Re: Need design suggestion , whether index organized table is good choice here

Hi,

I wouldn't suggest IOT in this case. I'd suggest an additional covering unique index with both columns (you can also switch your PK to use this new index and drop the old one). So you wouldn't need to lookup the table and it would decrease a load to buffer cache. Have you analyzed what percent of that table do you read more often than others?

Best regards,
Sayan Malakshinov
Oracle performance tuning expert
Oracle Database Developer Choice Award winner Oracle ACE
http://orasql.org

сб, 29 янв. 2022 г., 11:57 Pap <oracle.developer35_at_gmail.com>:

> Thank you Lok.
>
> In the sql monitor line no-14 is where it spent ~57% of the time and for
> that it did ~12million read requests and read ~89GB i.e. ~8KB per read
> request to result ~13million rows. And it spent ~1121 seconds in "cell
> single block physical read". So considering these figures , if we do the
> hash partitioning on the same primary key column , will it really give us
> a big difference? I was thinking as this column holds each distinct value ,
> so how beneficial will be the hash partitioning here for the select queries?
>
> Or are you pointing to physical reads bottleneck here, meaning to say the
> index segments will be more cached if they will be broken down to ~1024 or
> 2048 pieces using hash partition and thus may give us better read response
> time. But even then the table access by index rowid on line no-13, is going
> to take the same amount of time. Please correct me if I'm wrong?
>
> On Sat, Jan 29, 2022 at 11:15 AM Lok P <loknath.73_at_gmail.com> wrote:
>
>> What about hash partitioning on your primary key COL1?
>>
>> On Sat, 29 Jan 2022, 12:52 am Pap, <oracle.developer35_at_gmail.com> wrote:
>>
>>> Hello Listers, It's a 11.2.0.4 oracle database. We have one table (say
>>> REF_TAB) which has four columns, two of them(i.e. COL1, masked_COL1) are
>>> actually holding business attributes and the other two are created_date and
>>> created_by_user columns. The length of those two business columns are
>>> varchar2(34 bytes) and varchar2(36 bytes) respectively. And both of these
>>> columns hold all unique/distinct values. Col1 is the one on which the
>>> primary key is defined. The table currently has ~1.5billion rows in it and
>>> its size is ~160GB. It is estimated to grow to hold ~5-8billion rows.
>>>
>>> The table is always being queried on a filter/join on COL1 and that too
>>> as an outer join and will fetch the value of masked_col1. So in short the
>>> requirement is to scan the full table data based on outer join on column
>>> col1 like ( TRAN_TAB2.ANBR=REF_TAB.COL1 (+) ). And below is a sample
>>> query sql monitor showing even that table is getting access using primary
>>> key index but still is consuming all the time(sql_plan_line_id 13 and 14
>>> below). As sql monitor shows , Is those 1hr+ DB time for the 14
>>> million times index unique scan justified? And making it to go for full
>>> using hints is chewing up ~100gb+ temp space too. I can't not think of any
>>> partitioning strategy which can help us here to get the table access
>>> faster, as we have to look up whole column data here for incoming value of
>>> COL1.
>>>
>>> So want to understand how the access path to this table can be made
>>> faster? Will index organized tables be suggested in such a scenario and
>>> will help us in this kind of requirement? Or any other design strategy
>>> required here?
>>>
>>>
>>> Global Information
>>> ------------------------------
>>> Status : EXECUTING
>>>
>>> Instance ID : 4
>>>
>>> SQL Execution ID : 67108973
>>>
>>> Execution Started : 01/27/2022 07:43:01
>>>
>>> First Refresh Time : 01/27/2022 07:43:05
>>>
>>> Last Refresh Time : 01/27/2022 09:04:19
>>>
>>> Duration : 4879s
>>>
>>> Fetch Calls : 2782
>>>
>>>
>>> Global Stats
>>>
>>> =================================================================================================================
>>> | Elapsed | Cpu | IO | Application | Concurrency | Cluster |
>>> Fetch | Buffer | Read | Read | Cell |
>>> | Time(s) | Time(s) | Waits(s) | Waits(s) | Waits(s) | Waits(s) |
>>> Calls | Gets | Reqs | Bytes | Offload |
>>>
>>> =================================================================================================================
>>> | 5469 | 2065 | 2464 | 0.01 | 0.00 | 940 |
>>> 2782 | 124M | 24M | 202GB | 9.09% |
>>>
>>> =================================================================================================================
>>>
>>>
>>> ==============================================================================================================================================================================================================================
