SQL Tutorial 2019
If you are looking to learn everything about SQL you are watching the right video. This video is a 500 page book in one video! It contains over 100 queries that will teach everything from basic queries through Stored Procedures, Functions, Triggers and Database Design. You’ll learn about numerous SQL queries while building a real world sales transaction database.
All of the queries along with a transcript of the video follows below. Take your time and create the database and enter the queries to maximize your learning potential.
My Ultimate Python Course is available here for free until October the 3rd. If you sign up for it you’ll get all the current videos plus every video I upload in the future for free. I’ll cover everything from the basics through machine learning by the the time I finish it.
Transcript for my SQL Tutorial
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 |
SQL TUTORIAL A database is data that is structured into rows and columns like a spreadsheet. To receive or change data in a database you send it commands called queries. The database in turn returns a result based on that request. Databases contain many tables of data organized into rows and columns. Each column represents represents one type of data the database stores. Each row contains multiple pieces of data specific to each entity you are describing. For example we store information on customers here. Each individual value stored is called a cell. Primary keys are used to define unique entities in your tables. You can also have non-unique keys which are just referred to as non-primary keys. Let’s say we want to store possible countries in a table with an associated key for each country. We could then refer to those countries in our tables, but because they aren’t unique for each customer they can’t be used as primary keys. cust_id in the Sales table is called a foreign key because it refers to a row of data in a separate table being the Customer table. You can have multiple foreign keys in a table, but only one primary key. Primary keys can be made up of multiple columns and in that case they are called composite primary keys, but don’t worry about that for now. I’ll cover that later. Here it is possible to have multiple references to the customer table in the sales table. You can see that customer 9 is listed twice. That is known as a one to many relationship. I’ll get more into this later as well. Create a Database To create a database click the icon that looks like 3 stacked cylinders with a plus. That icon is labeled as Create a new schema in the connected server. MySQL calls databases schemas. Give your database a name and leave everything else as default Hit Apply and you’ll be asked to review the SQL script for creating a database which is CREATE SCHEMA ‘your_db_name’; Click Apply again Click the Schemas tab and you’ll see your test database in the list Create a Customer Table Double click ‘your_db_name’ in the left sidebar under Schemas Click the Query 1 tab and enter the following commands : CREATE TABLE customer( first_name VARCHAR(30) NOT NULL, last_name VARCHAR(30) NOT NULL, email VARCHAR(60) NOT NULL, company VARCHAR(60) NULL, street VARCHAR(50) NOT NULL, city VARCHAR(40) NOT NULL, state CHAR(2) NOT NULL DEFAULT "PA", zip MEDIUMINT UNSIGNED NOT NULL, phone VARCHAR(20) NOT NULL, birth_date DATE NULL, sex ENUM('M', 'F') NOT NULL, date_entered TIMESTAMP NOT NULL, id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY ); 3. Right click tables under ‘your_db_name’ and click Refresh all to see student What’s Going On Create table customer creates the table named customer When defining what data goes in each cell you must define the type of data you plan to store These are common numeric data types. You can see their minimum and maximum values. The Float type value is purely theoretical and its precision is dependent on your specific machine and deprecated. Double is similar to Float and also deprecated. Decimal are exact numeric data values. When defining them you use DECIMAL(P,D) where P is the precision for your numbers significant digits with a max value of 65. D is the maximum precision of digits that follow the decimal point. You can see the maximum and minimum values you can assign to time data types. You can define how many characters you wish to store in a Varchar as long as the maximum is 255 which is the max size for storing in a 8 bit number. A Blob can store up to 65,535 characters. MediumBlob can store 16,777,215. A LONGBLOB can store 4,294,967,295 but is dependent on a defined maximum packet size and memory. Enums are string values that can only be assigned one value from the list that is provided. You can have a max of 65,535 distinct elements. Sets are like Enums. Some additional data constraints include Not Null. If you mark data as not null that means it must have a value when a new row of data is created. Default designates a default value when a row is created without data. Unsigned means you cannot use negative values. Primary keys are identified as unique values assigned to a row. If you mark data as auto_increment each time a row of data is created you would mark this as a null value and the database will increment this value automatically based off of the previous value assigned to the last id created. You can only use auto_increment on one column per table and that column must be either a primary or unique key. How to Design a Database Slide 8 Each table should represent 1 real world object Each column stores 1 piece of information about that object Define how tables may relate to each other (Primary & Foreign Keys) Reduce redundant data (Normalizing a Database) Turning a Invoice into a Database Slide 9 I’ll take a company invoice and translate it into a database. First I create the customer table. I could have just had one field named Address, but when an element contains 2 or more pieces of information it is best to create separate elements. I'll add information that may be important like birthdate, sex, and the date the customer was added. (Create in Workbench) It also makes sense to do much the same with an employee except we'll define hire date. Duplication is a sign that the data should be in its own table. Tables should contain similar types of data. Tables shouldn't contain duplicate data. For example if we had all individual items ordered all in one table sales person, purchase order number, time of sale, and 4 different pieces of credit card data would be repeated an unknown number of times. Primary & Foreign Keys You define relationships between tables using primary and foreign keys. Primary keys are unique identifiers associated with a column in a table. Foreign keys are primary keys that are referenced by their ids in another table. How Tables Relate New Database with Multiple Tables and Foreign Keys CREATE TABLE customer( first_name VARCHAR(30) NOT NULL, last_name VARCHAR(30) NOT NULL, email VARCHAR(60) NOT NULL, company VARCHAR(60) NULL, street VARCHAR(50) NOT NULL, city VARCHAR(40) NOT NULL, state CHAR(2) NOT NULL DEFAULT "PA", zip MEDIUMINT UNSIGNED NOT NULL, phone VARCHAR(20) NOT NULL, birth_date DATE NULL, sex ENUM('M', 'F') NOT NULL, date_entered TIMESTAMP NOT NULL, id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY ); CREATE TABLE sales_person( first_name VARCHAR(30) NOT NULL, last_name VARCHAR(30) NOT NULL, email VARCHAR(60) NOT NULL, street VARCHAR(50) NOT NULL, city VARCHAR(40) NOT NULL, state CHAR(2) NOT