========================================================
 Detailed Analysis for Query: 15a 
 Source File: 15a.sql
 Date: Sun Dec 21 12:34:57 CST 2025
========================================================


################################################################
# CONFIGURATION: dp
################################################################

SET
SET
                                                                                                   QUERY PLAN                                                                                                    
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=78211.07..78211.08 rows=1 width=64) (actual time=552.357..552.361 rows=1.00 loops=1)
   Output: min(mi.info), min(t.title)
   Buffers: shared hit=271918 read=22998
   ->  Nested Loop  (cost=6991.57..78210.98 rows=18 width=60) (actual time=71.142..552.301 rows=328.00 loops=1)
         Output: mi.info, t.title
         Inner Unique: true
         Buffers: shared hit=271918 read=22998
         ->  Nested Loop  (cost=6991.15..78203.09 rows=18 width=64) (actual time=69.810..543.891 rows=328.00 loops=1)
               Output: mi.info, mk.keyword_id, t.title
               Buffers: shared hit=271008 read=22923
               ->  Nested Loop  (cost=6990.72..78201.31 rows=1 width=76) (actual time=56.946..534.006 rows=57.00 loops=1)
                     Output: at.movie_id, mc.movie_id, mi.info, mi.movie_id, t.title, t.id
                     Inner Unique: true
                     Buffers: shared hit=270863 read=22864
                     ->  Nested Loop  (cost=6990.58..78201.01 rows=1 width=80) (actual time=56.939..533.872 rows=57.00 loops=1)
                           Output: at.movie_id, mc.movie_id, mi.info, mi.movie_id, mi.info_type_id, t.title, t.id
                           Inner Unique: true
                           Buffers: shared hit=270749 read=22864
                           ->  Nested Loop  (cost=6990.45..78200.86 rows=1 width=84) (actual time=56.925..533.765 rows=57.00 loops=1)
                                 Output: at.movie_id, mc.movie_id, mc.company_type_id, mi.info, mi.movie_id, mi.info_type_id, t.title, t.id
                                 Buffers: shared hit=270691 read=22864
                                 ->  Nested Loop  (cost=6990.01..73579.58 rows=2479 width=33) (actual time=52.701..420.322 rows=5134.00 loops=1)
                                       Output: at.movie_id, mc.movie_id, mc.company_type_id, t.title, t.id
                                       Inner Unique: true
                                       Buffers: shared hit=185524 read=22569
                                       ->  Nested Loop  (cost=6989.58..69302.32 rows=4512 width=12) (actual time=52.684..385.093 rows=13699.00 loops=1)
                                             Output: at.movie_id, mc.movie_id, mc.company_type_id
                                             Buffers: shared hit=130729 read=22568
                                             ->  Hash Join  (cost=6989.16..64962.18 rows=6315 width=8) (actual time=52.421..327.937 rows=43837.00 loops=1)
                                                   Output: mc.movie_id, mc.company_type_id
                                                   Inner Unique: true
                                                   Hash Cond: (mc.company_id = cn.id)
                                                   Buffers: shared read=21785
                                                   ->  Seq Scan on public.movie_companies mc  (cost=0.00..57926.93 rows=17556 width=12) (actual time=0.246..260.496 rows=61664.00 loops=1)
                                                         Output: mc.id, mc.movie_id, mc.company_id, mc.company_type_id, mc.note
                                                         Filter: ((mc.note ~~ '%(200%)%'::text) AND (mc.note ~~ '%(worldwide)%'::text))
                                                         Rows Removed by Filter: 2547465
                                                         Buffers: shared read=18790
                                                   ->  Hash  (cost=5932.46..5932.46 rows=84536 width=4) (actual time=51.831..51.832 rows=84843.00 loops=1)
                                                         Output: cn.id
                                                         Buckets: 131072  Batches: 1  Memory Usage: 4007kB
                                                         Buffers: shared read=2995
                                                         ->  Seq Scan on public.company_name cn  (cost=0.00..5932.46 rows=84536 width=4) (actual time=0.174..26.124 rows=84843.00 loops=1)
                                                               Output: cn.id
                                                               Filter: ((cn.country_code)::text = '[us]'::text)
                                                               Rows Removed by Filter: 150154
                                                               Buffers: shared read=2995
                                             ->  Index Only Scan using movie_id_aka_title on public.aka_title at  (cost=0.42..0.66 rows=3 width=4) (actual time=0.001..0.001 rows=0.31 loops=43837)
                                                   Output: at.movie_id
                                                   Index Cond: (at.movie_id = mc.movie_id)
                                                   Heap Fetches: 0
