RAG addresses hallucinations of traditional RAG by leveraging the structured relationships in knowledge graphs, enabling more precise and contextually rich information retrieval. This structured approach minimizes hallucinations by grounding responses in factual, interconnected data, leading to more accurate and reliable answers.

# Two Major Methods to Implement Graph RAG

- **Build Your Graph RAG with NebulaGraph**: This method requires some coding work and solid AI knowledge. By leveraging NebulaGraph’s powerful graph database capabilities, you can construct a knowledge graph that integrates with your existing data sources. The approach of [Implementing Graph RAG with NebulaGraph](https://www.nebula-graph.io/posts/implement_graph_rag_with_nebulagraph) allows for customization and fine-tuning of the graph structure and retrieval algorithms, providing a robust solution for complex enterprise applications. It involves steps such as data ingestion, graph construction, and integration with large language models (LLMs) to enable sophisticated query handling and response generation.

- **Choose NebulaGraph RAG**: For those without coding experience, [NebulaGraph RAG](https://www.nebula-graph.io/posts/announcing_nebulagraph_RAG) offers an intelligent app-building platform that simplifies the process. This platform abstracts away the technical complexities, allowing users to build and deploy graph-powered AI applications with minimal effort. It provides a user-friendly interface for creating knowledge graphs, configuring retrieval parameters, and generating responses, making it accessible to a wider range of users within the organization.

# Build Tomorrow’s AI with NebulaGraph Today
The next AI breakthrough won’t come from bigger LLMs—it’ll come from smarter data infrastructure. NebulaGraph bridges the gap between LLMs’ linguistic brilliance and the structured intelligence of real-world knowledge. In the race to AGI, graph databases aren’t optional—they’re the bedrock. As OpenAI’s CTO once mused, “The future of AI isn’t just neural networks; it’s neural networks married to knowledge.”

Discover how graph databases like NebulaGraph are powering the future of AI and LLMs by enabling complex reasoning, reducing hallucinations, and unlocking scalable knowledge-driven applications.

Why Graph Databases Like NebulaGraph Are Essential for the Next Generation of AI and LLMs

**Author Introduction**

Hong Hailan specializes in leveraging large language models (LLMs) to automate the extraction of knowledge from unstructured medical records, construct knowledge graphs in traditional Chinese medicine, and uncover the experiences of renowned TCM practitioners.


# Background


Building knowledge graphs presents dual challenges: improving the efficiency of knowledge extraction and reducing human labor costs. To effectively address these issues, this paper builds on the schema layer and explores methods for automated knowledge extraction. The following sections detail the process of constructing a knowledge graph for the treatment of rhinitis by Master Gan Zuwang, a renowned traditional Chinese medicine practitioner.
![](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/%E9%BC%BB%E7%82%8E%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1/1.png)
The data for this study comes from clinical records of rhinitis patients, with a total of 1,081 detailed records selected for analysis. These valuable records contain various basic patient information, such as name, workplace, gender, age, diagnosis time, disease type, symptom details, diagnostic results, observations of tongue and pulse, treatment records, prescription content, and medication guidance.






# Knowledge Graph Construction Process Diagram





## Step 1: Prepare Sample Data

Utilize the 1,081 preprocessed medical records as sample data.


## Step 2: Define Extraction Targets

Based on the ontology-defined schema layer, establish extraction targets like diseases, related diseases, symptoms, examinations, tongue appearance, pulse appearance, and compatibility. Define extraction rules for each target type.




## Step 3: Automated Knowledge Extraction



Create a prompt template based on the defined extraction targets, using a "demonstration case + relationship list" format to guide large language models in automatically extracting medical record data and generating triples.



**The following is a specific Prompt example:**

Imagine you are a large language model designed to extract relationships in Traditional Chinese Medicine. Your task is to extract the relationships between symptoms and related diseases for rhinitis patients. Here's an example medical case:

"Rhinitis has been severe for three days, the condition naturally flares up, with excessive nasal discharge, difficulty blowing the nose. Headache. Nasal congestion. Nasal mucosal edema, with secretions. Thin white tongue coating, floating and tight pulse. Prescription: mulberry leaf 6g, peppermint 6g, platycodon root 6g, calamus 6g, spatholobus stem 10g, schizonepeta 6g, angelica dahurica 6g, xanthium fruit 10g, fagopyrum 10g, vitex 10g."

The output format is:[(“rhinitis”, “symptom”, “excessive nasal discharge”), (‘rhinitis’, ‘symptom’, ‘difficulty blowing the nose’), (‘rhinitis’, ‘symptom’, ‘headache’), (‘rhinitis’, ‘symptom’, ‘nasal congestion’), (‘rhinitis’, ‘examination’, ‘nasal mucosal edema’), (‘rhinitis’, ‘examination’, ‘secretions’), (‘rhinitis’, ‘tongue appearance’, ‘thin white tongue coating’), (‘rhinitis’, ‘pulse appearance’, ‘floating and tight pulse’), (‘rhinitis’, ‘combination’, ‘calamus 6g’), (‘rhinitis’, ‘combination’, ‘spatholobus stem 10g’), (‘rhinitis’, ‘combination’, ‘schizonepeta 6g’), (‘rhinitis’, ‘combination’, ‘angelica dahurica 6g’), (‘rhinitis’, ‘combination’, ‘xanthium fruit 10g’), (‘rhinitis’, ‘combination’, ‘fagopyrum 10g’), (‘rhinitis’, ‘combination’, ‘vitex 10g’)]. Follow this format to extract the following medical case in triplet format. The given sentence is: ‘***’. Given the relationship list: [‘symptom’, ‘related disease’, ‘combination’, ‘tongue appearance’, ‘examination’, ‘pulse appearance’], provide the relationships in the list. If they do not exist, output: none.


## Step 4: Clean the Extracted Results

Use regular expressions to clean the automatically generated triplet data by removing errors, redundancies, irrelevant, and incomplete triplets. This process ensures the accuracy and reliability of the data.


## Step 5: Store the Triplets

Store the final extracted triplet data in the NebulaGraph database.