NULL DEFAULT "PA", zip MEDIUMINT UNSIGNED NOT NULL, phone VARCHAR(20) NOT NULL, birth_date DATE NULL, sex ENUM('M', 'F') NOT NULL, date_hired TIMESTAMP NOT NULL, id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY ); // Will hold show types business, casual and athletic CREATE TABLE product_type( name VARCHAR(30) NOT NULL, id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY ); // We define a foreign key constraint to define a relationship between tables // Make sure the data constraints are exactly the same for the foreign key // and the referenced id in the other table CREATE TABLE product( type_id INT UNSIGNED NOT NULL, FOREIGN KEY (type_id) REFERENCES product_type(id), name VARCHAR(30) NOT NULL, supplier VARCHAR(30) NOT NULL, description BLOB NOT NULL, id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY ); // This table describes just the quality of an item. If I were to list quantity here it would // make it hard to look at this as a single item. Quantity should be kept in a completely different // table if needed. // Anything that gets in the way of being able to model an individual object should be put in // another table. CREATE TABLE item( product_id INT UNSIGNED NOT NULL, FOREIGN KEY (product_id) REFERENCES product(id), size INT NOT NULL, color VARCHAR(30) NOT NULL, picture VARCHAR(256) NOT NULL, price DECIMAL(6,2) NOT NULL, id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY ); // Only information pertaining to the order is here aside from products and prices // It simulates 2 people agreeing to do business, the time of that event, a purchase order and // the means of payment CREATE TABLE sales_order( cust_id INT UNSIGNED NOT NULL, sales_person_id INT UNSIGNED NOT NULL, FOREIGN KEY (cust_id) REFERENCES customer(id), FOREIGN KEY (sales_person_id) REFERENCES sales_person(id), time_order_taken TIMESTAMP NOT NULL, purchase_order_number INT NOT NULL, credit_card_number VARCHAR(16) NOT NULL, credit_card_exper_month TINYINT NOT NULL, credit_card_exper_day TINYINT NOT NULL, credit_card_secret_code TINYINT NOT NULL, name_on_card VARCHAR(50) NOT NULL, id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY ); // Each item that is part of an order goes in its own table. It is linked to the order with // sales_order_id // This simulates picking up a quantity of an individual item, with a certain discount and tax rate // The item itself is hidden in the item table and 100% defined specifically there in a way that // makes it easy to refer to it just by its id // If I were to list color, size or anything else here we would break that ability to consider // items in a self contained way CREATE TABLE sales_item( item_id INT UNSIGNED NOT NULL, sales_order_id INT UNSIGNED NOT NULL, FOREIGN KEY (item_id) REFERENCES item(id), FOREIGN KEY (sales_order_id) REFERENCES sales_order(id), quantity INT NOT NULL, discount DECIMAL(3,2) NULL DEFAULT 0, taxable BOOL NOT NULL DEFAULT 0, sales_tax_rate DECIMAL(5,2) NOT NULL DEFAULT 0, id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY ); // Do your best to consider the data in a database to be just that, data. It is often better // to perform calculations outside of the database versus storing those calculations such as // total sales amounts in a database. Altering Tables // Add a new column ALTER TABLE sales_item ADD day_of_week VARCHAR(8) // Modify a column (Change any Constraint this Way) ALTER TABLE sales_item MODIFY day_of_week VARCHAR(9) NOT NULL // Change name of a column ALTER TABLE sales_item CHANGE COLUMN day_of_week weekday // Drop a column ALTER TABLE sales_item DROP COLUMN weekday // Add a new table CREATE TABLE transaction_type( name VARCHAR(30) NOT NULL, payment_type VARCHAR(30) NOT NULL, id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY ); // Rename table RENAME TABLE transaction_type TO transaction // Create index based on a single column (Use UNIQUE INDEX for a unique index) CREATE INDEX transaction_id ON transaction(name) // Create an index based on 2 columns CREATE INDEX transaction_id_2 ON transaction(name, payment_type) // Delete data in a table TRUNCATE TABLE transaction // Drop a table DROP TABLE transaction Altering Tables in Workbench You can view and alter table columns by Right clicking them in the left column and clicking on Alter Table. Click on name or data type to rename. Change constraints in this order : Primary Key, Not Null, Unique, Binary, Unsigned, Zerofill (Pads displayed field with zeroes), Auto Increment, Generated (Contain Functions that auto create values) and default values. Double click <click to edit> to add a column. Right click Delete Selected to delete. Click Apply to set changes. Insert Update and Delete Database Values Use INSERT INTO to insert values into your tables INSERT INTO product_type (name, id) VALUES ('Business', NULL); INSERT INTO product_type (name, id) VALUES ('Casual', NULL); INSERT INTO product_type (name, id) VALUES ('Athletic', NULL); You can also insert multiple rows without defining column names if you put the values in the same order as the table data. INSERT INTO product VALUES (1, 'Grandview', 'Allen Edmonds', 'Classic broguing adds texture to a charming longwing derby crafted in America from lustrous leather', NULL), (1, 'Clarkston', 'Allen Edmonds', 'Sharp broguing touches up a charming, American-made derby fashioned from finely textured leather', NULL), (1, 'Derby', 'John Varvatos', 'Leather upper, manmade sole', NULL), (1, 'Ramsey', 'Johnston & Murphy', 'Leather upper, manmade sole', NULL), (1, 'Hollis', 'Johnston & Murphy', 'Leather upper, manmade sole', NULL), (2, 'Venetian Loafer', 'Mezlan', 'Suede upper, leather sole', NULL), (2, 'Malek', 'Johnston & Murphy', 'Contrast insets at the toe and sides bring updated attitude to a retro-inspired sneaker set on a sporty foam sole and triangle-lugged tread.', NULL), (3, 'Air Max 270 React', 'Nike', 'The reggae inspired Nike Air 270 React fuses forest green with shades of tan to reveal your righteous spirit', NULL), (3, 'Joyride', 'Nike', 'Tiny foam beads underfoot conform to your foot for cushioning that stands up to your mileage', NULL), (2, 'Air Force 1', 'Nike', 'A modern take on the icon that blends classic style and fresh, crisp details', NULL), (3, 'Ghost 12', 'Brooks', 'Just know that it still strikes a just-right balance of DNA LOFT softness and BioMoGo DNA responsiveness', NULL), (3, 'Revel 3', 'Brooks', 'Style to spare, now even softer.', NULL), (3, 'Glycerin 17', 'Brooks', 'A plush fit and super soft transitions make every stride luxurious', NULL); Insert Customers INSERT INTO customer (first_name, last_name, email, company, street, city, state, zip, phone, birth_date, sex, date_entered, id) VALUES ('Christopher', 'Jones', 'christopherjones@bp.com', 'BP', '347 Cedar St', 'Lawrenceville', 'GA', '30044', '348-848-8291', '1938-09-11', 'M', '2015-07-21 11:27:02', NULL), ('Matthew', 'Martinez', 'matthewmartinez@ge.com', 'GE', '602 Main Place', 'Fontana', 'CA', '92336', '117-997-7764', '1931-09-04', 'M', '2015-01-01 22:39:28', NULL), ('Melissa', 'Moore', 'melissamoore@aramark.com', 'Aramark', '463 Park Rd', 'Lakewood', 'NJ', '08701', '269-720-7259', '1967-08-27', 'M', '2017-10-20 21:59:29', NULL), ('Melissa', 'Brown', 'melissabrown@verizon.com', 'Verizon', '712 View Ave', 'Houston', 'TX', '77084', '280-570-5166', '1948-06-14', 'F', '2016-07-16 12:26:45', NULL), ('Jennifer', 'Thomas', 'jenniferthomas@aramark.com', 'Aramark', '231 Elm St', 'Mission', 'TX', '78572', '976-147-9254', '1998-03-14', 