<p><img src="https://user-images.githubusercontent.com/57335825/87520320-a4e20c00-c637-11ea-8053-7222b9c4f00a.png" alt="Pick of the Week" /></p>
<p>Normally the weekly issue covers NebulaGraph Updates and Community Q&As. If something major happens, it will also be covered in the additional Events of the Week section.</p>
<h2 id="post-events-of-the-week">Events of the Week</h2>
<p>This report was posted on DZone posted by the Tencent Cloud Security team. The report gives the testing environment and the test results. For more information, visit this URL: <a href="https://dzone.com/articles/performance-comparison-neo4j-vs-nebula-graph-vs-ja-1#:~:text=Seen%20from%20the%20above%20table,compared%20to%20Neo4j%20and%20HugeGraph.">https://discuss.nebula-graph.io/t/performance-comparison-neo4j-vs-nebula-graph-vs-janusgraph/619</a></p>
<p><img src="https://user-images.githubusercontent.com/57335825/90609906-8ed8e580-e1b9-11ea-9eab-eabae6583f03.png" alt="graph database performance comparison: neo4j vs janusgraph vs nebula graph" /></p>
<h2 id="post-nebulagraph-updates">NebulaGraph Updates</h2>
<p>The updates of Nebula in the last week:</p>
<p>• Optimizes the <code>GO</code> syntax. The query performance of a <code>GO</code> statement is optimized by allocating memory in advance, avoiding string copy, and so on. For more information, check this pull request: <a href="https://github.com/vesoft-inc/nebula/pull/2268">https://github.com/vesoft-inc/nebula/pull/2268</a>, which is contributed by <a href="https://github.com/xuguruogu">@xuguruogu</a></p>
<p>• Fixed the bug that the logs cannot be output because of the second try of starting the meta, storage, or graph service. For more information, check these pull requests:
<a href="https://github.com/vesoft-inc/nebula/pull/2289">https://github.com/vesoft-inc/nebula/pull/2289</a>, <a href="https://github.com/vesoft-inc/nebula/pull/2278">https://github.com/vesoft-inc/nebula/pull/2278</a></p>
<p>• Fixed the bug that the validity of the raft lease may be incorrect when replica_factor is set to 1. For more information, check this pull request: <a href="https://github.com/vesoft-inc/nebula/pull/2276">https://github.com/vesoft-inc/nebula/pull/2276</a></p>
<p>• Fixed the bug during compiling of JNI. For more information, check this pull request: <a href="https://github.com/vesoft-inc/nebula/pull/2271">https://github.com/vesoft-inc/nebula/pull/2271</a></p>
<h2 id="post-community-qa">Community Q&A</h2>
<p>Q: When the execution of a statement is successful, the screen prints "Execution succeeded (Time spent: 24.7027/24.7033 s)". What does "Time spent" here mean?</p>
<p>A: "Time spent" has two time values. The first one indicates the time consumed by the database system for the query process. It starts counting when the query engine receives a query statement sent from the console, and ends when the storage service obtains the data and all the required computing is done. The second one indicates the time consumed for the query process from the client side. It starts counting when the console sends the query, and ends when the console receives and prints the results on the screen.</p>
<h2 id="post-previous-pick-of-the-week">Previous Pick of the Week</h2>
<ol>
<li><a href="https://nebula-graph.io/posts/nebula-graph-pick-of-the-week-jul-31-2020/">GO with int Type</a></li>
<li><a href="https://nebula-graph.io/posts/nebula-graph-pick-of-the-week-jul-24-2020/">FETCH Syntax Goes Further with New Features</a></li>
<li><a href="https://nebula-graph.io/posts/nebula-graph-pick-of-the-week-jul-17-2020/">SQL vs nGQL & Job Manager in NebulaGraph</a></li>
</ol>

In this weekly issue, we are covering the graph database performance comparison among Neo4j, JanusGraph, and NebulaGraph, which was conducted by the Tencent Cloud Security team.

Features

System-testing

Dev-log

Performance

Use-cases

Community

Deployment

Tools

Query-language

Graph-computing

Tech-talk

Release-notes

Architecture

News

Source-code

Success-stories

Knowledge-graph

Pick of the Week at NebulaGraph - Performance Testing Report Comparing NebulaGraph with Other Graph DBMSs

Benchmarking the Mainstream Open Source Distributed Graph Databases at Meituan: NebulaGraph vs Dgraph vs JanusGraph

Graph Database vs Relational Database: What to Choose?

Difference Between Relational and Non Relational Database

Use cases of graph databases in real-time recommendation

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.