This paper directly extracts diseases and Chinese herbs into corresponding triplets to ensure the reliability of the knowledge extraction process using large language models. During the writing process, these triplets are further decomposed to ensure the rationality of the knowledge graph. Additionally, the entities and attributes of Chinese herbs are jointly extracted during the knowledge extraction. For example, the dosage of Fagopyrum 10g in a rhinitis patient’s prescription is extracted as ('rhinitis', 'combination', 'fagopyrum 10g').

Before storing the triplets, this paper uses regular expressions to extract the entities and their corresponding attributes. For instance, ('rhinitis', 'combination', 'fagopyrum 10g') is decomposed into ('rhinitis', 'combination', 'Fagopyrum') and ('fagopyrum', 'dosage', '10g'), as demonstrated in the constructed knowledge graph.




# Using NebulaGraph to build a knowledge graph




**Why NebulaGraph**？NebulaGraph is a high-performance and user-friendly graph database that offers researchers a new way to explore and analyze data thanks to its outstanding features and broad applications. It comes with a graph query language called **nGQL**, which provides users with a rich set of graph operation functions. Whether for basic CRUD operations on nodes and edges or complex graph algorithms and path queries, nGQL handles them effortlessly. This **flexibility and robust functionality make NebulaGraph an excellent choice for handling complex network structures and maximizing the value of medical case data**.

**Specific steps:**

* Algorithm pseudocode

* Construct prompt templates, related cases, and relationship lists

* Write functions to connect to **NebulaGraph**

* for i := 0 to N do // traverse medical case data

* Generate questions by combining data and templates

* Pass the questions to the LLM to get answers

* Clean the answers and remove unnecessary triples

* Build the IDs for patients, examinations, symptoms, and prescriptions in the current case

* for j := 0 to M do // iterate through the triple list


```
if relation'=="combination”:       
      Determine whether the Chinese herb in the triplet contains a dosage unit attribute. If it does, extract the attribute and store the extracted result in the knowledge graph. 
Else: Directly write the extracted results into the knowledge graph.
end for
```





# Experiment



This study utilizes a dataset of 353 pre-prepared medical cases as the foundation for knowledge extraction experiments using large language models (LLMs). In the experiment, other LLMs are employed for data annotation, followed by manual verification and correction to ensure the accuracy and reliability of the annotated data. This process serves to evaluate the knowledge extraction performance of each model. The Bert-BiLSTM-CRF model is selected as the baseline for joint entity and relationship extraction, and the results of automatic extraction using the Bert-BiLSTM-CRF model are compared with those obtained from large language models, primarily GLM3, GLM4, and ChatGPT-4. The evaluation metrics include F1 score, precision, and recall, with the experimental results presented in the table.
![](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/%E9%BC%BB%E7%82%8E%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1/2.png)



# Knowledge Graph Display


This study leverages the constructed knowledge graph to analyze and mine medication patterns used by a master physician in treating rhinitis, such as identifying the top 10 drugs most frequently prescribed for severe sneezing symptoms. The following Cypher query is executed to achieve this:

```
MATCH (v:Patient)-[:HAS_SYMPTOM]->()-[:SPECIFIC_SYMPTOM]->(v1) 
WHERE id(v1) = 'severe sneezing' 
MATCH (v:Patient)-[:PRESCRIBED]->(v3)-[e2:COMPONENT_OF]->(v4) 
RETURN v4, count(v4) AS cnt 
ORDER BY cnt DESC 
LIMIT 10;
```



This query identifies patients exhibiting severe sneezing symptoms, retrieves their treatment prescriptions, and sorts the drugs by frequency of occurrence, returning the top 10 most frequently prescribed drugs. The top 10 drugs are: Cicada Skin, Windproof, Ink Plant, Licorice, Earthworm, Black Soybean Coat, Chebulic Myrobalan, White Atractylodes, Rubia Root, and Astragalus.
![](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/%E9%BC%BB%E7%82%8E%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1/3.png)


This paper introduces how to build a knowledge graph for the treatment of rhinitis with traditional Chinese medicine using NebulaGraph.

Rhinitis Knowledge Graph in Traditional Medicine Using NebulaGraph

We are thrilled to announce the General Availability of the NebulaGraph Cloud Standard Tier with 14-day free trial! [NebulaGraph Cloud](https://www.nebula-graph.io/cloud-2025) is a fully-managed graph database service designed to support and enhance Graph RAG projects. It offers native support for ISO-GQL, the industry's first standardized graph query language, ensuring seamless integration with various AI models and tools. This support for ISO-GQL enables efficient querying and retrieval of data from the graph database, providing the necessary context for AI models to generate high-quality content.

# The Ideal Choice for Various Users

Graph databases excel at handling complex, interconnected data by modeling it as nodes and edges, which represent entities and their relationships, respectively. This structure enables efficient querying and analysis of data connections, making it ideal for applications where understanding relationships is crucial. The Standard Tier of NebulaGraph Cloud is tailored to meet the needs of different user groups:

- **Individual Developers**: For those looking to expand their skill set or build innovative projects, such as Graph RAG, the Standard Tier offers an affordable and accessible tool for learning and experimentation. It allows them to focus on developing their Graph RAG models without worrying about the underlying infrastructure complexities.

- **Data Scientists and Researchers**: The platform provides a robust environment for academic research and experimentation with Graph RAG. Researchers can leverage NebulaGraph Cloud to analyze complex datasets, develop novel Graph RAG algorithms, and gain valuable insights into the intersection of graph databases and generative AI.

- **Startup Founders and Small Teams**: For startups and small teams, the Standard Tier offers quick setup and minimal maintenance, allowing them to rapidly prototype and scale their AI projects. It helps them bring their innovative ideas to life without the need for extensive resources or expertise in managing graph databases.

# Key Features of NebulaGraph Cloud:

- **ISO-GQL Support**: NebulaGraph Cloud is the first to support the ISO-GQL standard graph query language on the cloud, allowing users to experience this powerful querying capability firsthand.

- **Beginner-Friendly**: Features no-code graph creation and interactive visual analysis, along with built-in demo datasets, making it accessible for beginners.

- **Flexible Data Modeling**: Supports multiple data types and complex relationship modeling, making it versatile for a variety of applications.

- **Observability**: Includes a monitoring dashboard with visual charts and continuous performance and resource utilization reporting.

