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Oracle s SQL analytical functions are powerful tools for business intelligence applications. Oracle claims a potential improvement of 200 to 500 percent in query performance with the use of the SQL analytical functions. The purpose behind using analytical functions is to perform complex summary computations without using a lot of code. Here are the main SQL analytical functions of the Oracle database: Ranking functions: These enable you to rank items in a data set according to some criteria. Oracle has several types of ranking functions, including RANK, DENSE_RANK, CUME_DIST, PERCENT_ RANK, and NTILE. Listing A-1 shows a simple example of how a ranking function can help you rank some sales data. Listing A-1. An Example of a Ranking Function SQL> SELECT sales_type, TO_CHAR(SUM(amount_sold), '9,999,999,999') SALES, RANK() OVER (ORDER BY SUM(amount_sold) ) AS original_rank, RANK() OVER (ORDER BY SUM(amount_sold) DESC NULLS LAST) AS derived_rank FROM sales, products, customers, time_frame, sales_types WHERE sales.prod_id=products.prod_id AND sales.cust_id=customers.cust_id AND sales.time_id=time_frame.time_id AND sales.sales_type_id=sales_types.sales_type_id AND timeframe.calendar_month_desc IN ('2008-07', '2008-08') AND country_id='INDIA' GROUP BY sales_type; SALES_TYPE ------------Direct Sales Internet Catalog Partners Tele Sales SQL> SALES --------5,744,263 3,625,993 1,858,386 1,500,213 604,656 ORIGINAL_RANK -------------5 4 3 2 1 DERIVED_RANK -----------1 2 3 4 5

When it comes to large networks, the principle of modularity could be applied to consider them as consisting of a number of smaller (small and medium-size) segments. The same approach that applies to NTP deployments on either a small network or a mediumsize network could then be applied to the different segments of a large one. If, however, a large network is viewed in a monolithic fashion as a single entity rather than a collection of smaller segments, consider the following approach as it is applied to an enterprise with about 6,000+ devices in need of NTP service: 1. Time source(s): A dozen of dedicated timeservers distributed throughout the enterprise in three clusters of four, each cluster serving about 2,000 other clients. 2. Topology: All timeservers within each cluster peer with one another in a fullmesh logical topology. One out of the four timeservers in each cluster has been designated to represent that cluster for the purpose of peering with the other two clusters. Figure 5-7 shows the logical topology for all of the timeservers. The lines between any two timeservers represent a peering relationship. Synchronization service to clients is not shown graphically, but imagine hundreds of lines extending from each S1 timeserver to represent the synchronization exchanges. The clusters of timeservers are physically distributed throughout the enterprise to minimize latency between them and their clients.

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Moving-window aggregates: These functions provide cumulative sums and moving averages. Period-over-period comparisons: These functions let you compare two periods (for example, How does the first quarter of 2008 compare with the first quarter of 2006 in terms of percentage growth ). Ratio-to-report comparisons: These make it possible to compare ratios (for example, What is August s enrollment as a percentage of the entire year s enrollment ). Statistical functions: These functions calculate correlations and regression functions so you can see cause-and-effect relationships among data. Inverse percentiles: These help you find the data corresponding to a percentile value (for example, Get me the names of the salespeople who correspond to the median sales value. ).

Hypothetical ranks and distributions: These help you figure out how a new value for a column fits into existing data in terms of its rank and distribution. Histograms: These functions return the number of the histogram data appropriate for each row in a table. First/last aggregates: These functions are appropriate when you are using the GROUP BY clause to sort data into groups. Aggregate functions let you specify the sort order for the groups.

/*@lineinfo:user-code*//*@lineinfo:173^5*/ return __jPt_result; } }/*@lineinfo:generated-code*/ Notice that the class extends the ORAData and ORADataFactory interfaces as expected and also generates a wrapper method getAddress() for the get_address method of the address object type. The following listing shows the contents of the generated MyAddressORAData.java file (edited for clarity). /*@lineinfo:filename=MyAddressORAData*//*@lineinfo:user-code*//*@lineinfo:1^1*/ package book.ch10.jpub; /* All imports deleted for clarity */ public class MyAddressORAData extends AddressORAData implements ORAData, ORADataFactory { private static final MyAddressORAData _MyAddressORADataFactory = new MyAddressORAData(); public static ORADataFactory getORADataFactory() { return _MyAddressORADataFactory; } public MyAddressORAData() { super(); } public MyAddressORAData(Connection conn) throws SQLException { super(conn); } public MyAddressORAData(DefaultContext ctx) throws SQLException { super(ctx); } public MyAddressORAData(String line1, String line2, String street, String city, String state, String zip) throws SQLException { setLine1(line1); setLine2(line2); setStreet(street); setCity(city); setState(state); setZip(zip); } /* ORAData interface */ protected ORAData createExact(Datum d, int sqlType) throws SQLException { return create(new MyAddressORAData(), d, sqlType); }

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