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Part 2 - Learned Data Structures and AlgorithmsTim Kraska <[email protected]>
First part can be found here:https://stratos.seas.harvard.edu/files/stratos/files/learnedselfdesignedsystemssigmod2019tutorialpart1.pdf
4.5 Ways to Instance Optimized Algorithms and Data Structures
1. Configure/synthesize traditional algorithm using a model
Fundamental Building Blocks
Sorting
B-TreeHash-Map
Scheduling
Join
PriorityQueue
BloomFilter
CachingRangeFilter
B-Tree
Fundamental Algorithms & Data Structures
Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis:The Case for Learned Index Structures. SIGMOD Conference 2018: 489-504
A-B
B-C
C-G
…
AA-AL
AL-AK
AK-AP
…BA-BE
BI-BL
BL-BR
… … ... ... …
….
… … … … …. … …. …
….….… … … … …. … …. … … … … … …. … …. …
Key(e.g., spoon #1)
Key(e.g., spoon #1)
Model
How To Build Models To Predict the Locationid date first_name last_name email address zip state credit_card_nb amount
1000 2017-01-01 Hobart Spracklin [email protected] 20565 High Crossing Plaza 56372 Minnesota 4405-6975-7285-5160 611.00$ 1001 2017-01-02 Billye Binnion [email protected] 3698 Upham Point 20260 District of Columbia 3533-7150-7728-9850 244.00$ 1002 2017-01-02 Johann Brockley [email protected] 23844 Artisan Place 98516 Washington 67597-1193-7985-5100 233.00$ 1003 2017-01-03 Artie [email protected] 6276 Toban Trail 78759 Texas 3537-4829-6134-5000 210.00$ 1004 2017-01-03 Delilah O'Currigan [email protected] 86016 New Castle Avenue 72199 Arkansas 3555-2017-2226-5780 286.00$ 1005 2017-01-04 Gretta Will [email protected] 0 Dottie Circle 68524 Nebraska 503844-1984-2085-5000 870.00$ 1006 2017-01-04 Gordon Kirsopp [email protected] 64060 Scott Park 20370 District of Columbia 633332-1895-2414-5000 687.00$ 1007 2017-01-05 Bendick Fagg [email protected] 94 Florence Hill 45440 Ohio 3528-9673-1815-8420 733.00$ 1008 2017-01-05 Dimitry Boyet [email protected] 35886 Golf Plaza 30066 Georgia 3576-6991-4041-3170 382.00$ 1009 2017-01-06 Ailsun Beinke [email protected] 1 Badeau Place 46295 Indiana 56022-2011-8072-1400 854.00$ 1010 2017-01-07 Lou Hallows [email protected] 1 Twin Pines Junction 91125 California 5602-2364-4079-0250 150.00$ 1011 2017-01-09 Tiffani Mathew [email protected] 0456 Meadow Vale Lane 75260 Texas 6387-6943-8910-4580 313.00$ 1012 2017-01-09 Perl Bridie [email protected] 07 Bluestem Junction 33124 Florida 3539-8662-2397-5880 558.00$ 1013 2017-01-09 Rosabelle Blasik [email protected] 7 Fairfield Pass 79699 Texas 5602-2297-6599-8560 941.00$ 1014 2017-01-10 Meggi Belamy [email protected] 0995 Manufacturers Street 10170 New York 3557-5094-7405-8340 875.00$ 1015 2017-01-10 Tadio Balderston [email protected] 80 Novick Road 75260 Texas 60485-3728-7119-9300 954.00$ 1016 2017-01-11 Gianina Oxteby [email protected] 72674 Fuller Avenue 89505 Nevada 4-0415-9268-2397 239.00$ 1017 2017-01-12 Brendan Doody [email protected] 87414 Golden Leaf Street 11480 New York 201-6348-4121-1314 308.00$ 1018 2017-01-13 Conway Coombs [email protected] 2810 Oakridge Park 32859 Florida 3529-1514-0357-9120 60.00$ 1019 2017-01-14 Germaine Bere [email protected] 82802 Oakridge Park 20041 District of Columbia 670961-0240-4054-9000 95.00$ 1020 2017-01-15 Davide Tolcharde [email protected] 89 Continental Avenue 79165 Texas 5018-7748-4325-9510 137.00$ 1021 2017-01-16 Nigel Artharg [email protected] 31 Mcbride Point 22301 Virginia 560225-6965-2870-0000 496.00$ 1022 2017-01-17 Rickard Trenholm [email protected] 93 Hoepker Parkway 70593 Louisiana 3541-5241-5383-9970 760.00$ 1023 2017-01-18 Juditha Dwane [email protected] 7914 Eliot Lane 14276 New York 5456-4410-0914-3180 474.00$ 1024 2017-01-19 Susan Ilden [email protected] 25204 Huxley Road 21684 Maryland 3574-8586-6367-9920 83.00$ 1025 2017-01-20 Abbey Triggle [email protected] 47 Debra Pass 74184 Oklahoma 3538-6047-6315-7710 513.00$ 1026 2017-01-21 Zsazsa Dunster [email protected] 7 Gerald Alley 40576 Kentucky 3562-0325-7709-3490 952.00$ 1027 2017-01-22 Grantham Friatt [email protected] 774 Prairieview Circle 29225 South Carolina 3571-1171-9476-8780 942.00$ 1028 2017-01-22 Ross Gaudin [email protected] 3102 Loeprich Trail 68197 Nebraska 5108-7578-4665-2710 572.00$ 1029 2017-01-22 Aluino Drover [email protected] 2717 Northridge Avenue 72199 Arkansas 670999-3171-8848-0000 318.00$ 1030 2017-01-23 Shurlock Braker [email protected] 30783 Jenna Alley 80945 Colorado 6331106-1894-9878-0000 166.00$ 1031 2017-01-24 Glenda Goodbody [email protected] 720 Pierstorff Way 7522 New Jersey 36-0593-2719-1684 412.00$ 1032 2017-01-24 Rollin Reddie [email protected] 09 Gina Park 65810 Missouri 4665-9188-1324-1040 383.00$ 1033 2017-01-26 Dorry Jenks [email protected] 1 Butterfield Road 85210 Arizona 3578-9195-0297-7730 636.00$ 1034 2017-01-26 Patti Emby [email protected] 26 Hoard Drive 91210 California 3585-8243-7506-2470 957.00$ 1035 2017-01-26 Nickie Menauteau [email protected] 035 Derek Junction 92110 California 3580-5488-8443-2070 484.00$ 1036 2017-01-28 Jon Kassman [email protected] 2171 Buena Vista Lane 89125 Nevada 3576-7358-0772-1380 64.00$ 1037 2017-01-29 Pansy Pelz [email protected] 15 Buena Vista Circle 55441 Minnesota 372-3019-6895-3127 498.00$ 1038 2017-01-30 Tedd Kingsley [email protected] 65 Crest Line Terrace 19892 Delaware 633447-9874-0606-9000 574.00$ 1039 2017-01-30 Brana Barnaby [email protected] 8 Mifflin Way 35225 Alabama 4911-0899-8885-2970 296.00$ 1040 2017-01-30 Alysa Knightly [email protected] 19 Talisman Lane 45454 Ohio 6771-5993-8703-9340 713.00$ 1041 2017-01-31 Erina Emmot [email protected] 4 Monument Plaza 32255 Florida 6706465-4418-7633-0000 997.00$ 1042 2017-01-31 Georgy Locket [email protected] 0 Huxley Parkway 37605 Tennessee 30-5661-6604-8367 285.00$ 1043 2017-02-01 Kordula Dobkin [email protected] 47 Golf View Point 94121 California 30-2114-0922-1485 677.00$ 1044 2017-02-01 Clarie Josskowitz [email protected] 1 Park Meadow Lane 39236 Mississippi 3546-0315-7744-3990 363.00$ 1045 2017-02-02 Reube Eatock [email protected] 5 Ruskin Park 97211 Oregon 3584-2997-8675-0280 505.00$ 1046 2017-02-04 Joellyn Bebis [email protected] 68430 Ridgeway Park 38197 Tennessee 5602-2261-7843-1310 917.00$ 1047 2017-02-05 Mavis Frigout [email protected] 5581 Montana Street 80910 Colorado 3579-6425-2725-3150 781.00$ 1048 2017-02-05 Sadye Probetts [email protected] 93 Duke Avenue 27615 North Carolina 3530-3785-5739-3570 464.00$ 1049 2017-02-06 Alberto Deeves [email protected] 39341 Brickson Park Plaza 79940 Texas 5010-1248-9700-6870 901.00$ 1050 2017-02-06 Terrence