Planner — IMDB — JOB-19B

SELECT MIN(n.name) AS voicing_actress,
       MIN(t.title) AS kung_fu_panda
FROM job.aka_name AS an,
     job.char_name AS chn,
     job.cast_info AS ci,
     job.company_name AS cn,
     job.info_type AS it,
     job.movie_companies AS mc,
     job.movie_info AS mi,
     job.name AS n,
     job.role_type AS rt,
     job.title AS t
WHERE ci.note = '(voice)'
  AND cn.country_code ='[us]'
  AND it.info = 'release dates'
  AND mc.note LIKE '%(200%)%'
  AND (mc.note LIKE '%(USA)%'
       OR mc.note LIKE '%(worldwide)%')
  AND mi.info IS NOT NULL
  AND (mi.info LIKE 'Japan:%2007%'
       OR mi.info LIKE 'USA:%2008%')
  AND n.gender ='f'
  AND n.name LIKE '%Angel%'
  AND rt.role ='actress'
  AND t.production_year BETWEEN 2007 AND 2008
  AND t.title LIKE '%Kung%Fu%Panda%'
  AND t.id = mi.movie_id
  AND t.id = mc.movie_id
  AND t.id = ci.movie_id
  AND mc.movie_id = ci.movie_id
  AND mc.movie_id = mi.movie_id
  AND mi.movie_id = ci.movie_id
  AND cn.id = mc.company_id
  AND it.id = mi.info_type_id
  AND n.id = ci.person_id
  AND rt.id = ci.role_id
  AND n.id = an.person_id
  AND ci.person_id = an.person_id
  AND chn.id = ci.person_role_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 4,041,763 4M Rank		Estimation Error Est Err 4,041,818 4M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 124 124 Rank	Estimation Error Est Err 15 15 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 5,028,925 5M Rank		Estimation Error Est Err 0 Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 0 Rank	Estimation Error Est Err 0 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 4,109,165 4.1M Rank		Estimation Error Est Err 4,024,338 4M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 694,945 695K Rank	Estimation Error Est Err 19 19 Rank	Estimation Error Est Err 491,693 492K Rank
Estimation Error Est Err 20,528,544 21M Rank		Estimation Error Est Err 4,108,686 4.1M Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 1,685,103 1.7M Rank	Estimation Error Est Err 25 25 Rank	Estimation Error Est Err 5,511,899 5.5M Rank
Estimation Error Est Err 275,068 275K Rank		Estimation Error Est Err 296,791 297K Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 501,076 501K Rank	Estimation Error Est Err 15 15 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 573 573 Rank	Estimation Error Est Err 573 573 Rank	Estimation Error Est Err 598 598 Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 837 837 Rank	Estimation Error Est Err 15 15 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 1,617 1.6K Rank	Estimation Error Est Err 1,610 1.6K Rank	Estimation Error Est Err 1,611 1.6K Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 1,610 1.6K Rank	Estimation Error Est Err 15 15 Rank	Estimation Error Est Err 0 Rank
Estimation Error Est Err 3,231,907 3.2M Rank		Estimation Error Est Err 115 115 Rank	Estimation Error Est Err 0 0 Rank	Estimation Error Est Err 3,231,962 3.2M Rank	Estimation Error Est Err 31 31 Rank	Estimation Error Est Err 3,231,922 3.2M Rank

Actual Query Plans

Query Plan per Engine ?
