# NGINX Benchmarking ## In this learning path - [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/nginx-on-azure/) - [Overview of Azure Cobalt 100 and NGINX](https://learn.arm.com/learning-paths/servers-and-cloud-computing/nginx-on-azure/backgroud/) - [Create an Arm-based Azure VM with Cobalt 100](https://learn.arm.com/learning-paths/servers-and-cloud-computing/nginx-on-azure/instance/) - [Install NGINX](https://learn.arm.com/learning-paths/servers-and-cloud-computing/nginx-on-azure/deploy/) - [NGINX Baseline Testing](https://learn.arm.com/learning-paths/servers-and-cloud-computing/nginx-on-azure/baseline/) - [NGINX Benchmarking](https://learn.arm.com/learning-paths/servers-and-cloud-computing/nginx-on-azure/benchmarking/) - [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/nginx-on-azure/_next-steps/) ## Benchmark NGINX with ApacheBench (ab) on Ubuntu Pro 24.04 LTS Use ApacheBench (**ab**) to measure NGINX performance on your Arm64 Azure VM. This section shows you how to install the tool, run a basic benchmark, interpret key metrics, and review a sample result from an Azure **D4ps_v6** instance. ## Install ApacheBench On **Ubuntu Pro 24.04 LTS**, ApacheBench is provided by the **apache2-utils** package: ``` sudo apt update sudo apt install -y apache2-utils ``` Verify the installation: ``` ab -V ``` Expected output: ``` __output__ This is ApacheBench, Version 2.3 <$Revision: 1923142 $> __output__ Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ __output__ Licensed to The Apache Software Foundation, http://www.apache.org/ ``` ## Run a basic benchmark The general syntax for running an ApacheBench test is: ``` ab -n -c ``` Example (1,000 requests, 50 concurrent, to the NGINX default page on localhost): ``` ab -n 1000 -c 50 http://localhost/ ``` This command sends 1,000 total requests with 50 concurrent connections to `http://localhost/`. Sample output: ``` __output__ This is ApacheBench, Version 2.3 <$Revision: 1903618 $> __output__ Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ __output__ Licensed to The Apache Software Foundation, http://www.apache.org/ __output__ Benchmarking localhost (be patient) __output__ Completed 100 requests __output__ Completed 200 requests __output__ Completed 300 requests __output__ Completed 400 requests __output__ Completed 500 requests __output__ Completed 600 requests __output__ Completed 700 requests __output__ Completed 800 requests __output__ Completed 900 requests __output__ Completed 1000 requests __output__ Finished 1000 requests __output__ __output__ __output__ Server Software: nginx/1.24.0 __output__ Server Hostname: localhost __output__ Server Port: 80 __output__ __output__ Document Path: / __output__ Document Length: 890 bytes __output__ __output__ Concurrency Level: 50 __output__ Time taken for tests: 0.032 seconds __output__ Complete requests: 1000 __output__ Failed requests: 0 __output__ Total transferred: 1132000 bytes __output__ HTML transferred: 890000 bytes __output__ Requests per second: 31523.86 [#/sec] (mean) __output__ Time per request: 1.586 [ms] (mean) __output__ Time per request: 0.032 [ms] (mean, across all concurrent requests) __output__ Transfer rate: 34848.65 [Kbytes/sec] received __output__ Connection Times (ms) __output__ min mean[+/-sd] median max __output__ Connect: 0 1 0.1 1 1 __output__ Processing: 0 1 0.1 1 1 __output__ Waiting: 0 1 0.2 1 1 __output__ Total: 1 2 0.1 2 2 __output__ Percentage of the requests served within a certain time (ms) __output__ 50% 2 __output__ 66% 2 __output__ 75% 2 __output__ 80% 2 __output__ 90% 2 __output__ 95% 2 __output__ 98% 2 __output__ 99% 2 __output__ 100% 2 (longest request) ``` ## Interpret benchmark results ApacheBench produces a variety of metrics, but the most useful ones highlight how well your server handles load. The requests per second value shows the average throughput, while the time per request (mean) indicates the latency experienced by each request. Ideally, the failed requests metric should remain at zero to confirm reliability. Finally, the transfer rate measures the effective bandwidth used by the responses, giving you insight into overall data flow efficiency. ## Benchmark summary on Arm64 The following results were collected on an Arm64 **D4ps_v6** VM running **Ubuntu Pro 24.04 LTS**: | Category | Metric | Value | |-----------------|-------------------------------|----------------------| | General info | Server Software | nginx/1.24.0 | | | Server Hostname | localhost | | | Server Port | 80 | | | Document Path | / | | | Document Length | 890 bytes | | Test setup | Concurrency Level | 50 | | | Time Taken for Tests | 0.032 sec | | | Complete Requests | 1,000 | | | Failed Requests | 0 | | Transfer stats | Total Transferred | 1,132,000 bytes | | | HTML Transferred | 890,000 bytes | | | Requests per Second | 31,523.86 #/sec | | | Time per Request (mean) | 1.586 ms | | | Time per Request (across all) | 0.032 ms | | | Transfer Rate | 34,848.65 KB/sec | | Connection times (ms) | Connect (min / mean / stdev / median / max | 0 / 1 / 0.1 / 1 / 1 | | | Processing (min / mean / stdev / median / max | 0 / 1 / 0.1 / 1 / 1 | | | Waiting (min / mean / stdev / median / max | 0 / 1 / 0.2 / 1 / 1 | | | Total (min / mean / stdev / median / max | 1 / 2 / 0.1 / 2 / 2 | ## Analysis of results from NGINX benchmarking on Arm-based Azure Cobalt-100 These results highlight the performance characteristics of NGINX on Arm64-based Azure VMs (such as **D4ps_v6**): - High Requests per second (31,523.86 requests/sec), demonstrating high throughput under concurrent load. - Response time per request averaged 1.586 ms, indicating efficient handling of requests with minimal delay. - Zero failed requests, confirming stability and reliability during testing. - Consistently low connection and processing times (mean ≈ 1 ms), ensuring smooth performance. Overall, NGINX on Arm64 provides a performant, cost‑efficient platform for web workloads on Azure. You can use the same benchmark to compare with equivalent x86-based instances to evaluate relative performance and cost efficiency across architectures.