Knowledge

Database index

Source đź“ť

252:. An index may be declared as UNIQUE, which creates an implicit constraint on the underlying table. Database systems usually implicitly create an index on a set of columns declared PRIMARY KEY, and some are capable of using an already-existing index to police this constraint. Many database systems require that both referencing and referenced sets of columns in a FOREIGN KEY constraint are indexed, thus improving performance of inserts, updates and deletes to the tables participating in the constraint. 693: 1604: 313:(not to be confused with clustered index described previously). The records for the tables sharing the value of a cluster key shall be stored together in the same or nearby data blocks. This may improve the joins of these tables on the cluster key, since the matching records are stored together and less I/O is required to locate them. The cluster configuration defines the data layout in the tables that are parts of the cluster. A cluster can be keyed with a 579:, are most efficient if the values they index do not repeat or repeat a small number of times. In contrast, the bitmap index is designed for cases where the values of a variable repeat very frequently. For example, the sex field in a customer database usually contains at most three distinct values: male, female or unknown (not recorded). For such variables, the bitmap index can have a significant performance advantage over the commonly used trees. 1614: 36: 1624: 444:. This query would yield an email address for every customer whose email address ends with "@wikipedia.org", but even if the email_address column has been indexed the database must perform a full index scan. This is because the index is built with the assumption that words go from left to right. With a 333:
For example, in a phone book organized by city first, then by last name, and then by first name, in a particular city, one can easily extract the list of all phone numbers. However, it would be very tedious to find all the phone numbers for a particular last name. One would have to look within each
273:
is specified by the index. The data rows may be spread throughout the table regardless of the value of the indexed column or expression. The non-clustered index tree contains the index keys in sorted order, with the leaf level of the index containing the pointer to the record (page and the row number
296:
Clustering alters the data block into a certain distinct order to match the index, resulting in the row data being stored in order. Therefore, only one clustered index can be created on a given database table. Clustered indices can greatly increase overall speed of retrieval, but usually only where
854:
A covering index can dramatically speed up data retrieval but may itself be large due to the additional keys, which slow down data insertion and update. To reduce such index size, some systems allow including non-key fields in the index. Non-key fields are not themselves part of the index ordering
255:
Some database systems support an EXCLUSION constraint that ensures that, for a newly inserted or updated record, a certain predicate holds for no other record. This can be used to implement a UNIQUE constraint (with equality predicate) or more complex constraints, like ensuring that no overlapping
156:
An index is a copy of selected columns of data, from a table, that is designed to enable very efficient search. An index normally includes a "key" or direct link to the original row of data from which it was copied, to allow the complete row to be retrieved efficiently. Some databases extend the
300:
Since the physical records are in this sort order on disk, the next row item in the sequence is immediately before or after the last one, and so fewer data block reads are required. The primary feature of a clustered index is therefore the ordering of the physical data rows in accordance with the
676:
It is used to index fields that are neither ordering fields nor key fields (there is no assurance that the file is organized on key field or primary key field). One index entry for every tuple in the data file (dense index) contains the value of the indexed attribute and pointer to the block or
329:
The order that the index definition defines the columns in is important. It is possible to retrieve a set of row identifiers using only the first indexed column. However, it is not possible or efficient (on most databases) to retrieve the set of row identifiers using only the second or greater
208:
Suppose a database contains N data items and one must be retrieved based on the value of one of the fields. A simple implementation retrieves and examines each item according to the test. If there is only one matching item, this can stop when it finds that single item, but if there are multiple
645:
A reverse-key index reverses the key value before entering it in the index. E.g., the value 24538 becomes 83542 in the index. Reversing the key value is particularly useful for indexing data such as sequence numbers, where new key values monotonically increase.
144:
at the cost of additional writes and storage space to maintain the index data structure. Indexes are used to quickly locate data without having to search every row in a database table every time said table is accessed. Indexes can be created using one or more
905:
No standard defines how to create indexes, because the ISO SQL Standard does not cover physical aspects. Indexes are one of the physical parts of database conception among others like storage (tablespace or filegroups). RDBMS vendors all give a
781:. While in principle indexes can utilize the common database concurrency control methods, specialized concurrency control methods for indexes exist, which are applied in conjunction with the common methods for a substantial performance gain. 762:
of the clustered index corresponds to the actual data, not simply a pointer to data that resides elsewhere, as is the case with a non-clustered index. Each relation can have a single clustered index and many unclustered indices.