# Special Launch Pricing:
**For the first 14 days after creating an instance, NebulaGraph Cloud Standard Tier will be available for free.** After the trial period, if the instance is not deleted, it will automatically start charging at a special discounted rate, with the benefit of this discounted pricing extending for the first six months.

Don’t miss this opportunity to innovate with NebulaGraph Cloud. **[Sign up for the 14-day free trial](https://cloud.nebula-graph.io/login) and experience the future of graph databases with native ISO-GQL support now!**

We are thrilled to announce the General Availability of the NebulaGraph Cloud Standard Tier with 14-day free trial!

Unlock the Power of Graphs: NebulaGraph Cloud Standard Tier Launches with 14 Day Free Trial!

SQL (Structured Query Language) has been the standard language for managing and querying relational databases for decades. Relational databases store data in tables and use structured schemas to define relationships between these tables. However, with the increasing complexity of data relationships in modern applications, there has been a growing need to model and query data in ways that go beyond traditional tabular structures.

Graph databases, such as Neo4j, Amazon Neptune, and NebulaGraph, have emerged to address these needs by storing data as nodes (entities) and edges (relationships). They are optimized for traversing and querying complex relationships, making them ideal for applications like social networks, recommendation engines, fraud detection, and network analysis.

In response to the rising popularity of graph databases and the demand for more flexible data modeling, several major SQL database systems have introduced support for graph queries. This includes the ability to create, manage, and query graph structures directly within a SQL database, leveraging the familiar syntax and capabilities of SQL. 

This introduction does bring benefits to organizations looking for unified data management as both relational and graph data can be managed within the same database system, simplifying their data architecture and reducing the need for multiple specialized databases. However, it does not necessarily mean that graph databases will no longer be needed or that they will be replaced. 
Here are several key points to consider regarding the impact of this development on graph databases:

# Data Model Complexity and Flexibility
The graph database is a natural fit for graph data. If the data inherently represents complex relationships and networks (e.g., social networks, knowledge graphs), a graph database provides a more natural and intuitive way to model and query the data. What’s more, graph databases offer more flexibility in evolving the data model. In relational databases, schema changes can be complex and disruptive, whereas graph databases allow for more dynamic and flexible schema evolution.

# Query Language and Usability
Graph databases often provide specialized query languages (e.g., Cypher for Neo4j, Gremlin for TinkerPop) that are designed specifically for graph traversal and pattern matching, making it easier to express complex queries. Since the ISO-GQL was officially released in April this year, the graph database industry is expecting this standard graph query language to provide a unified and standard experience for developers familiar with graph concepts, as using a standard graph-specific query language, can be more intuitive and efficient compared to using SQL extensions for graph queries.

# Real-Time Analytics and Insights
For applications requiring real-time insights and analytics, such as fraud detection or recommendation engines, graph databases are optimized for low-latency query execution and can handle real-time data updates and queries more effectively. Some graph databases are designed to integrate seamlessly with streaming data sources, enabling real-time graph updates and analytics. Apparently, under situations requiring real-time analytics and insights, even though relational databases can perform graph queries, they may not be ideal options.

# Scalability and Distributed Processing
In terms of scalability and distributed processing, relational databases with graph query support might not be competitive to dedicated graph databases. Many graph databases are designed to scale horizontally, distributing data and queries across multiple nodes in a cluster, for instance NebulaGraph. This can be particularly important for large-scale graph applications where the data volume and query complexity require distributed processing. Besides, dedicated graph databases often come with built-in, optimized graph algorithms (e.g., shortest path, centrality, community detection) that can be executed efficiently at scale. 

# Complementary Use Cases
Graph databases are optimized specifically for graph data and queries, offering advanced features such as efficient graph traversals, pattern matching, and handling of complex relationships. While SQL databases with graph query support can handle basic graph operations, dedicated graph databases are likely to remain superior for use cases that require high performance and scalability in graph processing. Therefore, SQL databases with graph capabilities and dedicated graph databases can coexist, each serving different needs and use cases.

# Performance Considerations
While SQL databases with graph query support can handle graph data, they might not match the performance and efficiency of dedicated graph databases, especially for large-scale graph processing and real-time analytics. Graph databases are designed with specific optimizations for graph workloads, such as index-free adjacency, which can significantly improve query performance for certain types of graph operations.

# Conclusion
In summary, while the support for graph queries in SQL databases enhances their capabilities and provides more flexibility, it does not render dedicated graph databases obsolete. Each type of database has its strengths and is suited for different use cases. Organizations will continue to choose the best tool for their specific requirements, and dedicated graph databases will remain crucial for applications that demand high-performance graph processing and advanced graph analytics. The coexistence of these technologies provides organizations with a broader range of tools to meet their diverse data management needs.

While the support for graph queries in SQL databases enhances their capabilities and provides more flexibility, it does not render dedicated graph databases obsolete.

Graph Databases vs. SQL with Graph Queries: Friends or Foes?

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

We are excited to announce the General Availability of NebulaGraph Enterprise v5.0, a groundbreaking release that establishes [NebulaGraph as the first distributed graph database to offer native support for the Graph Query Language (GQL)](https://www.nebula-graph.io/posts/nebulagraph_enterprise_5.0_gql_support).This milestone represents the pinnacle of three years of focused research and development, showcasing our commitment to innovation and excellence in the graph database industry.

Recognizing the importance of GQL early on, we embarked on this ambitious project in November 2021. The initial phase involved a thorough analysis of the GQL standards, followed by extensive design discussions. Over the next few years, we engaged in multiple development iterations and nearly a year of rigorous validation, continuously refining our implementation as the standards evolved. Today, we are proud to unveil NebulaGraph Enterprise v5.0.

# Why Native GQL Support Matters More Than GQL Compatibility

Implementing GQL at the architecture level is a complex and challenging endeavor, requiring a well-designed architecture and significant enhancements across the metadata, computation, and storage layers of the database. You could refer to the enhanced kernel engine in [Announcing NebulaGraph Enterprise v5.0 RC release: Delivering Full-Fledged GQL Support](https://www.nebula-graph.io/posts/rc_release_of_nebulagraph_enterprise_v5.0).

