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Its easy to think up something and keep stroking the thought to conclusion which looks aesthetically proper to you. Its entirely different game to actually convince someone else of your argument. But public speaking is like a barometer what you think and believe. These thoughts relate to the fact that I gave a talk at OSIW about search engines.

The talk was controversially called ‘Who is scared of google?’, with a sincere believe that search engines are going to evolve and stop where they are right now. Also that one could come up with specialized search engines using FOSS tools. The presentation available here

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Verdict is in. People want ‘normal’ looking search engine. A search engine invokes a mental map which is getting re-enforced in our mind. Even separating out certain search results in a box entails a risk of users overlooking those links assuming it to be ads.

In his article Jacob Neilson warns against trying to change the search user interface. This argues that search engines should not try to distinguish themselves with fancy front ends.
Article available here

Reading through lucene wiki, I came across a nice list of things to try for improving indexing performance. I am listing some of the most striking ones from the page

  • Flush by RAM usage instead of document count.
    Call writer.ramSizeInBytes() after every added doc then call flush() when it’s using too much RAM. This is especially good if you have small docs or highly variable doc sizes. You need to first set maxBufferedDocs large enough to prevent the writer from flushing based on document count. However, don’t set it too large otherwise you may hit. Somewhere around 2-3X your “typical” flush count should be OK.
  • Turn off compound file format.
    Call setUseCompoundFile(false). Building the compound file format takes time during indexing (7-33% in testing). However, note that doing this will greatly increase the number of file descriptors used by indexing and by searching, so you could run out of file descriptors if mergeFactor is also large.
  • Re-use Document and Field instances
    As of Lucene 2.3 (not yet released) there are new setValue(…) methods that allow you to change the value of a Field. This allows you to re-use a single Field instance across many added documents, which can save substantial GC cost.

    It’s best to create a single Document instance, then add multiple Field instances to it, but hold onto these Field instances and re-use them by changing their values for each added document. For example you might have an idField, bodyField, nameField, storedField1, etc. After the document is added, you then directly change the Field values (idField.setValue(…), etc), and then re-add your Document instance.

    Note that you cannot re-use a single Field instance within a Document, and, you should not change a Field’s value until the Document containing that Field has been added to the index. See Field for details.

  • Re-use a single Token instance in your analyzer
    Analyzers often create a new Token for each term in sequence that needs to be indexed from a Field. You can save substantial GC cost by re-using a single Token instance instead.
  • Use the char[] API in Token instead of the String API to represent token Text
    As of Lucene 2.3 (not yet released), a Token can represent its text as a slice into a char array, which saves the GC cost of new’ing and then reclaiming String instances. By re-using a single Token instance and using the char[] API you can avoid new’ing any objects for each term. See Token for details.
  • Shamelessly plugged from here

A simple keyword search “looking for a job as a fashion designer for an import/export company” on big three job search engines in India gives interesting results:

  • Naukri which claims to be number one jobs site provides no results for this query.
  • Timesjobs which takes ions to provide the results, which are way off from the theme of the query.
  • Monster India barely provides decent results for the query.

Going into the reasons why this query results in abject failure from such premiere jobs sites requires bit of dis-integration of the query.

  • We have a well formed sentence with lots of what are called Stopwords. After query parsing phase ideally query should be left with job, fashion designer, import/export and company. These keywords are only relevant to the query. This is where TimesJobs fails.
  • Most search engines set equal priority field priority. Monsterindia brings itself apart by giving higher priority to title of the jobs.
  • Detecting domain and job type would be a great way of enhancing keyword search. None of the engines do that till now.
  • import/export has a special character ‘/’ which is not handled well by search engines.

A good way to get these thing sorted would be to pre-process queries with appropriate analyzer.