COUNT_BIG SQL function


1. Usage of COUNT_BIG to show customer demographics

SQL Server Query 1

            
-- Analyze order counts by customer and customer demographics  
SELECT
c.CustomerID,
c.CompanyName,
c.ContactName,
c.Country,
COUNT_BIG(o.OrderID) AS TotalOrders, -- Use COUNT_BIG here
AVG(CAST(od.Quantity * od.UnitPrice * (1 - od.Discount) AS DECIMAL(18, 2))) AS AverageOrderValue,
MAX(CAST(od.Quantity * od.UnitPrice * (1 - od.Discount) AS DECIMAL(18, 2))) AS MaxOrderValue,
MIN(CAST(od.Quantity * od.UnitPrice * (1 - od.Discount) AS DECIMAL(18, 2))) AS MinOrderValue
FROM Customers AS c
JOIN Orders AS o ON c.CustomerID = o.CustomerID
JOIN [Order Details] AS od ON o.OrderID = od.OrderID
GROUP BY c.CustomerID, c.CompanyName, c.ContactName, c.Country
ORDER BY TotalOrders DESC;

Create SQL query with SqlQueryBuilder 1

            
var (sql1, parameters1) = new SqlQueryBuilder()  
.Select()
.Columns("c.CustomerID","c.CompanyName","c.ContactName","c.Country")
.Column(new COUNT_BIG(new Column("o.OrderID")), "TotalOrders")
.Column(new AVG(new CAST(new ColumnArithmatic("od.Quantity").MULTIPLY("od.UnitPrice").MULTIPLY()
.StartBracket(1).SUBTRACT("od.Discount").EndBracket(), SqlDataType.DECIMAL, new Tuple<int,int>(18,2)))
, "AverageOrderValue")
.Column(new MAX(new CAST(new ColumnArithmatic("od.Quantity").MULTIPLY("od.UnitPrice").MULTIPLY()
.StartBracket(1).SUBTRACT("od.Discount").EndBracket(), SqlDataType.DECIMAL, new Tuple<int, int>(18, 2)))
, "MaxOrderValue")
.Column(new MIN(new CAST(new ColumnArithmatic("od.Quantity").MULTIPLY("od.UnitPrice").MULTIPLY()
.StartBracket(1).SUBTRACT("od.Discount").EndBracket(), SqlDataType.DECIMAL, new Tuple<int, int>(18, 2)))
, "MinOrderValue")
.From("Customers", "c")
.Join(new List<IJoin>()
{
new INNERJOIN().TableName("Orders", "o")
.On(new Column("c.CustomerID").Equale(new Column("o.CustomerID"))),
new INNERJOIN().TableName("[Order Details]", "od")
.On(new Column("o.OrderID").Equale(new Column("od.OrderID")))
})
.GroupBy(new GroupBy("c.CustomerID","c.CompanyName","c.ContactName","c.Country"))
.OrderBy(new OrderBy()
.SetColumnDescending("TotalOrders"))
.Build();

Query build by SqlQueryBuilder 1

            
SELECT c.CustomerID,
       c.CompanyName,
       c.ContactName,
       c.Country,
       COUNT(o.OrderID) AS TotalOrders,
       AVG(CAST (od.Quantity * od.UnitPrice * (@pMAIN_2507200158528374730 - od.Discount) AS DECIMAL (18, 2))) AS AverageOrderValue,
       MAX(CAST (od.Quantity * od.UnitPrice * (@pMAIN_2507200158528374731 - od.Discount) AS DECIMAL (18, 2))) AS MaxOrderValue,
       MIN(CAST (od.Quantity * od.UnitPrice * (@pMAIN_2507200158528374732 - od.Discount) AS DECIMAL (18, 2))) AS MinOrderValue
FROM Customers AS c
     INNER JOIN
     Orders AS o
     ON c.CustomerID = o.CustomerID
     INNER JOIN
     [Order Details] AS od
     ON o.OrderID = od.OrderID
GROUP BY c.CustomerID, c.CompanyName, c.ContactName, c.Country
ORDER BY TotalOrders DESC;


            
        

Parameters (If used)

Name Value
@pMAIN_2507200158528374730 1
@pMAIN_2507200158528374731 1
@pMAIN_2507200158528374732 1

Query Results 1:

