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這篇文章主要講解了“數據庫中cluster factor對執行計劃的影響是什么”，文中的講解內容簡單清晰，易于學習與理解，下面請大家跟著小編的思路慢慢深入，一起來研究和學習“數據庫中cluster factor對執行計劃的影響是什么”吧！
測試環境：Linux 7.6 + ORACLE 19.6.1
CZH@czhpdb > create table test_ffs as select * from hr.employees; Table created. CZH@czhpdb > insert into test_ffs select * from test_ffs; Execution Plan ---------------------------------------------------------- Plan hash value: 296244252 --------------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | --------------------------------------------------------------------------------------------- | 0 | INSERT STATEMENT | | 107 | 7383 | 3 (0)| 00:00:01 | | 1 | LOAD TABLE CONVENTIONAL | TEST_FFS | | | | | | 2 | OPTIMIZER STATISTICS GATHERING | | 107 | 7383 | 3 (0)| 00:00:01 | | 3 | TABLE ACCESS FULL | TEST_FFS | 107 | 7383 | 3 (0)| 00:00:01 | --------------------------------------------------------------------------------------------- Note ----- - dynamic statistics used: statistics for conventional DML Statistics ---------------------------------------------------------- 72 recursive calls 89 db block gets 81 consistent gets 12 physical reads 21576 redo size 195 bytes sent via SQL*Net to client 394 bytes received via SQL*Net from client 1 SQL*Net roundtrips to/from client 3 sorts (memory) 0 sorts (disk) 107 rows processed
上面autotrace執行計劃可以看到兩個新特性：
# OPTIMIZER STATISTICS GATHERING：12cR1以后的新特性，direct path load時，空表第一次加載數據時會自動收集統計信息。
# Oracle Database 12c introduced online statistics gathering for CREATE TABLE AS SELECT statements and direct-path inserts.
Oracle Database 19c introduces real-time statistics
, which extend online support to conventional DML statements
. Because statistics can go stale between DBMS_STATS jobs, real-time statistics helps the optimizer generate more optimal plans.Whereas bulk load operations gather all necessary statistics, real-time statistics augment rather than replace traditional statistics.
· Oracle introduced new parameters
· "_optimizer_gather_stats_on_conventional_dml" and "_optimizer_use_stats_on_conventional_dml" which are true by default
· "_optimizer_stats_on_conventional_dml_sample_rate" at 100%
· How does real time statistics works?
· By default the "_optimizer_gather_stats_on_conventional_dml" is true so its automatically kicks off
· When a DML operation is currently modifying a table (conventional), Oracle Database dynamically computes values for the most essential statistics if the above parameter is on.
· Consider a example of table that is having lot of inserts and rows are increasing. Real-time statistics keep track of the increasing row count as rows are being inserted. If the optimizer performs a hard parse of a new query, then the optimizer can use the real-time statistics to obtain a more accurate cost estimate.
· DBA_TAB_COL_STATISTICS and DBA_TAB_STATISITICS has columns NOTES tell real time statistics have been used. STATS_ON_CONVENTIONAL_DML
SELECT NVL(PARTITION_NAME, 'GLOBAL') PARTITION_NAME, NUM_ROWS, BLOCKS, NOTES
FROM USER_TAB_STATISTICS
WHERE TABLE_NAME = 'SALES'
ORDER BY 1, 4;
PARTITION_NAM NUM_ROWS BLOCKS NOTES
------------- ---------- ---------- -------------------------
GLOBAL 1837686 3315 STATS_ON_CONVENTIONAL_DML
CZH@czhpdb > set autot off CZH@czhpdb > insert into test_ffs select * from test_ffs; CZH@czhpdb > insert into test_ffs select * from test_ffs; CZH@czhpdb > insert into test_ffs select * from test_ffs; CZH@czhpdb > insert into test_ffs select * from test_ffs; CZH@czhpdb > insert into test_ffs select * from test_ffs; CZH@czhpdb > insert into test_ffs select * from test_ffs; CZH@czhpdb > insert into test_ffs select * from test_ffs; CZH@czhpdb > insert into test_ffs select * from test_ffs; CZH@czhpdb > insert into test_ffs select * from test_ffs; CZH@czhpdb > insert into test_ffs select * from test_ffs; CZH@czhpdb > commit; CZH@czhpdb > CREATE INDEX IDX_TEST_FFS ON TEST_FFS(EMPLOYEE_ID); CZH@czhpdb > EXEC DBMS_STATS.GATHER_TABLE_STATS(user,’TEST_FFS’,cascade=>true);
# sqlplus中set autotrace與explain plan for都是CBO預估出來的執行計劃，可能與真實執行的并不相同，我們使用下面hint獲取真實執行計劃。
CZH@czhpdb > SELECT /*+ gather_plan_statistics */ salary from test_ffs where employee_id < 100;
no rows selected
真實執行計劃：
SYS@orcl2 > select * from table(dbms_xplan.display_cursor('c9qg9su5khysd',null,'allstats last'));
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID c9qg9su5khysd, child number 0
-------------------------------------
SELECT /*+ gather_plan_statistics */ salary from test_ffs where
employee_id < 100
Plan hash value: 296244252
----------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers |
----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 0 |00:00:00.01 | 2288 |
|* 1 | TABLE ACCESS FULL| TEST_FFS | 1 | 1 | 0 |00:00:00.01 | 2288 |
----------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------1 - filter("EMPLOYEE_ID"<100)
# 可以看到由于表中數據沒有employee_id < 100，我們認為明顯走索引的sql并未選擇索引，那肯定是由于某些原因，cbo認為走索引并不是最優執行路徑，我們就利用10053獲取為什么cbo認為全表掃描cost更低。
SYS@orcl2 > alter system flush shared_pool; System altered.
