分析函數(shù)是Oracle816引入的一個(gè)全新的概念,為我們分析數(shù)據(jù)提供了一種簡(jiǎn)單高效的處理方式.在分析函數(shù)出現(xiàn)以前,我們必須使用自聯(lián)查詢(xún),子查詢(xún)或者內(nèi)聯(lián)視圖,甚至復(fù)雜的存儲(chǔ)過(guò)程實(shí)現(xiàn)的語(yǔ)句,現(xiàn)在只要一條簡(jiǎn)單的sql語(yǔ)句就可以實(shí)現(xiàn)了,而且在執(zhí)行效率方面也有相當(dāng)大的提高.下面我將針對(duì)分析函數(shù)做一些具體的說(shuō)明.今天我主要給大家介紹一下以下幾個(gè)函數(shù)的使用方法1.; 自動(dòng)匯總函數(shù)rollup,cube,2.; rank 函數(shù), rank,dense_rank,row_number3.;;;;;lag,lead函數(shù)4.;;;;;sum,avg,的移動(dòng)增加,移動(dòng)平均數(shù)5.;;;;;ratio_to_report報(bào)表處理函數(shù)6.;;;;;first,last取基數(shù)的分析函數(shù)基礎(chǔ)數(shù)據(jù); Code:;;;;;[Copy to clipboard]06:34:23 SQL> select * from t;BILL_MONTH;;;AREA_CODE; NET_TYPE;;;;LOCAL_FARE--------------- ---------- ---------- --------------200405; 5761;;;;G;;;7393344.04200405; 5761;;;;J;;;5667089.85200405;;;;;;;5762;;;;G;;;6315075.96200405; 5762;;;;J;;;6328716.15200405; 5763;;;;G;;;8861742.59200405; 5763;;;;J;;;7788036.32200405; 5764;;;;G;;;6028670.45200405; 5764;;;;J;;;6459121.49200405; 5765;;;;G;;13156065.77200405; 5765;;;;J;;11901671.70200406; 5761;;;;G;;;7614587.96200406; 5761;;;;J;;;5704343.05200406; 5762;;;;G;;;6556992.60200406; 5762;;;;J;;;6238068.05200406; 5763;;;;G;;;9130055.46200406; 5763;;;;J;;;7990460.25200406; 5764;;;;G;;;6387706.01200406; 5764;;;;J;;;6907481.66200406; 5765;;;;G;;13562968.81200406; 5765;;;;J;;12495492.50200407; 5761;;;;G;;;7987050.65200407; 5761;;;;J;;;5723215.28200407; 5762;;;;G;;;6833096.68200407; 5762;;;;J;;;6391201.44200407; 5763;;;;G;;;9410815.91200407; 5763;;;;J;;;;;;;;;;;8076677.41200407; 5764;;;;G;;;6456433.23200407; 5764;;;;J;;;6987660.53200407; 5765;;;;G;;14000101.20200407; 5765;;;;J;;12301780.20200408; 5761;;;;G;;;8085170.84200408; 5761;;;;J;;;6050611.37200408; 5762;;;;G;;;6854584.22200408; 5762;;;;J;;;6521884.50200408; 5763;;;;G;;;9468707.65200408; 5763;;;;J;;;8460049.43200408; 5764;;;;G;;;6587559.23BILL_MONTH;;;AREA_CODE; NET_TYPE;;;;LOCAL_FARE--------------- ---------- ---------- --------------200408; 5764;;;;J;;;7342135.86200408; 5765;;;;G;;14450586.63200408; 5765;;;;J;;12680052.3840 rows selected.Elapsed: 00:00:00.001. 使用rollup函數(shù)的介紹Quote:; 下面是直接使用普通sql語(yǔ)句求出各地區(qū)的匯總數(shù)據(jù)的例子06:41:36 SQL> set autot on06:43:36 SQL> select area_code,sum(local_fare) local_fare06:43:502; from t06:43:513; group by area_code06:43:574; union all06:44:005; select '合計(jì)' area_code,sum(local_fare) local_fare06:44:066; from t06:44:087; /AREA_CODE;;;LOCAL_FARE---------- --------------5761; 54225413.045762; 52039619.605763; 69186545.025764; 53156768.465765 104548719.19合計(jì) 333157065.316 rows selected.Elapsed: 00:00:00.03Execution Plan----------------------------------------------------------0;;;SELECT STATEMENT Optimizer=ALL_ROWS (Cost=7 Card=1310 Bytes=; 24884)1;0UNION-ALL2;1;;SORT (GROUP BY) (Cost=5 Card=1309 