Wrong cardinality estimate for range scan

select * from v$version;
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Oracle Database 11g Enterprise Edition Release 11.2.0.2.0 - 64bit Production
PL/SQL Release 11.2.0.2.0 - Production
CORE    11.2.0.2.0      Production
TNS for Linux: Version 11.2.0.2.0 - Production
NLSRTL Version 11.2.0.2.0 - ProductionSQL : select * from GC_FULFILLMENT_ITEMS where MARKETPLACE_ID=:b1 and GC_FULFILLMENT_STATUS_ID=:b2;
Plan
| Id  | Operation                   | Name                        | Rows  | Bytes | Cost (%CPU)| Time     |
|   0 | SELECT STATEMENT            |                             |   474K|    99M|   102  (85)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID| GC_FULFILLMENT_ITEMS        |   474K|    99M|   102  (85)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN          | I_GCFI_GCFS_ID_SDOC_MKTPLID |   474K|       |    91  (95)| 00:00:01 |
Predicate Information (identified by operation id):
   2 - access("GC_FULFILLMENT_STATUS_ID"=TO_NUMBER(:B2) AND "MARKETPLACE_ID"=TO_NUMBER(:B1))
       filter("MARKETPLACE_ID"=TO_NUMBER(:B1))If i use literals than CBO uses cardinality =1 (I believe this is due it fix control :5483301 which i set to off In my environment)
select * from GC_FULFILLMENT_ITEMS where MARKETPLACE_ID=5 and GC_FULFILLMENT_STATUS_ID=2;
| Id  | Operation                   | Name                        | Rows  | Bytes | Cost (%CPU)| Time     |
|   0 | SELECT STATEMENT            |                             |     1 |   220 |     3   (0)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID| GC_FULFILLMENT_ITEMS        |     1 |   220 |     3   (0)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN          | I_GCFI_GCFS_ID_SDOC_MKTPLID |     1 |       |     2   (0)| 00:00:01 |
Predicate Information (identified by operation id):
   2 - access("GC_FULFILLMENT_STATUS_ID"=2 AND "MARKETPLACE_ID"=5)
       filter("MARKETPLACE_ID"=5)Here is column distribution and histogram information
Enter value for column_name: MARKETPLACE_ID
COLUMN_NAME          ENDPOINT_VALUE CUMMULATIVE_FREQUENCY  FREQUENCY ENDPOINT_ACTUAL_VALU
MARKETPLACE_ID                    1                     1          1
MARKETPLACE_ID                    3                  8548       8547
MARKETPLACE_ID                    4                 15608       7060
MARKETPLACE_ID                    5                 16385        777   --->
MARKETPLACE_ID                35691                 16398         13
MARKETPLACE_ID                44551                 16407          9
6 rows selected.
Enter value for column_name: GC_FULFILLMENT_STATUS_ID
COLUMN_NAME                    ENDPOINT_VALUE CUMMULATIVE_FREQUENCY  FREQUENCY ENDPOINT_ACTUAL_VALU
GC_FULFILLMENT_STATUS_ID                    5                 19602      19602
GC_FULFILLMENT_STATUS_ID                    6                 19612         10
GC_FULFILLMENT_STATUS_ID                    8                 19802        190
3 rows selected.
Actual distribution
select MARKETPLACE_ID,count(*) from GC_FULFILLMENT_ITEMS group by MARKETPLACE_ID order by 1;
MARKETPLACE_ID   COUNT(*)
             1       2099
             3   16339936
             4   13358682
             5    1471839   --->
         35691      33623
         44551      19881
         78931      40273
        101611          1
                  6309408
9 rows selected.
BHAVIK_DBA: GC1EU> select GC_FULFILLMENT_STATUS_ID,count(*) from GC_FULFILLMENT_ITEMS group by GC_FULFILLMENT_STATUS_ID order by 1;
GC_FULFILLMENT_STATUS_ID   COUNT(*)
                       1        880
                       2         63   --->
                       3         24
                       5   37226908
                       6      22099
                       7         18
                       8     325409
                       9        343
8 rows selected.10053 trace
  SINGLE TABLE ACCESS PATH
  Table: GC_FULFILLMENT_ITEMS  Alias: GC_FULFILLMENT_ITEMS
    Card: Original: 36703588.000000  Rounded: 474909  Computed: 474909.06  Non Adjusted: 474909.06
  Best:: AccessPath: IndexRange
  Index: I_GCFI_GCFS_ID_SDOC_MKTPLID
         Cost: 102.05  Degree: 1  Resp: 102.05  Card: 474909.06  Bytes: 0
  Outline Data:
  /*+
    BEGIN_OUTLINE_DATA
      IGNORE_OPTIM_EMBEDDED_HINTS
      OPTIMIZER_FEATURES_ENABLE('11.2.0.2')
      DB_VERSION('11.2.0.2')
      OPT_PARAM('_b_tree_bitmap_plans' 'false')
      OPT_PARAM('_optim_peek_user_binds' 'false')
      OPT_PARAM('_fix_control' '5483301:0')
      ALL_ROWS
      OUTLINE_LEAF(@"SEL$F5BB74E1")
      MERGE(@"SEL$2")
      OUTLINE(@"SEL$1")
      OUTLINE(@"SEL$2")
      INDEX_RS_ASC(@"SEL$F5BB74E1" "GC_FULFILLMENT_ITEMS"@"SEL$2" ("GC_FULFILLMENT_ITEMS"."GC_FULFILLMENT_STATUS_ID" "GC_FULFILLMENT_ITEMS"."SHIP_DELIVERY_OPTION_CODE" "GC_FULFILLMENT_ITEMS"."MARKETPLACE_ID"))
    END_OUTLINE_DATA
  */Is there any reason why CBO is using card=474909.06 ? Having fix control () in place, it should have set card=1 if it is considering GC_FULFILLMENT_STATUS_ID= 2 as "rare" value..isn't it ?

OraDBA02 wrote:
You are right Charles.
I was reading one of your blog and saw that.
As you said, it is an issue with SQLPLUS.
However, plan for the sql which is comming from application still shows the same (wrong cardinality) plan. It does not have TO_NUMBER function because of the reason that it does not experience data-type conversion that SQLPLUS has.
But YES...Plan is exactly the same with/without NO_NUMBER.OraDBA02,
I believe that some of the other people responding to this thread might have already described why the execution plan in the library cache is the same plan that you are seeing. One of the goals of using bind variables in SQL statements is to reduce the number of time consuming (and resource intensive) hard parses. That also means that a second goal is to share the same execution plan for future executions of the same SQL statement, even through bind variable values have changed. The catch here is that bind variable peeking, introduced with Oracle Database 9.0.1 (may be disabled by modifying a hidden parameter), helps the optimizer select the "best" (lowest calculated cost) execution plan for those specific bind variable values - the same plan may not be the "best" execution plan for other sets of bind variable values on future executions.
Histograms on one or more of the columns in the WHERE clause could either help or hinder the situation further. It might further help the first execution, but might further hinder future executions with different bind variable values. Oracle Database 11.1 introduced something called adaptive cursor sharing (and 11.2 introduced cardinality feedback) that in theory addresses issues where the execution plan should change for later executions with different bind variable values (but the SQL statement must execute poorly at least once).
There might be multiple child cursors in the library cache for the same SQL statement, each potentially with a different execution plan. I suggest finding the SQL_ID of the SQL statement that the application is submitting (you can do this by checking V$SQL or V$SQLAREA). Once you have the SQL_ID, go back to the SQL statement that I suggested for displaying the execution plan:
SELECT * FROM TABLE (DBMS_XPLAN.DISPLAY_CURSOR(NULL,NULL,'TYPICAL'));The first NULL in the above SQL statement is where you would specify the SQL_ID. If you leave the second NULL in place, the above SQL statement will retrieve the execution plan for all child cursors with that SQL_ID.
For instance, if the SQL_ID was 75chksrfa5fbt, you would execute the following:
SELECT * FROM TABLE (DBMS_XPLAN.DISPLAY_CURSOR('75chksrfa5fbt',NULL,'TYPICAL'));Usually, you can take it a step further to see the bind variables that were used during the optimization phase. To do that, you would add the +PEEKED_BINDS format parameter:
SELECT * FROM TABLE (DBMS_XPLAN.DISPLAY_CURSOR('75chksrfa5fbt',NULL,'TYPICAL +PEEKED_BINDS'));Note that there are various optimizer parameters that affect the optimizer's decisions, for instance, maybe the optimizer mode is set to FIRST_ROWS. Also possibly helpful is the +OUTLINE format parameter that might provide a clue regarding the value of some of the parameters affecting the optimizer.  The SQL statement that you would then enter is similar to the following:
SELECT * FROM TABLE (DBMS_XPLAN.DISPLAY_CURSOR('75chksrfa5fbt',NULL,'TYPICAL +PEEKED_BINDS +OUTLINE'));Additional information might be helpful. Please see the following two forum threads to see what kind of information you should gather:
When your query takes too long… : When your query takes too long ...
How to post a SQL statement tuning request: HOW TO: Post a SQL statement tuning request - template posting
Charles Hooper
http://hoopercharles.wordpress.com/
IT Manager/Oracle DBA
K&M Machine-Fabricating, Inc.

Similar Messages

  • CBO - Wrong Cardinality Estimate

    Hello,
    Version 10.2.0.3
    I am trying to understand the figures in the Explain Plan. I am not able to explain the cardinality of 70 on step 4.
    The query takes very long to execute (about 400 secs). I would expect HASH JOIN SEMI instead of NESTED LOOPS SEMI.
    I have tried to provide as much information as possible. I have just requested the 10053 trace, dont have it now.
    There is a primary key on ORDERS.ORDER_ID (NUMBER) column. However, we are forced to use to_char(order_id) to accomodate for COT_EXTERNAL_ID being VARCHAR2 field.
      1  select cdw.* from cdw_orders cdw where cdw.cot_external_id in
      2  (
      3  select to_char(order_id) from orders o where o.status_id in (12,16,22)
      4* )
    SQL> /
    Execution Plan
    Plan hash value: 733167152
    | Id  | Operation                     | Name                 | Rows  | Bytes | Cost (%CPU)| Time     |
    |   0 | SELECT STATEMENT              |                      |     2 |   280 |   326   (1)| 00:00:04 |
    |   1 |  NESTED LOOPS SEMI            |                      |     2 |   280 |   326   (1)| 00:00:04 |
    |   2 |   TABLE ACCESS FULL           | CDW_ORDERS           |  3362 |   433K|   293   (1)| 00:00:04 |
    |   3 |   INLIST ITERATOR             |                      |       |       |            |          |
    |*  4 |    TABLE ACCESS BY INDEX ROWID| ORDERS               |    70 |   560 |     1   (0)| 00:00:01 |
    |*  5 |     INDEX RANGE SCAN          | ORDERS_STATUS_ID_IDX |     2 |       |     1   (0)| 00:00:01 |
    Predicate Information (identified by operation id):
       4 - filter("CDW"."COT_EXTERNAL_ID"=TO_CHAR("ORDER_ID"))
       5 - access("O"."STATUS_ID"=12 OR "O"."STATUS_ID"=16 OR "O"."STATUS_ID"=22)Here is some of the details on the table columns and data.
    SQL> select column_name,num_distinct,density,num_nulls,num_buckets from all_tab_columns where table_name = 'ORDERS'
      2  and column_name in ('STATUS_ID','ORDER_ID');
    COLUMN_NAME                    NUM_DISTINCT                        DENSITY  NUM_NULLS NUM_BUCKETS
    ORDER_ID                             177951             .00000561952447584          0         254
    STATUS_ID                                23             .00000275335899280          0          23
    SQL> select num_rows from all_tables where table_name = 'ORDERS';
      NUM_ROWS
        177951
    SQL> select index_name,distinct_keys,clustering_factor,num_rows,sample_size from all_indexes where index_name = 'ORDERS_STATUS_ID_IDX'
      2  /
    INDEX_NAME                     DISTINCT_KEYS CLUSTERING_FACTOR   NUM_ROWS SAMPLE_SIZE
    ORDERS_STATUS_ID_IDX                      25             35893     177951      177951Histograms on column STATUS_ID
    SQL> select * from (
      2  select column_name,endpoint_value,endpoint_number- nvl(lag(endpoint_number) over (order by endpoint_value),0) count
      3  from all_tab_histograms where column_name = 'STATUS_ID' and table_name = 'ORDERS'
      4  ) where endpoint_value in (12,16,22);
    COLUMN_NAME                    ENDPOINT_VALUE      COUNT
    STATUS_ID                                  12        494
    STATUS_ID                                  16         24
    STATUS_ID                                  22       3064
    SQL> select max(endpoint_number) from all_tab_histograms where column_name = 'STATUS_ID' and table_name = 'ORDERS' ;
    MAX(ENDPOINT_NUMBER)
                    5641I tried to run the query for individual values instead of inlist to check the numbers.
      1  select cdw.* from cdw_orders cdw where cdw.cot_external_id in
      2  (
      3  select to_char(order_id) from orders o where o.status_id = 12
      4* )
    SQL> /
    Execution Plan
    Plan hash value: 3178043291
    | Id  | Operation                    | Name                 | Rows  | Bytes | Cost (%CPU)| Time     |
    |   0 | SELECT STATEMENT             |                      |     2 |   280 |    33  (19)| 00:00:01 |
    |   1 |  MERGE JOIN SEMI             |                      |     2 |   280 |    33  (19)| 00:00:01 |
    |   2 |   TABLE ACCESS BY INDEX ROWID| CDW_ORDERS           |  3362 |   433K|    21   (0)| 00:00:01 |
    |   3 |    INDEX FULL SCAN           | CDW_ORD_COT_EXT_ID   |  3362 |       |     2   (0)| 00:00:01 |
    |*  4 |   SORT UNIQUE                |                      | 15584 |   121K|    11  (46)| 00:00:01 |
    |*  5 |    VIEW                      | index$_join$_002     | 15584 |   121K|     9  (34)| 00:00:01 |
    |*  6 |     HASH JOIN                |                      |       |       |            |          |
    |*  7 |      INDEX RANGE SCAN        | ORDERS_STATUS_ID_IDX | 15584 |   121K|     1   (0)| 00:00:01 |
    |   8 |      INDEX FAST FULL SCAN    | PK_ORDERS            | 15584 |   121K|     5   (0)| 00:00:01 |
    Predicate Information (identified by operation id):
       4 - access("CDW"."COT_EXTERNAL_ID"=TO_CHAR("ORDER_ID"))
           filter("CDW"."COT_EXTERNAL_ID"=TO_CHAR("ORDER_ID"))
       5 - filter("O"."STATUS_ID"=12)
       6 - access(ROWID=ROWID)
       7 - access("O"."STATUS_ID"=12)For status_id = 12, the cardinality on step 7 for orders_status_id_idx is 15584 which is inline with the expectation ie., (494/5641)*177951 = 15583.7 ~ 15584.
    Now, I continue the same with status_is = 16
      1  select cdw.* from cdw_orders cdw where cdw.cot_external_id in
      2  (
      3  select to_char(order_id) from orders o where o.status_id = 16
      4* )
    SQL> /
    Execution Plan
    Plan hash value: 43581000
    | Id  | Operation                      | Name                 | Rows  | Bytes | Cost (%CPU)| Time     |
    |   0 | SELECT STATEMENT               |                      |  1363 |   186K|    10  (10)| 00:00:01 |
    |   1 |  TABLE ACCESS BY INDEX ROWID   | CDW_ORDERS           |     2 |   264 |     1   (0)| 00:00:01 |
    |   2 |   NESTED LOOPS                 |                      |  1363 |   186K|    10  (10)| 00:00:01 |
    |   3 |    SORT UNIQUE                 |                      |   757 |  6056 |     2   (0)| 00:00:01 |
    |   4 |     TABLE ACCESS BY INDEX ROWID| ORDERS               |   757 |  6056 |     2   (0)| 00:00:01 |
    |*  5 |      INDEX RANGE SCAN          | ORDERS_STATUS_ID_IDX |   757 |       |     1   (0)| 00:00:01 |
    |*  6 |    INDEX RANGE SCAN            | CDW_ORD_COT_EXT_ID   |     2 |       |     1   (0)| 00:00:01 |
    Predicate Information (identified by operation id):
       5 - access("O"."STATUS_ID"=16)
       6 - access("CDW"."COT_EXTERNAL_ID"=TO_CHAR("ORDER_ID"))Here also the cardinality on step 5 for orders_status_id_idx is as expected ie., (24/5641)*177951 = 757.1 ~ 757
    Finally, running the same for status_id = 22 surprises me
      1  select cdw.* from cdw_orders cdw where cdw.cot_external_id in
      2  (
      3  select to_char(order_id) from orders o where o.status_id = 22
      4* )
    SQL> /
    Execution Plan
    Plan hash value: 3496542905
    | Id  | Operation                    | Name                 | Rows  | Bytes | Cost (%CPU)| Time     |
    |   0 | SELECT STATEMENT             |                      |     2 |   280 |   326   (1)| 00:00:04 |
    |   1 |  NESTED LOOPS SEMI           |                      |     2 |   280 |   326   (1)| 00:00:04 |
    |   2 |   TABLE ACCESS FULL          | CDW_ORDERS           |  3362 |   433K|   293   (1)| 00:00:04 |
    |*  3 |   TABLE ACCESS BY INDEX ROWID| ORDERS               |    60 |   480 |     1   (0)| 00:00:01 |
    |*  4 |    INDEX RANGE SCAN          | ORDERS_STATUS_ID_IDX |     2 |       |     1   (0)| 00:00:01 |
    Predicate Information (identified by operation id):
       3 - filter("CDW"."COT_EXTERNAL_ID"=TO_CHAR("ORDER_ID"))
       4 - access("O"."STATUS_ID"=22)Like the ones for 12 and 16, I would have expected the cardinality on step 4 to be (3064/5641)*177951 = 96657, but I see only 2.
    This is where my doubt is. Is this got to do with 22 being a popular value ? Can someone explain this behaviour ?
    As a solution I am thinking of creating an index on to_char(order_id) - function based, hoping that the step 3 CDW.COT_EXTERNAL_ID = TO_CHAR(ORDER_ID) changes
    to access instead of filter. Let me know your thoughts on the index creation as well.
    Thanks,
    Rgds,
    Gokul
    Edited by: Gokul Gopal on 24-May-2012 02:40

