Planner — IMDB — JOB-9C

SELECT MIN(an.name) AS alternative_name,
       MIN(chn.name) AS voiced_character_name,
       MIN(n.name) AS voicing_actress,
       MIN(t.title) AS american_movie
FROM job.aka_name AS an,
     job.char_name AS chn,
     job.cast_info AS ci,
     job.company_name AS cn,
     job.movie_companies AS mc,
     job.name AS n,
     job.role_type AS rt,
     job.title AS t
WHERE ci.note IN ('(voice)',
                  '(voice: Japanese version)',
                  '(voice) (uncredited)',
                  '(voice: English version)')
  AND cn.country_code ='[us]'
  AND n.gender ='f'
  AND n.name LIKE '%An%'
  AND rt.role ='actress'
  AND ci.movie_id = t.id
  AND t.id = mc.movie_id
  AND ci.movie_id = mc.movie_id
  AND mc.company_id = cn.id
  AND ci.role_id = rt.id
  AND n.id = ci.person_id
  AND chn.id = ci.person_role_id
  AND an.person_id = n.id
  AND an.person_id = ci.person_id;

Engine Compare

Accuracy chart, rows processed ?
Scan Scan	Seek Seek	Join Probe Join	Sort Sort	Hash Build Hash	Aggregate Agg	Distribute Dist
Estimation Error Est Err 9,501,393 9.5M Rank		Estimation Error Est Err 9,457,067 9.5M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 97,613 98K Rank	Estimation Error Est Err 8,144 8.1K Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 45,473,479 45M Rank		Estimation Error Est Err 45,839,994 46M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 17,038,814 17M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 10,078,055 10M Rank		Estimation Error Est Err 10,345,213 10M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 1,981,762 2M Rank	Estimation Error Est Err 8,145 8.1K Rank	Estimation Error Est Err 910,888 911K Rank
Estimation Error Est Err 18,054,314 18M Rank		Estimation Error Est Err 9,784,066 9.8M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 3,345,820 3.3M Rank	Estimation Error Est Err 8,154 8.2K Rank	Estimation Error Est Err 8,579,978 8.6M Rank
Estimation Error Est Err 9,073,263 9.1M Rank		Estimation Error Est Err 3,576,999 3.6M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 3,524,308 3.5M Rank	Estimation Error Est Err 8,144 8.1K Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 163,133 163K Rank	Estimation Error Est Err 113,121 113K Rank	Estimation Error Est Err 121,360 121K Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 114,318 114K Rank	Estimation Error Est Err 8,149 8.1K Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 645,842 646K Rank	Estimation Error Est Err 273,615 274K Rank	Estimation Error Est Err 700,630 701K Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 954,826 955K Rank	Estimation Error Est Err 140,411 140K Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 6,971,147 7M Rank		Estimation Error Est Err 57,128 57K Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 6,975,972 7M Rank	Estimation Error Est Err 8,160 8.2K Rank	Estimation Error Est Err 6,967,845 7M Rank

Actual Query Plans

Query Plan per Engine ?
