<p><img src="https://user-images.githubusercontent.com/57335825/83610262-09d41d80-a534-11ea-875a-5e696df2c6f0.png" alt="Compare Multiple Projects with this GitHub Stats tool" /></p>
<p>If you have project code hosted on GitHub, chances are you might be interested in checking some numbers and stats such as stars, commits and pull requests.</p>
<p>You might also want to compare some similar projects in terms of the above mentioned stats, for whatever reasons that interest you.</p>
<p>We have the right tool for you: the simple and easy-to-use little tool called&nbsp;<a href="https://vesoft-inc.github.io/github-statistics/">GitHub Stats</a>.</p>
<p>Let's dive right in to what we can get out of it.</p>
<h2 id="post-getting-started">Getting started</h2>
<p>This interactive tool is really easy to use. Follow the three steps below and you'll get what you want in real-time:</p>
<ol>
<li>Head to the <a href="https://vesoft-inc.github.io/github-statistics/">GitHub repo</a> of the tool</li>
<li>Enter as many projects as you need to check on</li>
<li>Hit the Update button beside each metric</li>
</ol>
<p>In this article we are going to compare three most popular machine learning projects for you. &nbsp;</p>
<p><img src="https://user-images.githubusercontent.com/38887077/75141197-69e44800-572b-11ea-9dc5-308c7ed70dde.png" alt="image" /></p>
<h2 id="post-general-repository-informationnbsp">General repository information&nbsp;</h2>
<p><img src="https://user-images.githubusercontent.com/38887077/75141248-87191680-572b-11ea-882f-31ab726ee7af.png" alt="image" /></p>
<p>So basically these metrics can answer the following questions for you:</p>
<ol>
<li>Which is the freshest project?</li>
</ol>
<p>&nbsp; &nbsp; &nbsp; tensorflow wins because its last push happened just an hour ago.</p>
<ol start="2">
<li>Which project gets the most attention?</li>
</ol>
<p>&nbsp; &nbsp; &nbsp; tensorflow wins because it has the most watchers.</p>
<h2 id="post-popularity-metrics">Popularity metrics</h2>
<p>It's common that you want to know which project gains the most popularity.&nbsp;</p>
<p>But how can you measure popularity?</p>
<p>By stars and forks each project has gained.</p>
<p>See the screenshots below for the comparison results:</p>
<h3 id="post-star">Star</h3>
<p><img src="https://user-images.githubusercontent.com/38887077/75141279-95ffc900-572b-11ea-8476-41b51d52fe7c.png" alt="image" /></p>
<h3 id="post-fork">Fork</h3>
<p><img src="https://user-images.githubusercontent.com/38887077/75141281-98622300-572b-11ea-8ce2-37967ad6c6ac.png" alt="image" /></p>
<p>Apparently tensorflow gets the strongest momentum during the entire life cycle.</p>
<h2 id="post-maturity-metrics">Maturity metrics</h2>
<p>Besides popularity, you might also care about the maturity of a project, which is worth considering. Generally speaking, the more mature a project is, the more powerful as well as stable it is.</p>
<p>How to measure the maturity of a project?</p>
<p>By the most recent version, number of issues and number of pull requests.</p>
<p>See the screenshots below for the comparison results:</p>
<h3 id="post-release">Release</h3>
<p><img src="https://user-images.githubusercontent.com/38887077/75141348-b7f94b80-572b-11ea-81f2-8658ab9ecbc7.png" alt="image" /></p>
<h3 id="post-issues">Issues</h3>
<p><img src="https://user-images.githubusercontent.com/38887077/75141395-ce9fa280-572b-11ea-92fe-9f0980f51414.png" alt="image" /></p>
<h3 id="post-pull-requests">Pull Requests</h3>
<p><img src="https://user-images.githubusercontent.com/38887077/75141426-da8b6480-572b-11ea-8c4f-b32478af217a.png" alt="image" /></p>
<p>tensorflow and Pytorch have been maintained constantly while caffe is still at its 1.0 version with much fewer issues and pull requests than its two peers.</p>
<p>It's your decision to make which to follow.</p>
<h2 id="post-conclusion">Conclusion</h2>
<p>Data matters. And we hope this simple GitHub Stats tool can bring you the insight required. Also feel free to use it your own way.</p>
<p>Submit an issue if you find any valuable metric is missing in the tool. Here's the project repo again:&nbsp;<a href="https://vesoft-inc.github.io/github-statistics/">https://vesoft-inc.github.io/github-statistics/</a></p>
<p>Enjoy!</p>
<h2 id="post-you-might-also-like">You might also like</h2>
<ol>
<li><a href="https://nebula-graph.io/posts/nebula-graph-architecture-overview/">NebulaGraph Architecture — A Bird’s View</a></li>
<li><a href="https://nebula-graph.io/posts/nebula-graph-storage-engine-overview/">An Introduction to NebulaGraph's Storage Engine</a></li>
<li><a href="https://nebula-graph.io/posts/nebula-graph-query-engine-overview/">An Introduction to NebulaGraph’s Query Engine</a></li>
</ol>

This post introduces a React based tool to help you get the stats of GitHub metrics like stars, forks, commits, etc. so that you can make an informed decision which project to follow.

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

Compare Multiple Projects with this GitHub Stats tool

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


# 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.