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Basic column-value retrieval getInt() getShort() getLong() getFloat() getDouble() getString() getBoolean() getDate() getTime() getTimestamp() getByte() getBytes() getObject() Large object retrieval getAsciiStream() Gets input stream object for processing a character large object (CLOB)column Retrieves integer value from specified column Retrieves short integer value from specified column Retrieves long integer value from specified column Retrieves floating point numeric value from specified column Retrieves double-precision floating point value from specified column Retrieves character string value from specified column Retrieves true/false value from specified column Retrieves date value from specified column Retrieves time value from specified column Retrieves timestamp value from specified column Retrieves byte value from specified column Retrieves fixed-length or variable-length BINARY data from specified column Retrieves any type of data from specified column

GetBinaryStream() Gets input stream object for processing a binary large object (BLOB) column Other functions getMetaData() getWarnings() Table 19-17. Returns a ResultSetMetaData object with meta data for query Retrieves SQL warnings associated with the ResultSet

Imagine trying to build a large lens to cover a meter square in order to concentrate the sun. What would you build it out of Well for a start, the lens would be physically quite big if it had to cover a meter square, it would also use quite a large volume of material. This is not a particularly efficient way of doing things. Far better to build a lens that uses less material. This has a number of advantages. First of all, it uses less material. As a result of this, the lens is not only cheaper, but also lighter. This means if our lens is actively tracking the position of the sun by a mechanism, the mechanism can be lighter duty, as it does not need to move such a heavy load.

Table 1-2

19:

Here is a very simple Java program excerpt that shows how the objects and methods you have seen so far combine to perform simple query processing. It retrieves and prints out the office number, city, and region for each office in the OFFICES table:

// The connection object, strings and variables Connection dbconn; // the database connection Int num; // returned office number String city; // returned city String reg; // returned region String str1 = SELECT OFFICE, CITY, REGION FROM OFFICES ;

// Create a statement object for executing the query Statement stmt = dbconn.createStatement(); // Carry out query method returns a ResultSet object ResultSet answer = stmt.executeQuery(str1); // Loop through ResultSet a row at a time while (answer.next()) { // Retrieve each column of data num = answer.getInt( OFFICE ); city = answer.getString( CITY ); reg = answer.getString(3); // Print the row of results System.out.println(city + } // Explicitly close the cursor and connection answer.close(); dbconn.close();

Video Store Database Table Columns (Continued)

+ reg);

The methods used are straightforward and parallel the query-processing steps already seen for embedded SQL and C/C++ APIs. The ResultSet object maintains a cursor to note its current position within the query results. Its next method advances the cursor, row by row, through them. There is an explicit JDBC get method call to retrieve each column of data for each row. Java s strong typing and memory-protection schemes make this approach a requirement, but it carries significantly higher overhead than the C/C++ approach of binding program variables and having the database API automatically populate those variables when a next row is fetched. Finally, the close method call ends query processing.

The example also shows the two alternative methods for specifying which column s value should be retrieved by each get method call. You can specify the name of the column to be retrieved (used for the OFFICE and CITY columns), or its ordinal position within the columns of results (used for the REGION column). JDBC delivers this capability by overloading each of the get methods one version takes a string (column name) argument; the other takes an integer (column number) argument.

It is now widely acknowledged that climate change is happening, and that it is caused by man-made events. Of course, there is always the odd scientist, who wants to wave a flag, get some publicity and say that it is natural and that there is nothing we can do about it, but the consensus is that the extreme changes that we are seeing in recent times are a result of our actions over the past couple of hundred years. Sir David King, the U.K. s Chief Scientific Advisor says that climate change is the most severe problem that we are facing today more serious even than the threat of terrorism.