	Query Plan
	Estimate Actual Operator 1 1 AGGREGATE min, min 1 15 INNER JOIN HASH ON person_id = person_id85 1 3 │└INNER JOIN HASH ON id = info_type_id 1 1 │ │└TABLE SCAN info_type WHERE info = release dates 1 3 │ INNER JOIN HASH ON id74 = person_role_id 1 3 │ │└INNER JOIN HASH ON id64 = company_id 1 3 │ │ │└INNER JOIN HASH ON movie_id57 = movie_id36 1 1 │ │ │ │└INNER JOIN HASH ON id44 = person_id 4 15 │ │ │ │ │└INNER JOIN HASH ON id6 = role_id 1 1 │ │ │ │ │ │└TABLE SCAN role_type WHERE role = actress 4 60 │ │ │ │ │ INNER JOIN HASH ON id11 = movie_id36 13 6 │ │ │ │ │ │└INNER JOIN HASH ON id11 = movie_id 953 7 │ │ │ │ │ │ │└TABLE SCAN title WHERE production_year BETWEEN 2007 AND 2008 AND title LIKE '%Kung%Fu%Panda%' 28976 6 │ │ │ │ │ │ TABLE SCAN movie_info WHERE info29 LIKE '%2007%' OR info29 LIKE '%2008%' 672502 60 │ │ │ │ │ TABLE SCAN cast_info WHERE note = voice 4069 1 │ │ │ │ TABLE SCAN name WHERE gender = f AND name LIKE '%Angel%' 211482 3 │ │ │ TABLE SCAN movie_companies WHERE note60 LIKE '%(200%)%' AND note60 LIKE '%(USA)%' OR note60 LIKE '%(worldwide)%' 90648 2 │ │ TABLE SCAN company_name WHERE country_code = us 3140339 3140339 │ TABLE SCAN char_name 901343 901343 TABLE SCAN aka_name
	Estimate Actual Operator - ∞ PROJECT a1 AS voicing_actress, a2 AS kung_fu_panda - ∞ AGGREGATE MIN(name) AS a1, MIN(title) AS a2 - ∞ PROJECT name, title - ∞ PROJECT name, title - ∞ INNER JOIN HASH ON tuple(PROJECTION_1659.movie_id,PROJECTION_1659.movie_id,PROJECTION_1659.id) = tuple(PROJECTION_1650.movie_id,PROJECTION_1650.movie_id,PROJECTION_1650.movie_id) - ∞ │└PROJECT movie_id AS movie_id_right - ∞ │ PROJECT movie_id - ∞ │ INNER JOIN HASH ON PROJECTION_1656.info_type_id = PROJECTION_1653.id - 1 │ │└PROJECT id - 1 │ │ FILTER (1 AND info = 'release dates'_String) AS a71 - 1 │ │ TABLE SCAN info_type WHERE info = 'release dates' - ∞ │ PROJECT info_type_id, movie_id - ∞ │ FILTER (1 AND OR ( LIKE (info,'Japan:%2007%'_String), LIKE (info,'USA:%2008%'_String))) AS a63 - 69478 │ TABLE SCAN movie_info WHERE info LIKE 'Japan:%2007%' OR info LIKE 'USA:%2008%' - ∞ PROJECT movie_id_left, movie_id_right AS movie_id_left_2, id, name, title - ∞ PROJECT movie_id_left, movie_id_right, name, title, id - ∞ INNER JOIN HASH ON PROJECTION_1665.company_id = PROJECTION_1662.id - 0 │└PROJECT id AS id_right - 0 │ FILTER (1 AND country_code = 'us'_String) AS a58 - 0 │ TABLE SCAN company_name WHERE country_code = 'us' - ∞ PROJECT company_id, movie_id_left, movie_id_right, name, title, id_left - ∞ PROJECT movie_id_left, movie_id_right, company_id, name, title, id - ∞ INNER JOIN HASH ON PROJECTION_1671.person_role_id = PROJECTION_1668.id - 3140339 │└PROJECT id AS id_right - 3140339 │ PROJECT id - 3140339 │ TABLE SCAN char_name - ∞ PROJECT person_role_id, movie_id_left, movie_id_right, company_id, name, title, id_left - ∞ PROJECT person_role_id, movie_id_left, movie_id_right, company_id, name, title, id - ∞ INNER JOIN HASH ON tuple(PROJECTION_1677.person_id,PROJECTION_1677.id) = tuple(PROJECTION_1674.person_id,PROJECTION_1674.person_id) - 901343 │└PROJECT person_id AS person_id_right - 901343 │ PROJECT person_id - 901343 │ TABLE SCAN aka_name - ∞ PROJECT person_id