Analytic function syntax example

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Script Name Analytic function syntax example
Description The purpose is to understand the analytic function syntax by gradually adding each component. Analytic function general syntax: analytic_function(arguments) OVER ([analytic clause]) Analytic clause can contain: - PARTITION BY [query_partition_clause] - ORDER BY [order_by_clause] - [windowing_clause]: ROWS|RANGE, BETWEEN ... AND, CURRENT ROW etc.
Area SQL General / Functions
Contributor saiona
Created Saturday March 03, 2018

Statement 1

WITHOUT ANALYTIC CLAUSE

select department_id, salary, sum(salary) over () as "Overall salary total"  
from hr.employees


DEPARTMENT_ID SALARY Overall salary total
90 24000 691416
90 17000 691416
90 17000 691416
60 9000 691416
60 6000 691416
60 4800 691416
60 4800 691416
60 4200 691416
100 12008 691416
100 9000 691416
100 8200 691416
100 7700 691416
100 7800 691416
100 6900 691416
30 11000 691416
30 3100 691416
30 2900 691416
30 2800 691416
30 2600 691416
30 2500 691416
50 8000 691416
50 8200 691416
50 7900 691416
50 6500 691416
50 5800 691416
50 3200 691416
50 2700 691416
50 2400 691416
50 2200 691416
50 3300 691416
50 2800 691416
50 2500 691416
50 2100 691416
50 3300 691416
50 2900 691416
50 2400 691416
50 2200 691416
50 3600 691416
50 3200 691416
50 2700 691416
50 2500 691416
50 3500 691416
50 3100 691416
50 2600 691416
50 2500 691416
80 14000 691416
80 13500 691416
80 12000 691416
80 11000 691416
80 10500 691416
80 10000 691416
80 9500 691416
80 9000 691416
80 8000 691416
80 7500 691416
80 7000 691416
80 10000 691416
80 9500 691416
80 9000 691416
80 8000 691416
80 7500 691416
80 7000 691416
80 10500 691416
80 9500 691416
80 7200 691416
80 6800 691416
80 6400 691416
80 6200 691416
80 11500 691416
80 10000 691416
80 9600 691416
80 7400 691416
80 7300 691416
80 6100 691416
80 11000 691416
80 8800 691416
80 8600 691416
80 8400 691416
 - 7000 691416
80 6200 691416
50 3200 691416
50 3100 691416
50 2500 691416
50 2800 691416
50 4200 691416
50 4100 691416
50 3400 691416
50 3000 691416
50 3800 691416
50 3600 691416
50 2900 691416
50 2500 691416
50 4000 691416
50 3900 691416
50 3200 691416
50 2800 691416
50 3100 691416
50 3000 691416
50 2600 691416
50 2600 691416
10 4400 691416
20 13000 691416
20 6000 691416
40 6500 691416
70 10000 691416
110 12008 691416
110 8300 691416

107 rows selected.

DEPARTMENT_ID	SALARY	Overall salary total
90	24000	691416
90	17000	691416
90	17000	691416
60	9000	691416
60	6000	691416
60	4800	691416
60	4800	691416
60	4200	691416
100	12008	691416
100	9000	691416
100	8200	691416
100	7700	691416
100	7800	691416
100	6900	691416
30	11000	691416
30	3100	691416
30	2900	691416
30	2800	691416
30	2600	691416
30	2500	691416
50	8000	691416
50	8200	691416
50	7900	691416
50	6500	691416
50	5800	691416
50	3200	691416
50	2700	691416
50	2400	691416
50	2200	691416
50	3300	691416
50	2800	691416
50	2500	691416
50	2100	691416
50	3300	691416
50	2900	691416
50	2400	691416
50	2200	691416
50	3600	691416
50	3200	691416
50	2700	691416
50	2500	691416
50	3500	691416
50	3100	691416
50	2600	691416
50	2500	691416
80	14000	691416
80	13500	691416
80	12000	691416
80	11000	691416
80	10500	691416
80	10000	691416
80	9500	691416
80	9000	691416
80	8000	691416
80	7500	691416
80	7000	691416
80	10000	691416
80	9500	691416
80	9000	691416
80	8000	691416
80	7500	691416
80	7000	691416
80	10500	691416
80	9500	691416
80	7200	691416
80	6800	691416
80	6400	691416
80	6200	691416
80	11500	691416
80	10000	691416
80	9600	691416
80	7400	691416
80	7300	691416
80	6100	691416
80	11000	691416
80	8800	691416
80	8600	691416
80	8400	691416
-	7000	691416
80	6200	691416
50	3200	691416
50	3100	691416
50	2500	691416
50	2800	691416
50	4200	691416
50	4100	691416
50	3400	691416
50	3000	691416
50	3800	691416
50	3600	691416
50	2900	691416
50	2500	691416
50	4000	691416
50	3900	691416
50	3200	691416
50	2800	691416
50	3100	691416
50	3000	691416
50	2600	691416
50	2600	691416
10	4400	691416
20	13000	691416
20	6000	691416
40	6500	691416
70	10000	691416
110	12008	691416
110	8300	691416