'F', '2018-01-08 09:27:55', NULL), ('Stephanie', 'Martinez', 'stephaniemartinez@albertsons.com', 'Albertsons', '386 Second St', 'Lakewood', 'NJ', '08701', '820-131-6053', '1998-01-24', 'M', '2016-06-18 13:27:34', NULL), ('Daniel', 'Williams', 'danielwilliams@tjx.com', 'TJX', '107 Pine St', 'Katy', 'TX', '77449', '744-906-9837', '1985-07-20', 'F', '2015-07-03 10:40:18', NULL), ('Lauren', 'Anderson', 'laurenanderson@pepsi.com', 'Pepsi', '13 Maple Ave', 'Riverside', 'CA', '92503', '747-993-2446', '1973-09-09', 'F', '2018-02-01 16:43:51', NULL), ('Michael', 'Jackson', 'michaeljackson@disney.com', 'Disney', '818 Pine Ave', 'Mission', 'TX', '78572', '126-423-3144', '1951-03-03', 'F', '2017-04-02 21:57:36', NULL), ('Ashley', 'Johnson', 'ashleyjohnson@boeing.com', 'Boeing', '874 Oak Ave', 'Pacoima', 'CA', '91331', '127-475-1658', '1937-05-10', 'F', '2015-01-04 08:58:56', NULL), ('Brittany', 'Thomas', 'brittanythomas@walmart.com', 'Walmart', '187 Maple Ave', 'Brownsville', 'TX', '78521', '447-788-4913', '1986-10-22', 'F', '2018-05-23 08:04:32', NULL), ('Matthew', 'Smith', 'matthewsmith@ups.com', 'UPS', '123 Lake St', 'Brownsville', 'TX', '78521', '961-108-3758', '1950-06-16', 'F', '2018-03-15 10:08:54', NULL), ('Lauren', 'Wilson', 'laurenwilson@target.com', 'Target', '942 Fifth Ave', 'Mission', 'TX', '78572', '475-578-8519', '1965-12-26', 'M', '2017-07-16 11:01:01', NULL), ('Justin', 'Smith', 'justinsmith@boeing.com', 'Boeing', '844 Lake Ave', 'Lawrenceville', 'GA', '30044', '671-957-1492', '1956-03-16', 'F', '2017-10-07 10:50:08', NULL), ('Jessica', 'Garcia', 'jessicagarcia@toyota.com', 'Toyota', '123 Pine Place', 'Fontana', 'CA', '92336', '744-647-2359', '1996-08-05', 'F', '2016-09-14 12:33:05', NULL), ('Matthew', 'Jackson', 'matthewjackson@bp.com', 'BP', '538 Cedar Ave', 'Katy', 'TX', '77449', '363-430-1813', '1966-02-26', 'F', '2016-05-01 19:25:17', NULL), ('Stephanie', 'Thomas', 'stephaniethomas@apple.com', 'Apple', '804 Fourth Place', 'Brownsville', 'TX', '78521', '869-582-9955', '1988-08-26', 'F', '2018-10-21 22:01:57', NULL), ('Jessica', 'Jackson', 'jessicajackson@aramark.com', 'Aramark', '235 Pine Place', 'Chicago', 'IL', '60629', '587-334-1054', '1991-07-22', 'F', '2015-08-28 03:11:35', NULL), ('James', 'Martinez', 'jamesmartinez@kroger.com', 'Kroger', '831 Oak St', 'Brownsville', 'TX', '78521', '381-428-3119', '1927-12-22', 'F', '2018-01-27 07:41:48', NULL), ('Christopher', 'Robinson', 'christopherrobinson@ibm.com', 'IBM', '754 Cedar St', 'Pharr', 'TX', '78577', '488-694-7677', '1932-06-25', 'F', '2016-08-19 16:11:31', NULL); // See list of customers SELECT * FROM customer; Insert Sales Persons INSERT INTO sales_person (first_name, last_name, email, street, city, state, zip, phone, birth_date, sex, date_hired, id) VALUES ('Jennifer', 'Smith', 'jennifersmith@volkswagen.com', '610 Maple Place', 'Hawthorne', 'CA', '90250', '215-901-2287', '1941-08-09', 'F', '2014-02-06 12:22:48', NULL), ('Michael', 'Robinson', 'michaelrobinson@walmart.com', '164 Maple St', 'Pacoima', 'CA', '91331', '521-377-4462', '1956-04-23', 'M', '2014-09-12 17:27:23', NULL), ('Brittany', 'Jackson', 'brittanyjackson@disney.com', '263 Park Rd', 'Riverside', 'CA', '92503', '672-708-7601', '1934-07-05', 'F', '2015-01-17 02:51:55', NULL), ('Samantha', 'Moore', 'samanthamoore@ge.com', '107 Pine Place', 'Houston', 'TX', '77084', '893-423-2899', '1926-05-05', 'M', '2015-11-14 22:26:21', NULL), ('Jessica', 'Thompson', 'jessicathompson@fedex.com', '691 Third Place', 'Sylmar', 'CA', '91342', '349-203-4736', '1938-12-18', 'M', '2014-12-13 06:54:39', NULL) // See all sales persons SELECT * FROM sales_person; Correct Purchase Order Number // Make a unique number that can accommodate a large number ALTER TABLE sales_order MODIFY purchase_order_number BIGINT UNSIGNED NOT NULL UNIQUE; // Change secret code to accommodate numbers up to 999 ALTER TABLE sales_order MODIFY credit_card_secret_code SMALLINT UNSIGNED NOT NULL; Insert Items INSERT INTO item VALUES (2, 10, 'Gray', 'Coming Soon', 199.60, NULL), (11, 12, 'Red', 'Coming Soon', 155.65, NULL), (2, 11, 'Red', 'Coming Soon', 128.87, NULL), (11, 11, 'Green', 'Coming Soon', 117.52, NULL), (5, 8, 'Black', 'Coming Soon', 165.39, NULL), (7, 11, 'Brown', 'Coming Soon', 168.15, NULL), (5, 8, 'Gray', 'Coming Soon', 139.48, NULL), (5, 11, 'Blue', 'Coming Soon', 100.14, NULL), (4, 10, 'Brown', 'Coming Soon', 117.66, NULL), (8, 10, 'Brown', 'Coming Soon', 193.53, NULL), (7, 8, 'Light Brown', 'Coming Soon', 154.62, NULL), (12, 10, 'Green', 'Coming Soon', 188.32, NULL), (3, 12, 'Green', 'Coming Soon', 101.49, NULL), (7, 9, 'Black', 'Coming Soon', 106.39, NULL), (8, 12, 'Red', 'Coming Soon', 124.77, NULL), (5, 8, 'Black', 'Coming Soon', 86.19, NULL), (8, 12, 'Blue', 'Coming Soon', 196.86, NULL), (8, 8, 'Blue', 'Coming Soon', 123.27, NULL), (7, 11, 'Red', 'Coming Soon', 130.76, NULL), (9, 12, 'Black', 'Coming Soon', 152.98, NULL), (11, 8, 'Blue', 'Coming Soon', 175.58, NULL), (7, 11, 'Light Brown', 'Coming Soon', 146.83, NULL), (4, 8, 'Green', 'Coming Soon', 159.82, NULL), (12, 8, 'Light Brown', 'Coming Soon', 171.92, NULL), (1, 12, 'Light Brown', 'Coming Soon', 128.77, NULL), (2, 10, 'Gray', 'Coming Soon', 102.45, NULL), (10, 8, 'Green', 'Coming Soon', 186.86, NULL), (1, 8, 'Blue', 'Coming Soon', 139.73, NULL), (9, 8, 'Light Brown', 'Coming Soon', 151.57, NULL), (2, 10, 'Green', 'Coming Soon', 177.16, NULL), (3, 9, 'Gray', 'Coming Soon', 124.87, NULL), (8, 8, 'Black', 'Coming Soon', 129.40, NULL), (5, 9, 'Black', 'Coming Soon', 107.55, NULL), (5, 8, 'Light Brown', 'Coming Soon', 103.71, NULL), (11, 10, 'Green', 'Coming Soon', 152.31, NULL), (6, 12, 'Red', 'Coming Soon', 108.96, NULL), (7, 12, 'Blue', 'Coming Soon', 173.14, NULL), (3, 10, 'Green', 'Coming Soon', 198.44, NULL), (1, 9, 'Light Brown', 'Coming Soon', 119.61, NULL), (1, 10, 'Black', 'Coming Soon', 114.36, NULL), (7, 9, 'Light Brown', 'Coming Soon', 181.93, NULL), (5, 10, 'Black', 'Coming Soon', 108.32, NULL), (1, 12, 'Black', 'Coming Soon', 153.97, NULL), (2, 12, 'Gray', 'Coming Soon', 184.27, NULL), (2, 9, 'Blue', 'Coming Soon', 151.63, NULL), (6, 8, 'Brown', 'Coming Soon', 159.39, NULL), (11, 9, 'Red', 'Coming Soon', 150.49, NULL), (9, 10, 'Gray', 'Coming Soon', 139.26, NULL), (4, 8, 'Gray', 'Coming Soon', 166.87, NULL), (12, 9, 'Red', 'Coming Soon', 110.77, NULL); Insert into Sales Order INSERT INTO sales_order VALUES (1, 2, '2018-03-23 10:26:23', 20183231026, 5440314057399014, 3, 5, 415, 'Ashley Martin', NULL), (8, 2, '2017-01-09 18:58:15', 2017191858, 6298551651340835, 10, 27, 962, 'Michael Smith', NULL), (9, 3, '2018-12-21 21:26:57', 201812212126, 3194084144609442, 7, 16, 220, 'Lauren Garcia', NULL), (8, 2, '2017-08-20 15:33:17', 20178201533, 2704487907300646, 7, 10, 430, 'Jessica Robinson', NULL), (3, 4, '2017-09-19 13:28:35', 20179191328, 8102877849444788, 4, 15, 529, 'Melissa Jones', NULL), (14, 1, '2016-10-02 18:30:13', 20161021830, 7294221943676784, 10, 22, 323, 'Lauren Moore', NULL), (4, 2, '2016-03-21 07:24:30', 2016321724, 1791316080799942, 1, 24, 693, 'Joshua Wilson', NULL), (1, 1, '2018-08-04 12:22:06', 2018841222, 4205390666512184, 5, 16, 758, 'Jennifer Garcia', NULL), (8, 4, '2016-08-25 10:36:09', 20168251036, 3925972513042074, 1, 10, 587, 'Michael Thomas', NULL), (8, 4, '2018-08-10 20:24:52', 20188102024, 2515001187633555, 10, 7, 354, 'David Martin', NULL), (5, 2, '2016-11-28 15:21:48', 201611281521, 6715538212478349, 5, 25, 565, 'Jennifer Johnson', NULL), (5, 3, '2016-12-07 10:20:05', 20161271020, 