Primary keys int ids or UUIDs?

Introduction

Auto-incrementing integers are widely used for primary key generation in relational databases, but they are not the only option. Depending on the application’s requirements, there are several alternatives that may be more suitable. This technical note explores the pros and cons of auto-incrementing integer primary keys, introduces UUIDs (Universally Unique Identifiers) and other alternatives, and provides guidance on how to choose the best option for your database.

Auto-Incrementing Integer IDs

An auto-incrementing integer ID is the most common approach for primary keys. This method assigns a unique, sequential integer value to each new row in a table. For example, the first row might get the value 1, the next row 2, and so on. The mechanism for generating these IDs varies by database vendor. It could involve sequence objects or built-in features like the SERIAL data type in PostgreSQL.

Advantages:

• Simplicity: Auto-incrementing IDs are easy to implement and are supported natively by most database engines.
• Efficiency: Integer types (e.g., INT, BIGINT) require relatively little storage (typically 4 to 8 bytes). They also allow for easy sorting and indexing, as sequential IDs facilitate optimized B-tree structures.
• Storage Efficiency: Compared to larger data types, integer IDs reduce storage overhead, network transfer time, and memory usage.

Disadvantages:

• Predictability: Since auto-incrementing values are sequential, they can introduce security risks. A user who knows the ID of one record might guess the ID of another, potentially exposing sensitive information.
• Scalability: In distributed systems, relying on the database to generate IDs can create bottlenecks. Client applications often need to wait for the database to return the generated ID, slowing down transactions.
• Merge Conflicts: Merging databases that use auto-incrementing integers for primary keys can lead to ID conflicts, requiring complex data transformations.

UUIDs (Universally Unique Identifiers)

A UUID is a 128-bit globally unique identifier, represented as a string of 36 characters (including hyphens). It provides a more scalable and secure alternative to auto-incrementing integers.

Types of UUIDs

1. UUID V4: Randomly generated, comprising 122 random bits and 6 bits for versioning. Example: 550e8400-e29b-41d4-a716-446655440000.
2. UUID V7: Similar to UUID V4, but with a timestamp component, allowing for sorted ordering and better indexing.
3. ULID (Universally Unique Lexicographically Sortable Identifier): A UUID-like identifier designed to be lexicographically sortable and compact.
4. Snowflake ID: A type of distributed identifier that includes information like a timestamp, machine ID, and sequence number, making it smaller and still unique.

Advantages of UUIDs

• Decentralized Generation: UUIDs can be generated by the client or application, not just the database, reducing reliance on a central database for ID generation.
• Database Merging: UUIDs eliminate the risk of conflicts when merging multiple databases.
• Hard to Guess: The random nature of UUIDs makes them harder to predict, enhancing security.

Disadvantages of UUIDs

• Increased Storage: UUIDs typically require 16 bytes, more than the 4 to 8 bytes needed for integers. While some databases can store them in a compact binary format, they still require more space.
• Indexing Performance: Random UUIDs (e.g., UUID V4) are not sequential, which can result in inefficient indexing and slower query performance. However, timestamp-based UUIDs (e.g., UUID V7) or ULIDs mitigate this issue.
• Comparison Overhead: UUIDs, being larger and more complex, may take more time to compare in query operations compared to integers.

Comparison of UUID Types

• UUID V4: Best for cases where random uniqueness is crucial. However, its random nature can degrade index performance.
• UUID V7: Combines randomness with timestamp-based ordering, improving indexing and query performance. It is more efficient in distributed systems where ordered inserts benefit database operations.
• ULID: Compact and sortable, offering similar benefits to UUID V7 but with less storage overhead.
• Snowflake ID: Designed for distributed systems, Snowflake IDs include timestamps and other metadata, providing uniqueness across systems while remaining smaller than traditional UUIDs.

Alternatives to Auto-Incrementing and UUIDs

1. ULID: ULIDs are compact and lexicographically sortable, making them well-suited for databases requiring efficient index performance and smaller storage footprints.
2. Snowflake IDs: Commonly used in distributed systems like those at Twitter, Snowflake IDs generate globally unique, smaller identifiers with metadata for tracing origins, such as timestamps and machine IDs.

