Skip to content

M
mariadb
Commit e13406b2 authored by Igor Babaev's avatar Igor Babaev
Browse files
Options

Changed the queries of index_intersect.test to ensure platform 

independent execution plans.
Fixed a bug in Unique::unique_add that caused a crash for a query from
index_intersect_innodb on some platforms.
Fixed two bugs in opt_range.cc that led to the choice of not the
cheapest plans for index intersections.
parent 80377bbf
Hide whitespace changes
Inline Side-by-side
Showing with 712 additions and 529 deletions
mysql-test/r/index_intersect.result
View file @ e13406b2
......@@ -51,9 +51,9 @@ COUNT(*)
SELECT COUNT(*) FROM City WHERE Population > 1000000;
COUNT(*)
237
SELECT COUNT(*) FROM City WHERE Population > 500000;
SELECT COUNT(*) FROM City WHERE Population > 1500000;
COUNT(*)
539
129
SELECT COUNT(*) FROM City WHERE Population > 300000;
COUNT(*)
1062
......@@ -64,12 +64,12 @@ EXPLAIN
SELECT * FROM City WHERE
Name LIKE 'C%' AND Population > 1000000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name Population,Name 4,35 NULL 9 Using sort_intersect(Population,Name); Using where
1 SIMPLE City range Population,Name Name,Population 35,4 NULL 9 Using sort_intersect(Name,Population); Using where
EXPLAIN
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 500000;
Name LIKE 'M%' AND Population > 1500000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name Population,Name 4,35 NULL 21 Using sort_intersect(Population,Name); Using where
1 SIMPLE City range Population,Name Population,Name 4,35 NULL 5 Using sort_intersect(Population,Name); Using where
EXPLAIN
SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 300000;
......@@ -121,97 +121,29 @@ ID Name Country Population
3539 Caracas VEN 1975294
3795 Chicago USA 2896016
SELECT * FROM City USE INDEX ()
WHERE Name LIKE 'M%' AND Population > 500000;
WHERE Name LIKE 'M%' AND Population > 1500000;
ID Name Country Population
77 Mar del Plata ARG 512880
131 Melbourne AUS 2865329
215 Manaus BRA 1255049
223 Maceió BRA 786288
653 Madrid ESP 2879052
658 Málaga ESP 530553
766 Manila PHL 1581082
942 Medan IDN 1843919
947 Malang IDN 716862
1024 Mumbai (Bombay) IND 10500000
1042 Madurai IND 977856
1051 Meerut IND 753778
1366 Mosul IRQ 879000
1381 Mashhad IRN 1887405
1465 Milano ITA 1300977
1810 Montréal CAN 1016376
1816 Mississauga CAN 608072
1945 Mudanjiang CHN 570000
2259 Medellín COL 1861265
2300 Mbuji-Mayi COD 806475
2440 Monrovia LBR 850000
2487 Marrakech MAR 621914
2523 Monterrey MEX 1108499
2526 Mexicali MEX 764902
2530 Mérida MEX 703324
2537 Morelia MEX 619958
2698 Maputo MOZ 1018938
2711 Mandalay MMR 885300
2734 Managua NIC 959000
2826 Multan PAK 1182441
2975 Marseille FRA 798430
3070 Munich [München] DEU 1194560
3175 Mekka SAU 965700
3176 Medina SAU 608300
3214 Mogadishu SOM 997000
3364 Mersin (Içel) TUR 587212
3434 Mykolajiv UKR 508000
3492 Montevideo URY 1236000
3520 Minsk BLR 1674000
3540 Maracaíbo VEN 1304776
3580 Moscow RUS 8389200
3810 Memphis USA 650100
3811 Milwaukee USA 596974
SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 500000;
WHERE Name LIKE 'M%' AND Population > 1500000;
ID Name Country Population
77 Mar del Plata ARG 512880
131 Melbourne AUS 2865329
215 Manaus BRA 1255049
223 Maceió BRA 786288
653 Madrid ESP 2879052
658 Málaga ESP 530553
766 Manila PHL 1581082
942 Medan IDN 1843919
947 Malang IDN 716862
1024 Mumbai (Bombay) IND 10500000
1042 Madurai IND 977856
1051 Meerut IND 753778
1366 Mosul IRQ 879000
1381 Mashhad IRN 1887405
1465 Milano ITA 1300977
1810 Montréal CAN 1016376
1816 Mississauga CAN 608072
1945 Mudanjiang CHN 570000
2259 Medellín COL 1861265
2300 Mbuji-Mayi COD 806475
2440 Monrovia LBR 850000
2487 Marrakech MAR 621914
2523 Monterrey MEX 1108499
2526 Mexicali MEX 764902
2530 Mérida MEX 703324
2537 Morelia MEX 619958
2698 Maputo MOZ 1018938
2711 Mandalay MMR 885300
2734 Managua NIC 959000
2826 Multan PAK 1182441
2975 Marseille FRA 798430
3070 Munich [München] DEU 1194560
3175 Mekka SAU 965700
3176 Medina SAU 608300
3214 Mogadishu SOM 997000
3364 Mersin (Içel) TUR 587212
3434 Mykolajiv UKR 508000
3492 Montevideo URY 1236000
3520 Minsk BLR 1674000
3540 Maracaíbo VEN 1304776
3580 Moscow RUS 8389200
3810 Memphis USA 650100
3811 Milwaukee USA 596974
SELECT * FROM City USE INDEX ()
WHERE Name LIKE 'M%' AND Population > 300000;
ID Name Country Population
......@@ -413,33 +345,30 @@ COUNT(*)
SELECT COUNT(*) FROM City WHERE Population > 1000000;
COUNT(*)
237
SELECT COUNT(*) FROM City WHERE Population > 700000;
COUNT(*)
358
SELECT COUNT(*) FROM City WHERE Population > 500000;
COUNT(*)
539
SELECT COUNT(*) FROM City WHERE Country LIKE 'C%';
COUNT(*)
551
SELECT COUNT(*) FROM City WHERE Country LIKE 'L%';
SELECT COUNT(*) FROM City WHERE Country LIKE 'B%';
COUNT(*)
29
339
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'M' AND 'N' AND Population > 1000000 AND Country LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Population,Name 4,35 NULL 9 Using sort_intersect(Population,Name); Using where
1 SIMPLE City range Population,Country,Name Name,Population 35,4 NULL 9 Using sort_intersect(Name,Population); Using where
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'L%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Country 3 NULL 28 Using where
1 SIMPLE City range Population,Country,Name Population,Country 4,3 NULL 19 Using sort_intersect(Population,Country); Using where
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 500000 AND Country LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Country,Name 3,35 NULL 29 Using sort_intersect(Country,Name); Using where
1 SIMPLE City range Population,Country,Name Name 35 NULL 225 Using where
SELECT * FROM City USE INDEX ()
WHERE Name BETWEEN 'M' AND 'N' AND Population > 1000000 AND Country LIKE 'C%';
ID Name Country Population
......@@ -451,13 +380,15 @@ ID Name Country Population
1810 Montréal CAN 1016376
2259 Medellín COL 1861265
SELECT * FROM City USE INDEX ()
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
ID Name Country Population
2516 Guadalajara MEX 1647720
217 Guarulhos BRA 1095874
218 Goiânia BRA 1056330
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
ID Name Country Population
2516 Guadalajara MEX 1647720
217 Guarulhos BRA 1095874
218 Goiânia BRA 1056330
SELECT * FROM City USE INDEX ()
WHERE Name BETWEEN 'G' AND 'J' AND Population > 500000 AND Country LIKE 'C%';
ID Name Country Population
......@@ -472,23 +403,26 @@ ID Name Country Population
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 500000 AND Country LIKE 'C%';
ID Name Country Population
1895 Harbin CHN 4289800
1905 Hangzhou CHN 2190500
1914 Guiyang CHN 1465200
1928 Handan CHN 840000
1905 Hangzhou CHN 2190500
1895 Harbin CHN 4289800
1916 Hefei CHN 1369100
1950 Hegang CHN 520000
1927 Hohhot CHN 916700
1928 Handan CHN 840000
1937 Huainan CHN 700000
1950 Hegang CHN 520000
SELECT COUNT(*) FROM City WHERE ID BETWEEN 500 AND 999;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 501 AND 1000;
COUNT(*)
500
SELECT COUNT(*) FROM City WHERE ID BETWEEN 3500 AND 3999;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 1 AND 500;
COUNT(*)
500
SELECT COUNT(*) FROM City WHERE ID BETWEEN 1 AND 1000;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 2001 AND 2500;
COUNT(*)
1000
500
SELECT COUNT(*) FROM City WHERE ID BETWEEN 3701 AND 4000;
COUNT(*)
300
SELECT COUNT(*) FROM City WHERE Population > 700000;
COUNT(*)
358
......@@ -498,79 +432,110 @@ COUNT(*)
SELECT COUNT(*) FROM City WHERE Population > 300000;
COUNT(*)
1062
SELECT COUNT(*) FROM City WHERE Population > 600000;
COUNT(*)
428
SELECT COUNT(*) FROM City WHERE Country LIKE 'C%';
COUNT(*)
551
SELECT COUNT(*) FROM City WHERE Country LIKE 'A%';
COUNT(*)