Statement 2

PARTITION BY CLAUSE

select department_id, salary, sum(salary) over (partition by department_id) as "Department salary total" 
from hr.employees


DEPARTMENT_ID SALARY Department salary total
10 4400 4400
20 13000 19000
20 6000 19000
30 11000 24900
30 3100 24900
30 2900 24900
30 2800 24900
30 2600 24900
30 2500 24900
40 6500 6500
50 8000 156400
50 8200 156400
50 7900 156400
50 6500 156400
50 5800 156400
50 3200 156400
50 2700 156400
50 2400 156400
50 2200 156400
50 3300 156400
50 2800 156400
50 2500 156400
50 2100 156400
50 3300 156400
50 2900 156400
50 2400 156400
50 2200 156400
50 3600 156400
50 3200 156400
50 2700 156400
50 2500 156400
50 3500 156400
50 3100 156400
50 2600 156400
50 2500 156400
50 3200 156400
50 3100 156400
50 2500 156400
50 2800 156400
50 4200 156400
50 4100 156400
50 3400 156400
50 3000 156400
50 3800 156400
50 3600 156400
50 2900 156400
50 2500 156400
50 4000 156400
50 3900 156400
50 3200 156400
50 2800 156400
50 3100 156400
50 3000 156400
50 2600 156400
50 2600 156400
60 9000 28800
60 6000 28800
60 4800 28800
60 4800 28800
60 4200 28800
70 10000 10000
80 14000 304500
80 13500 304500
80 12000 304500
80 11000 304500
80 10500 304500
80 10000 304500
80 9500 304500
80 9000 304500
80 8000 304500
80 7500 304500
80 7000 304500
80 10000 304500
80 9500 304500
80 9000 304500
80 8000 304500
80 7500 304500
80 7000 304500
80 10500 304500
80 9500 304500
80 7200 304500
80 6800 304500
80 6400 304500
80 6200 304500
80 11500 304500
80 10000 304500
80 9600 304500
80 7400 304500
80 7300 304500
80 6100 304500
80 11000 304500
80 8800 304500
80 8600 304500
80 8400 304500
80 6200 304500
90 24000 58000
90 17000 58000
90 17000 58000
100 12008 51608
100 9000 51608
100 8200 51608
100 7700 51608
100 7800 51608
100 6900 51608
110 12008 20308
110 8300 20308
 - 7000 7000

107 rows selected.

