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Oracle 10g analytical SQL for Business Intelligence Reporting Simay Alpoge Next Information Systems, Inc. Next Information Systems, Inc.

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Presentation on theme: "Oracle 10g analytical SQL for Business Intelligence Reporting Simay Alpoge Next Information Systems, Inc. Next Information Systems, Inc."— Presentation transcript:

1 Oracle 10g analytical SQL for Business Intelligence Reporting Simay Alpoge Next Information Systems, Inc. Next Information Systems, Inc.

2 March 1, 2007Next Information Systems2 Oracle 10g analytical SQL for Business Intelligence Reporting AGENDA Windowing aggregate functions. Windowing aggregate functions. Reporting aggregate functions. Reporting aggregate functions. LAG/LEAD functions. LAG/LEAD functions. FIRST/LAST functions. FIRST/LAST functions. Hypothetical Rank and Distribution Functions. Hypothetical Rank and Distribution Functions. Defining histograms with CASE statement. Defining histograms with CASE statement. Data densification for business intelligence reporting. Data densification for business intelligence reporting. Analytical functions vs conventional techniques. Analytical functions vs conventional techniques.

3 March 1, 2007Next Information Systems3 Oracle 10g analytical SQL for Business Intelligence Reporting analytic_function([ arguments ]) OVER (analytic_clause) OVER (analytic_clause) where analytic_clause = [ query_partition_clause ] [ query_partition_clause ] [ order_by_clause [ windowing_clause ] ] [ order_by_clause [ windowing_clause ] ] and query_partition_clause = PARTITION BY { value_expr[, value_expr ]... | ( value_expr[, value_expr ]... ) } PARTITION BY { value_expr[, value_expr ]... | ( value_expr[, value_expr ]... ) }

4 March 1, 2007Next Information Systems4 Oracle 10g analytical SQL for Business Intelligence Reporting windowing_clause = { ROWS | RANGE } windowing_clause = { ROWS | RANGE } { BETWEEN { UNBOUNDED PRECEDING | CURRENT ROW | value_expr { PRECEDING | FOLLOWING } } value_expr { PRECEDING | FOLLOWING } } AND { UNBOUNDED FOLLOWING | CURRENT ROW | value_expr { PRECEDING | FOLLOWING } } OR { UNBOUNDED PRECEDING | CURRENT ROW | value_expr PRECEDING } }

5 March 1, 2007Next Information Systems5 Oracle 10g analytical SQL for Business Intelligence Reporting Processing Order of analytical functions in queries: 1. Joins, WHERE, GROUP BY, HAVING clauses 1. Joins, WHERE, GROUP BY, HAVING clauses performed. performed. 2. Partitions are created with GROUP BY. 2. Partitions are created with GROUP BY. Analytical functions are applied to each row in Analytical functions are applied to each row in each partition. each partition. 3. ORDER BY is processed. 3. ORDER BY is processed.

6 March 1, 2007Next Information Systems6 Oracle 10g analytical SQL for Business Intelligence Reporting Analytical functions divide a query result sets into partitions. Analytical functions divide a query result sets into partitions. Current row is the reference point to determine starting and ending point of the window in a partition. Current row is the reference point to determine starting and ending point of the window in a partition. Window size can be based on physical number of rows or logical interval. Window size can be based on physical number of rows or logical interval. Window size of each row can also vary based on specific condition. Window size of each row can also vary based on specific condition.

7 March 1, 2007Next Information Systems7 Oracle 10g analytical SQL for Business Intelligence Reporting Windowing aggregate functions: Windowing aggregate functions: Used to compute cumulative, moving, centered Used to compute cumulative, moving, centered aggregates. aggregates. Access to more than one row of a table Access to more than one row of a table without self-join. without self-join. Can ONLY be used in the SELECT and Can ONLY be used in the SELECT and ORDER BY clause of a query. ORDER BY clause of a query.

8 March 1, 2007Next Information Systems8 Oracle 10g analytical SQL for Business Intelligence Reporting Windowing function with LOGICAL offset Constant- RANGE 5 Interval-RANGE INTERVAL N DAY/MONTH/YEAR … expression Multiple sort keys with analytical ORDER BY RANGE BETWEEN UNBOUNDED PRECEDING/FOLLOWING Windowing function with PHYSICAL offset Ordering expression have to be unique. Ordering expression have to be unique.