                                                   Index Searches: 43837
                                                   Buffers: shared hit=130729 read=783
                                       ->  Index Scan using title_pkey on public.title t  (cost=0.43..0.95 rows=1 width=21) (actual time=0.002..0.002 rows=0.37 loops=13699)
                                             Output: t.id, t.title, t.imdb_index, t.kind_id, t.production_year, t.imdb_id, t.phonetic_code, t.episode_of_id, t.season_nr, t.episode_nr, t.series_years, t.md5sum
                                             Index Cond: (t.id = at.movie_id)
                                             Filter: (t.production_year > 2000)
                                             Rows Removed by Filter: 1
                                             Index Searches: 13699
                                             Buffers: shared hit=54795 read=1
                                 ->  Index Scan using movie_id_movie_info on public.movie_info mi  (cost=0.43..1.85 rows=1 width=51) (actual time=0.022..0.022 rows=0.01 loops=5134)
                                       Output: mi.id, mi.movie_id, mi.info_type_id, mi.info, mi.note
                                       Index Cond: (mi.movie_id = t.id)
                                       Filter: ((mi.note ~~ '%internet%'::text) AND (mi.info ~~ 'USA:% 200%'::text))
                                       Rows Removed by Filter: 72
                                       Index Searches: 5134
                                       Buffers: shared hit=85167 read=295
                           ->  Index Only Scan using company_type_pkey on public.company_type ct  (cost=0.13..0.15 rows=1 width=4) (actual time=0.001..0.001 rows=1.00 loops=57)
                                 Output: ct.id
                                 Index Cond: (ct.id = mc.company_type_id)
                                 Heap Fetches: 0
                                 Index Searches: 57
                                 Buffers: shared hit=58
                     ->  Index Scan using info_type_pkey on public.info_type it1  (cost=0.14..0.22 rows=1 width=4) (actual time=0.002..0.002 rows=1.00 loops=57)
                           Output: it1.id, it1.info
                           Index Cond: (it1.id = mi.info_type_id)
                           Filter: ((it1.info)::text = 'release dates'::text)
                           Index Searches: 57
                           Buffers: shared hit=114
               ->  Index Scan using movie_id_movie_keyword on public.movie_keyword mk  (cost=0.43..1.32 rows=46 width=8) (actual time=0.167..0.172 rows=5.75 loops=57)
                     Output: mk.id, mk.movie_id, mk.keyword_id
                     Index Cond: (mk.movie_id = t.id)
                     Index Searches: 57
                     Buffers: shared hit=145 read=59
         ->  Index Only Scan using keyword_pkey on public.keyword k  (cost=0.42..0.44 rows=1 width=4) (actual time=0.025..0.025 rows=1.00 loops=328)
               Output: k.id
               Index Cond: (k.id = mk.keyword_id)
               Heap Fetches: 0
               Index Searches: 328
               Buffers: shared hit=910 read=75
 Planning:
   Buffers: shared hit=665 read=63
 Planning Time: 11.943 ms
 Execution Time: 552.482 ms
(94 rows)


################################################################
# CONFIGURATION: goo_cost
################################################################

SET
SET
SET
                                                                                                QUERY PLAN                                                                                                 
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=86720.13..86720.14 rows=1 width=64) (actual time=3568.750..3568.753 rows=1.00 loops=1)
   Output: min(mi.info), min(t.title)
   Buffers: shared hit=5508047 read=4752
   ->  Nested Loop  (cost=6992.53..86720.12 rows=1 width=60) (actual time=47.855..3568.708 rows=328.00 loops=1)
         Output: mi.info, t.title
         Inner Unique: true
         Buffers: shared hit=5508047 read=4752
         ->  Nested Loop  (cost=6992.39..86719.82 rows=1 width=64) (actual time=47.842..3568.494 rows=328.00 loops=1)
               Output: mi.info, mi.info_type_id, t.title
               Join Filter: (mi.movie_id = t.id)
               Buffers: shared hit=5507391 read=4752
               ->  Nested Loop  (cost=6991.96..80036.62 rows=4435 width=33) (actual time=25.304..793.931 rows=192112.00 loops=1)
                     Output: mk.movie_id, t.title, t.id, at.movie_id, mc.movie_id
                     Inner Unique: true
                     Buffers: shared hit=800567 read=4738
                     ->  Nested Loop  (cost=6991.54..78094.34 rows=4435 width=37) (actual time=25.288..579.703 rows=192112.00 loops=1)
                           Output: mk.movie_id, mk.keyword_id, t.title, t.id, at.movie_id, mc.movie_id
                           Buffers: shared hit=224649 read=4319
                           ->  Nested Loop  (cost=6991.10..73675.58 rows=2479 width=29) (actual time=25.248..322.164 rows=5134.00 loops=1)
                                 Output: t.title, t.id, at.movie_id, mc.movie_id
                                 Inner Unique: true
                                 Buffers: shared hit=208093 read=1
                                 ->  Nested Loop  (cost=6990.67..69398.32 rows=4512 width=8) (actual time=25.231..292.848 rows=13699.00 loops=1)