Some might assume that implementing a new query language is as simple as syntactic translation or adaptation. However, our experience tells us otherwise. Pure syntactic adaptation is fraught with challenges. Existing graph languages, such as Gremlin and Cypher, performing syntax translation to GQL might be in lack of catalog and schema management, which are fundamental to GQL as it draws heavily from SQL standards.

Historical attempts to translate query languages have not been successful. For example, some graph database vendors that have dedicated effort to convert Cypher to its own query languages have seen little progress. This underscores the necessity of native GQL support at the architecture level for achieving optimal performance and full compliance with the standards.

# Key Enhancements in NebulaGraph Enterprise v5.0
## Performance Boosts Up to 3x Faster with 90% Less Memory Overhead

[NebulaGraph Enterprise v5.0 delivers a significant performance boost, achieving up to 3x faster query execution while reducing memory overhead by nearly 90%](https://www.nebula-graph.io/posts/technical_preview_nebulagraph_enterprise_v5.0#post-performance). This is evidenced by the results from the LDBC SNB Interactive benchmark, where the Enterprise v5.0 demonstrated an average speedup of 3.01 times compared to the previous version.

## Graph Projection: Efficient Subgraph Analysis

NebulaGraph Enterprise v5.0 introduces support for graph projection, a powerful feature that allows users to create subgraphs based on existing graphs or MATCH queries. This capability enables users to isolate and focus on specific portions of their graph data, making it more efficient to perform complex graph computations and queries. By working with subgraphs, users can significantly reduce the computational overhead and improve query performance, leading to faster insights and more responsive applications. 

## Multi-Level Permission Management
Security and compliance are paramount in modern data management. NebulaGraph Enterprise v5.0 supports multi-level permission management, offering fine-grained access control down to the property level. This hierarchical permission system spans service groups, schema, graphtype/graph, nodetype/edgetype, and property, ensuring that only authorized users can access sensitive data. This feature not only enhances security and compliance with data privacy regulations but also improves administrative efficiency by reducing the risk of human error.
## Query Optimization and Isolation
NebulaGraph Enterprise v5.0 introduces substantial advancements in query optimization and isolation, including query-level resource limits and index hints. The query-level resource limits allow each query statement to utilize a specified amount of CPU and memory resources, and users can individually set the response time for each query. The major benefit of this capability is that it prevents a few resource-intensive queries from monopolizing system resources and potentially bringing down the entire cluster. This granular control ensures that the system remains stable and responsive, even under heavy workloads. The index hints give users more control over query execution. By specifying which indexes to use, users can optimize query performance and ensure that the most efficient execution path is taken. These improvements are crucial for enterprises handling complex relational data where efficiency, consistency, and security are paramount.

## Upgraded UI Experience for Analysts and DBAs
The new UI experience of visualization tools in NebulaGraph Enterprise v5.0 offers significant improvements for both business analysts and DBAs:
- Explorer: Redesigned for better interaction and focus on analysis, the new Explorer interface allows users to save and load historical analysis results, making it easier to track and revisit previous analyses.
- Dashboard: The Dashboard will integrate alert monitoring and diagnostics, enabling quick root cause analysis. It will also support multi-tenant management and resource allocation, providing DBAs with powerful tools to manage and optimize their database environments.

# Conclusion

NebulaGraph Enterprise v5.0 represents a significant advancement in graph database technology, offering native GQL support to meet the growing demands of complex data environments. We invite you to explore the capabilities of NebulaGraph Enterprise v5.0 and join us in advancing the frontiers of graph database technology. [Reach out to us today!](https://www.nebula-graph.io/contact)


We are thrilled to announce that NebulaGraph Enterprise v5.0 is now GA! This groundbreaking release establishes NebulaGraph as the first distributed graph database to offer native support for GQL.

Announcing the General Availability of NebulaGraph Enterprise v5.0: The First Distributed GQL Database

In the digital age, recommendation systems have become an integral part of online experience, driving better user experience and engagement. A recommendation system, often integrated with ad serving systems, is designed to suggest highly relevant content or advertisements to users based on users’ profile and engagement history. However, the complexity of user interactions and preferences demands robust data management solutions. Enter graph databases—an innovative approach that enhances the efficiency and accuracy of recommendation systems. This blog explores how recommendation systems work and how graph databases empower recommendation systems, providing real-life examples and architectural insights.

# What are Recommendation Systems and How They Work
## Key Components of Recommendation Systems:
- User Data: Information about users, including their demographics, behavior, preferences, and past interactions. This data is crucial for understanding user interests and making personalized recommendations.
- Content Data: Information about the content or ads available for recommendation. This includes metadata, categories, keywords, and performance metrics.
- Recommendation Algorithms: The core of the system, these algorithms analyze user and content data to generate personalized recommendations. Common types include collaborative filtering, content-based filtering, and hybrid methods.
- Real-Time Processing: The ability to process data and generate recommendations in real-time, ensuring that suggestions are timely and relevant.
- Feedback Loop: Mechanisms to collect user feedback on recommendations (e.g., clicks, likes, shares) to continuously improve the accuracy and relevance of the system.

## Types of Recommendation Systems
Recommendation systems are powerful tools designed to analyze user behavior and preferences to deliver personalized content and suggestions. These systems operate by collecting extensive data on user interactions, such as browsing history, ratings, and purchase activities. Using this data, recommendation systems employ sophisticated algorithms to identify patterns and relationships within the data. 

Two common approaches are collaborative filtering and content-based filtering. Collaborative filtering analyzes the behavior and preferences of similar users to make recommendations; for example, if two users have a history of liking similar movies, the system might suggest a movie liked by one user to the other. Content-based filtering, on the other hand, focuses on the attributes of items and recommends items that are similar to those a user has previously enjoyed. By combining these methods, recommendation systems can generate highly personalized suggestions that cater to individual tastes and preferences. This tailored approach not only enhances the user experience by presenting relevant content but also drives higher engagement and satisfaction, ultimately benefiting both users and businesses.

