Unique ID
A unique ID (Unique Identifier) is a unique, non-duplicate number or string of characters assigned to identify data or objects, which is used to distinguish specific information within a system or database.
The characteristics of a unique identifier include the following.
- Uniqueness: the assignment of an ID that does not duplicate others within a given system or database allows information to be managed without confusion. The absence of duplicate unique IDs is important for maintaining reliability and consistency within a system.
- Persistence: once generated, IDs are essentially unchanged. This means that the same data can be referenced over time, enabling long-term data management and analysis.
- Ease of search and identification: the use of unique IDs allows for quick and efficient searches when identifying and retrieving objects in databases and programmes.
Reasons why a unique ID is necessary include
- Preventing data confusion and errors: the risk of data being confused is lower and the integrity of the system is easier to maintain. For example, even when there is a large amount of data, a unique ID can ensure that certain data is handled correctly.
- Information tracing and history management: traceability of specific information, such as user behaviour history or product distribution history, is ensured, which makes it easier to identify the cause of errors and faults when they occur.
- Enhanced security and access management: access to specific users and data can be managed using unique IDs, allowing for strict control of authorisation. This also reduces the risk of information leaks.
Considering these characteristics, examples of use include the following.
- User ID: A unique ID can be allocated to each user of the system, so that users with the same name and surname can be identified individually. A typical example is the user ID allocated when registering as a member of a website or application.
- Product code or serial number: If a unique serial number is assigned to each product, information can be managed consistently from production to sales and support. It also makes it easier to track which lots of a particular product were manufactured and to which customers it was sold.
- Primary key of the database: each row in the database is given a unique ID, which is set as the primary key. The use of a primary key makes it possible to link data between different tables and prevent duplication.
- Universally Unique Identifier (UUID): UUIDs are often used in software development as a standardised method for generating global, non-duplicate identifiers, especially in environments where multiple systems work together, such as distributed systems and cloud computing.
Unique IDs are an essential mechanism for accurately managing data and objects and efficiently accessing and manipulating them, and their importance is even greater, especially in systems that handle large amounts of data and in environments where multiple systems are linked together. As the design and management of appropriate IDs is the basis for the reliability and operational efficiency of the entire system, ID generation requires careful design based on consistency and requirements.
How to generate a unique ID
The methods for generating unique IDs and their characteristics are described below.
1. auto increment ID:
Description: A method of assigning a sequence of numbers starting from 1 as an ID, often used in databases.
Features:
– Simple and easy to manage as it is managed by sequential numbers.
– Commonly used in local databases and single-server environments.
– The order of data insertion is easy to understand, and it is easy to guess the insertion date from a specific ID.
Disadvantages:
– Not suitable for cloud computing or large-scale systems due to the risk of duplication in distributed environments or between multiple systems.
2. universally unique identifier (UUID):
Description: a UUID is a 128-bit unique identifier with a very high probability of non-duplication, making it suitable for use in distributed environments and global systems; standardised in RFC 4122, there are five main versions (UUIDv1 to v5).
Features:
– Can be used in distributed systems and across multiple databases, with very low potential for duplication.
– Notated in hexadecimal format, making it long and difficult to read, but highly unique.
Generated examples:
– UUID v1: generated using timestamps and MAC addresses, making it easy to determine the time of generation.
– UUID v4: method using only random numbers, very low chance of duplication.
– UUID v5: uses a namespace-based hash value, allowing consistent UUIDs to be generated based on specific names.
3. hash-based ID:
Description: A method of generating a unique string of characters using a hash algorithm such as SHA-1 or MD5. Useful for generating IDs based on specific data or elements.
Features:
– Hash values are highly unique and are used to maintain data integrity.
– The rules for generating IDs are stable, as the same hash value is generated if the inputs are the same.
– Suitable for comparing and verifying data, and checking that certain content has not been changed.
Disadvantages:
– Not suitable when short IDs are required, as the hash value is long.
– SHA-1 and MD5 may not be cryptographically secure, so algorithms such as SHA-256 should be used in cases where security considerations are required.
4. the Snowflake Algorithm:
Description: Snowflake is an identity generation algorithm developed by Twitter that uses a specific bit structure to generate identities in distributed systems without duplication; it is a mechanism for generating unique 64-bit identities and is mainly used in distributed systems and microservice environments.
Features:
– Capable of generating large numbers of IDs at high speed.
– Consists of a timestamp, data centre ID, machine ID and sequence number, so it is easy to see which server and time the ID was generated.
– Effective in systems where global uniqueness is required, as uniqueness can be maintained automatically.
Disadvantages:
– Requires an environment that supports the Snowflake algorithm.
– Requires knowledge of operation due to complexity of implementation.
5. timestamp ID using nanoseconds/milliseconds:
Description: uses the current timestamp as a unique ID. As it is based on timestamps, it is possible to generate IDs that are easy to sequence.
Features:
– The ID includes the date and time of generation, so the order is visually clear.
– Can also be used in distributed environments by adding additional random numbers or server IDs.