AS person_id_left, id, person_role_id, movie_id_left, movie_id_right, company_id, name, title, id - ∞ PROJECT person_id, person_role_id, movie_id_left, movie_id_right, company_id, name, id, title, id - ∞ INNER JOIN HASH ON PROJECTION_1683.person_id = PROJECTION_1680.id - ∞ │└PROJECT id AS id_right, name - ∞ │ FILTER (1 AND LIKE (name,'%Angel%'_String) AND gender = 'f'_String) AS a50 - 5409 │ TABLE SCAN name WHERE (gender = 'f') AND name LIKE '%Angel%' - ∞ PROJECT person_id, person_role_id, movie_id_left, movie_id_right, company_id, title, id AS id_left - ∞ PROJECT person_id, person_role_id, movie_id_left, movie_id_right, company_id, title, id - ∞ INNER JOIN HASH ON tuple(PROJECTION_1689.movie_id,PROJECTION_1689.movie_id) = tuple(PROJECTION_1686.id,PROJECTION_1686.id) - ∞ │└PROJECT id, title - ∞ │ FILTER (1 AND production_year >= 2007_UInt16 AND production_year <= 2008_UInt16 AND LIKE (title,'%Kung%Fu%Panda%'_String)) AS a40 - 7 │ TABLE SCAN title WHERE (production_year >= 2007) AND (production_year <= 2008) AND title LIKE '%Kung%Fu%Panda%' - ∞ PROJECT movie_id_left, movie_id_right, person_id, person_role_id, company_id - ∞ PROJECT person_id, person_role_id, movie_id_left, movie_id_right, company_id - ∞ INNER JOIN HASH ON PROJECTION_1695.role_id = PROJECTION_1692.id - 1 │└PROJECT id - 1 │ FILTER (1 AND role = 'actress'_String) AS a36 - 1 │ TABLE SCAN role_type WHERE role = 'actress' - ∞ PROJECT role_id, person_id, person_role_id, movie_id_left, movie_id_right, company_id - ∞ PROJECT person_id, person_role_id, movie_id_left, role_id, movie_id_right, company_id - ∞ INNER JOIN HASH ON PROJECTION_1701.movie_id = PROJECTION_1698.movie_id - ∞ │└PROJECT movie_id AS movie_id_right, company_id - ∞ │ FILTER ( LIKE (note,'%(200%)%'_String) AND OR ( LIKE (note,'%(USA)%'_String), LIKE (note,'%(worldwide)%'_String)) AND 1) AS a22 - 198519 │ TABLE SCAN movie_companies WHERE note LIKE '%(200%)%' AND (note LIKE '%(USA)%' OR note LIKE '%(worldwide)%') - 713828 PROJECT movie_id AS movie_id_left, person_id, person_role_id, role_id - 713828 FILTER (1 AND note = '(voice)'_String) AS a18 - 713828 TABLE SCAN cast_info WHERE note = '(voice)'
	Estimate Actual Operator - 1 AGGREGATE MIN(#0), MIN(#1) 0 15 PROJECT name, title 0 15 INNER JOIN HASH ON id = person_role_id 0 15 │└INNER JOIN HASH ON id = person_id 0 36 │ │└INNER JOIN HASH ON id = info_type_id 0 36 │ │ │└INNER JOIN HASH ON movie_id = movie_id 0 36 │ │ │ │└INNER JOIN HASH ON person_id = person_id 0 24 │ │ │ │ │└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 2 │ │ │ │ │ FILTER id BETWEEN 2 AND 2525745 505662 2 │ │ │ │ │ TABLE SCAN title WHERE production_year >= 2007 AND production_year <= 2008 AND (title LIKE '%Kung%Fu%Panda%') 901343 20 │ │ │ │ TABLE SCAN aka_name WHERE person_id <= 4061926 2967144 3 │ │ │ FILTER movie_id BETWEEN 2 AND 2525745 2967144 3 │ │ │ TABLE SCAN movie_info WHERE (info IS NOT NULL) AND ((info LIKE 'Japan:%2007%') OR (info LIKE 'USA:%2008%')) 2 1 │ │ FILTER id <= 110 2 1 │ │ TABLE SCAN info_type WHERE info = 'release dates' 2083746 1 │ FILTER id BETWEEN 4 AND 4061926 2083746 1 │ TABLE SCAN "name" WHERE gender = 'f' AND contains(name,'Angel') 3140339 1 TABLE SCAN char_name