Missenden [email protected] 36339 Kinsman Center 77060 Texas 3552-8788-4710-8910 966.00$ 1051 2017-02-06 Whitby Mowsdell [email protected] 6235 Lotheville Alley 93591 California 3546-2664-8937-4240 126.00$ 1052 2017-02-07 Everard Wedlock [email protected] 8858 Briar Crest Avenue 36134 Alabama 3583-3948-6685-7240 923.00$ 1053 2017-02-07 Karlee Dowd [email protected] 3074 Northland Hill 15255 Pennsylvania 3589-6791-1914-2140 933.00$ 1054 2017-02-08 Lynett Nial [email protected] 845 Welch Junction 20551 District of Columbia 3579-0213-7287-8220 63.00$ 1055 2017-02-08 Tedman Pledge [email protected] 20 Kensington Parkway 24014 Virginia 3546-6887-0454-7180 242.00$ 1056 2017-02-08 Kim Bambrick [email protected] 4 Towne Avenue 84170 Utah 6374-6917-5337-0770 515.00$ 1057 2017-02-09 Adaline Denholm [email protected] 6010 Browning Park 25775 West Virginia 3586-3802-3692-9110 882.00$ 1058 2017-02-10 Basile Caverhill [email protected] 1288 Rowland Center 23509 Virginia 6304-0277-2034-3630 317.00$ 1059 2017-02-10 Derril Hogsden [email protected] 07852 Pond Place 12255 New York 3555-8671-3458-7860 191.00$ 1060 2017-02-11 Curtice Noden [email protected] 7 Raven Circle 30033 Georgia 374-2885-3140-6071 384.00$ 1061 2017-02-12 Adiana Cocking [email protected] 51 Surrey Center 8619 New Jersey 3566-4963-4956-0470 577.00$ 1062 2017-02-13 Livvy Giacoboni [email protected] 37 Lien Terrace 61614 Illinois 3544-7235-0724-9800 741.00$ 1063 2017-02-13 Bob Nussen [email protected] 7136 Carberry Circle 33610 Florida 4911-5061-6903-3430 846.00$ 1064 2017-02-14 Adrien Curtois [email protected] 946 Lerdahl Crossing 77090 Texas 3557-4641-4387-7530 659.00$ 1065 2017-02-14 Sephira Corradi [email protected] 754 Commercial Park 81005 Colorado 5038713-1328-9363-0000 342.00$ 1066 2017-02-15 Drusilla Bligh [email protected] 98978 Dexter Junction 27105 North Carolina 374-6225-4978-9690 627.00$
How To Build Models To Predict the Locationid date first_name last_name email address zip state credit_card_nb amount
1000 2017-01-01 Hobart Spracklin [email protected] 20565 High Crossing Plaza 56372 Minnesota 4405-6975-7285-5160 611.00$ 1001 2017-01-02 Billye Binnion [email protected] 3698 Upham Point 20260 District of Columbia 3533-7150-7728-9850 244.00$ 1002 2017-01-02 Johann Brockley [email protected] 23844 Artisan Place 98516 Washington 67597-1193-7985-5100 233.00$ 1003 2017-01-03 Artie [email protected] 6276 Toban Trail 78759 Texas 3537-4829-6134-5000 210.00$ 1004 2017-01-03 Delilah O'Currigan [email protected] 86016 New Castle Avenue 72199 Arkansas 3555-2017-2226-5780 286.00$ 1005 2017-01-04 Gretta Will [email protected] 0 Dottie Circle 68524 Nebraska 503844-1984-2085-5000 870.00$ 1006 2017-01-04 Gordon Kirsopp [email protected] 64060 Scott Park 20370 District of Columbia 633332-1895-2414-5000 687.00$ 1007 2017-01-05 Bendick Fagg [email protected] 94 Florence Hill 45440 Ohio 3528-9673-1815-8420 733.00$ 1008 2017-01-05 Dimitry Boyet [email protected] 35886 Golf Plaza 30066 Georgia 3576-6991-4041-3170 382.00$ 1009 2017-01-06 Ailsun Beinke [email protected] 1 Badeau Place 46295 Indiana 56022-2011-8072-1400 854.00$ 1010 2017-01-07 Lou Hallows [email protected] 1 Twin Pines Junction 91125 California 5602-2364-4079-0250 150.00$ 1011 2017-01-09 Tiffani Mathew [email protected] 0456 Meadow Vale Lane 75260 Texas 6387-6943-8910-4580 313.00$ 1012 2017-01-09 Perl Bridie [email protected] 07 Bluestem Junction 33124 Florida 3539-8662-2397-5880 558.00$ 1013 2017-01-09 Rosabelle Blasik [email protected] 7 Fairfield Pass 79699 Texas 5602-2297-6599-8560 941.00$ 1014 2017-01-10 Meggi Belamy [email protected] 0995 Manufacturers Street 10170 New York 3557-5094-7405-8340 875.00$ 1015 2017-01-10 Tadio Balderston [email protected] 80 Novick Road 75260 Texas 60485-3728-7119-9300 954.00$ 1016 2017-01-11 Gianina Oxteby [email protected] 72674 Fuller Avenue 89505 Nevada 4-0415-9268-2397 239.00$ 1017 2017-01-12 Brendan Doody [email protected] 87414 Golden Leaf Street 11480 New York 201-6348-4121-1314 308.00$ 1018 2017-01-13 Conway Coombs [email protected] 2810 Oakridge Park 32859 Florida 3529-1514-0357-9120 60.00$ 1019 2017-01-14 Germaine Bere [email protected] 82802 Oakridge Park 20041 District of Columbia 670961-0240-4054-9000 95.00$ 1020 2017-01-15 Davide Tolcharde [email protected] 89 Continental Avenue 79165 Texas 5018-7748-4325-9510 137.00$ 1021 2017-01-16 Nigel Artharg [email protected] 31 Mcbride Point 22301 Virginia 560225-6965-2870-0000 496.00$ 1022 2017-01-17 Rickard Trenholm [email protected] 93 Hoepker Parkway 70593 Louisiana 3541-5241-5383-9970 760.00$ 1023 2017-01-18 Juditha Dwane [email protected] 7914 Eliot Lane 14276 New York 5456-4410-0914-3180 474.00$ 1024 2017-01-19 Susan Ilden [email protected] 25204 Huxley Road 21684 Maryland 3574-8586-6367-9920 83.00$ 1025 2017-01-20 Abbey Triggle [email protected] 47 Debra Pass 74184 Oklahoma 3538-6047-6315-7710 513.00$ 1026 2017-01-21 Zsazsa Dunster [email protected] 7 Gerald Alley 40576 Kentucky 3562-0325-7709-3490 952.00$ 1027 2017-01-22 Grantham Friatt [email protected] 774 Prairieview Circle 29225 South Carolina 3571-1171-9476-8780 942.00$ 1028 2017-01-22 Ross Gaudin [email protected] 3102 Loeprich Trail 68197 Nebraska 5108-7578-4665-2710 572.00$ 1029 2017-01-22 Aluino Drover [email protected] 2717 Northridge Avenue 72199 Arkansas 670999-3171-8848-0000 318.00$ 1030 2017-01-23 Shurlock Braker [email protected] 30783 Jenna Alley 80945 Colorado 6331106-1894-9878-0000 166.00$ 1031 2017-01-24 Glenda Goodbody [email protected] 720 Pierstorff Way 7522 New Jersey 36-0593-2719-1684 412.00$ 1032 2017-01-24 Rollin Reddie [email protected] 09 Gina Park 65810 Missouri 4665-9188-1324-1040 383.00$ 1033 2017-01-26 Dorry Jenks [email protected] 1 Butterfield Road 85210 Arizona 3578-9195-0297-7730 636.00$ 1034 2017-01-26 Patti Emby [email protected] 26 Hoard Drive 91210 California 3585-8243-7506-2470 957.00$ 1035 2017-01-26 Nickie Menauteau [email protected] 035 Derek Junction 92110 California 3580-5488-8443-2070 484.00$ 1036 2017-01-28 Jon Kassman [email protected] 2171 Buena Vista Lane 89125 Nevada 3576-7358-0772-1380 64.00$ 1037 2017-01-29 Pansy Pelz [email protected] 15 Buena Vista Circle 55441 Minnesota 372-3019-6895-3127 498.00$ 1038 2017-01-30 Tedd Kingsley [email protected] 65 Crest Line Terrace 19892 Delaware 633447-9874-0606-9000 574.00$ 1039 2017-01-30 Brana Barnaby [email protected] 8 Mifflin Way 35225 Alabama 4911-0899-8885-2970 296.00$ 1040 2017-01-30 Alysa Knightly [email protected] 19 Talisman Lane 45454 Ohio 6771-5993-8703-9340 