UUIDs

UUID Adoption, Drawbacks, UUIDv7, and Snowflake IDs

1. Why Developers Are Switching to UUIDs

Developers increasingly prefer UUIDs over auto-increment integers because:

✅ 1.1. Decentralized ID Generation

• No need for database-side sequencing.
• Multiple services/machines can create IDs without coordination.

✅ 1.2. Perfect for Distributed Architectures

• Works well with microservices, event-driven systems, and multi-region apps.
• Avoids “ID collisions” across services.

✅ 1.3. Security Through Obscurity

• Sequential numeric IDs expose the scale of your system.
• UUIDs prevent ID enumeration attacks like:
  • /users/1, /users/2, etc.

✅ 1.4. Easier Migration & Sharding

• UUIDs remain unique across shards.
• Smooth migrations across environments (dev/stage/prod).

✅ 1.5. Better for Client-Side Generated Data

• Offline-first apps can generate IDs even without a backend.

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2. Drawbacks of Traditional UUIDs (UUIDv4)

❌ 2.1. Poor Index Locality

• UUIDv4 is completely random.
• Inserts scatter across the B-Tree index.
• Causes:
  • Page splits
  • Fragmentation
  • Slower writes
  • Larger index size

❌ 2.2. Bad for Ordering

• No time component → cannot sort by creation time.
• Requires separate createdAt column to track chronology.

❌ 2.3. Larger Storage Cost

• 16 bytes vs 4 bytes (int) or 8 bytes (bigint).
• Impacts:
  • Indexes
  • Joins
  • Foreign key storage

❌ 2.4. Harder to Debug

• Hard to visually understand the sequence of operations or logs.

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3. Modern Solutions to UUID Problems

3.1 UUIDv7 (The new recommended standard)

UUIDv7 = timestamp + randomness.

✅ Benefits

• Sortable (monotonic increasing) → great for indexing.
• Better insertion locality (fixes UUIDv4’s major pain).
• Has microsecond timestamps.
• Globally unique without coordination.
• Ideal for database usage.

📌 Why it indexes better?

• Because the timestamp prefix ensures new IDs are near the end of the index → fewer page splits.

Example (Node.js, TypeScript)

import { uuidv7 } from 'uuidv7';
const id = uuidv7();

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3.2 Snowflake IDs (Twitter Snowflake, KSUID, ULID, etc.)

Structure (typically)

Timestamp | Machine ID | Sequence

✅ Advantages

• Lexicographically sortable.
• Very fast to generate.
• Smaller than UUID (64-bit or 128-bit).
• Good for:
  • High throughput systems
  • Distributed services
  • Ordered event logs

❌ Disadvantages

• Requires configuration:
  • Machine ID / worker ID
• Must avoid:
  • Clock drift
  • Duplicate worker IDs
• Some implementations are not globally coordinated.

Popular Alternatives

ID Type	Sortable	Randomness	Fits in bigint	Notes
UUIDv4	❌ No	High	❌ No	Random but bad for DB
UUIDv7	✅ Yes	Medium	❌ No	Best modern/default choice
ULID	✅ Yes	Medium	❌ No	URL-safe, readable
KSUID	✅ Yes	Medium	❌ No	Great for event logs
Snowflake	✅ Yes	Low	✅ Yes	Perfect for bigint PKs

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4. Should You Use UUID as Primary Key?

Best cases:

• Distributed systems
• Multi-region applications
• Event logs
• Offline clients
• Microservices
• Large teams where different services create data

When NOT to use UUID:

• Very high write load tables where index bloat matters.
• When you need compact storage (IoT, embedded systems).
• When your DB performance matters more than independence.

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5. Recommended Choices in 2024–2025

👍 Use UUIDv7 for:

• Most modern applications
• PostgreSQL / MySQL / MongoDB
• Systems needing sortable, index-friendly IDs
• Minimal configuration

👍 Use Snowflake IDs when:

• You want bigint‐compatible IDs (required by some ORMs).
• You need extremely high throughput (thousands–millions IDs/sec).
• You want sortable IDs but smaller than UUID.

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6. Summary

Option	Best For	Pros	Cons
UUIDv4	Legacy, simple setups	Random, unique	Bad indexing, non-sortable
UUIDv7	Most apps today	Sortable, index-friendly	128-bit storage cost
Snowflake ID	Bigint PKs, high-perf systems	Compact, sortable, fast	Needs worker IDs, clock sync
ULID/KSUID	Event systems, logs	Readable, sortable	Not as standardized

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7. Final Recommendation

➡ For general application development (Node.js + PostgreSQL), use UUIDv7.
➡ For extremely high throughput systems with bigint constraints, use Snowflake IDs.