    Hello Jonathan,
    Apologies, I was wrong about optimizer_index_cost_adj value to be set to 100. I gather from DBA the value is set to currently set to 1.
    I have pasted the 10053 trace file for value 22. I was not able to figure out the "jsel=min(1, 6.1094e-04)" bit.
    /dborafiles/COTP/bycota2/udump/bycota2_ora_2147_values_22.trc
    Oracle Database 10g Enterprise Edition Release 10.2.0.3.0 - 64bit Production
    With the Partitioning, Real Application Clusters, OLAP and Data Mining options
    ORACLE_HOME = /dboracle/orabase/product/10.2.0
    System name:     Linux
    Node name:     byl945d002
    Release:     2.6.9-55.ELsmp
    Version:     #1 SMP Fri Apr 20 16:36:54 EDT 2007
    Machine:     x86_64
    Instance name: bycota2
    Redo thread mounted by this instance: 2
    Oracle process number: 37
    Unix process pid: 2147, image: oracle@byl945d002 (TNS V1-V3)
    *** 2012-05-28 14:00:59.737
    *** ACTION NAME:() 2012-05-28 14:00:59.737
    *** MODULE NAME:(SQL*Plus) 2012-05-28 14:00:59.737
    *** SERVICE NAME:(SYS$USERS) 2012-05-28 14:00:59.737
    *** SESSION ID:(713.51629) 2012-05-28 14:00:59.737
    Registered qb: SEL$1 0x973e5458 (PARSER)
      signature (): qb_name=SEL$1 nbfros=1 flg=0
        fro(0): flg=4 objn=51893 hint_alias="CDW"@"SEL$1"
    Registered qb: SEL$2 0x973e6058 (PARSER)
      signature (): qb_name=SEL$2 nbfros=1 flg=0
        fro(0): flg=4 objn=51782 hint_alias="O"@"SEL$2"
    Predicate Move-Around (PM)
    PM: Considering predicate move-around in SEL$1 (#0).
    PM:   Checking validity of predicate move-around in SEL$1 (#0).
    CBQT: Validity checks passed for 5r4bhr2yrt5gz.
    apadrv-start: call(in-use=704, alloc=16344), compile(in-use=60840, alloc=63984)
    Current SQL statement for this session:
    select cdw.* from cdw_orders cdw where cdw.cot_external_id in (select to_char(o.order_id) from orders o where status_id = 22)
    Legend
    The following abbreviations are used by optimizer trace.
    CBQT - cost-based query transformation
    JPPD - join predicate push-down
    FPD - filter push-down
    PM - predicate move-around
    CVM - complex view merging
    SPJ - select-project-join
    SJC - set join conversion
    SU - subquery unnesting
    OBYE - order by elimination
    ST - star transformation
    qb - query block
    LB - leaf blocks
    DK - distinct keys
    LB/K - average number of leaf blocks per key
    DB/K - average number of data blocks per key
    CLUF - clustering factor
    NDV - number of distinct values
    Resp - response cost
    Card - cardinality
    Resc - resource cost
    NL - nested loops (join)
    SM - sort merge (join)
    HA - hash (join)
    CPUCSPEED - CPU Speed
    IOTFRSPEED - I/O transfer speed
    IOSEEKTIM - I/O seek time
    SREADTIM - average single block read time
    MREADTIM - average multiblock read time
    MBRC - average multiblock read count
    MAXTHR - maximum I/O system throughput
    SLAVETHR - average slave I/O throughput
    dmeth - distribution method
      1: no partitioning required
      2: value partitioned
      4: right is random (round-robin)
      512: left is random (round-robin)
      8: broadcast right and partition left
      16: broadcast left and partition right
      32: partition left using partitioning of right
      64: partition right using partitioning of left
      128: use hash partitioning dimension
      256: use range partitioning dimension
      2048: use list partitioning dimension
      1024: run the join in serial
      0: invalid distribution method
    sel - selectivity
    ptn - partition
    Peeked values of the binds in SQL statement
    PARAMETERS USED BY THE OPTIMIZER
      PARAMETERS WITH ALTERED VALUES
      sort_area_retained_size             = 65535
      optimizer_mode                      = first_rows_100
      optimizer_index_cost_adj            = 25
      optimizer_index_caching             = 100
      Bug Fix Control Environment
      fix  4611850 = enabled
      fix  4663804 = enabled
      fix  4663698 = enabled
      fix  4545833 = enabled
      fix  3499674 = disabled
      fix  4584065 = enabled
      fix  4602374 = enabled
      fix  4569940 = enabled
      fix  4631959 = enabled
      fix  4519340 = enabled
      fix  4550003 = enabled
      fix  4488689 = enabled
      fix  3118776 = enabled
      fix  4519016 = enabled
      fix  4487253 = enabled
      fix  4556762 = 15     
      fix  4728348 = enabled
      fix  4723244 = enabled
      fix  4554846 = enabled
      fix  4175830 = enabled
      fix  4722900 = enabled
      fix  5094217 = enabled
      fix  4904890 = enabled
      fix  4483286 = disabled
      fix  4969880 = disabled
      fix  4711525 = enabled
      fix  4717546 = enabled
      fix  4904838 = enabled
      fix  5005866 = enabled
      fix  4600710 = enabled
      fix  5129233 = enabled
      fix  5195882 = enabled
      fix  5084239 = enabled
      fix  4595987 = enabled
      fix  4134994 = enabled
      fix  5104624 = enabled
      fix  4908162 = enabled
      fix  5015557 = enabled
      PARAMETERS WITH DEFAULT VALUES
      optimizer_mode_hinted               = false
      optimizer_features_hinted           = 0.0.0
      parallel_execution_enabled          = true
      parallel_query_forced_dop           = 0
      parallel_dml_forced_dop             = 0
      parallel_ddl_forced_degree          = 0
      parallel_ddl_forced_instances       = 0
      _query_rewrite_fudge                = 90
      optimizer_features_enable           = 10.2.0.3
      _optimizer_search_limit             = 5
      cpu_count                           = 8
      active_instance_count               = 2
      parallel_threads_per_cpu            = 2
      hash_area_size                      = 131072
      bitmap_merge_area_size              = 1048576
      sort_area_size                      = 65536
      _sort_elimination_cost_ratio        = 0
      _optimizer_block_size               = 8192
      _sort_multiblock_read_count         = 2
      _hash_multiblock_io_count           = 0
      _db_file_optimizer_read_count       = 32
      _optimizer_max_permutations         = 2000
      pga_aggregate_target                = 602112 KB
      _pga_max_size                       = 204800 KB
      _query_rewrite_maxdisjunct          = 257
      _smm_auto_min_io_size               = 56 KB
      _smm_auto_max_io_size               = 248 KB
      _smm_min_size                       = 602 KB
      _smm_max_size                       = 102400 KB
      _smm_px_max_size                    = 301056 KB
      _cpu_to_io                          = 0
      _optimizer_undo_cost_change         = 10.2.0.3
      parallel_query_mode                 = enabled
      parallel_dml_mode                   = disabled
      parallel_ddl_mode                   = enabled
      sqlstat_enabled                     = false
      _optimizer_percent_parallel         = 101
      _always_anti_join                   = choose
      _always_semi_join                   = choose
      _optimizer_mode_force               = true
      _partition_view_enabled             = true
      _always_star_transformation         = false
      _query_rewrite_or_error             = false
      _hash_join_enabled                  = true
      cursor_sharing                      = exact
      _b_tree_bitmap_plans                = true
      star_transformation_enabled         = false
      _optimizer_cost_model               = choose
      _new_sort_cost_estimate             = true
      _complex_view_merging               = true
      _unnest_subquery                    = true
      _eliminate_common_subexpr           = true
      _pred_move_around                   = true
      _convert_set_to_join                = false
      _push_join_predicate                = true
      _push_join_union_view               = true
      _fast_full_scan_enabled             = true
      _optim_enhance_nnull_detection      = true
      _parallel_broadcast_enabled         = true
      _px_broadcast_fudge_factor          = 100
      _ordered_nested_loop                = true
      _no_or_expansion                    = false
      _system_index_caching               = 0
      _disable_datalayer_sampling         = false
      query_rewrite_enabled               = true
      query_rewrite_integrity             = enforced
      _query_cost_rewrite                 = true
      _query_rewrite_2                    = true
      _query_rewrite_1                    = true
      _query_rewrite_expression           = true
      _query_rewrite_jgmigrate            = true
      _query_rewrite_fpc                  = true
      _query_rewrite_drj                  = true
      _full_pwise_join_enabled            = true
      _partial_pwise_join_enabled         = true
      _left_nested_loops_random           = true
      _improved_row_length_enabled        = true
      _index_join_enabled                 = true
      _enable_type_dep_selectivity        = true
      _improved_outerjoin_card            = true
      _optimizer_adjust_for_nulls         = true
      _optimizer_degree                   = 0
      _use_column_stats_for_function      = true
      _subquery_pruning_enabled           = true
      _subquery_pruning_mv_enabled        = false
      _or_expand_nvl_predicate            = true
      _like_with_bind_as_equality         = false
      _table_scan_cost_plus_one           = true
      _cost_equality_semi_join            = true
      _default_non_equality_sel_check     = true
      _new_initial_join_orders            = true
      _oneside_colstat_for_equijoins      = true
      _optim_peek_user_binds              = true
      _minimal_stats_aggregation          = true
      _force_temptables_for_gsets         = false
      workarea_size_policy                = auto
      _smm_auto_cost_enabled              = true
      _gs_anti_semi_join_allowed          = true
      _optim_new_default_join_sel         = true
      optimizer_dynamic_sampling          = 2
      _pre_rewrite_push_pred              = true
      _optimizer_new_join_card_computation = true
      _union_rewrite_for_gs               = yes_gset_mvs
      _generalized_pruning_enabled        = true
      _optim_adjust_for_part_skews        = true
      _force_datefold_trunc               = false
      statistics_level                    = typical
      _optimizer_system_stats_usage       = true
      skip_unusable_indexes               = true
      _remove_aggr_subquery               = true
      _optimizer_push_down_distinct       = 0
      _dml_monitoring_enabled             = true
      _optimizer_undo_changes             = false
      _predicate_elimination_enabled      = true
      _nested_loop_fudge                  = 100
      _project_view_columns               = true
      _local_communication_costing_enabled = true
      _local_communication_ratio          = 50
      _query_rewrite_vop_cleanup          = true
      _slave_mapping_enabled              = true
      _optimizer_cost_based_transformation = linear
      _optimizer_mjc_enabled              = true
      _right_outer_hash_enable            = true
      _spr_push_pred_refspr               = true
      _optimizer_cache_stats              = false
      _optimizer_cbqt_factor              = 50
      _optimizer_squ_bottomup             = true
      _fic_area_size                      = 131072
      _optimizer_skip_scan_enabled        = true
      _optimizer_cost_filter_pred         = false
      _optimizer_sortmerge_join_enabled   = true
      _optimizer_join_sel_sanity_check    = true
      _mmv_query_rewrite_enabled          = true
      _bt_mmv_query_rewrite_enabled       = true
      _add_stale_mv_to_dependency_list    = true
      _distinct_view_unnesting            = false
      _optimizer_dim_subq_join_sel        = true
      _optimizer_disable_strans_sanity_checks = 0
      _optimizer_compute_index_stats      = true
      _push_join_union_view2              = true
      _optimizer_ignore_hints             = false
      _optimizer_random_plan              = 0
      _query_rewrite_setopgrw_enable      = true
      _optimizer_correct_sq_selectivity   = true
      _disable_function_based_index       = false
      _optimizer_join_order_control       = 3
      _optimizer_cartesian_enabled        = true
      _optimizer_starplan_enabled         = true
      _extended_pruning_enabled           = true
      _optimizer_push_pred_cost_based     = true
      _sql_model_unfold_forloops          = run_time
      _enable_dml_lock_escalation         = false
      _bloom_filter_enabled               = true
      _update_bji_ipdml_enabled           = 0
      _optimizer_extended_cursor_sharing  = udo
      _dm_max_shared_pool_pct             = 1
      _optimizer_cost_hjsmj_multimatch    = true
      _optimizer_transitivity_retain      = true
      _px_pwg_enabled                     = true
      optimizer_secure_view_merging       = true
      _optimizer_join_elimination_enabled = true
      flashback_table_rpi                 = non_fbt
      _optimizer_cbqt_no_size_restriction = true
      _optimizer_enhanced_filter_push     = true
      _optimizer_filter_pred_pullup       = true
      _rowsrc_trace_level                 = 0
      _simple_view_merging                = true
      _optimizer_rownum_pred_based_fkr    = true
      _optimizer_better_inlist_costing    = all
      _optimizer_self_induced_cache_cost  = false
      _optimizer_min_cache_blocks         = 10
      _optimizer_or_expansion             = depth
      _optimizer_order_by_elimination_enabled = true
      _optimizer_outer_to_anti_enabled    = true
      _selfjoin_mv_duplicates             = true
      _dimension_skip_null                = true
      _force_rewrite_enable               = false
      _optimizer_star_tran_in_with_clause = true
      _optimizer_complex_pred_selectivity = true
      _optimizer_connect_by_cost_based    = true
      _gby_hash_aggregation_enabled       = true
      _globalindex_pnum_filter_enabled    = true
      _fix_control_key                    = 0
      _optimizer_skip_scan_guess          = false
      _enable_row_shipping                = false
