Performance analysis for php devs

When writing web applications performance is something that is often in the back of our minds and often times we rely on folklore tales of things that are said to improve performance, but rarely ever do we challenge or confirm them. In this talk I want to showcase some tools that help us change this bad habit of generalizing performance advice and instead focus on how we can improve our application where it hurts.

This talk introduces both established and new tools for profiling PHP applications and running performance tests on our infrastructure. Along the way we will discuss how to continuously keep an eye on key performance metrics by making them part of our development process and what general advice might be useful in our day to day work as developers and if there are some tips we can safely ignore.

Transcript

1. User Group Vienna Performance Analysis ...but is it web scale?
2. Denis Brumann denis.brumann@sensiolabs.de @dbrumann Deutschland
3. WHAT WILL WE COVER? Tools Guidelines for performance testing General advice
4. WHY? Developer experience Save money Make money Save the environment
5. WHEN SHOULD I START DOING PERFORMANCE TESTS?
6. dev stage prod Profiler adsr/phpspy XHProf
7. ADSR/PHPSPY Low overhead
 sampling profiler for PHP 7 https://github.com/adsr/phpspy
10. XHPROF originally built for PHP 5 can be used in production https://tideways.com/profiler/xhprof-for-php7
13. XDEBUG do not use in production! debugging garbage collection stats profiler https://xdebug.org/
15. CACHEGRIND visualisation tool for profiles
 generated with XDebug kcachegrind, qcachegrind,
 wincachegrind
17. PHP-MEMINFO https://github.com/BitOne/php-meminfo
20. Profiler Example App:
 https://github.com/sensiolabs-de/flex-meetup
23. {{ render(controller('App\\Controller\\GroupController::listAction')) }} {{ render(controller('App\\Controller\\EventController::listAction')) }}
32. https://symfony.com/doc/current/testing/profiling.html
34. It can be used to simulate a heavy load on a server, group of servers, network or object to test its strength or to analyze overall performance under different load types. https://jmeter.apache.org/
48. WEB SERVER access & error logs DB size, throughput, slow queries CACHE size, hits vs. misses, connections SYSTEM cpu & mem usage, disk i/o
56. WHAT TO LOOK FOR? reading from & writing to disk network calls & latency database queries CPU & memory usage
57. WHAT TO DO TO IMPROVE
 APP PERFORMANCE?
58. WRITING EFFICIENT CODE single vs. double quotes \count() vs. count() avoid regexes tabs instead of spaces
59. WRITING EFFICIENT CODE single vs. double quotes \count() vs. count() avoid regexes tabs instead of spaces
60. MICRO-OPTIMIZATIONS RARELY ARE THE ISSUE focus on data often there are huge gains around: API-Requests, DB-Queries,
61. MICRO-OPTIMIZATIONS RARELY ARE THE ISSUE automate micro-optimizations, if you want, but focus on
 measured bottlenecks (see tools)
63. AGGRESSIVE CACHING
66. https://developers.google.com/web/fundamentals/performance/optimizing-content-efficiency/http-caching
67. Caching does not
 improve your app's performance Caching allows for easier scaling
69. WHAT TO DO? Measure Improve Measure again Compare Incremental changes Don't guess/assume Don't trust benchmarks Re-evaluate old data