Unique ID Generator in Distributed System | System Design #TechInterviewPreparation

Science & Technology

Unique ID Generator in Distributed System | System Design #TechInterviewPreparation

Hey everyone, welcome to CodeFarm! I hope you're all doing great. Today’s topic is Unique ID Generator, a crucial component in designing systems, especially in a distributed setup. This video is part of our ongoing series on system design. You can find links to other videos in the description box and pinned comment. If you want a comprehensive understanding of system design, make sure to check them out. So, let’s get started with Unique ID Generator!

Understanding the Problem Statement

Imagine you are building software for your company, consisting of multiple services such as:

  • User Service
  • Product Service

Each of these services has its own database because they are microservices and do not share the same database. Now, every record created in both User and Product services must have a unique ID. In a local context (within one service), an auto-incremental ID might suffice. But in a distributed system, this method becomes problematic. Here’s why:

  1. Distributed Logs and Monitoring: When combined logs from different services like User and Product are sent to a centralized application performance monitoring (APM) tool, having the same ID in different services can cause confusion.

  2. Global Uniqueness Requirement: We need IDs that are globally unique across all services (e.g., User, Product) for debugging and monitoring purposes.

Key Requirements

To summarize, the ID generation system must fulfill the following requirements:

  1. Uniqueness: Each ID generated must be globally unique.
  2. Numerical Values: IDs should be numeric with no alphabets.
  3. Space-Optimized: Preferably, IDs should be within 64 bits or less.
  4. Time-Ordered: IDs should be sortable by time.
  5. Fit for Distributed Systems: The solution should be scalable, fault-tolerant, and have low latency.
  6. Secure: IDs should not be easily predictable to avoid misuse by malicious actors.

Possible Solutions

1. Database Auto Increment

  • Pros: Simple, consistent within its local context, and provides transactional integrity.
  • Cons: Scalability issues, performance bottleneck, single point of failure, and IDs are not globally unique.

2. UID (Universally Unique Identifier)

  • Pros: Globally unique and widely used.
  • Cons: Larger size (128 bits) and might not be time-ordered.
  • Alternative: UID Version 7 introduces time-ordered IDs, but the size remains a constraint.

3. Central Server Approach

  • Pros: Scalable, high throughput, and flexible.
  • Cons: Complexity, latency due to network calls, and single point of failure.

4. Snowflake Algorithm

  • Pros: Scalable, ordered by time, high throughput, fits in 64 bits, and is distributed.
  • Cons: Requires clock synchronization and has a slightly complex implementation.

Detailed Analysis of Snowflake Algorithm

The Snowflake Algorithm divides the 64-bit identifier as follows:

  • 1 bit: Reserved for future use.
  • 41 bits: Time in milliseconds since a custom epoch.
  • 5 bits: Data center ID, allowing up to 32 data centers.
  • 5 bits: Worker ID for machine identifiers, allowing up to 32 workers per data center.
  • 12 bits: Auto-incremented sequence number for different requests within the same millisecond.

Conclusion

The choice of a unique ID generation strategy depends on the specific requirements of your system. Each method has its pros and cons:

  • Database Auto-Increment: Best for simple, consistent needs, but lacks global uniqueness.
  • UID Version 4/7: Suitable for global uniqueness and sorted by time with Version 7 but has a larger size.
  • Central Server: Good for flexibility and throughput but has a single point of failure.
  • Snowflake Algorithm: Offers a combination of benefits, including global uniqueness, time-order, scalability, and fault tolerance.

Please let me know in the comments which implementation you are interested in seeing or which method you prefer. Stay tuned for more videos in the system design series.

Keywords

  • Unique ID Generator
  • Distributed Systems
  • System Design
  • Database Auto Increment
  • UID
  • Snowflake Algorithm
  • Central Server
  • IDs in Microservices
  • Fault Tolerance
  • Scalability

FAQ

1. What is the main challenge in generating a unique ID in a distributed system? The main challenge is ensuring that each ID is globally unique across all services, and it should be time-ordered and space-efficient.

2. Why is UID Version 7 significant? UID Version 7 introduces time-ordered IDs, which are helpful in sorting by time while maintaining global uniqueness.

3. How does the Snowflake Algorithm ensure uniqueness and scalability? Snowflake Algorithm uses a compounding strategy of dividing the 64-bit ID into time, data center, worker ID, and sequence number, ensuring both uniqueness and scalability.

4. What makes database auto-increment not ideal for distributed systems? Database auto-increment IDs are unique only within the context of a single database, which causes conflicts when multiple services interact in a distributed environment.

5. Which ID generation method is recommended for high scalability and fault tolerance? The Snowflake Algorithm is recommended for its scalability, fault tolerance, and efficient use of 64-bit IDs.

Feel free to reach out in the comments for any further questions or clarifications!