5125085038984547, 10, 27, 565, 'Brittany Garcia', NULL), (13, 3, '2018-10-11 16:27:04', 201810111627, 5559881213107031, 7, 14, 593, 'Sarah Jackson', NULL), (14, 1, '2018-04-26 20:35:34', 20184262035, 2170089500922701, 7, 26, 105, 'Daniel Harris', NULL), (3, 2, '2016-11-14 04:32:50', 20161114432, 6389550669359545, 7, 19, 431, 'Brittany Williams', NULL), (18, 3, '2016-07-10 17:55:01', 20167101755, 7693323933630220, 4, 22, 335, 'Christopher Thomas', NULL), (12, 2, '2018-05-13 06:20:56', 2018513620, 1634255384507587, 1, 4, 364, 'Megan Garcia', NULL), (3, 4, '2016-03-04 20:52:36', 2016342052, 7720584466409961, 2, 7, 546, 'Justin Taylor', NULL), (17, 1, '2017-02-16 15:44:27', 20172161544, 7573753924723630, 3, 15, 148, 'Michael White', NULL), (19, 3, '2017-08-04 07:24:30', 201784724, 9670036242643402, 10, 24, 803, 'Melissa Taylor', NULL), (8, 2, '2018-07-08 15:51:11', 2018781551, 5865443195522495, 2, 2, 793, 'James Thompson', NULL), (18, 1, '2017-03-02 03:08:03', 20173238, 9500873657482557, 6, 22, 793, 'Daniel Williams', NULL), (7, 1, '2018-03-19 10:54:30', 20183191054, 7685678049357511, 2, 9, 311, 'Joshua Martinez', NULL), (18, 1, '2017-07-04 18:48:02', 2017741848, 2254223828631172, 6, 18, 621, 'Justin Taylor', NULL), (16, 1, '2018-07-23 21:44:51', 20187232144, 8669971462260333, 10, 3, 404, 'Ashley Garcia', NULL), (8, 4, '2016-05-21 16:26:49', 20165211626, 9485792104395686, 2, 4, 270, 'Andrew Taylor', NULL), (19, 4, '2018-09-04 18:24:36', 2018941824, 5293753403622328, 8, 4, 362, 'Matthew Miller', NULL), (9, 2, '2018-07-01 18:19:10', 2018711819, 7480694928317516, 10, 5, 547, 'Justin Thompson', NULL), (8, 4, '2018-09-10 20:15:06', 20189102015, 7284020879927491, 4, 15, 418, 'Samantha Anderson', NULL), (17, 2, '2016-07-13 16:30:53', 20167131630, 7769197595493852, 1, 19, 404, 'Jessica Thomas', NULL), (17, 4, '2016-09-22 22:58:11', 20169222258, 1394443435119786, 7, 5, 955, 'James Wilson', NULL), (17, 4, '2017-10-28 11:35:05', 201710281135, 6788591532433513, 8, 13, 512, 'Michael Williams', NULL), (12, 4, '2018-11-11 04:55:50', 20181111455, 1854718494260005, 3, 26, 928, 'Melissa Jones', NULL), (15, 4, '2016-08-11 23:05:58', 2016811235, 7502173302686796, 3, 11, 836, 'Michael Thompson', NULL), (2, 3, '2018-07-13 07:50:24', 2018713750, 5243198834590551, 10, 12, 725, 'Joseph Thomas', NULL), (9, 3, '2017-09-28 11:42:16', 20179281142, 7221309687109696, 2, 5, 845, 'James Martinez', NULL), (7, 1, '2016-01-09 18:15:08', 2016191815, 9202139348760334, 4, 4, 339, 'Samantha Wilson', NULL), (18, 1, '2016-03-14 17:33:26', 20163141733, 3066530074499665, 6, 23, 835, 'David Garcia', NULL), (12, 3, '2017-08-21 18:14:01', 20178211814, 1160849457958425, 8, 19, 568, 'Samantha Miller', NULL), (8, 1, '2018-09-12 19:25:25', 20189121925, 6032844702934349, 8, 13, 662, 'Justin Brown', NULL), (19, 2, '2016-11-06 03:07:33', 201611637, 1369214097312715, 9, 23, 330, 'Joseph Jones', NULL), (3, 4, '2016-06-06 01:07:15', 20166617, 7103644598069058, 1, 5, 608, 'Brittany Thomas', NULL), (13, 4, '2017-05-15 01:02:57', 201751512, 2920333635602602, 11, 14, 139, 'Stephanie Smith', NULL), (15, 4, '2016-03-27 02:18:18', 2016327218, 7798214190926405, 5, 13, 809, 'Stephanie Taylor', NULL), (9, 2, '2018-01-25 14:43:01', 20181251443, 4196223548846892, 10, 17, 115, 'Melissa Martin', NULL), (6, 3, '2017-01-08 13:54:49', 2017181354, 8095784052038731, 8, 23, 416, 'Amanda White', NULL), (12, 2, '2017-09-24 15:24:44', 20179241524, 6319974420646022, 2, 4, 755, 'Megan Anderson', NULL), (11, 2, '2018-04-09 18:53:22', 2018491853, 3258192259182097, 11, 22, 730, 'Samantha Thompson', NULL), (10, 2, '2018-01-11 22:20:29', 20181112220, 8336712415869878, 3, 18, 872, 'Melissa Wilson', NULL), (14, 3, '2018-11-10 03:08:36', 2018111038, 6942550153605236, 9, 18, 250, 'Jessica Johnson', NULL), (6, 4, '2016-06-26 16:48:19', 20166261648, 5789348928562200, 2, 7, 458, 'Christopher Jones', NULL), (5, 1, '2018-06-23 02:25:16', 2018623225, 8550095429571317, 9, 25, 590, 'Samantha Wilson', NULL), (18, 2, '2017-07-01 01:16:04', 201771116, 2651011719468438, 11, 11, 107, 'Andrew Miller', NULL), (12, 4, '2017-01-17 21:42:51', 20171172142, 7354378345646144, 3, 14, 772, 'Andrew Moore', NULL), (7, 3, '2016-01-07 22:56:31', 2016172256, 3429850164043973, 2, 6, 295, 'Joseph Taylor', NULL), (10, 1, '2016-01-27 01:14:53', 2016127114, 2480926933843246, 7, 3, 704, 'Ashley Taylor', NULL), (13, 1, '2018-09-15 08:15:17', 2018915815, 6626319262681476, 4, 8, 837, 'Stephanie Thomas', NULL), (9, 1, '2018-04-06 15:40:28', 2018461540, 4226037621059886, 10, 26, 896, 'Stephanie Jones', NULL), (17, 3, '2016-10-17 21:31:09', 201610172131, 7862008338119027, 10, 25, 767, 'Amanda Robinson', NULL), (12, 2, '2016-06-04 22:27:57', 2016642227, 4472081783581101, 10, 9, 279, 'Justin Williams', NULL), (9, 3, '2018-01-27 06:57:23', 2018127657, 2384491606066483, 11, 23, 417, 'Joshua Garcia', NULL), (14, 2, '2018-07-19 22:11:23', 20187192211, 2680467440231722, 10, 8, 545, 'Ashley Wilson', NULL), (19, 4, '2018-11-06 03:12:35', 2018116312, 3973342791188144, 10, 9, 749, 'Megan Martinez', NULL), (11, 2, '2017-01-15 14:11:54', 20171151411, 3042008865691398, 8, 3, 695, 'Brittany White', NULL), (10, 4, '2018-10-07 01:26:57', 2018107126, 7226038495242154, 8, 7, 516, 'Stephanie White', NULL), (12, 3, '2018-10-02 16:13:23', 20181021613, 7474287104417454, 11, 1, 184, 'Daniel Davis', NULL), (8, 1, '2018-08-12 23:54:52', 20188122354, 6454271840792089, 1, 19, 914, 'Michael Robinson', NULL), (11, 2, '2016-07-06 04:57:33', 201676457, 6767948287515839, 8, 7, 127, 'Samantha Anderson', NULL), (12, 2, '2018-09-06 10:34:03', 2018961034, 2724397042248973, 11, 11, 686, 'Ashley Harris', NULL), (16, 1, '2017-11-12 07:05:38', 2017111275, 4832060124173185, 11, 27, 697, 'Brittany White', NULL), (16, 4, '2016-06-08 17:38:18', 2016681738, 2187337846675221, 5, 9, 895, 'Megan Wilson', NULL), (3, 3, '2016-02-08 21:46:46', 2016282146, 8361948319742012, 6, 26, 157, 'Jessica Taylor', NULL), (8, 1, '2016-10-22 03:01:13', 2016102231, 1748352966511490, 8, 7, 815, 'Justin Davis', NULL), (5, 4, '2018-12-06 12:51:24', 20181261251, 3987075017699453, 7, 18, 557, 'Andrew Martinez', NULL), (4, 1, '2017-09-23 07:14:32', 2017923714, 4497706297852239, 2, 12, 756, 'Justin Moore', NULL), (5, 3, '2016-02-28 23:16:42', 20162282316, 9406399694013062, 1, 26, 853, 'Joseph Moore', NULL), (11, 4, '2016-05-24 14:37:36', 20165241437, 4754563147105980, 8, 8, 742, 'Amanda Brown', NULL), (1, 2, '2018-04-08 09:35:58', 201848935, 5031182534686567, 2, 11, 760, 'Andrew Thompson', NULL), (11, 1, '2017-10-07 20:45:13', 20171072045, 9736660892936088, 5, 19, 240, 'Megan Robinson', NULL), (19, 2, '2017-03-19 23:03:38', 2017319233, 1154891936822433, 2, 14, 554, 'Christopher Davis', NULL), (1, 1, '2018-04-26 11:58:53', 20184261158, 5672494499371853, 11, 18, 692, 'James Thomas', NULL), (1, 3, '2018-07-20 10:05:17', 2018720105, 9695318985866569, 2, 12, 107, 'Jennifer Martin', NULL), (7, 3, '2018-06-21 18:41:12', 20186211841, 2824438494479373, 1, 12, 296, 'Joseph Miller', NULL), (6, 1, '2016-04-07 08:47:40', 201647847, 5608599820055114, 7, 2, 163, 'Brittany Brown', NULL), (15, 3, '2016-07-22 19:25:23', 20167221925, 3011298350076480, 1, 9, 352, 'Jessica Jackson', NULL), (16, 4, '2016-10-14 10:17:30', 201610141017, 5250543218399397, 9, 16, 975, 'David Wilson', NULL), (3, 4, '2018-05-15 03:51:28', 2018515351, 8835896606865589, 11, 4, 675, 'Andrew Garcia', NULL), (19, 3, '2017-05-25 07:44:57', 2017525744, 9159566098395188, 6, 23, 112, 'Ashley Brown', NULL), (11, 2, '2017-12-02 