PlannerIMDB — JOB-17A

SELECT MIN(n.name) AS member_in_charnamed_american_movie,
       MIN(n.name) AS a1
FROM job.cast_info AS ci,
     job.company_name AS cn,
     job.keyword AS k,
     job.movie_companies AS mc,
     job.movie_keyword AS mk,
     job.name AS n,
     job.title AS t
WHERE cn.country_code ='[us]'
  AND k.keyword ='character-name-in-title'
  AND n.name LIKE 'B%'
  AND n.id = ci.person_id
  AND ci.movie_id = t.id
  AND t.id = mk.movie_id
  AND mk.keyword_id = k.id
  AND t.id = mc.movie_id
  AND mc.company_id = cn.id
  AND ci.movie_id = mc.movie_id
  AND ci.movie_id = mk.movie_id
  AND mc.movie_id = mk.movie_id;

Engine Compare

Accuracy chart, rows processed ?
Scan
Scan
Seek
Seek
Join Probe
Join
Sort
Sort
Hash Build
Hash
Aggregate
Agg
Distribute
Dist
Native storage
Estimation Error
Est Err
45,952,603
46M
Rank
Estimation Error
Est Err
46,257,319
46M
Rank
Estimation Error
Est Err
0
0
Rank
Estimation Error
Est Err
3,782,613
3.8M
Rank
Estimation Error
Est Err
258,289
258K
Rank
Estimation Error
Est Err
0
Rank
Native storage
Estimation Error
Est Err
46,249,115
46M
Rank
Estimation Error
Est Err
46,249,114
46M
Rank
Estimation Error
Est Err
0
0
Rank
Estimation Error
Est Err
380,784
381K
Rank
Estimation Error
Est Err
0
0
Rank
Estimation Error
Est Err
0
Rank
Native storage
Estimation Error
Est Err
82,527,815
83M
Rank
Estimation Error
Est Err
12,967,299
13M
Rank
Estimation Error
Est Err
0
0
Rank
Estimation Error
Est Err
5,982,178
6M
Rank
Estimation Error
Est Err
300,131
300K
Rank
Estimation Error
Est Err
75,526,170
76M
Rank
Apache Iceberg
Estimation Error
Est Err
50,442,373
50M
Rank
Estimation Error
Est Err
1,998,714,189
2G
Rank
Estimation Error
Est Err
0
0
Rank
Estimation Error
Est Err
1,032,400,133
1G
Rank
Estimation Error
Est Err
258,299
258K
Rank
Estimation Error
Est Err
81,848,211
82M
Rank
Native storage
Estimation Error
Est Err
1,432,376
1.4M
Rank
Estimation Error
Est Err
3,535,929
3.5M
Rank
Estimation Error
Est Err
0
0
Rank
Estimation Error
Est Err
6,072,368
6.1M
Rank
Estimation Error
Est Err
258,289
258K
Rank
Estimation Error
Est Err
0
Rank
Native storage
Estimation Error
Est Err
6,045,701
6M
Rank
Estimation Error
Est Err
6,045,701
6M
Rank
Estimation Error
Est Err
6,045,894
6M
Rank
Estimation Error
Est Err
0
0
Rank
Estimation Error
Est Err
6,045,894
6M
Rank
Estimation Error
Est Err
258,289
258K
Rank
Estimation Error
Est Err
0
Rank
Native storage
Estimation Error
Est Err
12,008,109
12M
Rank
Estimation Error
Est Err
12,008,108
12M
Rank
Estimation Error
Est Err
12,008,108
12M
Rank
Estimation Error
Est Err
5,706,950
5.7M
Rank
Estimation Error
Est Err
12,008,108
12M
Rank
Estimation Error
Est Err
258,304
258K
Rank
Estimation Error
Est Err
0
Rank
Apache Iceberg
Estimation Error
Est Err
3,803,373
3.8M
Rank
Estimation Error
Est Err
14,217,299
14M
Rank
Estimation Error
Est Err
0
0
Rank
Estimation Error
Est Err
14,038,563
14M
Rank
Estimation Error
Est Err
258,305
258K
Rank
Estimation Error
Est Err
3,066,686
3.1M
Rank

Actual Query Plans

Query Plan per Engine ?