# What are Graph Databases
## Definition and Structure
Graph databases are a type of NoSQL database designed to handle and represent complex relationships between data entities. Unlike traditional relational databases that use tables to store data, graph databases use nodes, edges, and properties to create a flexible and highly interconnected data structure. Nodes represent data entities, edges denote the relationships between these entities, and properties provide additional information about the nodes and edges. This structure allows graph databases to efficiently model, store, and query intricate relationships and dependencies within data. Graph databases are quite young compared with relational databases and well-known graph databases include Neo4j, NebulaGraph, Amazon Neptune, and TigerGraph.

They excel at managing complex relationships because their inherent design closely mirrors real-world networks, such as social connections, organizational hierarchies, and financial transactions. This enables rapid traversal and querying of relationships, providing powerful insights and enabling applications like fraud detection, recommendation systems, and network analysis to operate with high performance and scalability.

## Architectural Insights

1. Data Modeling:
    * Nodes: Represent users, items, categories, etc.
    * Edges: Capture interactions such as likes, views, purchases, etc.
    * Example: In a movie recommendation system, nodes could represent users and movies, while edges represent viewing history and ratings.
2. Querying:
    * Graph Traversal Queries: Efficiently find relevant items by traversing the graph.
    * Example: To recommend books, a query might traverse from a user node to books they’ve rated highly, then to other users who rated those books highly, and finally to other books those users have rated highly.
3. Performance Optimization:
    * Indexing and Caching: Improve query performance by indexing frequently accessed nodes and caching results.
    * Example: Caching the results of common recommendation queries can reduce latency and improve user experience.

# How Graph Databases Support Better Recommendation Efficiency and Accuracy

Graph databases, such as [NebulaGraph](https://www.nebula-graph.io/), are particularly well-suited for handling the complex relationships and interconnected data involved in both ad serving and recommendation systems. Here’s how they support efficiency:

1. Complex Relationships and Connections:
    * Graph Structure: Graph databases store data in nodes (entities) and edges (relationships), making it easy to model and query complex relationships.
    * Example: Users, ads, and their interactions can be represented as nodes, with edges capturing relationships like views, clicks, and purchases. [LinkedIn](https://www.linkedin.com/) uses graph databases to model professional connections, endorsements, and job recommendations. By understanding the intricate relationships between users, companies, and skills, LinkedIn can provide highly relevant job suggestions and networking opportunities.
2. Real-Time Recommendations:
    * Fast Traversal: Graph databases excel at traversing relationships quickly, enabling real-time generation of recommendations based on user behavior and preferences.
    * Example: Quickly finding similar users or content based on shared attributes or interactions. [Pinterest](https://www.pinterest.com/) leverages graph databases to recommend pins based on user interactions and the connections between pins, boards, and users. This allows Pinterest to deliver personalized content in real-time, enhancing user engagement.
3. Personalization and Targeting:
    * Rich User Profiles: Graph databases can store and query detailed user profiles, including their connections, behaviors, and preferences, allowing for highly personalized ad targeting.
    * Example: Identifying users with similar browsing patterns and recommending ads that performed well with similar users. [eBay](https://www.ebay.com/) uses graph databases to improve its recommendation engine by analyzing user behavior, purchase history, and item relationships. This enables eBay to provide personalized product suggestions, increasing the likelihood of purchases.
4. Scalability:
    * Handling Large Datasets: Graph databases are designed to handle large volumes of interconnected data, making them suitable for high-traffic environments.
    * Example: Managing millions of users and ads, and their interactions, without performance degradation. [Facebook](https://www.facebook.com/)’s social graph, which models the relationships between billions of users, posts, and interactions, relies on graph databases to provide scalable and efficient recommendations. This ensures that users receive timely and relevant content, even as the platform grows.
5. Data Integration and Enrichment:
    * Combining Data Sources: Graph databases can integrate data from various sources, enriching user profiles and improving recommendation accuracy.
    * Example: Combining browsing history, social media activity, and purchase history to create a comprehensive user profile.
6. Feedback Loop and Continuous Learning:
    * Adaptive Algorithms: Graph databases facilitate the continuous updating of user profiles and recommendation algorithms based on real-time feedback.
    * Example: Adjusting recommendations based on recent user interactions to improve relevance.

# Conclusion
In summary, graph databases enhance the efficiency of ad serving and recommendation systems by providing a powerful way to model, store, and query complex relationships. They enable real-time personalization, effective targeting, and scalability, ultimately leading to better user engagement and optimized ad performance. As demonstrated by industry leaders like LinkedIn, Pinterest, eBay, and Facebook, leveraging graph databases can significantly improve user engagement and satisfaction.


This blog explores how recommendation systems work and how graph databases empower recommendation systems, providing real-life examples and architectural insights.

From Data to Insight: Leveraging Graph Databases for Superior Recommendations

This RideWallet is a leading force in the urban mobility payment industry worldwide. Integrated on the world’s renowned mobility service platform, the RideWallet has seamlessly facilitated millions of transactions, ensuring that passengers can effortlessly pay for their rides and other mobility services throughout the ride. **In addition to the ride payment, it supports a wide range of payment methods, including credit/debit cards, mobile wallets, loans and various local payment options tailored to regional needs.** In a short sentence, the RideWallet originated from the mobility service and evolved into a comprehensive payment solution.

# Rapid Expansion in Latin America Brings Financial Risk Management Challenges

In recent years, the RideWallet has been expanding rapidly, particularly in the Latin American market; for example, in Mexico alone, the company has issued over 5 million loans. However, with the boost of the business, it faces significant challenges in financial risk management. **The increasing geographical footprint has led to a surge in data volume, necessitating a system that can deliver faster response times, heightened accuracy, and unwavering stability.** The company encountered several critical challenges in its financial risk management operations:

- **Real-time Risk Control for Loan and New Card Issuance**: The consumer loan business, aimed at individual applicants, experiences high concurrency due to a large user base. To ensure a good user experience during the application process, the risk control decision engine cannot tolerate query delays exceeding one second. This places high demands on the graph technology's performance.

- **Mid-loan and Card Usage Risk Control**: Given the company's focus on the Latin American market, particularly Mexico, the relatively lenient overseas loan and credit card usage policies pose significant risks. Some payment institutions do not even verify CVV codes, leading to frequent misuse of loan and credit card limits. Traditional risk control rules struggle to accurately assess risks during the usage process.

