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mariadb/mysql-test/main/index_intersect.result
Monty eb483c5181 Updated optimizer costs in multi_range_read_info_const() and sql_select.cc 
- multi_range_read_info_const now uses the new records_in_range interface
- Added handler::avg_io_cost()
- Don't calculate avg_io_cost() in get_sweep_read_cost if avg_io_cost is
  not 1.0.  In this case we trust the avg_io_cost() from the handler.
- Changed test_quick_select to use TIME_FOR_COMPARE instead of
  TIME_FOR_COMPARE_IDX to align this with the rest of the code.
- Fixed bug when using test_if_cheaper_ordering where we didn't use
  keyread if index was changed
- Fixed a bug where we didn't use index only read when using order-by-index
- Added keyread_time() to HEAP.
  The default keyread_time() was optimized for blocks and not suitable for
  HEAP. The effect was the HEAP prefered table scans over ranges for btree
  indexes.
- Fixed get_sweep_read_cost() for HEAP tables
- Ensure that range and ref have same cost for simple ranges
  Added a small cost (MULTI_RANGE_READ_SETUP_COST) to ranges to ensure
  we favior ref for range for simple queries.
- Fixed that matching_candidates_in_table() uses same number of records
  as the rest of the optimizer
- Added avg_io_cost() to JT_EQ_REF cost. This helps calculate the cost for
  HEAP and temporary tables better. A few tests changed because of this.
- heap::read_time() and heap::keyread_time() adjusted to not add +1.
  This was to ensure that handler::keyread_time() doesn't give
  higher cost for heap tables than for normal tables. One effect of
  this is that heap and derived tables stored in heap will prefer
  key access as this is now regarded as cheap.
- Changed cost for index read in sql_select.cc to match
  multi_range_read_info_const(). All index cost calculation is now
  done trough one function.
- 'ref' will now use quick_cost for keys if it exists. This is done
  so that for '=' ranges, 'ref' is prefered over 'range'.
- scan_time() now takes avg_io_costs() into account
- get_delayed_table_estimates() uses block_size and avg_io_cost()
- Removed default argument to test_if_order_by_key(); simplifies code
2020-03-27 03:58:32 +02:00

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DROP TABLE IF EXISTS t1,t2,t3,t4;
DROP DATABASE IF EXISTS world;
set names utf8;
CREATE DATABASE world;
use world;
CREATE TABLE Country (
Code char(3) NOT NULL default '',
Name char(52) NOT NULL default '',
SurfaceArea float(10,2) NOT NULL default '0.00',
Population int(11) NOT NULL default '0',
Capital int(11) default NULL,
PRIMARY KEY (Code),
UNIQUE INDEX (Name)
);
CREATE TABLE City (
ID int(11) NOT NULL auto_increment,
Name char(35) NOT NULL default '',
Country char(3) NOT NULL default '',
Population int(11) NOT NULL default '0',
PRIMARY KEY (ID),
INDEX (Population),
INDEX (Country)
);
CREATE TABLE CountryLanguage (
Country char(3) NOT NULL default '',
Language char(30) NOT NULL default '',
Percentage float(3,1) NOT NULL default '0.0',
PRIMARY KEY (Country, Language),
INDEX (Percentage)
);
SELECT COUNT(*) FROM Country;
COUNT(*)
239
SELECT COUNT(*) FROM City;
COUNT(*)
4079
SELECT COUNT(*) FROM CountryLanguage;
COUNT(*)
984
CREATE INDEX Name ON City(Name);
SET SESSION optimizer_switch='rowid_filter=off';
SET SESSION optimizer_switch='index_merge_sort_intersection=on';
SELECT COUNT(*) FROM City;
COUNT(*)
4079
SELECT COUNT(*) FROM City WHERE Name LIKE 'C%';
COUNT(*)
281
SELECT COUNT(*) FROM City WHERE Name LIKE 'M%';
COUNT(*)
301
SELECT COUNT(*) FROM City WHERE Population > 1000000;
COUNT(*)
237
SELECT COUNT(*) FROM City WHERE Population > 1500000;
COUNT(*)
129
SELECT COUNT(*) FROM City WHERE Population > 300000;
COUNT(*)
1062
SELECT COUNT(*) FROM City WHERE Population > 7000000;