      _row_shipping_threshold             = 80
      _row_shipping_explain               = false
      _optimizer_rownum_bind_default      = 10
      _first_k_rows_dynamic_proration     = true
      _optimizer_native_full_outer_join   = off
      Bug Fix Control Environment
      fix  4611850 = enabled
      fix  4663804 = enabled
      fix  4663698 = enabled
      fix  4545833 = enabled
      fix  3499674 = disabled
      fix  4584065 = enabled
      fix  4602374 = enabled
      fix  4569940 = enabled
      fix  4631959 = enabled
      fix  4519340 = enabled
      fix  4550003 = enabled
      fix  4488689 = enabled
      fix  3118776 = enabled
      fix  4519016 = enabled
      fix  4487253 = enabled
      fix  4556762 = 15     
      fix  4728348 = enabled
      fix  4723244 = enabled
      fix  4554846 = enabled
      fix  4175830 = enabled
      fix  4722900 = enabled
      fix  5094217 = enabled
      fix  4904890 = enabled
      fix  4483286 = disabled
      fix  4969880 = disabled
      fix  4711525 = enabled
      fix  4717546 = enabled
      fix  4904838 = enabled
      fix  5005866 = enabled
      fix  4600710 = enabled
      fix  5129233 = enabled
      fix  5195882 = enabled
      fix  5084239 = enabled
      fix  4595987 = enabled
      fix  4134994 = enabled
      fix  5104624 = enabled
      fix  4908162 = enabled
      fix  5015557 = enabled
      PARAMETERS IN OPT_PARAM HINT
    Column Usage Monitoring is ON: tracking level = 1
    COST-BASED QUERY TRANSFORMATIONS
    FPD: Considering simple filter push (pre rewrite) in SEL$1 (#0)
    FPD:   Current where clause predicates in SEL$1 (#0) :
             "CDW"."COT_EXTERNAL_ID"=ANY (SELECT TO_CHAR("O"."ORDER_ID") FROM "ORDERS" "O")
    Registered qb: SEL$1 0x974658b0 (COPY SEL$1)
      signature(): NULL
    Registered qb: SEL$2 0x9745e408 (COPY SEL$2)
      signature(): NULL
    Cost-Based Subquery Unnesting
    SU: No subqueries to consider in query block SEL$2 (#2).
    SU: Considering subquery unnesting in query block SEL$1 (#1)
    SU: Performing unnesting that does not require costing.
    SU: Considering subquery unnest on SEL$1 (#1).
    SU:   Checking validity of unnesting subquery SEL$2 (#2)
    SU:   Passed validity checks.
    SU:   Transforming ANY subquery to a join.
    Registered qb: SEL$5DA710D3 0x974658b0 (SUBQUERY UNNEST SEL$1; SEL$2)
      signature (): qb_name=SEL$5DA710D3 nbfros=2 flg=0
        fro(0): flg=0 objn=51893 hint_alias="CDW"@"SEL$1"
        fro(1): flg=0 objn=51782 hint_alias="O"@"SEL$2"
    Cost-Based Complex View Merging
    CVM: Finding query blocks in SEL$5DA710D3 (#1) that are valid to merge.
    SU:   Transforming ANY subquery to a join.
    Set-Join Conversion (SJC)
    SJC: Considering set-join conversion in SEL$5DA710D3 (#1).
    Query block (0x2a973e5458) before join elimination:
    SQL:******* UNPARSED QUERY IS *******
    SELECT "CDW".* FROM "COT_PLUS"."ORDERS" "O","COT_PLUS"."CDW_ORDERS" "CDW" WHERE "CDW"."COT_EXTERNAL_ID"=TO_CHAR("O"."ORDER_ID") AND "O"."STATUS_ID"=22
    Query block (0x2a973e5458) unchanged
    Predicate Move-Around (PM)
    PM: Considering predicate move-around in SEL$5DA710D3 (#1).
    PM:   Checking validity of predicate move-around in SEL$5DA710D3 (#1).
    PM:     PM bypassed: Outer query contains no views.
    JPPD: Applying transformation directives
    JPPD: Checking validity of push-down in query block SEL$5DA710D3 (#1)
    JPPD:   No view found to push predicate into.
    FPD: Considering simple filter push in SEL$5DA710D3 (#1)
    FPD:   Current where clause predicates in SEL$5DA710D3 (#1) :
             "CDW"."COT_EXTERNAL_ID"=TO_CHAR("O"."ORDER_ID") AND "O"."STATUS_ID"=22
    kkogcp: try to generate transitive predicate from check constraints for SEL$5DA710D3 (#1)
    predicates with check contraints: "CDW"."COT_EXTERNAL_ID"=TO_CHAR("O"."ORDER_ID") AND "O"."STATUS_ID"=22
    after transitive predicate generation: "CDW"."COT_EXTERNAL_ID"=TO_CHAR("O"."ORDER_ID") AND "O"."STATUS_ID"=22
    finally: "CDW"."COT_EXTERNAL_ID"=TO_CHAR("O"."ORDER_ID") AND "O"."STATUS_ID"=22
    First K Rows: Setup begin
    kkoqbc-start
                : call(in-use=1592, alloc=16344), compile(in-use=101000, alloc=134224)
    QUERY BLOCK TEXT
    select cdw.* from cdw_orders cdw where cdw.cot_external_id in (select to_char(o.order_id) from orders o where status_id = 22)
    QUERY BLOCK SIGNATURE
    qb name was generated
    signature (optimizer): qb_name=SEL$5DA710D3 nbfros=2 flg=0
      fro(0): flg=0 objn=51893 hint_alias="CDW"@"SEL$1"
      fro(1): flg=0 objn=51782 hint_alias="O"@"SEL$2"
    SYSTEM STATISTICS INFORMATION
      Using NOWORKLOAD Stats
      CPUSPEED: 714 millions instruction/sec
      IOTFRSPEED: 4096 bytes per millisecond (default is 4096)
      IOSEEKTIM: 10 milliseconds (default is 10)
    BASE STATISTICAL INFORMATION
    Table Stats::
      Table: CDW_ORDERS  Alias: CDW
        #Rows: 3375  #Blks:  1504  AvgRowLen:  132.00
    Index Stats::
      Index: CDW_ORD_COT_EXT_ID  Col#: 10
        LVLS: 1  #LB: 232  #DK: 1878  LB/K: 1.00  DB/K: 1.00  CLUF: 1899.00
      Index: CDW_ORD_REFERENCE_IDX  Col#: 13
        LVLS: 0  #LB: 0  #DK: 0  LB/K: 0.00  DB/K: 0.00  CLUF: 0.00
      Index: COMMITTED_IDX  Col#: 12
        LVLS: 1  #LB: 171  #DK: 1673  LB/K: 1.00  DB/K: 1.00  CLUF: 1657.00
      Index: OBJID_IDX  Col#: 16 17
        LVLS: 2  #LB: 318  #DK: 3372  LB/K: 1.00  DB/K: 1.00  CLUF: 1901.00
      Index: ORDID_IDX  Col#: 14
        LVLS: 0  #LB: 0  #DK: 0  LB/K: 0.00  DB/K: 0.00  CLUF: 0.00
    Table Stats::
      Table: ORDERS  Alias:  O
        #Rows: 178253  #Blks:  7300  AvgRowLen:  282.00
    Index Stats::
      Index: IDX_ORDERS_CONFIG  Col#: 80
        LVLS: 1  #LB: 215  #DK: 452  LB/K: 1.00  DB/K: 130.00  CLUF: 59161.00
      Index: IDX_ORDERS_REFRENCE_NUMBER  Col#: 6
        LVLS: 1  #LB: 428  #DK: 68698  LB/K: 1.00  DB/K: 1.00  CLUF: 115830.00
      Index: ORDERS_BILLING_SI_IDX  Col#: 13
        LVLS: 1  #LB: 84  #DK: 3049  LB/K: 1.00  DB/K: 8.00  CLUF: 27006.00
      Index: ORDERS_LATEST_ORD_IDX  Col#: 3
        LVLS: 0  #LB: 0  #DK: 0  LB/K: 0.00  DB/K: 0.00  CLUF: 0.00
      Index: ORDERS_ORDER_TYPE_IDX  Col#: 4
        LVLS: 2  #LB: 984  #DK: 64  LB/K: 15.00  DB/K: 932.00  CLUF: 59702.00
      Index: ORDERS_ORD_MINOR__IDX  Col#: 43 5
        LVLS: 2  #LB: 784  #DK: 112  LB/K: 7.00  DB/K: 375.00  CLUF: 42012.00
      Index: ORDERS_OWNING_ORG_IDX  Col#: 37
        LVLS: 0  #LB: 0  #DK: 0  LB/K: 0.00  DB/K: 0.00  CLUF: 0.00
      Index: ORDERS_PARENT_ORD_IDX  Col#: 2
        LVLS: 1  #LB: 206  #DK: 37492  LB/K: 1.00  DB/K: 1.00  CLUF: 58051.00
      Index: ORDERS_SD_CONFIG__IDX  Col#: 42
        LVLS: 2  #LB: 604  #DK: 10  LB/K: 60.00  DB/K: 3638.00  CLUF: 36389.00
      Index: ORDERS_SPECIAL_OR_IDX  Col#: 36
        LVLS: 1  #LB: 63  #DK: 2  LB/K: 31.00  DB/K: 556.00  CLUF: 1113.00
      Index: ORDERS_STATUS_ID_IDX  Col#: 5
        LVLS: 2  #LB: 635  #DK: 25  LB/K: 25.00  DB/K: 1440.00  CLUF: 36015.00
      Index: PK_ORDERS  Col#: 1
        LVLS: 1  #LB: 408  #DK: 178253  LB/K: 1.00  DB/K: 1.00  CLUF: 131025.00
    SINGLE TABLE ACCESS PATH
      Column (#5): STATUS_ID(NUMBER)
        AvgLen: 3.00 NDV: 20 Nulls: 0 Density: 2.7653e-06 Min: 2 Max: 33
        Histogram: Freq  #Bkts: 20  UncompBkts: 5567  EndPtVals: 20
      Table: ORDERS  Alias: O    
        Card: Original: 178253  Rounded: 95450  Computed: 95450.37  Non Adjusted: 95450.37
      Access Path: TableScan
        Cost:  1419.89  Resp: 1419.89  Degree: 0
          Cost_io: 1408.00  Cost_cpu: 101897352
          Resp_io: 1408.00  Resp_cpu: 101897352
    kkofmx: index filter:"O"."STATUS_ID"=22
      Access Path: index (skip-scan)
        SS sel: 0.53548  ANDV (#skips): 60
        SS io: 419.81 vs. table scan io: 1408.00
        Skip Scan chosen
      Access Path: index (SkipScan)
        Index: ORDERS_ORD_MINOR__IDX
        resc_io: 22918.81  resc_cpu: 204258888
        ix_sel: 0.53548  ix_sel_with_filters: 0.53548
        Cost: 5735.66  Resp: 5735.66  Degree: 1
      Access Path: index (AllEqRange)
        Index: ORDERS_STATUS_ID_IDX
        resc_io: 19629.00  resc_cpu: 180830676
        ix_sel: 0.53548  ix_sel_with_filters: 0.53548
        Cost: 4912.53  Resp: 4912.53  Degree: 1
      ****** trying bitmap/domain indexes ******
      Best:: AccessPath: TableScan
             Cost: 1419.89  Degree: 1  Resp: 1419.89  Card: 95450.37  Bytes: 0
    SINGLE TABLE ACCESS PATH
      Table: CDW_ORDERS  Alias: CDW    
        Card: Original: 3375  Rounded: 3375  Computed: 3375.00  Non Adjusted: 3375.00
      Access Path: TableScan
        Cost:  292.51  Resp: 292.51  Degree: 0
          Cost_io: 291.00  Cost_cpu: 12971896
          Resp_io: 291.00  Resp_cpu: 12971896
      Best:: AccessPath: TableScan
             Cost: 292.51  Degree: 1  Resp: 292.51  Card: 3375.00  Bytes: 0
    OPTIMIZER STATISTICS AND COMPUTATIONS
    GENERAL PLANS
    Considering cardinality-based initial join order.
    Permutations for Starting Table :0
    Join order[1]:  CDW_ORDERS[CDW]#0  ORDERS[O]#1
    Now joining: ORDERS[O]#1
    NL Join
      Outer table: Card: 3375.00  Cost: 292.51  Resp: 292.51  Degree: 1  Bytes: 132
      Inner table: ORDERS  Alias: O
      Access Path: TableScan
        NL Join:  Cost: 4788284.86  Resp: 4788284.86  Degree: 0
          Cost_io: 4748144.00  Cost_cpu: 343916534896
          Resp_io: 4748144.00  Resp_cpu: 343916534896
    kkofmx: index filter:"O"."STATUS_ID"=22
    OPTIMIZER PERCENT INDEX CACHING = 100
      Access Path: index (FullScan)
        Index: ORDERS_ORD_MINOR__IDX
        resc_io: 22497.00  resc_cpu: 217815366
        ix_sel: 1  ix_sel_with_filters: 0.53548
        NL Join: Cost: 19004464.41  Resp: 19004464.41  Degree: 1
          Cost_io: 18982134.75  Cost_cpu: 191314735126
          Resp_io: 18982134.75  Resp_cpu: 191314735126
    OPTIMIZER PERCENT INDEX CACHING = 100
      Access Path: index (AllEqJoin)
        Index: ORDERS_STATUS_ID_IDX
        resc_io: 1.00  resc_cpu: 7981
        ix_sel: 1.0477e-05  ix_sel_with_filters: 1.0477e-05
        NL Join: Cost: 1137.05  Resp: 1137.05  Degree: 1
          Cost_io: 1134.75  Cost_cpu: 19706236
          Resp_io: 1134.75  Resp_cpu: 19706236
      ****** trying bitmap/domain indexes ******
      Best NL cost: 1137.05
              resc: 1137.05 resc_io: 1134.75 resc_cpu: 19706236
              resp: 1137.05 resp_io: 1134.75 resp_cpu: 19706236
    adjusting AJ/SJ sel based on min/max ranges: jsel=min(1, 6.1094e-04)Semi Join Card:  2.06 = outer (3375.00) * sel (6.1094e-04)
    Join Card - Rounded: 2 Computed: 2.06
    SM Join
      Outer table:
        resc: 292.51  card 3375.00  bytes: 132  deg: 1  resp: 292.51
      Inner table: ORDERS  Alias: O
        resc: 1419.89  card: 95450.37  bytes: 8  deg: 1  resp: 1419.89
        using dmeth: 2  #groups: 1
        SORT resource      Sort statistics
          Sort width:         598 Area size:      616448 Max Area size:   104857600
          Degree:               1
          Blocks to Sort:      65 Row size:          156 Total Rows:           3375
          Initial runs:         1 Merge passes:        0 IO Cost / pass:          0
          Total IO sort cost: 0      Total CPU sort cost: 10349977
          Total Temp space used: 0
        SORT resource      Sort statistics
          Sort width:         598 Area size:      616448 Max Area size:   104857600
          Degree:               1
          Blocks to Sort:     223 Row size:           19 Total Rows:          95450
          Initial runs:         2 Merge passes:        1 IO Cost / pass:        122
          Total IO sort cost: 345      Total CPU sort cost: 85199490
          Total Temp space used: 3089000
      SM join: Resc: 2068.56  Resp: 2068.56  [multiMatchCost=0.00]
      SM cost: 2068.56
         resc: 2068.56 resc_io: 2044.00 resc_cpu: 210418716
         resp: 2068.56 resp_io: 2044.00 resp_cpu: 210418716
    SM Join (with index on outer)
      Access Path: index (FullScan)
        Index: CDW_ORD_COT_EXT_ID
        resc_io: 2132.00  resc_cpu: 18119160
        ix_sel: 1  ix_sel_with_filters: 1
        Cost: 533.53  Resp: 533.53  Degree: 1
      Outer table:
        resc: 533.53  card 3375.00  bytes: 132  deg: 1  resp: 533.53
      Inner table: ORDERS  Alias: O
        resc: 1419.89  card: 95450.37  bytes: 8  deg: 1  resp: 1419.89
        using dmeth: 2  #groups: 1
        SORT resource      Sort statistics
          Sort width:         598 Area size:      616448 Max Area size:   104857600
          Degree:               1
          Blocks to Sort:     223 Row size:           19 Total Rows:          95450
          Initial runs:         2 Merge passes:        1 IO Cost / pass:        122
          Total IO sort cost: 345      Total CPU sort cost: 85199490
          Total Temp space used: 3089000
      SM join: Resc: 2308.37  Resp: 2308.37  [multiMatchCost=0.00]
    HA Join
      Outer table:
        resc: 292.51  card 3375.00  bytes: 132  deg: 1  resp: 292.51
      Inner table: ORDERS  Alias: O
        resc: 1419.89  card: 95450.37  bytes: 8  deg: 1  resp: 1419.89
        using dmeth: 2  #groups: 1
        Cost per ptn: 1.67  #ptns: 1
        hash_area: 151 (max=25600)   Hash join: Resc: 1714.08  Resp: 1714.08  [multiMatchCost=0.00]
      HA cost: 1714.08
         resc: 1714.08 resc_io: 1699.00 resc_cpu: 129204369
         resp: 1714.08 resp_io: 1699.00 resp_cpu: 129204369
    Best:: JoinMethod: NestedLoopSemi
           Cost: 1137.05  Degree: 1  Resp: 1137.05  Card: 2.06  Bytes: 140
    Best so far: Table#: 0  cost: 292.5140  card: 3375.0000  bytes: 445500
                 Table#: 1  cost: 1137.0501  card: 2.0619  bytes: 280
    Number of join permutations tried: 1
    (newjo-save)    [0 1 ]
    Final - All Rows Plan:  Best join order: 1
      Cost: 1137.0501  Degree: 1  Card: 2.0000  Bytes: 280
      Resc: 1137.0501  Resc_io: 1134.7500  Resc_cpu: 19706236
      Resp: 1137.0501  Resp_io: 1134.7500  Resc_cpu: 19706236
    kkoipt: Query block SEL$5DA710D3 (#1)
    kkoqbc-end
              : call(in-use=156048, alloc=164408), compile(in-use=103696, alloc=134224)
    First K Rows: Setup end
    ***********************