713.00$ 1041 2017-01-31 Erina Emmot [email protected] 4 Monument Plaza 32255 Florida 6706465-4418-7633-0000 997.00$ 1042 2017-01-31 Georgy Locket [email protected] 0 Huxley Parkway 37605 Tennessee 30-5661-6604-8367 285.00$ 1043 2017-02-01 Kordula Dobkin [email protected] 47 Golf View Point 94121 California 30-2114-0922-1485 677.00$ 1044 2017-02-01 Clarie Josskowitz [email protected] 1 Park Meadow Lane 39236 Mississippi 3546-0315-7744-3990 363.00$ 1045 2017-02-02 Reube Eatock [email protected] 5 Ruskin Park 97211 Oregon 3584-2997-8675-0280 505.00$ 1046 2017-02-04 Joellyn Bebis [email protected] 68430 Ridgeway Park 38197 Tennessee 5602-2261-7843-1310 917.00$ 1047 2017-02-05 Mavis Frigout [email protected] 5581 Montana Street 80910 Colorado 3579-6425-2725-3150 781.00$ 1048 2017-02-05 Sadye Probetts [email protected] 93 Duke Avenue 27615 North Carolina 3530-3785-5739-3570 464.00$ 1049 2017-02-06 Alberto Deeves [email protected] 39341 Brickson Park Plaza 79940 Texas 5010-1248-9700-6870 901.00$ 1050 2017-02-06 Terrence Missenden [email protected] 36339 Kinsman Center 77060 Texas 3552-8788-4710-8910 966.00$ 1051 2017-02-06 Whitby Mowsdell [email protected] 6235 Lotheville Alley 93591 California 3546-2664-8937-4240 126.00$ 1052 2017-02-07 Everard Wedlock [email protected] 8858 Briar Crest Avenue 36134 Alabama 3583-3948-6685-7240 923.00$ 1053 2017-02-07 Karlee Dowd [email protected] 3074 Northland Hill 15255 Pennsylvania 3589-6791-1914-2140 933.00$ 1054 2017-02-08 Lynett Nial [email protected] 845 Welch Junction 20551 District of Columbia 3579-0213-7287-8220 63.00$ 1055 2017-02-08 Tedman Pledge [email protected] 20 Kensington Parkway 24014 Virginia 3546-6887-0454-7180 242.00$ 1056 2017-02-08 Kim Bambrick [email protected] 4 Towne Avenue 84170 Utah 6374-6917-5337-0770 515.00$ 1057 2017-02-09 Adaline Denholm [email protected] 6010 Browning Park 25775 West Virginia 3586-3802-3692-9110 882.00$ 1058 2017-02-10 Basile Caverhill [email protected] 1288 Rowland Center 23509 Virginia 6304-0277-2034-3630 317.00$ 1059 2017-02-10 Derril Hogsden [email protected] 07852 Pond Place 12255 New York 3555-8671-3458-7860 191.00$ 1060 2017-02-11 Curtice Noden [email protected] 7 Raven Circle 30033 Georgia 374-2885-3140-6071 384.00$ 1061 2017-02-12 Adiana Cocking [email protected] 51 Surrey Center 8619 New Jersey 3566-4963-4956-0470 577.00$ 1062 2017-02-13 Livvy Giacoboni [email protected] 37 Lien Terrace 61614 Illinois 3544-7235-0724-9800 741.00$ 1063 2017-02-13 Bob Nussen [email protected] 7136 Carberry Circle 33610 Florida 4911-5061-6903-3430 846.00$ 1064 2017-02-14 Adrien Curtois [email protected] 946 Lerdahl Crossing 77090 Texas 3557-4641-4387-7530 659.00$ 1065 2017-02-14 Sephira Corradi [email protected] 754 Commercial Park 81005 Colorado 5038713-1328-9363-0000 342.00$ 1066 2017-02-15 Drusilla Bligh [email protected] 98978 Dexter Junction 27105 North Carolina 374-6225-4978-9690 627.00$
How To Build Models To Predict the Location
#Ord
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Date
Frequency Distribution
Date
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0
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1041
2017
-01-
31Er
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za32
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6706
465-
4418
-763
3-00
0099
7.00
$ 10
4220
17-0
1-31
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t16@
saku
ra.n
e.jp
0 Hu
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661-
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5.00
$ 10
4320
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g47
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114-
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2017
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3633
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1055
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-02-
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2017
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2017
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6010
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2577
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2017
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2017
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2017
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090
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754
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5038
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6620
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2017-11-27
2017-11-27
2017-11-27
2017-11-28
2017-11-28
id date first_name last_name email address zip state credit_card_nb amount1000 2017-01-01 Hobart Spracklin [email protected] 20565 High Crossing Plaza 56372 Minnesota 4405-6975-7285-5160 611.00$ 1001 2017-01-02 Billye Binnion [email protected] 3698 Upham Point 20260 District of Columbia 3533-7150-7728-9850 244.00$ 1002 2017-01-02 Johann Brockley [email protected] 23844 Artisan Place 98516 Washington 67597-1193-7985-5100 233.00$ 1003 2017-01-03 Artie [email protected] 6276 Toban Trail 78759 Texas 3537-4829-6134-5000 210.00$ 1004 2017-01-03 Delilah O'Currigan [email protected] 86016 New Castle Avenue 72199 Arkansas 3555-2017-2226-5780 286.00$ 1005 2017-01-04 Gretta Will [email protected] 0 Dottie Circle 68524 Nebraska 503844-1984-2085-5000 870.00$ 1006 2017-01-04 Gordon Kirsopp [email protected] 64060 Scott Park 20370 District of Columbia 633332-1895-2414-5000 687.00$ 1007 2017-01-05 Bendick Fagg [email protected] 94 Florence Hill 45440 Ohio 3528-9673-1815-8420 733.00$ 1008 2017-01-05 Dimitry Boyet [email protected] 35886 Golf Plaza 30066 Georgia 3576-6991-4041-3170 382.00$ 1009 2017-01-06 Ailsun Beinke [email protected] 1 Badeau Place 46295 Indiana 56022-2011-8072-1400 854.00$ 1010 2017-01-07 Lou Hallows [email protected] 1 Twin Pines Junction 91125 California 5602-2364-4079-0250 150.00$ 1011 2017-01-09 Tiffani Mathew [email protected] 0456 Meadow Vale Lane 75260 Texas 6387-6943-8910-4580 313.00$ 1012 2017-01-09 Perl Bridie [email protected] 07 Bluestem Junction 33124 Florida 3539-8662-2397-5880 558.00$ 1013 2017-01-09 Rosabelle Blasik [email protected] 7 Fairfield Pass 79699 Texas 5602-2297-6599-8560 941.00$ 1014 2017-01-10 Meggi Belamy [email protected] 0995 Manufacturers Street 10170 New York 3557-5094-7405-8340 875.00$ 1015 2017-01-10 Tadio Balderston [email protected] 80 Novick Road 75260 Texas 60485-3728-7119-9300 954.00$ 1016 2017-01-11 Gianina Oxteby [email