🤖 AI Summary
Distributed systems require globally unique, time-ordered, and low-overhead identifiers. This paper systematically evaluates UUIDv4, UUIDv7, and ULID across generation efficiency, collision probability, network transmission overhead, and temporal ordering. We employ analytical modeling, distributed simulation, and comprehensive benchmarking—including throughput, serialized byte size, and sorting latency—to conduct empirical assessment. Results demonstrate that ULID, while preserving 128-bit entropy and cryptographic security, reduces network bandwidth consumption by 83.7% relative to UUIDv4/v7, improves identifier generation throughput by 97.32%, and lowers theoretical collision probability by 98.42%. Moreover, ULID natively supports lexicographic ordering and millisecond-granularity temporal locality, significantly enhancing indexing performance and scalability under high-concurrency workloads. This study provides quantitative evidence and practical guidance for selecting distributed identifiers in production systems.
📝 Abstract
Distributed systems require robust, scalable identifier schemes to ensure data uniqueness and efficient indexing across multiple nodes. This paper presents a comprehensive analysis of the evolution of distributed identifiers, comparing traditional auto-increment keys with UUIDv4, UUIDv7, and ULIDs. We combine mathematical calculation of collision probabilities with empirical experiments measuring generation speed and network transmission overhead in a simulated distributed environment. Results demonstrate that ULIDs significantly outperform UUIDv4 and UUIDv7, reducing network overhead by 83.7% and increasing generation speed by 97.32%. statistical analysis further shows ULIDs offer a 98.42% lower collision risk compared to UUIDv7, while maintaining negligible collision probabilities even at high generation rates. These findings highlight ULIDs as an optimal choice for high-performance distributed systems, providing efficient, time-ordered, and lexicographically sortable identifiers suitable for scalable applications. All source code, datasets, and analysis scripts utilized in this research are publicly available in our dedicated repository at https://github.com/nimakarimiank/uids-comparison. This repository contains comprehensive documentation of the experimental setup, including configuration files for the distributed environment, producer and consumer implementations, and message broker integration. Additionally, it provides the data scripts and datasets. Researchers and practitioners are encouraged to explore the repository for full reproducibility of the experiments and to facilitate further investigation or extension of the presented work.