In high-volume systems, user experience depends on fast API responses. A slow component in your application can degrade user experience and increase infrastructure costs. This analysis, based on benchmarks from my previous post, shows how the choice of a trace ID generator impacts application latency, user experience, and operational costs.

The Contenders: `UUID` vs. OpenTelemetry's `IdGenerator`

Our benchmarks compared two popular methods for generating trace IDs: 

java.util.UUID.randomUUID().toString()
io.opentelemetry.sdk.trace.RandomIdGenerator.generateTraceId()

Implementation Differences

The performance gap stems from fundamental design choices. Here’s a side-by-side comparison:

Feature UUID (Version 4) OpenTelemetry Trace ID
Randomness Source SecureRandom ThreadLocalRandom
Security Cryptographically Strong Non-Cryptographic
Random Bits 122 bits 128 bits
Concurrency Model Synchronized (locking) Thread-local (lock-free)
Standard RFC 4122 W3C Trace Context

The Key Takeaway: The Synchronization Bottleneck

The critical difference is the concurrency model. UUID uses a synchronized, shared SecureRandom instance, which creates a major bottleneck. Under concurrent load, threads must wait to access the generator, causing latency to increase significantly.

In contrast, OpenTelemetry's IdGenerator uses ThreadLocalRandom, giving each thread its own independent generator. This lock-free approach avoids contention entirely, allowing throughput to scale almost linearly with the number of threads. For distributed tracing—where the goal is performance and collision avoidance, not cryptographic security—this is the superior trade-off.

The Benchmark: Performance Under Pressure

Single Thread Performance Across JDK Versions

JDK Version Implementation Average Time (ns) Throughput (M ops/sec)
JDK 25 OpenTelemetry 14.321 ±0.130 69.8 ±0.6
UUID 247.141 ±8.476 4.0 ±0.1
JDK 21 OpenTelemetry 14.528 ±0.474 68.8 ±2.2
UUID 245.351 ±14.737 4.1 ±0.2
JDK 17 OpenTelemetry 14.414 ±0.241 69.4 ±1.2
UUID 862.166 ±13.122 1.2 ±0.0

Multi-Thread Performance (10 Threads) Across JDK Versions

While single-threaded performance provides a useful baseline, it doesn't reflect the reality of a typical web application. Modern services handle hundreds or thousands of concurrent requests, making multi-threaded performance the true test of an implementation's viability at scale. The following benchmark simulates this concurrent load with 10 threads, revealing the critical impact of synchronization on latency and throughput.

JDK Version Implementation Average Time (ns) Throughput (M ops/sec)
JDK 25 OpenTelemetry 24.967 ±1.525 400.5 ±24.4
UUID 3,761.317 ±65.991 2.7 ±0.1
JDK 21 OpenTelemetry 23.517 ±1.003 425.2 ±18.1
UUID 4,032.508 ±176.588 2.5 ±0.1
JDK 17 OpenTelemetry 23.236 ±0.393 430.4 ±7.3
UUID 12,327.410 ±886.314 0.8 ±0.1

Analysis: What the Numbers Tell Us

OpenTelemetry's Performance Stability

OpenTelemetry's performance is consistent. Latency remains low (~14 ns) in single-threaded mode and only increases slightly (~24 ns) under concurrent load. This shows the effectiveness of its lock-free, thread-local design, which scales very well.

UUID's Problem with Concurrency

While newer JDKs have improved UUID performance (a 3x improvement from JDK 17 to 21 in multi-threaded tests), its performance still degrades under load. The latency increases by 15-20x when moving from a single thread to ten threads. This is a direct result of the synchronization bottleneck in SecureRandom.

The Performance Gap

The performance difference becomes much larger under concurrent load.

  • On JDK 25, OpenTelemetry is 17x faster in a single thread and 150x faster with 10 threads.
  • On JDK 17, the gap is larger: 60x faster in a single thread and 530x faster with 10 threads.

This proves that for any modern, concurrent application, UUID.randomUUID() is a significant performance liability for tracing.

The Impact on Applications and Infrastructure

The benchmark results have clear consequences for applications and infrastructure. Here’s how the performance difference translates into real-world outcomes.

1. Slower Response Times and Poor User Experience

In a high-volume system, the 15-20x latency increase with UUID under concurrent load directly impacts every user request. This causes slower API responses and a poor user experience, especially during peak traffic. A system that is fast with one user can become very slow with many users.

It's also important to note that these benchmarks were run in a controlled environment without the overhead of competing application threads. In a real-world production workload, where the CPU is busy executing business logic and contending for other resources, the impact of UUID's locking mechanism would be even more severe. The 15-20x latency explosion we measured is likely a conservative estimate; the actual tax on a busy production server is much higher.

2. Inflated Infrastructure Costs

To compensate for the inefficiency of UUID, teams often need to add more servers. The poor scaling means more hardware is required to handle the same workload, which increases infrastructure costs. By choosing a lock-free generator, you can serve more users with less hardware, directly reducing operational spending.

3. Unpredictable System Performance

The stable performance of OpenTelemetry's generator allows for reliable capacity planning. In contrast, the poor scaling of UUID makes it difficult to predict resource needs and ensure system stability during traffic spikes. This uncertainty can lead to service degradations or outages.

Implementation Recommendations

Based on our comprehensive analysis across JDK versions and threading scenarios:

For New Projects: The Clear Winner

For any new Java application requiring distributed tracing, OpenTelemetry's IdGenerator should be the default choice. It is faster, scales better, and aligns with the W3C Trace Context standard, making it a future-proof decision.

For Existing Systems: A Strategic Migration

Priority for migration based on scenarios:

Scenario Migration Priority Expected Impact
JDK 17 + High Concurrency Critical 530x performance improvement
JDK 17 + Single Thread High 60x performance improvement
JDK 21/25 + High Concurrency Medium 150-170x performance improvement
JDK 21/25 + Single Thread Low 17x performance improvement

Conclusion

This analysis proves that a technical choice, like the method for generating trace IDs, has a direct and measurable impact on user experience and infrastructure costs. For any high-throughput service, optimizing this component is a straightforward way to build faster, more reliable applications that cost less to run. At scale, small performance differences become significant, and making informed technical decisions is critical for success.

The complete benchmark code and detailed results are available in the benchmark-trace-id-generator repository.