19:07:39', 2017122197, 9920715756046783, 2, 25, 490, 'Joshua Garcia', NULL), (7, 4, '2016-05-01 04:50:28', 201651450, 8393790616940265, 9, 22, 490, 'Matthew White', NULL), (15, 3, '2018-01-21 19:54:46', 20181211954, 8078408967493993, 6, 18, 316, 'Jessica Thomas', NULL), (6, 1, '2018-04-11 11:23:58', 20184111123, 3921559263693643, 11, 17, 221, 'Andrew Jackson', NULL), (13, 3, '2018-03-05 10:26:27', 2018351026, 4739593984654108, 10, 18, 925, 'Samantha White', NULL), (8, 4, '2018-11-15 14:53:55', 201811151453, 8752393645304583, 4, 14, 554, 'Daniel Jackson', NULL), (10, 1, '2017-09-03 12:57:29', 2017931257, 3434269111389638, 6, 18, 360, 'Megan Johnson', NULL), (7, 1, '2018-06-28 12:10:58', 20186281210, 6543388006451934, 5, 19, 491, 'Megan Thomas', NULL), (15, 3, '2018-07-13 12:21:29', 20187131221, 4717498129166869, 5, 21, 386, 'Megan Davis', NULL), (4, 1, '2016-08-01 16:26:39', 2016811626, 1822404586758111, 3, 2, 346, 'Joseph Davis', NULL), (3, 2, '2016-10-27 10:53:05', 201610271053, 8446943405552052, 11, 17, 266, 'Daniel Smith', NULL), (18, 3, '2018-10-20 15:28:54', 201810201528, 6433477195769821, 8, 26, 723, 'Lauren Smith', NULL); Insert Sales Item INSERT INTO sales_item VALUES (24, 270, 2, 0.11, 0, 0.0, NULL), (8, 237, 2, 0.16, 0, 0.0, NULL), (24, 290, 2, 0.06, 0, 0.0, NULL), (34, 283, 2, 0.13, 0, 0.0, NULL), (26, 255, 2, 0.13, 0, 0.0, NULL), (19, 226, 1, 0.19, 0, 0.0, NULL), (23, 202, 1, 0.13, 0, 0.0, NULL), (48, 224, 2, 0.15, 0, 0.0, NULL), (30, 211, 2, 0.06, 0, 0.0, NULL), (1, 260, 2, 0.18, 0, 0.0, NULL), (48, 202, 2, 0.12, 0, 0.0, NULL), (35, 234, 2, 0.07, 0, 0.0, NULL), (29, 213, 1, 0.15, 0, 0.0, NULL), (15, 298, 2, 0.13, 0, 0.0, NULL), (27, 235, 2, 0.07, 0, 0.0, NULL), (30, 205, 1, 0.05, 0, 0.0, NULL), (45, 233, 1, 0.09, 0, 0.0, NULL), (31, 220, 1, 0.18, 0, 0.0, NULL), (32, 288, 1, 0.13, 0, 0.0, NULL), (47, 243, 1, 0.09, 0, 0.0, NULL), (23, 220, 2, 0.16, 0, 0.0, NULL), (44, 286, 2, 0.18, 0, 0.0, NULL), (35, 275, 2, 0.12, 0, 0.0, NULL), (24, 249, 1, 0.08, 0, 0.0, NULL), (31, 237, 1, 0.14, 0, 0.0, NULL), (21, 211, 2, 0.14, 0, 0.0, NULL), (21, 271, 2, 0.06, 0, 0.0, NULL), (48, 201, 1, 0.06, 0, 0.0, NULL), (37, 287, 1, 0.11, 0, 0.0, NULL), (38, 266, 1, 0.13, 0, 0.0, NULL), (14, 207, 2, 0.13, 0, 0.0, NULL), (26, 285, 2, 0.2, 0, 0.0, NULL), (21, 283, 2, 0.16, 0, 0.0, NULL), (8, 215, 2, 0.18, 0, 0.0, NULL), (40, 232, 1, 0.19, 0, 0.0, NULL), (49, 238, 1, 0.15, 0, 0.0, NULL), (41, 213, 2, 0.06, 0, 0.0, NULL), (36, 259, 1, 0.1, 0, 0.0, NULL), (14, 246, 2, 0.14, 0, 0.0, NULL), (30, 277, 2, 0.19, 0, 0.0, NULL), (12, 278, 2, 0.18, 0, 0.0, NULL), (5, 221, 1, 0.18, 0, 0.0, NULL), (10, 213, 1, 0.09, 0, 0.0, NULL), (39, 209, 2, 0.2, 0, 0.0, NULL), (46, 251, 2, 0.13, 0, 0.0, NULL), (47, 298, 1, 0.15, 0, 0.0, NULL), (25, 283, 2, 0.09, 0, 0.0, NULL), (36, 256, 2, 0.12, 0, 0.0, NULL), (18, 208, 2, 0.12, 0, 0.0, NULL), (35, 217, 1, 0.14, 0, 0.0, NULL), (41, 270, 1, 0.14, 0, 0.0, NULL), (9, 221, 1, 0.07, 0, 0.0, NULL), (42, 246, 1, 0.09, 0, 0.0, NULL), (18, 274, 1, 0.1, 0, 0.0, NULL), (25, 214, 1, 0.16, 0, 0.0, NULL), (44, 257, 1, 0.13, 0, 0.0, NULL), (2, 284, 2, 0.06, 0, 0.0, NULL), (18, 268, 2, 0.08, 0, 0.0, NULL), (35, 264, 2, 0.16, 0, 0.0, NULL), (49, 279, 1, 0.07, 0, 0.0, NULL), (7, 203, 2, 0.14, 0, 0.0, NULL), (42, 240, 2, 0.15, 0, 0.0, NULL), (8, 248, 2, 0.18, 0, 0.0, NULL), (27, 282, 2, 0.08, 0, 0.0, NULL), (21, 263, 1, 0.1, 0, 0.0, NULL), (42, 221, 2, 0.08, 0, 0.0, NULL), (31, 223, 2, 0.18, 0, 0.0, NULL), (29, 207, 1, 0.11, 0, 0.0, NULL), (48, 229, 2, 0.14, 0, 0.0, NULL), (15, 249, 2, 0.15, 0, 0.0, NULL), (34, 237, 1, 0.16, 0, 0.0, NULL), (22, 235, 1, 0.19, 0, 0.0, NULL), (22, 229, 2, 0.11, 0, 0.0, NULL), (38, 292, 2, 0.08, 0, 0.0, NULL), (21, 211, 2, 0.17, 0, 0.0, NULL), (13, 272, 1, 0.09, 0, 0.0, NULL), (12, 207, 1, 0.17, 0, 0.0, NULL), (41, 211, 2, 0.13, 0, 0.0, NULL), (22, 226, 2, 0.09, 0, 0.0, NULL), (43, 291, 1, 0.13, 0, 0.0, NULL), (33, 260, 1, 0.1, 0, 0.0, NULL), (39, 282, 2, 0.2, 0, 0.0, NULL), (27, 272, 2, 0.17, 0, 0.0, NULL), (10, 279, 2, 0.12, 0, 0.0, NULL), (41, 278, 2, 0.15, 0, 0.0, NULL), (11, 243, 1, 0.05, 0, 0.0, NULL), (29, 276, 1, 0.08, 0, 0.0, NULL), (25, 260, 1, 0.15, 0, 0.0, NULL), (15, 283, 2, 0.09, 0, 0.0, NULL), (7, 246, 1, 0.07, 0, 0.0, NULL), (26, 224, 2, 0.1, 0, 0.0, NULL), (43, 222, 2, 0.08, 0, 0.0, NULL), (47, 299, 1, 0.06, 0, 0.0, NULL), (29, 226, 1, 0.12, 0, 0.0, NULL), (36, 236, 2, 0.06, 0, 0.0, NULL), (41, 215, 1, 0.08, 0, 0.0, NULL), (12, 247, 2, 0.15, 0, 0.0, NULL), (38, 217, 1, 0.05, 0, 0.0, NULL), (22, 232, 2, 0.13, 0, 0.0, NULL), (12, 299, 2, 0.11, 0, 0.0, NULL), (30, 227, 2, 0.15, 0, 0.0, NULL), (38, 240, 1, 0.15, 0, 0.0, NULL), (22, 236, 1, 0.09, 0, 0.0, NULL), (14, 255, 2, 0.07, 0, 0.0, NULL), (1, 269, 1, 0.07, 0, 0.0, NULL), (47, 288, 1, 0.1, 0, 0.0, NULL), (7, 272, 2, 0.07, 0, 0.0, NULL), (46, 213, 1, 0.18, 0, 0.0, NULL), (9, 210, 1, 0.15, 0, 0.0, NULL), (35, 240, 1, 0.13, 0, 0.0, NULL), (15, 282, 2, 0.07, 0, 0.0, NULL), (47, 234, 1, 0.14, 0, 0.0, NULL), (10, 253, 1, 0.08, 0, 0.0, NULL), (49, 234, 2, 0.06, 0, 0.0, NULL), (13, 243, 1, 0.19, 0, 0.0, NULL), (6, 267, 1, 0.08, 0, 0.0, NULL), (21, 211, 1, 0.12, 0, 0.0, NULL), (26, 294, 1, 0.13, 0, 0.0, NULL), (38, 266, 1, 0.19, 0, 0.0, NULL), (40, 268, 2, 0.16, 0, 0.0, NULL), (25, 284, 1, 0.18, 0, 0.0, NULL), (11, 228, 1, 0.18, 0, 0.0, NULL), (48, 220, 1, 0.12, 0, 0.0, NULL), (26, 203, 1, 0.12, 0, 0.0, NULL), (1, 275, 1, 0.19, 0, 0.0, NULL), (6, 258, 1, 0.12, 0, 0.0, NULL), (33, 243, 2, 0.11, 0, 0.0, NULL), (15, 270, 1, 0.15, 0, 0.0, NULL), (41, 272, 2, 0.14, 0, 0.0, NULL), (8, 277, 2, 0.18, 0, 0.0, NULL), (36, 285, 2, 0.18, 0, 0.0, NULL), (42, 257, 2, 0.18, 0, 0.0, NULL), (27, 271, 1, 0.19, 0, 0.0, NULL), (20, 240, 1, 0.18, 0, 0.0, NULL), (14, 223, 2, 0.16, 0, 0.0, NULL), (15, 273, 1, 0.12, 0, 0.0, NULL), (25, 260, 1, 0.12, 0, 0.0, NULL), (30, 210, 2, 0.11, 0, 0.0, NULL), (18, 290, 2, 0.09, 0, 0.0, NULL), (17, 206, 2, 0.13, 0, 0.0, NULL), (43, 217, 1, 0.08, 0, 0.0, NULL), (20, 233, 2, 0.11, 0, 0.0, NULL), (1, 294, 2, 0.1, 0, 0.0, NULL), (49, 222, 2, 0.09, 0, 0.0, NULL), (1, 255, 2, 0.1, 0, 0.0, NULL), (24, 259, 1, 0.14, 0, 0.0, NULL), (19, 245, 1, 0.17, 0, 0.0, NULL), (13, 280, 2, 0.1, 0, 0.0, NULL), (17, 250, 1, 0.08, 0, 0.0, NULL), (45, 203, 2, 0.13, 0, 0.0, NULL), (6, 292, 2, 0.19, 0, 0.0, NULL), (25, 204, 1, 0.08, 0, 0.0, NULL), (47, 281, 1, 0.16, 0, 0.0, NULL), (39, 239, 2, 0.17, 0, 0.0, NULL), (47, 279, 1, 0.12, 0, 0.0, NULL), (6, 208, 1, 0.17, 0, 0.0, NULL), (15, 260, 2, 0.11, 0, 0.0, NULL), (49, 266, 1, 0.15, 0, 0.0, NULL), (34, 244, 2, 0.09, 0, 0.0, NULL), (20, 210, 1, 0.1, 0, 0.0, NULL), (13, 235, 1, 0.12, 0, 0.0, NULL), (10, 243, 1, 0.13, 0, 0.0, NULL), (24, 251, 2, 0.09, 0, 0.0, NULL), (11, 242, 2, 0.14, 0, 0.0, NULL), (20, 254, 1, 0.17, 0, 0.0, NULL), (42, 235, 1, 0.1, 0, 0.0, NULL), (1, 247, 2, 0.17, 0, 0.0, NULL), (35, 298, 1, 0.11, 0, 0.0, NULL), (14, 225, 1, 0.18, 0, 0.0, NULL), (23, 241, 2, 0.13, 0, 0.0, NULL), (4, 274, 2, 0.15, 0, 0.0, NULL), (32, 247, 2, 0.11, 0, 0.0, NULL), (49, 272, 2, 0.17, 0, 0.0, NULL), (37, 259, 2, 0.11, 0, 0.0, NULL), (43, 298, 1, 0.16, 0, 0.0, NULL), (26, 228, 1, 0.15, 0, 0.0, NULL), (16, 287, 1, 0.16, 0, 0.0, NULL), (6, 249, 2, 0.07, 0, 0.0, NULL), (6, 214, 2, 0.2, 0, 0.0, NULL), (27, 288, 1, 0.19, 0, 0.0, NULL), (37, 238, 1, 0.13, 0, 0.0, NULL), (44, 208, 1, 0.18, 0, 0.0, NULL), (49, 213, 1, 0.11, 0, 0.0, NULL), (30, 261, 2, 0.09, 0, 0.0, NULL), (33, 245, 2, 0.09, 0, 0.0, NULL), (24, 270, 2, 0.05, 0, 0.0, NULL), (42, 249, 2, 0.14, 0, 0.0, NULL), (43, 