	Query Plan
	Estimate Actual Operator 1 1 AGGREGATE min, min, min, min 251 8144 INNER JOIN HASH ON person_id72 = id6 1048 4503 │└INNER JOIN HASH ON id56 = movie_id29 1004 4503 │ │└INNER JOIN HASH ON id46 = person_role_id 1051 4503 │ │ │└INNER JOIN HASH ON company_id = id36 2864 12383 │ │ │ │└INNER JOIN HASH ON movie_id = movie_id29 1397 5686 │ │ │ │ │└INNER JOIN HASH ON role_id = id 1 1 │ │ │ │ │ │└TABLE SCAN role_type WHERE role = actress 13279 5686 │ │ │ │ │ INNER JOIN HASH ON id6 = person_id 61047 50011 │ │ │ │ │ │└TABLE SCAN name WHERE gender = f AND name LIKE '%An%' 707897 255096 │ │ │ │ │ TABLE SCAN cast_info WHERE note IN(voice,voice uncredited,(voice : English version),(voice : Japanese version)) 2609129 2609129 │ │ │ │ TABLE SCAN movie_companies 90648 17162 │ │ │ TABLE SCAN company_name WHERE country_code = us 3140339 3140339 │ │ TABLE SCAN char_name 2528312 2528312 │ TABLE SCAN title 901343 901343 TABLE SCAN aka_name
	Estimate Actual Operator - 1 PROJECT a1 AS alternative_name, a2 AS voiced_character_name, a3 AS voicing_actress, a4 AS american_movie - 1 AGGREGATE MIN(name_right) AS a1, MIN(name_left) AS a2, MIN(name_right_2) AS a3, MIN(title) AS a4 - 0 PROJECT name, name, name, title - 0 PROJECT name, name, name, title - 0 INNER JOIN HASH ON tuple(PROJECTION_5753.id,PROJECTION_5753.id) = tuple(PROJECTION_5714.movie_id,PROJECTION_5714.movie_id) - 0 │└PROJECT movie_id, movie_id, name, name, name - 0 │ PROJECT name, movie_id, name, movie_id, name - 0 │ INNER JOIN HASH ON PROJECTION_5750.id = PROJECTION_5717.company_id - 639781 │ │└PROJECT company_id, name, movie_id, name, movie_id, name - 639781 │ │ PROJECT name, movie_id, name, movie_id, company_id, name - 639781 │ │ INNER JOIN HASH ON PROJECTION_5747.movie_id = PROJECTION_5720.movie_id - 347884 │ │ │└PROJECT movie_id AS movie_id_right, name, name, name - 347884 │ │ │ PROJECT name, movie_id, name, name - 347884 │ │ │ INNER JOIN HASH ON PROJECTION_5744.id = PROJECTION_5723.person_role_id - 427653 │ │ │ │└PROJECT person_role_id, name_right, movie_id, name AS name_right_2 - 427653 │ │ │ │ PROJECT name, person_role_id, movie_id, name - 427653 │ │ │ │ INNER JOIN HASH ON tuple(PROJECTION_5741.person_id,PROJECTION_5741.person_id) = tuple(PROJECTION_5726.person_id,PROJECTION_5726.id) - 416527 │ │ │ │ │└PROJECT person_id AS person_id_right, id, person_role_id, movie_id, name AS name_right - 416527 │ │ │ │ │ PROJECT person_id, person_role_id, movie_id, name, id - 416527 │ │ │ │ │ INNER JOIN HASH ON PROJECTION_5738.id = PROJECTION_5729.person_id - 7451973 │ │ │ │ │ │└PROJECT person_id, person_role_id, movie_id - 7451973 │ │ │ │ │ │ PROJECT person_id, person_role_id, movie_id - 7451973 │ │ │ │ │ │ INNER JOIN HASH ON PROJECTION_5735.role_id = PROJECTION_5732.id - 1 │ │ │ │ │ │ │└PROJECT id - 1 │ │ │ │ │ │ │ PROJECT id - 1 │ │ │ │ │ │ │ TABLE SCAN role_type WHERE role = 'actress' - 36244344 │ │ │ │ │ │ PROJECT role_id, person_id, person_role_id, movie_id - 36244344 │ │ │ │ │ │ PROJECT person_id, person_role_id, movie_id, role_id - 36244344 │ │ │ │ │ │ TABLE SCAN cast_info WHERE TRUE - 50011 │ │ │ │ │ PROJECT id, name - 50011 │ │ │ │ │ PROJECT id, name - 50011 │ │ │ │ │ TABLE SCAN name WHERE (gender = 'f') AND name LIKE '%An%' - 901343 │ │ │ │ PROJECT person_id AS person_id_left, name AS name_left - 901343 │ │ │ │ PROJECT name, person_id - 901343 │ │ │ │ TABLE SCAN aka_name - 3140339 │ │ │ PROJECT id, name AS name_left - 3140339 │ │ │ PROJECT name, id - 3140339 │ │ │ TABLE SCAN char_name - 2609129 │ │ PROJECT movie_id AS movie_id_left, company_id - 2609129 │ │ PROJECT movie_id, company_id - 2609129 │ │ TABLE SCAN movie_companies - 0 │ PROJECT id - 0 │ PROJECT id - 0 │ TABLE SCAN company_name WHERE country_code = 'us' - 2528312 PROJECT id, title - 2528312 PROJECT title, id - 2528312 TABLE SCAN title
	Estimate Actual Operator - 1 AGGREGATE MIN(#0), MIN(#1), MIN(#2), MIN(#3) 657 8144 PROJECT name, name, name, title 657 8144 INNER JOIN HASH ON id = movie_id 645 8144 │└INNER JOIN HASH ON id = person_role_id 645 8514 │ │└INNER JOIN HASH ON id = person_id 1113 505250 │ │ │└INNER JOIN HASH ON person_id = person_id 4439 189554 │ │ │ │└INNER JOIN HASH ON role_id = id 1 1 │ │ │ │ │└FILTER id <= 11 1 1 │ │ │ │ │ TABLE SCAN role_type WHERE role = 'actress' 53270 189554 │ │ │ │ INNER JOIN HASH ON movie_id = movie_id 18253 1153798 │ │ │ │ │└INNER JOIN HASH ON company_id = id 1644 84843 │ │ │ │ │ │└TABLE SCAN company_name WHERE country_code = 'us' 2609129 2609129 │ │ │ │ │ TABLE SCAN movie_companies 7248868 248264 │ │ │ │ FILTER (person_id >= 4) AND (movie_id BETWEEN 2 AND 2525745) 7248868 248264 │ │ │ │ FILTER (note = '(voice)') OR (note = '(voice: Japanese version)') OR (note = '(voice) (uncredited)') OR (note = '(voice: English version)') 36244344 6367014 │ │ │ │ TABLE SCAN cast_info WHERE note IN('(voice)','(voice: Japanese version)','(voice) (uncredited)','(voice: English version)') 901343 10451 │ │ │ TABLE SCAN aka_name WHERE person_id <= 4061926 2083746 230 │ │ FILTER id BETWEEN 4 AND 4061926 2083746 230 │ │ TABLE SCAN "name" WHERE gender = 'f' AND contains(name,'An') 3140339 605 │ TABLE SCAN char_name 2528312 990 TABLE SCAN title WHERE id >= 2 AND id <= 2525745
	Estimate Actual Operator 1 1 PROJECT alternative_name, voiced_character_name, voicing_actress, american_movie 1 1 AGGREGATE MIN(name), MIN(name), MIN(name), MIN(title) 90635 10 DISTRIBUTE GATHER 90635 10 AGGREGATE MIN(name), MIN(name), MIN(name), MIN(title) 90635 8144 INNER JOIN HASH ON movie_id = id AND movie_id = id 90635 8144 │└DISTRIBUTE GATHER 90635 8144 │ INNER JOIN HASH ON id = role_id 3 1 │ │└DISTRIBUTE GATHER 3 1 │ │ FILTER role = 'actress' 12 12 │ │ DISTRIBUTE ROUND ROBIN 12 12 │ │ TABLE SCAN role_type WHERE role = 'actress' 332330 8144 │ INNER JOIN HASH ON person_id = id AND person_id = id 332330 490029 │ │└DISTRIBUTE HASH ON person_id, person_id 332330 490029 │ │ 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' 1661629 930328 │ │ INNER JOIN HASH ON movie_id = movie_id 1608526 497319 │ │ │└DISTRIBUTE HASH ON movie_id 1608526 