If we are dealing with large amounts of data and have to use queries to find count of distinct values of some particular column or group of columns, the new 12.1.0.2 feature APPROX_COUNT_DISTINCT function can be significantly faster than using the traditional COUNT (DISTINCT expr) function with marginal differences in the exact result.
The examples below also show less optimizer cost overhead as well as temporary tablespace space usage as well.
We will also see how the traditional Top n queries we used to run has changed significantly in Oracle 12c with the FETCH FIRST n ROWS and OFFSET commands available in SQL*PLUS.
Let us run the query in the traditional way to determine the Top 5 products in terms of the number of individual customers.
Note the execution plan and the fact that sort operation has used some temporary space.
SQL> conn sh/sh Connected. SQL> select * from 2 (select prod_id, count(distinct cust_id) from sales 3 group by prod_id order by 2 desc ) 4 where rownumNow we run the same query using the 12c new features - APPROX_COUNT_DISTINCT and FETCH FIRST n ROWS
Note the difference in the execution plan and optimizer cost. We see two new operations - WINDOW SORT PUSHED RANK and HASH GROUP BY APPROX
The results are almost 98% accurate with the APPROX_COUNT_DISTINCT function in use.
SQL> select prod_id, APPROX_COUNT_DISTINCT (cust_id) from sales 2 group by prod_id order by 2 desc 3 FETCH FIRST 5 ROWS ONLY; PROD_ID APPROX_COUNT_DISTINCT(CUST_ID) ---------- ------------------------------ 30 6134 48 5883 40 5858 31 5463 130 5336 Execution Plan ---------------------------------------------------------- Plan hash value: 723256909 --------------------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Pstart| Pstop | --------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 5 | 260 | 559 (9)| 00:00:01 | | | | 1 | SORT ORDER BY | | 5 | 260 | 559 (9)| 00:00:01 | | | |* 2 | VIEW | | 5 | 260 | 558 (9)| 00:00:01 | | | |* 3 | WINDOW SORT PUSHED RANK| | 72 | 648 | 558 (9)| 00:00:01 | | | | 4 | HASH GROUP BY APPROX | | 72 | 648 | 558 (9)| 00:00:01 | | | | 5 | PARTITION RANGE ALL | | 918K| 8075K| 514 (1)| 00:00:01 | 1 | 28 | | 6 | TABLE ACCESS FULL | SALES | 918K| 8075K| 514 (1)| 00:00:01 | 1 | 28 | Predicate Information (identified by operation id): --------------------------------------------------- 2 - filter("from$_subquery$_002"."rowlimit_$$_rownumber"We can also use the OFFSET clause to now do some pagination and obtain the next 5 rows (after the top 5)
SQL> select prod_id, APPROX_COUNT_DISTINCT (cust_id) from sales 2 group by prod_id order by 2 desc 3 OFFSET 5 ROWS FETCH NEXT 5 ROWS ONLY; PROD_ID APPROX_COUNT_DISTINCT(CUST_ID) ---------- ------------------------------ 33 5278 120 5135 128 5135 133 5114 23 5104We can also use the PERCENT ROWS clause to return the top percentage of rows we desire
SQL> select prod_id, count(distinct cust_id) from sales 2 group by prod_id order by 2 desc 3 FETCH FIRST 10 PERCENT ROWS ONLY; PROD_ID COUNT(DISTINCTCUST_ID) ---------- ---------------------- 30 6154 48 6010 40 5972 31 5586 130 5428 33 5389 120 5224 133 5201 8 rows selected.When we ran all these queries these were the runtime statistics – almost the same in every case.
Statistics ---------------------------------------------------------- 7 recursive calls 0 db block gets 1623 consistent gets 1607 physical reads 0 redo size 732 bytes sent via SQL*Net to client 552 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 2 sorts (memory) 0 sorts (disk) 5 rows processedSince the database is 12.1.0.2 and we have enabled IN MEMORY option in the database let us what happens to the same query when we use the In Memory column store.
Note the difference in the optimizer cost, execution plan and 0 physical reads – In Memory option in operation which requires no change in the application or query!
SQL> alter table sales inmemory; Table altered. SQL> select prod_id, APPROX_COUNT_DISTINCT (cust_id) from sales 2 group by prod_id order by 2 desc 3 FETCH FIRST 5 ROWS ONLY; PROD_ID APPROX_COUNT_DISTINCT(CUST_ID) ---------- ------------------------------ 30 6134 48 5883 40 5858 31 5463 130 5336 Execution Plan ---------------------------------------------------------- Plan hash value: 723256909 --------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Pstart| Pstop | --------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 5 | 260 | 154 (33)| 00:00:01 | | | | 1 | SORT ORDER BY | | 5 | 260 | 154 (33)| 00:00:01 | | | |* 2 | VIEW | | 5 | 260 | 153 (33)| 00:00:01 | | | |* 3 | WINDOW SORT PUSHED RANK | | 72 | 648 | 153 (33)| 00:00:01 | | | | 4 | HASH GROUP BY APPROX | | 72 | 648 | 153 (33)| 00:00:01 | | | | 5 | PARTITION RANGE ALL | | 918K| 8075K| 110 (6)| 00:00:01 | 1 | 28 | | 6 | TABLE ACCESS INMEMORY FULL| SALES | 918K| 8075K| 110 (6)| 00:00:01 | 1 | 28 | --------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 2 - filter("from$_subquery$_002"."rowlimit_$$_rownumber"
