Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?

• From: "Mladen Gogala" <dmarc-noreply@xxxxxxxxxxxxx> (Redacted sender "mgogala@xxxxxxxxx" for DMARC)
• To: oracle-l@xxxxxxxxxxxxx
• Date: Tue, 27 Jan 2015 12:05:44 -0500

On 01/27/2015 09:11 AM, Chris Taylor wrote:

Env; 11.2.0.2 Linux 5.8
Machine:
4 CPU (8 Core, 16 Threads E5-4620)
256 GB RAM

This is a "data warehouse" DB but doesn't use DIM/FACT tables - more like a data dump database.

I have a query against 3 tables (2 of which are partitioned) and the estimated TEMP space used for this query is going to be 81 TB. I'm looking for suggestions on how to get this data set. For the partitioned tables, 1 table accesses 1 partition, and the other table uses 2 partitions. I've included the explain plan below and any help/suggestions are appreciated. (I've updated the table stats yesterday after I kept blowing out my 150GB temp space and the estimated temp space used was 22GB. After updating stats, it tells me the estimated Temp Space is going to be 81 TB).

PGA Agg Target is 20 GB right now.

I've explained the query using both the default HASH GROUP BY and a SORT GROUP BY and both estimate using 81 T of temp space.

Here's the query and the plan (let me know if the fixed formatting doesn't show up correctly and I'll use pastebin to format it if needed)

SELECT a.STS_PERF_PERIOD,
       a.PART_ACCT_NO,
       a.PWN_PART_AKA_NME,
       a.PWN_P_W_IND,
       a.AFL_SOCIETY_NME,
       b.sts_CHANNEL_TYPE,
       c.stp_s03_program_cd--TOTAL Music Minutes CALCULATION:
       ,
         SUM (
            CASE
               WHEN STG_STATUS = 'DP'
               THEN
                  0
               ELSE
                    a.perf_count
                  * ( (  a.BMI_FEATURE
                       + a.BMI_BG
                       + a.BMI_LOGO
                       + a.BMI_THEME
                       + a.BMI_BV
                       + a.ASCAP_FEATURE
                       + a.ASCAP_BG
                       + a.ASCAP_LOGO
                       + a.ASCAP_THEME
                       + a.ASCAP_BV
                       + a.FOREIGN_FEATURE
                       + a.FOREIGN_BG
                       + a.FOREIGN_LOGO
                       + a.FOREIGN_THEME
                       + a.FOREIGN_BV
                       + a.PD_FEATURE
                       + a.PD_BG
                       + a.PD_LOGO
                       + a.PD_THEME
                       + a.PD_BV
                       + a.SESAC_FEATURE
                       + a.SESAC_BG
                       + a.SESAC_LOGO
                       + a.SESAC_THEME
                       + a.SESAC_BV
                       + a.NA_FEATURE
                       + a.NA_BG
                       + a.NA_LOGO
                       + a.NA_THEME
                       + a.NA_BV))
            END)
       / 60
          AS "TOT_MUSIC_MINS"--BMI Music Minutes CALCULATION:
       ,
         SUM (
            CASE
               WHEN STG_STATUS = 'DP'
               THEN
                  0
               ELSE
                    a.perf_count
                  * ( (  a.BMI_FEATURE
                       + a.BMI_BG
                       + a.BMI_LOGO
                       + a.BMI_THEME
                       + a.BMI_BV))
            END)
       / 60
          AS "BMI_MUSIC_MINS"
  FROM PRDM.COWS_PURPLE_PART  a, --partition(P20141)
       prdm.cows_TCUE_STS_SCHEDULE b, --partition(P20141,P20142) ,
       prdm.cows_tcue_stp_program c
 WHERE     A.STS_PERF_PERIOD = b.sts_perf_period
       AND A.STS_DPS_TYPE = b.sts_dps_type
       AND A.STG_GROUP_NO = b.sts_group_no
       AND a.stg_group_seq_no = b.sts_group_seq_no
       AND b.sts_provider = c.stp_provider
       AND b.sts_program_no = c.stp_program_no
       AND a.STS_PERF_PERIOD = '20141'
       AND b.sts_perf_source = 'CB'
       AND b.COWS_DIST_PERIOD in ('20141','20142')
       AND a.PWN_P_W_IND = 'W'
       AND c.stp_s03_program_cd <> '36'
GROUP BY a.STS_PERF_PERIOD,
       a.PART_ACCT_NO,
       a.PWN_PART_AKA_NME,
       a.PWN_P_W_IND,
       a.AFL_SOCIETY_NME,
       b.sts_CHANNEL_TYPE,
       c.stp_s03_program_cd
/