- **Fraud Ring Detection**: For personal credit businesses, the risk control department is particularly concerned about fraud rings. Compared to individual loan application fraud, organized fraud poses a much greater threat. Therefore, accurately identifying high-risk communities is crucial to minimizing financial losses.

# NebulaGraph: Delivering Real-Time, High-Performance Risk Management Solutions

To address these complex challenges, NebulaGraph provided a comprehensive and efficient solution. NebulaGraph supports real-time graph construction, enabling full-graph risk control based on real-time application data. **With its high-performance data processing engine, the system can handle ultra-high concurrency, real-time bulk writes, and queries, ensuring stable operation even during peak times.**

**In terms of performance, NebulaGraph boasts exceptional computational capabilities, completing queries and calculations within the decision engine's tolerable time frame, ensuring the effectiveness of real-time rules.** The system employs advanced graph computation algorithms, combining real-time and asynchronous methods to extract relevant subgraphs from application data for asynchronous computation and then reintegrate them. This approach not only enhances processing efficiency but also ensures data accuracy and real-time performance.

Furthermore, NebulaGraph offers elastic scaling capabilities, allowing online scaling to ensure uninterrupted business operations. **The system can smoothly expand from 3 nodes to 12 nodes, and further scale online as needed to accommodate business growth, ensuring flexibility in response to increasing business demands.**

# The RideWallet's Use of NebulaGraph for Risk Control

The RideWallet leverages NebulaGraph in several key areas:

- **Real-time Risk Control During Loan Application**: Using the graph database, the system performs real-time queries to check if the applicant's IP address is a known blacklisted IP or if it connects to numerous devices. It also checks within three hops if the contact information provided by the user links to any blacklisted users.

- **Real-time Risk Control During Loan Usage**: Based on the fund flow graph, the system queries whether the user's loan funds are transferred to accounts that do not comply with the loan's intended use.

- **Risk Community Detection**: Using offline graph computations (WCC, Louvain), the system identifies communities closely linked to blacklisted nodes with high concentrations of black points. It extracts community features, such as the presence of multiple accounts with unidirectional transactions, a high proportion of midnight transactions, and several nodes with high PageRank values. These features are then fed into machine learning models for risk control decision-making.

![image](https://www-cdn.nebula-graph.io/blogs/RideWallet%20system%20architecture.png)
*System Architecture of the RideWallet*

# Achieving Significant Improvements with NebulaGraph's Advanced Capabilities

By adopting NebulaGraph's solution, RideWallet's credit and payment departments achieved significant improvements in multiple areas:

- **Business Value**: The enhanced risk control capabilities met the business expansion requirements, handling 3.3 billion edges and 630 million nodes. Through complex computations, the multi-stage risk control capabilities for application, credit approval, and loan usage based on graph technology were significantly improved, addressing data quality issues and enhancing overall risk control quality.

- **Technical Excellence**: NebulaGraph provided a stable, low-latency, high-throughput graph cluster capable of real-time updates, queries, and handling asynchronous computation tasks with a 4:9:1 ratio. Its distributed architecture and advanced graph computation technology ensured excellent performance in high-concurrency and complex computational environments, maintaining business continuity and stability.

- **Elastic Scalability**: NebulaGraph's elastic scaling capability allowed the system to smoothly expand from 3 nodes to 12 nodes, and further scale online as needed to accommodate business growth. This flexibility ensured the system could respond to business growth at any time, avoiding performance bottlenecks and service interruptions due to delayed scaling.

In summary, NebulaGraph's outstanding performance and powerful graph computation capabilities helped the customer achieve efficient real-time full-graph risk control, significantly enhancing both business and technical value.



This article introduce how a RideWallet leverages NebulaGraph for its Risk Management and the business results that NebulaGraph brings.

A RideWallet Elevates Financial Risk Management with NebulaGraph

In the ever-evolving digital landscape, the choice of a database is not just a technical decision; it's a strategic one. The right database can be the cornerstone of a business's success, while the wrong choice can lead to inefficiencies, data loss, and even business failure. In this article, let’s explore the critical factors that should guide your decision when selecting a database for modern applications.
# Part 1: The Database Product
## Data Model
The first step in choosing a database is understanding the data model that aligns with your business needs. Whether you're dealing with structured, semi-structured, or unstructured data, the data model should reflect the nature of your data and how it will be used. Relational databases are suitable for structured data, document databases for flexible data models, and graph databases for scenarios that require analyzing complex relationships. Each type of database has its unique strengths and optimal use cases, and the choice should be made considering the specific requirements of the business.

Relational databases organize data into tables with relationships defined by primary and foreign keys. They are ideal for structured data that fits neatly into rows and columns, such as financial transactions, customer information, or inventory records. Graph database, like Neo4j or NebulaGraph, which uses nodes, edges, and properties to represent and store data. They are excellent for managing complex networks of connections, such as social networks, recommendation engines, or fraud detection systems. For instance, an e-commerce platform might store product information, user reviews, and purchase history in documents. The flexibility of document databases allows the platform to change the data structure, such as adding new fields for product ratings or social media links, without significant reworking of the database schema.

## Scalability
As your business grows, so will your data. Scalability is crucial for future-proofing your database. As businesses grow, the data needs will inevitably expand, and choosing a database that can scale efficiently and cost-effectively is crucial for maintaining performance, reliability, and overall user satisfaction. Netflix's migration from Oracle to Cassandra is a well-documented case that highlights the importance of scalability when selecting a database for business. The shift allowed them to handle the exponential growth of their user base and data volume without compromising performance. 
- **Handling Growth**:
Businesses need to anticipate growth in data volume and user load. A scalable database ensures that the system can handle increased demand without significant rearchitecting or downtime.

- **Performance**:
As the user base grows, the performance of the database can directly impact the user experience. A scalable database maintains performance levels by distributing load effectively.

- **Cost Management**:
Scalable solutions often provide more cost-effective ways to grow. Horizontal scaling with commodity hardware can be cheaper than vertical scaling with high-end servers.

- **Reliability and Availability**:
Scalable databases often come with built-in mechanisms for fault tolerance and high availability, which are crucial for maintaining service levels and user trust.