	Estimate Actual Operator 1 1 PROJECT voicing_actress, kung_fu_panda 1 1 AGGREGATE MIN(name), MIN(title) 6989 10 DISTRIBUTE GATHER 6989 10 AGGREGATE MIN(name), MIN(title) 6989 15 INNER JOIN HASH ON movie_id = id AND movie_id = id AND movie_id = id 6989 64 │└DISTRIBUTE GATHER 6989 64 │ 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' 25628 64 │ INNER JOIN HASH ON person_id = id AND person_id = id 25628 11092 │ │└DISTRIBUTE GATHER 25628 11092 │ │ INNER JOIN HASH ON id = info_type_id 23 1 │ │ │└DISTRIBUTE GATHER 23 1 │ │ │ FILTER info = 'release dates' 113 113 │ │ │ DISTRIBUTE ROUND ROBIN 113 113 │ │ │ TABLE SCAN info_type WHERE info = 'release dates' 122571 11092 │ │ INNER JOIN HASH ON movie_id = movie_id AND movie_id = movie_id 104366 57755 │ │ │└DISTRIBUTE HASH ON movie_id, movie_id 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, person_id, movie_id, role_id, 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 (((pers... 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 ... 2967144 69478 │ │ DISTRIBUTE HASH ON movie_id, movie_id 2967144 69478 │ │ FILTER info IS NOT NULL AND (info LIKE 'Japan:%2007%' OR info LIKE 'USA:%2008%') 14835720 3212639 │ │ TABLE SCAN movie_info WHERE ((info IS NOT NULL AND (info LIKE 'Japan:%2007%' OR info LIKE 'USA:%2008%')) AND CASE MOD(HASH_REPARTITION(movie_id,movie_id),10) WHEN 1 THEN ((((movie_id >= 908) AND (movie_id <= 2525344)) AND ((movie_id >= 908) AND (movie_id <= 2525344))) AND TRUE) WHEN 3 THEN ((((movie_id >= 20403) AND (movie_id <= 2524106)) AND ((movie_id >= 20403) AND (movie_id <= 2524106))) AND TRUE) WHEN 5 THEN ((((movie_id >= 919) AND (movie_id <= 2520937)) AND (... 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) 505663 4 FILTER ((production_year >= 2007) AND (production_year <= 2008)) AND title LIKE '%Kung%Fu%Panda%' 2528312 1789790 TABLE SCAN title WHERE (((production_year >= 2007) AND (production_year <= 2008)) AND title LIKE '%Kung%Fu%Panda%') AND (((((id >= 862896) AND (id <= 2428090)) AND ((id >= 862896) AND (id <= 2428090))) AND ((id >= 862896) AND (id <= 2428090))) AND struct(id,id,id) IN( < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > , < expr > ,...
	Estimate Actual Operator 1 1 AGGREGATE MIN(n.name), MIN(t.title) 1 4 DISTRIBUTE GATHER 1 4 AGGREGATE MIN(n.name), MIN(t.title) 1020 15 INNER JOIN HASH ON mi.info_type_id = it.id 1020 1 │└DISTRIBUTE GATHER 36200000 1 │ TABLE SCAN info_type WHERE it.info = 'release dates' 1020 15 INNER JOIN HASH ON ci.movie_id = mi.movie_id 1020 15 │└DISTRIBUTE GATHER 162 15 │ INNER JOIN HASH ON ci.person_id = an.person_id 162 3 │ │└DISTRIBUTE GATHER 102 3 │ │ INNER JOIN HASH ON mc.company_id = cn.id 102 84843 │ │ │└DISTRIBUTE GATHER 3140000 84843 │ │ │ TABLE SCAN company_name WHERE (cn.country_code IS NOT NULL) AND (cn.country_code = 'us') 98 3 │ │ INNER JOIN HASH ON ci.movie_id = mc.movie_id 98 1 │ │ │└DISTRIBUTE GATHER 38 1 │ │ │ INNER JOIN HASH ON ci.movie_id = t.id 38 367 │ │ │ │└DISTRIBUTE GATHER 38 367 │ │ │ │ INNER JOIN HASH ON ci.person_id = n.id 38 203229 │ │ │ │ │└DISTRIBUTE GATHER 38 203229 │ │ │ │ │ INNER JOIN HASH ON ci.person_role_id = chn.id 38 203229 │ │ │ │ │ │└DISTRIBUTE GATHER 38 