dansup / rss-php
RSS & Atom Feeds for PHP is a very small and easy-to-use library for consuming an RSS and Atom feed
Requires
- php: ^5.6 || ^7.0
- guzzlehttp/guzzle: ^6.2
README
RSS & Atom Feeds for PHP is a very small and easy-to-use library for consuming an RSS and Atom feeds. This project is a forked of David Grudl's rs-php https://github.com/dg/rss-php
It requires PHP 5.5 and Guzzle 6.1 and is licensed under the New BSD License. You can obtain the latest version from our GitHub repository or install it via Composer:
php composer.phar require dansup/rss-php
Usage
Download RSS feed from URL:
$rss = Feed::loadRss($url);
The returned properties are SimpleXMLElement objects. Extracting the information from the channel is easy:
echo 'Title: ', $rss->title; echo 'Description: ', $rss->description; echo 'Link: ', $rss->link; foreach ($rss->item as $item) { echo 'Title: ', $item->title; echo 'Link: ', $item->link; echo 'Timestamp: ', $item->timestamp; echo 'Description ', $item->description; echo 'HTML encoded content: ', $item->{'content:encoded'}; }
Download Atom feed from URL:
$atom = Feed::loadAtom($url);
You can set your own Guzzle instance to the static client property
Feed::$client = new GuzzleHttp\Client(['headers' => ['User-Agent' => 'FeedPHP/1.0']]);
and it will be reused.
You can pass Guzzle request options (including auth user/password) on each call
$atom = Feed::loadAtom($url, ['auth' => ['peter', 'secret']);
You can also enable caching using https://github.com/Kevinrob/guzzle-cache-middleware
// Simple volatile memory cache example check docs for more options use GuzzleHttp\Client; use GuzzleHttp\HandlerStack; use Kevinrob\GuzzleCache\CacheMiddleware; // Create default HandlerStack $stack = HandlerStack::create(); // Add this middleware to the top with `push` $stack->push(new CacheMiddleware(), 'cache'); // Initialize the client with the handler option Feed::$client = new Client(['handler' => $stack]);
(c) David Grudl, 2008 (http://davidgrudl.com) (c) grunjol, 2017 (https://github.com/grunjol) (c) dansup, 2017 (https://github.com/dansup)