Design a Random ID Generation System

This article highlights how random ID generation systems are useful in today’s digital world in areas such as generating identity for a user, transaction numbers, session IDs, and for creating unique identification numbers for objects in database applications for instance. Such systems need to be designed with considerations for making them unique, secure, scalable, and performing optimally. This article will also explain the details of developing a random ID generation system.

Requirements of Random ID Generation System

The successive design of a random ID generation system outlines certain proposal of requirements for distinctiveness, security, modularity, and efficiency. These goals are important in the provision of high performance and satisfying all the requirements put in place by different applications like user identification, commerce, and database operations.

1. Functional Requirements:

• Unique ID Generation:
  • In this way, the ID created by the system is suitable in one instance of the application and at the same time useful for a single use without having the same create a conflict in records or in creating duff records.
  • This singularity is crucial for several of the most important use cases in the application, including user login, various transactions, and management of the databases.
• High Entropy:
  • It is essential to maintain higher entropy in the produced ID as a measure of increasing the security systems within the given system.
  • Thus, high entropy, fixing fore-most of unpredictability and resistance to brute-force attacks, implies an extra layer of protection from unauthorized access and data infiltration.
  • In this regard, the system incorporates cryptographic libraries to ensure randomness of the process of generating an ID so as to be as base as possible.
• Length Configuration:
  • Allowing users to specify the length of the generated IDs is critical as it complies with the varied needs of applications.
  • Since it is understood that various contexts may require a shorter or longer ID, the system provides users with an opportunity to get an ID of their choice.
  • Users can set parameters within the limit, and these adjust the length according to the scalability and complexity of applications they wish to develop.
• Character Set Configuration:
  • Allowing users to choose specific character sets for IDs proves essential in UD in order to accommodate the different needs of an application.
  • To be more precise, the context-independency of these character sets promotes the ability of the system to cope with different formats demanded by the various contexts of application; for instance, numeric for transaction numbers or alphanumeric for user tokens.

2. Non-Functional Requirements:

• Scalability: Simplicity enables the system to be quickly adaptable in handling bigger loads as the application gains users. Horizontal scaling can be done through microservices architecture and load balancing where instead of going to one instance it spreads all the requests evenly to eliminate performance decline in the event of high traffic.
• Reliability: In computer applications, a reliable framework is crucial for the ID generation system to ensure that it works seamlessly and gains the user’s confidence. This physically means that the system can generate proper and reliable ID without failure, and this is an important aspect in the successful provision of services.
• Security: Compared to the traditional process of generating secure ID numbers, security is paramount when generating an ID and hence should be done in a very secure manner where the process of generating the ID cannot be easily determined by and other party.
• Availability: These considerations make it vital to maintain high availability of the ID generation system, meaning that the system is continually working, and is able to generate IDs whenever needed. It is crucial for applications that need to provide continuity and dependability, especially applications that run 24/7.
• Latency: Minimising the latency in the ID generation process plays a crucial role in supported application, especially in the case of real-time applications where delays can be counterproductive to the usability of the application . In this way, the system optimizes the response time for improving user interaction.

Use Case Diagram of random ID generation system

As depicted in the use case diagram below, it shows the ways through which various users will be able to engage with Random ID Generation System which will be rich in core functionalities.

• Users can ask for random IDs, decide on how many character the ID should contain
• Decide what character set the program should take for generating a new ID information
• View log information of what IDs has been generated.

These functionalities provide the feature of flexibility with the control principle with the ID generation full flexibility at the user’s side to set the IDs according to the user needs. Arrow symbolizes the sequence of actions and shows the user working with the system and using it for several purposes effectively.

Capacity Estimation for Random ID Generation System

1. Expected Requests per Second (RPS)

For this, we must decide the probable number of ID generation requests per second that our application will have to process. This can be based on: historical user statistics, peer user statistics projected for the future, or other industry averages.For instance, if we expect 1000 users to be logged in to the system at the same time, and each of these users requests for an ID every 5 seconds, the expected RPS output would be equal to 200 (1000/5).

2. Storage Requirements

Approximately quantify it in bytes or some other suitable measurement units and know if logs or generated IDs are kept and how large their storage requirements might be.Determine how many bytes each of IIDs measures and multiply the result by the expected frequency of IIDs’ generation per time unit.For instance, when each ID amounts to 16 bytes and for generating 1 million IDs per day, the daily requirement is 16MB.

In this way the planning and configuration of the system become more efficient and can match the expected working load and in further extend – expand its working capacity. This helps to prevent any disappointments and to ensure that efficiency of the system that delivers highly responsive ID generation solution to the users is achieved.