  • Is index range scan the reason for query running long time

    I would like to know whether index range scan is the reason for the query running long time. Below is the explain plan. If so, how to optimise it? Please help
    Operation     Object     COST     CARDINALITY     BYTES
    SELECT STATEMENT ()          413     1000     265000
    COUNT (STOPKEY)                    
    FILTER ()                    
    TABLE ACCESS (BY INDEX ROWID)     ORDERS     413     58720     15560800
    INDEX (RANGE SCAN)     IDX_SERV_PROV_ID     13     411709     
    TABLE ACCESS (BY INDEX ROWID)     ADDRESSES     2     1     14
    INDEX (UNIQUE SCAN)     SYS_C004605     1     1     
    TABLE ACCESS (BY INDEX ROWID)     ADDRESSES     2     1     14
    INDEX (UNIQUE SCAN)     SYS_C004605     1     1     
    TABLE ACCESS (BY INDEX ROWID)     ADDRESSES     2     1     14
    INDEX (UNIQUE SCAN)     SYS_C004605     1     1

    The index range scan means that the optimiser has determined that it is better to read the index rather than perform a full table scan. So in answer to your question - quite possibly but the alternative might take even longer!
    The best thing to do is to review your query and check that you need every table included in the query and that you are accessing the tables via the best route. For example if you can access a table via primary key index that would be better than using a non-unique index. But the best way of reducing the time the query takes to run is to give it less tables (and indexes) to read.
    John Seaman
    http://www.asktheoracle.net

  • Direct Path Reads instead of Sequential Reads for index range scan

    Database is 11.2. I have two development schemas, with the same table loaded in each schema - a 5 million row table. The execution path for the sql statement is the same against both tables; it's doing an index range scan.
    But it would appear Oracle performs a direct path read against one schema, and performs sequential reads against the other schema. I don't understand why I'm seeing different behavior when the execution plan is the same. Any ideas? These are two different schemas in the same database.

    There is not enough information.So you even these tables located same database and you gathered statistics it is not mean both run time wait event statistics must be same.Really they are different tables.If both query use INDEX RANGE SCAN the it is not mean these plans are same.What about table and their index statistics? are they same? for example num_row or num_blocks of both tables are same? also about indexes.In additionally if you want to get exact reason you can enable sql trace(using dbms_monitor or setting sql_trace parameter to true according session) and need analyze result trace file using tkprof utility.In additionally in 11g here when query execution time oracle automatically choose direct read path(serial) based on size of tables and size of buffer cache(also here is available some hidden parameter to controlling this behavior).

  • How can I improve optimizers poor cardinality estimates?

    Hi all,
    I have a query that is taking too long and it looks like the cardinality estimates are way off. It seems particulary bas with the hash joins
    and I don't know how to get the optimizer to get a better estimate. The tables in the query were last analyzed a couple of weeks ago
    using dbms_stats and DBMS_STATS.AUTO_SAMPLE_SIZE and FOR ALL COLUMNS SIZE AUTO but looking at dba_tab_col_statistics there is only a frequency histogram
    on a column not used in the query. The data hasn't really changed that much since the last collection
    This is 11.2.0.2.1 on linux x86_64.
    create table test2 as
    select /*+ GATHER_PLAN_STATISTICS */ DISTINCT
    hts.resource_id resource_id,       
    a.start_time start_date,
    hta.attribute12 alias1
    FROM
    hxc_time_attribute_usages htu,
    hxc_time_attributes hta,       
    hxc_time_building_blocks a,
    hxc_time_building_blocks b,       
    hxc_time_building_blocks c,
    hxc_timecard_summary hts 
    WHERE    
    htu.time_attribute_id = hta.time_attribute_id       
    AND hta.attribute_category LIKE 'ELEMENT%'
    AND hta.attribute12 IS NOT NULL       
    AND htu.time_building_block_id = c.time_building_block_id       
    AND a.time_building_block_id = b.parent_building_block_id       
    AND b.time_building_block_id = c.parent_building_block_id       
    AND c.time_building_block_id = htu.time_building_block_id       
    AND a.scope = 'TIMECARD'       
    AND b.scope = 'DAY'       
    AND c.scope ='DETAIL'      
    AND hts.timecard_id = a.time_building_block_id
    Plan hash value: 1730726592
    | Id  | Operation                          | Name                      | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  | Writes |  OMem |  1Mem | Used-Mem | Used-Tmp|
    |   0 | CREATE TABLE STATEMENT             |                           |      1 |        |      0 |00:40:13.79 |     621K|   3182K|   2671K|       |       |          |         |
    |   1 |  LOAD AS SELECT                    |                           |      1 |        |      0 |00:40:13.79 |     621K|   3182K|   2671K|   529K|   529K|  529K (0)|         |
    |   2 |   SORT UNIQUE                      |                           |      1 |   1205 |    170K|00:40:13.37 |     618K|   3182K|   2671K|    11M|    11M|   10M (0)|         |
    |*  3 |    HASH JOIN                       |                           |      1 |   1205 |    135M|00:36:59.88 |     618K|   3182K|   2671K|  3325M|    63M|  371M (1)|      18M|
    |*  4 |     HASH JOIN                      |                           |      1 |  10829 |    143M|00:11:47.18 |     616K|    894K|    384K|  2047M|    32M|  539M (1)|    2748K|
    |*  5 |      HASH JOIN                     |                           |      1 |   9541 |     28M|00:06:43.60 |     500K|    448K|  54765 |   751M|    16M|  607M (1)|     456K|
    |*  6 |       HASH JOIN                    |                           |      1 |   8885 |   7561K|00:05:28.13 |     383K|    276K|      0 |   211M|  8846K|  278M (0)|         |
    |*  7 |        HASH JOIN                   |                           |      1 |  21193 |   2689K|00:05:00.55 |     266K|    160K|      0 |   169M|  9302K|  201M (0)|         |
    |*  8 |         TABLE ACCESS BY INDEX ROWID| HXC_TIME_ATTRIBUTES       |      1 |  20971 |   2637K|00:04:23.04 |     209K|    103K|      0 |       |       |          |         |
    |*  9 |          INDEX RANGE SCAN          | HXC_TIME_ATTRIBUTES_FK2   |      1 |  71213 |   2640K|00:01:25.09 |   15774 |  15764 |      0 |       |       |          |         |
    |  10 |         TABLE ACCESS FULL          | HXC_TIME_ATTRIBUTE_USAGES |      1 |   8451K|   8849K|00:00:08.71 |   56898 |  56825 |      0 |       |       |          |         |
    |* 11 |        TABLE ACCESS FULL           | HXC_TIME_BUILDING_BLOCKS  |      1 |   1094K|   2703K|00:00:08.01 |     116K|    116K|      0 |       |       |          |         |
    |* 12 |       TABLE ACCESS FULL            | HXC_TIME_BUILDING_BLOCKS  |      1 |   1094K|   3025K|00:00:13.12 |     116K|    116K|      0 |       |       |          |         |
    |* 13 |      TABLE ACCESS FULL             | HXC_TIME_BUILDING_BLOCKS  |      1 |   1206K|    284K|00:00:19.24 |     116K|    116K|      0 |       |       |          |         |
    |  14 |     TABLE ACCESS FULL              | HXC_TIMECARD_SUMMARY      |      1 |    118K|    124K|00:00:05.15 |    2212 |   1183 |      0 |       |       |          |         |
    Predicate Information (identified by operation id):
       3 - access("HTS"."TIMECARD_ID"="A"."TIME_BUILDING_BLOCK_ID")
       4 - access("A"."TIME_BUILDING_BLOCK_ID"="B"."PARENT_BUILDING_BLOCK_ID")
       5 - access("B"."TIME_BUILDING_BLOCK_ID"="C"."PARENT_BUILDING_BLOCK_ID")
       6 - access("HTU"."TIME_BUILDING_BLOCK_ID"="C"."TIME_BUILDING_BLOCK_ID")
       7 - access("HTU"."TIME_ATTRIBUTE_ID"="HTA"."TIME_ATTRIBUTE_ID")
       8 - filter("HTA"."ATTRIBUTE12" IS NOT NULL)
       9 - access("HTA"."ATTRIBUTE_CATEGORY" LIKE 'ELEMENT%')
           filter("HTA"."ATTRIBUTE_CATEGORY" LIKE 'ELEMENT%')
      11 - filter(("C"."SCOPE"='DETAIL' AND "C"."PARENT_BUILDING_BLOCK_ID" IS NOT NULL))
      12 - filter(("B"."SCOPE"='DAY' AND "B"."PARENT_BUILDING_BLOCK_ID" IS NOT NULL))
      13 - filter("A"."SCOPE"='TIMECARD')I can get a slight improvement if I set optimizer_dynamic_sampling=4
    Plan hash value: 2768898101
    | Id  | Operation                      | Name                      | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  | Writes |  OMem |  1Mem | Used-Mem | Used-Tmp|
    |   0 | CREATE TABLE STATEMENT         |                           |      1 |        |      0 |00:20:21.47 |     621K|    760K|    355K|       |       |          |         |
    |   1 |  LOAD AS SELECT                |                           |      1 |        |      0 |00:20:21.47 |     621K|    760K|    355K|   529K|   529K|  529K (0)|         |
    |   2 |   SORT UNIQUE                  |                           |      1 |    433K|    170K|00:20:21.07 |     618K|    760K|    354K|    11M|    11M|   10M (0)|         |
    |*  3 |    HASH JOIN                   |                           |      1 |    433K|    135M|00:17:36.89 |     618K|    760K|    354K|   171M|  9261K|  233M (0)|         |
    |*  4 |     TABLE ACCESS BY INDEX ROWID| HXC_TIME_ATTRIBUTES       |      1 |   2509K|   2637K|00:00:06.33 |     209K|      0 |      0 |       |       |          |         |
    |*  5 |      INDEX RANGE SCAN          | HXC_TIME_ATTRIBUTES_FK2   |      1 |  71213 |   2640K|00:00:02.17 |   15767 |      0 |      0 |       |       |          |         |
    |*  6 |     HASH JOIN                  |                           |      1 |   1444K|    272M|00:07:38.99 |     409K|    760K|    354K|  2047M|    32M|  546M (1)|    2717K|
    |*  7 |      HASH JOIN                 |                           |      1 |    446K|     30M|00:01:43.68 |     352K|    399K|  50235 |   639M|    17M|  694M (1)|     418K|
    |*  8 |       HASH JOIN                |                           |      1 |    377K|   8329K|00:00:41.18 |     235K|    232K|      0 |    17M|  2383K|   27M (0)|         |
    |*  9 |        HASH JOIN               |                           |      1 |    134K|    277K|00:00:23.28 |     118K|    116K|      0 |  4912K|  1573K| 7759K (0)|         |
    |  10 |         TABLE ACCESS FULL      | HXC_TIMECARD_SUMMARY      |      1 |    118K|    124K|00:00:00.08 |    2212 |      0 |      0 |       |       |          |         |
    |* 11 |         TABLE ACCESS FULL      | HXC_TIME_BUILDING_BLOCKS  |      1 |   1206K|    284K|00:00:22.13 |     116K|    116K|      0 |       |       |          |         |
    |* 12 |        TABLE ACCESS FULL       | HXC_TIME_BUILDING_BLOCKS  |      1 |   2988K|   3025K|00:00:05.05 |     116K|    116K|      0 |       |       |          |         |
    |* 13 |       TABLE ACCESS FULL        | HXC_TIME_BUILDING_BLOCKS  |      1 |   2514K|   2703K|00:00:03.65 |     116K|    116K|      0 |       |       |          |         |
    |  14 |      TABLE ACCESS FULL         | HXC_TIME_ATTRIBUTE_USAGES |      1 |   8451K|   8849K|00:00:08.23 |   56898 |  56818 |      0 |       |       |          |         |
    Predicate Information (identified by operation id):
       3 - access("HTU"."TIME_ATTRIBUTE_ID"="HTA"."TIME_ATTRIBUTE_ID")
       4 - filter("HTA"."ATTRIBUTE12" IS NOT NULL)
       5 - access("HTA"."ATTRIBUTE_CATEGORY" LIKE 'ELEMENT%')
           filter("HTA"."ATTRIBUTE_CATEGORY" LIKE 'ELEMENT%')
       6 - access("HTU"."TIME_BUILDING_BLOCK_ID"="C"."TIME_BUILDING_BLOCK_ID")
       7 - access("B"."TIME_BUILDING_BLOCK_ID"="C"."PARENT_BUILDING_BLOCK_ID")
       8 - access("A"."TIME_BUILDING_BLOCK_ID"="B"."PARENT_BUILDING_BLOCK_ID")
       9 - access("HTS"."TIMECARD_ID"="A"."TIME_BUILDING_BLOCK_ID")
      11 - filter("A"."SCOPE"='TIMECARD')
      12 - filter(("B"."SCOPE"='DAY' AND "B"."PARENT_BUILDING_BLOCK_ID" IS NOT NULL))
      13 - filter(("C"."SCOPE"='DETAIL' AND "C"."PARENT_BUILDING_BLOCK_ID" IS NOT NULL))
    Note
       - dynamic sampling used for this statement (level=4)But I still have a large difference in the Estimated and Actual, what can I do to help the optimizer get a better estimate?