  CustomerID CompanyName ContactName Country TotalOrders AverageOrderValue MaxOrderValue MinOrderValue
1 SAVEA Save-a-lot Markets Jose Pavarotti USA 116 899 5570 35
2 ERNSH Ernst Handel Roland Mendel Austria 102 1028 6050 36
3 QUICK QUICK-Stop Horst Kloss Germany 86 1282 15019 34
4 RATTC Rattlesnake Canyon Grocery Paula Wilson USA 71 719 10540 14
5 HUNGO Hungry Owl All-Night Grocers Patricia McKenna Ireland 55 908 9903 22
6 BERGS Berglunds snabbköp Christina Berglund Sweden 52 479 3557 20
7 FRANK Frankenversand Peter Franken Germany 48 555 2618 13
8 HILAA HILARION-Abastos Carlos Hernández Venezuela 45 505 2640 33
9 FOLKO Folk och fä HB Maria Larsson Sweden 45 657 4642 18
10 BONAP Bon app' Laurence Lebihan France 44 499 1500 35
11 QUEEN Queen Cozinha Lúcia Carvalho Brazil 40 642 6324 26
12 WHITC White Clover Markets Karl Jablonski USA 40 684 6587 26
13 KOENE Königlich Essen Philip Cramer Germany 39 792 7905 40
14 SUPRD Suprêmes délices Pascale Cartrain Belgium 39 617 2750 14
15 LEHMS Lehmanns Marktstand Renate Messner Germany 39 493 2228 38
16 WARTH Wartian Herkku Pirkko Koskitalo Finland 37 422 1310 17
17 LINOD LINO-Delicateses Felipe Izquierdo Venezuela 35 470 2088 48
18 BOTTM Bottom-Dollar Markets Elizabeth Lincoln Canada 35 594 2958 37
19 LILAS LILA-Supermercado Carlos González Venezuela 34 472 1980 57
20 HANAR Hanari Carnes Mario Pontes Brazil 32 1026 15810 71
21 MEREP Mère Paillarde Jean Fresnière Canada 32 902 8263 89
22 VAFFE Vaffeljernet Palle Ibsen Denmark 31 511 2462 48
23 LAMAI La maison d'Asie Annette Roulet France 31 300 2352 14
24 AROUT Around the Horn Thomas Hardy UK 30 446 3847 45
25 RICSU Richter Supermarkt Michael Holz Switzerland 30 644 4456 30
26 OTTIK Ottilies Käseladen Henriette Pfalzheim Germany 29 430 1050 60
27 TORTU Tortuga Restaurante Miguel Angel Paolino Mexico 29 372 3952 22
28 RICAR Ricardo Adocicados Janete Limeira Brazil 27 461 2194 34
29 GODOS Godos Cocina Típica José Pedro Freyre Spain 26 440 2475 54
30 WANDK Die Wandernde Kuh Rita Müller Germany 26 368 1280 47
31 BLONP Blondesddsl père et fils Frédérique Citeaux France 26 712 3465 48
32 SEVES Seven Seas Imports Hari Kumar UK 26 623 2067 46
33 VICTE Victuailles en stock Mary Saveley France 25 367 1972 8
34 OLDWO Old World Delicatessen Rene Phillips USA 24 632 2227 135
35 QUEDE Que Delícia Bernardo Batista Brazil 24 277 1000 24
36 ISLAT Island Trading Helen Bennett UK 23 267 855 24
37 PICCO Piccolo und mehr Georg Pipps Austria 23 1005 8432 100
38 BSBEV B's Beverages Victoria Ashworth UK 22 276 1380 34
39 GREAL Great Lakes Food Market Howard Snyder USA 22 841 7509 16
40 CHOPS Chop-suey Chinese Yang Wang Switzerland 22 561 1520 36
41 REGGC Reggiani Caseifici Maurizio Moroni Italy 22 320 1252 23
42 MAGAA Magazzini Alimentari Riuniti Giovanni Rovelli Italy 21 341 887 30
43 EASTC Eastern Connection Ann Devon UK 21 702 2079 75
44 SPLIR Split Rail Beer & Ale Art Braunschweiger USA 20 572 4005 48
45 FURIB Furia Bacalhau e Frutos do Mar Lino Rodriguez Portugal 20 321 778 36
46 GOURL Gourmet Lanchonetes André Fonseca Brazil 19 442 1600 38
47 FAMIA Familia Arquibaldo Aria Cruz Brazil 19 216 779 28
48 WELLI Wellington Importadora Paula Parente Brazil 19 319 1396 8
49 MAISD Maison Dewey Catherine Dewey Belgium 17 572 2090 99
50 WILMK Wilman Kala Matti Karttunen Finland 17 185 550 36
51 ANTON Antonio Moreno Taquería Antonio Moreno Mexico 17 413 945 17
52 FOLIG Folies gourmandes Martine Rancé France 16 729 3125 15
53 WOLZA Wolski Zajazd Zbyszek Piestrzeniewicz Poland 16 220 591 22
54 SANTG Santé Gourmet Jonas Bergulfsen Norway 16 358 2108 18
55 SIMOB Simons bistro Jytte Petersen Denmark 15 1121 10540 14
56 ROMEY Romero y tomillo Alejandra Camino Spain 14 104 340 7
57 TOMSP Toms Spezialitäten Karin Josephs Germany 14 341 1696 27
58 LONEP Lonesome Pine Restaurant Fran Wilson USA 14 304 1060 30
59 BLAUS Blauer See Delikatessen Hanna Moos Germany 14 231 714 30
60 PERIC Pericles Comidas clásicas Guillermo Fernández Mexico 14 303 1060 62
61 TRADH Tradição Hipermercados Anabela Domingues Brazil 13 526 1296 150
62 RANCH Rancho grande Sergio Gutiérrez Argentina 12 237 527 36
63 ALFKI Alfreds Futterkiste Maria Anders Germany 12 356 878 18
64 LACOR La corne d'abondance Daniel Tonini France 11 181 439 17
65 MORGK Morgenstern Gesundkost Alexander Feuer Germany 11 458 1044 114
66 OCEAN Océano Atlántico Ltda. Yvonne Moncada Argentina 11 314 1215 30
67 CACTU Cactus Comidas para llevar Patricio Simpson Argentina 11 164 364 12
68 PRINI Princesa Isabel Vinhos Isabel de Castro Portugal 10 504 1237 68
69 COMMI Comércio Mineiro Pedro Afonso Brazil 10 381 1485 48
70 VINET Vins et alcools Chevalier Paul Henriot France 10 148 344 24
71 LETSS Let's Stop N Shop Jaime Yorres USA 10 307 758 23
72 ANATR Ana Trujillo Emparedados y helados Ana Trujillo Mexico 10 140 348 28
73 DRACD Drachenblut Delikatessen Sven Ottlieb Germany 10 376 1650 42
74 FRANS Franchi S.p.A. Paolo Accorti Italy 10 154 530 18
75 HUNGC Hungry Coyote Import Store Yoshi Latimer USA 9 340 1701 29
76 TRAIH Trail's Head Gourmet Provisioners Helvetius Nagy USA 9 174 493 35
77 DUMON Du monde entier Janine Labrune France 9 179 585 54
78 THECR The Cracker Box Liu Wong USA 8 243 742 42
79 LAUGB Laughing Bacchus Wine Cellars Yoshi Tannamuri Canada 8 65 154 22
80 GALED Galería del gastrónomo Eduardo Saavedra Spain 8 104 186 47
81 THEBI The Big Cheese Liz Nixon USA 7 480 2635 69
82 CONSH Consolidated Holdings Elizabeth Brown UK 7 245 640 4
83 NORTS North/South Simon Crowther UK 6 108 220 42
84 FRANR France restauration Carine Schmitt France 6 528 1733 159
85 BOLID Bólido Comidas preparadas Martín Sommer Spain 6 705 1856 224
86 SPECD Spécialités du monde Dominique Perrier France 6 403 1317 52
87 GROSR GROSELLA-Restaurante Manuel Pereira Venezuela 4 372 990 77
88 CENTC Centro comercial Moctezuma Francisco Chang Mexico 2 50 80 20
89 LAZYK Lazy K Kountry Store John Steel USA 2 178 210 147


