
# Summary


NebulaGraph is an open-source, distributed, and easily scalable native graph database capable of hosting very large datasets containing hundreds of billions of points and trillions of edges and providing millisecond queries.

Thanks to their unique data model and efficient query performance, graph databases play an important role in many business scenarios. Here are several typical business applications of graph databases:

* **Knowledge Graph**: Businesses and organizations need to build domain knowledge graphs from various data silos to support intelligent Q&A and semantic search.
* **Social Network Analysis**: Social network platforms need to analyze relationships, interests, and interactions between users to offer personalized recommendations and targeted advertisements.
* **Financial Risk Control**: Financial institutions need to monitor anomalous transaction activity, detect potential fraud, and assess credit risk.
* **Recommendation system**: E-commerce and media platforms need to provide personalized product or content recommendations based on the user's browsing history, purchase records, and other information.

NebulaGraph Cloud is a **fully managed cloud service** designed for NebulaGraph, and this article will detail how to realize cost reductions and efficiencies in synergy with AWS offerings.(For further instructions, please [refer to the AWS Marketplace product landing page](https://aws.amazon.com/marketplace/pp/prodview-wctboqxrwqkjy?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)


# Platform architecture and components





## Platform architecture
![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/941.webp)



NebulaGraph Cloud is designed and built on K8S with the following considerations:

* K8S's Operator mode enables the automation of all internal cluster activities, significantly reducing development costs.
* K8S offers a range of abstraction capabilities to streamline the management of compute, storage, and network resources, which can help users avoid vendor lock-in and achieve complete cloud-agnostic
* K8S's inherent features such as scaling up and down, scheduling framework, resource monitoring, and service discovery simplify various management operations.


## Service component


NebulaGraph Cloud is composed of two main parts:


### Control Plane


The control plane provides user console access for organizational management, user rights control, database management, network security settings, monitoring, alerting, and various other features designed to assist users in managing their NebulaGraph clusters.


### Data Plane


The data plane receives commands from the control plane, including resource scheduling, provisioning, versioning, horizontal scaling, specification adjustments, backup and recovery, observability, metering, and additional functions.


## Cost-optimized design


Illustrated with a sample database configuration:


```
apiVersion: apps.nebula-cloud.io/v1alpha1
kind: Database
metadata:
  name: db123456
  namespace: db123456
spec:
  provider: aws
  region: "us-east-2"
  k8sRef:
     name: "k8s-ml65nmjg"
     namespace: "k8s-ml65nmjg"
  tier: standard
  graphInstanceType: NG.C4.M32.D0
  graphNodes: 1
  StorageInstanceType: NG.C4.M32.D50
  StorageNodes: 1
  version: v3.8.2c
```


The database instance is defined using a Custom Resource Definition (CRD), and a database controller manages its operations within the control plane. Dependent compute resources are defined using the CRD NodePool, which is an abstraction of the compute resources of each cloud vendor, and is used to manage NodeGroups in AWS's EKS.As previously mentioned, NebulaGraph is a compute-storage separated architecture, so a database instance will correspond to two NodePool objects.

```
# Graph resource pool
apiVersion: apps.nebula-cloud.io/v1alpha1
kind: NodePool
metadata:
  name: gnp-7d0b156e
  namespace: db123456
spec:
  databaseRef:
    name: db123456
    namespace: db123456
  instanceCount: 1
  instanceType: x
  k8sRef:
    name: k8s-ml65nmjg
    namespace: k8s-ml65nmjg
  labels:
    platform.nebula-cloud.io/database-name: db123456
    platform.nebula-cloud.io/graph-pool: db123456-f24a
  provider: aws
  region: us-east-2
  zoneIndex: 0
# Storage resource pool
apiVersion: apps.nebula-cloud.io/v1alpha1
kind: NodePool
metadata:
  name: snp-59e9cc63
  namespace: db123456
spec:
  databaseRef:
    name: db123456
    namespace: db123456
  instanceCount: 1
  instanceType: x
  k8sRef:
    name: k8s-ml65nmjg
    namespace: k8s-ml65nmjg
  labels:
    platform.nebula-cloud.io/database-name: db123456
    platform.nebula-cloud.io/Storage-pool: db123456-un6g
  provider: aws
  region: us-east-2
  zoneIndex: 0
```



## Specification adjustments

Choosing the appropriate instance size is a more challenging task than expected for both users and service providers. Users frequently struggle to determine the optimal instance size for their needs. For example, as a user's data grows from 100GB to 1TB, they may be uncertain about the required number of CPU cores or the amount of memory necessary at each stage. It is up to the user and the SA to collaborate and ascertain these requirements.

When using fully managed cloud services, we provide an instance specification tuning function, which supports individual tuning of Query nodes or Storage nodes to meet the needs of users in terms of cost control and performance standards. Users can determine whether a single-node processing bottleneck has been reached based on the historical curve of the monitoring panel, and the cloud platform will also issue an internal alert.

![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/8d951d16b31294fcfbf25667fe48bc48.png)





## Horizontal expansion and contraction


Horizontal scale-up and scale-down supports individual tuning of the Query node, which can be regarded as a stateless service. This node can be scaled up or down to manage fluctuations in query traffic. Similarly, the Storage node, responsible for storing graph data and providing read and write capabilities, can also be scaled, with several data slices distributed across each Storage node. Each instance of expansion or contraction necessitates data migration and data slice rebalancing, thereby requiring meticulous operation.

Before proceeding with node expansion, it is essential to prepare the corresponding computing resources. The database controller will send the current desired number of nodes to the infra-controller to trigger the NodeGroup expansion of EKS. Here we do not enable CA or Karpenter for the following reasons:

* Scale-up/down decisions depend on specific cluster conditions and resource requests, and if these conditions are not met, it may not perform the intended scale-up/down operation
* Cross-availability scenarios do not guarantee the expansion and contraction of a given region
* The Pending Pod-based model is not tightly integrated with our business systems.

![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/1725438154945.jpg)

## Automatic elastic expansion and contraction


Supporting different workloads through automatic scaling up/down is a critical goal for us. Since computing and storage are separated, we can increase or decrease CPU and memory resources based on the utilization of each service node.

The Elastic Scaling Service architecture is as follows:

Every 10 minutes, the Elastic Scaling Service calculates the amount of resources that should be allocated to a database instance based on historical and current values of the monitored data and ultimately determines whether compute resources should be increased or scaled down. It can drive both horizontal scaling and vertical scaling (adjusting instance specifications) to ensure that business peaks are popped ahead of time. We will continue to make continuous improvements on this foundation to shorten the time to scale up and scale down by incorporating more metrics and proactive prediction strategies.

![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/873527167f86b6180154c413ddf8e499.png)


# Design for Efficiency Improvement





## Network security access

![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/2debb93e640f5e28273aeef9229a9230.png)

Currently, we offer two types of network connections to our users: network whitelisting and private link private connections. To ensure network security and simplify configuration steps, we introduced a proxy service, ngproxy, between the database instances and the Network Load Balancing (NLB), which takes full advantage of the functionality provided by the NLB. 

The NLB is capable of setting a PPv2 header on every transmitted packet. ngproxy parses these packets to verify whether the user's endpoint ID matches the ID configured in the console, with any mismatched requests being rejected. The same principle applies to the public network. The same principle applies to the whitelisting of public network access. Only if the source address matches the address in the whitelist will it be allowed to pass.






## Backup and recovery


Data backup serves as the primary defense against data loss, whether due to hardware failure, software error, data corruption, or human error, ensuring the safety of your data.

We provide users with both manual and scheduled backups and upload the backup data to AWS's object storage S3. We do not use a backup recovery solution based on cloud storage snapshots, as SST-based backup recovery is more rapid and does not depend on the cloud vendor's underlying services. Note that DDL and DML statements will be blocked during data backup, so it is recommended to perform backup operations during low business peak periods.

Backup data catalog structure:


```
backup_root_url/
- BACKUP_2024_08_20_16_31_43
├── BACKUP_2024_08_20_16_31_43.meta
├── data
│   └── 10.0.0.12:9779
│       └── data0
│           └── 5
│               ├── data
│               │   ├── 000009.sst
│               │   ├── 000011.sst
│               │   ├── 000013.sst
│               │   ├── CURRENT
│               │   ├── MANIFEST-000004
│               │   └── OPTIONS-000007
│               └── wal
│                   ├── 1
│                   │   ├── 0000000000000000001.wal
│                   │   └── 0000000000000000005.wal
.....
│                   ├── 30
│                   │   ├── 0000000000000000001.wal
│                   │   ├── 0000000000000000004.wal
│                   │   └── 0000000000000000005.wal
└── meta
    ├── __disk_parts__.sst
    ├── __edges__.sst
    ├── __id__.sst
    ├── __indexes__.sst
    ├── __index__.sst
    ├── __last_update_time__.sst
    ├── __local_id__.sst
    ├── __parts__.sst
    ├── __roles__.sst
    ├── __spaces__.sst
    └── __tags__.sst
- BACKUP_2024_08_20_19_02_35
```


Recovering data to a new instance of the program, so that you can take full advantage of the elastic expansion of resources on the cloud, specify a point in time after the backup set to quickly pull up a new database instance, and the user can be verified that the new database instance is working properly after the release of the old instance.




## High availability


For those who demand the highest level of service reliability, we offer a cross-availability zone disaster recovery solution. In NebulaGraph, a zone is a collection of service nodes that divide multiple Storage nodes into manageable logical zones to achieve resource isolation. At the same time, the platform service controls the access of Query nodes to replica data within the designated Zone, thus reducing network latency and traffic costs incurred by cross-availability zone communication.