Disadvantages:
– Auxiliary random numbers are necessary due to the risk of duplication when generating large numbers of IDs at the same time.
– May be inappropriate where confidentiality is required, as it includes the date and time of generation.
6. custom generation algorithms:
Description: A method of generating IDs with unique rules according to system requirements and specifications. Specific business rules or data attributes can be incorporated into the identity.
Features:
– Can be generated based on custom business logic, making it easy to manage.
– Meaning can be read from IDs by including dates, categories, serial numbers, etc.
Disadvantages:
– Can be complex to implement uniqueness logic as it is self-managed.
– Design is important, as duplicate management needs to be carried out as required.
A variety of ID generation methods exist, depending on the application and system requirements. Incremental IDs are convenient for single databases, while distributed ID generation methods such as UUID and Snowflake are suitable for distributed systems. By choosing the appropriate method according to the requirements, efficient and reliable identity management can be achieved.
implementation example
Below are example implementations of each ID generation method in Python and JavaScript.
Increment IDs: Increment IDs are often generated automatically by databases, so the following shows a simple way to manage your own counters.
Example implementation in Python:
class IncrementIDGenerator:
def __init__(self):
self.current_id = 0
def generate_id(self):
self.current_id += 1
return self.current_id
id_gen = IncrementIDGenerator()
print(id_gen.generate_id()) # 1
print(id_gen.generate_id()) # 2Example implementation in JavaScript:
class IncrementIDGenerator {
constructor() {
this.currentID = 0;
}
generateID() {
return ++this.currentID;
}
}
const idGen = new IncrementIDGenerator();
console.log(idGen.generateID()); // 1
console.log(idGen.generateID()); // 22. universally unique identifier (UUID): UUIDs can be generated by the standard Python and JavaScript libraries.
Example implementation in Python:.
import uuid
unique_id = uuid.uuid4() # Generation of UUID v4.
print(unique_id)Example implementation in JavaScript: in a JavaScript environment, it is common to use an external library for UUID generation, and the following is an example using the uuid library.
// Install uuid library with npm: npm install uuid
const { v4: uuidv4 } = require('uuid');
const uniqueID = uuidv4();
console.log(uniqueID);3. hash-based ID: a method of generating a unique hash value based on specific data.
Example implementation in Python:
import hashlib
def generate_hash_id(data):
return hashlib.sha256(data.encode()).hexdigest()
print(generate_hash_id("example_data"))Example implementation in JavaScript: example in Node.js, using the crypto module.
const crypto = require('crypto');
function generateHashID(data) {
return crypto.createHash('sha256').update(data).digest('hex');
}
console.log(generateHashID("example_data"));4. the Snowflake algorithm: generating Snowflake IDs is a bit more complicated and usually involves the use of libraries.
Example implementation in Python: Python has a Snowflake ID generation library called flake-idgen.
# pip install flake-idgen
from flake_idgen import IdGenerator
generator = IdGenerator()
snowflake_id = generator.next()
print(snowflake_id)Example implementation in JavaScript: the node-snowflake library can also be used to generate Snowflake IDs in JavaScript.
// npm install node-snowflake
const Snowflake = require('node-snowflake').Snowflake;
const snowflakeID = Snowflake.nextId();
console.log(snowflakeID);5. timestamp + random number: how to combine a timestamp and a random number to generate an ID.
Example implementation in Python:
import time
import random
def generate_timestamp_id():
timestamp = int(time.time() * 1000) # Millisecond timestamps.
random_number = random.randint(1000, 9999) # 4-digit random number
return f"{timestamp}{random_number}"
print(generate_timestamp_id())Example implementation in JavaScript:
function generateTimestampID() {
const timestamp = Date.now(); // Re-second time stamps.
const randomNumber = Math.floor(1000 + Math.random() * 9000); // 4-digit random number
return `${timestamp}${randomNumber}`;
}
console.log(generateTimestampID());
6. custom generation algorithms: e.g. example of custom ID generation combining date and serial number.
Example implementation in Python:
from datetime import datetime
class CustomIDGenerator:
def __init__(self):
self.serial_number = 0
def generate_custom_id(self):
self.serial_number += 1
date_str = datetime.now().strftime("%Y%m%d")
return f"{date_str}-{self.serial_number:04d}"
id_gen = CustomIDGenerator()
print(id_gen.generate_custom_id()) # 20241120-0001
print(id_gen.generate_custom_id()) # 20241120-0002Example implementation in JavaScript:
class CustomIDGenerator {
constructor() {
this.serialNumber = 0;
}
generateCustomID() {
this.serialNumber += 1;
const dateStr = new Date().toISOString().slice(0, 10).replace(/-/g, '');
return `${dateStr}-${String(this.serialNumber).padStart(4, '0')}`;
}
}
const idGen = new CustomIDGenerator();
console.log(idGen.generateCustomID()); // 20241120-0001
console.log(idGen.generateCustomID()); // 20241120-0002reference book
Reference books are listed below. These books also touch on technologies related to unique ID generation, such as distributed systems, database design and algorithms.
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