203229 │ │ │ │ │ │ INNER JOIN HASH ON ci.role_id = rt.id 38 1 │ │ │ │ │ │ │└DISTRIBUTE GATHER 113 1 │ │ │ │ │ │ │ TABLE SCAN role_type WHERE rt.role = 'actress' 14800000 203229 │ │ │ │ │ │ TABLE SCAN cast_info WHERE (ci.note IS NOT NULL) AND (ci.note = '(voice)') AND (ci.person_role_id IS NOT NULL) 901000 3132389 │ │ │ │ │ TABLE SCAN char_name 235000 5024 │ │ │ │ TABLE SCAN name WHERE (n.gender IS NOT NULL) AND (n.gender = 'f') AND contains(n.name,'Angel') 2610000 2 │ │ │ TABLE SCAN title WHERE (t.production_year IS NOT NULL) AND (t.production_year >= 2007L) AND (t.production_year <= 2008L) AND t.title LIKE '%Kung%Fu%Panda%' 4170000 74351 │ │ 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)')) 12 594144 │ TABLE SCAN aka_name 2530000 15181 TABLE SCAN movie_info WHERE mi.info LIKE 'Japan:%2007%' OR mi.info LIKE 'USA:%2008%'
	Estimate Actual Operator 1 1 AGGREGATE MIN(name as name) AS Expr1020, MIN(title as title) AS Expr1021 1 15 INNER JOIN LOOP ON ci.person_role_id = chn.id 1 15 │└TABLE SEEK char_name AS chn 4 15 INNER JOIN LOOP ON n.id = an.person_id 4 15 │└TABLE SEEK aka_name AS an 1 3 FILTER name as name LIKE '%Angel%' AND gender as gender = 'f' 1 24 INNER JOIN LOOP ON Bmk1014 = Bmk1014 1 24 │└TABLE SEEK name AS n 1 24 PROJECT BmkToPage Bmk1014 AS Expr1067 1 24 INNER JOIN LOOP ON ci.person_id = n.id 1 24 │└TABLE SEEK name AS n 1 24 FILTER role as role = 'actress' 1 100 INNER JOIN LOOP ON Bmk1016 = Bmk1016 1 100 │└TABLE SEEK role_type AS rt 1 100 INNER JOIN LOOP ON ci.role_id = rt.id 1 100 │└TABLE SEEK role_type AS rt 1 100 FILTER note as note = '(voice)' 30 608 INNER JOIN LOOP ON Bmk1004 = Bmk1004 1 608 │└TABLE SEEK cast_info AS ci 30 608 PROJECT BmkToPage Bmk1004 AS Expr1065 30 608 INNER JOIN LOOP ON t.id = ci.movie_id 30 608 │└TABLE SEEK cast_info AS ci 1 4 FILTER country_code as country_code = 'us' 1 4 INNER JOIN LOOP ON Bmk1006 = Bmk1006 1 4 │└TABLE SEEK company_name AS cn 1 4 INNER JOIN LOOP ON mc.company_id = cn.id 1 4 │└TABLE SEEK company_name AS cn 1 4 FILTER note as note LIKE '%(200%)%' AND (note as note LIKE '%(USA)%' OR note as note LIKE '%(worldwide)%') 9 45 INNER JOIN LOOP ON Bmk1010 = Bmk1010 1 45 │└TABLE SEEK movie_companies AS mc 9 45 PROJECT BmkToPage Bmk1010 AS Expr1064 9 45 INNER JOIN LOOP ON t.id = mc.movie_id 2 45 │└TABLE SEEK movie_companies AS mc 1 6 FILTER info as info = 'release dates' 5 6 INNER JOIN LOOP ON Bmk1008 = Bmk1008 1 6 │└TABLE SEEK info_type AS it 5 6 INNER JOIN LOOP ON mi.info_type_id = it.id 1 6 │└TABLE SEEK info_type AS it 5 6 INNER JOIN LOOP ON t.id = mi.movie_id 1 6 │└TABLE SEEK movie_info AS mi WHERE info as info LIKE 'Japan:%2007%' OR info as info LIKE 'USA:%2008%' 27 7 TABLE SCAN title AS t WHERE (production_year as production_year >= 2007 AND production_year as production_year <= 2008) AND (title as title LIKE '%Kung%Fu%Panda%')
