Planner — IMDB — JOB-9B

SELECT MIN(an.name) AS alternative_name,
       MIN(chn.name) AS voiced_character,
       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 = '(voice)'
  AND cn.country_code ='[us]'
  AND mc.note LIKE '%(200%)%'
  AND (mc.note LIKE '%(USA)%'
       OR mc.note LIKE '%(worldwide)%')
  AND n.gender ='f'
  AND n.name LIKE '%Angel%'
  AND rt.role ='actress'
  AND t.production_year BETWEEN 2007 AND 2010
  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 4,047,595 4M Rank		Estimation Error Est Err 4,042,597 4M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 6,364 6.4K Rank	Estimation Error Est Err 40 40 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 5,472,264 5.5M Rank		Estimation Error Est Err 5,014,646 5M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 958,915 959K Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 5,202,028 5.2M Rank		Estimation Error Est Err 4,627,080 4.6M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 811,777 812K Rank	Estimation Error Est Err 41 41 Rank	Estimation Error Est Err 736,211 736K Rank
Estimation Error Est Err 17,808,554 18M Rank		Estimation Error Est Err 4,485,311 4.5M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 1,662,979 1.7M Rank	Estimation Error Est Err 50 50 Rank	Estimation Error Est Err 5,431,245 5.4M Rank
Estimation Error Est Err 278,612 279K Rank		Estimation Error Est Err 324,698 325K Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 541,805 542K Rank	Estimation Error Est Err 40 40 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 515,071 515K Rank	Estimation Error Est Err 316,551 317K Rank	Estimation Error Est Err 457,218 457K Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 182,713 183K Rank	Estimation Error Est Err 40 40 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 338,389 338K Rank	Estimation Error Est Err 53,203 53K Rank	Estimation Error Est Err 283,203 283K Rank	Estimation Error Est Err 52,979 53K Rank	Estimation Error Est Err 433,768 434K Rank	Estimation Error Est Err 49 49 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 3,921,813 3.9M Rank		Estimation Error Est Err 592 592 Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 3,921,871 3.9M Rank	Estimation Error Est Err 56 56 Rank	Estimation Error Est Err 3,921,794 3.9M Rank

Actual Query Plans

Query Plan per Engine ?
	Query Plan
	Estimate Actual Operator 1 1 AGGREGATE min, min, min, min 1 40 INNER JOIN HASH ON person_id72 = id6 3 30 │└INNER JOIN HASH ON id61 = person_role_id 3 30 │ │└INNER JOIN HASH ON movie_id = id46 6 75 │ │ │└INNER JOIN HASH ON company_id = id36 16 76 │ │ │ │└INNER JOIN HASH ON movie_id = movie_id29 95 367 │ │ │ │ │└INNER JOIN HASH ON role_id = id 1 1 │ │ │ │ │ │└TABLE SCAN role_type WHERE role = actress 841 367 │ │ │ │ │ INNER JOIN HASH ON id6 = person_id 4069 5409 │ │ │ │ │ │└TABLE SCAN name WHERE gender = f AND name LIKE '%Angel%' 672502 367 │ │ │ │ │ TABLE SCAN cast_info WHERE note = voice 211482 74 │ │ │ │ TABLE SCAN movie_companies WHERE note32 LIKE '%(200%)%' AND note32 LIKE '%(USA)%' OR note32 LIKE '%(worldwide)%' 90648 44 │ │ │ TABLE SCAN company_name WHERE country_code = us 562944 18 │ │ TABLE SCAN title WHERE production_year BETWEEN 2007 AND 2010 3140339 3140339 │ TABLE SCAN char_name 901343 901343 TABLE SCAN aka_name