COUNT(*)
14
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 index_merge Population,Name Name,Population 35,4 NULL # Using sort_intersect(Name,Population); Using where
EXPLAIN
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 1500000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge Population,Name Population,Name 4,35 NULL # 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 Name 35 NULL # Using index condition; Using where
EXPLAIN
SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 7000000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge Population,Name Population,Name 4,35 NULL # Using sort_intersect(Population,Name); Using where
SELECT * FROM City USE INDEX ()
WHERE Name LIKE 'C%' AND Population > 1000000;
ID Name Country Population
1026 Calcutta [Kolkata] IND 4399819
1027 Chennai (Madras) IND 3841396
151 Chittagong BGD 1392860
1892 Chongqing CHN 6351600
1898 Chengdu CHN 3361500
1900 Changchun CHN 2812000
1910 Changsha CHN 1809800
212 Curitiba BRA 1584232
2258 Cali COL 2077386
2485 Casablanca MAR 2940623
2515 Ciudad de México MEX 8591309
3539 Caracas VEN 1975294
3795 Chicago USA 2896016
608 Cairo EGY 6789479
71 Córdoba ARG 1157507
712 Cape Town ZAF 2352121
926 Conakry GIN 1090610
SELECT * FROM City
WHERE Name LIKE 'C%' AND Population > 1000000;
ID Name Country Population
1026 Calcutta [Kolkata] IND 4399819
1027 Chennai (Madras) IND 3841396
151 Chittagong BGD 1392860
1892 Chongqing CHN 6351600
1898 Chengdu CHN 3361500
1900 Changchun CHN 2812000
1910 Changsha CHN 1809800
212 Curitiba BRA 1584232
2258 Cali COL 2077386
2485 Casablanca MAR 2940623
2515 Ciudad de México MEX 8591309
3539 Caracas VEN 1975294
3795 Chicago USA 2896016
608 Cairo EGY 6789479
71 Córdoba ARG 1157507
712 Cape Town ZAF 2352121
926 Conakry GIN 1090610
SELECT * FROM City USE INDEX ()
WHERE Name LIKE 'M%' AND Population > 1500000;
ID Name Country Population
1024 Mumbai (Bombay) IND 10500000
131 Melbourne AUS 2865329
1381 Mashhad IRN 1887405
2259 Medellín COL 1861265
3520 Minsk BLR 1674000
3580 Moscow RUS 8389200
653 Madrid ESP 2879052
766 Manila PHL 1581082
942 Medan IDN 1843919
SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 1500000;
ID Name Country Population
1024 Mumbai (Bombay) IND 10500000
131 Melbourne AUS 2865329
1381 Mashhad IRN 1887405
2259 Medellín COL 1861265
3520 Minsk BLR 1674000
3580 Moscow RUS 8389200
653 Madrid ESP 2879052
766 Manila PHL 1581082
942 Medan IDN 1843919
SELECT * FROM City USE INDEX ()
WHERE Name LIKE 'M%' AND Population > 300000;
ID Name Country Population
1024 Mumbai (Bombay) IND 10500000
1042 Madurai IND 977856
1051 Meerut IND 753778
1074 Mysore IND 480692
1081 Moradabad IND 429214
1098 Malegaon IND 342595
131 Melbourne AUS 2865329
1366 Mosul IRQ 879000
1381 Mashhad IRN 1887405
1465 Milano ITA 1300977
1559 Matsuyama JPN 466133
1560 Matsudo JPN 461126
1578 Machida JPN 364197
1595 Miyazaki JPN 303784
1810 Montréal CAN 1016376
1816 Mississauga CAN 608072
1882 Mombasa KEN 461753
1945 Mudanjiang CHN 570000
2005 Ma´anshan CHN 305421
215 Manaus BRA 1255049
223 Maceió BRA 786288
2259 Medellín COL 1861265
2267 Manizales COL 337580
2300 Mbuji-Mayi COD 806475
2348 Masan KOR 441242
2440 Monrovia LBR 850000
2454 Macao MAC 437500
2487 Marrakech MAR 621914
2491 Meknès MAR 460000
250 Mauá BRA 375055
2523 Monterrey MEX 1108499
2526 Mexicali MEX 764902
2530 Mérida MEX 703324
2537 Morelia MEX 619958
2554 Matamoros MEX 416428
2557 Mazatlán MEX 380265
256 Moji das Cruzes BRA 339194
2698 Maputo MOZ 1018938
2699 Matola MOZ 424662
2711 Mandalay MMR 885300
2712 Moulmein (Mawlamyine) MMR 307900
2734 Managua NIC 959000
2756 Mushin NGA 333200
2757 Maiduguri NGA 320000
2826 Multan PAK 1182441
2975 Marseille FRA 798430
3070 Munich [München] DEU 1194560
3086 Mannheim DEU 307730