protected] 72674 Fuller Avenue 89505 Nevada 4-0415-9268-2397 239.00$ 1017 2017-01-12 Brendan Doody [email protected] 87414 Golden Leaf Street 11480 New York 201-6348-4121-1314 308.00$ 1018 2017-01-13 Conway Coombs [email protected] 2810 Oakridge Park 32859 Florida 3529-1514-0357-9120 60.00$ 1019 2017-01-14 Germaine Bere [email protected] 82802 Oakridge Park 20041 District of Columbia 670961-0240-4054-9000 95.00$ 1020 2017-01-15 Davide Tolcharde [email protected] 89 Continental Avenue 79165 Texas 5018-7748-4325-9510 137.00$ 1021 2017-01-16 Nigel Artharg [email protected] 31 Mcbride Point 22301 Virginia 560225-6965-2870-0000 496.00$ 1022 2017-01-17 Rickard Trenholm [email protected] 93 Hoepker Parkway 70593 Louisiana 3541-5241-5383-9970 760.00$ 1023 2017-01-18 Juditha Dwane [email protected] 7914 Eliot Lane 14276 New York 5456-4410-0914-3180 474.00$ 1024 2017-01-19 Susan Ilden [email protected] 25204 Huxley Road 21684 Maryland 3574-8586-6367-9920 83.00$ 1025 2017-01-20 Abbey Triggle [email protected] 47 Debra Pass 74184 Oklahoma 3538-6047-6315-7710 513.00$ 1026 2017-01-21 Zsazsa Dunster [email protected] 7 Gerald Alley 40576 Kentucky 3562-0325-7709-3490 952.00$ 1027 2017-01-22 Grantham Friatt [email protected] 774 Prairieview Circle 29225 South Carolina 3571-1171-9476-8780 942.00$ 1028 2017-01-22 Ross Gaudin [email protected] 3102 Loeprich Trail 68197 Nebraska 5108-7578-4665-2710 572.00$ 1029 2017-01-22 Aluino Drover [email protected] 2717 Northridge Avenue 72199 Arkansas 670999-3171-8848-0000 318.00$ 1030 2017-01-23 Shurlock Braker [email protected] 30783 Jenna Alley 80945 Colorado 6331106-1894-9878-0000 166.00$ 1031 2017-01-24 Glenda Goodbody [email protected] 720 Pierstorff Way 7522 New Jersey 36-0593-2719-1684 412.00$ 1032 2017-01-24 Rollin Reddie [email protected] 09 Gina Park 65810 Missouri 4665-9188-1324-1040 383.00$ 1033 2017-01-26 Dorry Jenks [email protected] 1 Butterfield Road 85210 Arizona 3578-9195-0297-7730 636.00$ 1034 2017-01-26 Patti Emby [email protected] 26 Hoard Drive 91210 California 3585-8243-7506-2470 957.00$ 1035 2017-01-26 Nickie Menauteau [email protected] 035 Derek Junction 92110 California 3580-5488-8443-2070 484.00$ 1036 2017-01-28 Jon Kassman [email protected] 2171 Buena Vista Lane 89125 Nevada 3576-7358-0772-1380 64.00$ 1037 2017-01-29 Pansy Pelz [email protected] 15 Buena Vista Circle 55441 Minnesota 372-3019-6895-3127 498.00$ 1038 2017-01-30 Tedd Kingsley [email protected] 65 Crest Line Terrace 19892 Delaware 633447-9874-0606-9000 574.00$ 1039 2017-01-30 Brana Barnaby [email protected] 8 Mifflin Way 35225 Alabama 4911-0899-8885-2970 296.00$ 1040 2017-01-30 Alysa Knightly [email protected] 19 Talisman Lane 45454 Ohio 6771-5993-8703-9340 713.00$ 1041 2017-01-31 Erina Emmot [email protected] 4 Monument Plaza 32255 Florida 6706465-4418-7633-0000 997.00$ 1042 2017-01-31 Georgy Locket [email protected] 0 Huxley Parkway 37605 Tennessee 30-5661-6604-8367 285.00$ 1043 2017-02-01 Kordula Dobkin [email protected] 47 Golf View Point 94121 California 30-2114-0922-1485 677.00$ 1044 2017-02-01 Clarie Josskowitz [email protected] 1 Park Meadow Lane 39236 Mississippi 3546-0315-7744-3990 363.00$ 1045 2017-02-02 Reube Eatock [email protected] 5 Ruskin Park 97211 Oregon 3584-2997-8675-0280 505.00$ 1046 2017-02-04 Joellyn Bebis [email protected] 68430 Ridgeway Park 38197 Tennessee 5602-2261-7843-1310 917.00$ 1047 2017-02-05 Mavis Frigout [email protected] 5581 Montana Street 80910 Colorado 3579-6425-2725-3150 781.00$ 1048 2017-02-05 Sadye Probetts [email protected] 93 Duke Avenue 27615 North Carolina 3530-3785-5739-3570 464.00$ 1049 2017-02-06 Alberto Deeves [email protected] 39341 Brickson Park Plaza 79940 Texas 5010-1248-9700-6870 901.00$ 1050 2017-02-06 Terrence Missenden [email protected] 36339 Kinsman Center 77060 Texas 3552-8788-4710-8910 966.00$ 1051 2017-02-06 Whitby Mowsdell [email protected] 6235 Lotheville Alley 93591 California 3546-2664-8937-4240 126.00$ 1052 2017-02-07 Everard Wedlock [email protected] 8858 Briar Crest Avenue 36134 Alabama 3583-3948-6685-7240 923.00$ 1053 2017-02-07 Karlee Dowd [email protected] 3074 Northland Hill 15255 Pennsylvania 3589-6791-1914-2140 933.00$ 1054 2017-02-08 Lynett Nial [email protected] 845 Welch Junction 20551 District of Columbia 3579-0213-7287-8220 63.00$ 1055 2017-02-08 Tedman Pledge [email protected] 20 Kensington Parkway 24014 Virginia 3546-6887-0454-7180 242.00$ 1056 2017-02-08 Kim Bambrick [email protected] 4 Towne Avenue 84170 Utah 6374-6917-5337-0770 515.00$ 1057 2017-02-09 Adaline Denholm [email protected] 6010 Browning Park 25775 West Virginia 3586-3802-3692-9110 882.00$ 1058 2017-02-10 Basile Caverhill [email protected] 1288 Rowland Center 23509 Virginia 6304-0277-2034-3630 317.00$ 1059 2017-02-10 Derril Hogsden [email protected] 07852 Pond Place 12255 New York 3555-8671-3458-7860 191.00$ 1060 2017-02-11 Curtice Noden [email protected] 7 Raven Circle 30033 Georgia 374-2885-3140-6071 384.00$ 1061 2017-02-12 Adiana Cocking [email protected] 51 Surrey Center 8619 New Jersey 3566-4963-4956-0470 577.00$ 1062 2017-02-13 Livvy Giacoboni [email protected] 37 Lien Terrace 61614 Illinois 3544-7235-0724-9800 741.00$ 1063 2017-02-13 Bob Nussen [email protected] 7136 Carberry Circle 33610 Florida 4911-5061-6903-3430 846.00$ 1064 2017-02-14 Adrien Curtois [email protected] 946 Lerdahl Crossing 77090 Texas 3557-4641-4387-7530 659.00$ 1065 2017-02-14 Sephira Corradi [email protected] 754 Commercial Park 81005 Colorado 5038713-1328-9363-0000 342.00$ 1066 2017-02-15 Drusilla Bligh [email protected] 98978 Dexter Junction 27105 North Carolina 374-6225-4978-9690 627.00$
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1001
2017
-01-
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2017
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2017
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00$
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2017
-01-
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1 Bu
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-01-
26Pa
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2017
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28Jo
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2017
-01-
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0 Hu
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4320
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-02-
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…
data_array[model(date)]
#Ord
ers
Date
State-Of-The-ArtB-Tree
260ns ???