107
SELECT COUNT(*) FROM City WHERE Country LIKE 'L%';
COUNT(*)
29
SELECT COUNT(*) FROM City WHERE Country BETWEEN 'S' AND 'Z';
COUNT(*)
682
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 501 AND 1000 AND Population > 700000 AND Country LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Country,Population 4,3,4 NULL 5 Using sort_intersect(PRIMARY,Country,Population); Using where
1 SIMPLE City range PRIMARY,Population,Country Population 4 NULL 359 Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country Population,Country 4,3 NULL 6 Using sort_intersect(Population,Country); Using where
1 SIMPLE City range PRIMARY,Population,Country Country,Population 3,4 NULL 6 Using sort_intersect(Country,Population); Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 300000 AND Country LIKE 'C%';
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'L%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country Country,PRIMARY 3,4 NULL 65 Using sort_intersect(Country,PRIMARY); Using where
1 SIMPLE City range PRIMARY,Population,Country Country 3 NULL 28 Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 3500 AND 3999 AND Population > 700000
WHERE ID BETWEEN 3701 AND 4000 AND Population > 1000000
AND Country BETWEEN 'S' AND 'Z';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Population 4,4 NULL 44 Using sort_intersect(PRIMARY,Population); Using where
1 SIMPLE City range PRIMARY,Population,Country Population,PRIMARY 4,4 NULL 17 Using sort_intersect(Population,PRIMARY); Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 1 AND 1000 AND Population > 700000
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z' ;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country Population 4 NULL 359 Using where
1 SIMPLE City range PRIMARY,Population,Country Population 4 NULL 429 Using where
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 500 AND 999 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 501 AND 1000 AND Population > 700000 AND Country LIKE 'C%';
ID Name Country Population
554 Santiago de Chile CHL 4703954
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 501 AND 1000 AND Population > 700000 AND Country LIKE 'C%';
ID Name Country Population
554 Santiago de Chile CHL 4703954
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
ID Name Country Population
1 Kabul AFG 1780000
56 Luanda AGO 2022000
69 Buenos Aires ARG 2982146
70 La Matanza ARG 1266461
71 Córdoba ARG 1157507
126 Yerevan ARM 1248700
130 Sydney AUS 3276207
131 Melbourne AUS 2865329
132 Brisbane AUS 1291117
133 Perth AUS 1096829
144 Baku AZE 1787800
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
ID Name Country Population
1 Kabul AFG 1780000
56 Luanda AGO 2022000
69 Buenos Aires ARG 2982146
70 La Matanza ARG 1266461
71 Córdoba ARG 1157507
126 Yerevan ARM 1248700
130 Sydney AUS 3276207
131 Melbourne AUS 2865329
132 Brisbane AUS 1291117
133 Perth AUS 1096829
144 Baku AZE 1787800
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 500 AND 999 AND Population > 300000 AND Country LIKE 'C%';
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'L%';
ID Name Country Population
554 Santiago de Chile CHL 4703954
555 Puente Alto CHL 386236
556 Viña del Mar CHL 312493
584 San José CRI 339131
2432 Vientiane LAO 531800
2434 Riga LVA 764328
2438 Beirut LBN 1100000
2440 Monrovia LBR 850000
2441 Tripoli LBY 1682000
2442 Bengasi LBY 804000
2447 Vilnius LTU 577969
2448 Kaunas LTU 412639
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 300000 AND Country LIKE 'C%';
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'L%';
ID Name Country Population
554 Santiago de Chile CHL 4703954
555 Puente Alto CHL 386236
556 Viña del Mar CHL 312493
584 San José CRI 339131
2432 Vientiane LAO 531800
2438 Beirut LBN 1100000
2440 Monrovia LBR 850000
2441 Tripoli LBY 1682000
2442 Bengasi LBY 804000
2447 Vilnius LTU 577969
2448 Kaunas LTU 412639
2434 Riga LVA 764328
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 3500 AND 3999 AND Population > 700000
WHERE ID BETWEEN 3701 AND 4000 AND Population > 700000
AND Country BETWEEN 'S' AND 'Z';
ID Name Country Population
3503 Toskent UZB 2117500
3539 Caracas VEN 1975294
3540 Maracaíbo VEN 1304776
3541 Barquisimeto VEN 877239
3542 Valencia VEN 794246
3769 Ho Chi Minh City VNM 3980000
3770 Hanoi VNM 1410000
3771 Haiphong VNM 783133
......@@ -590,14 +555,76 @@ ID Name Country Population
3806 Jacksonville USA 735167
3807 Columbus USA 711470
SELECT * FROM City
WHERE ID BETWEEN 3500 AND 3999 AND Population > 700000
WHERE ID BETWEEN 3701 AND 4000 AND Population > 700000
AND Country BETWEEN 'S' AND 'Z';
ID Name Country Population
3769 Ho Chi Minh City VNM 3980000
3770 Hanoi VNM 1410000
3771 Haiphong VNM 783133
3793 New York USA 8008278
3794 Los Angeles USA 3694820
3795 Chicago USA 2896016
3796 Houston USA 1953631
3797 Philadelphia USA 1517550
3798 Phoenix USA 1321045
3799 San Diego USA 1223400
3800 Dallas USA 1188580
3801 San Antonio USA 1144646
3802 Detroit USA 951270
3803 San Jose USA 894943
3804 Indianapolis USA 791926
3805 San Francisco USA 776733
3806 Jacksonville USA 735167
3807 Columbus USA 711470
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z' ;
ID Name Country Population
3048 Stockholm SWE 750348
3173 Riyadh SAU 3324000
3174 Jedda SAU 2046300
3175 Mekka SAU 965700
3176 Medina SAU 608300
3197 Pikine SEN 855287
3198 Dakar SEN 785071
3207 Freetown SLE 850000
3208 Singapore SGP 4017733
3214 Mogadishu SOM 997000
3224 Omdurman SDN 1271403
3225 Khartum SDN 947483
3226 Sharq al-Nil SDN 700887
3250 Damascus SYR 1347000
3251 Aleppo SYR 1261983
3263 Taipei TWN 2641312
3264 Kaohsiung TWN 1475505
3265 Taichung TWN 940589
3266 Tainan TWN 728060
3305 Dar es Salaam TZA 1747000
3320 Bangkok THA 6320174
3349 Tunis TUN 690600
3357 Istanbul TUR 8787958
3358 Ankara TUR 3038159
3359 Izmir TUR 2130359
3360 Adana TUR 1131198
3361 Bursa TUR 1095842
3362 Gaziantep TUR 789056
3363 Konya TUR 628364
3425 Kampala UGA 890800
3426 Kyiv UKR 2624000
3427 Harkova [Harkiv] UKR 1500000
3428 Dnipropetrovsk UKR 1103000
3429 Donetsk UKR 1050000
3430 Odesa UKR 1011000
3431 Zaporizzja UKR 848000
3432 Lviv UKR 788000
3433 Kryvyi Rig UKR 703000
3492 Montevideo URY 1236000
3503 Toskent UZB 2117500
3539 Caracas VEN 1975294
3540 Maracaíbo VEN 1304776
3541 Barquisimeto VEN 877239
3542 Valencia VEN 794246
3543 Ciudad Guayana VEN 663713
3769 Ho Chi Minh City VNM 3980000
3770 Hanoi VNM 1410000
3771 Haiphong VNM 783133
......@@ -616,14 +643,79 @@ ID Name Country Population
3805 San Francisco USA 776733
3806 Jacksonville USA 735167
3807 Columbus USA 711470
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 1 AND 1000 AND Population > 700000
AND Country BETWEEN 'S' AND 'Z' ;
ID Name Country Population
3808 Austin USA 656562
3809 Baltimore USA 651154
3810 Memphis USA 650100
SELECT * FROM City
WHERE ID BETWEEN 1 AND 1000 AND Population > 700000
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z' ;
ID Name Country Population
3176 Medina SAU 608300
3363 Konya TUR 628364
3810 Memphis USA 650100
3809 Baltimore USA 651154
3808 Austin USA 656562
3543 Ciudad Guayana VEN 663713
3349 Tunis TUN 690600
3226 Sharq al-Nil SDN 700887
3433 Kryvyi Rig UKR 703000
3807 Columbus USA 711470
3266 Tainan TWN 728060
3806 Jacksonville USA 735167
3048 Stockholm SWE 750348
3805 San Francisco USA 776733
3771 Haiphong VNM 783133
3198 Dakar SEN 785071
3432 Lviv UKR 788000
3362 Gaziantep TUR 789056
3804 Indianapolis USA 791926
3542 Valencia VEN 794246
3431 Zaporizzja UKR 848000
3207 Freetown SLE 850000
3197 Pikine SEN 855287
3541 Barquisimeto VEN 877239
3425 Kampala UGA 890800
3803 San Jose USA 894943
3265 Taichung TWN 940589
3225 Khartum SDN 947483
3802 Detroit USA 951270
3175 Mekka SAU 965700