One of the main challenges in managing stateful services across availability zones is the issue of zone affinity of storage volumes. To address this issue, we have taken the approach of placing a NodeGroup in each availability zone. This avoids the possibility that a newly expanded Node may be incorrectly placed in an incorrect availability zone, resulting in the Pod not being scheduled.

Drawing from past user experiences, we also support the solution of expanding capacity individually in a certain availability zone. The cloud-native community's HPA-like solution only solves the problem of the quantity after expansion, but it cannot be expanded to a specified region. Scaling according to the business traffic carried by each region can effectively improve the quality of service. If a region observes a sudden surge in QPS, the elastic scaling service of the data plane will allow the scaling of Query nodes in this region.

![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/a6c9b11cc02fcf83606ea5d3c5ee37f5.png)


# Practical exercises on the cloud




**Welcome to [NebulaGraph Cloud](https://cloud.nebula-graph.io) .** After initiating a subscription and creating a database, the next step demonstrates several ways to access the database:

## Visualization tools

Select an instance from the Database list, go to Data->Graph graph space management, and click Explorer Graph to enter the visualization application.

![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/89b9e3f86138ba8b72bcc6417c3e31e6.png)

Clicking on the Console in the upper right corner opens the console and allows you to execute queries for GQL statements.
![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/da4da5f0e3f63450cffd982bd39791fc.png)

## network whitelisting

On the Overview page of the database instance, click Connect and select the Public method.
![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/60437776c9f1654c165a796afbab220b.png)
When network whitelisting is not configured, the database instance cannot be accessed directly from the public network.


```
$ ./nebula-console -addr nebula-graph-ncqqo19eh0aus73e4un6g.aws.cloud.nebula-graph.io -port 9669 -u dbaas-test@vesoft.com -p $PASSWORD -enable_ssl
Welcome!
(dbaas-test@vesoft.com@nebula) [(none)]> show spaces;
+------------------------+
| Name                   |
+------------------------+
| "genshin"              |
| "hanxiaotest"          |
| "movie_recommendation" |
| "nba"                  |
+------------------------+
Got 4 rows (time spent 602µs/222.883326ms)
Mon, 02 Sep 2024 16:52:39 CST
```

## Privatelink private network connection

Follow the configuration steps for **Create Private Link Endpoint** to determine the VPC ID and Subnet ID where the service resides.

Run the command to create the Endpoint:


```
aws ec2 create-vpc-endpoint --vpc-id <YOUR-VPC-ID>  --region us-east-2 --service-name com.amazonaws.vpce.us-east-2.vpce-svc-04f25889c855f891b --vpc-endpoint-type Interface --subnet-ids  <YOUR-SUBNET-IDs>
```


Log in to the AWS console to view Endpoint status
![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/7cc3c33b5f8e6be1464ce3af6e4d2e0a.png)

Test access within the business VPC
![image|561x499](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/af0b663545e30935f090334da7bb2dac.png)
Next, you can maintain the data on the cloud through official eco-tools according to your business needs.


# Summary and outlook


This paper describes the architectural details of landing a fully managed cloud service for NebulaGraph on AWS based on cloud-native concepts, focusing on how AWS offerings can yield substantial benefits to users, and provide a new solution that combines cost and elasticity advantages for using NebulaGraph in the cloud.

While ensuring the quality of our services, we will persist in exploring possibilities to **reduce user costs and increase ease of use.** Our goal is for users to pay only for the resources they use, **implementing the concept of cost in every detail.**

Beta trials are currently underway, with a maximum spend of $1 per month, and you can subscribe via [AWS Marketplace ](https://aws.amazon.com/marketplace/pp/prodview-wctboqxrwqkjy?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)  or [Sign up for an account](https://cloud.nebula-graph.io).

If you are interested in NebulaGraph Cloud, please [join the group](https://join.slack.com/t/nebulagraph/shared_invite/zt-2k1eawak2-00D7XxOIZr8mDt0R4JfsSw) to unlock more DBaaS-related content. 




This paper describes the architectural details of landing a fully managed cloud service for NebulaGraph on AWS.

NebulaGraph Cloud on AWS：Auto Scale, Start Free

We are excited to announce the **beta preview of NebulaGraph Cloud** on AWS Global. Starting today, businesses and developers can experience the power of NebulaGraph Cloud for **up to $1 for a month**. This exclusive beta preview will run until the end of November, 2024, offering a unique opportunity to explore a scalable and high-performance graph database technology.

NebulaGraph Cloud excels at managing super-large graphs, making it an ideal solution for small businesses seeking robust data visualization tools. With NebulaGraph Cloud, users can effortlessly visualize and analyze complex datasets, unlocking valuable insights and driving informed decision-making.


![image](https://www-cdn.nebula-graph.io/blogs/dbaas-overview.png)
*The platform overview of NebulaGraph Cloud*

# Exclusive Beta Preview Offer:
- **Powerful Scalability and Performance**: NebulaGraph Cloud effortlessly supports billions of nodes and trillions of edge while ensuring millisecond-level response times, even under high concurrency. Your system is guaranteed to be always online! 

- **End-to-End Schema Management and Visualization**: Experience a seamless, no-coding-required journey from schema management to data import (including support for S3 data sources) and visual graph exploration. NebulaGraph Cloud offers an all-in-one solution that simplifies the graph database experience, making it accessible for users at any skill level.

- **Developer-Friendly Multi-Language Support**: NebulaGraph Cloud provides comprehensive support for multiple client languages, ensuring a smooth and efficient development process. Whether you’re a seasoned developer or just getting started, you’ll find our platform intuitive and accommodating.

- **Real-Time Observability**: Keep an eye on your database’s performance with real-time monitoring of operational status and resource consumption. NebulaGraph Cloud’s observability empowers you to maintain optimal system health and performance effortlessly.

- **Flexible and Cost-Efficient Pricing**: With NebulaGraph Cloud’s pay-as-you-go model, you only pay for what you use, ensuring maximum value for your investment. During the trial period, you could experience the full capabilities of NebulaGraph Cloud with only up to $1 per month!

![image](https://www-cdn.nebula-graph.io/blogs/dbaas-virtualization-2.png)
*NebulaGraph Cloud offers visualization for data*

![image](https://www-cdn.nebula-graph.io/blogs/dbaas-visualization.png)
*Data visualization in 3D mode*

To participate in the beta preview, you should [register](https://cloud.nebula-graph.io/login) as soon as possible. The beta preview will end on *Nov 30th*, so don't miss this chance to experience NebulaGraph Cloud's capabilities. 

***To learn more about the functionalities of beta preview, please refer to the [Terms of Use](https://www.nebula-graph.io/cloud_beta_terms).***

# How to Register:
- Visit [https://cloud.nebula-graph.io/login](https://cloud.nebula-graph.io/login)
- Sign up for the beta preview
- Start exploring NebulaGraph Cloud for up to $1 a month

For more information about NebulaGraph Cloud, please contact our service team at [cloud-service@vesoft.com](cloud-service@vesoft.com).



The beta preview of NebulaGraph Cloud on AWS Global goes live.

NebulaGraph Cloud Launches Beta Preview: Experience Graph Database as a Service for Up to $1 for a Month

Since NebulaGraph’s [joint introduction with LlamaIndex](https://www.nebula-graph.io/posts/graph-RAG) in August 2023, Graph RAG has been at the forefront of a technological revolution. It addresses the limitations of conventional search enhancement techniques by providing a more contextually aware and data-trained approach, ensuring that the responses delivered are not only precise but also well-informed.

Today, at the VLDB 2024 LLM+ KG Workshop, we are thrilled to announce the groundbreaking platform designed under the Graph RAG idea to simplify the creation of knowledge applications without the need for programming skills, NebulaGraph RAG.

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

*Wey Gu, the GenAI lead at NebulaGraph, presenting NebulaGraph RAG at VLDB 2024 LLM+ KG workshop*

# What is NebulaGraph RAG?
NebulaGraph RAG is a minimalistic knowledge application builder that puts the power of Graph RAG technology at your fingertips. It's the go-to solution for users to create search engines, chat bots, and content generation apps through a simple dialogue interface. By activating and circulating internal knowledge, NebulaGraph RAG maximizes its value, turning insights into actions.

# Why NebulaGraph RAG?
For many enterprises, the rich expertise and business insights of team members were often confined to a few, creating knowledge silos and consultation bottlenecks. For domain experts without a computer science background, GenAI technologies like DAG, Workflow, Agent, and RAG can be daunting, posing a significant barrier to the adoption of AI capabilities. As a result, only personnel of the data or AI department typically utilize these advanced tools, akin to how only a few people master complex BI and advanced statistical functions in Excel.

NebulaGraph RAG lowers the barrier to entry for these advanced tools, empowering every employee to create and optimize applications based on their domain knowledge. Guided by an intuitive dialogue interface, users can construct and refine applications as naturally as conversing with a colleague. From next-gen search engines to customer service Q&A systems, report generators, and troubleshooting tools, NebulaGraph RAG integrates cutting-edge technologies with simplicity at its core.

![image](https://www-cdn.nebula-graph.io/blogs/RAG-user-interface.png)
*User Application Interface: GraphRAG Answer Referenced Graph Context*

## Main Benefits for Users
- **Simplified Custom Solution Creation**: Enterprises can effortlessly build customized knowledge applications tailored to their complex needs, optimizing internal workflows without delving into technical details.

- **Inclusive Knowledge Application Building**: With a well-designed user interface and streamlined processes, employees can independently create knowledge bases and build applications. This inclusive approach accelerates the flow of information and resources within the enterprise, enhancing decision-making speed and accuracy.

