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String based A text-based query language. Depending on the focus, such a language can be as simple as handling words and as Figure 1.7 Searching process. From a user or program request, determine the list of words, find the complex as having Boolean appropriate documents matching those words, operators, approximation oper- eliminate the documents not matching, and order the ators, field restriction, and results by relevance. much more! Programmatic API based For advanced and tightly controlled queries a programmatic API is very neat. It gives the developer a flexible way to express complex queries and decide how to expose the query flexibility to users (it might be a service exposed through a Representational State Transfer (REST) interface).

Figure 10-5. The partial results of a recursive query The query runs the recursive member repeatedly until all possible paths are selected, that is, until the recursive member no longer returns results. In case of an incorrectly written recursive query that will run forever, the recursive member will run only 100 times by default unless you specify the MAXRECURSION option to limit how many times the query will run. To alter the query in Listing 10-5 to a potential unending loop, change a.ManagerID = b.EmployeeID to a.EmployeeID = b.EmployeeID. Just because the default MAXRECURSION value is 100 doesn t mean that a recursive query will return only 100 rows. In this example, the anchor returns the CEO, EmployeeID 109. The first time the recursive member runs, the results include all employees who report to 109. The next call returns the employees reporting to the employees returned in the last call, and so on. The values from one call feed the next call. Instead of filtering the anchor to find the CEO, you can supply any ManagerID value. If you specify a particular manager, instead of starting at the CEO, the results will start at the subordinates of the ManagerID supplied. On your own, change the criteria in the anchor member, and rerun the query to see what happens. Writing recursive queries is an advanced skill you may or may not need right away. Luckily, if you do need to write a recursive query, you will know where to find a simple example.

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Some tools will focus on the string-based query, some on the programmatic API, and some on both. Because the query language or API is focused on full-text search, it ends up being much simpler (in complexity) to write than its SQL equivalent and helps to reduce one of the problems we had with our SQL-based search engine: complexity. The second operation, let s call it analyzing, is responsible for taking sentences or lists of words and applying the similar operation performed at indexing time (chunk

9. You are now presented with a Run Now screen, similar to the one shown in Figure 4-32. When you are ready, click the Check button. You should see similar results to those shown in the figure, which demonstrate that your database is out of policy.

into words, stems, or phonetic description). This is critical because the result of this operation is the common language that indexing and searching use to talk to each other and happens to be the one stored in the index. If the same set of operations is not applied, the search won t find the indexed words not so useful! This common language is the cornerstone of full-text search performances (another problem we had with our SQL-based search engine). Based on the common language between indexing and searching, the third operation (finding documents) will read the index and retrieve the index information associated with each matching word (see figure 1.8). Remember, for each word, the index could store the list of matching documents, the frequency, the word positions in a document, and so on. The implicit deal here is that the document itself is not loaded, and that s one of the reasons why full-text search is efficient: The document does not have to be loaded to know whether it matches or not. The next operation (filtering and ordering) will process the information retrieved from the index and build the list of documents (or more precisely, handlers to documents). From the information available (matching documents per word, word frequency, and word position), the search engine is able to exclude documents from the matching list. More important, it is able to compute a score for each document. The higher its score, the higher a document will be in the result list. A lengthy discussion about scoring is available in chapter 12, but in the meantime let s have a look at some factors influencing its value:

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