Mastering System Metrics: QPS, TPS, PV, UV, DAU, and MAU Explained

1. QPS

QPS ( Queries Per Second) measures the number of queries a server can handle each second, representing the maximum request‑response throughput of a specific query server.

2. TPS

TPS ( Transactions Per Second) counts completed transactions per second; a transaction spans from the client sending a request to receiving the server’s response.

3. Difference Between QPS and TPS

Key distinctions:

TPS includes the entire request‑processing‑response cycle.

QPS counts every individual request to the server; a single page view may generate multiple QPS while counting as one TPS.

For example, visiting a page that triggers three server requests yields one TPS but three QPS. Similarly, a fast eater can finish ten bites per second (high TPS), while a slower eater may only manage one bite per second (low QPS), illustrating the conceptual difference.

4. Concurrency

Concurrency (or concurrency degree) indicates how many requests a system can handle simultaneously, reflecting its load‑handling capability.

5. Throughput

Throughput is the number of requests processed per unit time; both QPS and TPS are common quantitative measures of throughput.

System Throughput Factors

The system’s throughput depends on request CPU consumption, external interfaces, I/O, and other resources. Higher CPU cost per request or slower I/O reduces overall throughput.

Important parameters include QPS/TPS, concurrency, and average response time.

Relationship: QPS (or TPS) = Concurrency / Average Response Time.

When either concurrency or response time reaches its limit, overall throughput plateaus or even declines due to overload, context switching, and resource contention.

6. PV (Page View)

PV counts each page load or refresh as a separate view, typically derived from daily access logs.

7. UV (Unique Visitor)

UV counts distinct users visiting within a day, deduplicated by unique identifiers from logs or web server data.

8. DAU (Daily Active Users)

DAU measures the number of unique users who actively use a product in a single day, similar to UV but focused on active engagement.

9. MAU (Monthly Active Users)

MAU counts distinct active users over a month, providing a longer‑term activity metric.

10. Evaluating System Throughput

When designing a system, consider CPU, I/O, and external service latency to estimate performance. Besides QPS and concurrency, daily PV is another useful dimension for capacity planning.

Typical approach:

Identify the system’s peak TPS and daily PV, which often have a stable relationship aside from seasonal effects.

Use load testing or empirical estimation to determine peak TPS, then apply the TPS‑PV relationship to calculate maximum daily throughput.

11. Basic Concepts and Formulas for Software Performance Testing

Performance testing focuses on metrics that affect user experience and system stability.

User‑centric concerns include response time—the interval from a user action to the system’s visible result. Shorter response times improve perceived performance.

Administrator concerns cover resource utilization, server and database efficiency, scalability, maximum concurrent users, potential bottlenecks, hardware upgrades, and 24/7 availability.

Developer concerns involve architectural soundness, database design, code efficiency, memory usage, thread synchronization, and resource contention.