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You can create a full-text index for a document using a variation of the CREATE FULLTEXT INDEX statement. A statement that creates a full-text index for the Production.Document table follows: CREATE FULLTEXT INDEX ON Production.Document ( Document TYPE COLUMN FileExtension LANGUAGE 1033 ) KEY INDEX PK_Document_DocumentNode ON (AdvFTCatalog) WITH ( CHANGE_TRACKING AUTO, STOPLIST = SYSTEM ); GO This version is very similar to the CREATE FULLTEXT INDEX statement for a database, with one slight difference. In this variation, the varbinary(max) Document column is specified as the column to include in the full-text index. Immediately following the column name is the TYPE COLUMN clause, followed by a column containing the document type. The FileExtension column is specified in this instance. This column contains document types of .doc, which indicates a Microsoft Word document. The type column is particularly important, since it tells SQL Server which filter should be used to extract indexable information from the document. You can query the sys.fulltext_document_types catalog view to get a list of all the supported document types and their associated extensions.

If $var is unset or null, the expression expands to DEFAULT; otherwise, it expands to the contents of the variable: $ $ y $ $ x var= echo ${var:-y} var=x echo ${var:-y}

sys.openkeys: This catalog view returns information about all open encryption keys in the current session. Information returned includes the ID and name of the database that contains the key; IDs, names, and GUIDs of each open key; and the date and time the key was opened. sys.symmetric_keys: This catalog view returns a row for each symmetric key in the database. Information returned includes the name, ID, GUID, length, and algorithm of the symmetric key. Also returned are the ID of the principal who owns the key and the dates that the symmetric key was first created and last modified.

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As we mentioned earlier in the chapter, SQL Server automatically generates a random IV to help prevent statistical analysis attacks on columns of data The need to eliminate patterns from encrypted data is at odds with the need to index and quickly search the same data Indexing takes advantage of these patterns to organize data for efficient search and retrieval A hacker who knows the relative frequency with which certain pieces of encrypted data occur in a given column could use that information to deduce even further information about it For example, a corporate database containing employee information in a table encrypted without the use of random IVs might leak additional information from the patterns provided Consider the HumanResourcesEmployee table in the AdventureWorks database Most of the executive and managerial titles occur only once, while the lower-level positions may occur dozens of times.

Without the colon, the variable must be unset, not just null, for DEFAULT to be used (the result of the variable expansion is surrounded by slashes): $ var= $ echo /${var-y}/ // $ unset var $ echo /${var-y}//y/

Note At the time of this writing, the Zend Tool Project has only basic support for modules. I use it to create the module s directory structure and then manually create the controllers and view scripts.

A hacker might be able to infer additional information from this pattern, including information about which employees are paid the most The hacker might use knowledge like this to help focus his attack SQL Server s random IV generation helps to eliminate these patterns from encrypted data This has two main implications for T-SQL developers: The same IV used during encryption is required during decryption The encryption functions are nondeterministic, which means that encrypting the same plain text multiple times with the same key will not generate the same encrypted text The nondeterministic nature of the SQL 2008 encryption functions makes it useless to index an encrypted column directly Searching encrypted columns requires decrypting every value in the column and comparing them one by one This is very inefficient and can be a bottleneck in your applications if your tables are large.

Some methods have been suggested for increasing the efficiency of searching encrypted data These methods generally include storing a hash of the encrypted data for indexing The main problem with these methods is that they reintroduce the statistical patterns eliminated by the random IVs You can take several approaches to strike a balance between data security and search efficiency The most important recommendation is to not encrypt columns you will use heavily in your query search criteria (WHERE clause), sort criteria (ORDER BY clause), or grouping (GROUP BY clause) However, sometimes you might not have a choice you may need to encrypt a column that is part of your WHERE clause or other query criteria One thing you can do to make this more efficient is to narrow down your results using other criteria involving nonencrypted columns first.

${var:=DEFAULT}

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