- **Seamless Integration of Cutting-Edge Technology at Reduced Costs**: By leveraging the latest advanced RAG and Graph RAG technologies without additional development or costly training, businesses can quickly innovate in knowledge management and application creation, accelerating business innovation and value creation.

- **Instant Access to Internal Knowledge Bases**: The search and Q&A capabilities of NebulaGraph RAG enable businesses to rapidly construct search engines for internal knowledge bases, increasing the speed and efficiency with which employees access critical information.

![image](https://www-cdn.nebula-graph.io/blogs/RAG-build-apps.jpg)

*App building interface: Set up, maintain, and optimize apps through natural language*

# Ready to Elevate Your Knowledge Management into RAG Apps?

Compared to other toB RAG products on the market, NebulaGraph RAG excels not only in its exceptional Graph RAG capabilities, but also user-friendliness and accessibility. While other solutions may require extensive programming knowledge and technical expertise, NebulaGraph RAG ensures that even non-technical users can harness the power of advanced AI technologies. Leveraging NebulaGraph RAG will reduce costs of knowledge management, and surely foster a more inclusive and innovative work environment.
Interested in transforming your enterprise's knowledge application capabilities? [Fill out the form](https://www.nebula-graph.io/contact) for a free trial!

We are thrilled to announce NebulaGraph RAG,  the groundbreaking platform designed to simplify the creation of knowledge applications without the need for programming skills

Announcing NebulaGraph RAG: From Knowledge to Apps in Minutes

The advent of AIGC has introduced a new paradigm in content creation, where AI systems generate a wide array of media types, from text to complex visual art. However, the complexity of relationships within data can be challenging for traditional databases. Graph databases offer a solution with their ability to model and navigate the intricate connections between data points, providing a powerful infrastructure for AIGC systems.

# Graph Databases: A Catalyst for AIGC Innovation

## Complex Relationship Management
Traditional relational databases excel in handling structured data but often fall short when dealing with complex relationships. Graph databases, however, are specifically designed to manage complex relationships between entities. They visually represent the connections and dependencies between data points in a graph form. In AIGC scenarios, this capability is particularly vital as it involves understanding and analyzing vast amounts of text and data. Graph databases can efficiently store and query relationships between entities, aiding AI systems in comprehending contextual information deeply and uncovering the underlying complex relationships within data.

## Advanced Querying Capabilities
The advanced querying capabilities of graph databases, such as hybrid vector search and graph pattern matching, offer AIGC an unprecedented ability to retrieve and integrate information. The input for large models is no longer confined to a single document or sentence but can perform a leapfrog search across the entire knowledge graph, achieving cross-domain and cross-document associative queries. This "chain of thought" querying, made possible by the advanced querying features of graph databases, enables AI to mimic human thought processes, navigating flexibly between multiple knowledge points to generate comprehensive and accurate responses.

## Enhancing Efficiency and Quality
The application of graph algorithms through graph databases can significantly speed up AIGC processes and improve the quality of content. Graph databases support the storage of vectorized graphs, allowing graph algorithms to operate on them and solve certain problems within AIGC. For instance, graph algorithms can capture entities and their relationships within a paragraph or summarize and categorize all entities and their relationships in a book. This capability enables graph-based AIGC systems to understand vast amounts of natural language text efficiently without the need for expert intervention. Moreover, graph algorithms can quickly locate relevant information nodes and their relationships, reducing unnecessary information processing steps and thus greatly enhancing the efficiency of content generation and the quality of AIGC.

## Visualization of Reasoning Chains
The visual capabilities of graph databases provide an intuitive way to display the reasoning process of AIGC systems. Through graphical interfaces, users can clearly see every step and logical chain in the reasoning process. This visualization not only enhances the system's transparency and credibility but also helps users better understand the sources and basis of the generated content. In fields such as academic research and business decision-making, this visual reasoning chain is crucial for improving the accuracy and reliability of decision-making.

# Real-world Applications of Graph Databases in AIGC
## Intelligent Q&A Systems
With the support of graph databases, intelligent Q&A systems can not only perform information retrieval and reasoning through traditional knowledge graphs but also support vectorized knowledge graphs. This means the system can manage and retrieve vector data, integrating intelligent Q&A technology based on graph queries and graph calculations with advanced technology based on vector retrieval. This integration not only improves the accuracy and efficiency of Q&A but also significantly reduces additional data storage and management overhead, bringing a smoother and smarter experience to users. At the same time, with the aid of AIGC technology, the system can generate richer and more personalized content, further enhancing user experience and satisfaction.

## News Media and Short Videos
The combination of graph databases and AIGC technology has shown great potential in the fields of internet news media and self-media short videos. By efficiently managing complex relational data through graph databases, AIGC technology can accurately analyze user behavior and automatically generate personalized content. In news recommendations, the combination of the two can quickly match user interests with news hotspots; in the field of short videos, it can achieve intelligent editing and distribution, enhancing user experience and platform efficiency. This integrated application is leading a new trend in the production and dissemination of internet content.

## Content Creation Assistance
For content creators, graph databases provide immense value by integrating vast data resources from the internet into a comprehensive knowledge graph, facilitating tasks such as inspiration, material search, and content integration. Creators can use this graph for inspiration, material search, and content integration. The support of graph databases makes content creation more efficient, accurate, and creative.

# Core Value of Graph Databases to AIGC
## Enhancing Content Quality and Richness
Graph databases provide a comprehensive network of entity relationships as a knowledge base support for AIGC systems. This allows AIGC systems to fully consider the complex relationships and diverse connections between entities when generating content, resulting in content that is closer to reality, rich in depth and breadth. In addition, by combining the reasoning capabilities of large models, AIGC systems can also generate personalized content according to users' specific needs and preferences, further enhancing the richness and appeal of the content.

## Reducing Generation Costs
Although the construction and maintenance costs of graph databases are relatively high, once they are built and integrated with AIGC systems, their efficient data processing and utilization capabilities will significantly reduce the costs associated with content generation. Since graph databases have the advantages of fully utilizing existing data resources, greatly reducing expert intervention, and improving the efficiency of new data collection and processing, the costs at multiple stages of content generation tasks will be significantly reduced. This is of great significance for enterprises and institutions that require a large output of content.

## Improving the Accuracy and Interpretability of Reasoning
The graph model used in graph databases can help make the reasoning process clearer and traceable. When using graph databases as the basis for reasoning in AIGC systems, it can ensure that every step of the reasoning process has a clear logical basis and data support. This interpretability not only enhances users' trust in the generated content but also helps to identify potential problems and areas for improvement. At the same time, further improving the transparency and credibility of the reasoning process by visualizing the reasoning chain is also an important direction for future development.

# Challenges and Future Prospects
## Construction Costs and Technical Thresholds
Currently, the method of relying on experts to manually construct knowledge graphs is relatively costly and technically demanding, which limits the wider application of knowledge graphs. To reduce construction costs and improve usability, the market has begun to see the emergence of some automated construction tools and technical means, such as the automated AIGC approach we currently provide, which supports the low-cost and efficient construction of knowledge graphs. At the same time, strengthening cross-domain cooperation and exchange also helps to promote the popularization and expansion of the application range of graph database technology.

## Data Quality and Updating Issues
Data quality is crucial for AIGC, and data cleaning is often costly, which is a major challenge faced by the industry. Based on graph databases, users can use graph pattern matching, association relationship analysis, and other means to effectively identify and handle erroneous or low-quality data. To ensure the completeness, accuracy, and timeliness of the data in the graph database, we can take various measures, such as introducing missing association relationship prediction technology and generation technology, anomaly detection technology based on graph algorithms and graph analysis, to timely capture and process new data source information; at the same time, strengthen the data verification and cleaning process to ensure the accuracy and integrity of the data; and establish an effective data sharing and collaboration mechanism to promote the integration and utilization of cross-domain data.

# Industry Application Expansion and Standardization
As technology continues to mature and application scenarios expand, the integration of graph databases and AIGC will be applied in more industries and achieve significant results. To promote the healthy development and widespread application of this field, it is particularly important to establish unified standards and specifications to guide the research and development and application practices of technology. These standards and specifications can cover aspects such as data formats, interface protocols, and performance evaluation methods to ensure interoperability and compatibility between different systems and platforms. At the same time, standardization work can also promote technical exchange and cooperation across fields. The vector data management related functions in the database are being considered for inclusion in the future versions of SQL and GQL standards, and this topic is still actively discussed in the industry. This standardization process will undoubtedly help further collaboration and integration of AIGC and databases, thereby promoting the common progress and development of the entire industry.

# Conclusion
As a solid foundation for AIGC, graph databases have demonstrated powerful capabilities in organizing, querying, and reasoning information, significantly enhancing the quality and efficiency of content generation. With ongoing technological advancements and expanding application scenarios, the application prospects of graph databases in the field of AIGC will undoubtedly be broader. 

[The Graph RAG proposed by NebulaGraph](https://www.nebula-graph.io/posts/graph-RAG) last year is undoubtedly a profound insight and positive response to this trend. If you’re interested in developing your AI products empowered by one of the leading graph databases, NebulaGraph, feel free to [reach out](https://www.nebula-graph.io/contact).

Graph databases are the backbone for AIGC because the advanced querying capabilities enhance AI's ability to understand complex data relationships.

Graph Databases: The Ultimate Tool for Enhancing AIGC Quality and Efficiency

In the rapidly evolving digital landscape, Database-as-a-Service (DBaaS) has emerged as a pivotal solution for organizations seeking to streamline their database management. The rise of DBaaS is closely related to the popularization of cloud computing. As enterprises become increasingly dependent on data, DBaaS has become an important part of IT infrastructure, providing functions such as data backup and recovery, scalability, and performance monitoring.