3214 Mogadishu SOM 997000
3430 Odesa UKR 1011000
3429 Donetsk UKR 1050000
3361 Bursa TUR 1095842
3428 Dnipropetrovsk UKR 1103000
3360 Adana TUR 1131198
3801 San Antonio USA 1144646
3800 Dallas USA 1188580
3799 San Diego USA 1223400
3492 Montevideo URY 1236000
3251 Aleppo SYR 1261983
3224 Omdurman SDN 1271403
3540 Maracaíbo VEN 1304776
3798 Phoenix USA 1321045
3250 Damascus SYR 1347000
3770 Hanoi VNM 1410000
3264 Kaohsiung TWN 1475505
3427 Harkova [Harkiv] UKR 1500000
3797 Philadelphia USA 1517550
3305 Dar es Salaam TZA 1747000
3796 Houston USA 1953631
3539 Caracas VEN 1975294
3174 Jedda SAU 2046300
3503 Toskent UZB 2117500
3359 Izmir TUR 2130359
3426 Kyiv UKR 2624000
3263 Taipei TWN 2641312
3795 Chicago USA 2896016
3358 Ankara TUR 3038159
3173 Riyadh SAU 3324000
3794 Los Angeles USA 3694820
3769 Ho Chi Minh City VNM 3980000
3208 Singapore SGP 4017733
3320 Bangkok THA 6320174
3793 New York USA 8008278
3357 Istanbul TUR 8787958
SET SESSION sort_buffer_size = 2048;
EXPLAIN
SELECT * FROM City WHERE
......@@ -632,17 +724,12 @@ id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name Name,Population 35,4 NULL 9 Using sort_intersect(Name,Population); Using where
EXPLAIN
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 500000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name Name 35 NULL 164 Using where
EXPLAIN
SELECT * FROM City
WHERE Name LIKE 'C%' AND Population > 1000000 AND Country LIKE 'C%';
Name LIKE 'M%' AND Population > 1500000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Name,Population 35,4 NULL 9 Using sort_intersect(Name,Population); Using where
1 SIMPLE City range Population,Name Population,Name 4,35 NULL 5 Using sort_intersect(Population,Name); Using where
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Name 35 NULL 225 Using where
EXPLAIN
......@@ -652,14 +739,15 @@ id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Name 35 NULL 225 Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country Country,Population 3,4 NULL 6 Using sort_intersect(Country,Population); Using where
EXPLAIN
SELECT * FROM City
WHERE ID < 1000 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country Population 4 NULL 359 Using where
1 SIMPLE City range PRIMARY,Population,Country Population 4 NULL 429 Using where
SELECT * FROM City WHERE
Name LIKE 'C%' AND Population > 1000000;
ID Name Country Population
......@@ -681,60 +769,22 @@ ID Name Country Population
3539 Caracas VEN 1975294
3795 Chicago USA 2896016
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 500000;
Name LIKE 'M%' AND Population > 1500000;
ID Name Country Population
223 Maceió BRA 786288
131 Melbourne AUS 2865329
653 Madrid ESP 2879052
1042 Madurai IND 977856
658 Málaga ESP 530553
947 Malang IDN 716862
2734 Managua NIC 959000
215 Manaus BRA 1255049
2711 Mandalay MMR 885300
766 Manila PHL 1581082
2698 Maputo MOZ 1018938
77 Mar del Plata ARG 512880
3540 Maracaíbo VEN 1304776
2487 Marrakech MAR 621914
2975 Marseille FRA 798430
1381 Mashhad IRN 1887405
2300 Mbuji-Mayi COD 806475
942 Medan IDN 1843919
1024 Mumbai (Bombay) IND 10500000
1381 Mashhad IRN 1887405
2259 Medellín COL 1861265
3176 Medina SAU 608300
1051 Meerut IND 753778
3175 Mekka SAU 965700
131 Melbourne AUS 2865329
3810 Memphis USA 650100
2530 Mérida MEX 703324
3364 Mersin (Içel) TUR 587212
2526 Mexicali MEX 764902
1465 Milano ITA 1300977
3811 Milwaukee USA 596974
3520 Minsk BLR 1674000
1816 Mississauga CAN 608072
3214 Mogadishu SOM 997000
2440 Monrovia LBR 850000
2523 Monterrey MEX 1108499
3492 Montevideo URY 1236000
1810 Montréal CAN 1016376
2537 Morelia MEX 619958
3580 Moscow RUS 8389200
1366 Mosul IRQ 879000
1945 Mudanjiang CHN 570000
2826 Multan PAK 1182441
1024 Mumbai (Bombay) IND 10500000
3070 Munich [München] DEU 1194560
3434 Mykolajiv UKR 508000
SELECT * FROM City
WHERE Name BETWEEN 'M' AND 'N' AND Population > 1000000 AND Country LIKE 'C%';
ID Name Country Population
2259 Medellín COL 1861265
1810 Montréal CAN 1016376
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'B%';
ID Name Country Population
2516 Guadalajara MEX 1647720
218 Goiânia BRA 1056330
217 Guarulhos BRA 1095874
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 500000 AND Country LIKE 'C%';
ID Name Country Population
......@@ -747,36 +797,112 @@ ID Name Country Population
1927 Hohhot CHN 916700
1937 Huainan CHN 700000
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
ID Name Country Population
1 Kabul AFG 1780000
56 Luanda AGO 2022000
69 Buenos Aires ARG 2982146
70 La Matanza ARG 1266461
71 Córdoba ARG 1157507
126 Yerevan ARM 1248700
130 Sydney AUS 3276207
131 Melbourne AUS 2865329
132 Brisbane AUS 1291117
133 Perth AUS 1096829
144 Baku AZE 1787800
SELECT * FROM City
WHERE ID < 1000 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z';
ID Name Country Population
554 Santiago de Chile CHL 4703954
3176 Medina SAU 608300
3363 Konya TUR 628364
3810 Memphis USA 650100
3809 Baltimore USA 651154
3808 Austin USA 656562
3543 Ciudad Guayana VEN 663713
3349 Tunis TUN 690600
3226 Sharq al-Nil SDN 700887
3433 Kryvyi Rig UKR 703000
3807 Columbus USA 711470
3266 Tainan TWN 728060
3806 Jacksonville USA 735167
3048 Stockholm SWE 750348
3805 San Francisco USA 776733
3771 Haiphong VNM 783133
3198 Dakar SEN 785071
3432 Lviv UKR 788000
3362 Gaziantep TUR 789056
3804 Indianapolis USA 791926
3542 Valencia VEN 794246
3431 Zaporizzja UKR 848000
3207 Freetown SLE 850000
3197 Pikine SEN 855287
3541 Barquisimeto VEN 877239
3425 Kampala UGA 890800
3803 San Jose USA 894943
3265 Taichung TWN 940589
3225 Khartum SDN 947483
3802 Detroit USA 951270
3175 Mekka SAU 965700
3214 Mogadishu SOM 997000
3430 Odesa UKR 1011000
3429 Donetsk UKR 1050000
3361 Bursa TUR 1095842
3428 Dnipropetrovsk UKR 1103000
3360 Adana TUR 1131198
3801 San Antonio USA 1144646
3800 Dallas USA 1188580
3799 San Diego USA 1223400
3492 Montevideo URY 1236000
3251 Aleppo SYR 1261983
3224 Omdurman SDN 1271403
3540 Maracaíbo VEN 1304776
3798 Phoenix USA 1321045
3250 Damascus SYR 1347000
3770 Hanoi VNM 1410000
3264 Kaohsiung TWN 1475505
3427 Harkova [Harkiv] UKR 1500000
3797 Philadelphia USA 1517550
3305 Dar es Salaam TZA 1747000
3796 Houston USA 1953631
3539 Caracas VEN 1975294
3174 Jedda SAU 2046300
3503 Toskent UZB 2117500
3359 Izmir TUR 2130359
3426 Kyiv UKR 2624000
3263 Taipei TWN 2641312
3795 Chicago USA 2896016
3358 Ankara TUR 3038159
3173 Riyadh SAU 3324000
3794 Los Angeles USA 3694820
3769 Ho Chi Minh City VNM 3980000
3208 Singapore SGP 4017733
3320 Bangkok THA 6320174
3793 New York USA 8008278
3357 Istanbul TUR 8787958
SET SESSION sort_buffer_size = default;
DROP INDEX Country ON City;
CREATE INDEX CountryID ON City(Country,ID);
CREATE INDEX CountryName ON City(Country,Name);
EXPLAIN
SELECT * FROM City
WHERE Country LIKE 'M%' AND Population > 700000;
WHERE Country LIKE 'M%' AND Population > 1000000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,CountryID,CountryName Population,CountryID 4,3 NULL 23 Using sort_intersect(Population,CountryID); Using where
1 SIMPLE City range Population,CountryID,CountryName Population,CountryID 4,3 NULL 15 Using sort_intersect(Population,CountryID); Using where
EXPLAIN
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000;
WHERE Country='CHN' AND Population > 1500000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,CountryID,CountryName CountryID,Population 3,4 NULL 20 Using sort_intersect(CountryID,Population); Using where
1 SIMPLE City range Population,CountryID,CountryName Population,CountryID 4,3 NULL 11 Using sort_intersect(Population,CountryID); Using where
EXPLAIN
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000 AND Name LIKE 'C%';
