We recently rolled out a new space on Antiques Near Me called ANM Picks, which uses eBay's APIs to programmatically find high-quality antique auctions using the same metrics that Sean (my antique-dealer business partner) uses in his own business. We'll cover the product promotion elsewhere, but I want to write here about how it works under the hood.
The first iteration a few weeks ago simply involved an RSS feed being piped into the sidebar of the site. The site is primarily a Drupal 6 application, and Drupal has tools for handling feeds, but they're very heavy: They make everything a "node" (Drupal content item), and all external requests have to be run in series using PHP cron scripts on top of a memory-intensive Drupal process - i.e. they're good if you want to pull external content permanently into your CMS, but aren't suited for the kind of ephemeral, 3rd-party data that eBay serves. So I built a Node.js app that loaded the feed periodically, reformatted the items, and served them via Ajax onto the site.
The RSS feed gave us very limited control over the filters, however. To really curate the data intelligently, I needed to use eBay's APIs. They have dozens of APIs of varying ages and protocols. Fortunately for our purposes, all the services we needed spoke JSON. No single API gave us all the dimensions we needed (that would be too easy!), so the app evolved to make many requests to several different APIs and then cross-reference the results.
Node.js is great for working with 3rd-party web services because its IO is all asynchronous. Using async, my favorite Node.js flow-control module, the app can run dozens of requests in parallel or in a more finely-tuned
queue. (Node.js obviously didn't invent asynchronous flow control - Java and other languages can spawn off threads to do this - but I doubt they do it as easily from a code perspective.)
To work with the eBay APIs, I wrote a client library which I published to npm (
npm install ebay-api). Code is on Github. (Ironically, someone else published another eBay module as I was working on mine, independently; they're both incomplete in different ways, so maybe they'll converge eventually. I like that in the Node.js ecosystem, unlike in Drupal's, two nutcrackers are considered better than one.) The module includes a
ebayApiGetRequest method for a single request,
paginateGetRequest for a multi-page request, a parser for two of the services, a
flatten method that simplifies the data structure (to more easily query the results with MongoDB), and some other helper functions. There are examples in the code as well.
Back to my app: Once the data is mashed up and filtered, it's saved into MongoDB (using Mongoose for basic schema validation, but otherwise as free-form JSON, which Mongo is perfect for). A subset is then loaded into memory for the sidebar Favorites and ANM Picks. (The database is only accessed for an individual item if it's no longer a favorite.) All this is frequently reloaded to fetch new items and flush out old ones (under the eBay TOS, we're not allowed to show anything that has passed).
The app runs on a different port from the website, so to pipe it through cleanly, I'm using Varnish as a proxy. Varnish is already running in front of the Drupal site, so I added another
backend in the VCL and activate it based on a subdomain. Oddly, trying to toggle by URL (via
req.url) didn't work - it would intermittently load the wrong backend without a clear pattern - so the subdomain (via `req.http.host) was second best. It was still necessary to deal with Allow-Origin issues, but at least the URL scheme looked better, and the cache splits the load.
Having the data in MongoDB means we can multiply the visibility (and affiliate-marketing revenue) through social networks. Each item now has a Facebook Like widget which points back to a unique URL on our site (proxied through Drupal, with details visible until it has passed). The client-side JS subscribes to the widgets' events and pings our app, so we can track which items and categories are the most popular, and (by also tracking clicks) make sure eBay is being honest. We're tuning the algorithm to show only high-quality auctions, so the better it does, the more (we hope) they'll be shared organically.
Comments? Questions? Interested in using the eBay APIs with Node.js? Feel free to email me or comment below.