# What is DBaaS

Essentially, DBaaS is a fully programmable database system deployed in a public cloud and managed by the DBaaS provider. It is designed to be automated, highly available, secure, and requires minimal routine maintenance from the user's side. This service model simplifies database management by handling deployment, management, and operations, allowing end-users to concentrate on their core business objectives and application development. 

# Comparison between DBaaS and traditional database

Below is the comparison table in English, highlighting the key differences between Database-as-a-Service (DBaaS) and traditional databases from the perspectives of cost, risk, efficiency, and risks as well as challenges:

| Aspect          | Traditional Databases                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | DBaaS (Database-as-a-Service)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
|-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Costs           | Requires significant upfront investment in hardware, software licenses, and employment of DBAs for maintenance, leading to continuous operational costs.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | Typically follows a pay-as-you-go model, reducing hardware costs and avoiding large initial investments, transforming CapEx to OpEx, and allowing for more efficient resource allocation.                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
| Risk            | Enterprises bear the risks of maintenance and upgrades, including data security, system compatibility, and backup recovery.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | DBaaS providers manage database maintenance, upgrades, and security patches, reducing the risks associated with technical updates and system maintenance. Automated backup and disaster recovery features also mitigate the risks of data loss and system failures.                                                                                                                                                                                                                                                                                                                                                 |
| Efficiency      | Deployment and configuration can take a long time and require specialized knowledge for management.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | Enhances efficiency through automated management tasks and rapid deployment capabilities. Allows users to quickly configure and scale database resources through self-service portals, accelerating development and operational processes.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
| Risks and Challenges | In certain situations, such as when there are complex database logical relationships or when the database needs to be bound to specific workloads, traditional databases might be a better choice. | There is a risk of vendor lock-in, which may limit the ability of enterprises to migrate or change service providers in the future.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |

# Benefits of DBaaS

DBaaS (Database-as-a-Service) is particularly beneficial for companies without a dedicated database team, with the following detailed advantages:

- **Simplified Database Management**: DBaaS allows for easy management of database instances through a user-friendly control panel, including operations such as creation, scaling, backup, and recovery, without the need for in-depth knowledge of database technical details.

- **Cost Reduction**: DBaaS eliminates the costs associated with purchasing, configuring, and maintaining hardware equipment, as well as hiring professionals, enabling businesses to utilize resources more effectively.

- **Automated Maintenance**: Cloud service providers take care of automatic backup, monitoring, and upgrades for the database, ensuring high availability and stability.

- **Enhanced Security**: DBaaS providers typically implement strict security measures, including data encryption, authentication, and access control, to protect user data from threats.

- **Rapid Deployment and Configuration**: DBaaS allows developers and IT professionals to quickly establish configuration and provisioning standards, communicating with the database through simple APIs or low-code applications, without worrying about the database itself.

- **Future-Proofing**: DBaaS can scale quickly with the expansion of the business, without the need to purchase additional storage, ensuring that the database infrastructure always aligns with business needs.

- **Professional Database Management**: DBaaS solutions are managed by a team of experts from the provider, who are responsible for monitoring, backup, recovery, node repair, cluster resizing operations, node management, applying hotfixes, and upgrades.

- **Reduced Hassle and Overhead**: With DBaaS solutions, teams do not need to develop or hire specialized personnel, such as database administrators, DevOps, and security personnel to manage the database. There is also no need to select hardware, purchase it, wait for it to arrive, install it, or set it up, or maintain it, thus saving on the costs of hardware data center technicians.

- **Disaster Recovery**: Distributed DBaaS typically replicates your data across multiple availability zones within a region, so there is no single point of failure.  Adding more replicas and expanding clusters to multiple data centers are allowed as needed.

- **Ease of Use**: DBaaS service providers usually offer user-friendly interfaces and tools that make it easy for users to manage and access their databases, even without technical expertise.

- **Professional Support**: DBaaS providers typically offer technical support to help users troubleshoot any database issues that may arise, which is a valuable resource for users who may not have in-house IT staff or expertise.

# Who Should Consider DBaaS?

Both enterprises and developers can benefit from DBaaS. For **enterprises** DBaaS offers a way to manage large volumes of data efficiently while maintaining high availability and performance . **Developers**, on the other hand, can leverage DBaaS for rapid application development, testing, and deployment without the complexity of managing databases.

# What factors should you think about when adopting DBaaS?

When selecting DBaaS (Database-as-a-Service) as its data management solution, a company should consider the following key factors to determine if it meets its specific needs:

- **Technical Team's Expertise**: If a company lacks a professional database management team, DBaaS can provide convenient database management services, including automatic backup and recovery, allowing the company to focus more on the development of its core business.

- **Specific Scenarios of Project Requirements**: DBaaS is suitable for application scenarios that require rapid deployment and flexible scaling, such as temporary database needs for continuous integration or backend testing.

- **Applicability to Micro-services Architecture**: In a production environment that adopts a micro-services design composed of numerous databases, DBaaS can provide the capability to quickly go into production.

- **Cost-Benefit Analysis**: DBaaS can bring economic benefits by reducing the costs of hardware and software upgrades, lowering capital expenditures, and simplifying database management.

- **Trade-off of Control**: Using DBaaS may mean giving up direct control over databases and servers in some cases, as the cloud service provider is responsible for the underlying infrastructure and security.

- **Network Dependency**: DBaaS requires a stable Internet connection; any network issues can affect the availability and performance of the database.

- **Security Considerations**: Companies need to assess the security measures of DBaaS providers to ensure the safety of sensitive data and compliance with data protection regulations.

- **Risk of Vendor Lock-in**: Choosing DBaaS may expose companies to the risk of vendor lock-in, especially if the service is tightly integrated with a specific cloud platform.

- **Performance Requirements**: For applications that require high performance and large-scale processing capabilities, the performance of DBaaS may need to meet the demands of specific workloads.

- **Testing and Prototyping Exploration**: Before making a final decision, companies should consider creating and exploring DBaaS prototypes to assess whether their performance and functionality meet the actual needs of the application.

# Conclusion

The global DBaaS market is projected to experience significant growth, driven by the increasing demand for cost-efficient database management solutions and the widespread adoption of cloud-based services across various industries. As organizations continue to prioritize agility, scalability, and data-driven insights, DBaaS is set to become an integral part of the digital transformation journey.

For anyone considering DBaaS, it's crucial to evaluate the specific needs of your organization, the type of DBaaS that aligns with your tech stack, and the potential for scalability and integration with existing systems. With the right choice, DBaaS can be a powerful enabler of business growth and innovation.

This blog introduces what database-as-a-service is and why enterprises and developers should consider it as an option.

Why Database As A Service

We are thrilled to announce the RC release of the latest NebulaGraph Enterprise, **the first and now only distributed GQL database**, and a significant milestone in the evolution of graph database technology. This latest version introduces a redesigned architecture that fully leverages the power of GQL, an enhanced kernel engine for superior performance, and powerful capabilities such as sub-graph computing and multi-cluster management, to deliver the utmost in performance, reliability and security.

The latest version of NebulaGraph is engineered to deliver exceptional performance and efficiency, addressing the critical needs of modern data operations. Read on to learn how these powerful new capabilities can transform your data management practices and meet your specific business requirements.

# What New Powerful Features Does the Latest Version Bring?

# Upgraded Architecture
In addition to the role of a standard query language, GQL also standardizes the comprehensive definition of graph databases, including catalog, graphtype, graph, etc. Simply adding GQL compatibility to graph databases would not be able to unleash the essential power of GQL. Therefore, in the latest version, from the storage layer up to the computing layer, the team has made great optimizations to better offer GQL power from the architecture level.

# Enhanced Kernel Engine
The engine of the new version has been upgraded and optimized corresponding to the GQL standards, adding new features and capabilities to better meet customers’ needs.

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

- Graph-native Query Plans

The latest version of NebulaGraph Enterprise introduces query optimization rules: not only the query optimization based on relational algebra, but also graph-native query plans and operators catering to the use cases of enterprises.

- Upgraded Data Storage Mode

The execution capabilities of the storage layer have been upgraded to handle operators pushed down by the computing layer. The storage mode of the graph database has been optimized to achieve finer-grained sharding, which can be easily merged and split, thus mitigating the difficulties caused by the underlying data distribution. 

- Strengthened Executor

The new version supports concurrent execution environments, vectorized execution engines, more detailed memory management down to the query level, and push-down of subquery plans. The interactive communication protocols and data structures between the computing layer and the storage layer have been tuned to reduce data conversions and improve overall performance and memory efficiency. 

# Subgraph Computing Added
The new version introduces sub-graph computing to provide iterative computing on complex graph traversal in real-time. Sub-graph computing is broadly leveraged to analyze and process specific areas of a network without having to process the entire graph, saving both time and computational resources.

# Multi-cluster Management Introduced

In accordance with the GQL definition that the catalog and graphtype correspond to multiple graphs, the new version introduces the multi-cluster management. The unified management of multiple clusters supports third-party authentication and allows authorization based on the principle of minimum authorization (PoLP). The same cluster resources therefore can be shared to maximize resource utilization while different clusters have independent resources, permissions, and data, meeting performance and privacy security requirements.

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

# Stronger Integration and Customization Capabilities

The new version of NebulaGraph Enterprise brings stronger integration and customization capabilities to allow users to add various system plug-ins to complete system query computing and maintenance functions. These plug-ins include various algorithms, functions, authentication methods, auditing, log management, etc. Users can customize as needed through the provided plug-in interface, add or modify related plug-ins. Moreover, these plug-ins can also be managed in a refined manner through permission control.

