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A serial field keeps a number that increments. For example, when a node is added, the nid field of the node table is incremented. This is done by inserting a row and calling db_last_insert_id(). If a row is added by another thread between the insertion of a row and the retrieval of the last ID, the correct ID is still returned because it is tracked on a per-connection basis. A serial field must be indexed; it is usually indexed as the primary key. $field['fieldname'] = array( 'type' => 'serial', // Required. 'unsigned' => TRUE, // Defaults to FALSE. Serial numbers are usually positive. 'size' => 'small', // tiny | small | medium | normal | big 'not null' => TRUE, // Defaults to FALSE. Typically TRUE for serial fields. 'description' => t('Always state the purpose of your field.'), );

Table 21-6. Members of the DbCommand Type (continued)

ExecuteNonQuery() ExecuteScalar()

Floating point numbers are stored using the float data type. There is typically no difference between the tiny, small, medium, and normal sizes for a floating point number; in contrast, the big size specifies a double-precision field. $field['fieldname'] = array( 'type' => 'float', // Required. 'unsigned' => TRUE, // Defaults to FALSE. 'size' => 'normal', // tiny | small | medium | normal | big 'not null' => TRUE, // Defaults to FALSE. 'description' => t('Always state the purpose of your field.'), );

Executes a SQL non-query (e.g., an insert, update, delete, or create table). A lightweight version of the ExecuteReader() method that was designed specifically for singleton queries (e.g., obtaining a record count). Creates a prepared (or compiled) version of the command on the data source. As you might know, a prepared query executes slightly faster and is useful when you wish to execute the same query multiple times (typically with different parameters each time).

Prepare()

The numeric data type allows you to specify the precision and scale of a number. Precision is the total number of significant digits in the number; scale is the total number of digits to the right of the decimal point. For example, 123.45 has a precision of 5 and a scale of 2. The size key is not used. At the time of this writing, numeric fields are not used in the schema of the Drupal core. $field['fieldname'] = array( 'type' => 'numeric', // Required. 'unsigned' => TRUE, // Defaults to FALSE. 'precision' => 5, // Significant digits. 'scale' => 2, // Digits to the right of the decimal. 'not null' => TRUE, // Defaults to FALSE. 'description' => t('Always state the purpose of your field.'), );

Once you establish the active connection and SQL command, the next step is to submit the query to the data source As you might guess, you have a number of ways to do this The DbDataReader type (which implements IDataReader) is the simplest and fastest way to obtain information from a data store Recall that data readers represent a read-only, forward-only stream of data returned one record at a time Given this, data readers are useful only when submitting SQL selection statements to the underlying data store Data readers are useful when you need to iterate over large amounts of data quickly and you do not need to maintain an in-memory representation.

The function returns successfully (that is, a return code of 0) if the argument supplied on the command line is a valid dotted-quad IP address. You can test the function at the command line by sourcing the file containing the function: $ . isvalidip-func The function is now available at the shell prompt. Let s test it with a few IP addresses: $ for ip in 127.0.0.1 168.260.0.234 1.2.3.4 123.1OO.34.21 204.225.122.150 > do > if isvalidip "$ip" > then > printf "%15s: valid\n" "$ip" > else > printf "%15s: invalid\n" "$ip" > fi > done 127.0.0.1: valid 168.260.0.234: invalid 1.2.3.4: valid 123.1OO.34.21: invalid 204.225.122.150: valid