Query Plan
Native storage
Estimate    Actual  Operator
       1         1  AGGREGATE min
     506    258289  INNER JOIN HASH ON id53 = movie_id36
     485    258289  │└INNER JOIN HASH ON company_id = id43
     198    702849   │└INNER JOIN HASH ON movie_id15 = movie_id36
      97     93315    │└INNER JOIN HASH ON id23 = person_id
    1017   1038393     │└INNER JOIN HASH ON movie_id15 = movie_id
      32     41840      │└INNER JOIN HASH ON keyword_id = id
       1         1       │└TABLE SCAN keyword WHERE keyword = character - name - in - title
 4523930   4523930       TABLE SCAN movie_keyword
36244344  36244344      TABLE SCAN cast_info
  358143     39048     TABLE SCAN name WHERE name) B
 2609129   2609129    TABLE SCAN movie_companies
   90648      7839   TABLE SCAN company_name WHERE country_code = us
 2528312   2528312  TABLE SCAN title
Native storage
Estimate    Actual  Operator
       -         1  PROJECT a1 AS member_in_charnamed_american_movie, a1
       -         1  AGGREGATE MIN(name) AS a1
       -         0  PROJECT name
       -         0  PROJECT name
       -         0  INNER JOIN HASH ON PROJECTION_1249.id = PROJECTION_1216.person_id
       -         0  │└PROJECT person_id
       -         0   PROJECT person_id
       -         0   INNER JOIN HASH ON tuple(PROJECTION_1246.movie_id,PROJECTION_1246.movie_id,PROJECTION_1246.movie_id) = tuple(PROJECTION_1219.movie_id,PROJECTION_1219.movie_id,PROJECTION_1219.id)
       -         0   │└PROJECT movie_id_right, movie_id AS movie_id_right_2, id
       -         0    PROJECT movie_id, movie_id, id
       -         0    INNER JOIN HASH ON PROJECTION_1243.id = PROJECTION_1222.company_id
       -    148552    │└PROJECT company_id, movie_id, movie_id, id AS id_right
       -    148552     PROJECT movie_id, company_id, movie_id, id
       -    148552     INNER JOIN HASH ON tuple(PROJECTION_1240.movie_id,PROJECTION_1240.movie_id) = tuple(PROJECTION_1225.movie_id,PROJECTION_1225.id)
       -     41840     │└PROJECT movie_id AS movie_id_right, id
       -     41840      PROJECT movie_id, id
       -     41840      INNER JOIN HASH ON PROJECTION_1237.id = PROJECTION_1228.movie_id
       -     41840      │└PROJECT movie_id
       -     41840       PROJECT movie_id
       -     41840       INNER JOIN HASH ON PROJECTION_1234.keyword_id = PROJECTION_1231.id
       -         1       │└PROJECT id
       -         1        PROJECT id
       -         1        TABLE SCAN keyword WHERE keyword = 'character-name-in-title'
       -   4523930       PROJECT keyword_id, movie_id
       -   4523930       PROJECT movie_id, keyword_id
       -   4523930       TABLE SCAN movie_keyword
       -   2528312      PROJECT id
       -   2528312      PROJECT id
       -   2528312      TABLE SCAN title
       -   2609129     PROJECT movie_id AS movie_id_left, company_id
       -   2609129     PROJECT movie_id, company_id
       -   2609129     TABLE SCAN movie_companies
       -         0    PROJECT id AS id_left
       -         0    PROJECT id
       -         0    TABLE SCAN company_name WHERE country_code = 'us'
       -  36244344   PROJECT movie_id AS movie_id_left, person_id
       -  36244344   PROJECT movie_id, person_id
       -  36244344   TABLE SCAN cast_info
       -    343399  PROJECT id, name
       -    343399  PROJECT id, name
       -    343399  TABLE SCAN name WHERE startsWith(name,'B')
Native storage
Estimate    Actual  Operator
       1         1  PROJECT member_in_charnamed_american_movie, a1
       -         1  AGGREGATE MIN(#0)
       1    258289  PROJECT name
       1    258289  INNER JOIN HASH ON id = person_id
       7   2832555  │└INNER JOIN HASH ON movie_id = id