                                       Output: at.movie_id, mc.movie_id
                                       Buffers: shared hit=153297 read=1
                                       ->  Hash Join  (cost=6990.25..65058.18 rows=6315 width=4) (actual time=25.055..262.125 rows=43837.00 loops=1)
                                             Output: mc.movie_id
                                             Inner Unique: true
                                             Hash Cond: (mc.company_id = cn.id)
                                             Buffers: shared hit=21785 read=1
                                             ->  Hash Join  (cost=1.09..58022.94 rows=17556 width=8) (actual time=0.173..223.147 rows=61664.00 loops=1)
                                                   Output: mc.movie_id, mc.company_id
                                                   Inner Unique: true
                                                   Hash Cond: (mc.company_type_id = ct.id)
                                                   Buffers: shared hit=18790 read=1
                                                   ->  Seq Scan on public.movie_companies mc  (cost=0.00..57926.93 rows=17556 width=12) (actual time=0.008..217.794 rows=61664.00 loops=1)
                                                         Output: mc.id, mc.movie_id, mc.company_id, mc.company_type_id, mc.note
                                                         Filter: ((mc.note ~~ '%(200%)%'::text) AND (mc.note ~~ '%(worldwide)%'::text))
                                                         Rows Removed by Filter: 2547465
                                                         Buffers: shared hit=18790
                                                   ->  Hash  (cost=1.04..1.04 rows=4 width=4) (actual time=0.161..0.161 rows=4.00 loops=1)
                                                         Output: ct.id
                                                         Buckets: 1024  Batches: 1  Memory Usage: 9kB
                                                         Buffers: shared read=1
                                                         ->  Seq Scan on public.company_type ct  (cost=0.00..1.04 rows=4 width=4) (actual time=0.154..0.155 rows=4.00 loops=1)
                                                               Output: ct.id
                                                               Buffers: shared read=1
                                             ->  Hash  (cost=5932.46..5932.46 rows=84536 width=4) (actual time=24.763..24.763 rows=84843.00 loops=1)
                                                   Output: cn.id
                                                   Buckets: 131072  Batches: 1  Memory Usage: 4007kB
                                                   Buffers: shared hit=2995
                                                   ->  Seq Scan on public.company_name cn  (cost=0.00..5932.46 rows=84536 width=4) (actual time=0.009..17.935 rows=84843.00 loops=1)
                                                         Output: cn.id
                                                         Filter: ((cn.country_code)::text = '[us]'::text)
                                                         Rows Removed by Filter: 150154
                                                         Buffers: shared hit=2995
                                       ->  Index Only Scan using movie_id_aka_title on public.aka_title at  (cost=0.42..0.66 rows=3 width=4) (actual time=0.001..0.001 rows=0.31 loops=43837)
                                             Output: at.movie_id
                                             Index Cond: (at.movie_id = mc.movie_id)
                                             Heap Fetches: 0
                                             Index Searches: 43837
                                             Buffers: shared hit=131512
                                 ->  Index Scan using title_pkey on public.title t  (cost=0.43..0.95 rows=1 width=21) (actual time=0.002..0.002 rows=0.37 loops=13699)
                                       Output: t.id, t.title, t.imdb_index, t.kind_id, t.production_year, t.imdb_id, t.phonetic_code, t.episode_of_id, t.season_nr, t.episode_nr, t.series_years, t.md5sum
                                       Index Cond: (t.id = at.movie_id)
                                       Filter: (t.production_year > 2000)
                                       Rows Removed by Filter: 1
                                       Index Searches: 13699
                                       Buffers: shared hit=54796
                           ->  Index Scan using movie_id_movie_keyword on public.movie_keyword mk  (cost=0.43..1.32 rows=46 width=8) (actual time=0.043..0.048 rows=37.42 loops=5134)
                                 Output: mk.id, mk.movie_id, mk.keyword_id
                                 Index Cond: (mk.movie_id = t.id)
                                 Index Searches: 5134
                                 Buffers: shared hit=16556 read=4318
                     ->  Index Only Scan using keyword_pkey on public.keyword k  (cost=0.42..0.44 rows=1 width=4) (actual time=0.001..0.001 rows=1.00 loops=192112)
                           Output: k.id
                           Index Cond: (k.id = mk.keyword_id)
                           Heap Fetches: 0
                           Index Searches: 192112
                           Buffers: shared hit=575918 read=419