Does It Work? A First Attempt
State-Of-The-ArtB-Tree
260ns >80,000ns
Does It Work? A First Attempt
Traditional model architectures do not work
Frameworks are not designed for nano-second execution
Overfitting can be good ML+System Co-Design
underfitting desired overfitting
desired
Challenges
Traditional model architectures do not work
Frameworks are not designed for nano-second execution
Overfitting can be good ML+System Co-Design
underfitting desired overfitting
desired
Challenges
What model type should I use?
What model type should I use?
Whatever works!
What model type should I use?
Whatever works!• Often continuous functions • Possible model types include neural nets, regression
models, piece-wise linear functions, among other things• Model-type can be auto-tuned• Opens up a complete new toolbox of building data
structures and algorithms
Traditional model architectures do not work
Frameworks are not designed for nano-second execution
Overfitting can be good ML+System Co-Design
underfitting desired overfitting
desired
Challenges
Overfitting is a Good ThingThe Last Mile Problem
Pos
Key
Overfitting is a Good Thing
…….
…….
…….
…….
…….
…….…….
…….
Index over 100M records. Page-size: 100
Precision Gain: 100M --> 1M(Min/Max-Error: 1M)
Precision Gain: 1M --> 10k
Precision Gain: 10k --> 100
100M records(i.e., 1M pages)
Traditional model architectures do not work
Frameworks are not designed for nano-second execution
Overfitting can be good ML+System Co-Design
underfitting desired overfitting
desired
Solution
2-Stage RMI with Linear Modelpos0 = a0 + b0 * keypos1 = m1[pos0].a + m1[pos0].b * keyrecord = local-search(key, pos1)
Model 1.1
Model 2.1 Model 2.2 Model 2.3
Model 3.1 Model 3.2 Model 3.3 Model 3.4
…
…St
age
1St
age
3St
age
2
Position
Key
Recursive-Model Index (RMI)
Model 1.1
Model 2.1 Model 2.2 Model 2.3
Model 3.2 Model 3.4
…
…
Stag
e 1
Stag
e 3
Stag
e 2
Position
Key
B-Tree 3.3B-Tree 3.1
Hybrid RMI
Worst-Case Performance is the one of a B-Tree
Traditional model architectures do not work
Frameworks are not designed for nano-second execution
Overfitting can be good ML+System Co-Design
underfitting desired overfitting
desired
Solution
The Learning Index Framework (LIF)
• An index synthesis system
• Given an index configuration generate the best possible code
• Uses ideas from Tupleware [VLDB15]
• Simple models are trained “on-the-fly”, whereas for complex models we use Tensorflow and extract weights afterwards (i.e., no Tensorflowduring inference time)
• Best index configuration is found using auto-tuning (e.g., see TuPAQ[SOCC15]
State-Of-The-ArtB-Tree
265ns>80,000ns
Initial Results
85ns
Learned Index
13MB 0.7MB
You Might Have Seen Certain Blog Posts
0 50 100 150 200 250 300 350
256
32
4
0.5
Size (MB
)
Lookup-Time (ns)
FAST
LookupTable
Fixed-Size Read-OptimizedB-Tree w/ Interpolation Search
LearnedIndex
BetterW
orse
Better Worse
Initial Results
My OwnComparison
A Comparison To ARTful Indexes (Radix-Tree)
Experimental setup:• Dense: continuous keys from 0 to 256M
• Sparse: 256M keys where each bit is equally likely 0 or 1.
Viktor Leis, Alfons Kemper, Thomas Neumann: The Adaptive Radix Tree: ARTfulIndexing for Main-Memory Databases. ICDE 2013
A Comparison To ARTful Indexes (Radix-Tree)
Experimental setup: continuous keys from 0 to 256M
Reported lookup throughput: 10M/s ≈ 100ns(1)
Size: not measured, but paper says overhead of ≈8 Bytes per key (dense, best case): 256M * 8 Byte ≈ 1953MB
(1)Numbers from the paper
Viktor Leis, Alfons Kemper, Thomas Neumann: The Adaptive Radix Tree: ARTfulIndexing for Main-Memory Databases. ICDE 2013
Learned Index
Generate Code:Record lookup(key) {
return data[0 + 1 * key];}
Learned Index
Generate Code:Record lookup(key) {
return data[key];}
Learned Index
Generate Code:Record lookup(key) {
return data[key];}
Lookup Latency: 10ns (learned index) vs 100ns* (ARTfull)or one-order-of-magnitude better
Space: 0MB vs 1953MBInfinitely better :)
?
What about Updates and Inserts?
What about Updates and Inserts?Alex Galakatos, Michael Markovitch, Carsten Binnig, Rodrigo Fonseca, Tim Kraska: A-Tree: A Bounded Approximate Index StructureSIGMOD 2019 (talk was yesterday)
Jialin Ding, Umar Farooq Minhas, Hantian Zhang, Yinan Li, Chi Wang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David B. Lomet: ALEX: An Updatable Adaptive Learned Index. CoRR abs/1905.08898 (2019)
The Simple Approach: Delta Indexing
updates
Training a simple Multi-Variate Regression ModelCan be done in one pass over the data
Leverage the Distribution for Appends
Inse
rts
(e.g
., Ti
mes
tam
ps)
Time
New Inserts
If the Learned Model Can Generalize to InsertsInsert complexity is O(1) not O(Log N)
Ali Hadian, Thomas Heinis: Considerations for handling updates in learned index structures. aiDM@SIGMOD 2019: 3:1-3:4
Updates/Inserts • Less beneficial as the data still has to be stored sorted
• Idea: Leave space in the array where more updates/inserts are expected
• Can also be done with traditional trees.
• But, the error of learned indexes should increase with
per node in RMI whereas traditional indexes with! !
Still at the Beginning!
• Can we provide bounds for inserts?• When to retrain?• How to retrain models on the fly?• …
A lot of follow up work (all from 2019)Jialin Ding, Umar Farooq Minhas, Hantian Zhang, Yinan Li, Chi Wang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David B. Lomet: ALEX: An Updatable Adaptive Learned Index. CoRR abs/1905.08898, 2019
Ali Hadian, Thomas Heinis: Considerations for handling updates in learned index structures. aiDM@SIGMOD 2019
Alex Galakatos, Michael Markovitch, Carsten Binnig, Rodrigo Fonseca, Tim Kraska: A-Tree: A Bounded Approximate Index Structure, SIGMOD 2019
Yingjun Wu, Jia Yu, Yuanyuan Tian, Richard Sidle, Ronald Barber: Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations. SIGMOD Conference 2019: 1223-1240
Ali Hadian, Thomas Heinis: Interpolation-friendly B-trees: Bridging the Gap Between Algorithmic and Learned Indexes. EDBT 2019: 710-713
P Van Sandt, Y Chronis, JM Patel: Efficiently Searching In-Memory Sorted Arrays: Revenge of the Interpolation Search?, SIGMOD 2019Wenkun Xiang, Hao Zhang, Rui Cui, Xing Chu, Keqin Li, Wei Zhou: Pavo: A RNN-Based Learned Inverted Index, Supervised or Unsupervised? IEEE Access 7: 293-303 (2019)
Giorgio Vinciguerra, Paolo Ferragina, Michele Miccinesi: Superseding traditional indexes by orchestrating learning and geometry, CoRR abs/1903.00507 (2019)
Yingjun Wu, Jia Yu, Yuanyuan Tian, Richard Sidle, Ronald Barber: Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations, CoRR abs/1903.11203, 2019
Chuzhe Tang, Zhiyuan Dong, Minjie Wang, Zhaoguo Wang, Haibo Chen: Learned Indexes for Dynamic Workloads. CoRRabs/1902.00655, 2019
….