Bytes=24871)3;2;;;;TABLE Access (FULL) OF 'T' (Cost=2 Card=1309 Bytes=248; 71)4;1;;SORT (AGGREGATE)5;4;;;;TABLE ACCESS (FULL) OF 'T' (Cost=2 Card=1309 Bytes=170; 17)Statistics----------------------------------------------------------;;;;;;;0; recursive calls; 0; db block gets; 6; consistent gets; 0; physical reads; 0; redo size;;;;;561; bytes sent via SQL*Net to client;;;;;503; bytes received via SQL*Net from client; 2; SQL*Net roundtrips to/from client; 1; sorts (memory); 0; sorts (disk); 6; rows processed下面是使用分析函數(shù)rollup得出的匯總數(shù)據(jù)的例子06:44:09 SQL> select nvl(area_code,'合計(jì)') area_code,sum(local_fare) local_fare06:45:262; from t06:45:303; group by rollup(nvl(area_code,'合計(jì)'))06:45:504; /AREA_CODE;;;LOCAL_FARE---------- --------------5761; 54225413.045762; 52039619.605763; 69186545.025764; 53156768.465765 104548719.19;;;;;;;;;;333157065.316 rows selected.Elapsed: 00:00:00.00Execution Plan----------------------------------------------------------0;;;SELECT STATEMENT Optimizer=ALL_ROWS (Cost=5 Card=1309 Bytes=; 24871)1;0SORT (GROUP BY ROLLUP) (Cost=5 Card=1309 Bytes=24871)2;1;;TABLE ACCESS (FULL) OF 'T' (Cost=2 Card=1309 Bytes=24871; )Statistics----------------------------------------------------------; 0; recursive calls;;;;;;;0; db block gets; 4; consistent gets; 0; physical reads; 0; redo size;;;;;557; bytes sent via SQL*Net to client;;;;;503; bytes received via SQL*Net from client; 2; SQL*Net roundtrips to/from client; 1; sorts (memory); 0; sorts (disk); 6; rows processed從上面的例子我們不難看出使用rollup函數(shù),系統(tǒng)的sql語(yǔ)句更加簡(jiǎn)單,耗用的資源更少,從6個(gè)consistent gets降到4個(gè)consistent gets,假如基表很大的話(huà),結(jié)果就可想而知了.1. 使用cube函數(shù)的介紹Quote:為了介紹cube函數(shù)我們?cè)賮?lái)看看另外一個(gè)使用rollup的例子06:53:00 SQL> select area_code,bill_month,sum(local_fare) local_fare06:53:372; from t06:53:383; group by rollup(area_code,bill_month)06:53:494; /AREA_CODE; BILL_MONTH; LOCAL_FARE---------- --------------- --------------5761;;;;200405;;13060433.895761;;;;200406;;13318931.015761;;;;200407;;13710265.935761;;;;200408;;14135782.215761; 54225413.045762;;;;200405;;12643792.115762;;;;200406;;12795060.655762;;;;200407;;13224298.125762;;;;200408;;13376468.725762; 52039619.605763;;;;200405;;16649778.915763;;;;200406;;17120515.715763;;;;200407;;17487493.325763;;;;200408;;17928757.085763; 69186545.025764;;;;200405;;12487791.945764;;;;200406;;13295187.675764;;;;200407;;13444093.765764;;;;200408;;13929695.095764; 53156768.465765;;;;200405;;25057737.475765;;;;200406;;26058461.315765;;;;200407;;26301881.405765;;;;200408;;27130639.015765;;;;;;104548719.19;;333157065.3126 rows selected.Elapsed: 00:00:00.00系統(tǒng)只是根據(jù)rollup的第一個(gè)參數(shù)area_code對(duì)結(jié)果集的數(shù)據(jù)做了匯總處理,而沒(méi)有對(duì)bill_month做匯總分析處理,cube函數(shù)就是為了這個(gè)而設(shè)計(jì)的.