               ->  Index Scan using movie_id_movie_info on public.movie_info mi  (cost=0.43..1.49 rows=1 width=51) (actual time=0.014..0.014 rows=0.00 loops=192112)
                     Output: mi.id, mi.movie_id, mi.info_type_id, mi.info, mi.note
                     Index Cond: (mi.movie_id = mk.movie_id)
                     Filter: ((mi.note ~~ '%internet%'::text) AND (mi.info ~~ 'USA:% 200%'::text))
                     Rows Removed by Filter: 207
                     Index Searches: 192112
                     Buffers: shared hit=4706824 read=14
         ->  Index Scan using info_type_pkey on public.info_type it1  (cost=0.14..0.22 rows=1 width=4) (actual time=0.000..0.000 rows=1.00 loops=328)
               Output: it1.id, it1.info
               Index Cond: (it1.id = mi.info_type_id)
               Filter: ((it1.info)::text = 'release dates'::text)
               Index Searches: 328
               Buffers: shared hit=656
 Planning:
   Buffers: shared hit=728
 Planning Time: 2.276 ms
 Execution Time: 3568.887 ms
(97 rows)


################################################################
# CONFIGURATION: goo_result_size
################################################################

SET
SET
SET
                                                                                                 QUERY PLAN                                                                                                 
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=176422.39..176422.40 rows=1 width=64) (actual time=354.931..354.932 rows=1.00 loops=1)
   Output: min(mi.info), min(t.title)
   Buffers: shared hit=32596 read=9979
   ->  Nested Loop  (cost=2341.90..176422.38 rows=2 width=60) (actual time=71.499..354.891 rows=328.00 loops=1)
         Output: mi.info, t.title
         Inner Unique: true
         Buffers: shared hit=32596 read=9979
         ->  Nested Loop  (cost=2341.48..176421.50 rows=2 width=64) (actual time=71.480..354.353 rows=328.00 loops=1)
               Output: mk.keyword_id, t.title, mi.info
               Buffers: shared hit=31611 read=9979
               ->  Nested Loop  (cost=2341.05..176419.72 rows=1 width=76) (actual time=67.527..354.083 rows=57.00 loops=1)
                     Output: t.title, t.id, at.movie_id, mc.movie_id, mi.info, mi.movie_id
                     Inner Unique: true
                     Buffers: shared hit=31407 read=9979
                     ->  Nested Loop  (cost=2340.62..176418.77 rows=1 width=55) (actual time=67.511..353.664 rows=71.00 loops=1)
                           Output: at.movie_id, mc.movie_id, mi.info, mi.movie_id
                           Join Filter: (at.movie_id = mi.movie_id)
                           Buffers: shared hit=31123 read=9979
                           ->  Nested Loop  (cost=2340.20..176418.08 rows=1 width=51) (actual time=58.579..351.908 rows=668.00 loops=1)
                                 Output: mc.movie_id, mi.info, mi.movie_id
                                 Inner Unique: true
                                 Buffers: shared hit=29118 read=9979
                                 ->  Nested Loop  (cost=2340.07..176417.92 rows=1 width=55) (actual time=58.572..351.513 rows=668.00 loops=1)
                                       Output: mc.movie_id, mc.company_type_id, mi.info, mi.movie_id
                                       Inner Unique: true
                                       Buffers: shared hit=28449 read=9979
                                       ->  Nested Loop  (cost=2339.65..176416.60 rows=1 width=59) (actual time=58.560..342.259 rows=699.00 loops=1)
                                             Output: mc.movie_id, mc.company_id, mc.company_type_id, mi.info, mi.movie_id
                                             Buffers: shared hit=25787 read=9845
                                             ->  Nested Loop  (cost=2339.22..176374.86 rows=2 width=47) (actual time=58.541..277.063 rows=1771.00 loops=1)
                                                   Output: mi.info, mi.movie_id
                                                   Buffers: shared hit=19445 read=8562
                                                   ->  Seq Scan on public.info_type it1  (cost=0.00..2.41 rows=1 width=4) (actual time=0.007..0.012 rows=1.00 loops=1)
                                                         Output: it1.id, it1.info
                                                         Filter: ((it1.info)::text = 'release dates'::text)
                                                         Rows Removed by Filter: 112
                                                         Buffers: shared hit=1
                                                   ->  Bitmap Heap Scan on public.movie_info mi  (cost=2339.22..176372.41 rows=3 width=51) (actual time=58.533..276.909 rows=1771.00 loops=1)
                                                         Output: mi.id, mi.movie_id, mi.info_type_id, mi.info, mi.note
                                                         Recheck Cond: (it1.id = mi.info_type_id)
                                                         Filter: ((mi.note ~~ '%internet%'::text) AND (mi.info ~~ 'USA:% 200%'::text))
                                                         Rows Removed by Filter: 3034948
                                                         Heap Blocks: exact=25447
                                                         Buffers: shared hit=19444 read=8562