WHERE Country='CHN' AND Population > 1500000 AND Name LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name,CountryID,CountryName CountryName 38 NULL 13 Using where
1 SIMPLE City range Population,Name,CountryID,CountryName CountryName,Population 38,4 NULL 1 Using sort_intersect(CountryName,Population); Using where
SELECT * FROM City USE INDEX ()
WHERE Country LIKE 'M%' AND Population > 700000;
WHERE Country LIKE 'M%' AND Population > 1000000;
ID Name Country Population
2464 Kuala Lumpur MYS 1297526
2482 Bamako MLI 809552
2485 Casablanca MAR 2940623
2515 Ciudad de México MEX 8591309
2516 Guadalajara MEX 1647720
......@@ -788,23 +914,13 @@ ID Name Country Population
2522 León MEX 1133576
2523 Monterrey MEX 1108499
2524 Zapopan MEX 1002239
2525 Naucalpan de Juárez MEX 857511
2526 Mexicali MEX 764902
2527 Culiacán MEX 744859
2528 Acapulco de Juárez MEX 721011
2529 Tlalnepantla de Baz MEX 720755
2530 Mérida MEX 703324
2690 Chisinau MDA 719900
2696 Ulan Bator MNG 773700
2698 Maputo MOZ 1018938
2710 Rangoon (Yangon) MMR 3361700
2711 Mandalay MMR 885300
SELECT * FROM City
WHERE Country LIKE 'M%' AND Population > 700000;
ID Name Country Population
2464 Kuala Lumpur MYS 1297526
2482 Bamako MLI 809552
2485 Casablanca MAR 2940623
2690 Chisinau MDA 719900
2515 Ciudad de México MEX 8591309
2516 Guadalajara MEX 1647720
2517 Ecatepec de Morelos MEX 1620303
......@@ -821,13 +937,14 @@ ID Name Country Population
2528 Acapulco de Juárez MEX 721011
2529 Tlalnepantla de Baz MEX 720755
2530 Mérida MEX 703324
2690 Chisinau MDA 719900
2696 Ulan Bator MNG 773700
2698 Maputo MOZ 1018938
2482 Bamako MLI 809552
2710 Rangoon (Yangon) MMR 3361700
2711 Mandalay MMR 885300
2696 Ulan Bator MNG 773700
2698 Maputo MOZ 1018938
2464 Kuala Lumpur MYS 1297526
SELECT * FROM City USE INDEX ()
WHERE Country='CHN' AND Population > 1000000;
WHERE Country='CHN' AND Population > 1500000;
ID Name Country Population
1890 Shanghai CHN 9696300
1891 Peking CHN 7472000
......@@ -853,19 +970,8 @@ ID Name Country Population
1911 Nanchang CHN 1691600
1912 Fuzhou CHN 1593800
1913 Lanzhou CHN 1565800
1914 Guiyang CHN 1465200
1915 Ningbo CHN 1371200
1916 Hefei CHN 1369100
1917 Urumt?i [Ürümqi] CHN 1310100
1918 Anshan CHN 1200000
1919 Fushun CHN 1200000
1920 Nanning CHN 1161800
1921 Zibo CHN 1140000
1922 Qiqihar CHN 1070000
1923 Jilin CHN 1040000
1924 Tangshan CHN 1040000
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000;
WHERE Country='CHN' AND Population > 1500000;
ID Name Country Population
1890 Shanghai CHN 9696300
1891 Peking CHN 7472000
......@@ -891,31 +997,20 @@ ID Name Country Population
1911 Nanchang CHN 1691600
1912 Fuzhou CHN 1593800
1913 Lanzhou CHN 1565800
1914 Guiyang CHN 1465200
1915 Ningbo CHN 1371200
1916 Hefei CHN 1369100
1917 Urumt?i [Ürümqi] CHN 1310100
1918 Anshan CHN 1200000
1919 Fushun CHN 1200000
1920 Nanning CHN 1161800
1921 Zibo CHN 1140000
1922 Qiqihar CHN 1070000
1923 Jilin CHN 1040000
1924 Tangshan CHN 1040000
SELECT * FROM City USE INDEX ()
WHERE Country='CHN' AND Population > 1000000 AND Name LIKE 'C%';
WHERE Country='CHN' AND Population > 1500000 AND Name LIKE 'C%';
ID Name Country Population
1892 Chongqing CHN 6351600
1898 Chengdu CHN 3361500
1900 Changchun CHN 2812000
1910 Changsha CHN 1809800
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000 AND Name LIKE 'C%';
WHERE Country='CHN' AND Population > 1500000 AND Name LIKE 'C%';
ID Name Country Population
1892 Chongqing CHN 6351600
1898 Chengdu CHN 3361500
1900 Changchun CHN 2812000
1910 Changsha CHN 1809800
1898 Chengdu CHN 3361500
1892 Chongqing CHN 6351600
DROP DATABASE world;
use test;
SET SESSION optimizer_switch='index_merge_sort_intersection=on';
mysql-test/r/index_intersect_innodb.result
View file @ e13406b2
......@@ -52,9 +52,9 @@ COUNT(*)
SELECT COUNT(*) FROM City WHERE Population > 1000000;
COUNT(*)
237
SELECT COUNT(*) FROM City WHERE Population > 500000;
SELECT COUNT(*) FROM City WHERE Population > 1500000;
COUNT(*)
539
129
SELECT COUNT(*) FROM City WHERE Population > 300000;
COUNT(*)
1062
......@@ -65,17 +65,17 @@ EXPLAIN
SELECT * FROM City WHERE
Name LIKE 'C%' AND Population > 1000000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name Name,Population 35,4 NULL 16 Using sort_intersect(Name,Population); Using where
1 SIMPLE City range Population,Name Population,Name 4,35 NULL 16 Using sort_intersect(Population,Name); Using where
EXPLAIN
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 500000;
Name LIKE 'M%' AND Population > 1500000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name Population,Name 4,35 NULL 40 Using sort_intersect(Population,Name); Using where
1 SIMPLE City range Population,Name Population,Name 4,35 NULL 9 Using sort_intersect(Population,Name); Using where
EXPLAIN
SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 300000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name Population,Name 4,35 NULL 79 Using sort_intersect(Population,Name); Using where
1 SIMPLE City range Population,Name Name,Population 35,4 NULL 79 Using sort_intersect(Name,Population); Using where
EXPLAIN
SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 5000000;
......@@ -122,97 +122,29 @@ ID Name Country Population
3539 Caracas VEN 1975294
3795 Chicago USA 2896016
SELECT * FROM City USE INDEX ()
WHERE Name LIKE 'M%' AND Population > 500000;
WHERE Name LIKE 'M%' AND Population > 1500000;
ID Name Country Population
77 Mar del Plata ARG 512880
131 Melbourne AUS 2865329
215 Manaus BRA 1255049
223 Maceió BRA 786288
653 Madrid ESP 2879052
658 Málaga ESP 530553
766 Manila PHL 1581082
942 Medan IDN 1843919
947 Malang IDN 716862
1024 Mumbai (Bombay) IND 10500000
1042 Madurai IND 977856
1051 Meerut IND 753778
1366 Mosul IRQ 879000
1381 Mashhad IRN 1887405
1465 Milano ITA 1300977
1810 Montréal CAN 1016376
1816 Mississauga CAN 608072
1945 Mudanjiang CHN 570000
2259 Medellín COL 1861265
2300 Mbuji-Mayi COD 806475
2440 Monrovia LBR 850000
2487 Marrakech MAR 621914
2523 Monterrey MEX 1108499
2526 Mexicali MEX 764902
2530 Mérida MEX 703324
2537 Morelia MEX 619958
2698 Maputo MOZ 1018938
2711 Mandalay MMR 885300
2734 Managua NIC 959000
2826 Multan PAK 1182441
2975 Marseille FRA 798430
3070 Munich [München] DEU 1194560
3175 Mekka SAU 965700
3176 Medina SAU 608300
3214 Mogadishu SOM 997000
3364 Mersin (Içel) TUR 587212
3434 Mykolajiv UKR 508000
3492 Montevideo URY 1236000
3520 Minsk BLR 1674000
3540 Maracaíbo VEN 1304776
3580 Moscow RUS 8389200
3810 Memphis USA 650100
3811 Milwaukee USA 596974
SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 500000;
WHERE Name LIKE 'M%' AND Population > 1500000;
ID Name Country Population
77 Mar del Plata ARG 512880
131 Melbourne AUS 2865329
215 Manaus BRA 1255049
223 Maceió BRA 786288
653 Madrid ESP 2879052
658 Málaga ESP 530553
766 Manila PHL 1581082
942 Medan IDN 1843919
947 Malang IDN 716862
1024 Mumbai (Bombay) IND 10500000
1042 Madurai IND 977856
1051 Meerut IND 753778
1366 Mosul IRQ 879000
1381 Mashhad IRN 1887405
1465 Milano ITA 1300977
1810 Montréal CAN 1016376
1816 Mississauga CAN 608072
1945 Mudanjiang CHN 570000
2259 Medellín COL 1861265
2300 Mbuji-Mayi COD 806475
2440 Monrovia LBR 850000
2487 Marrakech MAR 621914
2523 Monterrey MEX 1108499
2526 Mexicali MEX 764902
2530 Mérida MEX 703324
2537 Morelia MEX 619958
2698 Maputo MOZ 1018938
2711 Mandalay MMR 885300
2734 Managua NIC 959000
2826 Multan PAK 1182441
2975 Marseille FRA 798430
3070 Munich [München] DEU 1194560
3175 Mekka SAU 965700
3176 Medina SAU 608300
3214 Mogadishu SOM 997000
3364 Mersin (Içel) TUR 587212
3434 Mykolajiv UKR 508000
3492 Montevideo URY 1236000
3520 Minsk BLR 1674000
3540 Maracaíbo VEN 1304776
3580 Moscow RUS 8389200
3810 Memphis USA 650100
3811 Milwaukee USA 596974
SELECT * FROM City USE INDEX ()