       0     68316   │└INNER JOIN HASH ON id = movie_id
       0     68316    │└INNER JOIN HASH ON id = company_id
      72    148552     │└INNER JOIN HASH ON movie_id = movie_id
      69     41838      │└INNER JOIN HASH ON keyword_id = id
       2         1       │└TABLE SCAN keyword WHERE keyword = 'character-name-in-title'
 4523930     41838       TABLE SCAN movie_keyword WHERE movie_id <= 2525745
 2609129    173613      TABLE SCAN movie_companies
    1644     84843     TABLE SCAN company_name WHERE country_code = 'us'
 2528312     25740    TABLE SCAN title WHERE id >= 2 AND id <= 2525745
36244344    764636   TABLE SCAN cast_info WHERE movie_id >= 2 AND movie_id <= 2525745
  833498    334764  FILTER id <= 4061926
  833498    341705  TABLE SCAN "name" WHERE name >= 'B' AND name < 'C'
Apache Iceberg
Estimate    Actual  Operator
       1         1  PROJECT member_in_charnamed_american_movie, a1
       1         1  AGGREGATE MIN(name)
  833499        10  DISTRIBUTE GATHER
  833499        10  AGGREGATE MIN(name)
  833499    258289  INNER JOIN HASH ON movie_id = id AND movie_id = id AND movie_id = id
  833499    258289  │└DISTRIBUTE HASH ON movie_id, movie_id, movie_id
  833499    258289   PROJECT movie_id, movie_id, movie_id, name
  833499    258289   INNER JOIN HASH ON id = person_id
  833499    343399   │└DISTRIBUTE HASH ON id
  833499    343399    FILTER name LIKE 'B%'
 4167491   4167491    TABLE SCAN name WHERE name LIKE 'B%' AND (name < 'B, Khaz')
 2682007   2832555   DISTRIBUTE HASH ON person_id
 2682007   2832555   INNER JOIN HASH ON id = keyword_id
   26834         1   │└DISTRIBUTE GATHER
   26834         1    FILTER keyword = 'character-name-in-title'
  134170    134170    DISTRIBUTE ROUND ROBIN
  134170    134170    TABLE SCAN keyword WHERE keyword = 'character-name-in-title'
13410035      916M   PROJECT person_id, movie_id, movie_id, movie_id, keyword_id
13410035      916M   INNER JOIN HASH ON movie_id = movie_id AND movie_id = movie_id
 4523930   4523930   │└DISTRIBUTE HASH ON movie_id, movie_id
 4523930   4523930    TABLE SCAN movie_keyword WHERE ((keyword_id >= 117) AND (keyword_id <= 117)) AND keyword_id IN 117
 7487593  32289229   DISTRIBUTE HASH ON movie_id, movie_id
 7487593  32289229   INNER JOIN HASH ON id = company_id
   47000     84843   │└DISTRIBUTE GATHER
   47000     84843    FILTER country_code = 'us'
  234997    234997    TABLE SCAN company_name WHERE country_code = 'us'
37437492  80274241   PROJECT person_id, movie_id, movie_id, company_id
37437492  80274241   INNER JOIN HASH ON movie_id = movie_id
 2609129   2609129   │└DISTRIBUTE HASH ON movie_id
 2609129   2609129    TABLE SCAN movie_companies WHERE (((company_id >= 1) AND (company_id <= 234997)) AND TRUE) AND CASE MOD(HASH_REPARTITION(movie_id,movie_id),10) WHEN 1 THEN ((((movie_id >= 64) AND (movie_id <= 2525771)) AND ((movie_id >= 64) AND (movie_id <= 2525771))) AND TRUE) WHEN 3 THEN ((((movie_id >= 11) AND (movie_id <= 2525785)) AND ((movie_id >= 11) AND (movie_id <= 2525785))) AND TRUE) WHEN 5 THEN ((((movie_id >= 211) AND (movie_id <= 2525833)) AND ((movie_id >= 211) AND ...
36244344  36244344   DISTRIBUTE HASH ON movie_id
36244344  36244344   TABLE SCAN cast_info WHERE CASE MOD(HASH_REPARTITION movie_id,10) WHEN 0 THEN (((movie_id >= 67) AND (movie_id <= 2525735)) AND TRUE) WHEN 1 THEN (((movie_id >= 45) AND (movie_id <= 2525732)) AND TRUE) WHEN 2 THEN (((movie_id >= 24) AND (movie_id <= 2525742)) AND TRUE) WHEN 3 THEN (((movie_id >= 2) AND (movie_id <= 2525729)) AND TRUE) WHEN 4 THEN (((movie_id >= 55) AND (movie_id <= 2525738)) AND TRUE) WHEN 5 THEN (((movie_id >= 54) AND (movie_id <= 2525731)) AND TRUE) ...
