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  • Writer's pictureViacheslav Miroshnychenko

Beyond Numbers: Navigating the Universally Unique World of UUIDs!

Navigating data across interconnected systems has been a challenge since the dawn of networked computing. An enduring solution to this predicament is the Universally Unique Identifier (UUID).

This distinctive data marker has stood the test of time and continues to be a prevalent identifier in the digital landscape. In this exploration, we delve into the intricacies of this enduring data identifier.




The article dives into the history of computing before networks, where computers were isolated, and the challenge of sharing data was a real hurdle.

Enter UUIDs, a 36-character alphanumeric label that uniquely identifies information within a computer system.

What's fascinating is how a company called Apollo Computer in 1980 laid the groundwork for computer networking, introducing the concept of UUIDs to label information shared between machines.

Fast forward to today, and UUIDs are everywhere, thanks to their low probability of duplication.

The article breaks down the five UUID versions, explaining how they incorporate time, device identifiers, or hashing algorithms.

It emphasizes the importance of using well-maintained libraries for UUID generation to comply with standards.

What caught my eye is how UUIDs are used in various applications, from web apps for frontend data labeling to analytics systems for tracking events within user sessions.

In distributed database systems, UUIDs prove handy for efficient data organization across multiple servers.

Overall, it's a deep dive into the world of UUIDs, shedding light on their evolution, applications, and the role they play in modern software architecture.



The article delves into the less obvious challenges associated with using UUIDs in PostgreSQL, focusing on issues related to working with random values.

Specifically, it addresses problems related to indexing random numbers, where values generated at the same time may end up scattered across different parts of the index.

The text provides a detailed explanation of the problem, conducts experiments comparing data retrieval from random and sequential UUID indexes, and offers insights into the underlying reasons for this behavior.

This content sheds light on the nuanced aspects of employing UUID keys in PostgreSQL, offering valuable information for developers and architects keen on understanding and mitigating potential challenges associated with random value indexing in their database systems.




The author highlights five key mistakes made during the migration process. Some of the key points discussed include:

  1. Underestimation of Migration Time: The article emphasizes the need for a realistic estimation of the time required for database migration. This is crucial to avoid unexpected delays and disruptions to normal operations.

  2. Handling Special Cases: The author delves into unforeseen special cases and scenarios that often catch developers off guard during the migration process. These cases require careful consideration to ensure a smooth transition.

  3. Overlooking Broken References: One of the highlighted mistakes involves overlooking references that can break during the migration. This emphasizes the importance of a comprehensive analysis of dependencies to prevent post-migration issues.

Additionally, the article provides insight into the motivation behind transitioning to UUIDs as primary keys, revealing that it was possible to achieve the desired outcome without resorting to a full migration to UUIDs.

This article serves as a valuable resource for developers and software architects looking to navigate the complexities of transitioning from a monolithic architecture to microservices, offering practical insights and lessons learned from real-world experiences.

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