2. Usage of COUNT_BIG to find most valuable customers based on orders

SQL Server Query 2

            
--    Identify customers with a very large number of orders (e.g., more than 100)  
-- This helps in identifying your most valuable customers.
SELECT
c.CustomerID,
c.CompanyName,
COUNT_BIG(o.OrderID) AS TotalOrders
FROM Customers AS c
JOIN Orders AS o ON c.CustomerID = o.CustomerID
GROUP BY c.CustomerID, c.CompanyName
HAVING COUNT_BIG(o.OrderID) > 10 -- Use COUNT_BIG in the HAVING clause
ORDER BY TotalOrders DESC;

Create SQL query with SqlQueryBuilder 2

            
var (sql2, parameters2) = new SqlQueryBuilder()  
.Select()
.Columns("c.CustomerID", "c.CompanyName")
.Column(new COUNT_BIG(new Column("o.OrderID")), "TotalOrders")
.From("Customers", "c")
.Join(new List<IJoin>()
{
new INNERJOIN().TableName("Orders","o")
.On(new Column("c.CustomerID").Equale(new Column("o.CustomerID")))
})
.GroupBy(new GroupBy("c.CustomerID", "CompanyName"))
.Having(new Having(new COUNT_BIG(new Column("o.OrderID")).GreaterThan(10)))
.OrderBy(new OrderBy().SetColumnDescending("TotalOrders"))
.Build();

Query build by SqlQueryBuilder 2

            
SELECT c.CustomerID,
       c.CompanyName,
       COUNT(o.OrderID) AS TotalOrders
FROM Customers AS c
     INNER JOIN
     Orders AS o
     ON c.CustomerID = o.CustomerID
GROUP BY c.CustomerID, CompanyName
HAVING COUNT(o.OrderID) > @pMAIN_2507200158528598090
ORDER BY TotalOrders DESC;


            
        

Parameters (If used)

Name Value
@pMAIN_2507200158528598090 10

Query Results 2:

  CustomerID CompanyName TotalOrders
1 SAVEA Save-a-lot Markets 31
2 ERNSH Ernst Handel 30
3 QUICK QUICK-Stop 28
4 FOLKO Folk och fä HB 19
5 HUNGO Hungry Owl All-Night Grocers 19
6 BERGS Berglunds snabbköp 18
7 RATTC Rattlesnake Canyon Grocery 18
8 HILAA HILARION-Abastos 18
9 BONAP Bon app' 17
10 FRANK Frankenversand 15
11 LEHMS Lehmanns Marktstand 15
12 WARTH Wartian Herkku 15
13 WHITC White Clover Markets 14
14 LILAS LILA-Supermercado 14
15 KOENE Königlich Essen 14
16 LAMAI La maison d'Asie 14
17 HANAR Hanari Carnes 14
18 BOTTM Bottom-Dollar Markets 14
19 AROUT Around the Horn 13
20 MEREP Mère Paillarde 13
21 QUEEN Queen Cozinha 13
22 LINOD LINO-Delicateses 12
23 SUPRD Suprêmes délices 12
24 REGGC Reggiani Caseifici 12
25 RICAR Ricardo Adocicados 11
26 VAFFE Vaffeljernet 11
27 BLONP Blondesddsl père et fils 11
28 GREAL Great Lakes Food Market 11


3. Usage of CHECKSUM to find distribution of orders by ship country

SQL Server Query 3

            
-- Analyze the distribution of orders by ship country, using COUNT_BIG  
SELECT
o.ShipCountry,
COUNT_BIG(o.OrderID) AS OrderCount,
CAST(COUNT_BIG(o.OrderID) * 100.0 / (SELECT COUNT_BIG(*) FROM Orders) AS DECIMAL(5, 2)) AS PercentageOfTotalOrders
FROM Orders AS o
GROUP BY o.ShipCountry
ORDER BY OrderCount DESC;


Create SQL query with SqlQueryBuilder 3

            
var (sql3, parameters3) = new SqlQueryBuilder()  
.Select()
.Column("o.ShipCountry", "ShipCountry")
.Column(new COUNT_BIG(new Column("o.OrderID")), "OrderCount")
.Column(new CAST(new ColumnArithmatic(new COUNT_BIG(new Column("o.OrderID"))).MULTIPLY(100.0)
.DIVIDE(new SqlQueryBuilder()
.Select()
.Column(new COUNT_BIG(new Column("*")),"OrderCount")
.From("Orders")), SqlDataType.DECIMAL, new Tuple<int,int>(5, 2)), "PercentageOfTotalOrders")
.From("Orders", "o")
.GroupBy(new GroupBy("o.ShipCountry"))
.OrderBy(new OrderBy().SetColumnDescending("OrderCount"))
.Build();