9 March 1, 2007Next Information Systems9 Oracle 10g analytical SQL for Business Intelligence Reporting Cumulative Aggregate SELECT REGION, QUARTER, SUM(SALES) Q_SALES, SUM(SALES) Q_SALES, SUM(SUM(SALES)) OVER (PARTITION BY REGION SUM(SUM(SALES)) OVER (PARTITION BY REGION ORDER BY REGION, QUARTER ORDER BY REGION, QUARTER ROWS UNBOUNDED PRECEDING) CUMULATIVE_SALES FROM SALES S, TIMES T, LOCATION L FROM SALES S, TIMES T, LOCATION L WHERE S.TIME_ID = T.TIME_ID WHERE S.TIME_ID = T.TIME_ID AND S.LOCATION_ID = L.LOCATION_ID AND S.LOCATION_ID = L.LOCATION_ID AND T.CALENDAR_YEAR = ‘2006’ AND T.CALENDAR_YEAR = ‘2006’ AND L.LOCATION_ID IN (234, 356,780) AND L.LOCATION_ID IN (234, 356,780) GROUP BY REGION, QUARTER GROUP BY REGION, QUARTER ORDER BY REGION, QUARTER ; ORDER BY REGION, QUARTER ;

10 March 1, 2007Next Information Systems10 Oracle 10g analytical SQL for Business Intelligence Reporting Region Quarter Q_Sales Cumulative_Sales East 01-2006 100.90 100.90 East 02-2006 150.75 251.65 East 03-2006 200.00 451.65 East 04-2006 500.00 951.65 West 01-2006 1100.00 1100.00 West 02-2006 875.00 1975.00 West 03-2006 950.78 2925.78 West 04-2006 1200.00 4125.78

11 March 1, 2007Next Information Systems11 Oracle 10g analytical SQL for Business Intelligence Reporting Centered Aggregate SELECT C_MONTH_ID, SUM(SALES) M_SALES, SUM(SALES) M_SALES, AVG(SUM(SALES)) OVER AVG(SUM(SALES)) OVER (ORDER BY C_MONTH_ID RANGE BETWEEN INTERVAL ‘1’ MONTH PRECEEDING AND ‘1’ MONTH FOLLOWING) 3_MONTH_SALES (ORDER BY C_MONTH_ID RANGE BETWEEN INTERVAL ‘1’ MONTH PRECEEDING AND ‘1’ MONTH FOLLOWING) 3_MONTH_SALES FROM SALES S, TIMES T FROM SALES S, TIMES T WHERE S.TIME_ID = T.TIME_ID WHERE S.TIME_ID = T.TIME_ID AND T.CALENDAR_YEAR = ‘2006’ AND T.CALENDAR_YEAR = ‘2006’ AND C_MONTH_ID BETWEEN 1 AND 6 AND C_MONTH_ID BETWEEN 1 AND 6 GROUP BY C_MONTH_ID GROUP BY C_MONTH_ID ORDER BY C_MONTH_ID ; ORDER BY C_MONTH_ID ;

12 March 1, 2007Next Information Systems12 Oracle 10g analytical SQL for Business Intelligence Reporting C_Month_Id M_Sales 3_Month_Sales 1 500.00 350.00 1 500.00 350.00 2 200.00 300.00 2 200.00 300.00 3 200.00 300.00 3 200.00 300.00 4 500.00 500.00 4 500.00 500.00 5 800.00 633.33 5 800.00 633.33 6 600.00 700.00 6 600.00 700.00

13 March 1, 2007Next Information Systems13 Oracle 10g analytical SQL for Business Intelligence Reporting Moving Aggregate SELECT REGION, QUARTER, SUM(SALES) Q_SALES, SUM(SALES) Q_SALES, AVG(SUM(SALES)) OVER (PARTITION BY REGION AVG(SUM(SALES)) OVER (PARTITION BY REGION ORDER BY REGION, QUARTER ORDER BY REGION, QUARTER ROWS 2 PRECEDING) MOVING_AVG_SALES FROM SALES S, TIMES T, LOCATION L FROM SALES S, TIMES T, LOCATION L WHERE S.TIME_ID = T.TIME_ID WHERE S.TIME_ID = T.TIME_ID AND S.LOCATION_ID = L.LOCATION_ID AND S.LOCATION_ID = L.LOCATION_ID AND T.CALENDAR_YEAR = ‘2006’ AND T.CALENDAR_YEAR = ‘2006’ AND L.LOCATION_ID IN (234, 356,780) AND L.LOCATION_ID IN (234, 356,780) GROUP BY REGION, QUARTER GROUP BY REGION, QUARTER ORDER BY REGION, QUARTER; ORDER BY REGION, QUARTER;