847:
To find the Name for ID 13, an index on (ID) is useful, but the record must still be read to get the Name. However, an index on (ID, Name) contains the required data field and eliminates the need to look up the record.
789:
In most cases, an index is used to quickly locate the data records from which the required data is read. In other words, the index is only used to locate data records in the table and not to return data.
668:
The primary index contains the key fields of the table and a pointer to the non-key fields of the table. The primary index is created automatically when the table is created in the database.
256:
time ranges or no intersecting geometry objects would be stored in the table. An index supporting fast searching for records satisfying the predicate is required to police such a constraint.
301:
index blocks that point to them. Some databases separate the data and index blocks into separate files, others put two completely different data blocks within the same physical file(s).
851:
Covering indexes are each for a specific table. Queries which JOIN/ access across multiple tables, may potentially consider covering indexes on more than one of these tables.
224:
used for this purpose. There are complex design trade-offs involving lookup performance, index size, and index-update performance. Many index designs exhibit logarithmic (
685:
A hash index in database is most commonly used index in data management. It is created on a column that contains unique values, such as a primary key or email address.
544: 173:, where index entries are created only for those records that satisfy some conditional expression. A further aspect of flexibility is to permit indexing on 1214: 448:
at the beginning of the search-term, the database software is unable to use the underlying index data structure (in other words, the WHERE-clause is
1197: 403:. To process this statement without an index the database software must look at the last_name column on every row in the table (this is known as a 1209: 1127: 217:. Since databases may contain many objects, and since lookup is a common operation, it is often desirable to improve performance. 793:
A covering index is a special case where the index itself contains the required data fields and can answer the required data.
1017: 297:
the data is accessed sequentially in the same or reverse order of the clustered index, or when a range of items is selected.
100: 1648: 1607: 72: 1280: 1169: 345:), if we search by giving exact values for all the three fields, search time is minimal—but if we provide the values for 1658: 969: 79: 119: 157:
power of indexing by letting developers create indexes on column values that have been transformed by functions or
334:
city's section for the entries with that last name. Some databases can do this, others just won't use the index.
1627: 1333: 1071:
Daren Bieniek; Randy Dess; Mike Hotek; Javier Loria; Adam Machanic; Antonio Soto; Adolfo Wiernik (January 2006).
1003: 53: 86: 1584: 1231: 596: 57: 1523: 411:) until the Smith entry has been found; this is much less computationally expensive than a full table scan. 361:. So, to improve the performance, one must ensure that the index is created on the order of search columns. 515:, the index available on this field is not used. Rather only a sequential search is performed, which takes 68: 1518: 660:
An inverted index maps a content word to the document containing it, thereby allowing full-text searches.
1549: 1268: 777:
An index is typically being accessed concurrently by several transactions and processes, and thus needs
1472: 1462: 1238: 338: 17: 1559: 1292: 703: 600: 1039: 284:
The indexed columns are typically non-primary key columns used in JOIN, WHERE, and ORDER BY clauses.
1101: 1089: 221: 158: 209:
matches, it must test everything. This means that the number of operations in the average case is
1508: 1162: 958: 855:
but only included at the leaf level, allowing for a covering index with less overall index size.