DEPARTMENT_ID	SALARY	Department salary total
10	4400	4400
20	13000	19000
20	6000	19000
30	11000	24900
30	3100	24900
30	2900	24900
30	2800	24900
30	2600	24900
30	2500	24900
40	6500	6500
50	8000	156400
50	8200	156400
50	7900	156400
50	6500	156400
50	5800	156400
50	3200	156400
50	2700	156400
50	2400	156400
50	2200	156400
50	3300	156400
50	2800	156400
50	2500	156400
50	2100	156400
50	3300	156400
50	2900	156400
50	2400	156400
50	2200	156400
50	3600	156400
50	3200	156400
50	2700	156400
50	2500	156400
50	3500	156400
50	3100	156400
50	2600	156400
50	2500	156400
50	3200	156400
50	3100	156400
50	2500	156400
50	2800	156400
50	4200	156400
50	4100	156400
50	3400	156400
50	3000	156400
50	3800	156400
50	3600	156400
50	2900	156400
50	2500	156400
50	4000	156400
50	3900	156400
50	3200	156400
50	2800	156400
50	3100	156400
50	3000	156400
50	2600	156400
50	2600	156400
60	9000	28800
60	6000	28800
60	4800	28800
60	4800	28800
60	4200	28800
70	10000	10000
80	14000	304500
80	13500	304500
80	12000	304500
80	11000	304500
80	10500	304500
80	10000	304500
80	9500	304500
80	9000	304500
80	8000	304500
80	7500	304500
80	7000	304500
80	10000	304500
80	9500	304500
80	9000	304500
80	8000	304500
80	7500	304500
80	7000	304500
80	10500	304500
80	9500	304500
80	7200	304500
80	6800	304500
80	6400	304500
80	6200	304500
80	11500	304500
80	10000	304500
80	9600	304500
80	7400	304500
80	7300	304500
80	6100	304500
80	11000	304500
80	8800	304500
80	8600	304500
80	8400	304500
80	6200	304500
90	24000	58000
90	17000	58000
90	17000	58000
100	12008	51608
100	9000	51608
100	8200	51608
100	7700	51608
100	7800	51608
100	6900	51608
110	12008	20308
110	8300	20308
-	7000	7000

Statement 3

PARTTION BY CLAUSE & ORDER BY CLAUSE

select department_id, last_name, first_name, salary, sum(salary) over (partition by department_id order by last_name, first_name) as "Cumulative salary total" 
from hr.employees


DEPARTMENT_ID LAST_NAME FIRST_NAME SALARY Cumulative salary total
10 Whalen Jennifer 4400 4400
20 Fay Pat 6000 6000
20 Hartstein Michael 13000 19000
30 Baida Shelli 2900 2900
30 Colmenares Karen 2500 5400
30 Himuro Guy 2600 8000
30 Khoo Alexander 3100 11100
30 Raphaely Den 11000 22100
30 Tobias Sigal 2800 24900
40 Mavris Susan 6500 6500
50 Atkinson Mozhe 2800 2800
50 Bell Sarah 4000 6800
50 Bissot Laura 3300 10100
50 Bull Alexis 4100 14200
50 Cabrio Anthony 3000 17200
50 Chung Kelly 3800 21000
50 Davies Curtis 3100 24100
50 Dellinger Julia 3400 27500
50 Dilly Jennifer 3600 31100
50 Everett Britney 3900 35000