WHERE Name LIKE 'M%' AND Population > 300000;
ID Name Country Population
......@@ -414,18 +346,15 @@ COUNT(*)
SELECT COUNT(*) FROM City WHERE Population > 1000000;
COUNT(*)
237
SELECT COUNT(*) FROM City WHERE Population > 700000;
COUNT(*)
358
SELECT COUNT(*) FROM City WHERE Population > 500000;
COUNT(*)
539
SELECT COUNT(*) FROM City WHERE Country LIKE 'C%';
COUNT(*)
551
SELECT COUNT(*) FROM City WHERE Country LIKE 'L%';
SELECT COUNT(*) FROM City WHERE Country LIKE 'B%';
COUNT(*)
29
339
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'M' AND 'N' AND Population > 1000000 AND Country LIKE 'C%';
......@@ -433,14 +362,14 @@ id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Population,Name,Country 4,35,3 NULL 2 Using sort_intersect(Population,Name,Country); Using where
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'L%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Country,Population 3,4 NULL 2 Using sort_intersect(Country,Population); Using where
1 SIMPLE City range Population,Country,Name Population,Country,Name 4,3,35 NULL 2 Using sort_intersect(Population,Country,Name); Using where
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 500000 AND Country LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Population,Country,Name 4,3,35 NULL 7 Using sort_intersect(Population,Country,Name); Using where
1 SIMPLE City range Population,Country,Name Name,Population,Country 35,4,3 NULL 7 Using sort_intersect(Name,Population,Country); Using where
SELECT * FROM City USE INDEX ()
WHERE Name BETWEEN 'M' AND 'N' AND Population > 1000000 AND Country LIKE 'C%';
ID Name Country Population
......@@ -452,13 +381,15 @@ ID Name Country Population
1810 Montréal CAN 1016376
2259 Medellín COL 1861265
SELECT * FROM City USE INDEX ()
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
ID Name Country Population
2516 Guadalajara MEX 1647720
217 Guarulhos BRA 1095874
218 Goiânia BRA 1056330
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
ID Name Country Population
2516 Guadalajara MEX 1647720
217 Guarulhos BRA 1095874
218 Goiânia BRA 1056330
SELECT * FROM City USE INDEX ()
WHERE Name BETWEEN 'G' AND 'J' AND Population > 500000 AND Country LIKE 'C%';
ID Name Country Population
......@@ -481,15 +412,18 @@ ID Name Country Population
1928 Handan CHN 840000
1937 Huainan CHN 700000
1950 Hegang CHN 520000
SELECT COUNT(*) FROM City WHERE ID BETWEEN 500 AND 999;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 501 AND 1000;
COUNT(*)
500
SELECT COUNT(*) FROM City WHERE ID BETWEEN 1 AND 500;
COUNT(*)
500
SELECT COUNT(*) FROM City WHERE ID BETWEEN 3500 AND 3999;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 2001 AND 2500;
COUNT(*)
500
SELECT COUNT(*) FROM City WHERE ID BETWEEN 1 AND 1000;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 3701 AND 4000;
COUNT(*)
1000
300
SELECT COUNT(*) FROM City WHERE Population > 700000;
COUNT(*)
358
......@@ -499,79 +433,199 @@ COUNT(*)
SELECT COUNT(*) FROM City WHERE Population > 300000;
COUNT(*)
1062
SELECT COUNT(*) FROM City WHERE Population > 600000;
COUNT(*)
428
SELECT COUNT(*) FROM City WHERE Country LIKE 'C%';
COUNT(*)
551
SELECT COUNT(*) FROM City WHERE Country LIKE 'A%';
COUNT(*)
107
SELECT COUNT(*) FROM City WHERE Country LIKE 'L%';
COUNT(*)
29
SELECT COUNT(*) FROM City WHERE Country BETWEEN 'S' AND 'Z';
COUNT(*)
682
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 501 AND 1000 AND Population > 700000 AND Country LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Country,Population 4,3,4 NULL 11 Using sort_intersect(PRIMARY,Country,Population); Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Population,Country 4,4,3 NULL 1 Using sort_intersect(PRIMARY,Population,Country); Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 300000 AND Country LIKE 'C%';
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'L%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Population,Country 4,4,3 NULL 34 Using sort_intersect(PRIMARY,Population,Country); Using where
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Country 4,3 NULL 10 Using sort_intersect(PRIMARY,Country); Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 3500 AND 3999 AND Population > 700000
WHERE ID BETWEEN 3701 AND 4000 AND Population > 1000000
AND Country BETWEEN 'S' AND 'Z';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Country,Population 4,3,4 NULL 12 Using sort_intersect(PRIMARY,Country,Population); Using where
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Country,Population 4,3,4 NULL 2 Using sort_intersect(PRIMARY,Country,Population); Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 1 AND 1000 AND Population > 700000
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z' ;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Country,Population 4,3,4 NULL 21 Using sort_intersect(PRIMARY,Country,Population); Using where
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Population,Country 4,4,3 NULL 30 Using sort_intersect(PRIMARY,Population,Country); Using where
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 500 AND 999 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 501 AND 1000 AND Population > 700000 AND Country LIKE 'C%';
ID Name Country Population
554 Santiago de Chile CHL 4703954
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 501 AND 1000 AND Population > 700000 AND Country LIKE 'C%';
ID Name Country Population
554 Santiago de Chile CHL 4703954
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
ID Name Country Population
1 Kabul AFG 1780000
56 Luanda AGO 2022000
69 Buenos Aires ARG 2982146
70 La Matanza ARG 1266461
71 Córdoba ARG 1157507
126 Yerevan ARM 1248700
130 Sydney AUS 3276207
131 Melbourne AUS 2865329
132 Brisbane AUS 1291117
133 Perth AUS 1096829
144 Baku AZE 1787800
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
ID Name Country Population
1 Kabul AFG 1780000
56 Luanda AGO 2022000
69 Buenos Aires ARG 2982146
70 La Matanza ARG 1266461
71 Córdoba ARG 1157507
126 Yerevan ARM 1248700
130 Sydney AUS 3276207
131 Melbourne AUS 2865329
132 Brisbane AUS 1291117
133 Perth AUS 1096829
144 Baku AZE 1787800
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 500 AND 999 AND Population > 300000 AND Country LIKE 'C%';
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'L%';
ID Name Country Population
554 Santiago de Chile CHL 4703954
555 Puente Alto CHL 386236
556 Viña del Mar CHL 312493
584 San José CRI 339131
2432 Vientiane LAO 531800
2434 Riga LVA 764328
2438 Beirut LBN 1100000
2440 Monrovia LBR 850000
2441 Tripoli LBY 1682000
2442 Bengasi LBY 804000
2447 Vilnius LTU 577969
2448 Kaunas LTU 412639
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 300000 AND Country LIKE 'C%';
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'L%';
ID Name Country Population
554 Santiago de Chile CHL 4703954
555 Puente Alto CHL 386236
556 Viña del Mar CHL 312493
584 San José CRI 339131
2432 Vientiane LAO 531800
2434 Riga LVA 764328
2438 Beirut LBN 1100000
2440 Monrovia LBR 850000
2441 Tripoli LBY 1682000
2442 Bengasi LBY 804000
2447 Vilnius LTU 577969
2448 Kaunas LTU 412639
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 3500 AND 3999 AND Population > 700000
WHERE ID BETWEEN 3701 AND 4000 AND Population > 700000
AND Country BETWEEN 'S' AND 'Z';
ID Name Country Population
3769 Ho Chi Minh City VNM 3980000
3770 Hanoi VNM 1410000
3771 Haiphong VNM 783133
3793 New York USA 8008278
3794 Los Angeles USA 3694820
3795 Chicago USA 2896016
3796 Houston USA 1953631
3797 Philadelphia USA 1517550
3798 Phoenix USA 1321045
3799 San Diego USA 1223400
3800 Dallas USA 1188580
3801 San Antonio USA 1144646
3802 Detroit USA 951270
3803 San Jose USA 894943
3804 Indianapolis USA 791926