>>> | Id | Operation | Name
>>> | Rows | Cost | Time | Start | Execs | Rows | Read |
>>> Read | Cell | Mem | Activity | Activity Detail
>>> |
>>> | | |
>>> | (Estim) | | Active(s) | Active | | (Actual) | Reqs |
>>> Bytes | Offload | | (%) | (# samples)
>>> |
>>>
>>> ==============================================================================================================================================================================================================================
>>> | 0 | SELECT STATEMENT |
>>> | | | 4875 | +4 | 1 | 14M | |
>>> | | | 1.11 | Cpu (53) |
>>> | 1 | NESTED LOOPS OUTER |
>>> | 166K | 1M | 4875 | +4 | 1 | 14M | |
>>> | | | 0.04 | Cpu (2) |
>>> | 2 | VIEW |
>>> | 166K | 901K | 4875 | +4 | 1 | 14M | |
>>> | | | 0.40 | Cpu (19) |
>>> | 3 | NESTED LOOPS OUTER |
>>> | 166K | 901K | 4875 | +4 | 1 | 14M | |
>>> | | | 0.04 | Cpu (2) |
>>> | 4 | HASH JOIN |
>>> | 166K | 402K | 4875 | +4 | 1 | 14M | |
>>> | | 1M | 0.29 | Cpu (14) |
>>> | 5 | JOIN FILTER CREATE | :BF0000
>>> | 1836 | 66073 | 1 | +4 | 1 | 1890 | |
>>> | | | | |
>>> | 6 | TABLE ACCESS STORAGE FULL | STAGE_TAB
>>> | 1836 | 66073 | 5 | +0 | 1 | 1890 | |
>>> | | | 0.02 | gc cr multi block request (1) |
>>> | 7 | JOIN FILTER USE | :BF0000
>>> | 48M | 336K | 4875 | +4 | 1 | 15M | |
>>> | | | 0.13 | Cpu (6) |
>>> | 8 | PARTITION RANGE SINGLE |
>>> | 48M | 336K | 4875 | +4 | 1 | 15M | |
>>> | | | 0.04 | Cpu (2) |
>>> | 9 | TABLE ACCESS STORAGE FULL | TRAN_TAB
>>> | 48M | 336K | 4878 | +1 | 1 | 15M | 11748 |
>>> 11GB | 92.40% | 7M | 0.25 | Cpu (11)
>>> |
>>> | | |
>>> | | | | | | | |
>>> | | | | cell smart table scan (1) |
>>> | 10 | PARTITION RANGE SINGLE |
>>> | 1 | 4 | 4875 | +4 | 14M | 2M | |
>>> | | | 0.53 | Cpu (25) |
>>> | 11 | TABLE ACCESS BY LOCAL INDEX ROWID | TRAN_TAB2
>>> | 1 | 4 | 4875 | +4 | 14M | 2M | 433K |
>>> 3GB | | | 2.52 | gc cr grant 2-way (26) |
>>> | | |
>>> | | | | | | | |
>>> | | | | Cpu (57) |
>>> | | |
>>> | | | | | | | |
>>> | | | | gcs drm freeze in enter server mode (2) |
>>> | | |
>>> | | | | | | | |
>>> | | | | cell single block physical read (35) |
>>> | 12 | INDEX RANGE SCAN | TRAN_TAB2_IX1
>>> | 1 | 3 | 4875 | +4 | 14M | 2M | 271K |
>>> 2GB | | | 2.40 | gc cr grant 2-way (18) |
>>> | | |
>>> | | | | | | | |
>>> | | | | Cpu (63) |
>>> | | |
>>> | | | | | | | |
>>> | | | | cell single block physical read (33) |
>>> | 13 | TABLE ACCESS BY INDEX ROWID | REF_TAB
>>> | 1 | 3 | 4877 | +2 | 14M | 13M | 11M |
>>> 88GB | | | 34.01 | gc cr block 2-way (1)
>>> |
>>> | | |
>>> | | | | | | | |
>>> | | | | gc cr disk read (6) |
>>> | | |
>>> | | | | | | | |
>>> | | | | Cpu (397) |
>>> | | |
>>> | | | | | | | |
>>> | | | | cell single block physical read (1214) |
>>> | -> 14 | INDEX UNIQUE SCAN | REF_TAB_PK
>>> | 1 | 2 | 4876 | +4 | 14M | 13M | 12M |
>>> 89GB | | | 57.09 | gc cr block 2-way (154)
>>> |
>>> | | |
>>> | | | | | | | |
>>> | | | | gc cr block 3-way (14) |
>>> | | |
>>> | | | | | | | |
>>> | | | | gc cr block busy (520) |
>>> | | |
>>> | | | | | | | |
>>> | | | | gc cr disk read (30) |
>>> | | |
>>> | | | | | | | |
>>> | | | | gc cr failure (1) |
>>> | | |
>>> | | | | | | | |
>>> | | | | gc cr grant 2-way (106) |
>>> | | |
>>> | | | | | | | |
>>> | | | | gc current block 2-way (4) |
>>> | | |
>>> | | | | | | | |
>>> | | | | gc current grant 2-way (3) |
>>> | | |
>>> | | | | | | | |
>>> | | | | gc remaster (2) |
>>> | | |
>>> | | | | | | | |
>>> | | | | Cpu (754) |
>>> | | |
>>> | | | | | | | |
>>> | | | | gcs drm freeze in enter server mode (6) |
>>> | | |
>>> | | | | | | | |
>>> | | | | latch: object queue header operation (1) |
>>> | | |
>>> | | | | | | | |
>>> | | | | cell single block physical read (1121) |
>>>
>>> ==============================================================================================================================================================================================================================
>>> Predicate Information (identified by operation id):
>>> ---------------------------------------------------
>>> 4 - access("TRAN_TAB"."SBID"="SFID")
>>> 6 - storage(("PART_DATE"=:B1 AND "ASP_NM"=:B2))
>>> filter(("PART_DATE"=:B1 AND "ASP_NM"=:B2))
>>> 9 - storage(("TRAN_TAB"."PART_DATE1"=:B1 AND
>>> SYS_OP_BLOOM_FILTER(:BF0000,"TRAN_TAB"."SBID")))
>>> filter(("TRAN_TAB"."PART_DATE1"=:B1 AND
>>> SYS_OP_BLOOM_FILTER(:BF0000,"TRAN_TAB"."SBID")))
>>> 11 - filter("TRAN_TAB2"."PART_DATE1"=:B1)
>>> 12 - access("TRAN_TAB"."TX_ID"="TRAN_TAB2"."TX_ID" AND
>>> "TRAN_TAB2"."P_CD"='XX')
>>> 14 - access("TRAN_TAB2"."ANBR"="REF_TAB"."COL1")
>>>
>>>
>>>
>>>

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