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At first glance, an ID might seem like the most boring part of your application. It's just a unique identifier, right?
But if you're building systems that scale - across regions, across teams, across microservices - your ID generation strategy can be the silent hero or the hidden landmine. And that’s exactly why companies like Netflix, Twitter, Stripe, and Shopify have ditched traditional auto-incrementing IDs in favor of UUIDs and Snowflake-like systems.
Let’s explore why UUIDs are not just random gibberish, but a critical architectural decision in high-scale systems - and what lessons we can steal from the giants.
The Problem With Auto-Increment IDs
Auto-incrementing integers are deceptively simple and convenient. They work great when:
But modern systems don’t live in that world anymore. The problems start to show when:
Collisions and Race Conditions
Multiple databases can't safely share an auto-increment counter without introducing locking or orchestration.
Poor Mergeability
Data from separate systems (say, multiple regions or services) becomes a nightmare to merge.
Predictability
Auto-increment IDs can leak sensitive information:
In fact, I once worked with a system where simply knowing the current user ID could let you enumerate every customer in the database with /users/{id}.
Enter UUIDs: Globally Unique by Design
UUID (Universally Unique Identifier) is a 128-bit number used to uniquely identify information in distributed systems - no central authority needed.
It looks like this:
123e4567-e89b-12d3-a456-426614174000
That randomness is not for show. It’s your ticket to generating globally unique IDs without coordination.
There are different versions of UUIDs - each designed with a specific use-case in mind.
Real-World Case Study: How Netflix Handles IDs
Netflix’s backend is a polyglot architecture - microservices, Kafka streams, data lakes, global deployment zones, all generating billions of events per day.
They don’t use UUID v4s directly.
Instead, they use a Snowflake-inspired ID generation system, pioneered by Twitter and now a standard for distributed ID generation.
A typical Netflix ID might be composed of:
This format:
Auto-incremented IDs simply don’t work in an architecture with:
Netflix has no central place to "ask for the next ID." Doing so would create latency, single points of failure, and tight coupling.
Instead, every instance can independently generate its own IDs - and they're still guaranteed to be unique and ordered.
UUID v7: The Future Is Timestamped
UUID v7 is gaining popularity because it solves many of the long-standing issues with UUID v4:
If you're designing a new system today, strongly consider UUID v7 or a Snowflake-style ID generator over v4 or auto-increment.
Here’s a story from a startup I consulted with:
They used UUID v4s for every table. Three years in, as the database grew, query performance dropped off a cliff. The culprit? Random insertion pattern caused by v4 UUIDs.
Every insert was touching a new part of the index tree, resulting in write amplification and cache inefficiency. They ended up migrating to ULIDs (like v7) to regain performance.
Lesson: Just because a UUID is “unique” doesn’t mean it’s “smart.”
Final Thoughts
IDs are often an afterthought in design - but they shouldn’t be. Netflix, Twitter, and others teach us that at scale, even your identifiers must be thoughtfully engineered.
If you’re building distributed systems, event-driven pipelines, or global-scale SaaS platforms, ditch auto-increment and random v4s. Embrace timestamped, sortable, decentralized IDs.
Your future self (and your infrastructure team) will thank you.
Footnote: Netflix doesn’t publicly publish the full internals of their ID generation, but numerous engineering talks, job postings, and system diagrams confirm that they use a variant of the Snowflake pattern for large-scale event tracking.
Further Reading:
But if you're building systems that scale - across regions, across teams, across microservices - your ID generation strategy can be the silent hero or the hidden landmine. And that’s exactly why companies like Netflix, Twitter, Stripe, and Shopify have ditched traditional auto-incrementing IDs in favor of UUIDs and Snowflake-like systems.
Let’s explore why UUIDs are not just random gibberish, but a critical architectural decision in high-scale systems - and what lessons we can steal from the giants.
The Problem With Auto-Increment IDs
Auto-incrementing integers are deceptively simple and convenient. They work great when:
- You have a single database.
- You can guarantee a single source of truth.
- You're not worried about collisions across systems.
But modern systems don’t live in that world anymore. The problems start to show when:
- You scale horizontally (e.g., microservices writing to different DBs).