14 March 1, 2007Next Information Systems14 Oracle 10g analytical SQL for Business Intelligence Reporting Region Quarter Q_Sales Moving_Avg_Sales East 01-2006 100.90 100.90 East 02-2006 150.75 125.83 East 03-2006 200.00 150.55 East 04-2006 500.00 283.58 West 01-2006 1100.00 1100.00 West 02-2006 875.00 987.50 West 03-2006 950.78 975.26 West 04-2006 1200.00 1008.59

15 March 1, 2007Next Information Systems15 Oracle 10g analytical SQL for Business Intelligence Reporting Reporting aggregate functions Reporting aggregate functions Returns same aggregate value for every row in a partition. It does multiple passes of data in a single query block. Excellent query performance result.

16 March 1, 2007Next Information Systems16 Oracle 10g analytical SQL for Business Intelligence Reporting SELECT store_name, prod_grp_desc, tot_sales, tot_costs FROM (SELECT prod_grp_name, store_name, store_name, SUM(sales) tot_sales, SUM(sales) tot_sales, SUM(costs) tot_costs, SUM(costs) tot_costs, MAX(SUM(sales)) OVER (PARTITION BY prod_grp_cd) top_sales, MAX(SUM(sales)) OVER (PARTITION BY prod_grp_cd) top_sales, MAX(SUM(costs)) OVER (PARTITION BY prod_grp_cd) top_costs MAX(SUM(costs)) OVER (PARTITION BY prod_grp_cd) top_costs FROM sales_hist sh, store s, product_grp p inv_major m FROM sales_hist sh, store s, product_grp p inv_major m WHERE sh.store_cd = s.store_cd AND sh.mjr_cd = m.inv_mjr_cd AND sh.mjr_cd = m.inv_mjr_cd AND m.prod_grp_cd = p.product_grp_cd AND m.prod_grp_cd = p.product_grp_cd AND sh.s_date = TO_DATE(’01-FEB-2007’) AND sh.s_date = TO_DATE(’01-FEB-2007’) GROUP BY prod_grp_name, store_name) GROUP BY prod_grp_name, store_name) WHERE tot_costs <= top_costs AND tot_sales = top_sales AND tot_sales = top_sales

17 March 1, 2007Next Information Systems17 Oracle 10g analytical SQL for Business Intelligence Reporting Inner query results PROD_GRP_ STORE_ TOT_ TOT_ TOP_ TOP_ NAME NAME SALES COSTS SALES COSTS ACCESSORIES 5 th Ave1000 200 1000 200 ACCESSORIES Brooklyn 500 150 1000 200 LADIES SHOES 5 th Ave 3000 750 3000 1000 LADIES SHOES Brooklyn2000 500 3000 1000 LADIES SHOES San Francisco2500 1000 3000 1000 CHILDREN Houston3000 650 4000 1000 CHILDREN 5 th Ave4000 900 4000 1000 CHILDREN Brooklyn3000 1000 4000 1000

18 March 1, 2007Next Information Systems18 Oracle 10g analytical SQL for Business Intelligence Reporting Final result Final result PROD_GRP_ STORE_ TOT_ TOT_ TOP_ TOP_ NAME NAME SALES COSTS SALES COSTS ACCESSORIES 5 th Ave1000 200 1000 200 LADIES SHOES 5 th Ave3000 750 3000 1000 CHILDREN 5 th Ave4000 900 4000 1000

19 March 1, 2007Next Information Systems19 Oracle 10g analytical SQL for Business Intelligence Reporting LAG/LEAD function Access of a row at a given offset prior to / after current position. Access to more than one row of a table Access to more than one row of a table at the same time without self-join. at the same time without self-join.