下面,讓我們看看使用cube函數(shù)的結(jié)果06:58:02 SQL> select area_code,bill_month,sum(local_fare) local_fare06:58:302; from t06:58:323; group by cube(area_code,bill_month)06:58:424; order by area_code,bill_month nulls last06:58:575; /AREA_CODE; BILL_MONTH; LOCAL_FARE---------- --------------- --------------5761;;;;200405;;;;;13060.435761;;;;200406;;;;;13318.935761;;;;200407;;;;;13710.275761;;;;200408;;;;;14135.785761;;54225.415762; ;;;;;200405;;;;;12643.795762;;;;200406;;;;;12795.065762;;;;200407;;;;;13224.305762;;;;200408;;;;;13376.475762;;52039.625763;;;;200405;;;;;16649.785763;;;;200406;;;;;17120.525763;;;;200407;;;;;17487.495763;;;;200408;;;;;17928.765763;;69186.545764;;;;200405;;;;;12487.795764;;;;200406;;;;;13295.195764;;;;200407;;;;;13444.095764;;;;200408;;;;;13929.695764;;53156.775765;;;;200405;;;;;25057.745765;;;;200406;;;;;26058.465765;;;;200407;;;;;26301.885765;;;;200408;;;;;27130.645765;104548.72200405;;;;;79899.53200406;;;;;82588.15200407;;;;;84168.03200408;;;;;86501.34;;;;;333157.0530 rows selected.Elapsed: 00:00:00.01可以看到,在cube函數(shù)的輸出結(jié)果比使用rollup多出了幾行統(tǒng)計(jì)數(shù)據(jù).這就是cube函數(shù)根據(jù)bill_month做的匯總統(tǒng)計(jì)結(jié)果] 1 rollup 和 cube函數(shù)的再深入Quote:從上面的結(jié)果中我們很輕易發(fā)現(xiàn),每個(gè)統(tǒng)計(jì)數(shù)據(jù)所對(duì)應(yīng)的行都會(huì)出現(xiàn)null,我們?nèi)绾蝸?lái)區(qū)分到底是根據(jù)那個(gè)字段做的匯總呢,這時(shí)候,oracle的grouping函數(shù)就粉墨登場(chǎng)了.假如當(dāng)前的匯總記錄是利用該字段得出的,grouping函數(shù)就會(huì)返回1,否則返回0; 1; select decode(grouping(area_code),1,'all area',to_char(area_code)) area_code,; 2 decode(grouping(bill_month),1,'all month',bill_month) bill_month,; 3 sum(local_fare) local_fare; 4; from t; 5; group by cube(area_code,bill_month); 6* order by area_code,bill_month nulls last07:07:29 SQL> /AREA_CODE; BILL_MONTH; LOCAL_FARE---------- --------------- --------------5761;;;;200405;;;;;13060.43 5761;;;;200406;;;;;13318.935761;;;;200407;;;;;13710.275761;;;;200408;;;;;14135.785761;;;;all month;;54225.415762;;;;200405;;;;;12643.795762;;;;200406;;;;;12795.065762;;;;200407;;;;;13224.305762;;;;200408;;;;;13376.475762;;;;all month;;52039.625763;;;;200405;;;;;16649.785763;;;;200406;;;;;17120.525763;;;;200407;;;;;17487.49 5763;;;;200408;;;;;17928.765763;;;;all month;;69186.545764;;;;200405;;;;;12487.795764;;;;200406;;;;;13295.195764;;;;200407;;;;;13444.095764;;;;200408;;;;;13929.695764;;;;all month;;53156.775765;;;;200405;;;;;25057.745765;;;;200406;;;;;26058.465765;;;;200407;;;;;26301.885765;;;;200408;;;;;27130.645765;;;;all month;104548.72 all area200405;;;;;79899.53all area200406;;;;;82588.15all area200407;;;;;84168.03all area200408;;;;;86501.34all areaall month;333157.0530 rows selected.Elapsed: 00:00:00.0107:07:31 SQL>可以看到,所有的空值現(xiàn)在都根據(jù)grouping函數(shù)做出了很好的區(qū)分,這樣利用rollup,cube和grouping函數(shù),我們做數(shù)據(jù)統(tǒng)計(jì)的時(shí)候就可以輕松很多了.