Analytic & Windowing functions in oracle


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Are you an Oracle developer or a DBA?

Do you know the difference between aggregate and analytic functions?

Without complex sub-queries or self-joins, do you know how to:

Calculate running/cumulative totals and moving/centered averages?

List products with revenues above or below their peers or product groups?

Compute the ratio of one category’s sales to the total sales?

Select the Top-N or Top N % of the customers/products?

Classify advertisers into quartiles/n-tiles based on the revenue potential?

Compare period-over-period (year-over-year, month-over-month) growth and rank advancement?
Convert rows into columns (pivot), columns into rows (unpivot) or aggregate strings?

Perform what-if analysis and hypothetical ranking?

Analytic functions are more performant because tables need to be scanned only once. They make you more productive because there is no need to write procedural code. No wonder Tom Kyte, a well-respected Oracle guru, says analytic functions are the best thing to happen after the sliced bread.

In the first half, I will cover the basics of the various analytic functions:
Others: FIRST/LAST, LEAD/LAG, hypothetical ranking,

In the second half, I will show how powerful these functions are with a few examples.

If there is time, I will cover enhanced aggregation (ROLLUP, CUBE, GROUPING SET extensions to GROUP BY clause)

This class would be useful for both developers and DBAs alike, especially for those working in Analytic, Business Intelligence, and Datawarehouse environments.

Are you already an expert in analytic functions? Then come and help me refine the content.

For more info, read

rollup, cross-tabulation across different dimensions using ROLLUP, CUBE and GROUPING SETS extension to GROUP BY clause

, most active time-periods (i.e. days when the most number of tickets are open in BZ, hours with the most take-off and landings, months with the highest sales, 5-minute periods with the maximum number of calls made, etc)

data densification?

their rank last year, this year, rank growth, running/cumulative total (Year-To-Date/Month-To-Date summation), moving averages, Year-Over-Year comparison, sales projection, average/min/max time between one sale and the next sale, products with above and below average sales.

overall average, sum, departmental average, sum, ranking, job wise ranking in one SQL.

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Analytic & Windowing functions in oracle