## Performance
Database performance is integral to a business’s success across various dimensions, including customer experience, operational efficiency, real-time analytics, resource optimization, and customer support. Investing in high-performance databases and regularly optimizing them can yield significant benefits and provide a competitive edge in the market. A database that can't deliver quick query responses will slow down your applications and frustrate your users. For example, e-commerce websites like Amazon or eBay rely heavily on database performance. When customers search for products, add items to their cart, or complete a purchase, these actions interact with the database. If the database is slow, customers may experience delays, leading to frustration and potentially abandoning their shopping carts. Financial institutions like JPMorgan Chase or Goldman Sachs rely on real-time data analytics for trading, risk management, and customer transactions. An underperforming database can delay these analytics, leading to slower decision-making and potential financial losses. For instance, in high-frequency trading, even milliseconds of delay can translate into significant financial impacts.

## Reliability
Reliability refers to the ability of a database system to perform its functions correctly and consistently over time, especially in the face of failures or unexpected conditions. It is about the database's durability and consistency. Consistency ensures that data is accurate and reliable across all nodes in a distributed system. Companies like Walmart use data analytics to track sales, manage inventory, and forecast demand. Reliable data is essential for these analytics to be accurate and actionable. Take the case of Uber, which switched from PostgreSQL to a custom MySQL setup to ensure data consistency across its global operations, despite the high write and read demands. 

​​Investing in a reliable database system is not just a technical necessity but a strategic business decision. By ensuring that their databases are reliable, businesses can not only avoid potential pitfalls but also gain a competitive edge in their respective industries.

## Security
The security of a database is paramount for any business, as it affects a wide range of operational, legal, and reputational aspects. Here are some key reasons why database security is crucial, along with real-life examples to illustrate each point:
- **Protection of Sensitive Information**
Databases often contain sensitive information such as personal data, financial records, and proprietary business information. Securing this data is essential to prevent unauthorized access and misuse.
Companies like Equifax store vast amounts of personal and financial data. In 2017, Equifax experienced a data breach that exposed sensitive information of 147 million people. The breach led to significant financial losses and reputation damage.
- **Compliance with Regulations**
Various industries are subject to stringent regulations regarding data protection. Non-compliance can result in hefty fines and legal repercussions.
Hospitals and clinics must comply with HIPAA regulations in the United States. A breach of patient data can lead to substantial fines and legal action. For example, Anthem Inc. faced a $16 million settlement in 2018 after a data breach exposed the personal information of nearly 79 million people.
- **Preventing Financial Loss**
Data breaches can lead to direct financial losses from theft, fraud, and the costs associated with addressing the breach. Additionally, businesses may face indirect losses from reputation damage.
- **Ensuring Business Continuity**
A security breach can disrupt business operations, leading to downtime, loss of productivity, and operational inefficiencies.

# Part 2: Cost, Community and Support
## Cost
When selecting a database solution, cost is often a primary concern for businesses. The choice between open-source and commercial editions can significantly impact the overall expense. Open-source databases, such as MySQL or PostgreSQL, are generally free to use, which can be appealing for startups and small businesses with limited budgets. However, the hidden costs associated with open-source solutions often lie in the need for skilled personnel. Companies may need to hire expensive experts to handle installation, customization, and ongoing maintenance, which can add up over time. On the other hand, commercial database solutions like Oracle or Microsoft SQL Server come with licensing fees that can be substantial. However, these costs are often offset by the comprehensive support and maintenance provided by the vendor, reducing the need for in-house expertise and allowing businesses to focus on their core operations.

## Community and Support
The availability of community and support can make or break a database solution for many businesses. Open-source databases benefit from vibrant communities of developers and users who contribute to forums, documentation, and code repositories. This can be an invaluable resource for troubleshooting and best practices. However, relying solely on community support can be unpredictable and time-consuming, especially for mission-critical applications. Commercial databases, on the other hand, come with dedicated support teams from the vendor, offering guaranteed response times and expert assistance. This level of support can be crucial for businesses that require high availability and cannot afford downtime. Additionally, commercial vendors often provide training and certification programs, ensuring that your team has the skills needed to effectively manage the database.

# Part 3: Pitfalls to Avoid

When selecting a database for modern applications, companies often encounter several pitfalls that can lead to inefficiencies, higher costs, and even data loss. Here are some common pitfalls and strategies to avoid them:

- **Ignoring Business Needs**: Choosing a database without aligning it with the specific needs of the business is a common mistake. For instance, not considering the nature of the data, the expected data volume, or the required query performance can lead to a system that doesn't meet the application's requirements.

- **Underestimating Scalability**: As businesses grow, so does their data. Failing to select a database that can scale horizontally or vertically to accommodate this growth can lead to performance bottlenecks and increased costs down the line.

- **Neglecting Performance Optimization**: Performance issues can arise if a database is not properly optimized. This includes not only the database configuration but also the queries, indexing strategies, and hardware resources.

- **Overlooking Data Quality**: Poor data quality can lead to inaccurate insights and decision-making. Companies must implement data validation rules and regularly cleanse their data to maintain accuracy and consistency.

- **Insufficient Security Measures**: Data breaches can be costly. It's crucial to choose a database that supports robust security features, including encryption, access controls, and regular security updates.
Lack of Disaster Recovery Planning: Without a solid backup and recovery plan, companies risk losing data due to hardware failures, human errors, or natural disasters. Regular backups and a clear recovery strategy are essential.

- **Inadequate Resource Allocation**: Databases require maintenance and management. Not allocating sufficient resources, including personnel and budget, can lead to poorly managed databases that don't perform as needed.

- **Rigid Data Models**: Some databases have rigid data models that don't accommodate changing business requirements well. It's important to select a database that can adapt to evolving data structures and relationships.

To avoid these pitfalls, companies should conduct a thorough analysis of their data requirements, projected growth, and compliance needs. By taking a comprehensive approach to database selection, companies can choose a solution that will support their business needs both now and in the future.

# Conclusion
Selecting a database is a complex decision that requires a deep understanding of your business needs and the capabilities of the database solutions available. By considering factors like data model, scalability, performance, consistency, reliability, security, cost, you can make an informed decision that will serve your business well into the future. Remember, the database is the backbone of your applications; choose wisely.