497319 │ │ │ INNER JOIN HASH ON person_role_id = id 1608526 517803 │ │ │ │└DISTRIBUTE HASH ON person_role_id 1608526 517803 │ │ │ │ INNER JOIN HASH ON person_id = person_id 901343 901343 │ │ │ │ │└DISTRIBUTE HASH ON person_id 901343 901343 │ │ │ │ │ TABLE SCAN aka_name 7248869 280995 │ │ │ │ DISTRIBUTE HASH ON person_id 7248869 280995 │ │ │ │ FILTER note IN('(voice)','(voice: Japanese version)','(voice) (uncredited)','(voice: English version)') 36244344 7656546 │ │ │ │ TABLE SCAN cast_info WHERE (note IN('(voice)','(voice: Japanese version)','(voice) (uncredited)','(voice: English version)') AND CASE MOD(HASH_REPARTITION person_id,10) WHEN 0 THEN (((person_id >= 5) AND (person_id <= 4167489)) AND TRUE) WHEN 1 THEN (((person_id >= 15) AND (person_id <= 4167473)) AND TRUE) WHEN 2 THEN (((person_id >= 69) AND (person_id <= 4167478)) AND TRUE) WHEN 3 THEN (((person_id >= 161) AND (person_id <= 4167352)) AND TRUE) WHEN 4 THEN ... 3140339 3140339 │ │ │ DISTRIBUTE HASH ON id 3140339 3140339 │ │ │ TABLE SCAN char_name WHERE CASE MOD(HASH_REPARTITION id,10) WHEN 0 THEN (((id >= 119) AND (id <= 3139884)) AND TRUE) WHEN 1 THEN (((id >= 63) AND (id <= 3139863)) AND TRUE) WHEN 2 THEN (((id >= 53) AND (id <= 3139889)) AND TRUE) WHEN 3 THEN (((id >= 1) AND (id <= 3139888)) AND TRUE) WHEN 4 THEN (((id >= 21) AND (id <= 3139890)) AND TRUE) WHEN 5 THEN (((id >= 57) AND (id <= 3139914)) AND TRUE) WHEN 6 THEN (((id >= 142) AND (id <= 3139883)) AND TRUE) WHEN 7 THEN ... 2609129 2609129 │ │ DISTRIBUTE HASH ON movie_id 2609129 2609129 │ │ TABLE SCAN movie_companies WHERE CASE MOD(HASH_REPARTITION movie_id,10) WHEN 0 THEN (((movie_id >= 920) AND (movie_id <= 2525688)) AND TRUE) WHEN 1 THEN (((movie_id >= 1126) AND (movie_id <= 2525697)) AND TRUE) WHEN 2 THEN (((movie_id >= 921) AND (movie_id <= 2525701)) AND TRUE) WHEN 3 THEN (((movie_id >= 912) AND (movie_id <= 2525700)) AND TRUE) WHEN 4 THEN (((movie_id >= 925) AND (movie_id <= 2525205)) AND TRUE) WHEN 5 THEN (((movie_id >= 908) AND (movie_id <= 25... 833499 50011 │ DISTRIBUTE HASH ON id, id 833499 50011 │ FILTER (gender = 'f') AND name LIKE '%An%' 4167491 983636 │ TABLE SCAN name WHERE ((gender = 'f') AND name LIKE '%An%') AND CASE MOD(HASH_REPARTITION(id,id),10) WHEN 1 THEN ((((id >= 169332) AND (id <= 2700427)) AND ((id >= 169332) AND (id <= 2700427))) AND TRUE) WHEN 3 THEN ((((id >= 4453) AND (id <= 2698015)) AND ((id >= 4453) AND (id <= 2698015))) AND TRUE) WHEN 5 THEN ((((id >= 1728587) AND (id <= 2700464)) AND ((id >= 1728587) AND (id <= 2700464))) AND TRUE) WHEN 7 THEN ((((id >= 1728872) AND (id <= 2700366)) AND ((id >= 1... 2528312 2528312 TABLE SCAN title WHERE (((id >= 34003) AND (id <= 2519219)) AND ((id >= 34003) AND (id <= 2519219))) AND TRUE
