This time we will implement a code style check to sure that our code not only works correctly but follows the style requirements. This is especially useful when working in a team and all members follow requirements.
This is the second part of a series on creating a PHP package.
In the previous part, we initialized a project using the composer and wrote the code itself. In this part, we will integrate the PHPUnit into our project and write tests.
Code should be covered with tests. This will help to avoid bugs when modifying the code. When we connect the CI to the repository, tests will run automatically as pull requests are created.
If you created a useful PHP library and want to share it with others or reuse it in your other projects, it will be convenient to pack it into a separate package. In this series of articles, we'll discover how to properly organize code into a reusable package, create a structure of the project, write unit tests, create an automatic code style check, implement CI, and much more. As an example, we will write step by step a small PHP library for syntax highlighting. It will accept text and return highlighted PHP code.
Sprite sheets have been used in game development for many years. In web development it allows you to reduce:
- a number of HTTP requests for downloading many separate images,
- webserver load as a result of fewer requests,
- download time as a combined sprite sheet is smaller than the individual files.
To demonstrate it, I compiled a Sonic sprite sheet from the "Sonic the Hedgehog 3" game.
Recently I've passed my second AWS certification exam "Developer - Associate". Although my preparation in many ways was similar to the preparation for Solutions Architect, which I wrote about earlier, this time the exam touched many other AWS services more deeply.
Until recently AWS Lambda did not support PHP, but with the advent of the runtime API and layers, now we able to implement an AWS Lambda runtime in any programming language including PHP. Each layer can contain libraries, a custom runtime, or other dependencies. You can create layers, or use layers published by AWS and other AWS customers.
There is a great practical example of creating a Custom Runtime for PHP if you want to build a PHP runtime layer by yourself. In this article, we'll use partner supported PHP layer for Lambda from stackery for a quick understanding of its use.
During the development of a web application, it’s useful to know what kind of load it can hold. Today the application’s speed is one of the most important indicators and it is worth taking it seriously. A slow running application can push your users away.
To check how much your application is ready for production, you can use some utilities to test your server performance. Slow request processing speed can also help to identify incorrect server settings or scripts.
We'll cover 2 tools for understanding server-side performance: Apache Benchmark (ab) and Siege.
Today Docker became an important part of the development process. Docker creates an isolated environment in containers within the operating system. This allows to get a transferable environment - everything in the container can be saved, copied and deployed to another system.
We'll prepare our own local docker environment for PHP development with components:
Last week I have successfully passed the AWS Certified Solutions Architect - Associate 2018 and want to tell about my experience passing AWS certification exam. Amazon recommend have at least one year of hands-on experience in this field on AWS, but it's optional. Before I started preparing for the exam, I had experience with S3 and a little bit with Lambda, API Gateway, and DynamoDB. All other experience I got during preparation.
Apache Kafka is a fast, real-time, distributed, fault-tolerant message broker.
Using Kafka, you can transfer streaming data to the cluster, which is generated continuously, for example, a history of website visits, financial transactions, online shopping orders, application logs, etc. This information can help to understand what is happening with the data right now, create recommendations, use machine learning or aggregate data for further analysis. All this takes seconds or minutes, instead of hours and days.