# What Does These New Capabilities Mean for You?
## Future-Proofing Against Technological Obsolescence
Standards are a trusted ally for everyone in the tech landscape and embracing GQL sooner than later ensures long-term viability in the rapidly evolving tech world. From CIOs to developers, each tech role in the organization benefits from the consistency of GQL: avoiding the steep learning curve of new technologies, enhancing productivity, seamless interoperability with the ecosystem, and preventing vendor lock-in.

## Up to 3 Times Performance Increase and 90% Memory Efficiency Increase
NebulaGraph Enterprise witnesses a leap forward in performance and memory efficiency in the new version. [In testing using the LDBC SNB Interactive benchmark, the latest version demonstrates a remarkable speedup over the previous one, achieving up to 3 times faster query execution](https://www.nebula-graph.io/posts/technical_preview_nebulagraph_enterprise_v5.0). Not only does the new version excel in speed, but it also significantly reduces memory usage, boasting a peak memory footprint that is 90% smaller than its predecessor. 

In practical terms, the new version of NebulaGraph Enterprise delivers more than 3 times the performance while utilizing just 10% of the memory resources, making it an exceptionally efficient and powerful choice for graph database needs.

## Elevated Simplicity and Adaptability

The ease of use and flexibility are taken to new heights of the new version of NebulaGraph Enterprise. With the addition of sub-graph computing, users can now effortlessly analyze and manage specific portions of their data, making complex queries more intuitive and efficient. The new plug-ins allow for enhanced customization, enabling businesses to tailor the database to their unique needs seamlessly. Furthermore, the multi-cluster management capability ensures that scaling and maintaining large, distributed systems is both straightforward and reliable. These advancements collectively empower users to harness the full potential of their graph data with greater simplicity and adaptability.

The launch of NebulaGraph Enterprise v5.0 is setting a new standard for the performance and flexibility of distributed graph databases. If you want to embrace this opportunity to future-proof your data infrastructure and achieve unprecedented levels of performance and efficiency, why not take a trial to unlock the full potential of GQL? [Reach out](https://www.nebula-graph.io/contact) today!


Introducing NebulaGraph Enterprise RC! The first distributed GQL database with a redesigned architecture, enhanced kernel engine, and sub-graph computing.

Announcing the latest NebulaGraph Enterprise RC release: Delivering Full-Fledged GQL Support

Imagine navigating through a complex web of interrelated parts in a large manufacturing factory's supply chain. From procurement to customer service, each element is interconnected, and a delay in one can create a ripple effect across the entire chain. This blog post explores how graph databases, with their unique structure, can simplify this process and bring significant efficiency improvements, saving both time and costs in supply chain management.

# Understanding Supply Chain Management and Key Factors to Its Efficiency
The supply chain is the network of people, organizations, resources, activities and technology involved in the production and sale of a product from the supplier delivering raw materials to the manufacturer to the end user. It’s all the steps to get a product or service from supplier to customer.

Supply Chain Management (SCM) is the active management of the supply chain to maximize customer value and gain a sustainable competitive advantage. It involves the coordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers.

![image](https://media.licdn.com/dms/image/C4E12AQFs6QMs0h6WFg/article-cover_image-shrink_720_1280/0/1647752062480?e=1726099200&v=beta&t=N2VbMBySvyWfLg28ATvNgXTqgjJEiYJ9NDie9S99JEE)

*Image source: https://www.linkedin.com/pulse/what-supply-chain-management-its-importance-dhruman-gohil/*


Some of the key factors to a well-managed supply chain are:
- Demand Forecasting: Accurate forecasting of demand is crucial as it impacts all other supply chain planning activities including production, inventory management and shipping.

- Inventory Management: Keeping the optimal level of inventory is a delicate balance; too much will lead to high holding costs while too little leading to stock-outs and lost sales.

- Technology Integration: SCM requires robust technology systems that can automate and streamline the processes involved from order processing and production scheduling to inventory control and customer service.

- Flexibility: The supply chain should be able to respond quickly and efficiently to changes in demand or supply.

- Visibility: A transparent supply chain helps companies identify inefficiencies or problems quicker and take action sooner.
Risk Management: Identifying and managing risks such as supply disruptions, demand volatility and currency fluctuations is also key to SCM.

- Sustainability: Increasingly, companies are looking at the sustainability of their supply chains in terms of environmental impact, social responsibility and economic performance.

# Understanding Graph Databases and Their Distinction from Traditional Databases
A graph database is a unique type of database designed to handle complex data relationships. It’s a NoSQL database that uses graph theory to store, map and query relationships. A graph database is fundamentally different from traditional databases, for example relational databases, because of the way it treats data relationships. In traditional databases, relationships between data are often secondary. They are inferred through JOIN operations that link together separate tables based on common attributes. This can lead to performance issues when dealing with large amounts of complex and interrelated data.

But graph databases, such as NebulaGraph, take a different approach. They put relationships first, storing data in a flexible graph structure of nodes and edges. Nodes are entities like a person or a product, edges are the relationships between those entities. This structure allows for high performance when querying relationships making it great for managing complex connected data.

![image](https://user-images.githubusercontent.com/57335825/100973399-57cfdb00-34ef-11eb-9c12-262e4922cac1.png)

# How Graph Databases Drive Efficiency in Supply Chain Management

In a large manufacturing factory, the supply chain is a spider’s web of interconnected parts. From procurement and production to distribution and customer service, each step is connected. A delay in procurement can impact production schedules which can then impact distribution and delivery times.

Think of a car manufacturing plant. A car is a complex machine with thousands of parts, each with its own supply chain. If one part fails, it could be a bigger issue in the supply chain that needs to be fixed.
With a traditional database it’s hard to understand these ripple effects. The tabular structure of traditional databases makes it difficult and time-consuming to follow the impact of a change through the entire supply chain. But with a graph database it’s much simpler. By clearly defining the relationships between different aspects of the supply chain, a graph database allows for quick and easy navigation through the data. This can lead to significant efficiency improvements, saving both time and costs.

Here are some ways graph databases can support enterprise supply chain management:
- Revealing Complex Relationships: Graph databases can show complex relationships between data, like materials, suppliers, production equipment and personnel in the supply chain. That’s very useful for optimizing production, reducing waste and improving efficiency.
- Tracking and Forecasting: Graph databases can help companies predict potential issues, like supply chain disruptions and equipment failures, by tracking every link in the production chain in real time. So companies can take action before they lose.
- Decision Support: By analyzing supply chain data, graph databases can support company decision making. For example, they can predict future demand based on historical data so companies can make better production plans.

To elaborate, graph databases, with their ability to show relationships between different data points, can give you a complete view of the entire supply chain. This enables companies to identify bottlenecks, inefficiencies, or risks, and take corrective action. Also they can help companies respond faster to changes or disruptions in the supply chain. Furthermore, by analyzing and forecasting based on historical data you can improve demand planning and inventory management. Lastly, the graph structure itself ensures data access is controlled and secure so your supply chain data is protected.

# Graph Databases Benefiting Different Departments in the Manufacturing Sector
The benefits of graph databases are not limited to a single department within the manufacturing industry. In fact, they can be leveraged by various departments to improve efficiency and decision-making.

Production planning can optimize production schedules by using graph databases, for example. They can create schedules that are more efficient in minimizing downtime and increasing productivity by understanding the relationships between different parts of the process. 

Procurement department can use graph databases to find the best way to get materials. They can make better procurement decisions by looking at the relationships among suppliers, costs, delivery times, and other factors. 

The quality control department can also use graph databases. By tracing the origins of quality issues, they can identify the responsible parties and take corrective action. This can lead to improved product quality and customer satisfaction.

# Conclusion

Graph databases are great for managing complex connected data in big factory supply chain management. They can help you detect and predict failures, optimize and decide. By detecting and predicting failures, optimizing and deciding they can deliver big efficiency gains.

Graph databases are great for managing complex connected data in big factory supply chain management. This blog post introduces how graph databases empowers supply chain management to be efficient.

How Graph Databases Drive Efficiency in Supply Chain Management

Welcome to an exploration multi-tenant architecture - it’s all about resource utilization and data security for software. This post will provide in-depth insights into multi-tenancy, its benefits and how companies like Salesforce use it. You’ll also see how [NebulaGraph Enterprise v5.0](https://www.nebula-graph.io/posts/nebulagraph_enterprise_5.0_gql_support) has embraced this architecture in its product design.

# What is multi-tenancy?
Tenants are equivalent to the "instance" concept of traditional databases to a certain extent. Tenants are completely isolated from each other. In terms of data security, cross-tenant data access is not allowed to ensure that there is no risk of user data assets being leaked. In terms of resource usage, tenants "exclusively" occupy their resource quotas. Generally speaking, a tenant is both a container for various database objects and a container for resources (CPU, Memory, IO, etc.).

Multi-tenancy in the context of databases means a single instance of software serves multiple users or tenants. In a multi-tenant database, the data for all tenants is stored in one database which can be either a physical or virtual one. The data for each tenant is separated from the data of other tenants by a simple partitioning of the database or by more complex methods like encryption or separate schemas for each tenant.

Multi-tenant architecture is used in scenarios where multiple users or organizations need to access the same database without access to each other’s data. For example, a SaaS provider would use multi-tenant management to provide database services to multiple customers using the same underlying software and hardware.

# How Multi-Tenancy Works

Multi-tenant architecture creates separate, isolated environments within a single physical infrastructure, like a virtual machine, server or cloud platform. This is done by partitioning the data storage and processing; each tenant has their own dedicated space in the system. A tenant interacts with the app and can access their own data.