Plan hash value: 2657901994
----------------------------------------------------------------------------------------------------------------------------------------------

| Id | Operation | Name | E-Rows |E-Bytes|E-Temp | Cost (%CPU)| E-Time | Pstart| Pstop |

----------------------------------------------------------------------------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 289G| 57T| | 451M (9)|501:19:35 | | | | 1 | HASH GROUP BY | | 289G| 57T| 81T| 451M (9)|501:19:35 | | | |* 2 | HASH JOIN | | 289G| 57T| 42M| 2176K (91)| 02:25:07 | | | | 3 | PART JOIN FILTER CREATE | :BF0000 | 680K| 35M| | 39040 (2)| 00:02:37 | | | |* 4 | HASH JOIN | | 680K| 35M| 4304K| 39040 (2)| 00:02:37 | | | | 5 | INLIST ITERATOR | | | | | | | | | | 6 | PARTITION RANGE ITERATOR | | 80046 | 3361K| | 3063 (1)| 00:00:13 |KEY(I) |KEY(I) | | 7 | TABLE ACCESS BY LOCAL INDEX ROWID| COWS_TCUE_STS_SCHEDULE | 80046 | 3361K| | 3063 (1)| 00:00:13 |KEY(I) |KEY(I) | |* 8 | INDEX RANGE SCAN | COWS_TCUE_STS_SCHED_IDX01 | 9052 | | | 147 (1)| 00:00:01 |KEY(I) |KEY(I) | |* 9 | INDEX FAST FULL SCAN | COWS_TCUE_STP_PROG_IDX01 | 24M| 256M| | 12602 (3)| 00:00:51 | | | | 10 | PARTITION RANGE SINGLE | | 10M| 1582M| | 96246 (2)| 00:06:25 |KEY(AP)|KEY(AP)| |* 11 | TABLE ACCESS FULL | COWS_PURPLE_PART | 10M| 1582M| | 96246 (2)| 00:06:25 | 21 | 21 |

----------------------------------------------------------------------------------------------------------------------------------------------
Query Block Name / Object Alias (identified by operation id):
-------------------------------------------------------------
 1 - SEL$1
 7 - SEL$1 / B@SEL$1
 8 - SEL$1 / B@SEL$1
 9 - SEL$1 / C@SEL$1
11 - SEL$1 / A@SEL$1
Outline Data
-------------
/*+
    BEGIN_OUTLINE_DATA
    USE_HASH_AGGREGATION(@"SEL$1")
    USE_HASH(@"SEL$1" "A"@"SEL$1")
    USE_HASH(@"SEL$1" "C"@"SEL$1")
    LEADING(@"SEL$1" "B"@"SEL$1" "C"@"SEL$1" "A"@"SEL$1")
    FULL(@"SEL$1" "A"@"SEL$1")

INDEX_FFS(@"SEL$1" "C"@"SEL$1" ("COWS_TCUE_STP_PROGRAM"."STP_S03_PROGRAM_CD" "COWS_TCUE_STP_PROGRAM"."STP_PROVIDER"

            "COWS_TCUE_STP_PROGRAM"."STP_PROGRAM_NO"))

INDEX_RS_ASC(@"SEL$1" "B"@"SEL$1" ("COWS_TCUE_STS_SCHEDULE"."COWS_DIST_PERIOD" "COWS_TCUE_STS_SCHEDULE"."STS_PERF_PERIOD"

            "COWS_TCUE_STS_SCHEDULE"."STS_PERF_SOURCE"))
    OUTLINE_LEAF(@"SEL$1")
    NO_PARALLEL
    DB_VERSION('11.2.0.2')
    OPTIMIZER_FEATURES_ENABLE('11.2.0.2')
    IGNORE_OPTIM_EMBEDDED_HINTS
    END_OUTLINE_DATA
*/
Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("A"."STS_PERF_PERIOD"="B"."STS_PERF_PERIOD" AND "A"."STS_DPS_TYPE"="B"."STS_DPS_TYPE" AND "A"."STG_GROUP_NO"="B"."STS_GROUP_NO" AND "A"."STG_GROUP_SEQ_NO"="B"."STS_GROUP_SEQ_NO") 4 - access("B"."STS_PROVIDER"="C"."STP_PROVIDER" AND "B"."STS_PROGRAM_NO"="C"."STP_PROGRAM_NO") 8 - access(("B"."COWS_DIST_PERIOD"='20141' OR "B"."COWS_DIST_PERIOD"='20142') AND "B"."STS_PERF_PERIOD"='20141' AND