Tree
Fundamental Algorithms & Data Structures
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
id date first_name last_name email address zip state credit_card_nb amount1000 2017-01-01 Hobart Spracklin [email protected] 20565 High Crossing Plaza 56372 Minnesota 4405-6975-7285-5160 611.00$ 1001 2017-01-02 Billye Binnion [email protected] 3698 Upham Point 20260 District of Columbia 3533-7150-7728-9850 244.00$ 1002 2017-01-02 Johann Brockley [email protected] 23844 Artisan Place 98516 Washington 67597-1193-7985-5100 233.00$ 1003 2017-01-03 Artie [email protected] 6276 Toban Trail 78759 Texas 3537-4829-6134-5000 210.00$ 1004 2017-01-03 Delilah O'Currigan [email protected] 86016 New Castle Avenue 72199 Arkansas 3555-2017-2226-5780 286.00$ 1005 2017-01-04 Gretta Will [email protected] 0 Dottie Circle 68524 Nebraska 503844-1984-2085-5000 870.00$ 1006 2017-01-04 Gordon Kirsopp [email protected] 64060 Scott Park 20370 District of Columbia 633332-1895-2414-5000 687.00$ 1007 2017-01-05 Bendick Fagg [email protected] 94 Florence Hill 45440 Ohio 3528-9673-1815-8420 733.00$ 1008 2017-01-05 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Dunster [email protected] 7 Gerald Alley 40576 Kentucky 3562-0325-7709-3490 952.00$ 1027 2017-01-22 Grantham Friatt [email protected] 774 Prairieview Circle 29225 South Carolina 3571-1171-9476-8780 942.00$ 1028 2017-01-22 Ross Gaudin [email protected] 3102 Loeprich Trail 68197 Nebraska 5108-7578-4665-2710 572.00$ 1029 2017-01-22 Aluino Drover [email protected] 2717 Northridge Avenue 72199 Arkansas 670999-3171-8848-0000 318.00$ 1030 2017-01-23 Shurlock Braker [email protected] 30783 Jenna Alley 80945 Colorado 6331106-1894-9878-0000 166.00$ 1031 2017-01-24 Glenda Goodbody [email protected] 720 Pierstorff Way 7522 New Jersey 36-0593-2719-1684 412.00$ 1032 2017-01-24 Rollin Reddie [email protected] 09 Gina Park 65810 Missouri 4665-9188-1324-1040 383.00$ 1033 2017-01-26 Dorry Jenks [email protected] 1 Butterfield Road 85210 Arizona 3578-9195-0297-7730 636.00$ 1034 2017-01-26 Patti Emby [email protected] 26 Hoard Drive 91210 California 3585-8243-7506-2470 957.00$ 1035 2017-01-26 Nickie 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protected] 1 Park Meadow Lane 39236 Mississippi 3546-0315-7744-3990 363.00$ 1045 2017-02-02 Reube Eatock [email protected] 5 Ruskin Park 97211 Oregon 3584-2997-8675-0280 505.00$ 1046 2017-02-04 Joellyn Bebis [email protected] 68430 Ridgeway Park 38197 Tennessee 5602-2261-7843-1310 917.00$ 1047 2017-02-05 Mavis Frigout [email protected] 5581 Montana Street 80910 Colorado 3579-6425-2725-3150 781.00$ 1048 2017-02-05 Sadye Probetts [email protected] 93 Duke Avenue 27615 North Carolina 3530-3785-5739-3570 464.00$ 1049 2017-02-06 Alberto Deeves [email protected] 39341 Brickson Park Plaza 79940 Texas 5010-1248-9700-6870 901.00$ 1050 2017-02-06 Terrence Missenden [email protected] 36339 Kinsman Center 77060 Texas 3552-8788-4710-8910 966.00$ 1051 2017-02-06 Whitby Mowsdell [email protected] 6235 Lotheville Alley 93591 California 3546-2664-8937-4240 126.00$ 1052 2017-02-07 Everard Wedlock [email protected] 8858 Briar Crest Avenue 36134 Alabama 3583-3948-6685-7240 923.00$ 1053 2017-02-07 Karlee 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Giacoboni [email protected] 37 Lien Terrace 61614 Illinois 3544-7235-0724-9800 741.00$ 1063 2017-02-13 Bob Nussen [email protected] 7136 Carberry Circle 33610 Florida 4911-5061-6903-3430 846.00$ 1064 2017-02-14 Adrien Curtois [email protected] 946 Lerdahl Crossing 77090 Texas 3557-4641-4387-7530 659.00$ 1065 2017-02-14 Sephira Corradi [email protected] 754 Commercial Park 81005 Colorado 5038713-1328-9363-0000 342.00$ 1066 2017-02-15 Drusilla Bligh [email protected] 98978 Dexter Junction 27105 North Carolina 374-6225-4978-9690 627.00$
How To Build an Index For First and Last Name, Date, ID, and Email
There is only oneorder on disk
Jump to results
SpiciesPans
Coffee
Garbage MilkCleaningUpdates
Fruits
Cups
Plates
Stuff we do not need but still do not want to throw away
Christmas Cookie Stuff
Cleaning Stuff
Cleaning Stuff
Cutting boards
Alcohol
BBQ Stuff
Storage:
Date
Amount
Date
Amount
P(X < Start Date)
Storage: …
Date
Amount
P(X < Start Date)
Storage: …
Scan here
Date
Amount
P(X < Start Date)
Storage: …
Scan here Scan here
Date
Amount
P(X < Start Date)
Storage: …
Scan here Scan here Scan here Scan here
Models and Layout Can BeData and Workload Dependent
Initial Results
In-Memory Column Store, Single Threaded, Traditional Data Compression, No SIMD Opt.Queries: Real-world workload: only scan-filter-aggregate (no joins, etc)
101
102
103
Full Colum
n Sc
an
Cluste
red
C
olum
n Index
Redshift-like
Z-O
rder
UB tre
e
Hyp
eroctree
K-d tree
Flood843
144
9.66
204
41.7
14.1
2.88
Perf-Monitoring Workload
Avg
. Que
ry T
ime
(ms)
100
101
102
Full Colum
n Sc
an
92.8
Cluste
red
C
olum
n Index
0.47
Redshift-like
Z-O
rder
10.9
Flood
0.15
Sales DW WorkloadA
vg. Q
uery
Tim
e (m
s)
UB tre
e
Hyp
eroctree
K-d tree
38.1
6.467.34
35.4
R-Tree
Initial Results
In-Memory Column Store, Single Threaded, Traditional Data Compression, No SIMD Opt.Queries: Real-world workload: only scan-filter-aggregate (no joins, etc)
101
102
103
Full Colum
n Sc
an
Cluste
red
C
olum
n Index
Redshift-like
Z-O
rder
UB tre
e
Hyp
eroctree
K-d tree
Flood843
144
9.66
204
41.7
14.1
2.88
Perf-Monitoring Workload
Avg
. Que
ry T
ime
(ms)
100
101
102
Full Colum
n Sc
an
92.8
Cluste
red
C
olum
n Index
0.47
Redshift-like
Z-O
rder
10.9
Flood
0.15
Sales DW WorkloadA
vg. Q
uery
Tim
e (m
s)
UB tre
e
Hyp
eroctree
K-d tree
38.1
6.467.34
35.4
R-Tree
Note: We are still at the beginning!Un
explo
red o
ppor
tuni
ties
GPUs/FPGAs ML Compression
Re-Generating IDs Sparse vs dense storage
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
Is This Key In My Set?
Maybe Yes No No
Maybe No
Is This Key In My Set?
Model
Maybe Yes
36% Space Improvement over Bloom Filter at Same False Positive Rate
Bloom Filter- Approach 1
No
Maybe Yes
Is This Key In My Set?
Model
Maybe Yes
Sandwiched Bloom Filter
No
Maybe No
Michael Mitzenmacher: A Model for Learned Bloom Filters and Optimizing by Sandwiching. NeurIPS 2018: 462-471
Follow up work
Michael Mitzenmacher: A Model for Learned Bloom Filters and Optimizing by Sandwiching. NeurIPS 2018: 462-471
Jack W. Rae, Sergey Bartunov, Timothy P. Lillicrap: Meta-Learning Neural Bloom Filters. ICML 2019: 5271-5280
Stephen Macke, Alex Beutel, Tim Kraska, Maheswaran Sathiamoorthy, Derek Zhiyuan Cheng, Ed H. Chi: Lifting the Curse of Multidimensional Data with Learned Existence Indexes, ML for Systems workshop at NeurIPS, 2018
….