 2528312   2528312  DISTRIBUTE HASH ON id, id, id
 2528312   2528312  TABLE SCAN title WHERE CASE MOD(HASH_REPARTITION(id,id,id),10) WHEN 0 THEN (((((id >= 3532) AND (id <= 2523559)) AND ((id >= 3532) AND (id <= 2523559))) AND ((id >= 3532) AND (id <= 2523559))) AND TRUE) WHEN 1 THEN (((((id >= 6313) AND (id <= 2521264)) AND ((id >= 6313) AND (id <= 2521264))) AND ((id >= 6313) AND (id <= 2521264))) AND TRUE) WHEN 2 THEN (((((id >= 11165) AND (id <= 2519716)) AND ((id >= 11165) AND (id <= 2519716))) AND ((id >= 11165) AND (id <= 2519716))) A...
Native storage
Estimate    Actual  Operator
       2         0  SEQUENCE
       1         1  ├─AGGREGATE MIN(n.name)
       1         2   DISTRIBUTE GATHER
       1         2   AGGREGATE MIN(n.name)
    3280    258289   INNER JOIN HASH ON ci.person_id = n.id
    3280   2832555   │└DISTRIBUTE GATHER
    3280   2832555    INNER JOIN HASH ON mc.movie_id = ci.movie_id
    3280     68316    │└DISTRIBUTE GATHER
     199     68316     INNER JOIN HASH ON mc.company_id = cn.id
     199     84843     │└DISTRIBUTE GATHER
  235000     84843      TABLE SCAN company_name WHERE (cn.country_code IS NOT NULL) AND (cn.country_code = 'us')
     192    148545     INNER JOIN HASH ON mk.movie_id = mc.movie_id
     192     41840     │└DISTRIBUTE GATHER
      75     41840      INNER JOIN HASH ON mk.movie_id = t.id
      75     41840      │└DISTRIBUTE GATHER
      71     41840       INNER JOIN HASH ON mk.keyword_id = k.id
      71         1       │└DISTRIBUTE GATHER
       -         1        TABLE SCAN keyword WHERE k.keyword = 'character-name-in-title'
 4170000   4519861       TABLE SCAN movie_keyword
36200000   2520276      TABLE SCAN title
 2610000   2605052     TABLE SCAN movie_companies
    1050   1179185    FILTER 
 2530000  36228386    TABLE SCAN cast_info
 4520000    341010   TABLE SCAN name WHERE startswith(n.name,'B')
       2         0  └─FILTER 
    3280         1    DISTRIBUTE HASH
    3280         1    AGGREGATE bloom_filter_agg(bloom_expr(mk.movie_id,mk.movie_id),41840L,524288L)
    3280     41840    SELECT
  134000  36228386    DISTRIBUTE HASH
  134000  36228386    DISTRIBUTE HASH
  134000  36228386    TABLE SCAN cast_info
Native storage
Estimate    Actual  Operator
       1         1  AGGREGATE MIN(partialagg1029) AS Expr1014
       5         1  AGGREGATE MIN(name as name) AS partialagg1029
     855    258303  FILTER name as name LIKE 'B%'
    9770   2832555  INNER JOIN LOOP ON Bmk1010 = Bmk1010
       1   2832555  │└TABLE SEEK name AS n
    9770   2832555  SORT Expr1033
    9770   2832555  PROJECT BmkToPage Bmk1010 AS Expr1033
    9770   2832555  INNER JOIN LOOP ON ci.person_id = n.id
       1   2832555  │└TABLE SEEK name AS n
    9770   2832555  INNER JOIN LOOP ON Bmk1000 = Bmk1000
       1   2832555  │└TABLE SEEK cast_info AS ci
    9770   2832555  SORT Expr1030
    9770   2832555  PROJECT BmkToPage Bmk1000 AS Expr1030
    9770   2832555  INNER JOIN LOOP ON mk.movie_id = ci.movie_id
      30   2832555  │└TABLE SEEK cast_info AS ci
     323     68316  FILTER country_code as country_code = 'us'
     898    148552  INNER JOIN LOOP ON Bmk1002 = Bmk1002
       1    148552  │└TABLE SEEK company_name AS cn