            "B"."STS_PERF_SOURCE"='CB')
 9 - filter("C"."STP_S03_PROGRAM_CD"<>'36')
11 - filter("A"."PWN_P_W_IND"='W' AND "A"."STS_PERF_PERIOD"='20141')
Column Projection Information (identified by operation id):
-----------------------------------------------------------

1 - (#keys=7) "A"."STS_PERF_PERIOD"[VARCHAR2,5], "A"."PART_ACCT_NO"[NUMBER,22], "A"."PWN_PART_AKA_NME"[VARCHAR2,30], "A"."PWN_P_W_IND"[VARCHAR2,1], "A"."AFL_SOCIETY_NME"[VARCHAR2,8], "B"."STS_CHANNEL_TYPE"[VARCHAR2,7], "C"."STP_S03_PROGRAM_CD"[VARCHAR2,2], SUM(CASE "STG_STATUS" WHEN 'DP' THEN 0 ELSE "A"."PERF_COUNT"*("A"."BMI_FEATURE"+"A"."BMI_BG"+"A"."BMI_LOGO"+"A"."BMI_THEME"+"A"."BMI_BV") END )[22], SUM(CASE "STG_STATUS" WHEN 'DP' THEN 0 ELSE "A"."PERF_COUNT"*("A"."BMI_FEATURE"+"A"."BMI_BG"+"A"."BMI_LOGO"+"A"."BMI_THEME"+"A"."BMI_BV"+"A"."ASCAP_FEATURE"+"A".

 
"ASCAP_BG"+"A"."ASCAP_LOGO"+"A"."ASCAP_THEME"+"A"."ASCAP_BV"+"A"."FOREIGN_FEATURE"+"A"."FOREIGN_BG"+"A"."FOREIGN_LOGO"+"A"."FOREIGN_TH
 
EME"+"A"."FOREIGN_BV"+"A"."PD_FEATURE"+"A"."PD_BG"+"A"."PD_LOGO"+"A"."PD_THEME"+"A"."PD_BV"+"A"."SESAC_FEATURE"+"A"."SESAC_BG"+"A"."SE

SAC_LOGO"+"A"."SESAC_THEME"+"A"."SESAC_BV"+"A"."NA_FEATURE"+"A"."NA_BG"+"A"."NA_LOGO"+"A"."NA_THEME"+"A"."NA_BV") END )[22] 2 - (#keys=4) "A"."STS_PERF_PERIOD"[VARCHAR2,5], "C"."STP_S03_PROGRAM_CD"[VARCHAR2,2], "B"."STS_CHANNEL_TYPE"[VARCHAR2,7], "A"."FOREIGN_LOGO"[NUMBER,22], "A"."PD_LOGO"[NUMBER,22], "A"."PERF_COUNT"[NUMBER,22], "STG_STATUS"[VARCHAR2,2], "A"."NA_LOGO"[NUMBER,22], "A"."SESAC_LOGO"[NUMBER,22], "A"."PART_ACCT_NO"[NUMBER,22], "A"."PWN_PART_AKA_NME"[VARCHAR2,30], "A"."PWN_P_W_IND"[VARCHAR2,1], "A"."AFL_SOCIETY_NME"[VARCHAR2,8], "A"."ASCAP_BG"[NUMBER,22], "A"."BMI_BG"[NUMBER,22], "A"."SESAC_BG"[NUMBER,22], "A"."NA_BG"[NUMBER,22], "A"."PD_BG"[NUMBER,22], "A"."FOREIGN_BG"[NUMBER,22], "A"."ASCAP_BV"[NUMBER,22], "A"."BMI_BV"[NUMBER,22], "A"."SESAC_BV"[NUMBER,22], "A"."NA_BV"[NUMBER,22], "A"."PD_BV"[NUMBER,22], "A"."FOREIGN_BV"[NUMBER,22], "A"."ASCAP_THEME"[NUMBER,22], "A"."BMI_THEME"[NUMBER,22], "A"."SESAC_THEME"[NUMBER,22], "A"."NA_THEME"[NUMBER,22], "A"."PD_THEME"[NUMBER,22], "A"."FOREIGN_THEME"[NUMBER,22], "A"."ASCAP_FEATURE"[NUMBER,22], "A"."BMI_FEATURE"[NUMBER,22], "A"."SESAC_FEATURE"[NUMBER,22], "A"."NA_FEATURE"[NUMBER,22], "A"."PD_FEATURE"[NUMBER,22], "A"."FOREIGN_FEATURE"[NUMBER,22],

     "A"."ASCAP_LOGO"[NUMBER,22], "A"."BMI_LOGO"[NUMBER,22]