Implementing UUIDs and Other IDs in PostgreSQL

PostgreSQL offers built-in support for UUIDs with a UUID data type. It stores UUIDs in a binary format, which is more efficient than storing them as text strings.

Example: Generating a UUID in PostgreSQL

CREATE TABLE users (
  id UUID DEFAULT gen_random_uuid(),
  name VARCHAR(100),
  PRIMARY KEY (id)
);

Here, gen_random_uuid() generates a UUID V4 by default. PostgreSQL also allows storing UUIDs in their native binary form for efficiency.

Considerations for Other Databases

Other relational databases, such as MySQL, Oracle, and SQL Server, also support UUIDs but may handle them differently. Typically, UUIDs are stored as strings or binary data types, depending on the vendor. When using UUIDs, it’s generally better to store them as binary data for performance and storage efficiency.

Summary and Recommendations

• Auto-Incrementing Integer IDs: Suitable for smaller applications or systems that do not require global uniqueness. They are easy to implement, but issues like security risks and scalability arise in larger, distributed systems.
• UUID V4: Offers globally unique, decentralized generation but suffers from performance issues due to its randomness. Best for systems that require extreme uniqueness and do not rely heavily on indexing.
• UUID V7: Combines the randomness of UUID V4 with timestamp-based sorting, improving scalability and performance in distributed systems.
• ULID: A more compact alternative to UUID V7, with the same sorting benefits and reduced storage requirements.
• Snowflake IDs: Ideal for large, distributed systems that require globally unique identifiers with additional metadata.

Choosing the right primary key depends on the specific requirements of your system:

• Use auto-incrementing integers for simple, small-scale applications.
• Use UUIDs for large, distributed systems where global uniqueness is critical.
• Use UUID V7 or ULIDs for systems that benefit from sorted indexing and scalability.
• Consider Snowflake IDs for distributed environments that require compact, traceable identifiers.

Usage in Python

There are several Python libraries that support generating and handling different types of unique identifiers, including auto-incrementing IDs, UUIDs, ULIDs, and Snowflake IDs.

UUIDs

Python’s standard library provides built-in support for generating UUIDs, which can cover UUIDv1, UUIDv3, UUIDv4, and UUIDv5.

Library: uuid

The uuid module is part of Python’s standard library and provides various methods for generating UUIDs.

Installation: No installation required as it’s part of the standard library.

Example Usage:

import uuid

# Generate a random UUID (UUIDv4)
uuid_v4 = uuid.uuid4()
print(f"UUIDv4: {uuid_v4}")

# Generate a time-based UUID (UUIDv1)
uuid_v1 = uuid.uuid1()
print(f"UUIDv1: {uuid_v1}")

ULIDs (Universally Unique Lexicographically Sortable Identifiers)

ULIDs are not part of Python’s standard library, but you can use third-party libraries to generate ULIDs.

Library: ulid-py

This library supports generating and parsing ULIDs, which are lexicographically sortable and compact compared to UUIDs.

Installation:

pip install ulid-py

Example Usage:

import ulid

# Generate a ULID
ulid_value = ulid.new()
print(f"ULID: {ulid_value}")

# Access the timestamp component
timestamp = ulid_value.timestamp()
print(f"Timestamp: {timestamp}")

Snowflake IDs

Snowflake IDs are often used in distributed systems to generate unique, ordered identifiers. There are several Python libraries that provide functionality for generating Snowflake IDs.

Library: snowflake-id

This is a lightweight library for generating Snowflake IDs in Python. It’s based on the Snowflake algorithm originally developed by Twitter.

Installation:

pip install snowflake-id

Example Usage:

from snowflake_id import snowflake

# Generate a Snowflake ID
snowflake_id = snowflake.generate()
print(f"Snowflake ID: {snowflake_id}")

ShortUUID (Shortened UUIDs)

For cases where you need a more compact identifier compared to a full UUID, you can use shortuuid, which generates shorter, more human-readable UUIDs based on the standard UUID format.

Library: shortuuid

shortuuid generates compact, URL-safe UUIDs using base57 encoding.

Installation:

pip install shortuuid

Example Usage:

import shortuuid

# Generate a short UUID
short_id = shortuuid.uuid()
print(f"Short UUID: {short_id}")

Auto-Incrementing IDs

While Python itself does not have built-in support for auto-incrementing IDs (which are generally handled by databases), you can simulate auto-incrementing behavior using simple counter variables or through database libraries like SQLAlchemy.