![image](https://miro.medium.com/v2/resize:fit:720/format:webp/1*dt0aQxbm0xQBqYZ4mcdaRA.jpeg)

*Image source: https://medium.com/@harish.somasundar14/database-multi-tenancy-7c8dbe848d50*

Because each multi-tenant environment is separate from each other, they can be customized to meet the needs of each individual tenant without affecting other environments. Tenants can customize features like user interface and data security settings for their environment. They can also have different rules for each domain for access control, resource allocation and feature availability.

# Benefits of Multi-tenant Architecture
Business can benefit from multi-tenant architecture in software while minimizing its challenges, including improved security, cost efficiency, and better resource utilization, etc.

- Data Security: Multi-tenant architecture with resource isolation secures data by keeping each tenant’s data separate and preventing one tenant from accessing another’s data. Plus it simplifies security management as security policies can be applied across all tenants.
- Cost Efficiency: Multi-tenancy provides cost savings as multiple tenants can share the same database infrastructure, no need for separate hardware and software for each tenant. By sharing resources and reducing the need for dedicated resources for each tenant, the company can cut down operational and maintenance costs. Plus automated operation and maintenance tools associated with multi-tenant management can reduce time and effort for routine tasks leading to more cost savings.
- Efficient Resource Utilization: In a large organization, different teams or departments may need to access and work with the same resources. With multi-tenancy a single instance of a software application can serve multiple tenants (i.e. teams or departments) thus maximizing resource utilization and reducing redundancy.
- Scalability: Multi-tenant architecture allows for easy scalability as scaling up is more efficient and less disruptive. When the company grows and more teams need access to the database new tenants can be added without the need for additional software instances.g
- Simplified Management: With multi-tenant architecture, managing updates, implementing changes, and troubleshooting issues can be done from a single point, simplifying the overall management process.

# Real-life Cases of Multi-tenant Architecture
Below are two examples for a real-life use case of multi-tenant architecture. In both examples, multi-tenant management is key to efficient resource utilization, cost savings and high data security and performance.

Salesforce leverages multi-tenant architecture to deliver its Customer Relationship Management (CRM) software as a service. This architecture allows Salesforce to provide a single instance of its application to multiple customers, which reduces costs, simplifies maintenance and upgrades, and allows for scalability as the number of clients grows.

Another industry example is the telecom sector. Telecom companies use multi-tenant architecture in their billing systems. These systems need to manage data for millions of customers, each with their own plans, usage patterns and billing cycles. By using multi-tenant architecture, these companies can isolate each customer’s data while managing resources efficiently, bill correctly and perform well.

#Examples of Databases with Multi-Tenant Architecture

Multi-tenancy is often used in Software as a Service (SaaS) applications where it can provide cost savings and scalability. For databases it’s more common in distributed databases and cloud-based systems where multiple users or tenants can share resources while keeping each other’s data private. Here are a few examples of distributed databases that support multi-tenant management and resource isolation:

- Apache Cassandra: Open-source, highly scalable and distributed NoSQL database that can handle large amounts of data across many commodity servers. It supports multi-tenancy and resource isolation, great for applications that need scalability and high availability.
- Microsoft Azure Cosmos DB: A globally distributed, multi-model database service that provides multi-tenancy and resource isolation. It's designed for applications with globally distributed users who need low latency access to data.
- NebulaGraph: Open-source, distributed graph database for super large-scale graphs with milliseconds of latency. Highly performant, horizontally scalable and fault-tolerant. Enterprise v5.0 has multi-tenant management and native GQL support.

# Multi-Tenant Architecture and NebulaGraph Enterprise v5.0
Graph databases as the underlying infrastructure have become platform oriented when integrated with real life business. This has led many tech teams to build graph based platforms to serve their business needs. Hence multi-tenant architecture has become a must have feature.

NebulaGraph Enterprise saw this trend and introduced multi-tenant architecture in its design since v5.0. This allows tenants to be managed in a single pane of glass, including support for third-party single sign on. The architecture also follows the principle of least privilege (PoLP) for authorization. This means users are given the minimum access – or permissions – they need to do their job. This reduces the risk of data breaches and unauthorized access.

The multi-tenant architecture of NebulaGraph Enterprise v5.0 also separates metadata management and data management, as well as permissions and roles. This separation aids in maintaining the integrity and security of data and user roles. Moreover, NebulaGraph architecture allows the same cluster resources to be shared, while different clusters have their own resources, permissions and data. This separation is necessary to meet performance targets and privacy and security requirements.

In summary, NebulaGraph multi-tenant architecture is all about resource utilization and data security and privacy. It serves the evolving needs of businesses that are building graph based platforms. If you are interested in trying NebulaGraph Enterprise v5.0, please [contact us](https://www.nebula-graph.io/contact) to try it out.


This post will provide in-depth insights into multi-tenancy, its benefits and how companies like Salesforce use it. You’ll also see how NebulaGraph Enterprise v5.0 has embraced this architecture in its product design.

Multi-Tenant Architecture: Enhancing Resource Utilization and Data Security

The latest offering of NebulaGraph Enterprise is **the first and now the only** distributed graph database that implements the ISO/IEC 39075: GQL, the first international graph query language standard. As standardized by GQL and promoted by NebulaGraph in all previous versions, the latest version adopts the property graph model for managing and storing user data mainly as vertices (nodes) or edges, each containing a set of labels and properties. Users get to use the market-proven MATCH (graph pattern matching) clauses in addition to a rich set of other SQL-like clauses to query such a property graph.

# GQL
Similar to previous versions of NebulaGraph, the schema of a property graph is to be created or altered by users and managed by the database. The following code snippet is an example of a DDL query written in GQL that tries to create a graph type containing a set of node types (i.e., City, Country, Continent, etc.) and edge types (i.e., CITY_IS_PART_OF_COUNTRY, COUNTRY_IS_PART_OF_CONTINENT, etc.). Each node or edge type has its type name (e.g., City), a set of optional labels (e.g., City, Place) and a set of optional properties (e.g., id, name, url).

```
CREATE GRAPH TYPE IF NOT EXISTS ldbc_type AS {
  NODE City (LABELS City&Place {id INT64 PRIMARY KEY, name STRING, url STRING}),
  NODE Country (LABELS Country&Place {id INT64 PRIMARY KEY, name STRING, url STRING}),
  NODE Continent (LABELS Continent&Place {id INT64 PRIMARY KEY, name STRING, url STRING}),
  ...
  EDGE CITY_IS_PART_OF_COUNTRY (City)-[:IS_PART_OF]->(Country),
  EDGE COUNTRY_IS_PART_OF_CONTINENT (Country)-[:IS_PART_OF]->(Continent),
  EDGE UNIVERSITY_IS_LOCATED_IN_CITY (University)-[:IS_LOCATED_IN]->(City),
  }
```

Once a graph type is created, we can use it to create as many graphs as we want, all of which would be confined to the same graph type. In the following, we create a graph with the name LDBC using the ldbc_type created above.

```
CREATE GRAPH IF NOT EXISTS LDBC TYPED ldbc_type
```

Assuming that we have loaded the LDBC graph with nodes and edges, we can query it using MATCH queries like the following that first declares two path patterns to be matched from the graph, order the results by the [person.id](http://person.id/) and [message.id](http://message.id/) properties, limit the cardinality of the result set to 20 and return a set of properties of interest.

```
MATCH
  (person:Person)-[:KNOWS]->(friend:Person),
  (message:Message)-[:HAS_CREATOR]->(friend)
ORDER BY
  person.id DESC,
  message.id ASC
LIMIT
  20
RETURN
  friend.id AS personId,
  friend.firstName AS personFirstName,
  friend.lastName AS personLastName,
  message.id AS postOrCommentId
```
For a concise description of GQL, please refer to [GQL Database Language](https://jtc1info.org/slug/gql-database-language/e9bddae) published by ISO and [Understanding GQL: A Comprehensive Overview of the Standard Graph Query Language](https://www.nebula-graph.io/posts/An_Comprehensive_Overview_of_the_Standard_Graph_Query_Language).

# Internals
Compared with the previous NebulaGraph series, we have made fundamental technical advancements in all components of the latest Enterprise version. The following diagram compares the two architectures. The internals of the v3.0 and the Enterprise new are placed on the left- and right-hand side, respectively. For the new version, the green boxes are new features and the red boxes are enhanced features over their counterparts in previous versions.

![image](https://www-cdn.nebula-graph.io/blogs/version%20comparison.png)

- Full-Fledged Support for GQL
  - **New GQL frontend** that parses and validates the GQL queries, conforming to the GQL standard.
  - Deprecate previous vendor-locked query languages.
- Distributed Architecture
  - In the market, NebulaGraph has been known for its **distributed architecture**, the computing and storage capacity of which scales out nicely to cover even 200 TB of data of huge graphs in a single cluster. The new version honors the same architecture with major enhancement on the implementation of **Raft**, a consensus protocol for distributed systems.
  - The latest Enterprise version **cluster** consists of a set of stateless **graphd** nodes and a set of **storaged** nodes organized in Raft groups. The metadata of each such cluster is managed by a **metad** service, consisting of metad nodes also organized in Raft groups. A single metad service can serve multiple clusters, if needed, in an isolated manner, all of which are referred to as a **supercluster**. This architecture is flexible and has been proven by the market to fit the needs of data management in enterprises.
- Planner & Optimizer
  - **Enterprise-grade RBO** (rule-based optimizations): The optimization of new version has a rich set of optimization rules including both the market-tested ones inherited from the previous series and new rules optimized for GQL. The new does not start itself from ground zero but from the shoulder of the old series.
  - Support **graph-native operators** that are specially designed and optimized for graphs in the query plans (e.g., breadth-first search in a graph) in addition to conventional relational algebra operators (e.g., joins).
- Data Structures
  - **Compact Vectorized Layout**: Within the memory, we store the data of graph elements (nodes and edges) in the newly designed Nebula Vector data structure that is compatible with Apache Arrow. Vector uses a compact layout to encode and store all data types such as strings, lists, records and so on, saving the memory overhead by up to 95% compared with the previous series.
  - **No serialization/deserialization RPC overhead**: The compact layout of vectors is RPC-friendly. The vectors are essentially a set of self-contained and sequentially stored data chunks in the main memory with no pointers. They can be passed via the RPC directly without any need to serialize or deserialize the data or allocating temporary memory spaces for this process.
  - **Memory Graph**: We now support storing graphs using adjacency lists directly in the memory. This graph-native structure allows fast graph traversals and property retrievals by accessing the graph topology and properties directly in the memory, supporting queries that are orders of magnitude faster than those constrained by network & disk I/Os and KV encoding/decoding. We have exciting features based on this memory graph in future versions.

- Executor
  - **Vectorized Executor**: Instead of processing each row one by one, the new version now fully implements all operators in the vectorized way that applies operations on all values in a vector simultaneously. This allows the executor to exploit the hardware benefits of modern multi-core processors and DRAMs. As an example, the vectorized hash join operator is now 2-3 times faster than that in the previous series.
  - **Fully concurrent and pipelined runtime**: Instead of processing each operator one by one using only one thread, the latest Enterprise version now splits a query plan into multiple pipelines that are executed concurrently. Within each pipeline, data in the form of vectors streams from the first source operator to the final pipeline breaker without being blocked. And, at each point during the execution, there can be as many concurrent pipelines as needed and tuned. This runtime improves the workload balancing and exploitation of multi-cores significantly and addresses the NUMA problem in modern multi-socket multi-core processors.
  - **Sub-query plan pushdown**: The latest Enterprise version can now push a complete sub-query plan from a graphd to multiple storaged for near-data processing (NDP). Previously, the v3.0 series benefited greatly from pushing some filters/predicates down to the storaged for performance improvements and network savings, despite that not all possible push-downs were implemented. We have greatly enhanced this feature from pushing down some filters/predicates to complete sub-query plans containing all possible operators, filters/predicates, etc. The push-downed sub-query plan is processed by the same pipelined and vectorized executors in the storaged.
  - **Fine-grained memory management**: To prevent OOM failures, we have implemented a fine-grained memory tracker that can track the memory usage of each query, each operator as well as small objects in the runtime. In the same way as in old series, users are free to set memory budgets and prevent the processes from being killed by OOM.
- Storage Engine
  - **Optimized KV Encoding**: We have optimized the KV encoding according to our latest understanding of how graph queries lookups, order and manipulate the underlying KV data. A new format is implemented to optimize prefix scans for nodes and edges.
  - **Bucket in partitions**: Like in the previous series, we continue to partition graph data in the shared-nothing architecture. In the new version, we split a partition into fixed-size buckets. All graph elements are mapped to corresponding buckets deterministically. The design allows faster data balancing and migration since data only needs to be moved at the bucket level. And, the new supports altering the number of partitions after creating a graph.

- Other Features
  - **Plugin**: We now support plugins into the database kernel, as in popular databases like PostgreSQL. Quite some important features have been implemented as built-in plugins such as functions, procedures, authentications, etc. Developers can implement their own plugins in C++ and load them into the database kernel.
  - **Procedure**: We support the development of procedures in C++. Users can call them in GQL queries. Procedures usually have better performance than plain queries. And, they can be reused once created. Users can also develop graph algorithms in procedures. We will support the development of GQL-based, Python-based procedures in future versions.
  - For a complete feature list, please wait for our upcoming release note.

# Performance
Now let’s look at how the latest Enterprise version performs in comparison with the latest enterprise version of the previous series.
The following chart shows the speedup of the new for the LDBC SNB Interactive benchmark, using the 99-th percentile of query latencies as the metric, on the same setup (a three-node cluster, 64-core Intel 8352Y CPU, 2.20GHz, 256 GB DRAM and 2 TB SSDs), with a scale factor (SF) of 100. **Enterprise v5.0 is 1.01 to 6.43 times faster, with an average speedup of 3.01. All the queries executed on the new are written in GQL**. For the old version, the queries are written in openCypher. Both languages are based on the same graph pattern matching theory, with GQL released as the international standard. It has always been our belief that users shall only need to express their queries using declarative query languages and enjoy a good level of performance with ease, rather than being forced to write queries as procedures in C++. C++ procedures are good patches to the database. But it would be a nightmare if some queries could only be achieved with it because they are hard to code, hard to debug, hard to maintain and hard to scale.

>Disclaimer: We used the LDBC SNB benchmark as a starting point. However, the test results aren’t audited, so we want to be clear that this is not an LDBC Benchmark test run, and these numbers are not LDBC Benchmark Results.

![image](https://www-cdn.nebula-graph.io/blogs/query%20latency.png)

If we zoom in on the Q1 for example (GQL query shown in the following) on which the new version is **3.82** times faster, v5.0’s peak memory footprint of all processes is **64.47%** smaller than that of previous one. So, in a nutshell, the new Enterprise version can use around 1/3 of the memory resources to produce more than 3 times faster performance in this case.

```
USE ldbc
MATCH 
  (p:Person{id:2}),
  (friend:Person{firstName:"Kyle"})
WHERE 
  p<>friend
MATCH 
  path1 = ANY SHORTEST PATH (p)-[:KNOWS]-{1,3}(friend)
RETURN 
  min(length(path1)) AS distance,
  friend GROUP BY friend
NEXT 
USE ldbc
ORDER BY
  distance ASC,
  friend.lastName ASC,
  friend.id ASC
LIMIT
  20
MATCH
  (friend)-[:IS_LOCATED_IN]->(friendCity:City)
OPTIONAL MATCH
  (friend)-[studyAt:STUDY_AT]->(uni:University)-[:IS_LOCATED_IN]->(uniCity:City)
RETURN 
  collect(CASE WHEN NOT uni.name IS NULL THEN RECORD 
      {uniName:uni.name,
      studyAtClassYear:studyAt.classYear, 
      uniCityName:uniCity.name} END
    ) AS unis,
  friend,
  friendCity,
  distance GROUP BY friend,
  friendCity,
  distance
NEXT 
USE ldbc
OPTIONAL MATCH
  (friend)-[workAt:WORK_AT]->(company:Company)-[:IS_LOCATED_IN]->(companyCountry:Country)
RETURN
  collect(CASE WHEN NOT company.name is null THEN RECORD 
      {companyName:company.name,
      workAtWorkFrom:workAt.workFrom,
      companyCountryName:companyCountry.name} END
    ) AS companies,
  friend,
  unis,
  friendCity,
  distance GROUP BY friend,
  unis,
  friendCity,
  distance
NEXT
USE ldbc
ORDER BY
  distance ASC,
  friend.lastName ASC,
  friend.id ASC
LIMIT 20
RETURN
  friend.id AS friendId,
  friend.lastName AS friendLastName,
  distance AS distanceFromPerson,
  friend.birthday AS friendBirthday,
  friend.creationDate AS friendCreationDate,
  friend.gender AS friendGender,
  friend.browserUsed AS friendBrowserUsed,
  friend.locationIP AS friendLocationIp,
  friend.email AS friendEmails,
  friend.speaks AS friendLanguages,
  friendCity.name AS friendCityName,
  unis AS friendUniversities,
  companies AS friendCompanies
```

# Summary
**ALL IN GQL**:  The upcoming NebulaGraph Enterprise release is the first and now the only distributed GQL database. For the first time in the history of graph databases, we are thrilled to welcome the arrival of GQL. It not only allows users to develop applications in a standardized way without worrying about using vendor-locked languages but also enables us to develop a full-fledged graph database with both the latest database technologies and comprehensive grammar support for graph-native features.

**Faster with Less Resources**: Evaluated using the LDBC SNB benchmark, the latest NebulaGraph Enterprise version is on average 3 times faster and around 3 times more memory efficient than the previous state-of-the-art enterprise version. We will release more evaluation results in more scenarios as the GA of the new Enterprise version approaches.

**If you're interested in take a trial, feel free to [reach out](https://www.nebula-graph.io/contact) to us.**

**About the author**

[Dr. Xuntao Cheng](https://www.linkedin.com/in/chengxt/) is the R&D leader of Vesoft and a member of the ISO/IEC JTC1/SC 32/WG3 Database Language expert group.



This blog dives into the new features and functions provided by NebulaGraph Enterprise.

Technical Preview of NebulaGraph Enterprise Latest Version

This global payments fintech unicorn offers a financial service interface for international transactions and global funds transfer for over 100,000 corporate entities over the world. This platform was established in 2015 and it serves as a hub for customers to receive payments from close to 180 countries and send money to over 150 countries, totaling over $50 billion in worldwide remittances as of January 2024.

# Challenges: Upgrading Risk Control System Amid Rising Financial Fraud

The company used to leverage a conventional relational database for their risk control system, yet the relational database has restrictions on data model representation. Besides, it was inefficient in processing multiple-table joins, leading to failed identification of risk in fraud conduct within financial transactions.

The company well understands that a successful financial platform relies on not only its ability to facilitate seamless transactions but also its capacity to guard assets and transactions, particularly with today’s sophisticated financial scams. Upon retrospective analysis of the fraudulent incidents, the company found that graph databases outperforms relational ones in revealing hidden data correlations, making them an optimum solution for such scenarios.