20 March 1, 2007Next Information Systems20 Oracle 10g analytical SQL for Business Intelligence Reporting SELECT SALES_TY, LAG_SALES, LEAD_SALES, SALES_MONTH, SALES_YEAR FROM (SELECT SUM(SALES) SALES_TY, TO_CHAR(SALES_DT, 'DD-MON') SALES_MONTH, TO_CHAR(SALES_DT, 'DD-MON') SALES_MONTH, TO_CHAR(SALES_DT,'RRRR') SALES_YEAR, TO_CHAR(SALES_DT,'RRRR') SALES_YEAR, LAG(SUM(SALES),1) OVER (ORDER BY TO_CHAR(SALES_DT, 'DD-MON')) AS LAG_SALES, AS LAG_SALES, LEAD(SUM(SALES),1) OVER (ORDER BY TO_CHAR(SALES_DT,'DD-MON')) AS LEAD_SALES AS LEAD_SALES FROM SALES WHERE TO_CHAR(SALES_DT,'RRRR') IN ('2005','2006') AND SALES_DT BETWEEN '20-AUG-2006' AND '22-AUG-2006' OR SALES_DT BETWEEN TO_DATE('20-AUG-2006','DD-MON-RRRR')- 364 AND TO_DATE('22-AUG-2006','DD-MON-RRRR') - 364 GROUP BY TO_CHAR(SALES_DT, 'DD-MON'), TO_CHAR(SALES_DT,'RRRR') ORDER BY SALES_MONTH DESC, SALES_YEAR DESC) WHERE SALES_YEAR = ‘2006’ ORDER BY SALES_MONTH

21 March 1, 2007Next Information Systems21 Oracle 10g analytical SQL for Business Intelligence Reporting Inner query results : SALES_ SALES_ SALES_ LAG_ LEAD_ TY MONTH YEAR SALES SALES 5000 20-AUG 2006 3500 3500 20-AUG 2005 5000 4500 21-AUG 2006 6700 6700 21-AUG 2005 4500 8300 22-AUG 2006 9500 9500 22-AUG 2005 8300

22 March 1, 2007Next Information Systems22 Oracle 10g analytical SQL for Business Intelligence Reporting Final query results : SALES_TY SALES_MONTH SALES_YEAR LAG_SALES LEAD_SALES 5000 20-AUG 2006 3500 4500 21-AUG 2006 6700 8300 22-AUG 2006 9500

23 March 1, 2007Next Information Systems23 Oracle 10g analytical SQL for Business Intelligence Reporting FIRST/LAST function SELECT prod_category, prod_name, sales, MIN(sales) KEEP (DENSE_RANK FIRST ORDER BY cost) OVER (PARTITION BY prod_category) low_sales, MIN(sales) KEEP (DENSE_RANK FIRST ORDER BY cost) OVER (PARTITION BY prod_category) low_sales, MAX(sales) KEEP (DENSE_RANK LAST ORDER BY cost) OVER (PARTITION BY prod_category) high_sales MAX(sales) KEEP (DENSE_RANK LAST ORDER BY cost) OVER (PARTITION BY prod_category) high_sales FROM sales_hist GROUP BY prod_category, prod_name, sales ORDER BY prod_category, sales

24 March 1, 2007Next Information Systems24 Oracle 10g analytical SQL for Business Intelligence Reporting Prod Prod Sales Cost Low High Category Name Sales Sales Accessories Leader Belt 200 50 100 500 … Accessories Leader Belt 100 50 100 300 Accessories Silk Scarf 600 100 200 600 … Accessories Silk Scarf 800 150 300 800 Handbag LV 5000 500 3000 35000 Handbag Coach 8000 400 8000 12000 RL Shirt 600 45 600 600 RL Shirt 600 45 600 600

25 March 1, 2007Next Information Systems25 Oracle 10g analytical SQL for Business Intelligence Reporting Prod Prod Sales Low High Category Name Sales Sales Accessories Leader Belt 100 100 300 Accessories Silk Scarf 600 200 600 Handbag LV 5000 3000 35000 Handbag Coach 8000 8000 12000 RL Shirt 600 600 600 RL Shirt 600 600 600

26 March 1, 2007Next Information Systems26 Oracle 10g analytical SQL for Business Intelligence Reporting Hypothetical rank and distribution functions: Primarly used for “What if analysis” Primarly used for “What if analysis” RANK RANKDENSE_RANKPERCENT_RANKCUM_DIST They can not be used as reporting or windowing aggregate functions. They can not be used as reporting or windowing aggregate functions.

