# Why I Use Snowflake IDs Instead of UUIDs - **URL:** https://harshcodez.com/blogs/snowflake-id-vs-uuid - **Markdown URL:** https://harshcodez.com/blogs/snowflake-id-vs-uuid.md - **JSON URL:** https://harshcodez.com/blogs/snowflake-id-vs-uuid.json - **Author:** Harshcodez - **Published Date:** 2026-08-17 - **Reading Time:** 8 min read - **Tags:** backend, database-design, databases, distributed-systems, system-design > UUIDs are unique, but at 128 bits they cost you storage, index size, and cache efficiency you don't need to pay for. Here's why Snowflake IDs — compact, sortable, and distributed-friendly — are often the better choice when you control your own infrastructure UUIDs are everywhere. You likely use them for users, orders, posts, employees, sessions, basically anything which requires a unique id. They are easy to create, distributed systems friendly and you never have to worry about generating two identical ids by different servers. But why do we need something else? Because **being unique does not necessarily mean being good**. The UUID can be perfectly valid, globally unique and at the same time a really bad choice for your database. You will use more storage, deal with larger indexes, and potentially give up some useful properties depending on the UUID version you use. It is just what gets ignored. Not only we need unique ids. We also need meaningful ids for our systems. Which is why I prefer Snowflake IDs. ## The Problem With UUIDs The problem with UUIDs isn't that they don't work. They work extremely well. They're globally unique, can be generated independently across multiple servers, and don't require a central ID generator. Modern UUIDs also solve many of the problems people traditionally associated with random identifiers. So what's the problem? **They're bigger than they need to be.** A UUID uses **128 bits, or 16 bytes**, while a typical Snowflake ID fits into **64 bits, or 8 bytes**. Eight bytes might not sound like much. But your database doesn't store an ID only once. It can appear in primary keys, foreign keys, indexes, caches, logs, and references across different services. When you're dealing with millions or billions of records, those extra bytes aren't completely free. And size isn't the only consideration. An ID is part of your system's data model. How it is generated, how it behaves when sorted, how much space it consumes, and how easily your infrastructure can work with it all matter. That's where Snowflake becomes interesting. Instead of using 128 bits for an identifier, a typical Snowflake implementation packs useful information such as **time, worker identity, and a sequence number into 64 bits.** The result is a compact ID that is unique, sortable by generation time, and designed specifically for distributed systems. UUIDs are a perfectly valid solution. But if you're building a system where you control the infrastructure, **there's a strong argument for using a smaller ID that gives you exactly what you need instead of carrying twice the bits.** ## What Is a Snowflake ID? A Snowflake ID is a 64-bit integer designed to generate unique IDs across multiple machines without requiring a central database to assign them. The concept is actually pretty simple. Instead of treating the entire ID as one random value, Snowflake divides those 64 bits into different parts. A common implementation uses: | Component | Bits | Purpose | | --------- | ---: | ------- | | Timestamp | 41 | Stores when the ID was generated | | Worker ID | 10 | Identifies the machine or process generating the ID | | Sequence | 12 | Allows multiple IDs to be generated within the same millisecond | With 10 bits for the worker ID, a system can support up to **1,024 workers**. The 12-bit sequence allows each worker to generate up to **4,096 IDs per millisecond**. And all of that fits inside just **64 bits**. The exact allocation doesn't have to be 41/10/12 either. You can adjust the number of bits based on your system's requirements. Need more workers? Allocate more bits to the worker ID. Need higher throughput per millisecond? Allocate more bits to the sequence. Need a longer timestamp range? Allocate more bits to the timestamp. You're essentially designing the identifier around your infrastructure instead of using a fixed-size identifier for every situation. Another important property is ordering. Because the timestamp occupies the most significant portion of the ID, Snowflake IDs generally increase as time moves forward. That means you get an identifier that is not only unique, but also naturally sortable by creation time. And that's what makes Snowflake interesting: **You're getting uniqueness, distribution, compactness, and time ordering in a single 64-bit value.** ## 64 Bits vs 128 Bits Let's start with the most obvious difference: **size**. A Snowflake ID is typically a 64-bit integer, while a UUID uses 128 bits. That means a Snowflake ID takes **8 bytes**, while a UUID takes **16 bytes** to represent the identifier itself. | ID | Size | | --------- | ------------------: | | Snowflake | 64 bits / 8 bytes | | UUID | 128 bits / 16 bytes | At first, saving 8 bytes probably doesn't sound like a big deal. If you have one user, it obviously isn't. But databases don't deal with one ID. Imagine a table with 100 million rows and the ID is the primary key. Ignoring database-specific overhead, storing 64-bit IDs instead of 128-bit IDs means the raw ID data for the ID column alone is roughly **800 MB instead of 1.6 GB**. And the primary key isn't the only place where that ID can exist. That same identifier can appear in foreign keys, indexes, join tables, caches, queues, logs, and other services. Every time you duplicate that identifier, you're potentially duplicating those extra 8 bytes. This is where the difference starts becoming meaningful. Smaller IDs also mean smaller index entries. Smaller indexes require less storage and can potentially fit more entries into the same database pages, which can improve cache efficiency and reduce the amount of data the database needs to move around. Obviously, database performance isn't determined by ID size alone. A well-designed UUID-based system can perform perfectly well. But when two approaches can provide the properties you need and one requires **half the bits**, it's worth asking why you're paying for the extra space. For me, that's one of the biggest advantages of Snowflake: **64 bits is enough to build a useful distributed ID. So why use 128?