# The Solution: NebulaGraph Boosted Their Fraud Detection Capabilities

The company evaluated several enterprise graph database solutions, including ArangoDB, Neo4j, TuGraph and NebulaGraph with regard to architectures, performance, and scalability. The selection saw the choice of NebulaGraph because of its shard-nothing distributed approach, horizontal scalability as well as professional technical support.

- Global Compliance with Distributed Architecture
Since the company operates globally, it is faced with many data compliance requests from various regional authorities such as anti-money laundering and consumer privacy protection. This requirement was met by NebulaGraph which has a shard-nothing distributed architecture design separating storage from computation.
- Superior Performance for Real-time Anti-fraud Detection
An essential goal of the selection was to support real-time anti-fraud operations. In such a context, the underlying graph database must be able to handle over 1,000 concurrent requests and offer millisecond-level response times. NebulaGraph's powerful native graph engine enables lightning fast QPS and TPS with millisecond latency, handles high levels of concurrency, making it an excellent fit for the company’s needs.
- Seamless Scalability for Future Growth
In consideration of the future expansion plans, the company laid emphasis on scalability. NebulaGraph provides robust horizontal scalability due to its shared-nothing distributed storage design. It was thus perceived as an ideal solution which could effectively handle their expanding business and data in the future.
- Premium Technical Support for Critical Infrastructure
Stability is a must-have for any foundational software and the company needed the provider of graph databases to have reliable technical support in graph technology. NebulaGraph, the leader of the distributed graph databases, boasts a deep bench of top experts in the graph technology industry. Thus, their professional technical support capabilities have been highly praised by the company from the initial project engagement to the final rollout.

# The Achievement: Secure Transactions through Risk Control Platform Built on NebulaGraph

NebulaGraph plays a vital role in this entire risk control platform across three sub-domains namely Onboarding Evaluation, Transaction Risk Review and Post-Transaction Analysis, all aimed at guaranteeing safety of transactions made on the system.

- Onboarding Evaluation: NebulaGraph is used by the company to analyze the potential relationships between new users’ network information, device fingerprints, and user profiles. Through a complete review of all this, it automatically filters out those with some high-risk connections.

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

- Transaction Risk Review: The system reviews each occurring transaction carefully based on a model of risk assessment. When transactions have been flagged for suspicion and necessitate manual review, NebulaGraph can be used to conduct detailed association analysis of both the transaction entity and its counterparty. This capability significantly aids their audit personnel in making informed decisions.

- Post-Transaction Analysis: Historical behavior of company clients is periodically scanned using data mining and machine learning techniques to detect unusual users or transactions. With NebulaGraph’s native support towards graph clustering algorithms, clusters of connected accounts can be identified very effectively. Such suspicious account clusters could be homogeneously labeled as well as discovered thereby being subjected to comprehensive manual scrutiny in order to mitigate future risks.

In addition to contributing to the risk control platform, NebulaGraph has been responsible for creating a holistic user knowledge graph for the company. This graph acts as an information base for RAG and assists by giving out short summaries of customer firms’ equity, operational status and partner data through LLM in the most user-friendly manner. These summaries are important points of reference for compliance departments that facilitate quick comprehension of their different businesses operating conditions.

In summary, NebulaGraph has played an essential role in the company’s pursuit to establish a secure, efficient and reliable financial system. Through its capacity of analyzing complex interactions and behaviors, it has enabled the firm to mitigate risks at all stages of users journey from on-boarding up to post event analysis.


A global fintech unicorn combats financial fraud with NebulaGraph, ensuring secure transactions.

A Global Fintech Unicorn Safeguards Financial Transactions with NebulaGraph

The importance of graph databases in data analysis cannot be overstated, and the recent development of subgraph computing is about to make them even more powerful. Although many people are already familiar with the idea behind graph computing, subgraph computing provides a new way of looking at things and greatly expands both what can be done with data as well as how it can be interpreted. In this post, we will introduce subgraph computing and explain its relevance to business.

# What is subgraph computing?
Subgraph computing is a term that is used when working with graph databases. In graph theory, a graph consists of vertices and edges connecting these vertices. A subgraph is any part of this full graph which includes some but not necessarily all of the nodes and/or edges from the main graph.

Subgraph computing is a specific kind of graph computing which deals with smaller, particular sections or 'subgraphs' within a bigger graph. This type of computing can be used to analyze and process specific areas of a network without having to process the entire graph, which can save time and computational resources. It's especially useful in large-scale data analysis where only certain parts of the data network are relevant for a particular analysis. For instance, in a social network, a subgraph could represent a group of people who are all friends with each other. Computing these subgraphs could give insights into the number of such tightly-knit friend groups in the network.

# What are the realistic use cases of subgraph computing?
- Social Network Analysis: Subgraph computing can be used to analyze specific communities or groups within a larger social network. For example, identifying subgroups of people with similar interests, behaviors, or connections.

- Recommendation Systems: In a recommendation network, nodes represent products and edges represent co-purchases. A subgraph may constitute a collection of merchandise which can be often bought collectively. Subgraph computing can help in analyzing user behavior within a specific context, and provide personalized recommendations based on that analysis.

- Fraud Detection: In a financial transaction network, nodes could represent individuals or entities, and edges could represent transactions. A subgraph might represent a suspicious pattern of transactions that often indicate fraudulent activity. By computing these subgraphs, a system could potentially identify and flag potential fraud cases.

- Bioinformatics: In organic networks, nodes can constitute proteins and edges can constitute interactions among them. A subgraph would possibly display a specific arrangement of proteins that form a practical complication. Analyzing those subgraphs can assist medical scientists aware of not unusual or massive protein complexes within the community.

- Network Security: In a pc community, nodes constitute devices like servers or routers, and edges represent connections. Subgraph computing can be used to identify and analyze specific parts of a network that are vulnerable to attacks or have already been compromised.

# Why does subgraph computing matter to commercial business?

In addition to the mentioned realistic use cases, subgraph computing brings business values to enterprises, including 

- Discovery of Patterns: Subgraph computing can help discover recurring patterns in complex networks. For instance, in attribution analysis, which is used in marketing to determine the most effective marketing channels, subgraphs could represent sequences of customer interactions leading to a purchase. Computing these subgraphs can help identify the most common or effective sequences.
- Anomaly Detection: In the context of capital flows, nodes could constitute debts and edges should constitute transactions. A subgraph may constitute a pattern of transactions that is indicative of cash laundering. By computing those subgraphs, you will become aware of anomalies and probably illicit activities.
- Predictive Analysis: Patterns found thru subgraph computing can also be used for predictive evaluation. For example, in a product recommendation community, styles of co-purchases can be used to expect future purchases.

# Comparison between graph computing and subgraph computing

While graph computing provides a wide assessment of a collection of graphs, which can be beneficial for knowledge of large-scale structures and tendencies, subgraph computing offers greater distinct and nearby information about the structure and properties of the graph. A user of graph databases would possibly need to depend on subgraphs to understand the structure of a complicated network, which includes the internet or a biological network. For instance, they may want to realize how many approaches a pandemic can spread via a network.

The two methods can be used to answer different types of questions. For example, if you're interested in understanding the overall structure of a set of data, graph computing might be more useful. If you're interested in finding patterns within a larger set of data, subgraph computing might be more useful.

In addition to providing more detailed and local information, subgraph computing offers better flexibility and efficiency for certain use cases.

- Flexibility: When dealing with large graphs, it's often impractical or unnecessary to query the whole graph.  For example, in case you're simply interested in a selected community inside a social community, you don't want to analyze the complete network. By focusing on subgraphs, you can isolate and analyze the relevant portions of the graph. This allows for more targeted analysis and reduces the amount of irrelevant data you have to deal with. In addition, it also allows for a more detailed analysis. For instance, by examining different subgraphs, you can identify local patterns or structures that may not be evident when looking at the graph as a whole.

- Efficiency: Subgraph computing is often more efficient than querying the whole graph. This is especially true for large graphs, where a full query can be computationally highly-priced and time-ingesting. By focusing on subgraphs, you can significantly reduce the computational load and speed up the analysis. This is not only more efficient, but it also means that the analysis can be more timely. In a rapidly changing network, a full graph query could be out of date by the time it's completed. Subgraph computing, on the other hand, can provide more up-to-date insights. Moreover, by reducing the computational load, subgraph computing also makes it possible to perform more in-depth analysis, such as running multiple queries or performing more complex computations.

As we wrap up, subgraph computing is more than just an extension of graph computing. It is an effective tool for reading complex networks and it can display crucial structures and styles that can be used for decision-making, prediction, and discovery. This transformative concept brings new intensity and measurement to data evaluation in graph databases. As we continue to discover its capacity, it's thrilling to imagine the effect it's going to have on the future of information analytics.

Subgraph computing is one of the key features that NebulaGraph Enterprise v5.0 offers. If you’re interested in taking a trail, feel free to [reach out](https://www.nebula-graph.io/contact) to us.




Subgraph computing is a method of data analysis in graph databases that focuses on smaller, specific sections or 'subgraphs' within a larger graph, saving time and computational resources.

Subgraph Computing Uncovered: What It Is and Why It Matters

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[NebulaGraph Enterprise v5.0](https://www.nebula-graph.io/posts/nebulagraph_enterprise_5.0_gql_support) is the first distributed graph database to offer native GQL support in that it is built on GQL and designed for GQL. For those eager to experience the future of graph database querying, a free trial awaits. [Reach out](https://www.nebula-graph.io/contact) today to discover the power of GQL for yourself.



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

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