27 March 1, 2007Next Information Systems27 Oracle 10g analytical SQL for Business Intelligence Reporting SELECT REGION, MAX(SCORE) MAX_SCORE, MIN(SCORE) MIN_SCORE, MIN(SCORE) MIN_SCORE, COUNT(SCORE) SCORE_COUNT, COUNT(SCORE) SCORE_COUNT, RANK (120) WITHIN GROUP (ORDER BY SCORE DESC NULLS FIRST) H_RANK RANK (120) WITHIN GROUP (ORDER BY SCORE DESC NULLS FIRST) H_RANK FROM LEAGUE_SCORES WHERE T_LEVEL = 3 AND G_TYPE = ‘BG14’ AND G_TYPE = ‘BG14’ GROUP BY REGION

28 March 1, 2007Next Information Systems28 Oracle 10g analytical SQL for Business Intelligence Reporting REGION MAX_SCORE MIN_SCORE SCORE_COUNT H_RANK Long Island 1002 80 1 Metro200515012 Northern1803100 7 Southern110595 1 Western3001016527

29 March 1, 2007Next Information Systems29 Oracle 10g analytical SQL for Business Intelligence Reporting Histograms with CASE SELECT SUM (CASE WHEN SALES BETWEEN 100 AND 5000 SELECT SUM (CASE WHEN SALES BETWEEN 100 AND 5000 THEN 1 ELSE 0 END) AS “100 – 5000”, THEN 1 ELSE 0 END) AS “100 – 5000”, SUM(CASE WHEN SALES BETWEEN 5001 AND 15000 SUM(CASE WHEN SALES BETWEEN 5001 AND 15000 THEN 1 ELSE 0 END) AS “5001 – 15000”, THEN 1 ELSE 0 END) AS “5001 – 15000”, SUM (CASE WHEN SALES BETWEEN 15001 AND 25000 SUM (CASE WHEN SALES BETWEEN 15001 AND 25000 THEN 1 ELSE 0 END ) AS “15001 – 25000” THEN 1 ELSE 0 END ) AS “15001 – 25000” FROM SALES WHERE REGION = ‘WEST’ WHERE REGION = ‘WEST’

30 March 1, 2007Next Information Systems30 Oracle 10g analytical SQL for Business Intelligence Reporting 100 – 5000 5001 – 15000 15001 – 25000 10518

31 March 1, 2007Next Information Systems31 Oracle 10g analytical SQL for Business Intelligence Reporting SELECT (CASE WHEN SALES BETWEEN 100 AND 5000 THEN ‘100 – 5000’ WHEN SALES BETWEEN 5001 AND 15000 WHEN SALES BETWEEN 5001 AND 15000 THEN ‘5001 – 15000’ WHEN SALES BETWEEN 15001 AND 25000 WHEN SALES BETWEEN 15001 AND 25000 THEN ‘15001 – 25000’ END ) AS SALES_BUCKET, COUNT(*) AS SALES_CNT FROM SALES WHERE REGION = ‘WEST’ WHERE REGION = ‘WEST’ GROUP BY (CASE WHEN SALES BETWEEN 100 AND 5000 GROUP BY (CASE WHEN SALES BETWEEN 100 AND 5000 THEN ‘100 – 5000’ THEN ‘100 – 5000’ WHEN SALES BETWEEN 5001 AND 15000 WHEN SALES BETWEEN 5001 AND 15000 THEN ‘5001 – 15000’ THEN ‘5001 – 15000’ WHEN SALES BETWEEN 15001 AND 25000 WHEN SALES BETWEEN 15001 AND 25000 THEN ‘15001 – 25000’ END ) THEN ‘15001 – 25000’ END )

32 March 1, 2007Next Information Systems32 Oracle 10g analytical SQL for Business Intelligence Reporting SALES_BUCKETSALES_CNT 100 – 5000 10 5001 – 15000 5 15001 – 2500018

33 March 1, 2007Next Information Systems33 Oracle 10g analytical SQL for Business Intelligence Reporting Data densification Process of converting sparse data into dense form. SELECT..... FROM table_reference PARTITION BY (expr [, expr ]... ) RIGHT OUTER JOIN table_reference Partition outer join fills the gaps in time series or any other dimensions.