50 Feeney Kevin 3000 38000
50 Fleaur Jean 3100 41100
50 Fripp Adam 8200 49300
50 Gates Timothy 2900 52200
50 Gee Ki 2400 54600
50 Geoni Girard 2800 57400
50 Grant Douglas 2600 60000
50 Jones Vance 2800 62800
50 Kaufling Payam 7900 70700
50 Ladwig Renske 3600 74300
50 Landry James 2400 76700
50 Mallin Jason 3300 80000
50 Markle Steven 2200 82200
50 Marlow James 2500 84700
50 Matos Randall 2600 87300
50 McCain Samuel 3200 90500
50 Mikkilineni Irene 2700 93200
50 Mourgos Kevin 5800 99000
50 Nayer Julia 3200 102200
50 OConnell Donald 2600 104800
50 Olson TJ 2100 106900
50 Patel Joshua 2500 109400
50 Perkins Randall 2500 111900
50 Philtanker Hazel 2200 114100
50 Rajs Trenna 3500 117600
50 Rogers Michael 2900 120500
50 Sarchand Nandita 4200 124700
50 Seo John 2700 127400
50 Stiles Stephen 3200 130600
50 Sullivan Martha 2500 133100
50 Taylor Winston 3200 136300
50 Vargas Peter 2500 138800
50 Vollman Shanta 6500 145300
50 Walsh Alana 3100 148400
50 Weiss Matthew 8000 156400
60 Austin David 4800 4800
60 Ernst Bruce 6000 10800
60 Hunold Alexander 9000 19800
60 Lorentz Diana 4200 24000
60 Pataballa Valli 4800 28800
70 Baer Hermann 10000 10000
80 Abel Ellen 11000 11000
80 Ande Sundar 6400 17400
80 Banda Amit 6200 23600
80 Bates Elizabeth 7300 30900
80 Bernstein David 9500 40400
80 Bloom Harrison 10000 50400
80 Cambrault Gerald 11000 61400
80 Cambrault Nanette 7500 68900
80 Doran Louise 7500 76400
80 Errazuriz Alberto 12000 88400
80 Fox Tayler 9600 98000
80 Greene Danielle 9500 107500
80 Hall Peter 9000 116500
80 Hutton Alyssa 8800 125300
80 Johnson Charles 6200 131500
80 King Janette 10000 141500
80 Kumar Sundita 6100 147600
80 Lee David 6800 154400
80 Livingston Jack 8400 162800
80 Marvins Mattea 7200 170000
80 McEwen Allan 9000 179000
80 Olsen Christopher 8000 187000
80 Ozer Lisa 11500 198500
80 Partners Karen 13500 212000
80 Russell John 14000 226000
80 Sewall Sarath 7000 233000
80 Smith Lindsey 8000 241000
80 Smith William 7400 248400
80 Sully Patrick 9500 257900
80 Taylor Jonathon 8600 266500
80 Tucker Peter 10000 276500
80 Tuvault Oliver 7000 283500
80 Vishney Clara 10500 294000
80 Zlotkey Eleni 10500 304500
90 De Haan Lex 17000 17000
90 King Steven 24000 41000
90 Kochhar Neena 17000 58000
100 Chen John 8200 8200
100 Faviet Daniel 9000 17200
100 Greenberg Nancy 12008 29208
100 Popp Luis 6900 36108
100 Sciarra Ismael 7700 43808
100 Urman Jose Manuel 7800 51608
110 Gietz William 8300 8300
110 Higgins Shelley 12008 20308
 - Grant Kimberely 7000 7000