	Estimate Actual Operator - 1 PROJECT a1 AS alternative_name, a2 AS voiced_character, 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_right, name_left, name_right_2, title - 0 PROJECT name_right, name_left, name_right_2, title - 0 INNER JOIN HASH ON tuple(PROJECTION_5682.movie_id,PROJECTION_5682.movie_id) = tuple(PROJECTION_5667.movie_id,PROJECTION_5667.id) - 0 │└PROJECT movie_id AS movie_id_right, id, title - 0 │ PROJECT movie_id, title, id - 0 │ INNER JOIN HASH ON PROJECTION_5673.company_id = PROJECTION_5670.id - 0 │ │└PROJECT id AS id_right - 0 │ │ PROJECT id - 0 │ │ TABLE SCAN company_name WHERE country_code = 'us' - 54971 │ PROJECT company_id, movie_id, title, id AS id_left - 54971 │ PROJECT movie_id, company_id, title, id - 54971 │ INNER JOIN HASH ON PROJECTION_5679.movie_id = PROJECTION_5676.id - 512825 │ │└PROJECT id, title - 512825 │ │ PROJECT id, title - 512825 │ │ TABLE SCAN title WHERE (production_year >= 2007) AND (production_year <= 2010) - 198519 │ PROJECT movie_id, company_id - 198519 │ PROJECT movie_id, company_id - 198519 │ TABLE SCAN movie_companies WHERE note LIKE '%(200%)%' AND (note LIKE '%(USA)%' OR note LIKE '%(worldwide)%') - 237 PROJECT movie_id AS movie_id_left, name, name, name - 237 PROJECT name, movie_id, name, name - 237 INNER JOIN HASH ON PROJECTION_5706.id = PROJECTION_5685.person_role_id - 265 │└PROJECT person_role_id, name_right, movie_id, name AS name_right_2 - 265 │ PROJECT name, person_role_id, movie_id, name - 265 │ INNER JOIN HASH ON tuple(PROJECTION_5703.person_id,PROJECTION_5703.person_id) = tuple(PROJECTION_5688.person_id,PROJECTION_5688.id) - 453 │ │└PROJECT person_id AS person_id_right, id, person_role_id, movie_id, name AS name_right - 453 │ │ PROJECT person_id, person_role_id, movie_id, name, id - 453 │ │ INNER JOIN HASH ON PROJECTION_5700.id = PROJECTION_5691.person_id - 222686 │ │ │└PROJECT person_id, person_role_id, movie_id - 222686 │ │ │ PROJECT person_id, person_role_id, movie_id - 222686 │ │ │ INNER JOIN HASH ON PROJECTION_5697.role_id = PROJECTION_5694.id - 1 │ │ │ │└PROJECT id - 1 │ │ │ │ PROJECT id - 1 │ │ │ │ TABLE SCAN role_type WHERE role = 'actress' - 713828 │ │ │ PROJECT role_id, person_id, person_role_id, movie_id - 713828 │ │ │ PROJECT person_id, person_role_id, movie_id, role_id - 713828 │ │ │ TABLE SCAN cast_info WHERE note = '(voice)' - 5409 │ │ PROJECT id, name - 5409 │ │ PROJECT id, name - 5409 │ │ TABLE SCAN name WHERE (gender = 'f') AND name LIKE '%Angel%' - 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
	Estimate Actual Operator - 1 AGGREGATE MIN(#0), MIN(#1), MIN(#2), MIN(#3) 0 40 PROJECT name, name, name, title 0 40 INNER JOIN HASH ON id = person_role_id 0 40 │└INNER JOIN HASH ON id = person_id 0 20424 │ │└INNER JOIN HASH ON person_id = person_id 0 7565 │ │ │└INNER JOIN HASH ON id = movie_id 0 26447 │ │ │ │└INNER JOIN HASH ON id = company_id 5 29100 │ │ │ │ │└INNER JOIN HASH ON movie_id = movie_id 26 222685 │ │ │ │ │ │└INNER JOIN HASH ON role_id = id 1 1 │ │ │ │ │ │ │└FILTER id <= 11 1 1 │ │ │ │ │ │ │ TABLE SCAN role_type