220:
An index is any data structure that improves the performance of lookup. There are many different
46: 369:
Indexes are useful for many applications but come with some limitations. Consider the following
1589: 1544: 1221: 938: 321:. The data block where the table record is stored is defined by the value of the cluster key. 1564: 1318: 174: 93: 1617: 1554: 1436: 1406: 1275: 1226: 982: 755: 627:
in the sorted data file. In clustered indices with duplicate keys, the sparse index points
620: 241: 607:
in the sorted data file. In clustered indices with duplicate keys, the dense index points
520: 8: 1574: 1467: 1452: 1379: 1204: 1053: 778: 357:
field to retrieve all matched records. Then a sequential lookup checks the matching with
739:
Indices can be implemented using a variety of data structures. Popular indices include
1653: 1569: 1513: 1482: 1431: 1323: 1263: 1155: 1027: 445: 228:(log(N))) lookup performance and in some applications it is possible to achieve flat ( 1389: 1243: 1013: 572: 146: 603:
in the data file. Every key in this file is associated with a particular pointer to
407:). With an index the database simply follows the index data structure (typically a 1579: 1426: 1416: 1384: 1072: 274:
in the data page in page-organized engines; row offset in file-organized engines).
141: 1487: 1457: 1411: 1192: 623:
in the data file. Every key in this file is associated with a particular pointer
567:
A bitmap index is a special kind of indexing that stores the bulk of its data as
404: 195: 915:
syntax with some specific options that depend on their software's capabilities.
619:
A sparse index in databases is a file with pairs of keys and pointers for every
1539: 1477: 1394: 1287: 1248: 990: 986: 932: 728: 655: 455:). This problem can be solved through the addition of another index created on 229: 225: 210: 137: 1147: 1642: 1358: 1343: 1070: 927: 772: 740: 640: 592: 202: 170: 562: 511:
When the wildcard characters are used on both sides of the search word as
177:, as well as expressions formed from an assortment of built-in functions. 1348: 1328: 249: 214: 692: 1492: 1401: 1363: 1338: 1009: 748: 318: 309:
When multiple databases and multiple tables are joined, it is called a
245: 759: 568: 169:
field in the index. Another option sometimes supported is the use of
288:
There can be more than one non-clustered index on a database table.
35: 1353: 1308: 1178: 744: 588: 576: 504:. This puts the wild-card at the right-most part of the query (now 451: 191: 281:
The physical order of the rows is not the same as the index order.
1258: 408: 314: 198: 150: 1083: 1253: 1313: 575:
on these bitmaps. The most commonly used indexes, such as
508:), which the index on reverse(email_address) can satisfy. 259: 140:
that improves the speed of data retrieval operations on a
370: 165:, which would only store the upper-case versions of the 796:
Consider the following table (other fields omitted):
523: 235: 1128:"Create indexes with included columns - SQL Server" 1005:
Chapter 8: Building Fast-Performing Database Models