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
F
a
bc
nmo
z
UOCANZBM
a
bc
nmo
z
(a) CDF Model Pre-Sorts (b) Compact & local sort
abcmnoz
Sorting
F
a
bc
nmo
z
UOCANZBM
a
bc
nmo
z
(a) CDF Model Pre-Sorts (b) Compact & local sort
abcmnoz
F
a
bc
nmo
z
UOCANZBM
a
bc
nmo
z
(a) CDF Model Pre-Sorts (b) Compact & local sort
abcmnoz
Sorting
Initial Results
200k 400k 600k 800k 1M
Number of Address Records
Tim
e in
Sec
onds
0.25
0.50
0.75
1.00
1.25std:sort QuickSort Learned Sort
1.5x – 2x
Initial ResultsRu
nnin
g tim
e (s
ec.)
0.0
1.0
2.0
3.0
0M 10M 20M 30M 40M 50M
std::sort Radix sort TimsortLearned Sort (pre-trained) Learned Sort
Runn
ing
time
(sec
.)
0.0
1.0
2.0
3.0
0M 10M 20M 30M 40M 50M
std::sort Radix sort TimsortLearned Sort (pre-trained) Learned Sort
32-bit ints; normal distribution (μ=0, σ=1e6) 128-bit ints; normal distribution (μ=0, σ=1e6)
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
Task
6
Task
5
Task
4
Scheduling
Task Queue
Servers
Task 1
Task 2
Task 2
Task 3
Task 1
Task 1
State-of-the art: Fair Scheduling
Learned scheduler improves average job
completion time by 21% over fair scheduling,
and 19-31% over best hand-crafted heuristics
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
HashFunctionKey ModelKey
Hash Map
Goal: Reduce Conflicts
25% - 70% Reduction in Hash-Map Conflicts
% Conflicts Hash Map % Conflicts Model ReductionMap Data 35.3% 07.9% 77.5%Web Data 35.3% 24.7% 30.0%Log Normal 35.4% 25.9% 26.7%
Figure 8: Reduction of Conflicts
micro-seconds), the model execution time is negligible and even small reductions in the conflict rate cansignificantly improve the overall performance. To conclude, learned hash functions are independent of theused Hash-map architecture and depending on the Hash-map architecture their complexity may or may notpay off.
5 Existence Index
The last common index type of DBMS are existence indexes, most importantly Bloom filters, a space efficientprobabilistic data structure to test whether an element is a member of a set. They are commonly used todetermine if a key exists on cold storage. For example, Bigtable uses them to determine if a key is containedin an SSTable [23].
Internally, Bloom filters use a bit array of size m and k hash functions, which each map a key to one ofthe m array positions (see Figure9(a)). To add an element to the set, a key is fed to the k hash-functions andthe bits of the returned positions are set to 1. To test if a key is a member of the set, the key is again fed intothe k hash functions to receive k array positions. If any of the bits at those k positions is 0, the key is not amember of a set. In other words, a Bloom filter does guarantee that there exists no false negatives, but haspotential false positives.
While Bloom filters are highly space-efficient, they can still occupy a significant amount of memory. Forexample for one billion records roughly ⇡ 1.76 Gigabytes are needed. For a FPR of 0.01% we would require⇡ 2.23 Gigabytes. There have been several attempts to improve the efficiency of Bloom filters [52], but thegeneral observation remains.
Yet, if there is some structure to determine what is inside versus outside the set, which can be learned, itmight be possible to construct more efficient representations. Interestingly, for existence indexes for databasesystems, the latency and space requirements are usually quite different than what we saw before. Given thehigh latency to access cold storage (e.g., disk or even band), we can afford more complex models while themain objective is to minimize the space for the index and the number of false positives. We outline twopotential ways to build existence indexes using learned models.
5.1 Learned Bloom filters
While both range and point indexes learn the distribution of keys, existence indexes need to learn a functionthat separates keys from everything else. Stated differently, a good hash function for a point index is one withfew collisions among keys, whereas a good hash function for a Bloom filter would be one that has lots ofcollisions among keys and lots of collisions among non-keys, but few collisions of keys and non-keys. Weconsider below how to learn such a function f and how to incorporate it into an existence index.
While traditional Bloom filters guarantee a false negative rate (FNR) of zero and a specific false positiverate (FPR) for any set of queries chosen a-priori [22], we follow the notion that we want to provide a specificFPR for realistic queries in particular while maintaining a FNR of zero. That is, we measure the FPR over aheldout dataset of queries, as is common in evaluating ML systems [30]. While these definitions differ, webelieve the assumption that we can observe the distribution of queries, e.g., from historical logs, holds in
15
Hash Map - Results
Skip
Independentof Hash-MapArchitecture
Hash Map – Example Results
Type Time (ns) Utilization Stanford AVX Cuckoo, 4 Byte value 31ns 99%Stanford AVX Cuckoo, 20 Byte record - Standard Hash 43ns 99% Commercial Cuckoo, 20Byte record - Standard Hash 90ns 95% In-place chained Hash-map, 20Byte record, learned hash functions 35ns 100%
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
0.5
1
1.5
2
2.5
0 10 20 30 40 50 60 70 80 90 100
Perf
orm
ance R
ela
tive t
o P
ostg
reSQ
L
Episodes
RL (Row Vectors)RL (Histogram)
RL (1-Hot)Postgres
0.5
1
1.5
2
2.5
0 10 20 30 40 50 60 70 80 90 100
Perf
orm
ance
Rel
ativ
e to
Ora
cle
Episodes
RL (Row Vectors)RL (Histogram)
RL (1-Hot)
OraclePostgreSQL on Oracle
Add reference
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling
Cache Policy
Bloom-Filter
Fundamental Algorithms & Data Structures
DNA-Search SQL Query Optimizer
Nearest Neighbor
DataCubes
We Have To Rewrite The BibleStart A New
Shared Components and Utilities
Relational Query Processor
Transactional Storage Manager
Client Communication Manager
Process Management
Admission Control
Dispatch and
Scheduling
Local Client ProtocolsRemote Client
Protocols
Query Parsing and Authentication
Query Rewrite
Query Optimization
Plan Executor
DDL and Utility Processing
Access Methods Buffer Manager
Log ManagerLock Manager
Catalog Manager
Memory Manager
Administration, Monitoring, &
Utilities
Replication and Loading Services
Batch Utilities
Database Architecture
Dispatch and
Scheduling
Query Rewrite
Query Optimization
Plan Executor
Access Methods
Joseph M. Hellerstein, Michael Stonebraker, and James Hamilton: Architecture of a Database System. Foundations and Trends in Databases 1(2): 141-259 (2007)
Shared Components and Utilities
Relational Query Processor
Transactional Storage Manager
Client Communication Manager
Process Management
Admission Control
Dispatch and
Scheduling
Local Client ProtocolsRemote Client
Protocols
Query Parsing and Authentication
Query Rewrite
Query Optimization
Plan Executor
DDL and Utility Processing
Access Methods Buffer Manager
Log ManagerLock Manager
Catalog Manager
Memory Manager
Administration, Monitoring, &
Utilities
Replication and Loading Services
Batch Utilities
Database Architecture
Admission Control
Dispatch and
Scheduling
Query Rewrite
Query Optimization
Plan Executor
DDL and Utility Processing
Access Methods
Buffer Manager
Log Manager
Catalog Manager
Memory Manager
Administration, Monitoring, &
Utilities
Replication and Loading Services
Batch Utilities
Local Client Protocols
Remote Client Protocols
Joseph M. Hellerstein, Michael Stonebraker, and James Hamilton: Architecture of a Database System. Foundations and Trends in Databases 1(2): 141-259 (2007)
Instance Optimality
Building a system from scratch for every single user is not economical
Machine Learning makes it possibleand we can leverage decades of ML research
The best performance for every user/application
Instance Optimality
An algorithm B is instance optimal over a class of algorithm A and a database d, if for every choice of a ∈ A:
Ronald Fagin, Amnon Lotem, Moni Naor: Optimal Aggregation Algorithms for Middleware. PODS 2001
Query Optimization
Query Execution
Data Access
- Data Cubes- AQP- Machine Learning
- Sorting- Joins- Aggregation- Scheduling
- Compression- Storage layout - Indexes
- Cardinality Estimation- Cost Model- Join Ordering
Data WorkloadHardware
Model Synthesis
Advanced Analytics
SageDB – Towards an Instance-Optimized Database System
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Data Science /Database Systems ApplicationsMobile SystemsNetwork Systems
• Router Table• Rule Engine• Bandwidth Optimization• …
• Index Structures• Storage Manager• Scheduling• …
• Compression• Location Services• Transport Protocols• …
• Data-driven development
• Intelligent Assistants• …
We Need To Start A New Bible
We Need To Start New Bibles
…
We Have To Start A New Bible• Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, and Ellen Vitercik: Learning to branch. ICML, 2018.• Thodoris Lykouris and Sergei Vassilvitskii: Competitive caching with machine learned advice. ICML, 2018.• Tim Kraska, Alex Beutel, Ed H Chi, Jeffrey Dean, and Neoklis Polyzotis. The case for learned index structures. SIGMOD, 2018.• Michael Mitzenmacher. A model for learned bloom filters and optimizing by sandwiching. NeurIPS, 2018.• Michael Mitzenmacher. Load balancing. unpublished• Manish Purohit, Zoya Svitkina and Ravi Kumar. Improving Online Algorithms via ML Predictions. NeurIPS, 2018.• Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett, Baran G¨ozc¨u, Ilija Bogunovic, and Volkan Cevher. Learning-based compressive subsampling. J-STSP, 2016.• Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis. Compressed sensing using generative models. ICML, 2017.• Ali Mousavi, Ankit B Patel, and Richard G Baraniuk. A deep learning approach to structured signal recovery. Allerton, 2015.• Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk. A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery. ICLR 2019• Hanjun Dai, Elias Khalil, Yuyu Zhang, Bistra Dilkina, and Le Song. Learning combinatorial optimization algorithms over graphs. NeurIPS, 2017.• H Mao, R Netravali, M Alizadeh. Neural adaptive video streaming with pensieve. SIGCOMM, 2017• H Mao, M Schwarzkopf, SB Venkatakrishnan, Z Meng, M Alizadeh. Learning Scheduling Algorithms for Data Processing Clusters. arXiv preprint arXiv:1810.01963• Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath. Deepcode: Feedback Codes via Deep Learning. NeurIPS, 2018• Saeed Amizadeh, Sergiy Matusevych, Markus Weimer. Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach. ICLR, 2019• Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill. Learning a SAT Solver from Single-Bit Supervision. ICLR, 2019• Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar. A new dog learns old tricks: RL finds classic optimization algorithms. ICLR, 2019• Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon. Learning Neural PDE Solvers with Convergence Guarantees. ICLR 2019• Morteza Mardani, Qingyun Sun, David Donoho, Vardan Papyan, Hatef Monajemi, Shreyas Vasanawala, John Pauly. Neural Proximal Gradient Descent for Compressive Imaging.
NeurIPS 2018• Andrés Muñoz Medina and Sergei Vassilvitskii. Revenue optimization with approximate bid predictions. NIPS, 2017.• MF Balcan, T Dick, C White. Data-Driven Clustering via Parameterized Lloyd's Families. NeurIPS, 2018• Mislav Balunovic, Pavol Bielik, Martin Vechev. Learning to Solve SMT Formulas. NeurIPS, 2018• Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos, Luis Lamb, Moshe Vardi. Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP• Jimenez, Daniel A. and Lin, Calvin. Neural Methods for Dynamic Branch Prediction, TOCS 2002• Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan: Learning Memory Access Patterns.
arXiv:1803.02329v1, 2018• W. Xiang and H. Zhang and R. Cui and X. Chu and K. Li, W. Zhou. Pavo: A RNN-Based Learned Inverted Index, Supervised or Unsupervised?. IEEE Access, 2019• Harrie Oosterhuis, J. Shane Culpepper, Maarten de Rijke. The Potential of Learned Index Structures for Index Compression. arXiv:1811.06678v1, 2018• Stephen Macke, Alex Beutel, Tim Kraska, Maheswaran Sathiamoorthy, Derek Zhiyuan Cheng, and Ed H. Chi. Lifting the Curse of Multidimensional Data with Learned Existence
Indexes. Workshop on ML for Systems at NeurIPS, 2018• Zhe Wang, Dylan Cashman, Mingwei Li, Jixian Li, Matthew Berger, Joshua A. Levine, Remco Chang, Carlos Scheidegger: NeuralCubes: Deep Representations for Visual Data
Exploration, CoRR abs/1808.08983, 2018…
4.5 Ways to Instance Optimized Algorithms and Data Structures
1. Configure/synthesize traditional algorithm using a model
4.5 Ways to Instance Optimized Algorithms and Data Structures
1. Configure/synthesize traditional algorithm using a model
2. CDF: empirical CDF model of the data
4.5 Ways to Instance Optimized Algorithms and Data Structures
1. Configure/synthesize traditional algorithm using a model
2. CDF: empirical CDF model of the data
3. Oracle: prediction model
4.5 Ways to Instance Optimized Algorithms and Data Structures
1. Configure/synthesize traditional algorithm using a model
2. CDF: empirical CDF model of the data
3. Oracle: prediction model
4. Full-Model: learning the entire algorithm/data structure
4.5 Ways to Instance Optimized Algorithms and Data Structures
1. Configure/synthesize traditional algorithm using a model
2. CDF: empirical CDF model of the data
3. Oracle: prediction model
4. Full-Model: learning the entire algorithm/data structure
5. Hybrids: Any combination of the above 4
Hash-MapTree Sorting
Join
Range-FilterMulti-Dim Index
…..
Scheduling Cache Policy
Bloom-Filter
Fundamental Algorithms & Data StructuresOur initial paper Work in Progress
DNA-Search
F(x)
CDF-Synth.
SQL Query Optimizer
Nearest Neighbor
DataCubes
Work in Progress
Oracle -based CDF-basedCDF-based CDF-based Oracle-based CDF-based
CDF-based/Full Full Full Full FullCDF-based
Data System for AI Lab DSAIL@CSAILRe
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Facu
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Spons
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ML
Facu
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Conclusion
General Purpose DB
Instance Optimized
Customized SystemsAdaptation to data and workload
4.5 Ways to Instance Optimized Algorithms and Data Structures
1. Configure/synthesize traditional algorithm using a model
2. CDF: empirical CDF model of the data
3. Oracle: prediction model
4. Full-Model: learning the entire algorithm/data structure
5. Hybrids: Any combination of the above 4
Can Change the Complexity Class
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…
data_array[model(date)]
#Ord
ers
Date
Big potential for GPUs/FPGAs/TPUs
Open Challenges
• Development• Benchmarks• Type of Models• Other algorithms• Adaptation to changes• Inserts/Updates• Complexity Analysis and Robustness Guarantees• Predictive Modeling• ….
How We Do Software Development Has To Change Fundamentally
How We Do Software Development Has To Change Fundamentally
Open Challenges
• Development• Benchmarks• Type of Models• Other algorithms• Adaptation to changes• Inserts/Updates• Complexity Analysis and Robustness Guarantees• Predictive Modeling• ….
Learned Algorithms and Data Structures• ML-Enhanced Algorithms, Data Structures, Systems Components Are On The
Rise
• They Provide A New Way To Build Highly Customized Systems (i.e., Instance-Optimized Systems)
• Many Data-Related Problems Can Be Reduced to Learning A CDF model
• We Found That RMIs (Mixture Of Experts With Very Simple Models) Are A Very powerful Building Block
• What Makes The Approach So Powerful Is The Use Of Continuous Functions And Easy To Vectorized Math Operations (the HW has changed!!!)
