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    copied!<p>If your slowdown is specifically the search of textual fields (as I surmise from your mentioning of LIKE), the best solution is building an index table (not to be confiused with DB table indexes that are also part of the answer).</p> <p>Build an index table mapping the unique ID of your records from main table to a set of words (1 word per row) of the textual field. If it matters, add the field of origin as a 3rd column in the index table, and if you want "relevance" features you may want to consider word count.</p> <p>Populate the index table with either a trigger (using splitting) or from your app - the latter might be better, simply call a stored proc with both the actual data to insert/update and the list of words already split up.</p> <p>This will immediately drastically speed up textual search as it will no longer do "LIKE", AND will be able to use indexes on index table (no pun intended) without interfering with indexing on SKU and the like on the main table.</p> <p>Also, ensure that all the relevant fields are <strong>indexed fully</strong> - not necessarily in the same compund index (SKU, sizing etc...), and any field that is searched as a range field (sizing or date) is a good candidate for a clustered index (as long as the records are inserted in approximate order of that field's increase or you don't care about insert/update speed as much).</p> <p>For anything mode detailed, you will need to post your table structure, existing indexes, the queries that are slow and the query plans you have now for those slow queries.</p> <p>Another item is to enure that as little of the fields are textual as possible, especially ones that are "decodable" - your comment mentioned "is it boxed" in the text fields set. If so, I assume the values are "yes"/"no" or some other very limited data set. If so, simply store a numeric code for valid values and do en/de-coding in your app, and search by the numeric code. Not a tremendous speed improvement but still an improvement.</p>
 

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