WHERE role = 'actress' 323 222685 │ │ │ │ │ │ FILTER (person_id >= 4) AND (movie_id BETWEEN 2 AND 2525745) 323 222686 │ │ │ │ │ │ TABLE SCAN cast_info WHERE note = '(voice)' 521825 7509 │ │ │ │ │ TABLE SCAN movie_companies WHERE (note LIKE '%(200%)%') AND (contains(note,'(USA)') OR contains(note,'(worldwide)')) 1644 44844 │ │ │ │ TABLE SCAN company_name WHERE country_code = 'us' 505662 1321 │ │ │ FILTER id BETWEEN 2 AND 2525745 505662 1321 │ │ │ TABLE SCAN title WHERE production_year >= 2007 AND production_year <= 2010 901343 2233 │ │ TABLE SCAN aka_name WHERE person_id <= 4061926 2083746 7 │ FILTER id BETWEEN 4 AND 4061926 2083746 7 │ TABLE SCAN "name" WHERE gender = 'f' AND contains(name,'Angel') 3140339 11 TABLE SCAN char_name
	Estimate Actual Operator 1 1 PROJECT alternative_name, voiced_character, voicing_actress, american_movie 1 1 AGGREGATE MIN(name), MIN(name), MIN(name), MIN(title) 28463 10 DISTRIBUTE GATHER 28463 10 AGGREGATE MIN(name), MIN(name), MIN(name), MIN(title) 28463 40 INNER JOIN HASH ON movie_id = id AND movie_id = id 28463 94 │└DISTRIBUTE GATHER 28463 94 │ 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' 104366 94 │ INNER JOIN HASH ON person_id = id AND person_id = id 104366 57755 │ │└DISTRIBUTE GATHER 104366 57755 │ │ 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' 521826 60838 │ │ PROJECT person_id, name, person_id, movie_id, role_id, name, movie_id, company_id 521826 60838 │ │ INNER JOIN HASH ON movie_id = movie_id 521826 198519 │ │ │└DISTRIBUTE HASH ON movie_id 521826 198519 │ │ │ FILTER note LIKE '%(200%)%' AND (note LIKE '%(USA)%' OR note LIKE '%(worldwide)%') 2609129 2609129 │ │ │ TABLE SCAN movie_companies WHERE (note LIKE '%(200%)%' AND (note LIKE '%(USA)%' OR note LIKE '%(worldwide)%')) AND (((company_id >= 1) AND (company_id <= 234997)) AND TRUE) 1608526 401120 │ │ DISTRIBUTE HASH ON movie_id 1608526 401120 │ │ INNER JOIN HASH ON person_role_id = id 1608526 420331 │ │ │└DISTRIBUTE HASH ON person_role_id 1608526 420331 │ │ │ INNER JOIN HASH ON person_id = person_id 901343 901343 │ │ │ │└DISTRIBUTE HASH ON person_id 901343 901343 │ │ │ │ TABLE SCAN aka_name 7248869 226878 │ │ │ DISTRIBUTE HASH ON person_id 7248869 226878 │ │ │ FILTER note = '(voice)' 36244344 7656546 │ │ │ TABLE SCAN cast_info WHERE (((note = '(voice)') 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 (((person_id >= 94) AND (person_id <= 4167420)) AND TRUE) WHEN 5 THEN (((person_i... 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 <= 3139886)) AND TRUE) WHEN 6 THEN (((id >= 142) AND (id <= 3139883)) AND TRUE) WHEN 7 THEN (((i... 833499 5409 │ FILTER (gender = 'f') AND name LIKE '%Angel%' 4167491 983636 │ TABLE SCAN name WHERE ((gender = 'f') AND name LIKE '%Angel%') AND ((((id >= 293010) AND (id <= 2700440)) AND ((id >= 293010) AND (id <= 2700440))) AND TRUE) 72238 457180 FILTER (production_year >= 2007) AND (production_year <= 2010) 2528312 2282552 TABLE SCAN title WHERE ((production_year >= 2007) AND (production_year <= 2010)) AND ((((id >= 468901) AND (id <= 2497704)) AND ((id >= 468901) AND (id <= 2497704))) AND struct(id,id) IN( < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < e...