3175 Mekka SAU 965700
3176 Medina SAU 608300
3214 Mogadishu SOM 997000
3364 Mersin (Içel) TUR 587212
3371 Malatya TUR 330312
3434 Mykolajiv UKR 508000
3435 Mariupol UKR 490000
3438 Makijivka UKR 384000
3492 Montevideo URY 1236000
3520 Minsk BLR 1674000
3522 Mogiljov BLR 356000
3540 Maracaíbo VEN 1304776
3545 Maracay VEN 444443
3547 Maturín VEN 319726
3580 Moscow RUS 8389200
3622 Magnitogorsk RUS 427900
3625 Murmansk RUS 376300
3636 Mahat?kala RUS 332800
3810 Memphis USA 650100
3811 Milwaukee USA 596974
3834 Mesa USA 396375
3837 Minneapolis USA 382618
3839 Miami USA 362470
462 Manchester GBR 430000
653 Madrid ESP 2879052
658 Málaga ESP 530553
661 Murcia ESP 353504
766 Manila PHL 1581082
77 Mar del Plata ARG 512880
778 Makati PHL 444867
781 Marikina PHL 391170
783 Muntinlupa PHL 379310
786 Malabon PHL 338855
80 Merlo ARG 463846
83 Moreno ARG 356993
87 Morón ARG 349246
942 Medan IDN 1843919
947 Malang IDN 716862
962 Manado IDN 332288
963 Mataram IDN 306600
SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 300000;
ID Name Country Population
1024 Mumbai (Bombay) IND 10500000
1042 Madurai IND 977856
1051 Meerut IND 753778
1074 Mysore IND 480692
1081 Moradabad IND 429214
1098 Malegaon IND 342595
131 Melbourne AUS 2865329
1366 Mosul IRQ 879000
1381 Mashhad IRN 1887405
1465 Milano ITA 1300977
1559 Matsuyama JPN 466133
1560 Matsudo JPN 461126
1578 Machida JPN 364197
1595 Miyazaki JPN 303784
1810 Montréal CAN 1016376
1816 Mississauga CAN 608072
1882 Mombasa KEN 461753
1945 Mudanjiang CHN 570000
2005 Ma´anshan CHN 305421
215 Manaus BRA 1255049
223 Maceió BRA 786288
2259 Medellín COL 1861265
2267 Manizales COL 337580
2300 Mbuji-Mayi COD 806475
2348 Masan KOR 441242
2440 Monrovia LBR 850000
2454 Macao MAC 437500
2487 Marrakech MAR 621914
2491 Meknès MAR 460000
250 Mauá BRA 375055
2523 Monterrey MEX 1108499
2526 Mexicali MEX 764902
2530 Mérida MEX 703324
2537 Morelia MEX 619958
2554 Matamoros MEX 416428
2557 Mazatlán MEX 380265
256 Moji das Cruzes BRA 339194
2698 Maputo MOZ 1018938
2699 Matola MOZ 424662
2711 Mandalay MMR 885300
2712 Moulmein (Mawlamyine) MMR 307900
2734 Managua NIC 959000
2756 Mushin NGA 333200
2757 Maiduguri NGA 320000
2826 Multan PAK 1182441
2975 Marseille FRA 798430
3070 Munich [München] DEU 1194560
3086 Mannheim DEU 307730
3175 Mekka SAU 965700
3176 Medina SAU 608300
3214 Mogadishu SOM 997000
3364 Mersin (Içel) TUR 587212
3371 Malatya TUR 330312
3434 Mykolajiv UKR 508000
3435 Mariupol UKR 490000
3438 Makijivka UKR 384000
3492 Montevideo URY 1236000
3520 Minsk BLR 1674000
3522 Mogiljov BLR 356000
3540 Maracaíbo VEN 1304776
3545 Maracay VEN 444443
3547 Maturín VEN 319726
3580 Moscow RUS 8389200
3622 Magnitogorsk RUS 427900
3625 Murmansk RUS 376300
3636 Mahat?kala RUS 332800
3810 Memphis USA 650100
3811 Milwaukee USA 596974
3834 Mesa USA 396375
3837 Minneapolis USA 382618
3839 Miami USA 362470
462 Manchester GBR 430000
653 Madrid ESP 2879052
658 Málaga ESP 530553
661 Murcia ESP 353504
766 Manila PHL 1581082
77 Mar del Plata ARG 512880
778 Makati PHL 444867
781 Marikina PHL 391170
783 Muntinlupa PHL 379310
786 Malabon PHL 338855
80 Merlo ARG 463846
83 Moreno ARG 356993
87 Morón ARG 349246
942 Medan IDN 1843919
947 Malang IDN 716862
962 Manado IDN 332288
963 Mataram IDN 306600
SELECT * FROM City USE INDEX ()
WHERE Name LIKE 'M%' AND Population > 7000000;
ID Name Country Population
1024 Mumbai (Bombay) IND 10500000
3580 Moscow RUS 8389200
SELECT * FROM City
WHERE Name LIKE 'M%' AND Population > 7000000;
ID Name Country Population
1024 Mumbai (Bombay) IND 10500000
3580 Moscow RUS 8389200
SELECT COUNT(*) FROM City WHERE Name BETWEEN 'M' AND 'N';
COUNT(*)
301
SELECT COUNT(*) FROM City WHERE Name BETWEEN 'G' AND 'J';
COUNT(*)
408
SELECT COUNT(*) FROM City WHERE Name BETWEEN 'G' AND 'K';