                                                         ->  Bitmap Index Scan on info_type_id_movie_info  (cost=0.00..2339.22 rows=214771 width=0) (actual time=56.109..56.110 rows=3036719.00 loops=1)
                                                               Index Cond: (mi.info_type_id = it1.id)
                                                               Index Searches: 1
                                                               Buffers: shared read=2559
                                             ->  Index Scan using movie_id_movie_companies on public.movie_companies mc  (cost=0.43..20.86 rows=1 width=12) (actual time=0.036..0.037 rows=0.39 loops=1771)
                                                   Output: mc.id, mc.movie_id, mc.company_id, mc.company_type_id, mc.note
                                                   Index Cond: (mc.movie_id = mi.movie_id)
                                                   Filter: ((mc.note ~~ '%(200%)%'::text) AND (mc.note ~~ '%(worldwide)%'::text))
                                                   Rows Removed by Filter: 1
                                                   Index Searches: 1771
                                                   Buffers: shared hit=6342 read=1283
                                       ->  Index Scan using company_name_pkey on public.company_name cn  (cost=0.42..1.32 rows=1 width=4) (actual time=0.013..0.013 rows=0.96 loops=699)
                                             Output: cn.id, cn.name, cn.country_code, cn.imdb_id, cn.name_pcode_nf, cn.name_pcode_sf, cn.md5sum
                                             Index Cond: (cn.id = mc.company_id)
                                             Filter: ((cn.country_code)::text = '[us]'::text)
                                             Rows Removed by Filter: 0
                                             Index Searches: 699
                                             Buffers: shared hit=2662 read=134
                                 ->  Index Only Scan using company_type_pkey on public.company_type ct  (cost=0.13..0.15 rows=1 width=4) (actual time=0.000..0.000 rows=1.00 loops=668)
                                       Output: ct.id
                                       Index Cond: (ct.id = mc.company_type_id)
                                       Heap Fetches: 0
                                       Index Searches: 668
                                       Buffers: shared hit=669
                           ->  Index Only Scan using movie_id_aka_title on public.aka_title at  (cost=0.42..0.66 rows=3 width=4) (actual time=0.002..0.002 rows=0.11 loops=668)
                                 Output: at.movie_id
                                 Index Cond: (at.movie_id = mc.movie_id)
                                 Heap Fetches: 0
                                 Index Searches: 668
                                 Buffers: shared hit=2005
                     ->  Index Scan using title_pkey on public.title t  (cost=0.43..0.95 rows=1 width=21) (actual time=0.005..0.005 rows=0.80 loops=71)
                           Output: t.id, t.title, t.imdb_index, t.kind_id, t.production_year, t.imdb_id, t.phonetic_code, t.episode_of_id, t.season_nr, t.episode_nr, t.series_years, t.md5sum
                           Index Cond: (t.id = at.movie_id)
                           Filter: (t.production_year > 2000)
                           Rows Removed by Filter: 0
                           Index Searches: 71
                           Buffers: shared hit=284
               ->  Index Scan using movie_id_movie_keyword on public.movie_keyword mk  (cost=0.43..1.32 rows=46 width=8) (actual time=0.004..0.004 rows=5.75 loops=57)
                     Output: mk.id, mk.movie_id, mk.keyword_id
                     Index Cond: (mk.movie_id = t.id)
                     Index Searches: 57
                     Buffers: shared hit=204
         ->  Index Only Scan using keyword_pkey on public.keyword k  (cost=0.42..0.44 rows=1 width=4) (actual time=0.001..0.001 rows=1.00 loops=328)
               Output: k.id
               Index Cond: (k.id = mk.keyword_id)
               Heap Fetches: 0
               Index Searches: 328
               Buffers: shared hit=985
 Planning:
   Buffers: shared hit=728
 Planning Time: 2.180 ms
 Execution Time: 355.013 ms
(96 rows)


################################################################
# CONFIGURATION: goo_combined
################################################################

SET
SET
SET
                                                                                                QUERY PLAN                                                                                                 
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=86720.13..86720.14 rows=1 width=64) (actual time=3287.981..3287.990 rows=1.00 loops=1)
   Output: min(mi.info), min(t.title)
   Buffers: shared hit=5512799
   ->  Nested Loop  (cost=6992.53..86720.12 rows=1 width=60) (actual time=50.276..3287.941 rows=328.00 loops=1)
         Output: mi.info, t.title
         Inner Unique: true
         Buffers: shared hit=5512799
         ->  Nested Loop  (cost=6992.39..86719.82 rows=1 width=64) (actual time=50.255..3287.716 rows=328.00 loops=1)
               Output: mi.info, mi.info_type_id, t.title
               Join Filter: (mi.movie_id = t.id)
               Buffers: shared hit=5512143