Library: SQLAlchemy

If you’re working with databases like PostgreSQL or MySQL, SQLAlchemy provides support for auto-incrementing primary keys via database-specific mechanisms (e.g., SERIAL in PostgreSQL, AUTO_INCREMENT in MySQL).

Installation:

pip install sqlalchemy

Example Usage:

from sqlalchemy import create_engine, Column, Integer, String, Sequence
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Base = declarative_base()

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, Sequence('user_id_seq'), primary_key=True)
    name = Column(String)

engine = create_engine('postgresql://user:password@localhost/mydb')
Base.metadata.create_all(engine)

Summary of Python Libraries

• UUIDs: Use the built-in uuid module for UUIDv1, UUIDv4, and other variants.
• ULIDs: Use ulid-py for generating lexicographically sortable identifiers.
• Snowflake IDs: Use snowflake-id for distributed, timestamp-based unique IDs.
• Short UUIDs: Use shortuuid for compact UUIDs.
• Auto-Incrementing IDs: Simulate with counters or use database libraries like SQLAlchemy for native support.

Each of these libraries provides flexible ways to generate unique identifiers based on the requirements of your application, whether it’s a small system or a large-scale distributed system.

In the context of DDD

In the context of Domain-Driven Design (DDD), the choice of primary key and identifier strategies, such as auto-incrementing integers or UUIDs, plays a significant role in aligning the technical infrastructure with domain concepts. Identifiers in DDD are often referred to as Domain Identifiers or IDs, and they are used to uniquely identify Entities and Aggregates within the domain model.

In DDD, an Entity is defined by its identity rather than its attributes and is generally mutable, which makes the uniqueness and correctness of identifiers critical. Let’s see how different identifier strategies fit within DDD principles and design considerations.

Identifiers and Domain Concepts

In DDD, each Entity must have a unique identifier that persists over time, independent of changes to the Entity’s attributes. This ensures that Entities can be correctly referenced and related within the model. The choice of identifier strategy can affect the integrity, scalability, and performance of the system.

Auto-Incrementing IDs in DDD:

Auto-incrementing integers are a common choice in many applications, especially for Entities that reside in relational databases. However, in DDD, the downsides of this approach are more pronounced:
- Tight coupling to persistence: Auto-incrementing integers are often generated by the database layer, meaning the identity of the Entity is assigned by infrastructure rather than by the domain. This can violate DDD principles, where the domain should be in control of key concepts like identity.
- Lack of portability: In distributed or multi-database systems, merging or synchronizing data between databases can lead to ID conflicts. In DDD, Aggregates and Entities should ideally have globally unique IDs, which auto-incrementing integers do not naturally support.
- Security risks: As discussed earlier, sequential IDs can be predictable, and in scenarios where IDs are exposed through APIs, malicious users could attempt to access or manipulate data by guessing identifiers.

In DDD, an identifier should be a first-class concept in the domain, meaning the domain model should dictate how identities are generated and managed, rather than the database. Using auto-incrementing IDs often shifts control of identity to the database, violating this principle.

UUIDs in DDD:

UUIDs (Universally Unique Identifiers), particularly UUIDv4 and UUIDv7, are more aligned with DDD principles in many contexts:
- Globally Unique Identity: UUIDs ensure that each Entity or Aggregate has a globally unique identifier, which is crucial in systems where entities need to be recognized across distributed boundaries. This aligns with DDD’s approach to ensuring uniqueness and consistency within large, complex domains.
- Decoupling from Persistence: UUIDs can be generated by the domain layer rather than the database, which decouples the identity management from the infrastructure layer. This allows the domain to take full control over the creation and management of identifiers, adhering to the DDD principle of domain isolation from the persistence mechanism.
- Security: UUIDs, especially when used in the context of APIs or external-facing systems, mitigate the predictability issues of auto-incrementing IDs, providing better security through opaque identifiers.