3 - "B"."STS_PERF_PERIOD"[VARCHAR2,5], "B"."STS_PERF_PERIOD"[VARCHAR2,5], "B"."STS_DPS_TYPE"[VARCHAR2,10], "B"."STS_GROUP_NO"[NUMBER,22], "B"."STS_GROUP_SEQ_NO"[NUMBER,22], "C"."STP_S03_PROGRAM_CD"[VARCHAR2,2],

     "B"."STS_CHANNEL_TYPE"[VARCHAR2,7]

4 - (#keys=2) "B"."STS_PERF_PERIOD"[VARCHAR2,5], "B"."STS_GROUP_NO"[NUMBER,22], "B"."STS_GROUP_SEQ_NO"[NUMBER,22], "B"."STS_CHANNEL_TYPE"[VARCHAR2,7], "B"."STS_DPS_TYPE"[VARCHAR2,10], "C"."STP_S03_PROGRAM_CD"[VARCHAR2,2] 5 - "B"."STS_PROGRAM_NO"[NUMBER,22], "B"."STS_PROVIDER"[VARCHAR2,1], "B"."STS_GROUP_NO"[NUMBER,22], "B"."STS_GROUP_SEQ_NO"[NUMBER,22], "B"."STS_CHANNEL_TYPE"[VARCHAR2,7], "B"."STS_DPS_TYPE"[VARCHAR2,10],

     "B"."STS_PERF_PERIOD"[VARCHAR2,5]

6 - "B"."STS_PROGRAM_NO"[NUMBER,22], "B"."STS_PROVIDER"[VARCHAR2,1], "B"."STS_GROUP_NO"[NUMBER,22], "B"."STS_GROUP_SEQ_NO"[NUMBER,22], "B"."STS_CHANNEL_TYPE"[VARCHAR2,7], "B"."STS_DPS_TYPE"[VARCHAR2,10],

     "B"."STS_PERF_PERIOD"[VARCHAR2,5]

7 - "B"."STS_PROGRAM_NO"[NUMBER,22], "B"."STS_PROVIDER"[VARCHAR2,1], "B"."STS_GROUP_NO"[NUMBER,22], "B"."STS_GROUP_SEQ_NO"[NUMBER,22], "B"."STS_CHANNEL_TYPE"[VARCHAR2,7], "B"."STS_DPS_TYPE"[VARCHAR2,10],

     "B"."STS_PERF_PERIOD"[VARCHAR2,5]
 8 - "B".ROWID[ROWID,10], "B"."STS_PERF_PERIOD"[VARCHAR2,5]

9 - "C"."STP_PROGRAM_NO"[NUMBER,22], "C"."STP_PROVIDER"[VARCHAR2,1], "C"."STP_S03_PROGRAM_CD"[VARCHAR2,2] 10 - "A"."STS_PERF_PERIOD"[VARCHAR2,5], "A"."STS_DPS_TYPE"[VARCHAR2,10], "A"."PERF_COUNT"[NUMBER,22], "STG_STATUS"[VARCHAR2,2], "A"."STG_GROUP_NO"[NUMBER,22], "A"."STG_GROUP_SEQ_NO"[NUMBER,22], "A"."PART_ACCT_NO"[NUMBER,22], "A"."PWN_PART_AKA_NME"[VARCHAR2,30], "A"."PWN_P_W_IND"[VARCHAR2,1], "A"."AFL_SOCIETY_NME"[VARCHAR2,8], "A"."ASCAP_BG"[NUMBER,22], "A"."BMI_BG"[NUMBER,22], "A"."SESAC_BG"[NUMBER,22], "A"."NA_BG"[NUMBER,22], "A"."PD_BG"[NUMBER,22], "A"."FOREIGN_BG"[NUMBER,22], "A"."ASCAP_BV"[NUMBER,22], "A"."BMI_BV"[NUMBER,22], "A"."SESAC_BV"[NUMBER,22], "A"."NA_BV"[NUMBER,22], "A"."PD_BV"[NUMBER,22], "A"."FOREIGN_BV"[NUMBER,22], "A"."ASCAP_THEME"[NUMBER,22], "A"."BMI_THEME"[NUMBER,22], "A"."SESAC_THEME"[NUMBER,22], "A"."NA_THEME"[NUMBER,22], "A"."PD_THEME"[NUMBER,22], "A"."FOREIGN_THEME"[NUMBER,22], "A"."ASCAP_FEATURE"[NUMBER,22], "A"."BMI_FEATURE"[NUMBER,22], "A"."SESAC_FEATURE"[NUMBER,22], "A"."NA_FEATURE"[NUMBER,22], "A"."PD_FEATURE"[NUMBER,22], "A"."FOREIGN_FEATURE"[NUMBER,22], "A"."ASCAP_LOGO"[NUMBER,22], "A"."BMI_LOGO"[NUMBER,22], "A"."SESAC_LOGO"[NUMBER,22], "A"."NA_LOGO"[NUMBER,22],