** ## Your Database Cares About ID Size An ID doesn't just sit inside a database row. If you're using it as a primary key, your database is also building an index around it. Other tables may reference it through foreign keys, and those columns can have indexes of their own. This is where the difference between 64 bits and 128 bits becomes more interesting. Most relational databases use B-tree-style indexes for these kinds of workloads. The database organizes keys into pages, and the more keys that can fit into each page, the more efficiently the index can be traversed. A smaller key means more entries can fit into the same amount of index space. Consider a simplified example: | ID | Size | | --------- | -------: | | Snowflake | 8 bytes | | UUID | 16 bytes | Ignoring all other database overhead, you can fit roughly **twice as many Snowflake IDs as UUIDs into the same amount of raw key storage**. That doesn't mean Snowflake automatically makes every database query twice as fast. Real databases have page headers, pointers, row overhead, caching behavior, and many other factors. But it does mean the database has **less data to store and move around for the key itself**. And indexes can become surprisingly large. A table might have a primary-key index, several secondary indexes, and foreign-key indexes across related tables. If your identifier appears throughout the schema, the cost of a larger identifier compounds. This is one of those optimizations that doesn't look impressive when you're developing locally with 10,000 rows. At 10 million rows, you start noticing it. At 500 million rows, you really care. And when you're designing a system that might eventually reach that scale, choosing a compact identifier from the beginning is a pretty easy optimization to take. Snowflake doesn't magically fix database performance. It simply gives the database **less data to deal with** every time that ID becomes part of an index. ## Distributed ID Generation One of the main ideas behind Snowflake is to solve a problem that becomes more and more important as your application grows: generating IDs across multiple workers without making every worker talk to a central ID generator. Imagine you have ten application servers processing requests at the time. Each server needs to generate IDs on its own. You don't want every request to wait for a database sequence. You don't want a central service sitting in the middle of every ID generation request. Snowflake solves this by giving each worker its identity. The worker ID becomes part of the generated ID while the sequence number handles IDs generated by that worker during the same millisecond. For example with a 10-bit worker ID and 12-bit sequence: | Component | Capacity | | ------------------------------------- | --------: | | Workers | 1,024 | | IDs per worker per millisecond | 4,096 | | Theoretical IDs per second per worker | 4,096,000 | > That's the theoretical maximum from the sequence field alone; actual throughput depends on the implementation and workload This means ID generation can happen locally. There is no network request just to get an ID. There is no central database sequence that every server has to coordinate through. There is no counter that becomes a bottleneck. Of course, this comes with a responsibility: worker IDs must actually be unique. If two workers accidentally use the same worker ID at the same time, they can generate conflicting IDs.” When worker identity is managed properly Snowflake gives you a very predictable model for distributed ID generation. That's the part I really like about it. The ID generator doesn't need to know what every other worker is doing. Each worker can generate its IDs independently while still producing IDs that are globally unique, within the system. ## The Catch: Snowflake Needs Coordination Snowflake isn't magic. It has a trade-off. Your workers need worker IDs. If two workers accidentally use the worker ID at the same time they can create conflicting IDs. So your system needs a way to assign and manage those worker IDs. UUIDs generally don't require this kind of worker-ID coordination. A machine can generate an ID independently without first being assigned a worker ID. So yes UUIDs are simpler in this way. If you already have a system where you know exactly which worker is running where giving a worker ID is a small cost, for a small ordered distributed ID. Snowflake gives you control. In return you have to handle that control. ## UUIDs aren't bad. They're standardized, widely supported, easy to generate, and for many applications, they're more than enough. But **more than enough isn't always the same as optimal**. If you control your infrastructure and need IDs that are compact, distributed, and time-ordered, Snowflake is a very compelling alternative. You get a 64-bit identifier instead of 128 bits, local ID generation without a central bottleneck, and useful structure built directly into the ID. There is a little more infrastructure to manage, especially around worker IDs and clock behavior. But in a system where those things are already under your control, that trade-off is often worth it. For me, the decision comes down to a simple question: **If 64 bits can give me everything I need from an ID, why use 128?** That's why I choose Snowflake. This isn't an argument that UUIDs are bad. UUIDs are an excellent general-purpose choice. I'm specifically talking about systems where I control the infrastructure and can take advantage of Snowflake's smaller, structured 64-bit format.