194:software includes indexing technology that enables 60:. Unsourced material may be challenged and removed. 727:Another type of index used in database systems is 538: 27:Data structure for query optimization in databases 571:(bitmaps) and answers most queries by performing 1640: 269:The data is present in arbitrary order, but the 1177: 952: 364: 972:Oracle® Database Concepts 10g Release 1 (10.1) 963: 1163: 1102:"11.9. Index-Only Scans and Covering Indexes" 1077:SQL Server 2005 Implementation and Management 1058:SQL Server 2005 Books Online (September 2007) 1001: 959:PostgreSQL 9.1.2 Documentation: CREATE TABLE 766: 161:. For example, an index could be created on 149:, providing the basis for both rapid random 1170: 1156: 185: 153:and efficient access of ordered records. 120:Learn how and when to remove this message 734: 1125: 1090:Covering Indexes for Query Optimization 260:Index architecture and indexing methods 14: 1641: 1151: 981:Database Systems: The Complete Book. 687: 205:is inefficient for large databases. 58:adding citations to reliable sources 29: 1623: 551: 24: 941: â€“ Method for data management 900: 671: 25: 1670: 784: 722: 649: 337:In the phone book example with a 236:Policing the database constraints 1622: 1612: 1603: 1602: 691: 663: 634: 264: 34: 1613: 907: 859: 614: 556: 460: 456: 415: 374: 353:only, the search uses only the 324: 45:needs additional citations for 1119: 1094: 1064: 1046: 995: 975: 935: â€“ Type of database index 582: 533: 527: 13: 1: 1073:"Chapter 4: Creating Indices" 945: 858:This can be done in SQL with 680: 414:Consider this SQL statement: 244:, such as UNIQUE, EXCLUSION, 1054:"Clustered Index Structures" 365:Applications and limitations 291: 7: 1649:Database management systems 1179:Database management systems 921: 459:and a SQL query like this: 343:city, last_name, first_name 240:Indexes are used to police 201:to improve performance, as 147:columns of a database table 10: 1675: 1585:Object–relational database 770: 653: 638: 573:bitwise logical operations 560: 304: 277:In a non-clustered index, 1659:Database index techniques 1598: 1560:Federated database system 1532: 1501: 1445: 1372: 1301: 1293:Blockchain-based database 1185: 767:Index concurrency control 498:'%@wikipedia.org' 438:'%@wikipedia.org' 1106:PostgreSQL Documentation 629:to the lowest search key 341:created on the columns ( 180: 595:with pairs of keys and 186:Support for fast lookup 1590:Transaction processing 1545:Database normalization 1488:Query rewriting system 939:Search engine indexing 700:This section is empty. 540: 457:reverse(email_address) 175:user-defined functions 1565:Referential integrity 1002:Gavin Powell (2006). 735:Index implementations 541: 1555:Distributed database 983:Hector Garcia-Molina 970:Overview of Clusters 756:Microsoft SQL Server 539:{\displaystyle O(N)} 521: 242:database constraints 54:improve this article 1575:Relational calculus 1453:Concurrency control 1132:learn.microsoft.com 779:concurrency control 609:to the first record 1570:Relational algebra 1514:Query optimization 1319:Armstrong's axioms 1079:. Microsoft Press. 536: 232:(1)) performance. 1636: 1635: 1244:Wide-column store 1239:Document-oriented 1060:. 4 October 2012. 1019:978-0-7645-7490-0 991:Jennifer D. Widom 987:Jeffrey D. Ullman 845: 844: 720: 719: 587:A dense index in 130: 129: 122: 104: 16:(Redirected from 1666: 1626: 1625: 1616: 1615: 1606: 1605: 1580:Relational model 1550:Database storage 1427:Stored procedure 1172: 1165: 1158: 1149: 1148: 1142: 1141: 1139: 1138: 1123: 1117: 1116: 1114: 1113: 1098: 1092: 1087: 1081: 1080: 1068: 1062: 1061: 1050: 1044: 1043: 1037: 1033: 1031: 1023: 999: 993: 979: 973: 967: 961: 956: 914: 913: 910: 896: 895: 892: 889: 886: 883: 880: 877: 874: 871: 868: 865: 862: 799: 798: 715: 712: 702:You can help by 695: 688: 552:Types of indexes 547: 545: 543: 542: 537: 507: 503: 502: 499: 496: 493: 490: 487: 484: 481: 478: 475: 472: 469: 466: 463: 458: 443: 442: 439: 436: 433: 430: 427: 424: 421: 418: 402: 401: 398: 395: 392: 389: 386: 383: 380: 377: 360: 356: 352: 348: 344: 330:indexed column. 