  1. 1. Analytic and Window Functions in Oracle Logan Palanisamy
  2. 2. Agenda Difference between aggregate and analytic functions Introduction to various analytic functions Functions that are both aggregate and analytic Break More examples Enhanced Aggregation (CUBE, ROLLUP)
  3. 3. Meeting Basics Put your phones/pagers on vibrate/mute Messenger: Change the status to offline or in-meeting Remote attendees: Mute yourself (*6). Ask questions via Adobe Connect.
  4. 4. Aggregates vs. Analytics Aggregate functions  Rows are collapsed. One row per group  Non-Group-By columns not allowed in SELECT list. Analytic functions  Rows are not collapsed  As many rows in the output as in the input  No restrictions on the columns in the SELECT list  Evaluated after joins, WHERE, GROUP BY, HAVING clauses  Nesting not allowed  Can appear only in the SELECT or ORDER BY clause analytic_aggr_diff.sql
  5. 5. Analytics vs. other methods Show the dept, empno, sal and the sum of all salaries in their dept Three possible ways  Using Joins  Using Scalar Sub-queries  Using Analytic Functions analytics_vs_others.sql
  6. 6. Anatomy of an analytic funcion function (arg1, ..., argN) OVER ([partition_by_clause] [order_by_clause [windowing_clause]]) The OVER keyword partition_by_clause: Optional. Not related to table/index partitions. Analogous to GROUP BY order_by_clause: Mandatory for Ranking and Windowing functions. Optional or meaningless for others windowing_clause: Optional. Should always be preceded by ORDER BY clause
  7. 7. Types of analytical functions Ranking functions FIRST_VALUE/LAST_VALUE/NTH_VALUE Windowing functions Reporting functions LAG/LEAD FIRST/LAST
  8. 8. Ranking Functions ROW_NUMBER() RANK() – Skips ranks after duplicate ranks DENSE_RANK() – Doesnt skip rank after duplicate ranks NTILE(n) – Sorts the rows into N equi-sized buckets CUME_DIST() – % of rows with values lower or equal PERCENT_RANK() - (rank of row -1)/(#rows – 1) function OVER ([PARTITION BY <c1,c2..>] ORDER BY <c3, ..>) PARTITION BY clause: Optional ORDER BY clause: Mandatory rank_dense_rank.sql
  9. 9. FIRST_VALUE/LAST_VALUE/NTH_VALUE Returns the first/last/nth value from an ordered set FIRST_VALUE(expr, [IGNORE NULLS]) OVER ([partitonby_clause] orderby_clause) IGNORE NULLS options helps you "carry forward". Often used in "Data Densification" Operates on Default Window (unbounded preceding and current row) when a window is not explicitly specified. NTH_VALUE introduced in 11gR2 flnth_value.sql
  10. 10. Window functions Used for computing cumulative/running totals (YTD, MTD, QTD), moving/centered averages function(args) OVER([partition_by_clause] order_by_clause [windowing_clause]) ORDER BY clause: mandatory. Windowing Clause: Optional. Defaults to: UNBOUNDED PRECEDING and CURRENT ROW anchored or sliding windows Two ways to specify windows: ROWS, RANGE [ROW | RANGE ] BETWEEN <start_exp> AND <end_exp> window.sql
  11. 11. ROWS type windows Physical offset. Number of rows before or after current row Non deterministic results if rows are not sorted uniquely Any number of columns in the ORDER BY clause ORDER BY columns can be of any type function(args) OVER ([partition_by_clause] order by c1, .., cN ROWS between <start_exp> and <end_exp>) windows_rows.sql
  12. 12. RANGE type Windows Logical offset non-unique rows treated as one logical row Only one column allowed in ORDER BY clause ORDER BY column should be numeric or date function(args) OVER ([partition_by_clause] order by c1 RANGE between <start_exp> and <end_exp>) windows_range.sql
  13. 13. Reporting function Computes the ratio of a value to the sum of a set of values RATIO_TO_REPORT(arg) OVER ([PARTITION BY <c1, .., cN>] PARTITION BY clause: Optional. ratio_to_report.sql
  14. 14. LAG/LEAD Gives the ability to access other rows without self-join. Allows you to treat cursor as an array Useful for making inter-row calculations (year-over-year comparison, time between events) LEAD (expr, <offset>, <default value>) [IGNORE NULLS] OVER ([partioning_clause] orderby_clause) Physical offset. Can be fixed or varying. default offset is 1 default value: value returned if offset points to a non- existent row IGNORE NULLS determines whether null values of are included or eliminated from the calculation. lead_lag.sql
  15. 15. FIRST/LAST Very different from FIRST_VALUE/LAST_VALUE Returns the results of aggregate/analytic function applied on column B on the first or last ranked rows sorted by column A function (expr_with_colB) KEEP (DENSE_RANK FIRST/LAST ORDER BY colA) [OVER (<partitioning_clause)>)] Slightly different syntax. Note the word KEEP analytic clause is optional. first_last.sql
  16. 16. Above/Below average calculation Find the list of employees whose salary is higher than the department average. above_average.sql