High-Level Design of Random ID Generation System

1. Actors

• User: Represents the end user, who submits the ID request, and is concerned with speed, code detail, and memory.
• Admin: Used to denote the administrator setting up the inherent characteristics of the system.

2. Components

• API Gateway: It acts as a starting point through which the client requests pass before being forwarded to the correct microservices.
• ID Generation Microservice: Currently it is tasked with generating IDs for other systems.
• Configuration Service: Responsible for configuring the operating system settings of all systems.
• Logging Service: Some of the functions include the ability to capture and store IDs and other system-related events that may take place.
• Database: Stores configuration settings and logs and uses them to ensure that all applications function optimally.

3. Interactions

• Here, all the entities, with ID generation requests towards the user, are the API Gateway.
• Depending on the requested platform, the Admin sends configuration requests to the API Gateway.
• The ID Generation functionality is implemented in the ID Generation Microservice;, the API Gateway directs requests to this service.
• It passes these parameters to the Configuration Service within the API Gateway.
• The Logging Service-Fig. 3 Log requests are forwarded to the API Gateway.
• Logging Service deals with logging the IDs generated by using the ID Generation Microservice.
• It can also write newly generated IDs to the Database if required as well, as a part of the ID Generation Microservice.
• As previously described, the Configuration Service refers to the Database as a potential location for storing configuration settings.
• The logs collected by the Logging Service are placed in the Database.

Low-Level Design for Random ID Generation System

Pseudocode for ID Generation

import random
import string

def generate_random_id(length=16, char_set=string.ascii_letters + string.digits):
    return ''.join(random.choice(char_set) for _ in range(length))

1. Actors

• User: This module is responsible for generating the ID for a request and also participates in the handling of the system.
• Admin: It provides the option to set the ID length appropriately and the character set that is needed to be assigned to the system.

2. Components

• ID Generation Service: Analyze its know-how, which corresponds to the management of the main functions of generating IDs.
  • IDGenerator: For this purpose, the main component needed for ID generations will be the sequence generator.
  • RandomNumberGenerator: Used to produce random numbers for high entropy IDs because random numbers should be secure.
  • UUIDGenerator: Generates UUIDs.
• Configuration Service: Manages system configuration according to the specifications of the organization.
  • ConfigManager: Integral part of managing various parameters or configuration settings.
  • LengthConfig: The configuration ID of a user’s ID is managed with this class.
  • CharacterSetConfig: It is used to manage and set the character set that the system will use.
• Logging Service: Responsible for controlling and regulating the produced IDs.
  • LogManager: Handles logging operations.
  • IDLog: Specifically, it produces an array of IDs and stores these as a log of generated IDs.

3. Interactions

• The User firstly, communicates with the IDGenerator for IDs to be generated.
• The given IDGenerator class uses the RandomNumberGenerator and UUIDGenerator classes to create IDs.
• Existing ID tags include LongId, AppId, ContextId, and SessionId, and these IDs are recorded in the IDLog by the LogManager.
• The Admin uses the ConfigManager to make configurations which change the LengthConfig and CharacterSetConfig and therefore sends configuration values to the IDGenerator.
• The low-level design focuses on detailing some of the intricate relations between different parts and elements, making the overall architecture of the system quite clear and rather exhaustive.

Database Design of Random ID Generation System

1. Tables

• Configurations: Stores configuration settings for ID generation.
  • config_id (Primary Key)
  • parameter
  • value
• Logs: Stores logs of generated IDs.
  • log_id (Primary Key)
  • generated_id
  • timestamp

2. Entities

• User: Means individuals who create IDs, so it refers to users.
• Admin: Prescribes individuals who set up the system and ensure that It works as planned by the developers.
• GeneratedID: Stores generated IDs.
• Configuration: Stores configuration settings.
• Log: Logs all IDs that it has produced and other related activities.
• CharacterSetConfig: Stores with specified character set of alphanumeric characters for IDs generation.
• LengthConfig: Variable storage defining the length of the stores to be used in the generation of IDs.

3. Attributes

• User: regular_user : user_id, username, email, password_hash, created_at, updated_at.
• Admin: admin_id, username email, password_hash created at, updated at.
• GeneratedID: It represents an id, a value of the id, a time, and a number related to a specific user.
• Configuration: Configurations:, config_id, config_name, config_value, admin_id, updated_at.
• Log: We have used the following database fields in our Log table: log_id, log message, log level, stamp, generated ID, user ID, admin ID.
• CharacterSetConfig: char_set_id, allowed_characters, config_id.
• LengthConfig: length_id to define Min length, Max length of the connection allowed and the Config Id.