271:logical ordering 168: 164: 163:upper(last_name) 125: 118: 114: 111: 105: 103: 69:"Database index" 62: 38: 30: 21: 1674: 1673: 1669: 1668: 1667: 1665: 1664: 1663: 1639: 1638: 1637: 1632: 1594: 1540:Database models 1528: 1497: 1483:Query optimizer 1458:Data dictionary 1441: 1412:Transaction log 1368: 1324:Codd's 12 rules 1297: 1227:Column-oriented 1193:Object-oriented 1181: 1176: 1146: 1145: 1136: 1134: 1124: 1120: 1111: 1109: 1100: 1099: 1095: 1088: 1084: 1069: 1065: 1052: 1051: 1047: 1035: 1034: 1025: 1024: 1020: 1010:Wrox Publishing 1000: 996: 980: 976: 968: 964: 957: 953: 948: 924: 918: 911: 908: 903: 901:Standardization 893: 890: 887: 884: 881: 878: 875: 872: 869: 866: 863: 860: 787: 775: 769: 737: 725: 716: 710: 707: 683: 674: 672:Secondary index 666: 658: 652: 643: 637: 631:in each block. 617: 611:with that key. 585: 565: 559: 554: 522: 519: 518: 516: 513:%wikipedia.org% 506:gro.aidepikiw@% 505: 500: 497: 494: 491: 488: 485: 482: 479: 476: 473: 470: 467: 464: 461: 440: 437: 434: 431: 428: 425: 422: 419: 416: 405:full table scan 399: 397:'Smith' 396: 393: 390: 387: 384: 381: 378: 375: 367: 358: 354: 350: 346: 342: 339:composite index 327: 307: 294: 267: 262: 238: 222:data structures 196:sub-linear time 188: 183: 166: 162: 126: 115: 109: 106: 63: 61: 51: 39: 28: 23: 22: 15: 12: 11: 5: 1672: 1662: 1661: 1656: 1651: 1634: 1633: 1631: 1630: 1620: 1610: 1599: 1596: 1595: 1593: 1592: 1587: 1582: 1577: 1572: 1567: 1562: 1557: 1552: 1547: 1542: 1536: 1534: 1533:Related topics 1530: 1529: 1527: 1526: 1521: 1516: 1511: 1509:Administration 1505: 1503: 1499: 1498: 1496: 1495: 1490: 1485: 1480: 1478:Query language 1475: 1470: 1465: 1460: 1455: 1449: 1447: 1443: 1442: 1440: 1439: 1434: 1429: 1424: 1419: 1414: 1409: 1404: 1399: 1398: 1397: 1392: 1387: 1376: 1374: 1370: 1369: 1367: 1366: 1361: 1356: 1351: 1346: 1341: 1336: 1331: 1326: 1321: 1316: 1311: 1305: 1303: 1299: 1298: 1296: 1295: 1290: 1285: 1284: 1283: 1273: 1272: 1271: 1261: 1256: 1251: 1246: 1241: 1236: 1235: 1234: 1224: 1219: 1218: 1217: 1212: 1202: 1201: 1200: 1189: 1187: 1183: 1182: 1175: 1174: 1167: 1160: 1152: 1144: 1143: 1118: 1093: 1082: 1063: 1045: 1018: 994: 974: 962: 950: 949: 947: 944: 943: 942: 936: 933:Inverted index 930: 923: 920: 902: 899: 843: 842: 839: 836: 832: 831: 828: 825: 821: 820: 817: 814: 810: 809: 806: 803: 786: 785:Covering index 783: 771:Main article: 768: 765: 741:balanced trees 736: 733: 729:linear hashing 724: 723:Linear hashing 721: 718: 717: 698: 696: 682: 679: 673: 670: 665: 662: 656:Inverted index 654:Main article: 651: 650:Inverted index 648: 639:Main article: 636: 633: 616: 613: 584: 581: 561:Main article: 558: 555: 553: 550: 535: 532: 529: 526: 366: 363: 326: 323: 306: 303: 293: 290: 286: 285: 282: 266: 263: 261: 258: 237: 234: 187: 184: 182: 179: 142:database table 138:data structure 134:database index 128: 127: 42: 40: 33: 26: 9: 6: 4: 3: 2: 1671: 1660: 1657: 1655: 1652: 1650: 1647: 1646: 1644: 1629: 1621: 1619: 1611: 1609: 1601: 1600: 