    Hi Dom
    Thank you for your input it is always appreciated!
    I tried running with a manual workarea and a sort_area_size of 2000000000 but the result was worse then before.
    Plan hash value: 1730726592
    | Id  | Operation                          | Name                      | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  | Writes |  OMem |  1Mem | Used-Mem | Used-Tmp|
    |   0 | CREATE TABLE STATEMENT             |                           |      1 |        |      0 |00:36:15.63 |    1085K|   2926K|   2475K|       |       |          |         |
    |   1 |  LOAD AS SELECT                    |                           |      1 |        |      0 |00:36:15.63 |    1085K|   2926K|   2475K|   529K|   529K|  529K (0)|         |
    |   2 |   SORT UNIQUE                      |                           |      1 |   1205 |    170K|00:36:14.83 |    1083K|   2926K|   2475K|    11M|    11M|   10M (0)|         |
    |*  3 |    HASH JOIN                       |                           |      1 |   1205 |    135M|00:32:54.89 |    1083K|   2926K|   2475K|  3325M|    63M| 2048M (1)|      19M|
    |*  4 |     HASH JOIN                      |                           |      1 |  10829 |    143M|01:06:20.59 |     651K|    623K|    188K|  2047M|    32M| 2048M (1)|    1681K|
    |*  5 |      HASH JOIN                     |                           |      1 |   9541 |     28M|00:05:51.56 |     500K|    317K|      0 |   751M|    16M| 1088M (0)|         |
    |*  6 |       HASH JOIN                    |                           |      1 |   8885 |   7561K|00:03:03.10 |     383K|    201K|      0 |   211M|  8846K|  379M (0)|         |
    |*  7 |        HASH JOIN                   |                           |      1 |  21193 |   2689K|00:02:21.61 |     266K|  84965 |      0 |   169M|  9302K|  299M (0)|         |
    |*  8 |         TABLE ACCESS BY INDEX ROWID| HXC_TIME_ATTRIBUTES       |      1 |  20971 |   2637K|00:01:45.51 |     209K|  28147 |      0 |       |       |          |         |
    |*  9 |          INDEX RANGE SCAN          | HXC_TIME_ATTRIBUTES_FK2   |      1 |  71213 |   2640K|00:00:43.17 |   15769 |   6921 |      0 |       |       |          |         |
    |  10 |         TABLE ACCESS FULL          | HXC_TIME_ATTRIBUTE_USAGES |      1 |   8451K|   8849K|00:00:08.75 |   56898 |  56818 |      0 |       |       |          |         |
    |* 11 |        TABLE ACCESS FULL           | HXC_TIME_BUILDING_BLOCKS  |      1 |   1094K|   2703K|00:00:22.74 |     116K|    116K|      0 |       |       |          |         |
    |* 12 |       TABLE ACCESS FULL            | HXC_TIME_BUILDING_BLOCKS  |      1 |   1094K|   3025K|00:00:07.85 |     116K|    116K|      0 |       |       |          |         |
    |* 13 |      TABLE ACCESS FULL             | HXC_TIME_BUILDING_BLOCKS  |      1 |   1206K|    284K|00:00:07.42 |     117K|    116K|      0 |       |       |          |         |
    |  14 |     TABLE ACCESS FULL              | HXC_TIMECARD_SUMMARY      |      1 |    118K|    124K|00:00:01.57 |    2250 |    282 |      0 |       |       |          |         |
    Query Block Name / Object Alias (identified by operation id):
       1 - SEL$1
       8 - SEL$1 / HTA@SEL$1
       9 - SEL$1 / HTA@SEL$1
      10 - SEL$1 / HTU@SEL$1
      11 - SEL$1 / C@SEL$1
      12 - SEL$1 / B@SEL$1
      13 - SEL$1 / A@SEL$1
      14 - SEL$1 / HTS@SEL$1
    Predicate Information (identified by operation id):
       3 - access("HTS"."TIMECARD_ID"="A"."TIME_BUILDING_BLOCK_ID")
       4 - access("A"."TIME_BUILDING_BLOCK_ID"="B"."PARENT_BUILDING_BLOCK_ID")
       5 - access("B"."TIME_BUILDING_BLOCK_ID"="C"."PARENT_BUILDING_BLOCK_ID")
       6 - access("HTU"."TIME_BUILDING_BLOCK_ID"="C"."TIME_BUILDING_BLOCK_ID")
       7 - access("HTU"."TIME_ATTRIBUTE_ID"="HTA"."TIME_ATTRIBUTE_ID")
       8 - filter("HTA"."ATTRIBUTE12" IS NOT NULL)
       9 - access("HTA"."ATTRIBUTE_CATEGORY" LIKE 'ELEMENT%')
           filter("HTA"."ATTRIBUTE_CATEGORY" LIKE 'ELEMENT%')
      11 - filter(("C"."SCOPE"='DETAIL' AND "C"."PARENT_BUILDING_BLOCK_ID" IS NOT NULL))
      12 - filter(("B"."SCOPE"='DAY' AND "B"."PARENT_BUILDING_BLOCK_ID" IS NOT NULL))
      13 - filter("A"."SCOPE"='TIMECARD')
    66 rows selected.So I tried setting some cardinality hints, but again, it doesn;t seemd to have helped.
    select /*+ GATHER_PLAN_STATISTICS
    CARDINALITY(A@SEL$1 284000)
    CARDINALITY(HTA@SEL$1 2637000)
    CARDINALITY(C@SEL$1 270000)
    CARDINALITY(B@SEL$1 300000) */
    DISTINCT
    hts.resource_id resource_id,       
    a.start_time start_date,
    hta.attribute12 alias1  
    FROM hxc_time_attribute_usages htu,
    hxc_time_attributes hta,       
    hxc_time_building_blocks a,
    hxc_time_building_blocks b,       
    hxc_time_building_blocks c,
    hxc_timecard_summary hts 
    WHERE    
    htu.time_attribute_id =hta.time_attribute_id       
    AND hta.attribute_category LIKE 'ELEMENT%'
    AND hta.attribute12 IS NOT NULL       
    AND htu.time_building_block_id = c.time_building_block_id       
    AND a.time_building_block_id = b.parent_building_block_id       
    AND b.time_building_block_id = c.parent_building_block_id       
    AND c.time_building_block_id = htu.time_building_block_id       
    AND a.scope = 'TIMECARD'       
    AND b.scope = 'DAY'       
    AND c.scope ='DETAIL'        AND hts
    Plan hash value: 1839838244
    | Id  | Operation                      | Name                      | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  | Writes |  OMem |  1Mem | Used-Mem | Used-Tmp|
    |   0 | CREATE TABLE STATEMENT         |                           |      1 |        |      0 |00:18:00.46 |    1104K|   4415K|   3927K|       |       |       |          |
    |   1 |  LOAD AS SELECT                |                           |      1 |        |      0 |00:18:00.46 |    1104K|   4415K|   3927K|   529K|   529K|  529K (0)|       |
    |   2 |   SORT UNIQUE                  |                           |      1 |   9678 |    170K|00:17:59.95 |    1101K|   4415K|   3926K|    11M|    11M|   10M (0)|       |
    |*  3 |    HASH JOIN                   |                           |      1 |   9678 |    135M|01:02:43.31 |    1101K|   4415K|   3926K|  4318M|    98M|  305M (1)|    27M|
    |*  4 |     HASH JOIN                  |                           |      1 |  30691 |    272M|00:12:31.27 |     409K|   1009K|    602K|  2047M|    32M|  498M (1)|    2523K|
    |*  5 |      HASH JOIN                 |                           |      1 |   9485 |     30M|00:05:41.24 |     352K|    649K|    300K|  2047M|    33M|  461M (1)|    2355K|
    |*  6 |       HASH JOIN                |                           |      1 |  22123 |     31M|00:01:56.55 |     349K|    367K|  17535 |   493M|    18M|  661M (1)|   146K|
    |*  7 |        HASH JOIN               |                           |      1 |  79434 |   8102K|00:00:53.71 |     233K|    233K|      0 |   116M|    10M|  195M (0)|       |
    |*  8 |         TABLE ACCESS FULL      | HXC_TIME_BUILDING_BLOCKS  |      1 |    270K|   2703K|00:00:24.01 |     116K|    116K|      0 |       |       |
    |*  9 |         TABLE ACCESS FULL      | HXC_TIME_BUILDING_BLOCKS  |      1 |    300K|   3025K|00:00:10.42 |     116K|    116K|      0 |       |       |       |          |
    |* 10 |        TABLE ACCESS FULL       | HXC_TIME_BUILDING_BLOCKS  |      1 |    284K|    284K|00:00:11.44 |     116K|    116K|      0 |       |       |       |          |
    |  11 |       TABLE ACCESS FULL        | HXC_TIMECARD_SUMMARY      |      1 |    118K|    124K|00:00:01.88 |    2212 |    256 |      0 |       |       |       |          |
    |  12 |      TABLE ACCESS FULL         | HXC_TIME_ATTRIBUTE_USAGES |      1 |   8451K|   8849K|00:00:11.09 |   56898 |  56818 |      0 |       |       |       |          |
    |* 13 |     TABLE ACCESS BY INDEX ROWID| HXC_TIME_ATTRIBUTES       |      1 |   2637K|   2637K|00:03:20.70 |     210K|  56613 |      0 |       |       |       |          |
    |* 14 |      INDEX RANGE SCAN          | HXC_TIME_ATTRIBUTES_FK2   |      1 |  71213 |   2640K|00:01:34.62 |   16142 |  15527 |      0 |       |       |       |          |
    Query Block Name / Object Alias (identified by operation id):
       1 - SEL$1
       8 - SEL$1 / C@SEL$1
       9 - SEL$1 / B@SEL$1
      10 - SEL$1 / A@SEL$1
      11 - SEL$1 / HTS@SEL$1
      12 - SEL$1 / HTU@SEL$1
      13 - SEL$1 / HTA@SEL$1
      14 - SEL$1 / HTA@SEL$1
    Predicate Information (identified by operation id):
       3 - access("HTU"."TIME_ATTRIBUTE_ID"="HTA"."TIME_ATTRIBUTE_ID")
       4 - access("HTU"."TIME_BUILDING_BLOCK_ID"="C"."TIME_BUILDING_BLOCK_ID")
       5 - access("HTS"."TIMECARD_ID"="A"."TIME_BUILDING_BLOCK_ID")
       6 - access("A"."TIME_BUILDING_BLOCK_ID"="B"."PARENT_BUILDING_BLOCK_ID")
       7 - access("B"."TIME_BUILDING_BLOCK_ID"="C"."PARENT_BUILDING_BLOCK_ID")
       8 - filter(("C"."SCOPE"='DETAIL' AND "C"."PARENT_BUILDING_BLOCK_ID" IS NOT NULL))
       9 - filter(("B"."SCOPE"='DAY' AND "B"."PARENT_BUILDING_BLOCK_ID" IS NOT NULL))
      10 - filter("A"."SCOPE"='TIMECARD')
      13 - filter("HTA"."ATTRIBUTE12" IS NOT NULL)
      14 - access("HTA"."ATTRIBUTE_CATEGORY" LIKE 'ELEMENT%')
           filter("HTA"."ATTRIBUTE_CATEGORY" LIKE 'ELEMENT%')
    68 rows selected.
    SQL>What else do you think I should try, or did I do the cardinality bit wrong, because I don't seem to be able to hint the HASH Join, only the table scan?
    Thanks

  • Query performance issues - Poor cardinality estimate?