107 rows selected.

DEPARTMENT_ID	LAST_NAME	FIRST_NAME	SALARY	Cumulative salary total
10	Whalen	Jennifer	4400	4400
20	Fay	Pat	6000	6000
20	Hartstein	Michael	13000	19000
30	Baida	Shelli	2900	2900
30	Colmenares	Karen	2500	5400
30	Himuro	Guy	2600	8000
30	Khoo	Alexander	3100	11100
30	Raphaely	Den	11000	22100
30	Tobias	Sigal	2800	24900
40	Mavris	Susan	6500	6500
50	Atkinson	Mozhe	2800	2800
50	Bell	Sarah	4000	6800
50	Bissot	Laura	3300	10100
50	Bull	Alexis	4100	14200
50	Cabrio	Anthony	3000	17200
50	Chung	Kelly	3800	21000
50	Davies	Curtis	3100	24100
50	Dellinger	Julia	3400	27500
50	Dilly	Jennifer	3600	31100
50	Everett	Britney	3900	35000
50	Feeney	Kevin	3000	38000
50	Fleaur	Jean	3100	41100
50	Fripp	Adam	8200	49300
50	Gates	Timothy	2900	52200
50	Gee	Ki	2400	54600
50	Geoni	Girard	2800	57400
50	Grant	Douglas	2600	60000
50	Jones	Vance	2800	62800
50	Kaufling	Payam	7900	70700
50	Ladwig	Renske	3600	74300
50	Landry	James	2400	76700
50	Mallin	Jason	3300	80000
50	Markle	Steven	2200	82200
50	Marlow	James	2500	84700
50	Matos	Randall	2600	87300
50	McCain	Samuel	3200	90500
50	Mikkilineni	Irene	2700	93200
50	Mourgos	Kevin	5800	99000
50	Nayer	Julia	3200	102200
50	OConnell	Donald	2600	104800
50	Olson	TJ	2100	106900
50	Patel	Joshua	2500	109400
50	Perkins	Randall	2500	111900
50	Philtanker	Hazel	2200	114100
50	Rajs	Trenna	3500	117600
50	Rogers	Michael	2900	120500
50	Sarchand	Nandita	4200	124700
50	Seo	John	2700	127400
50	Stiles	Stephen	3200	130600
50	Sullivan	Martha	2500	133100
50	Taylor	Winston	3200	136300
50	Vargas	Peter	2500	138800
50	Vollman	Shanta	6500	145300
50	Walsh	Alana	3100	148400
50	Weiss	Matthew	8000	156400
60	Austin	David	4800	4800
60	Ernst	Bruce	6000	10800
60	Hunold	Alexander	9000	19800
60	Lorentz	Diana	4200	24000
60	Pataballa	Valli	4800	28800
70	Baer	Hermann	10000	10000
80	Abel	Ellen	11000	11000
80	Ande	Sundar	6400	17400
80	Banda	Amit	6200	23600
80	Bates	Elizabeth	7300	30900
80	Bernstein	David	9500	40400
80	Bloom	Harrison	10000	50400
80	Cambrault	Gerald	11000	61400
80	Cambrault	Nanette	7500	68900
80	Doran	Louise	7500	76400
80	Errazuriz	Alberto	12000	88400
80	Fox	Tayler	9600	98000
80	Greene	Danielle	9500	107500
80	Hall	Peter	9000	116500
80	Hutton	Alyssa	8800	125300
80	Johnson	Charles	6200	131500
80	King	Janette	10000	141500
80	Kumar	Sundita	6100	147600
80	Lee	David	6800	154400
80	Livingston	Jack	8400	162800
80	Marvins	Mattea	7200	170000
80	McEwen	Allan	9000	179000
80	Olsen	Christopher	8000	187000
80	Ozer	Lisa	11500	198500
80	Partners	Karen	13500	212000
80	Russell	John	14000	226000
80	Sewall	Sarath	7000	233000
80	Smith	Lindsey	8000	241000
80	Smith	William	7400	248400
80	Sully	Patrick	9500	257900
80	Taylor	Jonathon	8600	266500
80	Tucker	Peter	10000	276500
80	Tuvault	Oliver	7000	283500
80	Vishney	Clara	10500	294000
80	Zlotkey	Eleni	10500	304500
90	De Haan	Lex	17000	17000
90	King	Steven	24000	41000
90	Kochhar	Neena	17000	58000
100	Chen	John	8200	8200
100	Faviet	Daniel	9000	17200
100	Greenberg	Nancy	12008	29208
100	Popp	Luis	6900	36108
100	Sciarra	Ismael	7700	43808
100	Urman	Jose Manuel	7800	51608
110	Gietz	William	8300	8300
110	Higgins	Shelley	12008	20308
-	Grant	Kimberely	7000	7000

Statement 4

PARTITION BY CLAUSE, ORDER BY CLAUSE & WINDOWING CLAUSE

select department_id, last_name, first_name, salary, sum(salary) over (partition by department_id order by last_name, first_name rows 2 preceding) as "Only add preceding two" 
from hr.employees