34 March 1, 2007Next Information Systems34 Oracle 10g analytical SQL for Business Intelligence Reporting Product Year MonthDaySales Oracle Fusion 200701 01100 Oracle Fusion 200701 08 370 Global Economy 200701 04 300 Global Economy 200701 07 500

35 March 1, 2007Next Information Systems35 Oracle 10g analytical SQL for Business Intelligence Reporting Select product, day, NVL(sales,0) SALES FROM (Select Day, Product, SUM(Sales) Sales FROM sales s, f_calendar f, products p WHERE s.sale_date = f.cal_date AND s.product_id = p.product_id AND f.cal_year = ‘2007’ AND f.cal_mnth = ’01’ AND f.day between ’01’ and ’08’ GROUP BY Product, Day) x PARTITION BY (product) RIGHT OUTER JOIN (SELECT day FROM f_calendar WHERE cal_year = ‘2007’ AND cal_mnth = ’01’ WHERE cal_year = ‘2007’ AND cal_mnth = ’01’ AND day between ’01’ and ’08’) ff AND day between ’01’ and ’08’) ff ON (ff.day = x.day)) ORDER BY product, day

36 March 1, 2007Next Information Systems36 Oracle 10g analytical SQL for Business Intelligence Reporting Product DaySales Oracle Fusion 01 100 Oracle Fusion 02 0 Oracle Fusion 03 0 Oracle Fusion 04 0 Oracle Fusion 05 0 Oracle Fusion 06 0 Oracle Fusion 07 0 Oracle Fusion 08 370 Global Economy 01 0 Global Economy 02 0 Global Economy 03 0 Global Economy 04 300 Global Economy 05 0 Global Economy 06 0 Global Economy 07 500 Global Economy 08 0

37 March 1, 2007Next Information Systems37 Oracle 10g analytical SQL for Business Intelligence Reporting Partition outer join repeating value Inventory table : Product Time_id Quantity Oracle Fusion03-Feb-07 10 Oracle Fusion05-Feb-07 30 Oracle Fusion10-Feb-07 35 Global Economy 03-Feb-07 45 Global Economy 05-Feb-07 15 Global Economy 10-Feb-07 25

38 March 1, 2007Next Information Systems38 Oracle 10g analytical SQL for Business Intelligence Reporting SELECT PRODUCT,TIME_ID, QUANTITY, LAST_VALUE (QUANTITY, IGNORE NULLS) OVER (PARTITION BY product ORDER BY time_id ) R_QUANTITY FROM ( SELECT times.time_id, product, quantity FROM inventory PARTITION BY (product) RIGHT OUTER JOIN times ON (times.time_id=inventory.time_id) );

39 March 1, 2007Next Information Systems39 Oracle 10g analytical SQL for Business Intelligence Reporting Product Time_id Quantity R_Quantity Oracle Fusion03-Feb-07 10 10 Oracle Fusion04-Feb-07 10 Oracle Fusion05-Feb-07 3030 Oracle Fusion06-Feb-07 30 Oracle Fusion07-Feb-07 30 Oracle Fusion08-Feb-07 30 Oracle Fusion09-Feb-07 30 Oracle Fusion10-Feb-07 3535 Global Economy 03-Feb-07 4545 Global Economy 04-Feb-07 45 Global Economy 05-Feb-07 1515 Global Economy 06-Feb-07 15 Global Economy 07-Feb-07 15 Global Economy 08-Feb-07 15 Global Economy 09-Feb-07 15 Global Economy 10-Feb-07 2525

40 March 1, 2007Next Information Systems40 Oracle 10g analytical SQL for Business Intelligence Reporting Analytical functions vs conventional techniques Cumulative/Moving aggregation – PL/SQL tables, permenant/temporary tables. Windowing – Multiple sub-queries/views. Densification – Temporary/Permenant tables. UNION/UNION ALL Views

41 March 1, 2007Next Information Systems41 Oracle 10g analytical SQL for Business Intelligence Reporting References: http://www.oracle.com/technology/documentation/index.html http://www.asktom.oracle.com THANK YOU. alpoges@aol.com

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