               ->  Nested Loop  (cost=6991.96..80036.62 rows=4435 width=33) (actual time=30.917..533.866 rows=192112.00 loops=1)
                     Output: mk.movie_id, t.title, t.id, at.movie_id, mc.movie_id
                     Inner Unique: true
                     Buffers: shared hit=805305
                     ->  Nested Loop  (cost=6991.54..78094.34 rows=4435 width=37) (actual time=30.903..358.316 rows=192112.00 loops=1)
                           Output: mk.movie_id, mk.keyword_id, t.title, t.id, at.movie_id, mc.movie_id
                           Buffers: shared hit=228968
                           ->  Nested Loop  (cost=6991.10..73675.58 rows=2479 width=29) (actual time=30.885..321.412 rows=5134.00 loops=1)
                                 Output: t.title, t.id, at.movie_id, mc.movie_id
                                 Inner Unique: true
                                 Buffers: shared hit=208094
                                 ->  Nested Loop  (cost=6990.67..69398.32 rows=4512 width=8) (actual time=30.864..293.542 rows=13699.00 loops=1)
                                       Output: at.movie_id, mc.movie_id
                                       Buffers: shared hit=153298
                                       ->  Hash Join  (cost=6990.25..65058.18 rows=6315 width=4) (actual time=30.691..263.698 rows=43837.00 loops=1)
                                             Output: mc.movie_id
                                             Inner Unique: true
                                             Hash Cond: (mc.company_id = cn.id)
                                             Buffers: shared hit=21786
                                             ->  Hash Join  (cost=1.09..58022.94 rows=17556 width=8) (actual time=0.022..219.706 rows=61664.00 loops=1)
                                                   Output: mc.movie_id, mc.company_id
                                                   Inner Unique: true
                                                   Hash Cond: (mc.company_type_id = ct.id)
                                                   Buffers: shared hit=18791
                                                   ->  Seq Scan on public.movie_companies mc  (cost=0.00..57926.93 rows=17556 width=12) (actual time=0.008..214.741 rows=61664.00 loops=1)
                                                         Output: mc.id, mc.movie_id, mc.company_id, mc.company_type_id, mc.note
                                                         Filter: ((mc.note ~~ '%(200%)%'::text) AND (mc.note ~~ '%(worldwide)%'::text))
                                                         Rows Removed by Filter: 2547465
                                                         Buffers: shared hit=18790
                                                   ->  Hash  (cost=1.04..1.04 rows=4 width=4) (actual time=0.008..0.013 rows=4.00 loops=1)
                                                         Output: ct.id
                                                         Buckets: 1024  Batches: 1  Memory Usage: 9kB
                                                         Buffers: shared hit=1
                                                         ->  Seq Scan on public.company_type ct  (cost=0.00..1.04 rows=4 width=4) (actual time=0.005..0.010 rows=4.00 loops=1)
                                                               Output: ct.id
                                                               Buffers: shared hit=1
                                             ->  Hash  (cost=5932.46..5932.46 rows=84536 width=4) (actual time=30.579..30.580 rows=84843.00 loops=1)
                                                   Output: cn.id
                                                   Buckets: 131072  Batches: 1  Memory Usage: 4007kB
                                                   Buffers: shared hit=2995
                                                   ->  Seq Scan on public.company_name cn  (cost=0.00..5932.46 rows=84536 width=4) (actual time=0.007..21.371 rows=84843.00 loops=1)
                                                         Output: cn.id
                                                         Filter: ((cn.country_code)::text = '[us]'::text)
                                                         Rows Removed by Filter: 150154
                                                         Buffers: shared hit=2995
                                       ->  Index Only Scan using movie_id_aka_title on public.aka_title at  (cost=0.42..0.66 rows=3 width=4) (actual time=0.001..0.001 rows=0.31 loops=43837)
                                             Output: at.movie_id
                                             Index Cond: (at.movie_id = mc.movie_id)
                                             Heap Fetches: 0
                                             Index Searches: 43837
                                             Buffers: shared hit=131512
                                 ->  Index Scan using title_pkey on public.title t  (cost=0.43..0.95 rows=1 width=21) (actual time=0.002..0.002 rows=0.37 loops=13699)
                                       Output: t.id, t.title, t.imdb_index, t.kind_id, t.production_year, t.imdb_id, t.phonetic_code, t.episode_of_id, t.season_nr, t.episode_nr, t.series_years, t.md5sum
                                       Index Cond: (t.id = at.movie_id)
                                       Filter: (t.production_year > 2000)
                                       Rows Removed by Filter: 1