1597: 1591: 1588: 1586: 1583: 1581: 1578: 1576: 1573: 1571: 1568: 1566: 1563: 1561: 1558: 1556: 1553: 1551: 1548: 1546: 1543: 1541: 1538: 1537: 1535: 1531: 1525: 1522: 1520: 1517: 1515: 1512: 1510: 1507: 1506: 1504: 1500: 1494: 1491: 1489: 1486: 1484: 1481: 1479: 1476: 1474: 1471: 1469: 1466: 1464: 1461: 1459: 1456: 1454: 1451: 1450: 1448: 1444: 1438: 1435: 1433: 1430: 1428: 1425: 1423: 1420: 1418: 1415: 1413: 1410: 1408: 1405: 1403: 1400: 1396: 1393: 1391: 1388: 1386: 1383: 1382: 1381: 1378: 1377: 1375: 1371: 1365: 1362: 1360: 1359:Surrogate key 1357: 1355: 1352: 1350: 1347: 1345: 1344:Candidate key 1342: 1340: 1337: 1335: 1332: 1330: 1327: 1325: 1322: 1320: 1317: 1315: 1312: 1310: 1307: 1306: 1304: 1300: 1294: 1291: 1289: 1286: 1282: 1279: 1278: 1277: 1274: 1270: 1267: 1266: 1265: 1262: 1260: 1257: 1255: 1252: 1250: 1247: 1245: 1242: 1240: 1237: 1233: 1230: 1229: 1228: 1225: 1223: 1220: 1216: 1213: 1211: 1208: 1207: 1206: 1203: 1199: 1196: 1195: 1194: 1191: 1190: 1188: 1184: 1180: 1173: 1168: 1166: 1161: 1159: 1154: 1153: 1150: 1133: 1129: 1126:MikeRayMSFT. 1122: 1107: 1103: 1097: 1091: 1086: 1078: 1074: 1067: 1059: 1055: 1049: 1041: 1029: 1021: 1015: 1011: 1007: 1006: 998: 992: 988: 984: 978: 971: 966: 960: 955: 951: 940: 937: 934: 931: 929: 928:Index locking 926: 925: 919: 916: 898: 856: 852: 849: 840: 837: 834: 833: 829: 826: 823: 822: 818: 815: 812: 811: 808:Other Fields 807: 804: 801: 800: 797: 794: 791: 782: 780: 774: 773:Index locking 764: 761: 757: 752: 750: 746: 742: 732: 730: 714: 705: 701: 697: 694: 690: 689: 686: 678: 669: 664:Primary index 661: 657: 647: 642: 641:Reverse index 635:Reverse index 632: 630: 626: 622: 612: 610: 606: 602: 598: 594: 590: 580: 578: 574: 570: 564: 549: 530: 524: 514: 509: 483:email_address 465:email_address 454: 453: 447: 432:email_address 420:email_address 412: 410: 406: 372: 362: 340: 335: 331: 322: 320: 316: 312: 302: 298: 289: 283: 280: 279: 278: 275: 272: 265:Non-clustered 257: 253: 251: 247: 243: 233: 231: 227: 223: 218: 216: 212: 206: 204: 203:linear search 200: 197: 193: 178: 176: 172: 171:partial index 160: 154: 152: 148: 143: 139: 135: 124: 121: 113: 102: 99: 95: 92: 88: 85: 81: 78: 74: 71: â€“  70: 66: 65:Find sources: 59: 55: 49: 48: 43:This article 41: 37: 32: 31: 19: 1421: 1135:. Retrieved 1131: 1121: 1110:. Retrieved 1108:. 2023-02-09 1105: 1096: 1085: 1076: 1066: 1057: 1048: 1004: 997: 977: 965: 954: 917: 904: 857: 853: 850: 846: 795: 792: 788: 776: 753: 738: 726: 708: 704:adding to it 699: 684: 675: 667: 659: 644: 628: 625:to the block 624: 618: 615:Sparse index 608: 604: 586: 566: 563:Bitmap index 557:Bitmap index 512: 510: 449: 413: 368: 336: 332: 328: 325:Column order 310: 308: 299: 295: 287: 276: 270: 268: 254: 239: 219: 207: 189: 155: 133: 131: 116: 107: 97: 90: 83: 76: 64: 52:Please help 47:verification 44: 1628:WikiProject 1519:Replication 1407:Transaction 1349:Foreign key 1329:CAP theorem 1276:Multi-model 1036:|work= 583:Dense index 373:statement: 317:index or a 250:FOREIGN KEY 246:PRIMARY KEY 215:linear time 159:expressions 1643:Categories 1493:Query plan 1446:Components 1364:Unique key 1281:comparison 1215:comparison 1205:Relational 1198:comparison 1137:2023-04-08 1112:2023-04-08 946:References 711:April 2023 681:Hash index 599:for every 569:bit arrays 379:first_name 359:first_name 351:first_name 319:hash table 80:newspapers 18:Index file 1654:