COUNT(*)
512
SELECT COUNT(*) FROM City WHERE Population > 1000000;
COUNT(*)
237
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 'B%';
COUNT(*)
339
SELECT COUNT(*) FROM City WHERE Country LIKE 'J%';
COUNT(*)
256
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 index_merge Population,Country,Name Name,Population 35,4 NULL # Using sort_intersect(Name,Population); Using where
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'K' AND Population > 1000000 AND Country LIKE 'J%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge Population,Country,Name Population,Country,Name 4,3,35 NULL # Using sort_intersect(Population,Country,Name); Using where
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'K' AND Population > 500000 AND Country LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge Population,Name,Country Name,Country,Population # NULL # Using sort_intersect(Name,Country,Population); Using where
SELECT * FROM City USE INDEX ()
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 '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 USE INDEX ()
WHERE Name BETWEEN 'G' AND 'K' AND Population > 1000000 AND Country LIKE 'J%';
ID Name Country Population
1533 Jokohama [Yokohama] JPN 3339594
1541 Hiroshima JPN 1119117
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'K' AND Population > 1000000 AND Country LIKE 'J%';
ID Name Country Population
1533 Jokohama [Yokohama] JPN 3339594
1541 Hiroshima JPN 1119117
SELECT * FROM City USE INDEX ()
WHERE Name BETWEEN 'G' AND 'K' AND Population > 500000 AND Country LIKE 'C%';
ID Name Country Population
1895 Harbin CHN 4289800
1904 Jinan CHN 2278100
1905 Hangzhou CHN 2190500
1914 Guiyang CHN 1465200
1916 Hefei CHN 1369100
1923 Jilin CHN 1040000
1927 Hohhot CHN 916700
1928 Handan CHN 840000
1937 Huainan CHN 700000
1938 Jixi CHN 683885
1944 Jinzhou CHN 570000
1950 Hegang CHN 520000
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'K' AND Population > 500000 AND Country LIKE 'C%';
ID Name Country Population
1895 Harbin CHN 4289800
1904 Jinan CHN 2278100
1905 Hangzhou CHN 2190500
1914 Guiyang CHN 1465200
1916 Hefei CHN 1369100
1923 Jilin CHN 1040000
1927 Hohhot CHN 916700
1928 Handan CHN 840000
1937 Huainan CHN 700000
1938 Jixi CHN 683885
1944 Jinzhou CHN 570000
1950 Hegang CHN 520000
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 2001 AND 2500;
COUNT(*)
500
SELECT COUNT(*) FROM City WHERE ID BETWEEN 3701 AND 4000;
COUNT(*)
300
SELECT COUNT(*) FROM City WHERE Population > 700000;
COUNT(*)
358
SELECT COUNT(*) FROM City WHERE Population > 1000000;
COUNT(*)
237
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 'H%';
COUNT(*)
22
SELECT COUNT(*) FROM City WHERE Country BETWEEN 'S' AND 'Z';
COUNT(*)
682
EXPLAIN
SELECT * FROM City
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 index_merge PRIMARY,Population,Country Population,PRIMARY,Country 4,4,3 NULL # Using sort_intersect(Population,PRIMARY,Country); Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 1 AND 500 AND Population > 700000 AND Country LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge PRIMARY,Population,Country Population,PRIMARY,Country 4,4,3 NULL # Using sort_intersect(Population,PRIMARY,Country); Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'H%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge PRIMARY,Population,Country Country,PRIMARY 3,4 NULL # Using sort_intersect(Country,PRIMARY); Using where
EXPLAIN
SELECT * FROM City
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 index_merge PRIMARY,Population,Country Population,PRIMARY 4,4 NULL # Using sort_intersect(Population,PRIMARY); Using where
EXPLAIN
SELECT * FROM City
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 # Using index condition; Using where
SELECT * FROM City USE INDEX ()