3805 San Francisco USA 776733
3806 Jacksonville USA 735167
3807 Columbus USA 711470
SELECT * FROM City
WHERE ID BETWEEN 3701 AND 4000 AND Population > 700000
AND Country BETWEEN 'S' AND 'Z';
ID Name Country Population
3769 Ho Chi Minh City VNM 3980000
3770 Hanoi VNM 1410000
3771 Haiphong VNM 783133
3793 New York USA 8008278
3794 Los Angeles USA 3694820
3795 Chicago USA 2896016
3796 Houston USA 1953631
3797 Philadelphia USA 1517550
3798 Phoenix USA 1321045
3799 San Diego USA 1223400
3800 Dallas USA 1188580
3801 San Antonio USA 1144646
3802 Detroit USA 951270
3803 San Jose USA 894943
3804 Indianapolis USA 791926
3805 San Francisco USA 776733
3806 Jacksonville USA 735167
3807 Columbus USA 711470
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z' ;
ID Name Country Population
3048 Stockholm SWE 750348
3173 Riyadh SAU 3324000
3174 Jedda SAU 2046300
3175 Mekka SAU 965700
3176 Medina SAU 608300
3197 Pikine SEN 855287
3198 Dakar SEN 785071
3207 Freetown SLE 850000
3208 Singapore SGP 4017733
3214 Mogadishu SOM 997000
3224 Omdurman SDN 1271403
3225 Khartum SDN 947483
3226 Sharq al-Nil SDN 700887
3250 Damascus SYR 1347000
3251 Aleppo SYR 1261983
3263 Taipei TWN 2641312
3264 Kaohsiung TWN 1475505
3265 Taichung TWN 940589
3266 Tainan TWN 728060
3305 Dar es Salaam TZA 1747000
3320 Bangkok THA 6320174
3349 Tunis TUN 690600
3357 Istanbul TUR 8787958
3358 Ankara TUR 3038159
3359 Izmir TUR 2130359
3360 Adana TUR 1131198
3361 Bursa TUR 1095842
3362 Gaziantep TUR 789056
3363 Konya TUR 628364
3425 Kampala UGA 890800
3426 Kyiv UKR 2624000
3427 Harkova [Harkiv] UKR 1500000
3428 Dnipropetrovsk UKR 1103000
3429 Donetsk UKR 1050000
3430 Odesa UKR 1011000
3431 Zaporizzja UKR 848000
3432 Lviv UKR 788000
3433 Kryvyi Rig UKR 703000
3492 Montevideo URY 1236000
3503 Toskent UZB 2117500
3539 Caracas VEN 1975294
3540 Maracaíbo VEN 1304776
3541 Barquisimeto VEN 877239
3542 Valencia VEN 794246
3543 Ciudad Guayana VEN 663713
3769 Ho Chi Minh City VNM 3980000
3770 Hanoi VNM 1410000
3771 Haiphong VNM 783133
......@@ -590,15 +644,58 @@ ID Name Country Population
3805 San Francisco USA 776733
3806 Jacksonville USA 735167
3807 Columbus USA 711470
3808 Austin USA 656562
3809 Baltimore USA 651154
3810 Memphis USA 650100
SELECT * FROM City
WHERE ID BETWEEN 3500 AND 3999 AND Population > 700000
AND Country BETWEEN 'S' AND 'Z';
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z' ;
ID Name Country Population
3048 Stockholm SWE 750348
3173 Riyadh SAU 3324000
3174 Jedda SAU 2046300
3175 Mekka SAU 965700
3176 Medina SAU 608300
3197 Pikine SEN 855287
3198 Dakar SEN 785071
3207 Freetown SLE 850000
3208 Singapore SGP 4017733
3214 Mogadishu SOM 997000
3224 Omdurman SDN 1271403
3225 Khartum SDN 947483
3226 Sharq al-Nil SDN 700887
3250 Damascus SYR 1347000
3251 Aleppo SYR 1261983
3263 Taipei TWN 2641312
3264 Kaohsiung TWN 1475505
3265 Taichung TWN 940589
3266 Tainan TWN 728060
3305 Dar es Salaam TZA 1747000
3320 Bangkok THA 6320174
3349 Tunis TUN 690600
3357 Istanbul TUR 8787958
3358 Ankara TUR 3038159
3359 Izmir TUR 2130359
3360 Adana TUR 1131198
3361 Bursa TUR 1095842
3362 Gaziantep TUR 789056
3363 Konya TUR 628364
3425 Kampala UGA 890800
3426 Kyiv UKR 2624000
3427 Harkova [Harkiv] UKR 1500000
3428 Dnipropetrovsk UKR 1103000
3429 Donetsk UKR 1050000
3430 Odesa UKR 1011000
3431 Zaporizzja UKR 848000
3432 Lviv UKR 788000
3433 Kryvyi Rig UKR 703000
3492 Montevideo URY 1236000
3503 Toskent UZB 2117500
3539 Caracas VEN 1975294
3540 Maracaíbo VEN 1304776
3541 Barquisimeto VEN 877239
3542 Valencia VEN 794246
3543 Ciudad Guayana VEN 663713
3769 Ho Chi Minh City VNM 3980000
3770 Hanoi VNM 1410000
3771 Haiphong VNM 783133
......@@ -617,14 +714,9 @@ ID Name Country Population
3805 San Francisco USA 776733
3806 Jacksonville USA 735167
3807 Columbus USA 711470
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 1 AND 1000 AND Population > 700000
AND Country BETWEEN 'S' AND 'Z' ;
ID Name Country Population
SELECT * FROM City
WHERE ID BETWEEN 1 AND 1000 AND Population > 700000
AND Country BETWEEN 'S' AND 'Z' ;
ID Name Country Population
3808 Austin USA 656562
3809 Baltimore USA 651154
3810 Memphis USA 650100
SET SESSION sort_buffer_size = 2048;
EXPLAIN
SELECT * FROM City WHERE
......@@ -633,19 +725,14 @@ id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name Population,Name 4,35 NULL 16 Using sort_intersect(Population,Name); Using where
EXPLAIN
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 500000;
Name LIKE 'M%' AND Population > 1500000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name Name,Population 35,4 NULL 40 Using sort_intersect(Name,Population); Using where
EXPLAIN
SELECT * FROM City
WHERE Name LIKE 'C%' AND Population > 1000000 AND Country LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Population,Name 4,35 NULL 16 Using sort_intersect(Population,Name); Using where
1 SIMPLE City range Population,Name Population,Name 4,35 NULL 9 Using sort_intersect(Population,Name); Using where
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Population,Name 4,35 NULL 36 Using sort_intersect(Population,Name); Using where
1 SIMPLE City range Population,Country,Name Population,Country,Name 4,3,35 NULL 2 Using sort_intersect(Population,Country,Name); Using where
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 500000 AND Country LIKE 'C%';
......@@ -653,14 +740,15 @@ id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Country,Name Name,Population,Country 35,4,3 NULL 7 Using sort_intersect(Name,Population,Country); Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Population,Country 4,4,3 NULL 1 Using sort_intersect(PRIMARY,Population,Country); Using where
EXPLAIN
SELECT * FROM City
WHERE ID < 1000 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Country,Population 4,3,4 NULL 17 Using sort_intersect(PRIMARY,Country,Population); Using where
1 SIMPLE City range PRIMARY,Population,Country PRIMARY,Country,Population 4,3,4 NULL 30 Using sort_intersect(PRIMARY,Country,Population); Using where
SELECT * FROM City WHERE
Name LIKE 'C%' AND Population > 1000000;
ID Name Country Population
......@@ -682,60 +770,22 @@ ID Name Country Population
3539 Caracas VEN 1975294
3795 Chicago USA 2896016
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 500000;
Name LIKE 'M%' AND Population > 1500000;
ID Name Country Population
77 Mar del Plata ARG 512880
131 Melbourne AUS 2865329
215 Manaus BRA 1255049
223 Maceió BRA 786288
653 Madrid ESP 2879052
658 Málaga ESP 530553
766 Manila PHL 1581082
942 Medan IDN 1843919
947 Malang IDN 716862
1024 Mumbai (Bombay) IND 10500000
1042 Madurai IND 977856
1051 Meerut IND 753778
1366 Mosul IRQ 879000
1381 Mashhad IRN 1887405
1465 Milano ITA 1300977
1810 Montréal CAN 1016376
1816 Mississauga CAN 608072
1945 Mudanjiang CHN 570000
2259 Medellín COL 1861265
2300 Mbuji-Mayi COD 806475
2440 Monrovia LBR 850000
2487 Marrakech MAR 621914
2523 Monterrey MEX 1108499
2526 Mexicali MEX 764902
2530 Mérida MEX 703324
2537 Morelia MEX 619958
2698 Maputo MOZ 1018938
2711 Mandalay MMR 885300
2734 Managua NIC 959000
2826 Multan PAK 1182441
2975 Marseille FRA 798430
3070 Munich [München] DEU 1194560
3175 Mekka SAU 965700
3176 Medina SAU 608300
3214 Mogadishu SOM 997000
3364 Mersin (Içel) TUR 587212
3434 Mykolajiv UKR 508000
3492 Montevideo URY 1236000
3520 Minsk BLR 1674000
3540 Maracaíbo VEN 1304776
3580 Moscow RUS 8389200
3810 Memphis USA 650100
3811 Milwaukee USA 596974
SELECT * FROM City
WHERE Name BETWEEN 'M' AND 'N' AND Population > 1000000 AND Country LIKE 'C%';
ID Name Country Population
1810 Montréal CAN 1016376
2259 Medellín COL 1861265
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'B%';
ID Name Country Population
2516 Guadalajara MEX 1647720