DEPARTMENT_ID LAST_NAME FIRST_NAME SALARY Only add preceding two
10 Whalen Jennifer 4400 4400
20 Fay Pat 6000 6000
20 Hartstein Michael 13000 19000
30 Baida Shelli 2900 2900
30 Colmenares Karen 2500 5400
30 Himuro Guy 2600 8000
30 Khoo Alexander 3100 8200
30 Raphaely Den 11000 16700
30 Tobias Sigal 2800 16900
40 Mavris Susan 6500 6500
50 Atkinson Mozhe 2800 2800
50 Bell Sarah 4000 6800
50 Bissot Laura 3300 10100
50 Bull Alexis 4100 11400
50 Cabrio Anthony 3000 10400
50 Chung Kelly 3800 10900
50 Davies Curtis 3100 9900
50 Dellinger Julia 3400 10300
50 Dilly Jennifer 3600 10100
50 Everett Britney 3900 10900
50 Feeney Kevin 3000 10500
50 Fleaur Jean 3100 10000
50 Fripp Adam 8200 14300
50 Gates Timothy 2900 14200
50 Gee Ki 2400 13500
50 Geoni Girard 2800 8100
50 Grant Douglas 2600 7800
50 Jones Vance 2800 8200
50 Kaufling Payam 7900 13300
50 Ladwig Renske 3600 14300
50 Landry James 2400 13900
50 Mallin Jason 3300 9300
50 Markle Steven 2200 7900
50 Marlow James 2500 8000
50 Matos Randall 2600 7300
50 McCain Samuel 3200 8300
50 Mikkilineni Irene 2700 8500
50 Mourgos Kevin 5800 11700
50 Nayer Julia 3200 11700
50 OConnell Donald 2600 11600
50 Olson TJ 2100 7900
50 Patel Joshua 2500 7200
50 Perkins Randall 2500 7100
50 Philtanker Hazel 2200 7200
50 Rajs Trenna 3500 8200
50 Rogers Michael 2900 8600
50 Sarchand Nandita 4200 10600
50 Seo John 2700 9800
50 Stiles Stephen 3200 10100
50 Sullivan Martha 2500 8400
50 Taylor Winston 3200 8900
50 Vargas Peter 2500 8200
50 Vollman Shanta 6500 12200
50 Walsh Alana 3100 12100
50 Weiss Matthew 8000 17600
60 Austin David 4800 4800
60 Ernst Bruce 6000 10800
60 Hunold Alexander 9000 19800
60 Lorentz Diana 4200 19200
60 Pataballa Valli 4800 18000
70 Baer Hermann 10000 10000
80 Abel Ellen 11000 11000
80 Ande Sundar 6400 17400
80 Banda Amit 6200 23600
80 Bates Elizabeth 7300 19900
80 Bernstein David 9500 23000
80 Bloom Harrison 10000 26800
80 Cambrault Gerald 11000 30500
80 Cambrault Nanette 7500 28500
80 Doran Louise 7500 26000
80 Errazuriz Alberto 12000 27000
80 Fox Tayler 9600 29100
80 Greene Danielle 9500 31100
80 Hall Peter 9000 28100
80 Hutton Alyssa 8800 27300
80 Johnson Charles 6200 24000
80 King Janette 10000 25000
80 Kumar Sundita 6100 22300
80 Lee David 6800 22900
80 Livingston Jack 8400 21300
80 Marvins Mattea 7200 22400
80 McEwen Allan 9000 24600
80 Olsen Christopher 8000 24200
80 Ozer Lisa 11500 28500
80 Partners Karen 13500 33000
80 Russell John 14000 39000
80 Sewall Sarath 7000 34500
80 Smith Lindsey 8000 29000
80 Smith William 7400 22400
80 Sully Patrick 9500 24900
80 Taylor Jonathon 8600 25500
80 Tucker Peter 10000 28100
80 Tuvault Oliver 7000 25600
80 Vishney Clara 10500 27500
80 Zlotkey Eleni 10500 28000
90 De Haan Lex 17000 17000
90 King Steven 24000 41000
90 Kochhar Neena 17000 58000
100 Chen John 8200 8200
100 Faviet Daniel 9000 17200
100 Greenberg Nancy 12008 29208
100 Popp Luis 6900 27908
100 Sciarra Ismael 7700 26608
100 Urman Jose Manuel 7800 22400
110 Gietz William 8300 8300
110 Higgins Shelley 12008 20308
 - Grant Kimberely 7000 7000

107 rows selected.

