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7966055
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2011-11-01T11:58:41.003
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2011-11-01T11:58:41.003
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Since you have not provided a sample document (object) format take this as a sample collection called 'stories'. <pre><code>{ "_id" : ObjectId("4eafd693627b738f69f8f1e3"), "body" : "There was a king", "author" : "tom" } { "_id" : ObjectId("4eafd69c627b738f69f8f1e4"), "body" : "There was a queen", "author" : "tom" } { "_id" : ObjectId("4eafd72c627b738f69f8f1e5"), "body" : "There was a queen", "author" : "tom" } { "_id" : ObjectId("4eafd74e627b738f69f8f1e6"), "body" : "There was a jack", "author" : "tom" } { "_id" : ObjectId("4eafd785627b738f69f8f1e7"), "body" : "There was a humpty and dumpty . Humtpy was tall . Dumpty was short .", "author" : "jane" } { "_id" : ObjectId("4eafd7cc627b738f69f8f1e8"), "body" : "There was a cat called Mini . Mini was clever cat . ", "author" : "jane" } </code></pre> For the given dataset, you can use the following javascript code to get to your solution. The collection "authors_unigrams" contains the result. All the code is supposed to be run using mongo console (http://www.mongodb.org/display/DOCS/mongo+-+The+Interactive+Shell). First, we need to mark of all the new documents that have come afresh into the 'stories' collection. We do it using following command. It will add a new attribute called "mr_status" into each document and assign value "inprocess". Later, we will see that map-reduce operation will only take those documents in account which are having the value "inprocess" for the field "mr_status". This way, we can avoid reconsidering all the documents for map-reduce operation that have been already considered in any of the previous attempt, making the operation efficient as asked. <pre><code>db.stories.update({mr_status:{$exists:false}},{$set:{mr_status:"inprocess"}},false,true); </code></pre> Second, we define both map() and reduce() function. <pre><code>var map = function() { uniqueWords = function (words){ var arrWords = words.split(" "); var arrNewWords = []; var seenWords = {}; for(var i=0;i<arrWords.length;i++) { if (!seenWords[arrWords[i]]) { seenWords[arrWords[i]]=true; arrNewWords.push(arrWords[i]); } } return arrNewWords; } var unigrams = uniqueWords(this.body) ; emit(this.author, {unigrams:unigrams}); }; var reduce = function(key,values){ Array.prototype.uniqueMerge = function( a ) { for ( var nonDuplicates = [], i = 0, l = a.length; i<l; ++i ) { if ( this.indexOf( a[i] ) === -1 ) { nonDuplicates.push( a[i] ); } } return this.concat( nonDuplicates ) }; unigrams = []; values.forEach(function(i){ unigrams = unigrams.uniqueMerge(i.unigrams); }); return { unigrams:unigrams}; }; </code></pre> Third, we actually run the map-reduce function. <pre><code>var result = db.stories.mapReduce( map, reduce, {query:{author:{$exists:true},mr_status:"inprocess"}, out: {reduce:"authors_unigrams"} }); </code></pre> Fourth, we mark all the records that have been considered for map-reduce in last run as processed by setting "mr_status" as "processed". <pre><code>db.stories.update({mr_status:"inprocess"},{$set:{mr_status:"processed"}},false,true); </code></pre> Optionally, you can see the result collection "authors_unigrams" by firing following command. <pre><code>db.authors_unigrams.find(); </code></pre>
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1. POMost efficient way to generate a list of Unigrams from a text field in MongoDB
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1. USSamyak Bhuta
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1. POMost efficient way to generate a list of Unigrams from a text field in MongoDB
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