Query build by SqlQueryBuilder 3

            
SELECT o.ShipCountry AS ShipCountry,
       COUNT(o.OrderID) AS OrderCount,
       CAST (COUNT(o.OrderID) * @pMAIN_2507200158528691430 / (SELECT COUNT(*) AS OrderCount
                                                              FROM Orders) AS DECIMAL (5, 2)) AS PercentageOfTotalOrders
FROM Orders AS o
GROUP BY o.ShipCountry
ORDER BY OrderCount DESC;


            
        

Parameters (If used)

Name Value
@pMAIN_2507200158528691430 100

Query Results 3:

  ShipCountry OrderCount PercentageOfTotalOrders
1 USA 122 14.70
2 Germany 122 14.70
3 Brazil 83 10.00
4 France 77 9.28
5 UK 56 6.75
6 Venezuela 46 5.54
7 Austria 40 4.82
8 Sweden 37 4.46
9 Canada 30 3.61
10 Mexico 28 3.37
11 Italy 28 3.37
12 Spain 23 2.77
13 Finland 22 2.65
14 Ireland 19 2.29
15 Belgium 19 2.29
16 Switzerland 18 2.17
17 Denmark 18 2.17
18 Argentina 16 1.93
19 Portugal 13 1.57
20 Poland 7 0.84
21 Norway 6 0.72


4. Usage of COUNT_BIG as windows function to show change in customer data

SQL Server Query 4

            
-- Find orders per customer  
SELECT
o.OrderID,
o.CustomerID,
COUNT_BIG(*) OVER (PARTITION BY o.CustomerID) AS OrdersPerCustomer
FROM
Orders o;

Create SQL query with SqlQueryBuilder 4

            
var (sql4, parameters4) = new SqlQueryBuilder()  
.Select()
.Column("o.OrderID", "o.CustomerID")
.Column(new COUNT_BIG(new Column("*")).OVER(new OVER().PARTITION_BY(new Column("o.CustomerID"))), "OrdersPerCustomer")
.From("Orders", "o")
.Build();

Query build by SqlQueryBuilder 4

            
SELECT o.OrderID,
       o.CustomerID,
       COUNT(*) OVER (PARTITION BY o.CustomerID) AS OrdersPerCustomer
FROM Orders AS o;


            
        

Parameters (If used)

Name Value

Query Results 4:

  OrderID CustomerID OrdersPerCustomer
1 10643 ALFKI 6
2 10692 ALFKI 6
3 10702 ALFKI 6
4 10835 ALFKI 6
5 10952 ALFKI 6
6 11011 ALFKI 6
7 10308 ANATR 4
8 10625 ANATR 4
9 10759 ANATR 4
10 10926 ANATR 4
11 10365 ANTON 7
12 10507 ANTON 7
13 10535 ANTON 7
14 10573 ANTON 7
15 10677 ANTON 7
16 10682 ANTON 7
17 10856 ANTON 7
18 10355 AROUT 13
19 10383 AROUT 13
20 10453 AROUT 13
21 10558 AROUT 13
22 10707 AROUT 13
23 10741 AROUT 13
24 10743 AROUT 13
25 10768 AROUT 13
26 10793 AROUT 13
27 10864 AROUT 13
28 10920 AROUT 13
29 10953 AROUT 13
30 11016 AROUT 13
31 10278 BERGS 18
32 10280 BERGS 18
33 10384 BERGS 18
34 10444 BERGS 18
35 10445 BERGS 18
36 10524 BERGS 18
37 10572 BERGS 18
38 10626 BERGS 18
39 10654 BERGS 18
40 10672 BERGS 18
41 10689 BERGS 18
42 10733 BERGS 18
43 10778 BERGS 18
44 10837 BERGS 18
45 10857 BERGS 18
46 10866 BERGS 18
47 10875 BERGS 18
48 10924 BERGS 18
49 10501 BLAUS 7
50 10509 BLAUS 7
51 10582 BLAUS 7
52 10614 BLAUS 7
53 10853 BLAUS 7
54 10956 BLAUS 7
55 11058 BLAUS 7
56 10265 BLONP 11
57 10297 BLONP 11
58 10360 BLONP 11
59 10436 BLONP 11
60 10449 BLONP 11
61 10559 BLONP 11
62 10566 BLONP 11
63 10584 BLONP 11
64 10628 BLONP 11
65 10679 BLONP 11
66 10826 BLONP 11
67 10326 BOLID 3
68 10801 BOLID 3
69 10970 BOLID 3
70 10331 BONAP 17
71 10340 BONAP 17
72 10362 BONAP 17
73 10470 BONAP 17
74 10511 BONAP 17
75 10525 BONAP 17
76 10663 BONAP 17
77 10715 BONAP 17
78 10730 BONAP 17
79 10732 BONAP 17
80 10755 BONAP 17
81 10827 BONAP 17
82 10871 BONAP 17
83 10876 BONAP 17
84 10932 BONAP 17
85 10940 BONAP 17
86 11076 BONAP 17
87 10389 BOTTM 14
88 10410 BOTTM 14
89 10411 BOTTM 14
90 10431 BOTTM 14
91 10492 BOTTM 14
92 10742 BOTTM 14
93 10918 BOTTM 14
94 10944 BOTTM 14
95 10949 BOTTM 14
96 10975 BOTTM 14
97 10982 BOTTM 14
98 11027 BOTTM 14
99 11045 BOTTM 14
100 11048 BOTTM 14
101 10289 BSBEV 10
102 10471 BSBEV 10
103 10484 BSBEV 10
104 10538 BSBEV 10
105 10539 BSBEV 10
106 10578 BSBEV 10
107 10599 BSBEV 10
108 10943 BSBEV 10
109 10947 BSBEV 10
110 11023 BSBEV 10
111 10521 CACTU 6
112 10782 CACTU 6
113 10819 CACTU 6
114 10881 CACTU 6
115 10937 CACTU 6
116 11054 CACTU 6
117 10259 CENTC 1
118 10254 CHOPS 8
119 10370 CHOPS 8
120 10519 CHOPS 8
121 10731 CHOPS 8
122 10746 CHOPS 8
123 10966 CHOPS 8
124 11029 CHOPS 8
125 11041 CHOPS 8
126 10290 COMMI 5
127 10466 COMMI 5
128 10494 COMMI 5
129 10969 COMMI 5
130 11042 COMMI 5
131 10435 CONSH 3
132 10462 CONSH 3
133 10848 CONSH 3
134 10363 DRACD 6
135 10391 DRACD 6
136 10797 DRACD 6
137 10825 DRACD 6
138 11036 DRACD 6
139 11067 DRACD 6
140 10311 DUMON 4
141 10609 DUMON 4
142 10683 DUMON 4
143 10890 DUMON 4
144 10364 EASTC 8
145 10400 EASTC 8
146 10532 EASTC 8
147 10726 EASTC 8
148 10987 EASTC 8
149 11024 EASTC 8
150 11047 EASTC 8
151 11056 EASTC 8
152 10258 ERNSH 30
153 10263 ERNSH 30
154 10351 ERNSH 30
155 10368 ERNSH 30
156 10382 ERNSH 30
157 10390 ERNSH 30
158 10402 ERNSH 30
159 10403 ERNSH 30
160 10430 ERNSH 30
161 10442 ERNSH 30
162 10514 ERNSH 30
163 10571 ERNSH 30
164 10595 ERNSH 30
165 10633 ERNSH 30
166 10667 ERNSH 30
167 10698 ERNSH 30
168 10764 ERNSH 30
169 10771 ERNSH 30
170 10773 ERNSH 30
171 10776 ERNSH 30
172 10795 ERNSH 30
173 10836 ERNSH 30
174 10854 ERNSH 30
175 10895 ERNSH 30
176 10968 ERNSH 30
177 10979 ERNSH 30
178 10990 ERNSH 30
179 11008 ERNSH 30
180 11017 ERNSH 30
181 11072 ERNSH 30
182 10347 FAMIA 7
183 10386 FAMIA 7
184 10414 FAMIA 7
185 10512 FAMIA 7
186 10581 FAMIA 7
187 10650 FAMIA 7
188 10725 FAMIA 7
189 10408 FOLIG 5
190 10480 FOLIG 5
191 10634 FOLIG 5
192 10763 FOLIG 5
193 10789 FOLIG 5
194 10264 FOLKO 19
195 10327 FOLKO 19
196 10378 FOLKO 19
197 10434 FOLKO 19
198 10460 FOLKO 19
199 10533 FOLKO 19
200 10561 FOLKO 19
201 10703 FOLKO 19
202 10762 FOLKO 19
203 10774 FOLKO 19
204 10824 FOLKO 19
205 10880 FOLKO 19
206 10902 FOLKO 19
207 10955 FOLKO 19
208 10977 FOLKO 19
209 10980 FOLKO 19
210 10993 FOLKO 19
211 11001 FOLKO 19
212 11050 FOLKO 19
213 10267 FRANK 15
214 10337 FRANK 15
215 10342 FRANK 15
216 10396 FRANK 15
217 10488 FRANK 15
218 10560 FRANK 15
219 10623 FRANK 15