                                       Index Searches: 13699
                                       Buffers: shared hit=54796
                           ->  Index Scan using movie_id_movie_keyword on public.movie_keyword mk  (cost=0.43..1.32 rows=46 width=8) (actual time=0.002..0.005 rows=37.42 loops=5134)
                                 Output: mk.id, mk.movie_id, mk.keyword_id
                                 Index Cond: (mk.movie_id = t.id)
                                 Index Searches: 5134
                                 Buffers: shared hit=20874
                     ->  Index Only Scan using keyword_pkey on public.keyword k  (cost=0.42..0.44 rows=1 width=4) (actual time=0.001..0.001 rows=1.00 loops=192112)
                           Output: k.id
                           Index Cond: (k.id = mk.keyword_id)
                           Heap Fetches: 0
                           Index Searches: 192112
                           Buffers: shared hit=576337
               ->  Index Scan using movie_id_movie_info on public.movie_info mi  (cost=0.43..1.49 rows=1 width=51) (actual time=0.014..0.014 rows=0.00 loops=192112)
                     Output: mi.id, mi.movie_id, mi.info_type_id, mi.info, mi.note
                     Index Cond: (mi.movie_id = mk.movie_id)
                     Filter: ((mi.note ~~ '%internet%'::text) AND (mi.info ~~ 'USA:% 200%'::text))
                     Rows Removed by Filter: 207
                     Index Searches: 192112
                     Buffers: shared hit=4706838
         ->  Index Scan using info_type_pkey on public.info_type it1  (cost=0.14..0.22 rows=1 width=4) (actual time=0.000..0.000 rows=1.00 loops=328)
               Output: it1.id, it1.info
               Index Cond: (it1.id = mi.info_type_id)
               Filter: ((it1.info)::text = 'release dates'::text)
               Index Searches: 328
               Buffers: shared hit=656
 Planning:
   Buffers: shared hit=728
 Planning Time: 2.748 ms
 Execution Time: 3288.138 ms
(97 rows)


################################################################
# CONFIGURATION: geqo
################################################################

SET
SET
                                                                                                   QUERY PLAN                                                                                                    
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=78204.16..78204.17 rows=1 width=64) (actual time=389.904..389.907 rows=1.00 loops=1)
   Output: min(mi.info), min(t.title)
   Buffers: shared hit=295065 read=10
   ->  Nested Loop  (cost=6991.59..78204.16 rows=1 width=60) (actual time=29.922..389.868 rows=328.00 loops=1)
         Output: mi.info, t.title
         Inner Unique: true
         Buffers: shared hit=295065 read=10
         ->  Nested Loop  (cost=6991.45..78204.00 rows=1 width=64) (actual time=29.915..389.744 rows=328.00 loops=1)
               Output: mc.company_type_id, t.title, mi.info
               Inner Unique: true
               Buffers: shared hit=294736 read=10
               ->  Nested Loop  (cost=6991.30..78203.51 rows=2 width=68) (actual time=29.901..389.662 rows=328.00 loops=1)
                     Output: mc.company_type_id, t.title, mi.info, mi.info_type_id
                     Inner Unique: true
                     Buffers: shared hit=294734 read=10
                     ->  Nested Loop  (cost=6990.88..78202.64 rows=2 width=72) (actual time=29.883..389.149 rows=328.00 loops=1)
                           Output: mc.company_type_id, t.title, mi.info, mi.info_type_id, mk.keyword_id
                           Buffers: shared hit=293749 read=10
                           ->  Nested Loop  (cost=6990.45..78200.86 rows=1 width=84) (actual time=28.194..388.883 rows=57.00 loops=1)
                                 Output: mc.movie_id, mc.company_type_id, at.movie_id, t.title, t.id, mi.info, mi.movie_id, mi.info_type_id
                                 Buffers: shared hit=293545 read=10
                                 ->  Nested Loop  (cost=6990.01..73579.58 rows=2479 width=33) (actual time=23.522..312.874 rows=5134.00 loops=1)
                                       Output: mc.movie_id, mc.company_type_id, at.movie_id, t.title, t.id
                                       Inner Unique: true
                                       Buffers: shared hit=208093
                                       ->  Nested Loop  (cost=6989.58..69302.32 rows=4512 width=12) (actual time=23.500..286.657 rows=13699.00 loops=1)
                                             Output: mc.movie_id, mc.company_type_id, at.movie_id
                                             Buffers: shared hit=153297
                                             ->  Hash Join  (cost=6989.16..64962.18 rows=6315 width=8) (actual time=23.320..257.917 rows=43837.00 loops=1)
                                                   Output: mc.movie_id, mc.company_type_id
                                                   Inner Unique: true
                                                   Hash Cond: (mc.company_id = cn.id)
                                                   Buffers: shared hit=21785
                                                   ->  Seq Scan on public.movie_companies mc  (cost=0.00..57926.93 rows=17556 width=12) (actual time=0.012..222.951 rows=61664.00 loops=1)
                                                         Output: mc.id, mc.movie_id, mc.company_id, mc.company_type_id, mc.note