	Estimate Actual Operator 1 1 AGGREGATE MIN(an.name), MIN(chn.name), MIN(n.name), MIN(t.title) 1 1 DISTRIBUTE GATHER 1 1 AGGREGATE MIN(an.name), MIN(chn.name), MIN(n.name), MIN(t.title) 608 8144 INNER JOIN HASH ON ci.movie_id = mc.movie_id 608 7726 │└DISTRIBUTE GATHER 150 7726 │ INNER JOIN HASH ON ci.person_id = an.person_id 150 5686 │ │└DISTRIBUTE GATHER 142 5686 │ │ INNER JOIN HASH ON ci.movie_id = t.id 142 5686 │ │ │└DISTRIBUTE GATHER 142 5686 │ │ │ INNER JOIN HASH ON ci.person_role_id = chn.id 142 5686 │ │ │ │└DISTRIBUTE GATHER 142 5686 │ │ │ │ INNER JOIN HASH ON ci.role_id = rt.id 142 1 │ │ │ │ │└DISTRIBUTE GATHER 901000 1 │ │ │ │ │ TABLE SCAN role_type WHERE rt.role = 'actress' 12100 5686 │ │ │ │ INNER JOIN HASH ON ci.person_id = n.id 12100 801259 │ │ │ │ │└DISTRIBUTE GATHER 235000 801259 │ │ │ │ │ TABLE SCAN cast_info WHERE (ci.note IN('(voice)','(voice: Japanese version)','(voice) (uncredited)','(voice: English version)')) AND (ci.person_role_id IS NOT NULL) 2610000 49644 │ │ │ │ TABLE SCAN name WHERE (n.gender IS NOT NULL) AND (n.gender = 'f') AND contains(n.name,'An') 2530000 3132165 │ │ │ TABLE SCAN char_name 3140000 2520139 │ │ TABLE SCAN title 4170000 893159 │ TABLE SCAN aka_name 238 1147575 INNER JOIN HASH ON mc.company_id = cn.id 238 84843 │└DISTRIBUTE GATHER 12 84843 │ TABLE SCAN company_name WHERE (cn.country_code IS NOT NULL) AND (cn.country_code = 'us') 36200000 2596845 TABLE SCAN movie_companies
	Estimate Actual Operator 1 1 AGGREGATE MIN(partialagg1048) AS Expr1016, MIN(partialagg1049) AS Expr1017, MIN(partialagg1050) AS Expr1018, MIN(partialagg1051) AS Expr1019 5 10 AGGREGATE MIN(name as name) AS partialagg1048, MIN(name as name) AS partialagg1049, MIN(name as name) AS partialagg1050, MIN(title as title) AS partialagg1051 15469 140401 INNER JOIN HASH ON ci.role_id = rt.id 1 1 │└FILTER role as role = 'actress' 12 12 │ INNER JOIN LOOP ON Bmk1012 = Bmk1012 1 12 │ │└TABLE SEEK role_type AS rt 12 12 │ TABLE SEEK role_type AS rt 17016 140401 INNER JOIN HASH ON an.person_id = n.id 3506 47973 │└INNER JOIN HASH ON mc.company_id = cn.id 84576 84843 │ │└TABLE SCAN company_name AS cn WHERE country_code as country_code = 'us' 9751 47973 │ INNER JOIN HASH ON mc.movie_id = t.id 4482 62580 │ │└INNER JOIN LOOP ON Bmk1014 = Bmk1014 1 62580 │ │ │└TABLE SEEK title AS t 4482 62580 │ │ PROJECT BmkToPage Bmk1014 AS Expr1601 4482 62580 │ │ INNER JOIN LOOP ON ci.movie_id = t.id 1 62580 │ │ │└TABLE SEEK title AS t 4482 62580 │ │ INNER JOIN LOOP ON Bmk1002 = Bmk1002 1 62580 │ │ │└TABLE SEEK char_name AS chn 4482 62580 │ │ PROJECT BmkToPage Bmk1002 AS Expr1598 4482 62580 │ │ INNER JOIN LOOP ON ci.person_role_id = chn.id 1 62580 │ │ │└TABLE SEEK char_name AS chn 9964 68065 │ │ INNER JOIN HASH ON n.id = ci.person_id 86757 276269 │ │ │└TABLE SCAN cast_info AS ci WHERE (note as note = '(voice)' OR note as note = '(voice) (uncredited)' OR note as note = '(voice: English version)' OR note as note = '(voice: Japanese version)') AND BLOOM(role_id as role_id) 478 8477 │ │ TABLE SCAN name AS n WHERE name as name LIKE '%An%' AND gender as gender = 'f' AND BLOOM(id as id) 26091 23271 │ TABLE SEEK movie_companies AS mc WHERE BLOOM(company_id as company_id) AND BLOOM(movie_id as movie_id) 901 2638 TABLE SCAN aka_name AS an WHERE BLOOM(person_id as person_id)