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 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 1 AND 500 AND Population > 700000 AND Country LIKE 'C%';
ID Name Country Population
SELECT * FROM City
WHERE ID BETWEEN 1 AND 500 AND Population > 700000 AND Country LIKE 'C%';
ID Name Country Population
SELECT * FROM City USE INDEX ()
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'H%';
ID Name Country Population
2409 Zagreb HRV 706770
SELECT * FROM City
WHERE ID BETWEEN 2001 AND 2500 AND Population > 300000 AND Country LIKE 'H%';
ID Name Country Population
2409 Zagreb HRV 706770
SELECT * FROM City USE INDEX ()
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
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
SELECT * FROM City
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
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 = IF(@@version_compile_machine like '%64%', 2048, 1536);
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 index_merge Population,Name Name,Population 35,4 NULL # Using sort_intersect(Name,Population); Using where
EXPLAIN
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 1500000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge Population,Name Population,Name 4,35 NULL # Using sort_intersect(Population,Name); Using where
EXPLAIN
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'K' AND Population > 1000000 AND Country LIKE 'J%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge Population,Country,Name Population,Country 4,3 NULL # 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 Name 35 NULL # Using index condition; Using where
EXPLAIN
SELECT * FROM City
WHERE ID BETWEEN 1 AND 500 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 Population 4 NULL # Using index condition; Using where
EXPLAIN
SELECT * FROM City
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 # Using index condition; Using where
SELECT * FROM City WHERE
Name LIKE 'C%' AND Population > 1000000;
ID Name Country Population
1026 Calcutta [Kolkata] IND 4399819
1027 Chennai (Madras) IND 3841396
151 Chittagong BGD 1392860
1892 Chongqing CHN 6351600
1898 Chengdu CHN 3361500
1900 Changchun CHN 2812000
1910 Changsha CHN 1809800
212 Curitiba BRA 1584232
2258 Cali COL 2077386
2485 Casablanca MAR 2940623
2515 Ciudad de México MEX 8591309
3539 Caracas VEN 1975294
3795 Chicago USA 2896016
608 Cairo EGY 6789479
71 Córdoba ARG 1157507
712 Cape Town ZAF 2352121
926 Conakry GIN 1090610
SELECT * FROM City WHERE
Name LIKE 'M%' AND Population > 1500000;
ID Name Country Population
1024 Mumbai (Bombay) IND 10500000
131 Melbourne AUS 2865329
1381 Mashhad IRN 1887405
2259 Medellín COL 1861265
3520 Minsk BLR 1674000
3580 Moscow RUS 8389200
653 Madrid ESP 2879052
766 Manila PHL 1581082
942 Medan IDN 1843919
SELECT * FROM City
WHERE Name BETWEEN 'G' AND 'J' AND Population > 700000 AND Country LIKE 'J%';
ID Name Country Population
1541 Hiroshima JPN 1119117
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
1916 Hefei CHN 1369100
1927 Hohhot CHN 916700
1928 Handan CHN 840000
1937 Huainan CHN 700000
1950 Hegang CHN 520000
SELECT * FROM City
WHERE ID BETWEEN 1 AND 500 AND Population > 700000 AND Country LIKE 'C%';
ID Name Country Population
SELECT * FROM City
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
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 > 1000000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge Population,CountryID,CountryName Population,CountryID 4,3 NULL # Using sort_intersect(Population,CountryID); Using where
EXPLAIN
SELECT * FROM City
WHERE Country='USA' AND Population > 1000000;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge Population,CountryID,CountryName Population,CountryID 4,3 NULL # Using sort_intersect(Population,CountryID); Using where