    Hi,
    I have a query which is taking far longer than estimated by the explain plan (estimate 1min, query still running after several hours).
    Plan hash value: 3287246760
    | Id  | Operation                             | Name           | Rows  | Bytes | Cost (%CPU)| Time     |
    |   0 | SELECT STATEMENT                      |                |     1 |   195 |  3795   (1)| 00:00:46 |
    |   1 |  VIEW                                 |                |     1 |   195 |  3795   (1)| 00:00:46 |
    |   2 |   WINDOW SORT                         |                |     1 |   151 |  3795   (1)| 00:00:46 |
    |   3 |    VIEW                               |                |     1 |   151 |  3794   (1)| 00:00:46 |
    |   4 |     SORT UNIQUE                       |                |     1 |   147 |  3794   (1)| 00:00:46 |
    |   5 |      WINDOW BUFFER                    |                |     1 |   147 |  3794   (1)| 00:00:46 |
    |   6 |       SORT GROUP BY PIVOT             |                |     1 |   147 |  3794   (1)| 00:00:46 |
    |   7 |        NESTED LOOPS                   |                |       |       |            |          |
    |   8 |         NESTED LOOPS                  |                |     1 |   147 |  3793   (1)| 00:00:46 |
    |   9 |          NESTED LOOPS                 |                |     3 |   297 |  1503   (1)| 00:00:19 |
    |* 10 |           HASH JOIN                   |                |   238 | 15470 |    75   (7)| 00:00:01 |
    |  11 |            MAT_VIEW ACCESS FULL       | VENTILATION    | 17994 |   404K|    35   (0)| 00:00:01 |
    |  12 |            VIEW                       |                | 17994 |   738K|    39  (11)| 00:00:01 |
    |  13 |             SORT UNIQUE               |                | 17994 |   702K|    39  (11)| 00:00:01 |
    |  14 |              WINDOW SORT              |                | 17994 |   702K|    39  (11)| 00:00:01 |
    |* 15 |               VIEW                    |                | 17994 |   702K|    37   (6)| 00:00:01 |
    |  16 |                WINDOW SORT            |                | 17994 |   632K|    37   (6)| 00:00:01 |
    |  17 |                 MAT_VIEW ACCESS FULL  | VENTILATION    | 17994 |   632K|    35   (0)| 00:00:01 |
    |  18 |           INLIST ITERATOR             |                |       |       |            |          |
    |* 19 |            TABLE ACCESS BY INDEX ROWID| LABEVENTS      |     1 |    34 |     6   (0)| 00:00:01 |
    |* 20 |             INDEX RANGE SCAN          | LABEVENTS_O5   |     5 |       |     3   (0)| 00:00:01 |
    |* 21 |          INDEX RANGE SCAN             | CHARTEVENTS_O5 |  4937 |       |    12   (0)| 00:00:01 |
    |* 22 |         TABLE ACCESS BY INDEX ROWID   | CHARTEVENTS    |     1 |    48 |   763   (0)| 00:00:10 |
    Predicate Information (identified by operation id):
      10 - access("ICUS"."SUBJECT_ID"="FVGT48H"."SUBJECT_ID" AND
                  SYS_EXTRACT_UTC("FVGT48H"."BEGIN_TIME")=SYS_EXTRACT_UTC("ICUS"."BEGIN_TIME"))
      15 - filter((INTERNAL_FUNCTION("END_TIME")-INTERNAL_FUNCTION("BEGIN_TIME"))DAY(9) TO
                  SECOND(9)>INTERVAL'+02 00:00:00' DAY(2) TO SECOND(0))
      19 - filter(SYS_EXTRACT_UTC("LE"."CHARTTIME")>=SYS_EXTRACT_UTC("FVGT48H"."BEGIN_TIME") AND
                  SYS_EXTRACT_UTC("LE"."CHARTTIME")<=SYS_EXTRACT_UTC("FVGT48H"."END_TIME"))
      20 - access("ICUS"."ICUSTAY_ID"="LE"."ICUSTAY_ID" AND ("LE"."ITEMID"=50013 OR
                  "LE"."ITEMID"=50019))
           filter("LE"."ICUSTAY_ID" IS NOT NULL)
      21 - access("LE"."ICUSTAY_ID"="CE"."ICUSTAY_ID")I tried removing the nested loops using the NO_USE_NL hints, which give the following plan:
    | Id  | Operation                            | Name           | Rows  | Bytes |TempSpc| Cost (%CPU)| Time     |
    |   0 | SELECT STATEMENT                     |                |     1 |   195 |       | 22789   (1)| 00:04:34 |
    |   1 |  VIEW                                |                |     1 |   195 |       | 22789   (1)| 00:04:34 |
    |   2 |   WINDOW SORT                        |                |     1 |   151 |       | 22789   (1)| 00:04:34 |
    |   3 |    VIEW                              |                |     1 |   151 |       | 22788   (1)| 00:04:34 |
    |   4 |     SORT UNIQUE                      |                |     1 |   147 |       | 22788   (1)| 00:04:34 |
    |   5 |      WINDOW BUFFER                   |                |     1 |   147 |       | 22788   (1)| 00:04:34 |
    |   6 |       SORT GROUP BY PIVOT            |                |     1 |   147 |       | 22788   (1)| 00:04:34 |
    |*  7 |        HASH JOIN                     |                |     1 |   147 |       | 22787   (1)| 00:04:34 |
    |   8 |         VIEW                         |                | 17994 |   738K|       |    39  (11)| 00:00:01 |
    |   9 |          SORT UNIQUE                 |                | 17994 |   702K|       |    39  (11)| 00:00:01 |
    |  10 |           WINDOW SORT                |                | 17994 |   702K|       |    39  (11)| 00:00:01 |
    |* 11 |            VIEW                      |                | 17994 |   702K|       |    37   (6)| 00:00:01 |
    |  12 |             WINDOW SORT              |                | 17994 |   632K|       |    37   (6)| 00:00:01 |
    |  13 |              MAT_VIEW ACCESS FULL    | VENTILATION    | 17994 |   632K|       |    35   (0)| 00:00:01 |
    |* 14 |         HASH JOIN                    |                | 11873 |  1217K|  5800K| 22747   (1)| 00:04:33 |
    |* 15 |          HASH JOIN                   |                | 86060 |  4790K|       | 16141   (2)| 00:03:14 |
    |  16 |           MAT_VIEW ACCESS FULL       | VENTILATION    | 17994 |   404K|       |    35   (0)| 00:00:01 |
    |* 17 |           TABLE ACCESS FULL          | LABEVENTS      |   176K|  5869K|       | 16105   (2)| 00:03:14 |
    |  18 |          INLIST ITERATOR             |                |       |       |       |            |          |
    |  19 |           TABLE ACCESS BY INDEX ROWID| CHARTEVENTS    |   104K|  4911K|       |  6024   (1)| 00:01:13 |
    |* 20 |            INDEX RANGE SCAN          | CHARTEVENTS_O4 |   104K|       |       |   220   (1)| 00:00:03 |
    Predicate Information (identified by operation id):
       7 - access("ICUS"."SUBJECT_ID"="FVGT48H"."SUBJECT_ID" AND
                  SYS_EXTRACT_UTC("FVGT48H"."BEGIN_TIME")=SYS_EXTRACT_UTC("ICUS"."BEGIN_TIME"))
           filter(SYS_EXTRACT_UTC("LE"."CHARTTIME")>=SYS_EXTRACT_UTC("FVGT48H"."BEGIN_TIME") AND
                  SYS_EXTRACT_UTC("LE"."CHARTTIME")<=SYS_EXTRACT_UTC("FVGT48H"."END_TIME"))
      11 - filter((INTERNAL_FUNCTION("END_TIME")-INTERNAL_FUNCTION("BEGIN_TIME"))DAY(9) TO
                  SECOND(9)>INTERVAL'+02 00:00:00' DAY(2) TO SECOND(0))
      14 - access("LE"."ICUSTAY_ID"="CE"."ICUSTAY_ID")
           filter(SYS_EXTRACT_UTC("CHARTTIME")<SYS_EXTRACT_UTC("LE"."CHARTTIME"))
      15 - access("ICUS"."ICUSTAY_ID"="LE"."ICUSTAY_ID")
      17 - filter("LE"."ICUSTAY_ID" IS NOT NULL AND ("LE"."ITEMID"=50013 OR "LE"."ITEMID"=50019))
      20 - access("CE"."ITEMID"=185 OR "CE"."ITEMID"=186 OR "CE"."ITEMID"=190 OR "CE"."ITEMID"=3420)The cardinality estimate looks way off to me - I'm expecting several thousand rows. I have up-to-date statistics.
    Can anyone help?
    Thanks,
    Dan

    WITH chf_patients AS (
    -- Exclude patients with CHF by ICD9 code
    select subject_id,
           hadm_id
      from mimic2v26.icd9
    where code in ('398.91','402.01','402.91','428.0','428.0', '428.1', '404.13', '404.93', '428.9', '404.91')
    , icustays AS (
        /* Our ICU Stay population */
    SELECT *
      FROM MIMIC2V26.ICUSTAY_DETAIL
    WHERE ICUSTAY_AGE_GROUP = 'adult'
       AND SUBJECT_ID NOT IN (select subject_id from chf_patients)
    --   AND SUBJECT_ID < 50
    --select * from icustays;
    -- Combine ventilation periods separated by < 48 hours.
    , combine_ventilation as (
    select subject_id,
           icustay_id,
           begin_time,
    --       end_time as end_first_vent,
    --       lead(begin_time,1) over (partition by icustay_id order by begin_time) as next_begin_time,
    --       lead(begin_time,1) over (partition by icustay_id order by begin_time) - begin_time as time_to_next,
           case when (lead(begin_time,1) over (partition by icustay_id order by begin_time) - begin_time) < interval '2' day
            then lead(end_time,1) over (partition by icustay_id order by begin_time)
            else end_time end as end_time
      from mimic2devel.ventilation
    --select * from combine_ventilation;
    --select * from combine_ventilation where end_of_ventilation != end_time;
    -- Get the first ventilation period which is >  48 hours.
    , first_vent_gt_48hrs as (
    select distinct subject_id,
           first_value(begin_time) over (partition by subject_id order by begin_time) as begin_time,
           first_value(end_time) over (partition by subject_id order by begin_time) as end_time
      from combine_ventilation where end_time - begin_time > interval '48' hour
    --select * from first_vent_gt_48hrs;
    -- Find the ICU stay when it occurred
    , icustay_first_vent_gt_48hrs as (
    select fvgt48h.subject_id,
           icus.icustay_id,
           fvgt48h.begin_time,
           fvgt48h.end_time
      from first_vent_gt_48hrs fvgt48h
      join mimic2devel.ventilation icus on icus.subject_id = fvgt48h.subject_id and fvgt48h.begin_time = icus.begin_time
    --select /*+gather_plan_statistics*/ * from icustay_first_vent_gt_48hrs;
    , pao2_fio2_during_ventilation as (
    select /*+ NO_USE_NL(le ifvgt48h) */
           le.subject_id,
           le.hadm_id,
           le.icustay_id,
           charttime,
           case when itemid = 50019 then 'PAO2'
                when itemid = 50013 then 'FIO2'
           end as item_type,
           -- Some FIO2s are fractional instead of percentage
           case when itemid = 50013 and valuenum > 1 then round(valuenum / 100,2)
                else round(valuenum,2)
           end as valuenum
      from mimic2v26.labevents le
      join icustay_first_vent_gt_48hrs ifvgt48h on ifvgt48h.icustay_id = le.icustay_id and le.charttime between ifvgt48h.begin_time and ifvgt48h.end_time
    where le.itemid = 50019 or le.itemid = 50013
    --select * from pao2_fio2_during_ventilation;
    -- Check that FIO2s have valid range
    , vent_data_pivot as (
    select * from (
        select subject_id, hadm_id, icustay_id, charttime, item_type, valuenum from pao2_fio2_during_ventilation)
        pivot ( max(valuenum) as valuenum for item_type in ('FIO2' as fio2, 'PAO2' as pao2) )
    --select * from vent_data_pivot;
    -- Fill in prior FIO2 from chartevents
    , get_prior_fio2s as (
    select /*+ NO_USE_NL(vdp ce) */
           distinct
           vdp.subject_id,
           vdp.hadm_id,
           vdp.icustay_id,
           vdp.charttime as pao2_charttime,
           vdp.fio2_valuenum,
           vdp.pao2_valuenum,
    --       ce.itemid,
    --       ce.charttime as chart_charttime,
    --       ce.value1num,
           first_value(ce.value1num) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc) as most_recent_fio2_raw,
           case when first_value(ce.value1num) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc) > 1
              then round(first_value(ce.value1num) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc) / 100,2)
              else round(first_value(ce.value1num) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc),2)
           end as most_recent_fio2,
           first_value(ce.charttime) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc) as most_recent_fio2_charttime,
           vdp.charttime - first_value(ce.charttime) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc) as time_since_fio2,
    --       first_value(ce.charttime) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc) as most_recent_charttime
           case when first_value(ce.value1num) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc) > 1
              then round(vdp.pao2_valuenum/(first_value(ce.value1num) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc) / 100),2)
              else round(vdp.pao2_valuenum/(first_value(ce.value1num) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc)),2)
           end as pf_ratio,
           case when first_value(ce.value1num) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc) > 1
              then
                case when vdp.pao2_valuenum/(first_value(ce.value1num) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc) / 100) < 200 then 1 else 0 end
              else
                case when vdp.pao2_valuenum/(first_value(ce.value1num) over (partition by ce.icustay_id, vdp.charttime order by ce.charttime desc)) < 200 then 1 else 0 end
           end as pf_ratio_below_thresh
      from vent_data_pivot vdp
      join mimic2v26.chartevents ce on vdp.icustay_id = ce.icustay_id and ce.charttime < vdp.charttime
    where itemid in (190,3420,186,185)
    --select * from get_prior_fio2s order by icustay_id, charttime;
    , pf_data as (
    select subject_id,
           hadm_id,
           icustay_id,
           pao2_charttime,
           lead(pao2_charttime) over (partition by icustay_id order by pao2_charttime) as next_pao2_charttime,
           fio2_valuenum,
           pao2_valuenum,
           lead(pao2_valuenum) over (partition by icustay_id order by pao2_charttime) as next_pao2_valuenum,
           most_recent_fio2_raw,
           most_recent_fio2,
           most_recent_fio2_charttime,
           time_since_fio2,
           pf_ratio,
           lead(pf_ratio) over (partition by icustay_id order by pao2_charttime) as next_pf_ratio,
           pf_ratio_below_thresh,
           lead(pf_ratio_below_thresh) over (partition by icustay_id order by pao2_charttime) as next_pf_ratio_below_thresh
      from get_prior_fio2s
    select * from pf_data;Table structure is available here:
    http://mimic.physionet.org/schema/latest/
    Can I still get a TKPROF if the query doesn't complete? I'll have a go and post the results shortly.
    Thanks,
    Dan

  • How does the CBO calculate the selectivity for range predicates on ROWID ?

    Hi all,
    I'm wondering how the CBO estimate the selectivity for range predicates based on ROWID columns.
    For example, for the following query the CBO estimates there's going to be 35004 rows returned instead of 7:
    SQL> SELECT count(*)
      FROM intsfi i
    WHERE
    ROWID>='AAADxyAAWAAHDLIAAB' AND ROWID<='AAADxyAAWAAHDLIAAH';  2    3    4
      COUNT(*)
             7
    Elapsed: 00:00:02.31
    SQL> select * from table(dbms_xplan.display_cursor(null,null,'iostats last'));
    PLAN_TABLE_OUTPUT
    SQL_ID  aqbdu2p2t6w0z, child number 1
    SELECT count(*)   FROM intsfi i  WHERE  ROWID>='AAADxyAAWAAHDLIAAB' AND
    ROWID<='AAADxyAAWAAHDLIAAH'
    Plan hash value: 1610739540
    | Id  | Operation             | Name    | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
    |   0 | SELECT STATEMENT      |         |      1 |        |      1 |00:00:02.31 |   68351 |
    |   1 |  SORT AGGREGATE       |         |      1 |      1 |      1 |00:00:02.31 |   68351 |
    |*  2 |   INDEX FAST FULL SCAN| INTSFI2 |      1 |  35004 |      7 |00:00:02.31 |   68351 |
    Predicate Information (identified by operation id):
       2 - filter((ROWID>='AAADxyAAWAAHDLIAAB' AND ROWID<='AAADxyAAWAAHDLIAAH'))According to Jonathan Lewis' book, for a normal column the selectivity would have been:
    (value_column1-value_column2)/(high_value-low_value)+1/num_distinct+1/num_distinct
    But here with the ROWID column, how does the CBO make its computation ?
    SINGLE TABLE ACCESS PATH
      Single Table Cardinality Estimation for INTSFI[I]
      Table: INTSFI  Alias: I
        Card: Original: 14001681.000000  Rounded: 35004  Computed: 35004.20  Non Adjusted: 35004.20