#如果不清空shared_pool或者使游標失效，軟解析開啟10053事件，不會生成trace文件。
CZH@czhpdb > ALTER SESSION SET EVENTS '10053 trace name context forever,level 1'; Session altered. CZH@czhpdb > SELECT /*+ gather_plan_statistics */ salary from test_ffs where employee_id < 100; no rows selected CZH@czhpdb > ALTER SESSION SET EVENTS '10053 trace name context off'; Session altered.
19c 10053：
# 可以從下面10053看到DK(distinct key),CLUF(clustering factor),IX_SEL,下一步將根據幾個參數計算為何CBO認為走索引cost會高于全表掃描。
*************************************** BASE STATISTICAL INFORMATION *********************** Table Stats:: Table: TEST_FFS Alias: TEST_FFSonline table stats for conventional DML (block count: 2263 row count: 219029) used on (TEST_FFS) block count: 5 -> 2263, row count: 107 -> 219136 #Rows: 219136 SSZ: 0 LGR: 0 #Blks: 2263 AvgRowLen: 69.00 NEB: 0 ChainCnt: 0.00 ScanRate: 0.00 SPC: 0 RFL: 0 RNF: 0 CBK: 0 CHR: 0 KQDFLG: 193 #IMCUs: 0 IMCRowCnt: 0 IMCJournalRowCnt: 0 #IMCBlocks: 0 IMCQuotient: 0.000000 Index Stats:: Index: IDX_TEST_FFS Col#: 1 LVLS: 1 #LB: 458 #DK: 107 LB/K: 4.00 DB/K: 1524.00 CLUF: 163174.00 NRW: 219136.00 SSZ: 0.00 LGR: 0.00 CBK: 0.00 GQL: 0.00 CHR: 0.00 KQDFLG: 8192 BSZ: 1 KKEISFLG: 1 try to generate single-table filter predicates from ORs for query block SEL$1 (#0) finally: "TEST_FFS"."EMPLOYEE_ID"<100 ======================================= SPD: BEGIN context at query block level ======================================= Query Block SEL$1 (#0) Return code in qosdSetupDirCtx4QB: NOCTX ===================================== SPD: END context at query block level ===================================== Access path analysis for TEST_FFS *************************************** SINGLE TABLE ACCESS PATH Single Table Cardinality Estimation for TEST_FFS[TEST_FFS] SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE kkecdn: Single Table Predicate:"TEST_FFS"."EMPLOYEE_ID"<100 online column stats for conventional DML used on (TEST_FFS.EMPLOYEE_ID) min: 100.00 -> 100.00, max: 206.00 -> 206.00, nnl: 0 -> 0, acl: 4 -> 0 Column (#1): EMPLOYEE_ID(NUMBER) AvgLen: 22 NDV: 107 Nulls: 0 Density: 0.009346 Min: 100.000000 Max: 206.000000 Using density: 0.009346 of col #1 as selectivity of unpopular value pred Table: TEST_FFS Alias: TEST_FFS Card: Original: 219136.000000 Rounded: 2048 Computed: 2048.000000 Non Adjusted: 2048.000000 Scan IO Cost (Disk) = 615.000000 Scan CPU Cost (Disk) = 49272938.720000 Cost of predicates: io = NOCOST, cpu = 50.000000, sel = 0.009346 flag = 2048 ("TEST_FFS"."EMPLOYEE_ID"<100) Total Scan IO Cost = 615.000000 (scan (Disk)) + 0.000000 (io filter eval) (= 0.000000 (per row) * 219136.000000 (#rows)) = 615.000000 Total Scan CPU Cost = 49272938.720000 (scan (Disk)) + 10956800.000000 (cpu filter eval) (= 50.000000 (per row) * 219136.000000 (#rows)) = 60229738.720000 Access Path: TableScan Cost: 621.167026 Resp: 621.167026 Degree: 0 