EXPLAIN
SELECT * FROM City
WHERE Country='USA' AND Population > 1500000 AND Name LIKE 'C%';
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge Population,Name,CountryID,CountryName CountryName,Population 38,4 NULL # Using sort_intersect(CountryName,Population); Using where
SELECT * FROM City USE INDEX ()
WHERE Country LIKE 'M%' AND Population > 1000000;
ID Name Country Population
2464 Kuala Lumpur MYS 1297526
2485 Casablanca MAR 2940623
2515 Ciudad de México MEX 8591309
2516 Guadalajara MEX 1647720
2517 Ecatepec de Morelos MEX 1620303
2518 Puebla MEX 1346176
2519 Nezahualcóyotl MEX 1224924
2520 Juárez MEX 1217818
2521 Tijuana MEX 1212232
2522 León MEX 1133576
2523 Monterrey MEX 1108499
2524 Zapopan MEX 1002239
2698 Maputo MOZ 1018938
2710 Rangoon (Yangon) MMR 3361700
SELECT * FROM City
WHERE Country LIKE 'M%' AND Population > 1000000;
ID Name Country Population
2464 Kuala Lumpur MYS 1297526
2485 Casablanca MAR 2940623
2515 Ciudad de México MEX 8591309
2516 Guadalajara MEX 1647720
2517 Ecatepec de Morelos MEX 1620303
2518 Puebla MEX 1346176
2519 Nezahualcóyotl MEX 1224924
2520 Juárez MEX 1217818
2521 Tijuana MEX 1212232
2522 León MEX 1133576
2523 Monterrey MEX 1108499
2524 Zapopan MEX 1002239
2698 Maputo MOZ 1018938
2710 Rangoon (Yangon) MMR 3361700
SELECT * FROM City USE INDEX ()
WHERE Country='USA' AND Population > 1000000;
ID Name Country Population
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
SELECT * FROM City
WHERE Country='USA' AND Population > 1000000;
ID Name Country Population
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
SELECT * FROM City USE INDEX ()
WHERE Country='USA' AND Population > 1500000 AND Name LIKE 'C%';
ID Name Country Population
3795 Chicago USA 2896016
SELECT * FROM City
WHERE Country='USA' AND Population > 1500000 AND Name LIKE 'C%';
ID Name Country Population
3795 Chicago USA 2896016
EXPLAIN
SELECT * FROM City, Country
WHERE City.Name LIKE 'C%' AND City.Population > 1000000 AND
Country.Code=City.Country;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE City index_merge Population,Name,CountryID,CountryName Name,Population 35,4 NULL # Using sort_intersect(Name,Population); Using where
1 SIMPLE Country eq_ref PRIMARY PRIMARY 3 world.City.Country #
DROP DATABASE world;
use test;
CREATE TABLE t1 (
f1 int,
f4 varchar(32),
f5 int,
PRIMARY KEY (f1),
KEY (f4)
);
INSERT INTO t1 VALUES
(5,'H',1), (9,'g',0), (527,'i',0), (528,'y',1), (529,'S',6),
(530,'m',7), (531,'b',2), (532,'N',1), (533,'V',NULL), (534,'l',1),
(535,'M',0), (536,'w',1), (537,'j',5), (538,'l',0), (539,'n',2),
(540,'m',2), (541,'r',2), (542,'l',2), (543,'h',3),(544,'o',0),
(956,'h',0), (957,'g',0), (958,'W',5), (959,'s',3), (960,'w',0),
(961,'q',0), (962,'e',NULL), (963,'u',7), (964,'q',1), (965,'N',NULL),
(966,'e',0), (967,'t',3), (968,'e',6), (969,'f',NULL), (970,'j',0),
(971,'s',3), (972,'I',0), (973,'h',4), (974,'g',1), (975,'s',0),
(976,'r',3), (977,'x',1), (978,'v',8), (979,'j',NULL), (980,'z',7),
(981,'t',9), (982,'j',5), (983,'u',NULL), (984,'g',6), (985,'w',1),
(986,'h',1), (987,'v',0), (988,'v',0), (989,'c',2), (990,'b',7),
(991,'z',0), (992,'M',1), (993,'u',2), (994,'r',2), (995,'b',4),
(996,'A',2), (997,'u',0), (998,'a',0), (999,'j',2), (1,'I',2);
EXPLAIN
SELECT * FROM t1
WHERE (f1 < 535 OR f1 > 985) AND ( f4='r' OR f4 LIKE 'a%' ) ;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE t1 range PRIMARY,f4 f4 35 NULL # Using index condition; Using where
SELECT * FROM t1
WHERE (f1 < 535 OR f1 > 985) AND ( f4='r' OR f4 LIKE 'a%' ) ;
f1 f4 f5
994 r 2
996 A 2
998 a 0
DROP TABLE t1;
SET SESSION optimizer_switch='index_merge_sort_intersection=on';
SET SESSION optimizer_switch='rowid_filter=default';
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