- You have geo-redundant deployments.
- You ingest millions of concurrent events (e.g., Netflix's stream logs, Stripe’s transactions, Shopify’s orders).
Collisions and Race ConditionsMultiple databases can't safely share an auto-increment counter without introducing locking or orchestration.
Poor MergeabilityData from separate systems (say, multiple regions or services) becomes a nightmare to merge.
PredictabilityAuto-increment IDs can leak sensitive information:
- How many users have signed up
- Volume of orders or transactions
- Sequence of operations
In fact, I once worked with a system where simply knowing the current user ID could let you enumerate every customer in the database with /users/{id}.
Enter UUIDs: Globally Unique by Design
UUID (Universally Unique Identifier) is a 128-bit number used to uniquely identify information in distributed systems - no central authority needed.
It looks like this:
123e4567-e89b-12d3-a456-426614174000
That randomness is not for show. It’s your ticket to generating globally unique IDs without coordination.
There are different versions of UUIDs - each designed with a specific use-case in mind.
| UUID Version | Description | Use Case |
|---|---|---|
| v1 | Timestamp + MAC address | Time-sortable, but leaks host info |
| v4 | Randomly generated | Most common, but unordered |
| v5 | Hash of namespace and name | Deterministic UUIDs |
| v7 (new) | Timestamp-first, random suffix | Perfect for databases & logs |
Netflix’s backend is a polyglot architecture - microservices, Kafka streams, data lakes, global deployment zones, all generating billions of events per day.
They don’t use UUID v4s directly.
Instead, they use a Snowflake-inspired ID generation system, pioneered by Twitter and now a standard for distributed ID generation.
A typical Netflix ID might be composed of:
- 41 bits of timestamp (milliseconds since epoch)
- 10 bits for machine ID
- 12 bits for sequence (to avoid collision in the same millisecond)
This format:
- Keeps IDs unique across all instances.
- Makes them time-sortable for logs, metrics, and debugging.
- Avoids the database indexing problem that plagues UUID v4.
Why They Don’t Use Auto-IncrementFun fact: The original Snowflake system could generate 4096 unique IDs per machine per millisecond.
Auto-incremented IDs simply don’t work in an architecture with:
- Dozens of microservices writing independently
- Global regions that can go into active-active mode
- Systems that must work even when partially disconnected
Netflix has no central place to "ask for the next ID." Doing so would create latency, single points of failure, and tight coupling.
Instead, every instance can independently generate its own IDs - and they're still guaranteed to be unique and ordered.
UUID v7: The Future Is Timestamped
UUID v7 is gaining popularity because it solves many of the long-standing issues with UUID v4:
- Ordered generation = better database index locality
- Encodes time = more useful for logs, debugging, analytics
- Still decentralized = no coordination needed
If you're designing a new system today, strongly consider UUID v7 or a Snowflake-style ID generator over v4 or auto-increment.
Bonus: When UUIDs Go Wrong
Here’s a story from a startup I consulted with:
They used UUID v4s for every table. Three years in, as the database grew, query performance dropped off a cliff. The culprit? Random insertion pattern caused by v4 UUIDs.
Every insert was touching a new part of the index tree, resulting in write amplification and cache inefficiency. They ended up migrating to ULIDs (like v7) to regain performance.
Lesson: Just because a UUID is “unique” doesn’t mean it’s “smart.”
Final Thoughts
IDs are often an afterthought in design - but they shouldn’t be. Netflix, Twitter, and others teach us that at scale, even your identifiers must be thoughtfully engineered.
If you’re building distributed systems, event-driven pipelines, or global-scale SaaS platforms, ditch auto-increment and random v4s. Embrace timestamped, sortable, decentralized IDs.
Your future self (and your infrastructure team) will thank you.
Footnote: Netflix doesn’t publicly publish the full internals of their ID generation, but numerous engineering talks, job postings, and system diagrams confirm that they use a variant of the Snowflake pattern for large-scale event tracking.
Further Reading:
- Twitter Snowflake:
- UUID v7 spec:
- Stripe’s custom ID formats:
- ULID: