dperf:基于 DPDK 的高性能网络负载测试工具
dperf is a high-performance network traffic generator and load testing tool based on DPDK.
High Performance
Built on DPDK, dperf can generate massive traffic using a single x86 server — achieving tens of millions of HTTP Connections Per Second (CPS), hundreds of Gbps throughput, and billions of concurrent connections.
Comprehensive Statistics
Provides detailed real-time metrics and identifies every packet drop or error.
Versatile Use Cases
All results were measured using the following hardware and configuration:
| Client Cores | Server Cores | HTTP CPS (Million) | Client CPU Usage (%) | Server CPU Usage (%) |
|---|---|---|---|---|
| 1 | 1 | 4 | 74 | 71 |
| 2 | 2 | 8 | 74 | 72 |
| 4 | 4 | 16 | 73 | 70 |
| 8 | 8 | 32 | 70 | 68 |
| 16 | 16 | 64 | 70 | 68 |
| Client Cores | Server Cores | RX Throughput | TX Throughput | Client CPU Usage (%) | Server CPU Usage (%) |
|---|---|---|---|---|---|
| 1 | 1 | 98.3 Gbps | 98.3 Gbps | 78 | 80 |
| 2 | 2 | 196.7 Gbps | 196.7 Gbps | 78 | 82 |
| Client Cores | Server Cores | Connections (Billion) | Client CPU Usage (%) | Server CPU Usage (%) | Memory Usage (GB) |
|---|---|---|---|---|---|
| 1 | 1 | 1 | 48 | 48 | 60 |
| 2 | 2 | 2 | 48 | 48 | 120 |
| 4 | 4 | 4 | 48 | 48 | 240 |
| Client Cores | RX PPS (Mpps) | TX PPS (Mpps) | Client CPU Usage (%) |
|---|---|---|---|
| 1 | 16.8 | 16.8 | 99 |
| 6 | 105.2 | 105.2 | 99 |
| 12 | 204.6 | 204.6 | 99 |
dperf prints real-time statistics every second, including CPS, TPS, PPS, packet drops, socket errors, and HTTP status counts. Example output:
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See the official website at https://dperf.org/.
We welcome contributions! Please see the CONTRIBUTING file for details.
We gratefully acknowledge xnetin for providing the high-performance testing platform used in our benchmarking experiments.
Jianzhang Peng holds a Ph.D. in Computer Science from the University of Science and Technology of China (USTC). He previously worked as a Principal Engineer at Baidu, where he contributed to the development of high-performance L4 load balancer systems. He initiated and developed the dperf project during his time at Baidu, and continues to maintain it as an open-source contributor. His current focus is on low-latency network protocol stacks for quantitative trading systems.
dperf is licensed under the Apache License 2.0.
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