My preferred way until recently was Deviant Art, because it had a lot of content and very nice filtering – I could specify my exact resolution.

However, as of some time ago, Deviant Art browsing by resolution disappeared, as evident from trying to find it on their site and from the comments on this post announcing the capability in the first place.

The Solution

Sometimes the solution lies right in front of our eyes – we just have to open them to see it.

A while ago, Google Image Search expanded their support for image size filtering (small, medium, large) to include the actual image size via this query:

imagesize:NNNNxNNNN

And because it's Google, you can add search terms to narrow down the subject.

Here are some examples.

Example 1: "wallpaper"

imagesize:1920×1200 wallpaper

The image size is so specific that it's usually not even necessary to include the word "wallpaper" – most of the results will already be wallpapers, except maybe for a few desktop screenshots.

Example 2: "nature"

imagesize:1920×1200 nature

Example 3: "futuristic"

imagesize:1680×1050 futuristic

Example 4: "cars"

imagesize:1680×1050 cars

Example 5: "babe"

imagesize:1680×1050 babe (be careful if you have SafeSearch off ;-] )

Starter Links Per Resolution

Here are some quick starter links for common resolutions to get you started:

320×396

320×480

800×600

1024×600

1024×768

1152×864

1280×1024

1280×720

1280×768

1280×800

1280×960

1360×768

1366×768

1400×1050

1440×900

1600×1200

1600×900

1680×1050

1920×1080

1920×1200

2560×1024

2560×1600

Conclusion

What sites do you use to get your wallpapers, specifically allowing filtering by resolution? No spam links please.

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Similarities

• Both Solr and Sphinx satisfy all of your requirements. They're fast and designed to index and search large bodies of data efficiently.
• Both have a long list of high-traffic sites using them (Solr, Sphinx)
• Both offer commercial support. (Solr, Sphinx)
• Both offer client API bindings for several platforms/languages (Sphinx, Solr)
• Both can be distributed to increase speed and capacity (Sphinx, Solr)

Here are some differences

• Solr, being an Apache project, is obviously is Apache2-licensed. Sphinx is GPLv2. This means that if you ever need to embed or extend (not just "use") Sphinx in a commercial application, you'll have to buy a commercial license.
• Solr is easily embeddable in Java applications.
• Solr is built on top of Lucene, which is a proven technology over 7 years old with a huge user base (this is only a small part). Whenever Lucene gets a new feature or speedup, Solr gets it too. Many of the devs committing to Solr are also Lucene committers.
• Sphinx integrates more tightly with RDBMSs, especially MySQL.
• Solr can be integrated with Hadoop to build distributed applications
• Solr can be integrated with Nutch to quickly build a fully-fledged web search engine with crawler.
• Solr can index proprietary formats like Microsoft Word, PDF, etc. Sphinx can't.
• Solr comes with a spell-checker out of the box.
• Solr comes with facet support out of the box. Faceting in Sphinx takes more work.
• Sphinx doesn't allow partial index updates for field data.
• In Sphinx, all document ids must be unique unsigned non-zero integer numbers. Solr doesn't even require a unique key for many operations, and unique keys can be either integers or strings.
• Solr supports field collapsing to avoid duplicating similar results. Sphinx doesn't seem to provide any feature like this.

Related questions

• http://stackoverflow.com/questions/1284083/choosing-a-stand-alone-full-text-search-server-sphinx-or-solr
• http://stackoverflow.com/questions/1132284/full-text-searching-with-rails
• http://stackoverflow.com/questions/737275/pros-cons-of-full-text-search-engine-lucene-sphinx-postgresql-full-text-searc

Conclusion

In my experience, Solr is very-very fast on the query side. It is also very powerful. The indexing side is very CPU and memory intensive and is an unfortunate side effect of having such a feature-rich, fast application. Nevertheless, I highly recommend Solr.

For disclaimer purposes, I have not had much experience with Sphinx and, again, all credit for this comparison goes to mausch.

By the way, here's a really good resource for Solr 1.4 that just came out: Solr 1.4 Enterprise Search. I have this book and it's quite helpful in explaining such topics as multicore setup, search methods, replication, etc.

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