217 Guarulhos BRA 1095874
218 Goiânia BRA 1056330
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 500000 AND Country LIKE 'C%';
ID Name Country Population
......@@ -748,36 +798,112 @@ ID Name Country Population
1937 Huainan CHN 700000
1950 Hegang CHN 520000
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
ID Name Country Population
1 Kabul AFG 1780000
56 Luanda AGO 2022000
69 Buenos Aires ARG 2982146
70 La Matanza ARG 1266461
71 Córdoba ARG 1157507
126 Yerevan ARM 1248700
130 Sydney AUS 3276207
131 Melbourne AUS 2865329
132 Brisbane AUS 1291117
133 Perth AUS 1096829
144 Baku AZE 1787800
SELECT * FROM City
WHERE ID < 1000 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z';
ID Name Country Population
554 Santiago de Chile CHL 4703954
3048 Stockholm SWE 750348
3173 Riyadh SAU 3324000
3174 Jedda SAU 2046300
3175 Mekka SAU 965700
3176 Medina SAU 608300
3197 Pikine SEN 855287
3198 Dakar SEN 785071
3207 Freetown SLE 850000
3208 Singapore SGP 4017733
3214 Mogadishu SOM 997000
3224 Omdurman SDN 1271403
3225 Khartum SDN 947483
3226 Sharq al-Nil SDN 700887
3250 Damascus SYR 1347000
3251 Aleppo SYR 1261983
3263 Taipei TWN 2641312
3264 Kaohsiung TWN 1475505
3265 Taichung TWN 940589
3266 Tainan TWN 728060
3305 Dar es Salaam TZA 1747000
3320 Bangkok THA 6320174
3349 Tunis TUN 690600
3357 Istanbul TUR 8787958
3358 Ankara TUR 3038159
3359 Izmir TUR 2130359
3360 Adana TUR 1131198
3361 Bursa TUR 1095842
3362 Gaziantep TUR 789056
3363 Konya TUR 628364
3425 Kampala UGA 890800
3426 Kyiv UKR 2624000
3427 Harkova [Harkiv] UKR 1500000
3428 Dnipropetrovsk UKR 1103000
3429 Donetsk UKR 1050000
3430 Odesa UKR 1011000
3431 Zaporizzja UKR 848000
3432 Lviv UKR 788000
3433 Kryvyi Rig UKR 703000
3492 Montevideo URY 1236000
3503 Toskent UZB 2117500
3539 Caracas VEN 1975294
3540 Maracaíbo VEN 1304776
3541 Barquisimeto VEN 877239
3542 Valencia VEN 794246
3543 Ciudad Guayana VEN 663713
3769 Ho Chi Minh City VNM 3980000
3770 Hanoi VNM 1410000
3771 Haiphong VNM 783133
3793 New York USA 8008278
3794 Los Angeles USA 3694820
3795 Chicago USA 2896016
3796 Houston USA 1953631
3797 Philadelphia USA 1517550
3798 Phoenix USA 1321045
3799 San Diego USA 1223400
3800 Dallas USA 1188580
3801 San Antonio USA 1144646
3802 Detroit USA 951270
3803 San Jose USA 894943
3804 Indianapolis USA 791926
3805 San Francisco USA 776733
3806 Jacksonville USA 735167
3807 Columbus USA 711470
3808 Austin USA 656562
3809 Baltimore USA 651154
3810 Memphis USA 650100
SET SESSION sort_buffer_size = default;
DROP INDEX Country ON City;
CREATE INDEX CountryID ON City(Country,ID);
CREATE INDEX CountryName ON City(Country,Name);
EXPLAIN
SELECT * FROM City
WHERE Country LIKE 'M%' AND Population > 700000;
WHERE Country LIKE 'M%' AND Population > 1000000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,CountryID,CountryName Population,CountryID 4,3 NULL 24 Using sort_intersect(Population,CountryID); Using where
1 SIMPLE City range Population,CountryID,CountryName Population,CountryID 4,3 NULL 16 Using sort_intersect(Population,CountryID); Using where
EXPLAIN
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000;
WHERE Country='CHN' AND Population > 1500000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,CountryID,CountryName CountryID,Population 3,4 NULL 21 Using sort_intersect(CountryID,Population); Using where
1 SIMPLE City range Population,CountryID,CountryName Population,CountryID 4,3 NULL 11 Using sort_intersect(Population,CountryID); Using where
EXPLAIN
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000 AND Name LIKE 'C%';
WHERE Country='CHN' AND Population > 1500000 AND Name LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City range Population,Name,CountryID,CountryName CountryName,Population 38,4 NULL 1 Using sort_intersect(CountryName,Population); Using where
SELECT * FROM City USE INDEX ()
WHERE Country LIKE 'M%' AND Population > 700000;
WHERE Country LIKE 'M%' AND Population > 1000000;
ID Name Country Population
2464 Kuala Lumpur MYS 1297526
2482 Bamako MLI 809552
2485 Casablanca MAR 2940623
2515 Ciudad de México MEX 8591309
2516 Guadalajara MEX 1647720
......@@ -789,17 +915,8 @@ ID Name Country Population
2522 León MEX 1133576
2523 Monterrey MEX 1108499
2524 Zapopan MEX 1002239
2525 Naucalpan de Juárez MEX 857511
2526 Mexicali MEX 764902
2527 Culiacán MEX 744859
2528 Acapulco de Juárez MEX 721011
2529 Tlalnepantla de Baz MEX 720755
2530 Mérida MEX 703324
2690 Chisinau MDA 719900
2696 Ulan Bator MNG 773700
2698 Maputo MOZ 1018938
2710 Rangoon (Yangon) MMR 3361700
2711 Mandalay MMR 885300
SELECT * FROM City
WHERE Country LIKE 'M%' AND Population > 700000;
ID Name Country Population
......@@ -828,7 +945,7 @@ ID Name Country Population
2710 Rangoon (Yangon) MMR 3361700
2711 Mandalay MMR 885300
SELECT * FROM City USE INDEX ()
WHERE Country='CHN' AND Population > 1000000;
WHERE Country='CHN' AND Population > 1500000;
ID Name Country Population
1890 Shanghai CHN 9696300
1891 Peking CHN 7472000
......@@ -854,19 +971,8 @@ ID Name Country Population
1911 Nanchang CHN 1691600
1912 Fuzhou CHN 1593800
1913 Lanzhou CHN 1565800
1914 Guiyang CHN 1465200
1915 Ningbo CHN 1371200
1916 Hefei CHN 1369100
1917 Urumt?i [Ürümqi] CHN 1310100
1918 Anshan CHN 1200000
1919 Fushun CHN 1200000
1920 Nanning CHN 1161800
1921 Zibo CHN 1140000
1922 Qiqihar CHN 1070000
1923 Jilin CHN 1040000
1924 Tangshan CHN 1040000
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000;
WHERE Country='CHN' AND Population > 1500000;
ID Name Country Population
1890 Shanghai CHN 9696300
1891 Peking CHN 7472000
......@@ -892,26 +998,15 @@ ID Name Country Population
1911 Nanchang CHN 1691600
1912 Fuzhou CHN 1593800
1913 Lanzhou CHN 1565800
1914 Guiyang CHN 1465200
1915 Ningbo CHN 1371200
1916 Hefei CHN 1369100
1917 Urumt?i [Ürümqi] CHN 1310100
1918 Anshan CHN 1200000
1919 Fushun CHN 1200000
1920 Nanning CHN 1161800
1921 Zibo CHN 1140000
1922 Qiqihar CHN 1070000
1923 Jilin CHN 1040000
1924 Tangshan CHN 1040000
SELECT * FROM City USE INDEX ()
WHERE Country='CHN' AND Population > 1000000 AND Name LIKE 'C%';
WHERE Country='CHN' AND Population > 1500000 AND Name LIKE 'C%';
ID Name Country Population
1892 Chongqing CHN 6351600
1898 Chengdu CHN 3361500
1900 Changchun CHN 2812000
1910 Changsha CHN 1809800
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000 AND Name LIKE 'C%';
WHERE Country='CHN' AND Population > 1500000 AND Name LIKE 'C%';
ID Name Country Population
1892 Chongqing CHN 6351600
1898 Chengdu CHN 3361500
......
mysql-test/suite/innodb/r/innodb_mysql.result
View file @ e13406b2
......@@ -679,8 +679,6 @@ INSERT INTO t1(b,c) SELECT b,c FROM t2;
UPDATE t2 SET c='2007-01-03';
INSERT INTO t1(b,c) SELECT b,c FROM t2;
set @@sort_buffer_size=8192;
Warnings:
Warning 1292 Truncated incorrect sort_buffer_size value: '8192'
SELECT COUNT(*) FROM t1;
COUNT(*)
3072
......
mysql-test/suite/pbxt/r/subselect.result
View file @ e13406b2
......@@ -3667,8 +3667,6 @@ CREATE TABLE t1 (a int, b int auto_increment, PRIMARY KEY (b));
CREATE TABLE t2 (x int auto_increment, y int, z int,
PRIMARY KEY (x), FOREIGN KEY (y) REFERENCES t1 (b));
SET SESSION sort_buffer_size = 32 * 1024;
Warnings:
Warning 1292 Truncated incorrect sort_buffer_size value: '32768'
SELECT SQL_NO_CACHE COUNT(*)
FROM (SELECT a, b, (SELECT x FROM t2 WHERE y=b ORDER BY z DESC LIMIT 1) c
FROM t1) t;
......@@ -4104,8 +4102,6 @@ INSERT INTO `t1` VALUES ('asdf','2007-02-08 01:11:26');
INSERT INTO `t2` VALUES ('abcdefghijk');
INSERT INTO `t2` VALUES ('asdf');
SET session sort_buffer_size=8192;
Warnings:
Warning 1292 Truncated incorrect sort_buffer_size value: '8192'
SELECT (SELECT 1 FROM t1 WHERE t1.a=t2.a ORDER BY t1.b LIMIT 1) AS d1 FROM t2;
d1
1
......