283, 1, 0.16, 0, 0.0, NULL), (39, 277, 2, 0.12, 0, 0.0, NULL), (1, 265, 1, 0.19, 0, 0.0, NULL), (42, 277, 1, 0.1, 0, 0.0, NULL), (2, 237, 2, 0.11, 0, 0.0, NULL), (24, 259, 2, 0.07, 0, 0.0, NULL), (42, 288, 1, 0.17, 0, 0.0, NULL), (45, 221, 1, 0.18, 0, 0.0, NULL), (10, 275, 2, 0.05, 0, 0.0, NULL), (15, 209, 2, 0.15, 0, 0.0, NULL), (24, 282, 2, 0.09, 0, 0.0, NULL), (30, 287, 1, 0.15, 0, 0.0, NULL), (22, 257, 1, 0.19, 0, 0.0, NULL) -------- Getting Data from One Table -------- Here we will learn about SELECT, FROM, WHERE, ORDER BY and LIMIT. You've seen a few of these already. Here I'll retrieve all data from from the table sales_item. SELECT * FROM sales_item; WHERE is used to define which rows are included in the results based on a condition. Show all sales with a discount greater than 15% Conditional Operators = : Equal < : Less than > : Greater than <= : Less than or Equal >= : Greater than or Equal <> : Not Equal != : Not Equal SELECT * FROM sales_item WHERE discount > .15; Logical Operators AND, OR and NOT are logical operators. Use them to combine conditions. Find the order dates for all orders in December, 2018. SELECT time_order_taken FROM sales_order WHERE time_order_taken > '2018-12-01' AND time_order_taken < '2018-12-31'; You can use BETWEEN to get the same results SELECT time_order_taken FROM sales_order WHERE time_order_taken BETWEEN '2018-12-01' AND '2018-12-31'; ORDER BY determines which column is used to define the order of results. The default order is from low to high. SELECT * FROM sales_item WHERE discount > .15 ORDER BY discount; The following gives results from high to low SELECT * FROM sales_item WHERE discount > .15 ORDER BY discount DESC; LIMIT limits the number of rows in the result. Get just the top 5. You could use LIMIT 5, 10 to get the next 5 SELECT * FROM sales_item WHERE discount > .15 ORDER BY discount DESC LIMIT 5; You can limit the results. Get the name, phone number and state where state is Texas. We can use CONCAT to merge to columns. We can then use AS to define a new column name. SELECT CONCAT(first_name, ' ', last_name) AS Name, phone, state FROM customer WHERE state = 'TX'; You can perform calculations. Get the total value of all business shoes in inventory. SELECT product_id, SUM(price) AS Value FROM item WHERE product_id = 1; You can use distinct to eliminate duplicates in results. Get a list of states we have customers in. SELECT DISTINCT state FROM customer ORDER BY state; Find all states where we have customers not including 'CA' SELECT DISTINCT state FROM customer WHERE state != 'CA' ORDER BY state; The IN phrase can be used to test if a value is in a list. Find customer states that are in my list. You can also use NOT IN. SELECT DISTINCT state FROM customer WHERE state IN ('CA', 'NJ') ORDER BY state; -------- Getting Data from Multiple Tables -------- We can get results from multiple tables with either inner joins, outer joins, or unions. The most common join is the inner join. You join data from 2 tables in the FROM clause with the JOIN keyword. The ON keyword is used to define the join condition. Get all items ordered ever and sort them by id while listing their price : SELECT item_id, price FROM item INNER JOIN sales_item ON item.id = sales_item.item_id ORDER BY item_id; We use the join condition to find ids that are equal in the tables item and sales_item. These joins are normally done using the primary and foreign keys in the tables as we did here. When we join tables while checking for equality between a common column this is called a equijoin. You can define multiple join conditions with logical operators : SELECT item_id, price FROM item INNER JOIN sales_item ON item.id = sales_item.item_id AND price > 120.00 ORDER BY item_id; Now let's join 3 tables. Get the orders, quantity and the total sale. SELECT sales_order.id, sales_item.quantity, item.price, (sales_item.quantity * item.price) AS Total FROM sales_order JOIN sales_item ON sales_item.sales_order_id = sales_order.id JOIN item ON item.id = sales_item.item_id ORDER BY sales_order.id; Other arithmetic operators include : Addition : + Subtraction : - Division : / Integer Division : DIV Modulus : % You can also define the join conditions using WHERE, but this is not considered to be a best practice. SELECT item_id, price FROM item, sales_item WHERE item.id = sales_item.item_id AND price > 120.00 ORDER BY item_id; -------- Outer Joins -------- Outer joins return all of the rows from one of the tables being joined even if no matches are found. A Left Outer Join returns all rows from the table being joined on the left. The Right Outer Join returns all rows from the table on the right. It's common practice to avoid Right Outer joins though. Here I'll get product information from 2 tables SELECT name, supplier, price FROM product LEFT JOIN item ON item.product_id = product.id ORDER BY name; -------- Cross Joins -------- Cross joins include data from each row in both tables. I'll grab information from the item and sales_item table. This will produce many results. Since there are no join conditions in a Cross Join you will rarely use them. SELECT sales_order_id, quantity, product_id FROM item CROSS JOIN sales_item ORDER BY sales_order_id; -------- Unions -------- Unions combine the results of 2 or more select statements in one result. Each result must return the same number of columns and data in each column must have the same data type. Let's say we want to send birthday cards to all customers and sales persons for the month of December we could do this. Always put the Order By statement last. The column names are taken from those provided in the 1st select statement. SELECT first_name, last_name, street, city, zip, birth_date FROM customer WHERE MONTH(birth_date) = 12 UNION SELECT first_name, last_name, street, city, zip, birth_date FROM sales_person WHERE MONTH(birth_date) = 12 ORDER BY birth_date; -------- IS NULL -------- Null is used when a value is not known. IS NULL can be used to search for potential problems. Search for items with NULL prices SELECT product_id, price FROM item WHERE price = NULL; You can also use IS NOT NULL -------- LIKE & REGEXP -------- Like is used to search for simple string matches. Match any customers whose name begins with M SELECT first_name, last_name FROM customer WHERE first_name LIKE "M%"; % matches for zero or more characters. _ Matches any single character. SELECT first_name, last_name FROM customer WHERE first_name LIKE 'A_____'; Return all customers whose 1st name begins with D, or whose last name ends with an n SELECT first_name, last_name FROM customer WHERE first_name LIKE 'D%' OR last_name LIKE '%n'; REGEXP SLIDE REGEXP is used to search for complex patterns using regular expressions. Match 1st name that starts with Ma SELECT first_name, last_name FROM customer WHERE first_name REGEXP '^Ma'; Match names that end with ez SELECT first_name, last_name FROM customer WHERE last_name REGEXP 'ez$'; Match last names that contain ez or son SELECT first_name, last_name FROM customer WHERE last_name REGEXP 'ez|son'; Match last names that contain w, x, y or z SELECT first_name, last_name FROM customer WHERE last_name REGEXP '[w-z]'; -------- SUMMARIZING RESULTS -------- GROUP BY defines how the results are grouped. COUNT returns the total number of records that match. We'll use GROUP BY to return a single row for each unique value. How many customers have birthdays in certain months SELECT MONTH(birth_date) AS Month, COUNT(*) AS Amount