                                                         Filter: ((mc.note ~~ '%(200%)%'::text) AND (mc.note ~~ '%(worldwide)%'::text))
                                                         Rows Removed by Filter: 2547465
                                                         Buffers: shared hit=18790
                                                   ->  Hash  (cost=5932.46..5932.46 rows=84536 width=4) (actual time=23.138..23.138 rows=84843.00 loops=1)
                                                         Output: cn.id
                                                         Buckets: 131072  Batches: 1  Memory Usage: 4007kB
                                                         Buffers: shared hit=2995
                                                         ->  Seq Scan on public.company_name cn  (cost=0.00..5932.46 rows=84536 width=4) (actual time=0.011..17.283 rows=84843.00 loops=1)
                                                               Output: cn.id
                                                               Filter: ((cn.country_code)::text = '[us]'::text)
                                                               Rows Removed by Filter: 150154
                                                               Buffers: shared hit=2995
                                             ->  Index Only Scan using movie_id_aka_title on public.aka_title at  (cost=0.42..0.66 rows=3 width=4) (actual time=0.001..0.001 rows=0.31 loops=43837)
                                                   Output: at.movie_id
                                                   Index Cond: (at.movie_id = mc.movie_id)
                                                   Heap Fetches: 0
                                                   Index Searches: 43837
                                                   Buffers: shared hit=131512
                                       ->  Index Scan using title_pkey on public.title t  (cost=0.43..0.95 rows=1 width=21) (actual time=0.002..0.002 rows=0.37 loops=13699)
                                             Output: t.id, t.title, t.imdb_index, t.kind_id, t.production_year, t.imdb_id, t.phonetic_code, t.episode_of_id, t.season_nr, t.episode_nr, t.series_years, t.md5sum
                                             Index Cond: (t.id = at.movie_id)
                                             Filter: (t.production_year > 2000)
                                             Rows Removed by Filter: 1
                                             Index Searches: 13699
                                             Buffers: shared hit=54796
                                 ->  Index Scan using movie_id_movie_info on public.movie_info mi  (cost=0.43..1.85 rows=1 width=51) (actual time=0.015..0.015 rows=0.01 loops=5134)
                                       Output: mi.id, mi.movie_id, mi.info_type_id, mi.info, mi.note
                                       Index Cond: (mi.movie_id = t.id)
                                       Filter: ((mi.note ~~ '%internet%'::text) AND (mi.info ~~ 'USA:% 200%'::text))
                                       Rows Removed by Filter: 72
                                       Index Searches: 5134
                                       Buffers: shared hit=85452 read=10
                           ->  Index Scan using movie_id_movie_keyword on public.movie_keyword mk  (cost=0.43..1.32 rows=46 width=8) (actual time=0.004..0.004 rows=5.75 loops=57)
                                 Output: mk.id, mk.movie_id, mk.keyword_id
                                 Index Cond: (mk.movie_id = t.id)
                                 Index Searches: 57
                                 Buffers: shared hit=204
                     ->  Index Only Scan using keyword_pkey on public.keyword k  (cost=0.42..0.44 rows=1 width=4) (actual time=0.001..0.001 rows=1.00 loops=328)
                           Output: k.id
                           Index Cond: (k.id = mk.keyword_id)
                           Heap Fetches: 0
                           Index Searches: 328
                           Buffers: shared hit=985
               ->  Memoize  (cost=0.15..0.23 rows=1 width=4) (actual time=0.000..0.000 rows=1.00 loops=328)
                     Output: it1.id
                     Cache Key: mi.info_type_id
                     Cache Mode: logical
                     Estimates: capacity=2 distinct keys=2 lookups=2 hit percent=0.00%
                     Hits: 327  Misses: 1  Evictions: 0  Overflows: 0  Memory Usage: 1kB
                     Buffers: shared hit=2
                     ->  Index Scan using info_type_pkey on public.info_type it1  (cost=0.14..0.22 rows=1 width=4) (actual time=0.007..0.007 rows=1.00 loops=1)
                           Output: it1.id
                           Index Cond: (it1.id = mi.info_type_id)
                           Filter: ((it1.info)::text = 'release dates'::text)
                           Index Searches: 1
                           Buffers: shared hit=2
         ->  Index Only Scan using company_type_pkey on public.company_type ct  (cost=0.13..0.15 rows=1 width=4) (actual time=0.000..0.000 rows=1.00 loops=328)
               Output: ct.id
               Index Cond: (ct.id = mc.company_type_id)
               Heap Fetches: 0
               Index Searches: 328
               Buffers: shared hit=329
 Planning:
   Buffers: shared hit=728
 Planning Time: 17.766 ms
 Execution Time: 390.092 ms
(101 rows)