	Estimate Actual Operator 1 ∞ PROJECT min AS voicing_actress, min_61 AS kung_fu_panda 1 1 AGGREGATE MIN(min_62) AS min, MIN(min_63) AS min_61 - 16 DISTRIBUTE GATHER - 16 AGGREGATE MIN(name_40) AS min_62, MIN(title) AS min_63 - 15 INNER JOIN HASH ON movie_id = id_54 15904 7 │└DISTRIBUTE HASH ON id_54 15904 7 │ PROJECT id AS id_54, title 15904 7 │ FILTER (production_year BETWEEN 2007 AND 2008) AND (title LIKE '%Kung%Fu%Panda%') 15904 7 │ TABLE SCAN title - 15 INNER JOIN HASH ON role_id = id_50 12 1 │└DISTRIBUTE GATHER 12 1 │ PROJECT id AS id_50 12 1 │ FILTER role = 'actress' 12 1 │ TABLE SCAN role_type - 15 INNER JOIN HASH ON person_id_10 = id_39 4167491 5409 │└DISTRIBUTE HASH ON id_39 4167491 5409 │ PROJECT id AS id_39, name AS name_40 4167491 5409 │ FILTER (gender = 'f') AND (name LIKE '%Angel%') 4167491 5409 │ TABLE SCAN name - 15 INNER JOIN HASH ON info_type_id = id_22 113 1 │└DISTRIBUTE GATHER 113 1 │ PROJECT id AS id_22 113 1 │ FILTER info = 'release dates' 113 1 │ TABLE SCAN info_type - 15 INNER JOIN HASH ON movie_id = movie_id_33 13352148 6 │└DISTRIBUTE HASH ON movie_id_33 13352148 6 │ PROJECT movie_id AS movie_id_33, info_type_id 13352148 6 │ FILTER (((info >= 'Japan:') AND (info < 'Japan;')) OR ((info >= 'USA:') AND (info < 'USA;'))) AND ((info LIKE 'Japan:%2007%') OR (info LIKE 'USA:%2008%')) 13352148 6 │ TABLE SCAN movie_info - 15 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 - 15 INNER JOIN HASH ON movie_id = movie_id_27 2348216 4 │└DISTRIBUTE HASH ON movie_id_27 2348216 4 │ PROJECT movie_id AS movie_id_27, company_id 2348216 4 │ FILTER (note LIKE '%(200%)%') AND ((note LIKE '%(USA)%') OR (note LIKE '%(worldwide)%')) 2348216 4 │ TABLE SCAN movie_companies - 5 INNER JOIN HASH ON person_role_id = id_0 3140339 3140339 │└DISTRIBUTE HASH ON id_0 3140339 3140339 │ PROJECT id AS id_0 3140339 3140339 │ TABLE SCAN char_name - 5 INNER JOIN HASH ON person_id_10 = person_id 901343 1296 │└DISTRIBUTE GATHER 901343 1296 │ TABLE SCAN aka_name 32619910 1 PROJECT person_id AS person_id_10, movie_id, person_role_id, role_id 32619910 1 FILTER note = '(voice)' 32619910 1 TABLE SCAN cast_info
	Estimate Actual Operator 1 1 AGGREGATE MIN(name), MIN(title) 1 15 INNER JOIN LOOP ON id = person_id AND person_id = id AND (person_id = id) 1 36 │└INNER JOIN LOOP ON id = info_type_id 1 36 │ │└INNER JOIN LOOP ON movie_id = movie_id AND movie_id = id AND (movie_id = id) 1 36 │ │ │└INNER JOIN LOOP ON id = company_id 1 36 │ │ │ │└INNER JOIN LOOP ON id = person_role_id 5 36 │ │ │ │ │└INNER JOIN LOOP ON id = role_id 5 275 │ │ │ │ │ │└INNER JOIN LOOP ON person_id = person_id 5 100 │ │ │ │ │ │ │└INNER JOIN LOOP ON movie_id = movie_id AND movie_id = id AND (movie_id = id) 5 5 │ │ │ │ │ │ │ │└INNER JOIN LOOP ON movie_id = id 30 7 │ │ │ │ │ │ │ │ │└TABLE SEEK title AS t WHERE t.title LIKE '%Kung%Fu%Panda%' 7 7 │ │ │ │ │ │ │ │ TABLE SEEK movie_companies AS mc WHERE (mc.note LIKE '%(200%)%') AND ((mc.note LIKE '%(USA)%') OR (mc.note LIKE '%(worldwide)%')) 4 100 │ │ │ │ │ │ │ TABLE SEEK cast_info AS ci WHERE ci.note = '(voice)' 200 275 │ │ │ │ │ │ TABLE SEEK aka_name AS an 4 4 │ │ │ │ │ TABLE SEEK role_type AS rt WHERE rt.role = 'actress' 36 36 │ │ │ │ TABLE SEEK char_name AS chn 36 36 │ │ │ TABLE SEEK company_name AS cn WHERE cn.country_code = 'us' 36 36 │ │ TABLE SEEK movie_info AS mi WHERE (mi.info LIKE 'Japan:%2007%') OR (mi.info LIKE 'USA:%2008%') 36 36 │ TABLE SEEK info_type AS it WHERE it.info = 'release dates' 36 36 TABLE SEEK name AS n WHERE (n.name LIKE '%Angel%') AND (n.gender = 'f')