	Estimate Actual Operator 1 ∞ PROJECT min AS alternative_name, min_50 AS voiced_character_name, min_51 AS voicing_actress, min_52 AS american_movie 1 1 AGGREGATE MIN(min_53) AS min, MIN(min_54) AS min_50, MIN(min_55) AS min_51, MIN(min_56) AS min_52 - 16 DISTRIBUTE GATHER - 16 AGGREGATE MIN(name) AS min_53, MIN(name_1) AS min_54, MIN(name_29) AS min_55, MIN(title) AS min_56 - 8144 INNER JOIN HASH ON movie_id = id_43 2528312 2528312 │└DISTRIBUTE HASH ON id_43 2528312 2528312 │ PROJECT id AS id_43, title 2528312 2528312 │ TABLE SCAN title - 8144 INNER JOIN HASH ON role_id = id_39 12 1 │└DISTRIBUTE GATHER 12 1 │ PROJECT id AS id_39 12 1 │ FILTER role = 'actress' 12 1 │ TABLE SCAN role_type - 8144 INNER JOIN HASH ON person_id_10 = id_28 4167491 50011 │└DISTRIBUTE HASH ON id_28 4167491 50011 │ PROJECT id AS id_28, name AS name_29 4167491 50011 │ FILTER (gender = 'f') AND (name LIKE '%An%') 4167491 50011 │ TABLE SCAN name - 8144 INNER JOIN HASH ON company_id = id_14 234997 84843 │└DISTRIBUTE GATHER 234997 84843 │ PROJECT id AS id_14 234997 84843 │ FILTER country_code = 'us' 234997 84843 │ TABLE SCAN company_name - 8144 INNER JOIN HASH ON movie_id = movie_id_23 2609129 1153798 │└DISTRIBUTE HASH ON movie_id_23 2609129 1153798 │ PROJECT movie_id AS movie_id_23, company_id 2609129 1153798 │ TABLE SCAN movie_companies - 7726 INNER JOIN HASH ON person_role_id = id_0 3140339 3140339 │└DISTRIBUTE HASH ON id_0 3140339 3140339 │ PROJECT id AS id_0, name AS name_1 3140339 3140339 │ TABLE SCAN char_name - 8204 INNER JOIN HASH ON person_id_10 = person_id 901343 10525 │└DISTRIBUTE GATHER 901343 10525 │ TABLE SCAN aka_name 32619910 3318 PROJECT person_id AS person_id_10, movie_id, person_role_id, role_id 32619910 3318 FILTER note IN('(voice)','(voice) (uncredited)','(voice: English version)','(voice: Japanese version)') 32619910 3318 TABLE SCAN cast_info
	Estimate Actual Operator 1 1 AGGREGATE MIN(name), MIN(name), MIN(name), MIN(title) 5 5 AGGREGATE PARTIAL MIN(name), PARTIAL MIN(name), PARTIAL MIN(name), PARTIAL MIN(title) 1595 8144 INNER JOIN LOOP ON id = company_id 4370 18345 │└INNER JOIN LOOP ON movie_id = id AND movie_id = movie_id AND (movie_id = movie_id) 1530 7726 │ │└INNER JOIN LOOP ON id = movie_id 1530 7726 │ │ │└INNER JOIN LOOP ON id = person_role_id 306 1640 │ │ │ │└INNER JOIN HASH ON role_id = id 1 1 │ │ │ │ │└TABLE SCAN role_type AS rt WHERE rt.role = 'actress' 18380 8204 │ │ │ │ INNER JOIN LOOP ON person_id = person_id AND person_id = id AND (person_id = id) 7860 10525 │ │ │ │ │└INNER JOIN LOOP ON person_id = id 36375 50011 │ │ │ │ │ │└TABLE SCAN name AS n WHERE (n.name LIKE '%An%') AND (n.gender = 'f') 100022 50011 │ │ │ │ │ TABLE SEEK aka_name AS an 126300 10525 │ │ │ │ TABLE SEEK cast_info AS ci WHERE ci.note IN('(voice)','(voice: Japanese version)','(voice) (uncredited)','(voice: English version)') 8204 8204 │ │ │ TABLE SEEK char_name AS chn 7726 7726 │ │ TABLE SEEK title AS t 38630 18310 │ TABLE SEEK movie_companies AS mc 18345 18345 TABLE SEEK company_name AS cn WHERE cn.country_code = 'us'