4. Relationships

• User to GeneratedID: A user creates a multitude of IDs.
• Admin to Configuration: An admin gets to decide many things.
• GeneratedID to Log: In this scheme, it will be possible for each of the generated IDs to have many logs.
• User to Log: This may simply mean that a user can produce many logs.
• Admin to Log: There are very many logs that an admin can set to contain specific information.
• Configuration to CharacterSetConfig: In such a configuration, one can have a character set configuration.
• Configuration to LengthConfig: This indicates that a configuration can include a length configuration.

Microservices and APIs Used in a Random ID Generation System

The entire random ID generation process is divided into modules so that the system may be scaled as needed and may need to utilize microservices. This is a type of architecture that decomposes the entire system into loosely coupled services that work through Application Programming Interfaces. Below are the detailed descriptions of the microservices and APIs used:

1. API Gateway

API Gateway is responsible to service all client requests which are intended to use and access resources. This one responds to incoming requests and dispatches them to the required microservice; this way, the clients do not have to know about the internal division of the application.

Responsibilities:

• Request routing
• Authentication and authorization
• Load balancing
• Rate limiting
• Caching

APIs:

• POST /generate-id: That forwards the request to the ID Generation Microservice to generate the new ID.
• GET /configurations: Retrieves the current settings from the Configuration Service in order to get the current values of various configuration parameters.
• POST /configurations: Retrieves and changes the existing configuration through the Configuration Service.
• GET /logs: It gets logs from the log service Logging Service.

2. ID Generation Microservice

This core service presents the ability to create appropriate IDs using the configuration parameters given. This secures high entropy and uniqueness of the IDs produced in the process.

Responsibilities:

• Generating unique IDs
• The first requirement is high entropy and randomness of the generated sequences This means that a large number of different random states and/or spin configurations should be obtainable.
• Specifying configurations associated with the ID length and the character set.

APIs:

• POST /generate: Creates a new unique ID as defined by the current parameters in its configuration.
• Request Body: Type of the text: letter containing 10 characters of which 10 are alphanumeric.
• Response: It became a popular product as described by Rita: { "id": "aB3dE6Gh9J" }

3. Configuration Service

This process decides the configuration that is used in generating different IDs. It enables the administrators to modify the settings which include the number of characters in the ID and the character chosen.

Responsibilities:

• Managing configuration settings.
• Opening configuration update we can offer API for access.
• Saving the configuration data into the database.

APIs:

• GET /configurations: Retrieves the current configuration settings.
• Response: { "length": 10, "characterSet": "alphanumeric" }
• POST /configurations: Updates the configuration settings.
• Request Body: { "length": 12, "characterSet": "alphanumeric+special" }

4. Logging Service

The Logging Service captures logs of generated IDs and other system events. This helps in monitoring and troubleshooting the system.

Responsibilities:

• Logging generated IDs.
• Storing system events and errors.
• Providing APIs to retrieve logs.

APIs:

• POST /logs: Logs an event or ID generation activity.
• Request Body: { "event": "ID Generated", "id": "aB3dE6Gh9J", "timestamp": "2024-05-23T12:34:56Z" }
• GET /logs: Retrieves a list of logs.
• Response: [ { "event": "ID Generated", "id": "aB3dE6Gh9J", "timestamp": "2024-05-23T12:34:56Z" }, ... ]

Scalability in the Random ID Generation System

scalability is one of the most important factors to consider when it comes to designing a random ID generation system, as this makes the system more capable of handling high loads without showing a significant decline in performance. In the following sub-sections, let us examine various ways in which it is possible to design the system to ensure that the scale factor is optimally met.

Key Principles of Scalability

• Horizontal Scaling: Bring more instances of the same microservices to process more loads of traffics.
• Load Balancing: The capability of spreading the new arriving requests equably across multiple instance.
• Statelessness: The following includes— Microservices that must be stateless since this makes it possible to scale them out.
• Asynchronous Processing: Substituting asynchronous communication as often as possible for synchronous forms in the workflow to enhance the system’s throughput.
• Database Sharding: Database partitioning - the process of dividing the physical database in to multiple logical sub-databases to enhance the performance.
• Caching: The second solution is tentang caching systems so as to relieve the burden that falls on the database.

Conclusion

On this article it takes considerable alignments to develop a good functional and non-functional requirement of the random ID generation system. By adopting an architecture of microservices, we guarantee that we will be able to develop a more scalable, reliable, and fast system. To address this, it is essential to accurately define and anticipate workload and design the database to withstand the load and ensure it remains stable, secure, and reliable. The high level and low level of design provide a way in which the system can be implemented to meet the various requirements of modern applications with regards to the use of IDs whereby the system can also incorporate measures to ensure that the IDs it generates have tight securities and are unique.