mysql-test/t/index_intersect.test
View file @ e13406b2
......@@ -44,7 +44,7 @@ SELECT COUNT(*) FROM City;
SELECT COUNT(*) FROM City WHERE Name LIKE 'C%';
SELECT COUNT(*) FROM City WHERE Name LIKE 'M%';
SELECT COUNT(*) FROM City WHERE Population > 1000000;
SELECT COUNT(*) FROM City WHERE Population > 500000;
SELECT COUNT(*) FROM City WHERE Population > 1500000;
SELECT COUNT(*) FROM City WHERE Population > 300000;
SELECT COUNT(*) FROM City WHERE Population > 5000000;
......@@ -60,7 +60,7 @@ SELECT * FROM City WHERE
EXPLAIN
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 500000;
Name LIKE 'M%' AND Population > 1500000;
EXPLAIN
SELECT * FROM City
......@@ -84,10 +84,10 @@ SELECT * FROM City
SELECT * FROM City USE INDEX ()
WHERE Name LIKE 'M%' AND Population > 500000;
WHERE Name LIKE 'M%' AND Population > 1500000;
SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 500000;
WHERE Name LIKE 'M%' AND Population > 1500000;
SELECT * FROM City USE INDEX ()
......@@ -104,16 +104,15 @@ SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 5000000;
# The output of the next 7 queries tells us about selectivities
# The output of the next 6 queries tells us about selectivities
# of the conditions utilized in 3 queries following after them
SELECT COUNT(*) FROM City WHERE Name BETWEEN 'M' AND 'N';
SELECT COUNT(*) FROM City WHERE Name BETWEEN 'G' AND 'J';
SELECT COUNT(*) FROM City WHERE Population > 1000000;
SELECT COUNT(*) FROM City WHERE Population > 700000;
SELECT COUNT(*) FROM City WHERE Population > 500000;
SELECT COUNT(*) FROM City WHERE Country LIKE 'C%';
SELECT COUNT(*) FROM City WHERE Country LIKE 'L%';
SELECT COUNT(*) FROM City WHERE Country LIKE 'B%';
# The pattern of the WHERE condition used in the following 3 queries is
......@@ -129,7 +128,7 @@ SELECT * FROM City
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'L%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
EXPLAIN
SELECT * FROM City
......@@ -149,10 +148,10 @@ SELECT * FROM City
SELECT * FROM City USE INDEX ()
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
SELECT * FROM City USE INDEX ()
......@@ -162,17 +161,20 @@ SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 500000 AND Country LIKE 'C%';
# The output of the next 9 queries tells us about selectivities
# The output of the next 12 queries tells us about selectivities
# of the conditions utilized in 5 queries following after them
SELECT COUNT(*) FROM City WHERE ID BETWEEN 500 AND 999;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 3500 AND 3999;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 1 AND 1000;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 501 AND 1000;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 1 AND 500;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 2001 AND 2500;
SELECT COUNT(*) FROM City WHERE ID BETWEEN 3701 AND 4000;
SELECT COUNT(*) FROM City WHERE Population > 700000;
SELECT COUNT(*) FROM City WHERE Population > 1000000;
SELECT COUNT(*) FROM City WHERE Population > 300000;
SELECT COUNT(*) FROM City WHERE Population > 600000;
SELECT COUNT(*) FROM City WHERE Country LIKE 'C%';
SELECT COUNT(*) FROM City WHERE Country LIKE 'A%';
SELECT COUNT(*) FROM City WHERE Country LIKE 'L%';
SELECT COUNT(*) FROM City WHERE Country BETWEEN 'S' AND 'Z';
......@@ -181,7 +183,7 @@ SELECT COUNT(*) FROM City WHERE Country BETWEEN 'S' AND 'Z';
# with key1 happens to be a primary key (it matters only for InnoDB)
# Varying values of the constants in the conjuncts of the condition
# we can get index intersection either over all three keys, or over
# different pairs, or a range sacn over one of these keys.
# different pairs, or a range scan over one of these keys.
# Bear in mind that the condition (Country LIKE 'A%') is actually
# equivalent to the condition (Country BETWEEN 'A' AND 'B') for the
# tested instance the table City.
......@@ -189,24 +191,24 @@ SELECT COUNT(*) FROM City WHERE Country BETWEEN 'S' AND 'Z';
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 501 AND 1000 AND Population > 700000 AND Country LIKE 'C%';
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 300000 AND Country LIKE 'C%';
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'L%';
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 3500 AND 3999 AND Population > 700000
WHERE ID BETWEEN 3701 AND 4000 AND Population > 1000000
AND Country BETWEEN 'S' AND 'Z';
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 1 AND 1000 AND Population > 700000
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z' ;
......@@ -216,41 +218,41 @@ SELECT * FROM City
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 500 AND 999 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 501 AND 1000 AND Population > 700000 AND Country LIKE 'C%';
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 501 AND 1000 AND Population > 700000 AND Country LIKE 'C%';
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 500 AND 999 AND Population > 300000 AND Country LIKE 'C%';
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'L%';
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 300000 AND Country LIKE 'C%';
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'L%';
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 3500 AND 3999 AND Population > 700000
WHERE ID BETWEEN 3701 AND 4000 AND Population > 700000
AND Country BETWEEN 'S' AND 'Z';
SELECT * FROM City
WHERE ID BETWEEN 3500 AND 3999 AND Population > 700000
WHERE ID BETWEEN 3701 AND 4000 AND Population > 700000
AND Country BETWEEN 'S' AND 'Z';
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 1 AND 1000 AND Population > 700000
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z' ;
SELECT * FROM City
WHERE ID BETWEEN 1 AND 1000 AND Population > 700000
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z' ;
......@@ -267,16 +269,12 @@ SELECT * FROM City WHERE
EXPLAIN
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 500000;
Name LIKE 'M%' AND Population > 1500000;
EXPLAIN
SELECT * FROM City
WHERE Name LIKE 'C%' AND Population > 1000000 AND Country LIKE 'C%';
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 1000000 AND Country LIKE 'B%';
EXPLAIN
SELECT * FROM City
......@@ -285,11 +283,12 @@ SELECT * FROM City
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
EXPLAIN
SELECT * FROM City
WHERE ID < 1000 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z';
#Yet the query themselves return the correct results in this case as well
......@@ -299,24 +298,22 @@ SELECT * FROM City WHERE
Name LIKE 'C%' AND Population > 1000000;
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 500000;
Name LIKE 'M%' AND Population > 1500000;
SELECT * FROM City
WHERE Name BETWEEN 'M' AND 'N' AND Population > 1000000 AND Country LIKE 'C%';
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'M%';
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'B%';
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 500000 AND Country LIKE 'C%';
SELECT * FROM City
WHERE ID BETWEEN 500 AND 999 AND Population > 1000000 AND Country LIKE 'A%';
WHERE ID BETWEEN 1 AND 500 AND Population > 1000000 AND Country LIKE 'A%';
SELECT * FROM City
WHERE ID < 1000 AND Population > 700000 AND Country LIKE 'C%';
WHERE ID BETWEEN 3001 AND 4000 AND Population > 600000
AND Country BETWEEN 'S' AND 'Z';
SET SESSION sort_buffer_size = default;
......@@ -343,39 +340,39 @@ ANALYZE TABLE City;
EXPLAIN
SELECT * FROM City
WHERE Country LIKE 'M%' AND Population > 700000;
WHERE Country LIKE 'M%' AND Population > 1000000;
EXPLAIN
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000;
WHERE Country='CHN' AND Population > 1500000;
EXPLAIN
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000 AND Name LIKE 'C%';
WHERE Country='CHN' AND Population > 1500000 AND Name LIKE 'C%';
# Check that the previous 3 plans return the right results when executed
SELECT * FROM City USE INDEX ()
WHERE Country LIKE 'M%' AND Population > 700000;
WHERE Country LIKE 'M%' AND Population > 1000000;
SELECT * FROM City
WHERE Country LIKE 'M%' AND Population > 700000;
SELECT * FROM City USE INDEX ()
WHERE Country='CHN' AND Population > 1000000;
WHERE Country='CHN' AND Population > 1500000;
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000;
WHERE Country='CHN' AND Population > 1500000;
SELECT * FROM City USE INDEX ()
WHERE Country='CHN' AND Population > 1000000 AND Name LIKE 'C%';
WHERE Country='CHN' AND Population > 1500000 AND Name LIKE 'C%';
SELECT * FROM City
WHERE Country='CHN' AND Population > 1000000 AND Name LIKE 'C%';
WHERE Country='CHN' AND Population > 1500000 AND Name LIKE 'C%';
DROP DATABASE world;
......
sql/opt_range.cc
View file @ e13406b2
......@@ -5112,7 +5112,7 @@ double get_unique_intersect_cost(COMMON_INDEX_INTERSECTION_INFO *common,
common->max_memory_size,
common->compare_factor,
TRUE, in_memory);
if (in_memory)
if (*in_memory)
*in_memory_cost= cost;
}
return cost;
......@@ -5124,7 +5124,7 @@ double get_cpk_filter_cost(INDEX_SCAN_INFO *index_scan,
INDEX_SCAN_INFO *cpk_scan,
double compare_factor)
{
return log((double) cpk_scan->range_count) / (compare_factor * M_LN2) *
return log((double) (cpk_scan->range_count+1)) / (compare_factor * M_LN2) *
index_scan->records;
}
......@@ -5174,7 +5174,8 @@ bool check_index_intersect_extension(PARTIAL_INDEX_INTERSECTION_INFO *curr,
return FALSE;
records_in_scans= curr->records_in_scans + index_scan_records;
next->in_memory= curr->in_memory;
if ((next->in_memory= curr->in_memory))
next->in_memory_cost= curr->in_memory_cost;
records= records_in_index_intersect_extension(curr, ext_index_scan);
if (idx && records > curr->records)
......@@ -5207,7 +5208,7 @@ bool check_index_intersect_extension(PARTIAL_INDEX_INTERSECTION_INFO *curr,
{
double cost2;
bool in_memory_save= next->in_memory;
if (!idx)
if (idx)
{
next->length= curr->length+1;
records2= records_in_index_intersect_extension(next, cpk_scan);
......
sql/sql_class.h
View file @ e13406b2
......@@ -2983,7 +2983,8 @@ public:
{
DBUG_ENTER("unique_add");
DBUG_PRINT("info", ("tree %u - %lu", tree.elements_in_tree, max_elements));
if (tree.elements_in_tree > max_elements && flush())
if (!(tree.flag & TREE_ONLY_DUPS) &&
tree.elements_in_tree >= max_elements && flush())
DBUG_RETURN(1);
DBUG_RETURN(!tree_insert(&tree, ptr, 0, tree.custom_arg));
}
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment
GitLab Nexedi Edition | About GitLab | About Nexedi | 沪ICP备2021021310号-2 | 沪ICP备2021021310号-7