     898    148552  INNER JOIN LOOP ON mc.company_id = cn.id
       1    148552  │└TABLE SEEK company_name AS cn
     899    148552  INNER JOIN LOOP ON Bmk1006 = Bmk1006
       1    148552  │└TABLE SEEK movie_companies AS mc
     899    148552  INNER JOIN LOOP ON mk.movie_id = mc.movie_id
       9    148552  │└TABLE SEEK movie_companies AS mc
      90     41840  SORT movie_id
      90     41840  INNER JOIN LOOP ON Bmk1008 = Bmk1008
       1     41840  │└TABLE SEEK movie_keyword AS mk
      90     41840  INNER JOIN LOOP ON k.id = mk.keyword_id
      90     41840  │└TABLE SEEK movie_keyword AS mk
       1         1  TABLE SCAN keyword AS k WHERE keyword as keyword = 'character-name-in-title'
Apache Iceberg
Estimate    Actual  Operator
       1         ∞  PROJECT min AS member_in_charnamed_american_movie, min AS a1
       1         1  AGGREGATE MIN(min_35) AS min
       -        16  DISTRIBUTE GATHER
       -        16  AGGREGATE MIN(name_20) AS min_35
       -    258289  INNER JOIN HASH ON movie_id = id_27
 2528312   2528312  │└DISTRIBUTE HASH ON id_27
 2528312   2528312   PROJECT id AS id_27
 2528312   2528312   TABLE SCAN title
       -    258289  INNER JOIN HASH ON person_id = id_19
 3750742    343399  │└DISTRIBUTE HASH ON id_19
 3750742    343399   PROJECT id AS id_19, name AS name_20
 3750742    343399   FILTER (name >= 'B') AND (name < 'C') AND (name LIKE 'B%')
 3750742    343399   TABLE SCAN name
       -   2792158  INNER JOIN HASH ON keyword_id = id_4
  134170         1  │└DISTRIBUTE GATHER
  134170         1   PROJECT id AS id_4
  134170         1   FILTER keyword = 'character-name-in-title'
  134170         1   TABLE SCAN keyword
       -   2792158  INNER JOIN HASH ON movie_id = movie_id_15
 4523930     41840  │└DISTRIBUTE HASH ON movie_id_15
 4523930     41840   PROJECT movie_id AS movie_id_15, keyword_id
 4523930     41840   TABLE SCAN movie_keyword
       -   2791268  INNER JOIN HASH ON company_id = id_0
  234997     84843  │└DISTRIBUTE GATHER
  234997     84843   PROJECT id AS id_0
  234997     84843   FILTER country_code = 'us'
  234997     84843   TABLE SCAN company_name
       -   2791268  INNER JOIN HASH ON movie_id = movie_id_9
 2609129     68275  │└DISTRIBUTE HASH ON movie_id_9
 2609129     68275   PROJECT movie_id AS movie_id_9, company_id
 2609129     68275   TABLE SCAN movie_companies
36244344    736703  TABLE SCAN cast_info
Native storage
Estimate    Actual  Operator
       1         1  AGGREGATE MIN(name), MIN(name)
     112    258289  INNER JOIN LOOP ON id = person_id
    1380   2832555  │└INNER JOIN LOOP ON movie_id = id
      63     68316   │└INNER JOIN LOOP ON id = company_id
     173    148552    │└INNER JOIN LOOP ON movie_id = id
      34     41840     │└INNER JOIN LOOP ON id = movie_id
      34     41840      │└INNER JOIN LOOP ON keyword_id = id
       1         1       │└TABLE SEEK keyword AS k
     305     41840       TABLE SEEK movie_keyword AS mk
   41840     41840      TABLE SEEK title AS t
  209200    148532     TABLE SEEK movie_companies AS mc
  148552    148552    TABLE SEEK company_name AS cn WHERE cn.country_code = 'us'
 2664324   2832381   TABLE SEEK cast_info AS ci
 2832555   2832555  TABLE SEEK name AS n WHERE n.name LIKE 'B%'