  17. 17. Top-N queries Find the full details of "set of" employees with the top- N salaries Find the two most recent hires in each department List the names and employee count of departments with the highest employee count top_n.sql
  18. 18. Top-N% queries List the top 5% of the customers by revenue top_np.sql
  19. 19. Multi Top-N queries For each customer, find out  the maximum sale in the last 7 days  the date of that sale  the maximum sale in the last 30 days  the date of that sale multi_top.sql
  20. 20. De-Duping Deleting duplicate records dedup.txt
  21. 21. Hypothetical Ranking CUME_DIST, DENSE_RANK, RANK, PERCENT_RANK Used for what-if analysis hypothetical_rank.sql
  22. 22. Inverse Percentile functions Return the value corresponding to a certain percentile (opposite of CUME_DIST) PERCENTILE_CONT (continuous) PERCENTILE_DISC (discrete) PERCENTILE_CONT(0.5) is the same as MEDIAN inverse_p.sql
  23. 23. String Aggregation: LISTAGG, STRAGG Concatenated string of values for a particular group (e.g. employees working in a dept) Tom Kytes STRAGG 11gR2 has LISTAGG 10g has COLLECT listagg.sql
  24. 24. Pivoting/Unpivoting Pivoting  transposes rows to columns  DECODE/CASE and GROUP BY used Unpivoting  Converts columns to rows  Join the base table with a one column serial number table 11gR2 introduced PIVOT and UNPIVOT clauses to SELECT pivot.sql
  25. 25. Data Densification Data normally stored in sparse form (e.g. No rows if there is no sales for a particular period) Missing data needed for comparison (e.g. month- over-month comparison) Data Densification comes in handy LAG (col, INGORE NULLS), and PARTITION BY OUTER JOIN are used. /sql-filling-in-gaps-in-the-source-data/
  26. 26. When not to use analytics When a simple group by would do the job when_not_to_use_analytics.sq
  27. 27. Drawback of analytics Lot of sorting. Set PGA_AGGREGATE_TARGET/SORT_AREA_SIZE appropriately New versions reduce the number of sorts (same partition_by and order_by clauses on multiple analytic functions use single sort) NO::P11_QUESTION_ID:1137250200346660664 alytic-agony/
  28. 28. Recap of Analytic Functions Analytic Functions:  Were introduced in 8.1.6 (~1998)  Are supported within PL/SQL only from 10g. Use "view" or "dynamic sql" older versions.  Compute the aggregates while preserving the details  Eliminate the need for self-joins or multiple passes on the same table  Reduce the amount of data transferred between DB and client.  Can be used only in SELECT and ORDER BY clauses. Use sub- queries if there is a need to filter.  Are computed at the end - after join, where, group by, having clauses
  29. 29. Advanced Aggregation GROUP BY col1, col2 GROUP BY ROLLUP(col1, col2) GROUP BY CUBE(col1, col2) GROUP BY GROUPING SETS ((col1, col2), col1)
  30. 30. ROLLUP GROUP BY ROLLUP(col1, col2) Generates subtotals automatically Generally used in hierarchical dimensions (region, state, city), (year, quarter, month, day) n + 1 different groupings where n is the number of expressions in the ROLLUP operator in the GROUP BY clause. Order of the columns in ROLLUP matter. ROLLUP(col1, col2), ROLLUP(col2, col1) produce different outputs
  31. 31. CUBE GROUP BY CUBE(col1, col2) Gives subtotals automatically for every possible combination Used in cross-tabular reports. Suitable when dimensions are independent of each other 2n different groupings where n is the number of expressions in the CUBE operator in the GROUP BY clause. Have to be careful with higher values for n Order of the columns in CUBE doesn’t really matter. CUBE(col1, col2), CUBE(col2, col1) produce same results, but in a different order.
  32. 32. Grouping Sets GROUP BY GROUPING SETS (col1, (col1, col2)) Explicitly lists the needed groupings GROUPING, GROUPING_ID, GROUP_ID functions help you differentiate one grouping from the other. Advanced aggregation functions more efficient than their UNION ALL equivalents (why?) Grouping Equivalent GROUPING SETS advanced_agg.sql CUBE(a,b) GROUPING SETS((a,b), (a), (b), ()) ROLLUP(a,b) GROUPING SETS((a,b), (a), ()) ROLLUP(b,a) GROUPING SETS((a,b), (b), ()) ROLLUP(a) GROUPING SETS((a), ())
  33. 33. Composite Columns Treat multiple columns as a single column Composite_columns.sql
  34. 34. Concatenated Groupings GROUP BY GROUPING SETs (a,b), GROUPING SETS (c,d) The above is same as GROUP BY GROUPING SETS ((a,c), (a,d), (b,c), (b,d))
  35. 35. References nalytic_functions.html html oracle/05-mar/o25dba.html
  36. 36. References Chapter 12 of "Expert Database Architecture" by Tom Kyte Business-Savy SQL by Ganesh Variar, Oracle magazine, Mar/April 2002 h?p_string=rock+and+roll alytic&objID=f75&dateRange=all&numResults=30& forumID=75&rankBy=10001&start=0
  37. 37. Q&A devel_oracle@
  38. 38. Predicate merging in views with analytics create view v select .. over(partition by ...) from t; select ... from v where col1 = A In some cases predicates dont get merged. Reasons:  ESTION_ID:12864646978683#30266389821111  QUESTION_ID:1137250200346660664  9151&#4169151