Cost_io: 615.000000 Cost_cpu: 60229739 Resp_io: 615.000000 Resp_cpu: 60229739 ****** Costing Index IDX_TEST_FFS SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER Using density: 0.009346 of col #1 as selectivity of unpopular value pred Access Path: index (RangeScan) Index: IDX_TEST_FFS resc_io: 1531.000000 resc_cpu: 11906445 ix_sel: 0.009346 ix_sel_with_filters: 0.009346 Cost: 1532.219121 Resp: 1532.219121 Degree: 1 Best:: AccessPath: TableScan Cost: 621.167026 Degree: 1 Resp: 621.167026 Card: 2048.000000 Bytes: 0.000000 online column stats for conventional DML used on (TEST_FFS.SALARY) min: 2100.00 -> 2100.00, max: 24000.00 -> 24000.00, nnl: 0 -> 0, acl: 4 -> 0 ***************************************
cluster factor表示索引順序與表存儲數據一致性，順序掃描索引時，如果索引鍵值掃描到鍵值對應的表數據行對應的數據塊發生變化時，則cluster factor加1，所以cluster factor最低為表數據塊，最大為表數據行，與表存儲順序高度相關，如果表是按照順序插入，則cluster factor較低，如果表數據為無序插入，則cluster factor較高，這就是為什么同樣表數據情況下，執行計劃會有時候有差別的原因。
索引掃描成本公式：
INDEX ACCESS I/O COST=BLEVEL+CEIL(#LEAF_BLOCKS*IX_SEL)
TABLE_ACCESS I/O COST=CEIL(CLUSTERING_FACTOR*IX_SEL_WITH_FILTERS)
IX_SEL與IX_SEL_WITH_FILTERS為索引選擇率與索引帶謂詞選擇率，一般為1/(DISTINCT KEY)值，本例中走全表掃描時，IX_SEL=1/107=0.009345，則計算走索引成本為：
ACCESS INDEX COST=INDEX ACCESS I/O COST + TABLE ACCESS I/O COST=2+CEIL(458*0.009345)+CEIL(163174*0.009345)=1540
近似等于CBO預計出來的1532，是高于全表掃的COST 615的，所以選擇走了全表掃描。
重建表，order by排序，降低cluster factor
CZH@czhpdb > create table test_ffs_03 as select * from test_ffs_02 order by employee_id; Table created. CZH@czhpdb > create index idx_test_ffs_03 on test_ffs_03(employee_id); Index created. CZH@czhpdb > select clustering_factor,index_name from user_indexes where index_name='IDX_TEST_FFS_03'; CLUSTERING_FACTOR INDEX_NAME ---------------------------------------- -------------------- 1128 IDX_TEST_FFS_03
# 可以看到cluster factor明顯降低。
CZH@czhpdb > select /*+ gather_plan_statistics */ salary from test_ffs_03 where employee_id < 100;
no rows selected
SYS@orcl2 > select * from table(dbms_xplan.display_cursor('8fpk2b8vzn5y2',null,'allstats last'));
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID 8fpk2b8vzn5y2, child number 0
-------------------------------------
select /*+ gather_plan_statistics */ salary from test_ffs_03 where
employee_id < 100
Plan hash value: 704625359
--------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads |
--------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 0 |00:00:00.01 | 2 | 1 |
| 1 | TABLE ACCESS BY INDEX ROWID BATCHED| TEST_FFS_03 | 1 | 1024 | 0 |00:00:00.01 | 2 | 1 |
|* 2 | INDEX RANGE SCAN | IDX_TEST_FFS_03 | 1 | 1024 | 0 |00:00:00.01 | 2 | 1 |
--------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("EMPLOYEE_ID"<100)感謝各位的閱讀，以上就是“數據庫中cluster factor對執行計劃的影響是什么”的內容了，經過本文的學習后，相信大家對數據庫中cluster factor對執行計劃的影響是什么這一問題有了更深刻的體會，具體使用情況還需要大家實踐驗證。這里是億速云，小編將為大家推送更多相關知識點的文章，歡迎關注！
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