	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) 156 40 INNER JOIN HASH ON ci.person_id = an.person_id 156 30 │└DISTRIBUTE GATHER 61 30 │ INNER JOIN HASH ON ci.movie_id = mc.movie_id 61 97 │ │└DISTRIBUTE GATHER 38 97 │ │ INNER JOIN HASH ON ci.movie_id = t.id 38 367 │ │ │└DISTRIBUTE GATHER 36 367 │ │ │ INNER JOIN HASH ON ci.person_role_id = chn.id 36 367 │ │ │ │└DISTRIBUTE GATHER 36 367 │ │ │ │ INNER JOIN HASH ON ci.role_id = rt.id 36 1 │ │ │ │ │└DISTRIBUTE GATHER 901000 1 │ │ │ │ │ TABLE SCAN role_type WHERE rt.role = 'actress' 12100 367 │ │ │ │ INNER JOIN HASH ON ci.person_id = n.id 12100 650505 │ │ │ │ │└DISTRIBUTE GATHER 235000 650505 │ │ │ │ │ TABLE SCAN cast_info WHERE (ci.note IS NOT NULL) AND (ci.note = '(voice)') AND (ci.person_role_id IS NOT NULL) 2610000 5024 │ │ │ │ TABLE SCAN name WHERE (n.gender IS NOT NULL) AND (n.gender = 'f') AND contains(n.name,'Angel') 2530000 3128054 │ │ │ TABLE SCAN char_name 4170000 481586 │ │ TABLE SCAN title WHERE (t.production_year IS NOT NULL) AND (t.production_year >= 2007L) AND (t.production_year <= 2010L) 36 160034 │ INNER JOIN HASH ON mc.company_id = cn.id 36 84843 │ │└DISTRIBUTE GATHER 3140000 84843 │ │ TABLE SCAN company_name WHERE (cn.country_code IS NOT NULL) AND (cn.country_code = 'us') 12 180047 │ TABLE SCAN movie_companies WHERE (mc.note IS NOT NULL) AND mc.note LIKE '%(200%)%' AND (contains(mc.note,'(USA)') OR contains(mc.note,'(worldwide)')) 36200000 671968 TABLE SCAN aka_name
	Estimate Actual Operator 1 1 AGGREGATE MIN(partialagg1044) AS Expr1016, MIN(partialagg1045) AS Expr1017, MIN(partialagg1046) AS Expr1018, MIN(partialagg1047) AS Expr1019 5 7 AGGREGATE MIN(name as name) AS partialagg1044, MIN(name as name) AS partialagg1045, MIN(name as name) AS partialagg1046, MIN(title as title) AS partialagg1047 331 42 INNER JOIN LOOP ON Bmk1002 = Bmk1002 1 42 │└TABLE SEEK char_name AS chn 331 42 PROJECT BmkToPage Bmk1002 AS Expr1864 331 42 INNER JOIN LOOP ON ci.person_role_id = chn.id 1 42 │└TABLE SEEK char_name AS chn 736 42 INNER JOIN LOOP ON Bmk1000 = Bmk1000 1 42 │└TABLE SEEK aka_name AS an 349 42 INNER JOIN LOOP ON n.id = an.person_id 4 42 │└TABLE SEEK aka_name AS an 151 32 INNER JOIN LOOP ON Bmk1014 = Bmk1014 0 32 │└TABLE SEEK title AS t WHERE production_year as production_year >= 2007 AND production_year as production_year <= 2010 356 85 SORT Expr1057 356 85 PROJECT BmkToPage Bmk1014 AS Expr1057 356 85 INNER JOIN LOOP ON mc.movie_id = t.id 1 85 │└TABLE SEEK title AS t 356 85 FILTER name as name LIKE '%Angel%' AND gender as gender = 'f' 4032 26447 INNER JOIN LOOP ON Bmk1010 = Bmk1010 1 26447 │└TABLE SEEK name AS n 4032 26447 SORT Expr1051 4032 26447 PROJECT BmkToPage Bmk1010 AS Expr1051 4032 26447 INNER JOIN LOOP ON ci.person_id = n.id 1 26447 │└TABLE SEEK name AS n 4032 26447 SORT person_id 4032 26447 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 4435 26447 INNER JOIN HASH ON ci.movie_id = mc.movie_id 72076 177065 │└INNER JOIN HASH ON mc.company_id = cn.id 84576 84843 │ │└TABLE SCAN company_name AS cn WHERE country_code as country_code = 'us' 200411 177065 │ TABLE SCAN movie_companies AS mc WHERE note as note LIKE '%(200%)%' AND (note as note LIKE '%(USA)%' OR note as note LIKE '%(worldwide)%') AND BLOOM(company_id as company_id) 4435 23278 TABLE SCAN cast_info AS ci WHERE (BLOOM(movie_id as movie_id,N'IN ROW')) AND (note as note = '(voice)' AND BLOOM(role_id as role_id))
	Estimate Actual Operator 1 ∞ PROJECT min AS alternative_name, min_50 AS voiced_character, 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 - 40 INNER JOIN HASH ON movie_id = id_43 53012 512825 │└DISTRIBUTE HASH ON id_43 53012 512825 │ PROJECT id AS id_43, title 53012 512825 │ FILTER production_year BETWEEN 2007 AND 2010 53012 512825 │ TABLE SCAN title - 94 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 - 94 INNER JOIN HASH ON person_id_10 = id_28 4167491 5409 │└DISTRIBUTE HASH ON id_28 4167491 5409 │ PROJECT id AS id_28, name AS name_29 4167491 5409 │ FILTER (gender = 'f') AND (name LIKE '%Angel%') 4167491 5409 │ TABLE SCAN name - 94 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 - 94 INNER JOIN HASH ON movie_id = movie_id_23 2348216 177065 │└DISTRIBUTE HASH ON movie_id_23 2348216 177065 │ PROJECT movie_id AS movie_id_23, company_id 2348216 177065 │ FILTER (note LIKE '%(200%)%') AND ((note LIKE '%(USA)%') OR (note LIKE '%(worldwide)%')) 2348216 177065 │ TABLE SCAN movie_companies - 58 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 - 61 INNER JOIN HASH ON person_id_10 = person_id 901343 1296 │└DISTRIBUTE GATHER 901343 1296 │ TABLE SCAN aka_name 32619910 35 PROJECT person_id AS person_id_10, movie_id, person_role_id, role_id 32619910 35 FILTER note = '(voice)' 32619910 35 TABLE SCAN cast_info
	Estimate Actual Operator 1 1 AGGREGATE MIN(name), MIN(name), MIN(name), MIN(title) 3 40 INNER JOIN LOOP ON person_id = person_id AND person_id = id AND (person_id = id) 1 30 │└INNER JOIN LOOP ON id = person_role_id 4 30 │ │└INNER JOIN LOOP ON id = person_id 157 1891 │ │ │└INNER JOIN HASH ON role_id = id 1 1 │ │ │ │└TABLE SCAN role_type AS rt WHERE rt.role = 'actress' 7560 26990 │ │ │ INNER JOIN LOOP ON movie_id = id 9552 47009 │ │ │ │└INNER JOIN LOOP ON id = movie_id 12063 44266 │ │ │ │ │└INNER JOIN HASH ON company_id = id 201896 84843 │ │ │ │ │ │└TABLE SEEK company_name AS cn 132148 198519 │ │ │ │ │ TABLE SCAN movie_companies AS mc WHERE (mc.note LIKE '%(200%)%') AND ((mc.note LIKE '%(USA)%') OR (mc.note LIKE '%(worldwide)%')) 177065 177065 │ │ │ │ TABLE SEEK title AS t WHERE (t.production_year >= 2007) AND (t.production_year <= 2010) 47009 47009 │ │ │ TABLE SEEK cast_info AS ci WHERE ci.note = '(voice)' 7565 7565 │ │ TABLE SEEK name AS n WHERE (n.name LIKE '%Angel%') AND (n.gender = 'f') 30 30 │ TABLE SEEK char_name AS chn 60 39 TABLE SEEK aka_name AS an