220 10653 FRANK 15
221 10670 FRANK 15
222 10675 FRANK 15
223 10717 FRANK 15
224 10791 FRANK 15
225 10859 FRANK 15
226 10929 FRANK 15
227 11012 FRANK 15
228 10671 FRANR 3
229 10860 FRANR 3
230 10971 FRANR 3
231 10422 FRANS 6
232 10710 FRANS 6
233 10753 FRANS 6
234 10807 FRANS 6
235 11026 FRANS 6
236 11060 FRANS 6
237 10328 FURIB 8
238 10352 FURIB 8
239 10464 FURIB 8
240 10491 FURIB 8
241 10551 FURIB 8
242 10604 FURIB 8
243 10664 FURIB 8
244 10963 FURIB 8
245 10366 GALED 5
246 10426 GALED 5
247 10568 GALED 5
248 10887 GALED 5
249 10928 GALED 5
250 10303 GODOS 10
251 10550 GODOS 10
252 10629 GODOS 10
253 10872 GODOS 10
254 10874 GODOS 10
255 10888 GODOS 10
256 10911 GODOS 10
257 10948 GODOS 10
258 11009 GODOS 10
259 11037 GODOS 10
260 10423 GOURL 9
261 10652 GOURL 9
262 10685 GOURL 9
263 10709 GOURL 9
264 10734 GOURL 9
265 10777 GOURL 9
266 10790 GOURL 9
267 10959 GOURL 9
268 11049 GOURL 9
269 10528 GREAL 11
270 10589 GREAL 11
271 10616 GREAL 11
272 10617 GREAL 11
273 10656 GREAL 11
274 10681 GREAL 11
275 10816 GREAL 11
276 10936 GREAL 11
277 11006 GREAL 11
278 11040 GREAL 11
279 11061 GREAL 11
280 10268 GROSR 2
281 10785 GROSR 2
282 10250 HANAR 14
283 10253 HANAR 14
284 10541 HANAR 14
285 10645 HANAR 14
286 10690 HANAR 14
287 10770 HANAR 14
288 10783 HANAR 14
289 10886 HANAR 14
290 10903 HANAR 14
291 10922 HANAR 14
292 10925 HANAR 14
293 10981 HANAR 14
294 11022 HANAR 14
295 11052 HANAR 14
296 10257 HILAA 18
297 10395 HILAA 18
298 10476 HILAA 18
299 10486 HILAA 18
300 10490 HILAA 18
301 10498 HILAA 18
302 10552 HILAA 18
303 10601 HILAA 18
304 10613 HILAA 18
305 10641 HILAA 18
306 10705 HILAA 18
307 10796 HILAA 18
308 10863 HILAA 18
309 10901 HILAA 18
310 10957 HILAA 18
311 10960 HILAA 18
312 10976 HILAA 18
313 11055 HILAA 18
314 10375 HUNGC 5
315 10394 HUNGC 5
316 10415 HUNGC 5
317 10600 HUNGC 5
318 10660 HUNGC 5
319 10298 HUNGO 19
320 10309 HUNGO 19
321 10335 HUNGO 19
322 10373 HUNGO 19
323 10380 HUNGO 19
324 10429 HUNGO 19
325 10503 HUNGO 19
326 10516 HUNGO 19
327 10567 HUNGO 19
328 10646 HUNGO 19
329 10661 HUNGO 19
330 10687 HUNGO 19
331 10701 HUNGO 19
332 10712 HUNGO 19
333 10736 HUNGO 19
334 10897 HUNGO 19
335 10912 HUNGO 19
336 10985 HUNGO 19
337 11063 HUNGO 19
338 10315 ISLAT 10
339 10318 ISLAT 10
340 10321 ISLAT 10
341 10473 ISLAT 10
342 10621 ISLAT 10
343 10674 ISLAT 10
344 10749 ISLAT 10
345 10798 ISLAT 10
346 10829 ISLAT 10
347 10933 ISLAT 10
348 10323 KOENE 14
349 10325 KOENE 14
350 10456 KOENE 14
351 10457 KOENE 14
352 10468 KOENE 14
353 10506 KOENE 14
354 10542 KOENE 14
355 10630 KOENE 14
356 10718 KOENE 14
357 10799 KOENE 14
358 10817 KOENE 14
359 10849 KOENE 14
360 10893 KOENE 14
361 11028 KOENE 14
362 10858 LACOR 4
363 10927 LACOR 4
364 10972 LACOR 4
365 10973 LACOR 4
366 10350 LAMAI 14
367 10358 LAMAI 14
368 10371 LAMAI 14
369 10413 LAMAI 14
370 10425 LAMAI 14
371 10454 LAMAI 14
372 10493 LAMAI 14
373 10500 LAMAI 14
374 10610 LAMAI 14
375 10631 LAMAI 14
376 10787 LAMAI 14
377 10832 LAMAI 14
378 10923 LAMAI 14
379 11051 LAMAI 14
380 10495 LAUGB 3
381 10620 LAUGB 3
382 10810 LAUGB 3
383 10482 LAZYK 2
384 10545 LAZYK 2
385 10279 LEHMS 15
386 10284 LEHMS 15
387 10343 LEHMS 15
388 10497 LEHMS 15
389 10522 LEHMS 15
390 10534 LEHMS 15
391 10536 LEHMS 15
392 10557 LEHMS 15
393 10592 LEHMS 15
394 10593 LEHMS 15
395 10772 LEHMS 15
396 10862 LEHMS 15
397 10891 LEHMS 15
398 10934 LEHMS 15
399 11070 LEHMS 15
400 10579 LETSS 4
401 10719 LETSS 4
402 10735 LETSS 4
403 10884 LETSS 4
404 10283 LILAS 14
405 10296 LILAS 14
406 10330 LILAS 14
407 10357 LILAS 14
408 10381 LILAS 14
409 10461 LILAS 14
410 10499 LILAS 14
411 10543 LILAS 14
412 10780 LILAS 14
413 10823 LILAS 14
414 10899 LILAS 14
415 10997 LILAS 14
416 11065 LILAS 14
417 11071 LILAS 14
418 10405 LINOD 12
419 10485 LINOD 12
420 10638 LINOD 12
421 10697 LINOD 12
422 10729 LINOD 12
423 10811 LINOD 12
424 10838 LINOD 12
425 10840 LINOD 12
426 10919 LINOD 12
427 10954 LINOD 12
428 11014 LINOD 12
429 11039 LINOD 12
430 10307 LONEP 8
431 10317 LONEP 8
432 10544 LONEP 8
433 10662 LONEP 8
434 10665 LONEP 8
