This project investigates the performance of the most popular web server backend frameworks and technologies: ASP.NET Web API, Django, Express.js, Go, Laravel, Phalcon, PHP (no framework), Rails, Spring MVC. To estimate performance selected benchmarking algorithms were adapted to and run in a minimal web application project constructed using each framework, and execution times were measured.

The benchamrking algorithms are representative of three common CPU problem areas:

1. Integer arithmetic: a straight-forward unoptimized square matrix multiplication algorithm,

2. Prime number generation: sieve of Atkin, source: GeeksforGeeks,

3. String sorting: quicksort, source: GeeksforGeeks.

For each algorithm a referential implementation in C is provided in the 'Other/Algorithms' directory. Note: the C implementation of Quicksort compares integers and in that form it is more suitable for reference.

The underlying assumption is that the performance on these algorithms corresponds sufficiently to performance on the typical web application usages.

For each framework the algorithms are adapted adhering to the established development conventions as much as possible without violating their original form. The input of each algorithm is generated upfront, in a script or method called 'init', and is stored in memcached. An external cache store had to be utilized as not all technologies preserve data between requests in memory. As a consequence, cache reads are included in the measurement results. Algorithm output is discarded. All the default framework middleware that is redundant to the task has been removed. While in general best efforts have been made for each framework to run optimally in a production-like server environment, optimizations requiring extensive familiarity with the specific frameworks and / or underlying server software were not made. Details of the execution evironments are listed in the section Development and server setup. After the server has been set up, HTTP requests were being repeatedly sent to each endpoint which executes an algorithm.

The ApacheBench tool was used for sending requests to the server, in the following general manner:

ab -n 5000 -c 100 server-endpoint-url > algorithm_name.txt

Making 5.000 requests to each corresponding URL, issued in batches of 100, constitutes high-load testing. While the mean time per request would likely be lower under lower server load at least for some algorithm-framework pairs, this is not expected to largely affect the comparative performance results.

The evaluation was made on a machine having 4 CPU cores with base frequency of 3.6 GHz and 16 GB of RAM running a Windows 10 Enterprise OS. The tool output can be found in the 'results' subdirectory for each framework directory.

Effective algorithm execution parameters:

• Matrix multiplication: matrix dimension - 100.

• Sieve of Atkin: limit - 250.000.

• Quicksort: collection size - 30.000, string length - 100.

The values of the parameters were chosen so that the following desired properties hold:

1. most of the request processing time is spent running the algorithm itself as opposed to routing or middleware,

2. time per request and memory consumption are kept at reasonable levels.

This section lists framework and server version along with other development or environmental setup details for each framework in alphabetical order, with the exception of the cache server which is given upfront.

Memcached 1.4.5 64-bit server was used in the following manner:

memcached -I 10m

The size of the maximum value that can be stored is increased from the default 1 MB to 10 MB due to the input data of matrix multiplication and quicksort algorithms being of few megabytes in size given the effective parameters.

IIS Express 10.0.14358.1000 running CLR v4.0.

Python 3.7.0 64-bit, Django 2.1.1. Server is Apache httpd 2.4.34 64-bit VC15 with mod_wsgi.

Node.js 8.12.0 64-bit, Express.js 4.16.3.

Go 1.11 64-bit.

Laravel 5.7.1. See PHP and server details under PHP. ThreadsPerChild for mpm_worker_module had to be increased to 50 from the default 25 in httpd-mpm.conf.

Phalcon 3.4.1. See PHP and server details under PHP. Using mod_fcgid 2.3.9 64-bit VC15 as a workaround for an issue with thread-safe PHP; this configuration yields slightly lower performance compared to using the php7_module.

PHP 7.2.9 64-bit Thread Safe VC15, run on server Apache httpd 2.4.34 64-bit VC15. Nginx is the more appropriate choice for PHP server, however performing the experiment on Windows eliminated this possibility. The older 'memcache' module is used instead of 'memcached' for cache server communication, again due to the specific platform that is used. php.ini and httpd.conf files are provided in 'env-config-files' subdirectory.

Ruby 2.5.1p57, Rails 5.2.1. Run on Puma 3.12.0 with min threads being 10 and max threads being 150.

Apache Tomcat 9.0.12, JRE 10.0.2 64-bit, Spring 5.0.0.RELEASE. Tomcat's memory pool was increased to 4096MB from the default 256MB using Tomcat9w.exe, Java tab, Maximum memory pool field.

The statistics reported from Apache Bench were Time per request (mean) in milliseconds and [+/-sd] over Mean for row Total in percentages.

The following table summarizes the results, ordering the frameworks by descending performance, the sum of the means is used to resolve ambiguous cases.

The Express.js framework was unable to handle the high-load for quicksort algorithm execution and had to be run sequentially; the mean statistic for this run is adjusted by the effective concurrency level.

Django and Rails achieve comparatively lower performance on matrix multiplication which is to be expected since the respective implementations preserve the original algorithm form when this does not result in the preferred and optimal problem solution.

The high standard deviation and few failed requests in the results for Phalcon are likely a consequence of the high load on mod_fcgid despite its overly-permitting configuration.

The following chart summarizes the results graphically, separate views of the top 6 and bottom 3 y-axis values are provided to better accentuate these areas of interest.