However, UUIDs also introduce challenges:
- Storage overhead: In terms of storage and indexing, UUIDs are larger than integers, which can have performance implications. In DDD, the performance trade-offs should be weighed carefully based on the domain’s needs.
- Indexing efficiency: Randomly generated UUIDs (UUIDv4) can lead to inefficient indexing and slower query performance. Using a time-ordered UUID like UUIDv7 or ULID addresses this issue by allowing more efficient indexing and sorting, which is crucial in complex, query-heavy domains.

Aggregates and Distributed Systems

In DDD, Aggregates are clusters of related Entities that are treated as a single unit of consistency. The Aggregate Root is the Entity responsible for enforcing the consistency rules for the entire Aggregate. In a distributed system, where different services may own different Aggregates, unique identification becomes crucial to ensure that each Aggregate can be tracked and referenced correctly across service boundaries.

UUIDs and Aggregate Roots:

Using UUIDs for Aggregate Roots aligns with DDD’s goal of creating self-contained, uniquely identifiable Aggregates. In distributed systems, UUIDs:
- Provide global uniqueness, ensuring that Aggregates remain uniquely identifiable even across different services and databases.
- Allow deterministic generation: UUIDs can be generated without querying the database, allowing distributed services or systems to generate IDs without worrying about collisions or dependencies on a central source of truth.
- Facilitate data synchronization: When merging data across services or instances of databases (a common scenario in microservices), UUIDs simplify the process because there’s no risk of conflicting IDs.

Auto-Incrementing IDs and Aggregates:

For Aggregate Roots in distributed systems, auto-incrementing IDs can introduce several issues:
- ID conflicts: When data is synchronized or merged across distributed systems or databases, the same auto-incrementing IDs might be assigned to different Entities, leading to conflicts and the need for complex reconciliation processes.
- Sequential dependence: Auto-incrementing IDs often require synchronous database operations, which can hinder performance and scalability in distributed systems, where asynchronous, decoupled operations are favored.

Value Objects and Identifiers

In DDD, Value Objects are immutable and identified by their attributes rather than by a unique ID. However, when Value Objects are part of complex Aggregates or used in relationships with other Entities, they may still require unique identifiers.

For instance, if a Value Object represents something like a product SKU or an order number, using a ULID or Snowflake ID might be appropriate. These identifiers are compact and sortable, making them efficient to store and easy to generate across distributed systems.

Strategic Design and Identity Choices

In DDD, strategic design emphasizes the alignment of technical solutions with the needs of the business domain. The choice between auto-incrementing IDs, UUIDs, or other identifier strategies can have a significant impact on:
- Scalability: Systems that need to scale horizontally or across distributed services benefit more from globally unique identifiers like UUIDs or Snowflake IDs. These allow for asynchronous, decoupled operations that are critical in distributed architectures.
- Domain Integrity: Since DDD focuses on maintaining the integrity of domain concepts across boundaries, using predictable IDs (such as auto-incrementing integers) might introduce security and consistency risks. UUIDs or similarly opaque identifiers maintain stronger domain boundaries.
- Decoupling: Decoupling the domain model from the persistence layer is a core principle in DDD. Choosing identifier strategies like UUIDs ensures that the domain model controls identity, allowing it to evolve independently of infrastructure decisions.

Practical Considerations in DDD

• When to use Auto-Incrementing IDs: If the system is simple, non-distributed, and the performance overhead of larger IDs is a concern, auto-incrementing IDs may be sufficient. For example, if you are working within a single database, and there are no external integrations or complex scaling needs, this strategy can still work without violating DDD principles.
• When to use UUIDs or ULIDs: For distributed systems, microservices architectures, or applications that require scalability and resilience, UUIDs, ULIDs, or Snowflake IDs are a better fit. They ensure domain independence, scalability, and prevent ID conflicts in large, distributed, or replicated environments.

Conclusion

In the context of Domain-Driven Design, the choice of identifier strategy plays a key role in both the technical and strategic aspects of the system. While auto-incrementing IDs may suffice in simple, centralized systems, they often fall short in more complex or distributed domains. UUIDs, ULIDs, and Snowflake IDs provide better alignment with DDD principles by offering globally unique, decentralized identity generation, which allows the domain model to remain independent of the infrastructure. The decision on which strategy to use should be guided by the system’s scalability, performance, and security requirements, ensuring that the identifier strategy complements the domain model and architectural goals.

Page last modified: 2024-09-25 08:35:47