     "A"."PD_LOGO"[NUMBER,22], "A"."FOREIGN_LOGO"[NUMBER,22]

11 - "A"."STS_PERF_PERIOD"[VARCHAR2,5], "A"."STS_DPS_TYPE"[VARCHAR2,10], "A"."PERF_COUNT"[NUMBER,22], "STG_STATUS"[VARCHAR2,2], "A"."STG_GROUP_NO"[NUMBER,22], "A"."STG_GROUP_SEQ_NO"[NUMBER,22], "A"."PART_ACCT_NO"[NUMBER,22], "A"."PWN_PART_AKA_NME"[VARCHAR2,30], "A"."PWN_P_W_IND"[VARCHAR2,1], "A"."AFL_SOCIETY_NME"[VARCHAR2,8], "A"."ASCAP_BG"[NUMBER,22], "A"."BMI_BG"[NUMBER,22], "A"."SESAC_BG"[NUMBER,22], "A"."NA_BG"[NUMBER,22], "A"."PD_BG"[NUMBER,22], "A"."FOREIGN_BG"[NUMBER,22], "A"."ASCAP_BV"[NUMBER,22], "A"."BMI_BV"[NUMBER,22], "A"."SESAC_BV"[NUMBER,22], "A"."NA_BV"[NUMBER,22], "A"."PD_BV"[NUMBER,22], "A"."FOREIGN_BV"[NUMBER,22], "A"."ASCAP_THEME"[NUMBER,22], "A"."BMI_THEME"[NUMBER,22], "A"."SESAC_THEME"[NUMBER,22], "A"."NA_THEME"[NUMBER,22], "A"."PD_THEME"[NUMBER,22], "A"."FOREIGN_THEME"[NUMBER,22], "A"."ASCAP_FEATURE"[NUMBER,22], "A"."BMI_FEATURE"[NUMBER,22], "A"."SESAC_FEATURE"[NUMBER,22], "A"."NA_FEATURE"[NUMBER,22], "A"."PD_FEATURE"[NUMBER,22], "A"."FOREIGN_FEATURE"[NUMBER,22], "A"."ASCAP_LOGO"[NUMBER,22], "A"."BMI_LOGO"[NUMBER,22], "A"."SESAC_LOGO"[NUMBER,22], "A"."NA_LOGO"[NUMBER,22],

     "A"."PD_LOGO"[NUMBER,22], "A"."FOREIGN_LOGO"[NUMBER,22]
Note
-----
 - automatic DOP: skipped because of IO calibrate statistics are missing

- Warning: basic plan statistics not available. These are only collected when:

     * hint 'gather_plan_statistics' is used for the statement or

* parameter 'statistics_level' is set to 'ALL', at session or system level

This looks like an ordinary hash join, not a star transformation. Is there any way you could use bitmap indexes to achieve the star transformation, which really the only way that will make such a huge fact/detail query run reasonably fast. If only there was some kind of a physical contraption that could use Bloom filter to quickly eliminate unnecessary blocks from the fact table during the full table scan. And if it use some kind of compression that would lower the number of blocks to be read, that would be an additional boost.

--
Mladen Gogala
Oracle DBA
http://mgogala.freehostia.com

• Follow-Ups:
  • RE: Large Dataset - Estimated 87TB needed for TEMP - suggestions?
    • From: Mark W. Farnham
  • Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?
    • From: Chris Taylor

• References:
  • Large Dataset - Estimated 87TB needed for TEMP - suggestions?
    • From: Chris Taylor

Other related posts:

• » Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Chris Taylor
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Laimutis . Nedzinskas
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Chris Taylor
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Chris Taylor
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Sayan Sergeevich Malakshinov
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Chris Taylor
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Sayan Sergeevich Malakshinov
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Chris Taylor
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Chris Taylor
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Sayan Malakshinov
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions? - Mladen Gogala
• » RE: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Mark W. Farnham
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Chris Taylor
• » RE: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Mark W. Farnham
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Chris Taylor
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Chris Taylor
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Sayan Malakshinov
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Mladen Gogala
• » Re: Large Dataset - Estimated 87TB needed for TEMP - suggestions?- Sidney Chen

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