    Hi Jonathan,
    Some Clarifications
    =============
    DELETE /*+ ROWID(I) */ FROM INTSFI I WHERE
    (I.DAVAL<=TO_DATE('12032008','DDMMYYYY') AND (EXISTS(SELECT 1 FROM
    INTSFI S WHERE S.COINT=I.COINT AND S.NUCPT=I.NUCPT AND S.CTSIT=I.CTSIT
    AND NVL(S.RGCID,-1)=NVL(I.RGCID,-1) AND S.CODEV=I.CODEV AND
    S.COMAR=I.COMAR AND S.DAVAL>I.DAVAL) AND I.COMAR IN (SELECT P.COMAR
    FROM PURMAR P WHERE P.NUPUR=1))) AND ROWID>='AAADxyAAWAAHDLIAAB' AND
    ROWID<='AAADxyAAWAAHDLIAAH'
    Plan hash value: 1677274993
    | Id  | Operation                      | Name    | Starts | E-Rows | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
    |   0 | DELETE STATEMENT               |         |      1 |        |      0 |00:00:05.94 |   53247 |    |          |          |
    |   1 |  DELETE                        | INTSFI  |      1 |        |      0 |00:00:05.94 |   53247 |    |          |          |
    |*  2 |   HASH JOIN SEMI               |         |      1 |   9226 |      7 |00:00:05.94 |   53180 |   783K|   783K|  471K (0)|
    |   3 |    NESTED LOOPS                |         |      1 |   9226 |      7 |00:00:00.01 |      10 |    |          |          |
    |*  4 |     TABLE ACCESS BY ROWID RANGE| INTSFI  |      1 |   9226 |      7 |00:00:00.01 |       6 |    |          |          |
    |*  5 |     INDEX UNIQUE SCAN          | PURMAR1 |      7 |      1 |      7 |00:00:00.01 |       4 |    |          |          |
    |   6 |    INDEX FAST FULL SCAN        | INTSFI1 |      1 |     14M|   7543K|00:00:01.73 |   53170 |    |          |          |
    Predicate Information (identified by operation id):
       2 - access("S"."COINT"="I"."COINT" AND "S"."NUCPT"="I"."NUCPT" AND "S"."CTSIT"="I"."CTSIT" AND
                  NVL("S"."RGCID",(-1))=NVL("I"."RGCID",(-1)) AND "S"."CODEV"="I"."CODEV" AND "S"."COMAR"="I"."COMAR")
           filter("S"."DAVAL">"I"."DAVAL")
       4 - access(ROWID>='AAADxyAAWAAHDLIAAB' AND ROWID<='AAADxyAAWAAHDLIAAH')
           filter("I"."DAVAL"<=TO_DATE(' 2008-03-12 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))
       5 - access("P"."NUPUR"=1 AND "I"."COMAR"="P"."COMAR")
    When I force the NESTED LOOP SEMI JOIN the query runs faster:
    DELETE /*+ ROWID(I) */ FROM INTSFI I WHERE
    (I.DAVAL<=TO_DATE('12032008','DDMMYYYY') AND (EXISTS(SELECT /*+ NL_SJ
    */ 1 FROM INTSFI S WHERE S.COINT=I.COINT AND S.NUCPT=I.NUCPT AND
    S.CTSIT=I.CTSIT AND NVL(S.RGCID,-1)=NVL(I.RGCID,-1) AND S.CODEV=I.CODEV
    AND S.COMAR=I.COMAR AND S.DAVAL>I.DAVAL) AND I.COMAR IN (SELECT P.COMAR
    FROM PURMAR P WHERE P.NUPUR=1))) AND ROWID>='AAADxyAAWAAHDLIAAB' AND
    ROWID<='AAADxyAAWAAHDLIAAH'
    Plan hash value: 2031485112
    | Id  | Operation                      | Name    | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
    |   0 | DELETE STATEMENT               |         |      1 |        |      0 |00:00:00.01 |      94 |
    |   1 |  DELETE                        | INTSFI  |      1 |        |      0 |00:00:00.01 |      94 |
    |   2 |   NESTED LOOPS SEMI            |         |      1 |   9226 |      7 |00:00:00.01 |      27 |
    |   3 |    NESTED LOOPS                |         |      1 |   9226 |      7 |00:00:00.01 |       9 |
    |*  4 |     TABLE ACCESS BY ROWID RANGE| INTSFI  |      1 |   9226 |      7 |00:00:00.01 |       5 |
    |*  5 |     INDEX UNIQUE SCAN          | PURMAR1 |      7 |      1 |      7 |00:00:00.01 |       4 |
    |*  6 |    INDEX RANGE SCAN            | INTSFI1 |      7 |     14M|      7 |00:00:00.01 |      18 |
    Predicate Information (identified by operation id):
       4 - access(ROWID>='AAADxyAAWAAHDLIAAB' AND ROWID<='AAADxyAAWAAHDLIAAH')
           filter("I"."DAVAL"<=TO_DATE(' 2008-03-12 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))
       5 - access("P"."NUPUR"=1 AND "I"."COMAR"="P"."COMAR")
       6 - access("S"."COINT"="I"."COINT" AND "S"."NUCPT"="I"."NUCPT" AND
                  "S"."CTSIT"="I"."CTSIT" AND "S"."CODEV"="I"."CODEV" AND "S"."COMAR"="I"."COMAR" AND
                  "S"."DAVAL">"I"."DAVAL")
           filter(NVL("S"."RGCID",(-1))=NVL("I"."RGCID",(-1)))the above post is from Ahmed AANGOUR
    Case 1 - . If you check Plan hash value: 16772749938
    =====
    TABLE ACCESS BY ROWID RANGE| INTSFI  For every row access from INTSFI - it fetches a record from INDEX UNIQUE SCAN | PURMAR1
    If we check A-rows = 9226
    9226 * 7 = 64582 request across the table - perhaps with hint of rowid it fetches exact rows from PURMAR1
    in this case i think going for hash join with ordered hints (jonathan as you suggest go for leading hint's instead of ordered) - from INTSFI - PURMAR1 - instead of going for IN clause would get the rows that satifies the ("P"."NUPUR"=1 AND "I"."COMAR"="P"."COMAR")
    |*  2 |   HASH JOIN SEMI               |         |      1 |   9226 |      7 |00:00:05.94 |   53180 |   783K|   783K|  471K (0)|
    |   3 |    NESTED LOOPS                |         |      1 |   9226 |      7 |00:00:00.01 |      10 |    |          |          |
    |*  4 |     TABLE ACCESS BY ROWID RANGE| INTSFI  |      1 |   9226 |      7 |00:00:00.01 |       6 |    |          |          |
    |*  5 |     INDEX UNIQUE SCAN          | PURMAR1 |      7 |      1 |      7 |00:00:00.01 |       4 |    |          |          |My understanding with above plan would change to
    HASH JOIN
    TABLE ACCESS BY ROWID RANGE| INTSFI
    INDEX UNIQUE SCAN | PURMAR1
    HASH JOIN
    INDEX FAST FULL SCAN | INTSFI1
    Which migt be feasible.
    2 .
    DELETE /*+ ROWID(I) */ FROM INTSFI I WHERE
    (I.DAVAL<=TO_DATE('12032008','DDMMYYYY') AND (EXISTS(SELECT /*+ NL_SJ
    */ 1 FROM INTSFI S WHERE S.COINT=I.COINT AND S.NUCPT=I.NUCPT AND
    S.CTSIT=I.CTSIT AND NVL(S.RGCID,-1)=NVL(I.RGCID,-1) AND S.CODEV=I.CODEV
    AND S.COMAR=I.COMAR AND S.DAVAL>I.DAVAL) AND I.COMAR IN (SELECT P.COMAR
    FROM PURMAR P WHERE P.NUPUR=1))) AND ROWID>='AAADxyAAWAAHDLIAAB' AND
    ROWID<='AAADxyAAWAAHDLIAAH'Ahmed AANGOUR, modified the query by /*+ NL_SJ */ hint, Instead of that in to remove the most of the rows as we join the tables using subquery, I still doubt it
    to go push_predicate hints - still doubt it.
    Jonathan your comments are most valuable in the above two cases..
    Looking forward to calrify my understanding with concepts of indexes for above test cases
    - Pavan Kumar N

  • Wrong cardinality with date literal

    Hi guys,
    There is table T with column D (DATE). The table consists of data from 1999 year.
    NUM_ROWS = 33.7M
    The column D does NOT have histogram.
    There is index on the column D (and columns C1, C2).
    create index ... on T (D, C1, C2)There is a SQL and plan
    SQL> explain plan for
      2  select *
      3    from t
      4   where t.D > to_date('01.01.2010', 'dd.mm.rrrr')
      5  ;
    Explained
    SQL> @plan
    Session altered
    Plan hash value: 2411994532
    | Id  | Operation                   | Name                  | Rows  | Bytes | Cost (%CPU)| Time     |
    |   0 | SELECT STATEMENT            |                       |     1 |   278 |     5   (0)| 00:00:01 |
    |   1 |  TABLE ACCESS BY INDEX ROWID| T                     |     1 |   278 |     5   (0)| 00:00:01 |
    |*  2 |   INDEX RANGE SCAN          | D                     |     1 |       |     4   (0)| 00:00:01 |
    Predicate Information (identified by operation id):
       2 - access("T"."D">TO_DATE(' 2010-01-01 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))
    14 rows selectedNotice, cardinality = 1 (!!!) Why?
    When min data is 1999 year, and max is 2010 year in the SQL above I expect get cardinality about 1/10 of NUM_ROWS.
    But cardinality = 1.
    This is the question.
    As I know (68992.1), in this case selectivity is calculate as 1 - (High - Value / High - Low). But in my 10053 trace I see very small value 1.6733e-08.
    SINGLE TABLE ACCESS PATH
      BEGIN Single Table Cardinality Estimation
      Column (#25): D(DATE)
        AvgLen: 8.00 NDV: 7765172 Nulls: 0 Density: 1.2878e-07 Min: 2451269 Max: 2453250
      Using prorated density: 1.6733e-08 of col #25 as selectivity of out-of-range value pred
      Table: T  Alias: T    
        Card: Original: 29880736  Rounded: 1  Computed: 0.50  Non Adjusted: 0.50
      END   Single Table Cardinality Estimation
      Access Path: TableScan
        Cost:  785120.33  Resp: 785120.33  Degree: 0
          Cost_io: 572319.00  Cost_cpu: 57593829444
          Resp_io: 572319.00  Resp_cpu: 57593829444
      Using prorated density: 1.6733e-08 of col #25 as selectivity of out-of-range value pred
      Using prorated density: 1.6733e-08 of col #25 as selectivity of out-of-range value pred
      Access Path: index (RangeScan)
        Index: TIDX
        resc_io: 5.00  resc_cpu: 31164
        ix_sel: 1.6733e-08  ix_sel_with_filters: 1.6733e-08
        Cost: 5.12  Resp: 5.12  Degree: 1And the second one - how can I translate HIGH_VALUE and LOW_VALUE from dba_tab_col_statistics from RAW to DATE?

    793769 wrote:
    SINGLE TABLE ACCESS PATH
    BEGIN Single Table Cardinality Estimation
    Column (#25): D(DATE)
    AvgLen: 8.00 NDV: 7765172 Nulls: 0 Density: 1.2878e-07 Min: 2451269 Max: 2453250
    Using prorated density: 1.6733e-08 of col #25 as selectivity of out-of-range value predAnd the second one - how can I translate HIGH_VALUE and LOW_VALUE from dba_tab_col_statistics from RAW to DATE?I know you've said that the question is answered - but
    a) The "out of range value" is a good clue that the low/high values are not what you expect
    b) For dates, the values are the Julian format of the date, and in your case we see:
    SQL> select to_char(to_date('01.01.2010','dd.mm.rrrr'),'J') from dual;
    TO_CHAR
    2455198
    1 row selected.Your high value is well below 1st Jan 2010.
    The value you have can be derived from :
    SQL> select to_char(to_date('2453250','J'),'dd-mon-yyyy') from dual;
    TO_CHAR(TO_
    01-sep-2004
    1 row selected.Regards
    Jonathan Lewis

  • Wrong optimizer estimate ?

    Hi all,
    while doing performance tests of some new queries in our application I noticed
    one query where the estimated rows differ much from the actual rows in the
    execution plan (Line 7 in the attached execution plan). Now I ask you kindly
    to help me finding out if this is a bug in the optimizers estimation or is
    there another reason (maybe strange data) which could explain the difference.
    Fortunately there is no performance problem with the query yet, but I've
    learned that even small differences in data combined with small
    miscalculations may have a huge impact on the query performance in the future.
    For better readability I attached the tests I did so far at the end of this message.
    If there are any numbers, statistics ... helping to debug this further, please let
    me know and I'll post them to the list.
    In the tests you'll find the query in question along with the
    execution plan gathered with the gather_plan_statistics hint. There are two
    executions plans with two different sets of optimizer statistics. (first test
    without histograms, the second test with histograms). Also attached the column
    stats for the main table involved. The table t_sendung_import1 has only 1 row.
    I did the tests on 11.2.0.2
    Thank you very much in advance for helping my deepen my oracle knowledge.
    Very best regards
    Michael
    PLAN_TABLE_OUTPUT (without histograms)
    BEGIN
       DBMS_STATS.
       GATHER_TABLE_STATS (ownname            => 'xxx',
                           tabname            => 'produkttitelinstanzen',
                           estimate_percent   => NULL,
                           method_opt         => 'for all columns size 1',
                           cascade            => TRUE);
    END;
    SQL_ID  0x4sfjdj0wshv, child number 0
    select  /*+ gather_plan_statistics */
            pt1.id quellprodukt_id,
             pt2.id zielprodukt_id
    from   
            t_sendung_import1 timp,
            produkttitelinstanzen pt1,
            produkttitelinstanzen pt2
    where  
            timp.id                    = pt1.produkt_id
         and pt1.produkt_id        <> pt2.produkt_id
        and pt1.titelsuche_id          = pt2.titelsuche_id
         and pt1.objektbereich_id   = 0
         and pt1.objektbereich_id   = pt2.objektbereich_id
         and pt1.produkttitelart_id = 1
         and pt1.produkttitelart_id = pt2.produkttitelart_id
         and pt1.reihenfolge        = 0
         and pt1.reihenfolge        = pt2.reihenfolge
         and timp.jobid = 1111
    Plan hash value: 2472102189
    | Id  | Operation                      | Name                  | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
    |   0 | SELECT STATEMENT               |                       |      1 |        |    197 |00:00:00.01 |     125 |
    |   1 |  NESTED LOOPS                  |                       |      1 |        |    197 |00:00:00.01 |     125 |
    |   2 |   NESTED LOOPS                 |                       |      1 |      1 |    198 |00:00:00.01 |      25 |
    |   3 |    NESTED LOOPS                |                       |      1 |      1 |      1 |00:00:00.01 |       7 |
    |*  4 |     INDEX RANGE SCAN           | T_SENDUNG_IMPORT1_PK  |      1 |      1 |      1 |00:00:00.01 |       2 |
    |   5 |     TABLE ACCESS BY INDEX ROWID| PRODUKTTITELINSTANZEN |      1 |      1 |      1 |00:00:00.01 |       5 |
    |*  6 |      INDEX RANGE SCAN          | PRTI_UK2_I            |      1 |      1 |      1 |00:00:00.01 |       4 |
    |*  7 |    INDEX RANGE SCAN            | PRTI_TISU_FK_I        |      1 |     12 |    198 |00:00:00.01 |      18 |
    |*  8 |   TABLE ACCESS BY INDEX ROWID  | PRODUKTTITELINSTANZEN |    198 |      1 |    197 |00:00:00.01 |     100 |
    Predicate Information (identified by operation id):
       4 - access("TIMP"."JOBID"=1111)
       6 - access("TIMP"."ID"="PT1"."PRODUKT_ID" AND "PT1"."PRODUKTTITELART_ID"=1 AND
                  "PT1"."OBJEKTBEREICH_ID"=0 AND "PT1"."REIHENFOLGE"=0)
       7 - access("PT1"."TITELSUCHE_ID"="PT2"."TITELSUCHE_ID")
       8 - filter(("PT2"."REIHENFOLGE"=0 AND "PT2"."OBJEKTBEREICH_ID"=0 AND "PT2"."PRODUKTTITELART_ID"=1 AND
                  "PT1"."PRODUKT_ID"<>"PT2"."PRODUKT_ID"))
    PLAN_TABLE_OUTPUT (with histograms)
    BEGIN
       DBMS_STATS.
       GATHER_TABLE_STATS (ownname            => 'xxx',
                           tabname            => 'produkttitelinstanzen',
                           estimate_percent   => NULL,
                           method_opt         => 'for all columns size auto',
                           cascade            => TRUE);
    END;
    SQL_ID  6k2stg2srtq2w, child number 0
    select  /*+ gather_plan_statistics */
            pt1.id quellprodukt_id,
             pt2.id zielprodukt_id
    from   
            t_sendung_import1 timp,
            produkttitelinstanzen pt1,
            produkttitelinstanzen pt2
    where  
            timp.id                    = pt1.produkt_id
         and pt1.produkt_id        <> pt2.produkt_id
        and pt1.titelsuche_id          = pt2.titelsuche_id
         and pt1.objektbereich_id   = 0
         and pt1.objektbereich_id   = pt2.objektbereich_id
         and pt1.produkttitelart_id = 1
         and pt1.produkttitelart_id = pt2.produkttitelart_id
         and pt1.reihenfolge        = 0
         and pt1.reihenfolge        = pt2.reihenfolge
         and timp.jobid = 1111
    Plan hash value: 2472102189
    | Id  | Operation                      | Name                  | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
    |   0 | SELECT STATEMENT               |                       |      1 |        |    197 |00:00:00.01 |     129 |
    |   1 |  NESTED LOOPS                  |                       |      1 |        |    197 |00:00:00.01 |     129 |
    |   2 |   NESTED LOOPS                 |                       |      1 |  39799 |    198 |00:00:00.01 |      29 |
    |   3 |    NESTED LOOPS                |                       |      1 |      1 |      1 |00:00:00.01 |      11 |
    |*  4 |     INDEX RANGE SCAN           | T_SENDUNG_IMPORT1_PK  |      1 |      1 |      2 |00:00:00.01 |       3 |
    |   5 |     TABLE ACCESS BY INDEX ROWID| PRODUKTTITELINSTANZEN |      2 |      1 |      1 |00:00:00.01 |       8 |
    |*  6 |      INDEX RANGE SCAN          | PRTI_UK2_I            |      2 |      1 |      1 |00:00:00.01 |       7 |
    |*  7 |    INDEX RANGE SCAN            | PRTI_TISU_FK_I        |      1 |     11 |    198 |00:00:00.01 |      18 |
    |*  8 |   TABLE ACCESS BY INDEX ROWID  | PRODUKTTITELINSTANZEN |    198 |  57767 |    197 |00:00:00.01 |     100 |
    Predicate Information (identified by operation id):
       4 - access("TIMP"."JOBID"=1111)
       6 - access("TIMP"."ID"="PT1"."PRODUKT_ID" AND "PT1"."PRODUKTTITELART_ID"=1 AND
                  "PT1"."OBJEKTBEREICH_ID"=0 AND "PT1"."REIHENFOLGE"=0)
       7 - access("PT1"."TITELSUCHE_ID"="PT2"."TITELSUCHE_ID")
       8 - filter(("PT2"."PRODUKTTITELART_ID"=1 AND "PT2"."REIHENFOLGE"=0 AND "PT2"."OBJEKTBEREICH_ID"=0 AND
                  "PT1"."PRODUKT_ID"<>"PT2"."PRODUKT_ID"))
    39 rows selected.
    COLUMN_NAME                    NUM_DISTINCT                           LOW_VALUE                                                                                                                        HIGH_VALUE                                                                                                                       DENSITY                                NUM_NULLS                              AVG_COL_LEN                            NUM_BUCKETS                            HISTOGRAM      
    ID                             22878255                               C102                                                                                                                             C432034B63                                                                                                                       4,37096273295319E-8                    0                                      6                                      1                                      NONE           
    OBJEKTBEREICH_ID               113                                    80                                                                                                                               C403011A04                                                                                                                       2,1854813664766E-8                     0                                      3                                      113                                    FREQUENCY      
    TITELTEXT_ID                   2129449                                C415010102                                                                                                                       C419190123                                                                                                                       0,000602515818749885                   0                                      6                                      254                                    HEIGHT BALANCED
    TITELSUCHE_ID                  1992312                                C415010102                                                                                                                       C41909231C                                                                                                                       0,000740863626550299                   0                                      6                                      254                                    HEIGHT BALANCED
    REIHENFOLGE                    393                                    80                                                                                                                               C2045D                                                                                                                           0,0020138646157787                     0                                      3                                      254                                    HEIGHT BALANCED
    PRODUKT_ID                     8581895                                C40B1B5923                                                                                                                       C50329633844                                                                                                                     1,16524380687482E-7                    0                                      7                                      1                                      NONE           
    PRODUKTTITELART_ID             9                                      80                                                                                                                               C109                                                                                                                             2,1854813664766E-8                     0                                      3                                      9                                      FREQUENCY      
    ORIGINALTITELINSTANZ_ID        22874695                               C42A563F05 