FROM customer GROUP BY Month ORDER BY Month; HAVING narrows the results based on a condition. Let's only get months if more than 1 person has a birthday that month SELECT MONTH(birth_date) AS Month, COUNT(*) AS Amount FROM customer GROUP BY Month HAVING Amount > 1 ORDER BY Month; Get total purchases from each company SELECT cust_id, company, COUNT(*) AS Purchases FROM sales_order JOIN customer ON customer.id = cust_id GROUP BY cust_id; -------- AGGREGATE FUNCTIONS -------- Aggregate functions return a single value from multiple parameters. For example sum all our inventory SELECT SUM(price) FROM item; Get count, sum, min, max and average value of our items SELECT COUNT(*) AS Items, SUM(price) AS Value, ROUND(AVG(price), 2) AS Avg, MIN(price) AS Min, MAX(price) AS Max FROM item; SLIDE -------- WORKING WITH VIEWS -------- Views are select statements thats result is stored in your database. Let's create a view that contains our main purchase order info. CREATE VIEW purchase_order_overview AS SELECT sales_order.purchase_order_number, customer.company, sales_item.quantity, product.supplier, product.name, item.price, (sales_item.quantity * item.price) AS Total, CONCAT(sales_person.first_name, ' ', sales_person.last_name) AS Salesperson FROM sales_order JOIN sales_item ON sales_item.sales_order_id = sales_order.id JOIN item ON item.id = sales_item.item_id JOIN customer ON sales_order.cust_id = customer.id JOIN product ON product.id = item.product_id JOIN sales_person ON sales_person.id = sales_order.sales_person_id ORDER BY purchase_order_number; When data in the database is updated so is the view. You can use the view in all the same ways you can a regular table. SELECT * FROM purchase_order_overview; You can drop this view with DROP VIEW purchase_order_overview. Views allow you to hide, or change data in other tables while keeping the view the same. This is beneficial in hiding data from users who shouldn't have access to it. If you want a view to be updatable it can't include : DISTINCT UNION Aggregate Functions GROUP BY or HAVING To make my view updatable I'd change it to CREATE VIEW purchase_order_overview AS SELECT sales_order.purchase_order_number, customer.company, sales_item.quantity, product.supplier, product.name, item.price, sales_person.first_name AS Sales F Name, sales_person.last_name AS Sales L Name FROM sales_order JOIN sales_item ON sales_item.sales_order_id = sales_order.id JOIN item ON item.id = sales_item.item_id JOIN customer ON sales_order.cust_id = customer.id JOIN product ON product.id = item.product_id JOIN sales_person ON sales_person.id = sales_order.sales_person_id ORDER BY purchase_order_number; Recalculate Total SELECT *, (quantity * price) AS Total FROM purchase_order_overview; -------- STORED PROGRAMS -------- You can write programs that are similar to traditional programming languages. There are different types of stored programs. Stored Procedures can be executed by an application that has access to your database. For example PHP could call for this code to execute. Stored Functions can be executed by SQL statements. Triggers execute when Insert, Update or Delete commands are executed. Events execute at scheduled times. Create a Stored Procedure that gets a list of customer company names -- Define that we are using the customer_db DB USE customer_db; -- If I already created a procedure with this name -- delete the previous and update it with new code DROP PROCEDURE IF EXISTS get_customers; -- Switch delimiter from ; to // because we use ; to -- define the end of our statements in our procedure DELIMITER // -- Create stored procedure named sample CREATE PROCEDURE get_customers() -- Code assigned to the procedure starts at BEGIN BEGIN SELECT customer.company FROM customer; END // -- Change delimiter back to ; DELIMITER ; -- Execute stored procedure CALL get_customers(); Create a procedure that returns inventory value based on supplier -- Define that we are using the customer_db DB USE customer_db; DROP PROCEDURE IF EXISTS get_supplier_value; DELIMITER // -- Accept an argument being the suppliers name CREATE PROCEDURE get_supplier_value(IN the_supplier VARCHAR(30)) BEGIN -- Declare 2 variables to hold results of query DECLARE supplier_name VARCHAR(30); DECLARE price_sum DECIMAL(8, 2); -- Get results of query and store them in variables SELECT product.supplier, SUM(item.price) INTO supplier_name, price_sum FROM product, item WHERE product.supplier = the_supplier; -- Output results of query SELECT CONCAT(supplier_name, ' Inventory Value : $', price_sum) AS output; END // DELIMITER ; -- Pass Nike to the procedure CALL get_supplier_value('Nike'); Accept multiple values using IN and OUT parameters -- Define that we are using the customer_db DB USE customer_db; DROP PROCEDURE IF EXISTS get_customer_birthday; DELIMITER // -- To receive multiple values you must use OUT parameters -- IN designates a parameter expected to be passed to a procedure -- it is pass by value and its value is only affected in the procedure -- OUT designates a variable whose value will be passed back to -- the calling program. The procedure can't access an OUT parameters -- value when it's passed. -- INOUT parameters can both be passed in and returned CREATE PROCEDURE get_customer_birthday(IN the_month INT, OUT bd_month INT, OUT bd_day INT, OUT f_name VARCHAR(20), OUT l_name VARCHAR(20), OUT p_num VARCHAR(20) ) BEGIN -- Get results of query and store them in variables SELECT MONTH(birth_date), DAY(birth_date), first_name, last_name, phone FROM customer WHERE MONTH(birth_date) = the_month; END // DELIMITER ; -- Pass the month and the OUT parameters CALL get_customer_birthday(9, @bd_month, @bd_day, @f_name, @l_name, @p_num); Check order status with IF ELSEIF and ELSE USE customer_db; DROP PROCEDURE IF EXISTS check_month_orders; DELIMITER // CREATE PROCEDURE check_month_orders(IN the_month INT) BEGIN DECLARE total_orders INT; SELECT COUNT(purchase_order_number) INTO total_orders FROM sales_order WHERE MONTH(time_order_taken) = the_month; -- Different output using if IF total_orders > 5 THEN SELECT CONCAT(total_orders, ' Orders : Doing Good') AS output; ELSEIF total_orders < 5 THEN SELECT CONCAT(total_orders, ' Orders : Doing Bad') AS output; ELSE SELECT CONCAT(total_orders, ' Orders : On Target') AS output; END IF; END // DELIMITER ; -- Pass Nike to the procedure CALL check_month_orders(9); Doing the same with CASE -- Define that we are using the customer_db DB USE customer_db; DROP PROCEDURE IF EXISTS check_month_orders; DELIMITER // CREATE PROCEDURE check_month_orders(IN the_month INT) BEGIN DECLARE total_orders INT; SELECT COUNT(purchase_order_number) INTO total_orders FROM sales_order WHERE MONTH(time_order_taken) = the_month; -- Case executes different code depending on an exact value -- for total_orders or a range of values CASE WHEN total_orders < 1 THEN SELECT CONCAT(total_orders, ' Orders : Terrible') AS output; WHEN total_orders > 1 < 5 THEN SELECT CONCAT(total_orders, ' Orders : Get Better') AS output; WHEN total_orders = 5 THEN SELECT CONCAT(total_orders, ' Orders : On Target') AS output; ELSE SELECT CONCAT(total_orders, ' Orders : Doing Good') AS output; END CASE; END // DELIMITER ; -- Pass Nike to the procedure CALL check_month_orders(9); You can also CASE total_orders WHEN 1 THEN SELECT CONCAT(total_orders, ' Orders : Terrible') AS output; Sum values up to a max number using while USE customer_db; DROP PROCEDURE IF EXISTS loop_test; DELIMITER // CREATE PROCEDURE