435 10867 LONEP 8
436 10883 LONEP 8
437 11018 LONEP 8
438 10275 MAGAA 10
439 10300 MAGAA 10
440 10404 MAGAA 10
441 10467 MAGAA 10
442 10635 MAGAA 10
443 10754 MAGAA 10
444 10784 MAGAA 10
445 10818 MAGAA 10
446 10939 MAGAA 10
447 10950 MAGAA 10
448 10529 MAISD 7
449 10649 MAISD 7
450 10760 MAISD 7
451 10892 MAISD 7
452 10896 MAISD 7
453 10978 MAISD 7
454 11004 MAISD 7
455 10332 MEREP 13
456 10339 MEREP 13
457 10376 MEREP 13
458 10424 MEREP 13
459 10439 MEREP 13
460 10505 MEREP 13
461 10565 MEREP 13
462 10570 MEREP 13
463 10590 MEREP 13
464 10605 MEREP 13
465 10618 MEREP 13
466 10619 MEREP 13
467 10724 MEREP 13
468 10277 MORGK 5
469 10575 MORGK 5
470 10699 MORGK 5
471 10779 MORGK 5
472 10945 MORGK 5
473 10517 NORTS 3
474 10752 NORTS 3
475 11057 NORTS 3
476 10409 OCEAN 5
477 10531 OCEAN 5
478 10898 OCEAN 5
479 10958 OCEAN 5
480 10986 OCEAN 5
481 10305 OLDWO 10
482 10338 OLDWO 10
483 10441 OLDWO 10
484 10594 OLDWO 10
485 10680 OLDWO 10
486 10706 OLDWO 10
487 10808 OLDWO 10
488 10855 OLDWO 10
489 10965 OLDWO 10
490 11034 OLDWO 10
491 10260 OTTIK 10
492 10407 OTTIK 10
493 10508 OTTIK 10
494 10554 OTTIK 10
495 10580 OTTIK 10
496 10684 OTTIK 10
497 10766 OTTIK 10
498 10833 OTTIK 10
499 10999 OTTIK 10
500 11020 OTTIK 10
501 10322 PERIC 6
502 10354 PERIC 6
503 10474 PERIC 6
504 10502 PERIC 6
505 10995 PERIC 6
506 11073 PERIC 6
507 10353 PICCO 10
508 10392 PICCO 10
509 10427 PICCO 10
510 10489 PICCO 10
511 10530 PICCO 10
512 10597 PICCO 10
513 10686 PICCO 10
514 10747 PICCO 10
515 10844 PICCO 10
516 11053 PICCO 10
517 10336 PRINI 5
518 10397 PRINI 5
519 10433 PRINI 5
520 10477 PRINI 5
521 11007 PRINI 5
522 10261 QUEDE 9
523 10291 QUEDE 9
524 10379 QUEDE 9
525 10421 QUEDE 9
526 10587 QUEDE 9
527 10647 QUEDE 9
528 10720 QUEDE 9
529 10794 QUEDE 9
530 10989 QUEDE 9
531 10372 QUEEN 13
532 10406 QUEEN 13
533 10487 QUEEN 13
534 10637 QUEEN 13
535 10659 QUEEN 13
536 10704 QUEEN 13
537 10728 QUEEN 13
538 10786 QUEEN 13
539 10868 QUEEN 13
540 10913 QUEEN 13
541 10914 QUEEN 13
542 10961 QUEEN 13
543 11068 QUEEN 13
544 10273 QUICK 28
545 10285 QUICK 28
546 10286 QUICK 28
547 10313 QUICK 28
548 10345 QUICK 28
549 10361 QUICK 28
550 10418 QUICK 28
551 10451 QUICK 28
552 10515 QUICK 28
553 10527 QUICK 28
554 10540 QUICK 28
555 10549 QUICK 28
556 10588 QUICK 28
557 10658 QUICK 28
558 10691 QUICK 28
559 10694 QUICK 28
560 10721 QUICK 28
561 10745 QUICK 28
562 10765 QUICK 28
563 10788 QUICK 28
564 10845 QUICK 28
565 10865 QUICK 28
566 10878 QUICK 28
567 10938 QUICK 28
568 10962 QUICK 28
569 10991 QUICK 28
570 10996 QUICK 28
571 11021 QUICK 28
572 10448 RANCH 5
573 10716 RANCH 5
574 10828 RANCH 5
575 10916 RANCH 5
576 11019 RANCH 5
577 10262 RATTC 18
578 10272 RATTC 18
579 10294 RATTC 18
580 10314 RATTC 18
581 10316 RATTC 18
582 10346 RATTC 18
583 10401 RATTC 18
584 10479 RATTC 18
585 10564 RATTC 18
586 10569 RATTC 18
587 10598 RATTC 18
588 10761 RATTC 18
589 10820 RATTC 18
590 10852 RATTC 18
591 10889 RATTC 18
592 10988 RATTC 18
593 11000 RATTC 18
594 11077 RATTC 18
595 10288 REGGC 12
596 10428 REGGC 12
597 10443 REGGC 12
598 10562 REGGC 12
599 10586 REGGC 12
600 10655 REGGC 12
601 10727 REGGC 12
602 10812 REGGC 12
603 10908 REGGC 12
604 10942 REGGC 12
605 11010 REGGC 12
606 11062 REGGC 12
607 10287 RICAR 11
608 10299 RICAR 11
609 10447 RICAR 11
610 10481 RICAR 11
611 10563 RICAR 11
612 10622 RICAR 11
613 10648 RICAR 11
614 10813 RICAR 11
615 10851 RICAR 11
616 10877 RICAR 11
617 11059 RICAR 11
618 10255 RICSU 10
619 10419 RICSU 10
620 10537 RICSU 10
621 10666 RICSU 10
622 10751 RICSU 10
623 10758 RICSU 10
624 10931 RICSU 10
625 10951 RICSU 10
626 11033 RICSU 10
627 11075 RICSU 10
628 10281 ROMEY 5
629 10282 ROMEY 5
630 10306 ROMEY 5
631 10917 ROMEY 5
632 11013 ROMEY 5
633 10387 SANTG 6
634 10520 SANTG 6
635 10639 SANTG 6
636 10831 SANTG 6
637 10909 SANTG 6
638 11015 SANTG 6
639 10324 SAVEA 31
640 10393 SAVEA 31
641 10398 SAVEA 31
642 10440 SAVEA 31
643 10452 SAVEA 31
644 10510 SAVEA 31
645 10555 SAVEA 31
646 10603 SAVEA 31
647 10607 SAVEA 31
648 10612 SAVEA 31
649 10627 SAVEA 31