DEPARTMENT_ID	LAST_NAME	FIRST_NAME	SALARY	Only add preceding two
10	Whalen	Jennifer	4400	4400
20	Fay	Pat	6000	6000
20	Hartstein	Michael	13000	19000
30	Baida	Shelli	2900	2900
30	Colmenares	Karen	2500	5400
30	Himuro	Guy	2600	8000
30	Khoo	Alexander	3100	8200
30	Raphaely	Den	11000	16700
30	Tobias	Sigal	2800	16900
40	Mavris	Susan	6500	6500
50	Atkinson	Mozhe	2800	2800
50	Bell	Sarah	4000	6800
50	Bissot	Laura	3300	10100
50	Bull	Alexis	4100	11400
50	Cabrio	Anthony	3000	10400
50	Chung	Kelly	3800	10900
50	Davies	Curtis	3100	9900
50	Dellinger	Julia	3400	10300
50	Dilly	Jennifer	3600	10100
50	Everett	Britney	3900	10900
50	Feeney	Kevin	3000	10500
50	Fleaur	Jean	3100	10000
50	Fripp	Adam	8200	14300
50	Gates	Timothy	2900	14200
50	Gee	Ki	2400	13500
50	Geoni	Girard	2800	8100
50	Grant	Douglas	2600	7800
50	Jones	Vance	2800	8200
50	Kaufling	Payam	7900	13300
50	Ladwig	Renske	3600	14300
50	Landry	James	2400	13900
50	Mallin	Jason	3300	9300
50	Markle	Steven	2200	7900
50	Marlow	James	2500	8000
50	Matos	Randall	2600	7300
50	McCain	Samuel	3200	8300
50	Mikkilineni	Irene	2700	8500
50	Mourgos	Kevin	5800	11700
50	Nayer	Julia	3200	11700
50	OConnell	Donald	2600	11600
50	Olson	TJ	2100	7900
50	Patel	Joshua	2500	7200
50	Perkins	Randall	2500	7100
50	Philtanker	Hazel	2200	7200
50	Rajs	Trenna	3500	8200
50	Rogers	Michael	2900	8600
50	Sarchand	Nandita	4200	10600
50	Seo	John	2700	9800
50	Stiles	Stephen	3200	10100
50	Sullivan	Martha	2500	8400
50	Taylor	Winston	3200	8900
50	Vargas	Peter	2500	8200
50	Vollman	Shanta	6500	12200
50	Walsh	Alana	3100	12100
50	Weiss	Matthew	8000	17600
60	Austin	David	4800	4800
60	Ernst	Bruce	6000	10800
60	Hunold	Alexander	9000	19800
60	Lorentz	Diana	4200	19200
60	Pataballa	Valli	4800	18000
70	Baer	Hermann	10000	10000
80	Abel	Ellen	11000	11000
80	Ande	Sundar	6400	17400
80	Banda	Amit	6200	23600
80	Bates	Elizabeth	7300	19900
80	Bernstein	David	9500	23000
80	Bloom	Harrison	10000	26800
80	Cambrault	Gerald	11000	30500
80	Cambrault	Nanette	7500	28500
80	Doran	Louise	7500	26000
80	Errazuriz	Alberto	12000	27000
80	Fox	Tayler	9600	29100
80	Greene	Danielle	9500	31100
80	Hall	Peter	9000	28100
80	Hutton	Alyssa	8800	27300
80	Johnson	Charles	6200	24000
80	King	Janette	10000	25000
80	Kumar	Sundita	6100	22300
80	Lee	David	6800	22900
80	Livingston	Jack	8400	21300
80	Marvins	Mattea	7200	22400
80	McEwen	Allan	9000	24600
80	Olsen	Christopher	8000	24200
80	Ozer	Lisa	11500	28500
80	Partners	Karen	13500	33000
80	Russell	John	14000	39000
80	Sewall	Sarath	7000	34500
80	Smith	Lindsey	8000	29000
80	Smith	William	7400	22400
80	Sully	Patrick	9500	24900
80	Taylor	Jonathon	8600	25500
80	Tucker	Peter	10000	28100
80	Tuvault	Oliver	7000	25600
80	Vishney	Clara	10500	27500
80	Zlotkey	Eleni	10500	28000
90	De Haan	Lex	17000	17000
90	King	Steven	24000	41000
90	Kochhar	Neena	17000	58000
100	Chen	John	8200	8200
100	Faviet	Daniel	9000	17200
100	Greenberg	Nancy	12008	29208
100	Popp	Luis	6900	27908
100	Sciarra	Ismael	7700	26608
100	Urman	Jose Manuel	7800	22400
110	Gietz	William	8300	8300
110	Higgins	Shelley	12008	20308
-	Grant	Kimberely	7000	7000

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