16. Distributed Unique ID Generators: Twitter Snowflake, UUIDv7 & Database Ticket Servers • High-Level System Design (HLD): Distributed Systems at Scale • Rishabh Ki Kaksha

High-Level System Design (HLD): Distributed Systems at Scale › 16. Distributed Unique ID Generators: Twitter Snowflake, UUIDv7 & Database Ticket Servers

45 mins

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Course Syllabus

Module 1: Distributed Systems Core & Scalability Fundamentals

01. Scalability Dimensions: Vertical Scaling vs Horizontal Scaling & Latency vs Throughput 02. CAP Theorem vs PACELC Theorem: Consistency, Availability & Partition Tolerance Tradeoffs 03. Consistency Models: Strong Consistency, Eventual Consistency, Read-Your-Writes & Monotonic Reads

Module 2: Networking, Load Balancing & Reverse Proxies

04. Layer 4 vs Layer 7 Load Balancing: Round Robin, Weighted, Least Connections & IP Hash 05. High-Availability Load Balancers: Keepalived, VRRP, Anycast DNS & Failover Clustering 06. API Gateway Pattern: Authentication, Rate Limiting, Request Routing & Circuit Breakers

Module 3: Database Scaling: Sharding, Replication & Distributed Transactions

07. Database Replication: Master-Slave vs Multi-Master & Synchronous vs Asynchronous Replication 08. Database Sharding: Horizontal Partitioning, Directory-Based Sharding & Rebalancing Hotspots 09. Distributed Transactions: Two-Phase Commit (2PC) vs Saga Pattern (Choreography vs Orchestration)

Module 4: Caching Architectures, CDN & Consistent Hashing

10. Caching Strategies: Cache-Aside, Read-Through, Write-Through, Write-Around & Write-Back 11. Cache Invalidation Nightmares: Cache Avalanche, Cache Stampede, Thundering Herd & Cache Penetration 12. Consistent Hashing with Virtual Nodes (Solving Cache Clustering & Dynamic Scaling) 13. Content Delivery Networks (CDN): Edge Caching, Dynamic Site Acceleration & Origin Shielding

Module 5: Message Brokers, Event-Driven Architecture & Stream Processing

14. Message Queues vs Event Streams: RabbitMQ vs Apache Kafka (Partitioning, Consumer Groups) 15. Exactly-Once Processing Semantics (EOS), Idempotency Keys & Deduplication Architecture 16. Distributed Unique ID Generators: Twitter Snowflake, UUIDv7 & Database Ticket Servers

Module 6: FAANG Classic HLD Architectural Case Studies

17. System Design Case Study 1: URL Shortener (TinyURL / Bitly) at 100M Daily Requests 18. System Design Case Study 2: Distributed Rate Limiter & Web Application Firewall 19. System Design Case Study 3: Real-Time Chat & Notification System (WhatsApp / Slack) 20. System Design Case Study 4: Social Media News Feed (Twitter/Instagram) with Fan-out on Write/Read 21. System Design Case Study 5: Video Streaming Platform (YouTube / Netflix) Transcoding Architecture 22. System Design Case Study 6: Ride Hailing Geospatial Dispatch Architecture (Uber / Ola)

Interactive Lesson Studio

# 16. Distributed Unique ID Generators: Twitter Snowflake, UUIDv7 & Database Ticket Servers

> High-Level Design (HLD) Notes by Rishabh - Topic 16

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### 1. Why Auto-Increment IDs Fail in Distributed Systems ⚠️

Single database ka `AUTO_INCREMENT` primary key multi-master sharded architecture me single point of failure aur write throughput bottleneck ban jata hai.

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### 2. Twitter Snowflake 64-bit ID Structure (The Gold Standard ❄️)

1 64-bit BigInt number jo time-sortable hota hai:

```
[ 1 Bit: Sign (0) ] [ 41 Bits: Epoch Milliseconds ] [ 10 Bits: Machine/Datacenter ID ] [ 12 Bits: Sequence Counter ]
```

*   **41 Bits Timestamp:** ~69 Years of unique time-sorted millisecond IDs.
*   **10 Bits Machine ID:** $2^{10} = 1024$ distinct independent worker nodes.
*   **12 Bits Sequence Number:** $2^{12} = 4096$ unique IDs **per single millisecond per machine** (Over 4 Million IDs/sec/node!).

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### 3. UUIDv4 vs UUIDv7 (B-Tree Index Fragmentation 🪓)

*   **UUIDv4 (Completely Random 128-bit):** Database B-Tree index me random inserts karta hai ➔ Severe page splits, cache thrashing, 80% slow DB writes!
*   **UUIDv7 (Time-Ordered 128-bit):** Initial 48 bits me Unix Epoch timestamp hota hai ➔ **Sequential continuous B-Tree appends (Fast as standard BigInts!)**.

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### 4. Master Comparison Matrix

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### 5. Interview Ke 3 Sawal Is Topic Se

1. **Twitter Snowflake me Clock Drift (NTP backwards step) hone par kya hota hai?** -> Agar system clock piche chali jaye, toh generator wait karta hai ya exception throw karta hai taaki duplicate timestamp collision na ho.
2. **Snowflake ID me se creation timestamp kaise extract karte hain?** -> `(id >> 22) + custom_epoch_timestamp` bitwise right shift karke.
3. **Flickr Ticket Server me odd/even `AUTO_INCREMENT_INCREMENT = 2` strategy kyu use hoti thi?** -> 2 Master databases ke beech ID collisions prevent karne ke liye (Master 1 generates 1, 3, 5; Master 2 generates 2, 4, 6).

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**Next Topic:** 17. System Design Case Study 1: URL Shortener (TinyURL / Bitly) at 100M Daily Requests

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