650 10657 SAVEA 31
651 10678 SAVEA 31
652 10700 SAVEA 31
653 10711 SAVEA 31
654 10713 SAVEA 31
655 10714 SAVEA 31
656 10722 SAVEA 31
657 10748 SAVEA 31
658 10757 SAVEA 31
659 10815 SAVEA 31
660 10847 SAVEA 31
661 10882 SAVEA 31
662 10894 SAVEA 31
663 10941 SAVEA 31
664 10983 SAVEA 31
665 10984 SAVEA 31
666 11002 SAVEA 31
667 11030 SAVEA 31
668 11031 SAVEA 31
669 11064 SAVEA 31
670 10359 SEVES 9
671 10377 SEVES 9
672 10388 SEVES 9
673 10472 SEVES 9
674 10523 SEVES 9
675 10547 SEVES 9
676 10800 SEVES 9
677 10804 SEVES 9
678 10869 SEVES 9
679 10341 SIMOB 7
680 10417 SIMOB 7
681 10556 SIMOB 7
682 10642 SIMOB 7
683 10669 SIMOB 7
684 10802 SIMOB 7
685 11074 SIMOB 7
686 10738 SPECD 4
687 10907 SPECD 4
688 10964 SPECD 4
689 11043 SPECD 4
690 10271 SPLIR 9
691 10329 SPLIR 9
692 10349 SPLIR 9
693 10369 SPLIR 9
694 10385 SPLIR 9
695 10432 SPLIR 9
696 10756 SPLIR 9
697 10821 SPLIR 9
698 10974 SPLIR 9
699 10252 SUPRD 12
700 10302 SUPRD 12
701 10458 SUPRD 12
702 10463 SUPRD 12
703 10475 SUPRD 12
704 10767 SUPRD 12
705 10841 SUPRD 12
706 10846 SUPRD 12
707 10885 SUPRD 12
708 10930 SUPRD 12
709 11035 SUPRD 12
710 11038 SUPRD 12
711 10310 THEBI 4
712 10708 THEBI 4
713 10805 THEBI 4
714 10992 THEBI 4
715 10624 THECR 3
716 10775 THECR 3
717 11003 THECR 3
718 10249 TOMSP 6
719 10438 TOMSP 6
720 10446 TOMSP 6
721 10548 TOMSP 6
722 10608 TOMSP 6
723 10967 TOMSP 6
724 10276 TORTU 10
725 10293 TORTU 10
726 10304 TORTU 10
727 10319 TORTU 10
728 10518 TORTU 10
729 10576 TORTU 10
730 10676 TORTU 10
731 10842 TORTU 10
732 10915 TORTU 10
733 11069 TORTU 10
734 10292 TRADH 6
735 10496 TRADH 6
736 10606 TRADH 6
737 10830 TRADH 6
738 10834 TRADH 6
739 10839 TRADH 6
740 10574 TRAIH 3
741 10577 TRAIH 3
742 10822 TRAIH 3
743 10367 VAFFE 11
744 10399 VAFFE 11
745 10465 VAFFE 11
746 10591 VAFFE 11
747 10602 VAFFE 11
748 10688 VAFFE 11
749 10744 VAFFE 11
750 10769 VAFFE 11
751 10921 VAFFE 11
752 10946 VAFFE 11
753 10994 VAFFE 11
754 10251 VICTE 10
755 10334 VICTE 10
756 10450 VICTE 10
757 10459 VICTE 10
758 10478 VICTE 10
759 10546 VICTE 10
760 10806 VICTE 10
761 10814 VICTE 10
762 10843 VICTE 10
763 10850 VICTE 10
764 10248 VINET 5
765 10274 VINET 5
766 10295 VINET 5
767 10737 VINET 5
768 10739 VINET 5
769 10301 WANDK 10
770 10312 WANDK 10
771 10348 WANDK 10
772 10356 WANDK 10
773 10513 WANDK 10
774 10632 WANDK 10
775 10640 WANDK 10
776 10651 WANDK 10
777 10668 WANDK 10
778 11046 WANDK 10
779 10266 WARTH 15
780 10270 WARTH 15
781 10320 WARTH 15
782 10333 WARTH 15
783 10412 WARTH 15
784 10416 WARTH 15
785 10437 WARTH 15
786 10455 WARTH 15
787 10526 WARTH 15
788 10553 WARTH 15
789 10583 WARTH 15
790 10636 WARTH 15
791 10750 WARTH 15
792 10781 WARTH 15
793 11025 WARTH 15
794 10256 WELLI 9
795 10420 WELLI 9
796 10585 WELLI 9
797 10644 WELLI 9
798 10803 WELLI 9
799 10809 WELLI 9
800 10900 WELLI 9
801 10905 WELLI 9
802 10935 WELLI 9
803 10269 WHITC 14
804 10344 WHITC 14
805 10469 WHITC 14
806 10483 WHITC 14
807 10504 WHITC 14
808 10596 WHITC 14
809 10693 WHITC 14
810 10696 WHITC 14
811 10723 WHITC 14
812 10740 WHITC 14
813 10861 WHITC 14
814 10904 WHITC 14
815 11032 WHITC 14
816 11066 WHITC 14
817 10615 WILMK 7
818 10673 WILMK 7
819 10695 WILMK 7
820 10873 WILMK 7
821 10879 WILMK 7
822 10910 WILMK 7
823 11005 WILMK 7
824 10374 WOLZA 7
825 10611 WOLZA 7
826 10792 WOLZA 7
827 10870 WOLZA 7
828 10906 WOLZA 7
829 10998 WOLZA 7
830 11044 WOLZA 7


4. Usage of COUNT_BIG with DISTINCT to find distinct supplier

SQL Server Query 5

            
-- Find distinct count of suppliers  
SELECT
COUNT_BIG(DISTINCT SupplierID) AS NumberOfSuppliers
FROM
Suppliers;

Create SQL query with SqlQueryBuilder 5

            
var (sql5, parameters5) = new SqlQueryBuilder()  
.Select()
.Column(new COUNT_BIG(new Column("SupplierID"), true), "NumberOfSuppliers")
.From("Suppliers")
.Build();

Query build by SqlQueryBuilder 5

            
SELECT COUNT(DISTINCT SupplierID) AS NumberOfSuppliers
FROM Suppliers;


            
        

Parameters (If used)

Name Value

Query Results 5:

  NumberOfSuppliers
1 29