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This allows for an extremely high degree of flexibility while building your indexes. The one thing the authors want to emphasize, though, is that when you query an index that was built with a specific analyzer, unless you really know what you re doing, you should employ the identical analyzer when querying. This guarantees that expectations concerning the data you re querying are valid and there will be no surprises. This gremlin really rears it ugly head in the case of punctuation. Read the Javadoc of the org.apache.lucene.analysis.standard.StandardTokenizer class for an example of exactly what this tokenizer does. It could cause you problems if you re not aware that it was employed to index the data you re querying. How do we get around this problem As of this writing, with Lucene you re out of luck. Either you have to know which analyzer was used to create the index via documentation, or the index creator could store the analyzer class type somehow, somewhere, so that it can be retrieved and reused. Hibernate Search, on the other hand, allows you to retrieve at query time which analyzer was employed at index time. We ll get to how Hibernate Search does this in just a minute, but first we want demonstrate how to manually apply an analyzer to a query. You never know when this will come in handy.

Use the AdventureWorks2008 database to complete this exercise. 1. Make sure that the Include Actual Execution Plan setting is turned on before typing and executing the following code. Compare the execution plans to see whether the CTE query performs better than the OVER clause query. USE AdventureWorks2008; GO --1 WITH SumSale AS (SELECT SUM(TotalDue) AS SumTotalDue, CustomerID FROM Sales.SalesOrderHeader GROUP BY CustomerID) SELECT o.CustomerID, TotalDue, TotalDue / SumTotalDue * 100 AS PercentOfSales FROM SumSale INNER JOIN Sales.SalesOrderHeader AS o ON SumSale.CustomerID = o.CustomerID ORDER BY CustomerID;

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A filter implementation doesn t have to build its DocIdSet from the data stored in the Lucene index. It s perfectly possible to extract information from an external service and use that information to compute the filter. Back to our DVD store example: Around Christmas, customers become upset when the items displayed in the results are out of stock. Our website needs to filter out items that are no longer in stock. Stock information is updated per batch every half day. It s possible to build a filter that removes items that are out of stock. Listing 8.12 shows a more complex Filter implementation than what we ve seen so far. It makes use of an external service, holds an internal cache, and rebuilds data when needed. It also shows some of the techniques used to find a Lucene document id based on indexed information (that is, correlating unique information indexed in a document to a document id).

As noted, two different indexes are created in this example. The first one is a unique clustered index, based on the identity column of the TransactionDetails.TransactionTypes table. This column was chosen because we will be linking

public class StockFilter extends Filter { private volatile long lastUpdateTime;

to this table using the TransactionType column Rarely, if ever, will we link on any other column within this table The overhead is microscopic, though, due to the few records we will be entering, and it is therefore not really a concern It also allows us to see where to place the keyword within the example The second index, built on the TransactionDetailsTransactions table, cannot be a unique index; there will be multiple entries of the same value because there are multiple transactions for the same type However, it is still possible to make this index clustered Changing the transaction type on a transaction will be rare, or, if we had a full audit trail built within our system, we may ban such an action.

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