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    copied!<p>I think using Apache Solr or ElasticSearch you get more flexibility and performance, but this is supported using <a href="http://www.mongodb.org/display/DOCS/Aggregation+Framework" rel="noreferrer">Aggregation Framework</a>.</p> <p>The main problem using MongoDB is you have to query it N Times: First for get matching results and then once per group; while using a full text search engine you get it all in one query.</p> <p><strong>Example</strong></p> <pre><code>//'tags' filter simulates the search //this query gets the products db.products.find({tags: {$all: ["tag1", "tag2"]}}) //this query gets the size facet db.products.aggregate( {$match: {tags: {$all: ["tag1", "tag2"]}}}, {$group: {_id: "$size"}, count: {$sum:1}}, {$sort: {count:-1}} ) //this query gets the color facet db.products.aggregate( {$match: {tags: {$all: ["tag1", "tag2"]}}}, {$group: {_id: "$color"}, count: {$sum:1}}, {$sort: {count:-1}} ) //this query gets the brand facet db.products.aggregate( {$match: {tags: {$all: ["tag1", "tag2"]}}}, {$group: {_id: "$brand"}, count: {$sum:1}}, {$sort: {count:-1}} ) </code></pre> <p>Once the user filters the search using facets, you have to add this filter to query predicate and match predicate as follows.</p> <pre><code>//user clicks on "Brand 1" facet db.products.find({tags: {$all: ["tag1", "tag2"]}, brand: "Brand 1"}) db.products.aggregate( {$match: {tags: {$all: ["tag1", "tag2"]}}, brand: "Brand 1"}, {$group: {_id: "$size"}, count: {$sum:1}}, {$sort: {count:-1}} ) db.products.aggregate( {$match: {tags: {$all: ["tag1", "tag2"]}}, brand: "Brand 1"}, {$group: {_id: "$color"}, count: {$sum:1}}, {$sort: {count:-1}} ) db.products.aggregate( {$match: {tags: {$all: ["tag1", "tag2"]}}, brand: "Brand 1"}, {$group: {_id: "$brand"}, count: {$sum:1}}, {$sort: {count:-1}} ) </code></pre>
 

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