    Yes, these are two different testcases with the same query, one without histograms (size 1) and one with historgrams (size auto) to see if the optimizer makes better estimates with histograms which seems not to be the case.

  • Re: Confidence of Cardinality Estimates (CBO)

    Iordan Iotzov wrote:
    Teradata has the concept of confidence of cardinality estimates -
    http://developer.teradata.com/database/articles/can-we-speak-confidentially-exposing-explain-confidence-levels
    In short, the optimizer tries to figure out the amount of “guesswork” that is included into a cardinality estimate.
    Is there anything similar in Oracle? I am looking for anything - supported or not!
    Nice idea, but no.
    (although, internally, there are points where it does know that it is guessing (which is where optimizer_dynamic_sampliing at level 3 comes into play) or where it is aware that predicate independence is not a realistic assumption (which is where level 4 comes into play)).
    Regards
    Jonathan Lewis

    Hi Jonathan,
    In my limited 12c experience, presence or absence of adaptive execution plans (STATISTICS COLLECTOR) is a proxy for cardinality confidence only in few situations.
    That is, Oracle 12c generates an adaptive execution plan for most joins, even for joins in simple queries that pose no cardinality confidence challenges.
    From what I have seen so far, the only situation where the CBO is confident enough about its estimates to skip the adaptive execution plan step (STATISTICS COLLECTOR) is for single value primary key/unique index scans.
    One possible explanation is that the cost of adding an adaptive execution plan step is so low, that Oracle adds it almost indiscriminately.
    I ran some tests ( http://wp.me/p1DHW2-7o ) and came across some interesting results:
         ->The cost of adaptive execution plan is actually negative for nested loops. That is, turning adaptive execution plans off would make a nested loop run slower. The difference is small, but it seems to be statistically significant.
         ->The cost of adaptive exec plan is negligible for hash joins. That is, no statistically significant difference was found between HJ queries using adaptive exec plans and those that do not use adaptive exec
    plans.
    Regards,
    Iordan Iotzov

  • Confidence of Cardinality Estimates (CBO)

    Teradata has the concept of confidence of cardinality estimates -
    http://developer.teradata.com/database/articles/can-we-speak-confidentially-exposing-explain-confidence-levels
    In short, the optimizer tries to figure out the amount of “guesswork” that is included into a cardinality estimate.
    Is there anything similar in Oracle? I am looking for anything - supported or not!
    Thanks,
    Iordan Iotzov
    http://iiotzov.wordpress.com/

    Hi Jonathan,
    In my limited 12c experience, presence or absence of adaptive execution plans (STATISTICS COLLECTOR) is a proxy for cardinality confidence only in few situations.
    That is, Oracle 12c generates an adaptive execution plan for most joins, even for joins in simple queries that pose no cardinality confidence challenges.
    From what I have seen so far, the only situation where the CBO is confident enough about its estimates to skip the adaptive execution plan step (STATISTICS COLLECTOR) is for single value primary key/unique index scans.
    One possible explanation is that the cost of adding an adaptive execution plan step is so low, that Oracle adds it almost indiscriminately.
    I ran some tests ( http://wp.me/p1DHW2-7o ) and came across some interesting results:
         ->The cost of adaptive execution plan is actually negative for nested loops. That is, turning adaptive execution plans off would make a nested loop run slower. The difference is small, but it seems to be statistically significant.
         ->The cost of adaptive exec plan is negligible for hash joins. That is, no statistically significant difference was found between HJ queries using adaptive exec plans and those that do not use adaptive exec
    plans.
    Regards,
    Iordan Iotzov

  • What is mean by index range scan and fast full scan

    What is mean by the following execution plans
    1)Table access by index rowid
    2) Index range scan
    3) Index fast full scan
    4) global index by rowid
    ..etc
    where i can get this information.In what situation CBO take these paths.Can you pls give me a link where i can find all these.I read these long time ago but not able to recollect
    Thanks
    Anand

    Oracle® Database Performance Tuning Guide
    10g Release 2 (10.2)
    Part Number B14211-01
    13.5 Understanding Access Paths for the Query Optimizer
    http://download-east.oracle.com/docs/cd/B19306_01/server.102/b14211/optimops.htm#sthref1281

  • Taking more time in INDEX RANGE SCAN compare to the full table scan

    Hi all ,
    Below are the version og my database.
    SQL> select * from v$version;
    BANNER
    Oracle Database 10g Enterprise Edition Release 10.2.0.4.0 - 64bi
    PL/SQL Release 10.2.0.4.0 - Production
    CORE 10.2.0.4.0 Production
    TNS for HPUX: Version 10.2.0.4.0 - Production
    NLSRTL Version 10.2.0.4.0 - Production
    I have gather the table statistics and plan change for sql statment.
    SELECT P1.COMPANY, P1.PAYGROUP, P1.PAY_END_DT, P1.PAYCHECK_OPTION,
    P1.OFF_CYCLE, P1.PAGE_NUM, P1.LINE_NUM, P1.SEPCHK  FROM  PS_PAY_CHECK P1
    WHERE P1.FORM_ID = :1 AND P1.PAYCHECK_NBR = :2 AND
    P1.CHECK_DT = :3 AND P1.PAYCHECK_OPTION <> 'R'
    Plan before the gather stats.
    Plan hash value: 3872726522
    | Id  | Operation         | Name         | Rows  | Bytes | Cost (%CPU)| Time     |
    |   0 | SELECT STATEMENT  |              |       |       | *14306* (100)|          |
    |   1 |  *TABLE ACCESS FULL| PS_PAY_CHECK* |     1 |    51 | 14306   (4)| 00:02:52 |
    Plan after the gather stats:
    Operation     Object Name     Rows     Bytes     Cost
    SELECT STATEMENT Optimizer Mode=CHOOSE
              1           4
      *TABLE ACCESS BY INDEX ROWID     SYSADM.PS_PAY_CHECK*     1     51     *4*
        *INDEX RANGE SCAN     SYSADM.PS0PAY_CHECK*     1           3After gather stats paln look good . but when i am exeuting the query it take 5 hours. before the gather stats it finishing the within 2 hours. i do not want to restore my old statistics. below are the data for the tables.and when i am obserrving it lot of db files scatter rea
    NAME                                 TYPE        VALUE
    _optimizer_cost_based_transformation string      OFF
    filesystemio_options                 string      asynch
    object_cache_optimal_size            integer     102400
    optimizer_dynamic_sampling           integer     2
    optimizer_features_enable            string      10.2.0.4
    optimizer_index_caching              integer     0
    optimizer_index_cost_adj             integer     100
    optimizer_mode                       string      choose
    optimizer_secure_view_merging        boolean     TRUE
    plsql_optimize_level                 integer     2
    SQL> select count(*) from sysadm.ps_pay_check;
    select num_rows,blocks from dba_tables where table_name ='PS_PAY_CHECK';
      COUNT(*)
       1270052
    SQL> SQL> SQL>
      NUM_ROWS     BLOCKS
       1270047      63166
    Event                                 Waits    Time (s)   (ms)   Time Wait Class
    db file sequential read           1,584,677       6,375      4   36.6   User I/O
    db file scattered read            2,366,398       5,689      2   32.7   User I/Oplease let me know why it taking more time in INDEX RANGE SCAN compare to the full table scan?

    suresh.ratnaji wrote:
    NAME                                 TYPE        VALUE
    _optimizer_cost_based_transformation string      OFF
    filesystemio_options                 string      asynch
    object_cache_optimal_size            integer     102400
    optimizer_dynamic_sampling           integer     2
    optimizer_features_enable            string      10.2.0.4
    optimizer_index_caching              integer     0
    optimizer_index_cost_adj             integer     100
    optimizer_mode                       string      choose
    optimizer_secure_view_merging        boolean     TRUE
    plsql_optimize_level                 integer     2
    please let me know why it taking more time in INDEX RANGE SCAN compare to the full table scan?Suresh,
    Any particular reason why you have a non-default value for a hidden parameter, optimizercost_based_transformation ?
    On my 10.2.0.1 database, its default value is "linear". What happens when you reset the value of the hidden parameter to default?

  • Dblink + local function: INDEX RANGE SCAN not used

    Hi All,
    I have an sql query to remote database:
    SELECT N FROM [email protected] WHERE cd_n = 60
    It works with INDEX RANGE SCAN for "N" field of table "tab", it's ok.
    Now I'm replacing the constant value with the local database function:
    SELECT N FROM [email protected] WHERE cd_n = dannis.foo()
    Then 'INDEX RANGE SCAN' is removed out from query execution plan :-(
    I've tried some tricks as
    /*+ rule index(user.tab TAB$PK) */,
    driving_site(tab),
    to_number(dannis.foo()),
    (select dannis.foo from [email protected])
    and so on...
    but INDEX RANGE SCAN wasn't appear while using the local function.
    Is it true when dblink is used in combination with local function then INDEX RANGE SCAN will never used?
    /Oracle 9.0.1.2/
    Thanx,
    dannis.

    See Optimizer not taking the hint

  • Index Range Scan / Deleted Leaf Blocks

    Hello guys,
    i have such a scenario on a big index / table which i can not reproduce on my test database, so i need to know how oracle handles the index range scan.
    For example:
    TABLE TAB with the following columns NR (number), I_DATE (date), TEXT (VARCHAR2(50))
    INDEX I_TAB on the column I_DATE.
    Now the index has blevel 2 and many leaf blocks. And now my question.
    Query: SQL> SELECT * from TAB WHERE I_DATE < 10.10.2004
    The index had stored some values which are a less than 2003 but these ones are already deleted (so the leaf blocks are gone to the freelist), but it was not reorganized.
    The execution plan is a INDEX RANGE SCAN on the INDEX I_TAB. Does the branch block still have pointers to the deleted leaf blocks which contained only 2003 values before (and so the INDEX RANGE SCAN scans all these blocks too) or are the pointers to these leaf blocks deleted in the branch block?
    Thanks and Regards
    Stefan

    You can verify it by yourself. See following:
    SELECT count(*) FROM index_test;
    ==> 1569408
    SELECT count(*) FROM index_test WHERE id <= 2;
    ==> 12
    -- Delete all except first 12 rows
    DELETE FROM index_test WHERE id > 2;
    -- Query and SQL Trace
    BEGIN
    FOR C IN (SELECT /*+index(index_test index_test_idx) deleted */ * FROM INDEX_TEST WHERE ID < 1000000) LOOP
    NULL;
    END LOOP;
    END;
    SELECT /*+index(index_test index_test_idx) deleted */ *
    FROM
    INDEX_TEST WHERE ID < 1000000
    call count cpu elapsed disk query current rows
    Parse 1 0.00 0.00 0 0 0 0
    Execute 1 0.00 0.00 0 0 0 0
    Fetch 1 0.00 0.00 0 3490 0 12
    total 3 0.00 0.01 0 3490 0 12
    ==> 3490 logical reads only for 12 rows and range scan??
    -- Index tree dump
    ALTER SESSION SET EVENTS 'IMMEDIATE TRACE NAME TREEDUMP LEVEL 67513'
    ----- begin tree dump
    branch: 0x1000124 16777508 (0: nrow: 6, level: 2)
    branch: 0x100b1ca 16822730 (-1: nrow: 557, level: 1)
    leaf: 0x1000125 16777509 (-1: nrow: 512 rrow: 12)
    leaf: 0x1000126 16777510 (0: nrow: 484 rrow: 0)
    leaf: 0x1000127 16777511 (1: nrow: 479 rrow: 0)
    leaf: 0x1000128 16777512 (2: nrow: 479 rrow: 0)
    leaf: 0x1000139 16777529 (3: nrow: 479 rrow: 0)
    leaf: 0x100013a 16777530 (4: nrow: 478 rrow: 0)
    branch: 0x100b401 16823297 (0: nrow: 558, level: 1)
    leaf: 0x100b1c9 16822729 (-1: nrow: 449 rrow: 0)
    leaf: 0x100b1cb 16822731 (0: nrow: 449 rrow: 0)
    leaf: 0x100b1cc 16822732 (1: nrow: 449 rrow: 0)
    ==> leaf:3488, branch: 7
    This means that almost all the branch and leaf nodes are read only for 12 keys.
    You can cross check this with the result of "10200" event which traces cr reads. You would find out that the blocks that are read by the query are exactly same as all the index blocks.
    This is what you mean? that the deleted leaf blocks(which contain no actual data) are read by range scan? Through the simple test, the anwer is "yes".

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