This article explores the critical factors that should guide your decision when selecting a database for modern applications.

Database Selection: Critical Factors for Modern Applications

Snapchat, the camera-first social media platform, promotes authentic connections between its users through ephemeral photos, videos, and AR experiences. As of June of 2024, Snapchat reported having more than 432 million daily active users worldwide, ranking among the top mobile platforms in the US. **The user base growth poses growing challenges for the underlying infrastructure, requiring efficient tools to handle complex data workloads to navigate its vast, dense, interconnected social graph.** Snapchat’s Infrastructure team was seeking an efficient database solution to manage and analyze the massive interconnected user data, aiming to enhance their friend recommendation use case.

# The Challenge: Performance Bottleneck Restricts Real-time Friend Recommendations

To create better recommendations for friend suggestions, the underlying system needs to compute heuristics that leverage users and relationships, stored in aggregate and anonymized as metadata in vertices and edges. During the retrieval stage of the recommendation pipeline, these heuristics are used to compute affinity to the query user, and often require multiple hops, a task where graph databases excel compared to traditional relational databases. Moreover, the graph model is more natural for users to express data relations. At the end of 2022, Snapchat's Infrastructure team began exploring several graphDB solutions to examine their feasibility and performance.

# The Solution: NebulaGraph’s High Availability and Performance Make It the Ideal Choice

**After a thorough evaluation of various graph databases, the team decided to adopt NebulaGraph as it integrates seamlessly with their in-house environment and uniquely satisfies their production requirements of serving diverse online requests.**

NebulaGraph stood out as the team’s top choice with the following technical advantages:
- **High availability and resilience to failures**: NebulaGraph commits to delivering reliable performance with a 99.95% uptime, ensuring smooth operation during updates and upgrades.. Its high availability stands to remain unaffected in the face of failures such as node or network breakdowns, and even zone failures. This resilience is critical, considering the common occurrence of failures in large-scale clusters.
- **High performance**: NebulaGraph was among the very few top-performing databases, distinguished by its ability to store and process graphs with trillions of vertices and edges, while delivering impressive QPS and latency. It ensures highly simultaneous access, swift graph traversals, and optimized memory utilization.
- **Horizontal scalability**: NebulaGraph's shared-nothing distributed architecture offers linear scalability. This feature allows Snapchat to independently scale computing and storage, achieving desired performance at a cost-effective rate.
- **Seamless integration with Kubernetes**: NebulaGraph, with its cloud-native nature, can be effortlessly hosted within a Kubernetes environment, including AWS EKS, GCP GKE, Azure AKS, and more. Moreover, NebulaGraph holds full compatibility with the Kubernetes Operator Framework. This compatibility significantly reduces Snapchat's maintenance costs while ensuring uninterrupted service. The ability to run seamlessly in a Kubernetes environment further enhances its high-availability, offering customers peace of mind.
- **AAA security compliance**: NebulaGraph enhances Snapchat's network security through authentication, authorization, and accounting, safeguarding their user data. NebulaGraph supports both local and LDAP authentication that authorizes each user a role comprising various privileges, including metadata and data read/write access, and even fine-grained vertex and edge level permission management. Additionally, NebulaGraph logs user activities and database operations for future auditing. It also secures client-to-NebulaGraph and intra-NebulaGraph node traffic with TLS encryption. To protect data privacy, NebulaGraph supports TTL (Time-To-Live), ensuring that expired data is automatically and permanently removed from the database. Users can also delete data by dropping and clearing the database, providing control over data retention and privacy.

# The Result: Enhanced Performance, Reduced Costs and Simplified Maintenance

With the power of NebulaGraph, Snapchat has rolled out new friend recommendation workloads in production with some highlighted results, including:
- **Performance Amplification**: NebulaGraph's efficient handling of massive data sets has enabled Snapchat to employ new distributed online graph query algorithms, previously unattainable in traditional KV-store databases, while ensuring user data is stored in aggregate and anonymized as metadata in vertices and edges. This has led to a noticeable increase in new friendships formed through friends-of-friends recommendations. 
- **Large Graph Support**: NebulaGraph’s sharded architecture allowed ingestion of 12TB graphs and real-time queries at p90 <20ms. 
- **High Availability**: NebulaGraph has maintained 99.95% uptime since its initial deployment, and has shown it’s capable of handling large-scale ingestion, online query, through multiple traffic spike events. This has led to reduced operational overhead for the Snapchat Infrastructure team.
- **Cost-Effective Hardware**: Leveraging more affordable machine types for scaling, Snapchat can host NebulaGraph more economically compared to using a single, powerful machine with equivalent capacity.

**“We evaluated 9 graph DB solutions over the last year and found NebulaGraph capable of meeting our data size, query SLO, and QPS needs. NebulaGraph’s native Kubernetes support also makes it a nice fit to Snapchat’s underlying multi-cloud service mesh infrastructure. Since launch, we've thrown the book at NebulaGraph, adding 3 production clusters with more complex workloads and spiky online traffic patterns. Each time, it's scaled effortlessly, exceeding our expectations.” - Martin Qian, Sr. Manager Infrastructure at Snap.**

# Looking Ahead: Evolving the Entire Infrastructure with NebulaGraph

Moving beyond friend recommendations, Snapchat's Infrastructure team plans to leverage NebulaGraph in three new use cases:
1.	ML Edge Feature Storage: Eliminating the overhead of materializing costly multi-hop ML features, NebulaGraph can be used to power real-time inference and training queries.
2.	In-app User Engagement: Storing and querying relationships between users, content, lenses, locations and more will enable training graph convolution neural nets and relationship embeddings, boosting personalization.
3.	Off-app Analytics: Tackling identity resolution for Ads Attribution, and further data exploration opportunities beyond the app.

Additionally, NebulaGraph enables data sovereignty by allowing Snapchat to manage its own deployments of database instances. This shared-nothing data model ensures all of Snapchat's user data is maintained within Snapchat Infrastructure's controlled environments. This guarantees that enterprise data sovereignty is upheld and maximizes adoption flexibility.

This blog introduces why and how Snapchat leverages NebulaGraph for enhancing their real-time friend recommendations.

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