loop_test(IN i INT) BEGIN DECLARE j INT DEFAULT 1; DECLARE tot_sum INT DEFAULT 0; WHILE j <= i DO SET tot_sum = tot_sum + j; SET j = j + 1; END WHILE; SELECT CONCAT('sum = ', tot_sum) AS sum; END // DELIMITER ; CALL loop_test(5); Replace While with Repeat for same results REPEAT SET tot_sum = tot_sum + j; SET j = j + 1; UNTIL j > i END REPEAT; Cursors Cursors allow you iterate through rows and work with them individually. Get a comma separated list of companies using a cursor USE customer_db; DROP PROCEDURE IF EXISTS get_companies; DELIMITER // -- We will receive the variable and pass the company list back CREATE PROCEDURE get_companies(INOUT company_list VARCHAR(1000)) BEGIN -- When set to 1 we know we are at the end of the list DECLARE finished INTEGER DEFAULT 0; -- Will hold each comapny name from the list DECLARE company_name VARCHAR(30) DEFAULT ""; -- declare cursor for customer list which is populated -- by SELECT DECLARE cur_company CURSOR FOR SELECT company FROM customer; -- Handles when no more companies were found DECLARE CONTINUE HANDLER FOR NOT FOUND SET finished = 1; -- Initializes the result set OPEN cur_company; -- Will continue looping until LEAVE is called get_company: LOOP -- Retrieves the next row of data FETCH cur_company INTO company_name; -- Leave loop when no more rows to process IF finished = 1 THEN LEAVE get_company; END IF; -- Add company to result SET company_list = CONCAT(company_name, ', ', company_list); END LOOP get_company; -- Release memory assigned for the cursor CLOSE cur_company; END // DELIMITER ; -- Generate a list of companies using a cursor SET @company_list = ''; CALL get_companies(@company_list); SELECT @company_list; -------- ERROR HANDLING -------- You can handle errors in your procedures to avoid errors and provide understandable error messages. We'll create an error where the user tries to use a key already in use and provide a custom error message -- Define that we are using the customer_db DB USE customer_db; DROP PROCEDURE IF EXISTS create_product_type; DELIMITER // CREATE PROCEDURE create_product_type(IN prod_name VARCHAR(30), IN prod_id INT) BEGIN -- You are going to block the Duplicate Key error code 1062 -- This is thrown when you try to use a key already in use. DECLARE EXIT HANDLER FOR 1062 BEGIN SELECT CONCAT('You tried to use an Key ID that already exists. You can\'t use ID : ', prod_id); END; -- This will catch any other errors not caught by the above DECLARE EXIT HANDLER FOR SQLSTATE '23000' BEGIN SELECT CONCAT('An Unknown Error Occurred'); END; INSERT INTO product_type(name, id) VALUES(prod_name, prod_id); -- This isn't executed because you have DECLARE EXIT HANDLER above -- If you had EXIT CONTINUE it would execute but your error message wouldn't SELECT * FROM product_type; END // DELIMITER ; CALL create_product_type('Business Casual', 1); There is a complete list of error messages here https://dev.mysql.com/doc/refman/5.7/en/server-error-reference.html -------- RAISING USER DEFINED ERRORS -------- You can catch your own custom errors. Here I'll block a user from entering a duplicate value in the product_type table -- Define that we are using the customer_db DB USE customer_db; DROP PROCEDURE IF EXISTS insert_product_type; DELIMITER // CREATE PROCEDURE insert_product_type(IN prod_name VARCHAR(30), IN prod_id INT) BEGIN -- Will hold the result of checking for a name in product_type DECLARE name_in_table INT; -- Define the name for our custom error event DECLARE duplicate_prod_name CONDITION FOR SQLSTATE '50000'; -- This is called if the custom error is thrown DECLARE EXIT HANDLER FOR duplicate_prod_name BEGIN SELECT CONCAT('You can\'t enter a product name already in the table'); END; -- Check if the name passed is already in the table SET name_in_table = (SELECT COUNT(*) FROM product_type WHERE product_type.name = prod_name); -- If name is already in the table trigger an error -- SIGNAL is used to trigger an error IF name_in_table = 1 THEN SIGNAL duplicate_prod_name; ELSE INSERT INTO product_type(name, id) VALUES(prod_name, prod_id); END IF; END // DELIMITER ; CALL insert_product_type('Athletic', 4); -------- TRANSACTIONS -------- You can combine multiple SQL statements using a transaction. Within the transaction you can define when statements are committed and roll back statement execution if something goes wrong. -- Define that we are using the customer_db DB USE customer_db; DROP PROCEDURE IF EXISTS insert_sales_item; DELIMITER // CREATE PROCEDURE insert_sales_item() BEGIN -- Will be true or false based on if errors occur DECLARE sql_error TINYINT DEFAULT FALSE; DECLARE CONTINUE HANDLER FOR SQLEXCEPTION SET sql_error = TRUE; START TRANSACTION; INSERT INTO sales_item VALUES (3, 263, 1, 0.09, 0, 0.0, NULL); INSERT INTO sales_item VALUES (26, 283, 1, 0.07, 0, 0.0, NULL); INSERT INTO sales_item VALUES (23, 253, 1, 0.05, 0, 0.0, NULL); IF sql_error = FALSE THEN COMMIT; SELECT 'Sales Items Inserted'; ELSE ROLLBACK; SELECT 'Transaction Rolled Back'; END IF; END // DELIMITER ; CALL insert_sales_item(); -------- STORED FUNCTIONS -------- You can create your own functions called stored functions. Your functions can only return a single value however. Since you know how to create stored procedures this will be easy to understand. Here I'll create a function that adds up and order you define. DELIMITER // -- Create function and define argument it receives CREATE FUNCTION get_order_total(sales_order_id INT) -- Define the return type RETURNS DECIMAL -- Deterministic means it returns the same result with same argument passed -- We define we read data, but don't insert DETERMINISTIC READS SQL DATA BEGIN -- Will store result DECLARE order_total DECIMAL(8, 2); -- Sum all totals based on sales_order.id and put the result in order_total SELECT SUM(sales_item.quantity * item.price) AS Total INTO order_total FROM sales_order JOIN sales_item ON sales_item.sales_order_id = sales_order.id JOIN item ON item.id = sales_item.item_id WHERE sales_order.id = sales_order_id GROUP BY sales_order.id ORDER BY sales_order.id; -- Return the result RETURN(order_total); END// DELIMITER ; Call for the function to execute SELECT sales_order.id, get_order_total(sales_order.id) AS Order_Total FROM sales_order ORDER BY sales_order.id; -------- TRIGGERS -------- Triggers can be executed automatically when a SQL query is executed. I'll create a procedure that checks if a customer with the new first and last name exists and if it does I throw an error DROP PROCEDURE IF EXISTS customer_in_db; DELIMITER // CREATE PROCEDURE customer_in_db( f_name VARCHAR(30), l_name VARCHAR(30) ) BEGIN DECLARE match_found INT; SELECT COUNT(*) INTO match_found FROM customer WHERE first_name = f_name AND last_name = l_name; IF match_found > 0 THEN SIGNAL SQLSTATE '50000' SET MESSAGE_TEXT = 'Customer already exists'; END IF; END// DELIMITER ; Now create a trigger that is fired if an insert is issued on customer DELIMITER // CREATE TRIGGER customers_before_insert -- Call Trigger if customer is updated BEFORE INSERT ON customer FOR EACH ROW BEGIN CALL customer_in_db(NEW.first_name, NEW.last_name); END// DELIMITER ; Now I try to insert a duplicate customer and it is blocked. INSERT INTO customer (first_name, last_name, email, company, street, city, state, zip, phone, birth_date, sex, date_entered, id) VALUES ('Melissa', 'Moore', 'laurenthomas@aramark.com', 'Aramark', '322 Pine St', 'Mission', 'TX', '78572', '236-237-1929', '1967-07-23', 'M', '2016-08-15 11:37:14', NULL); |
Leave a Reply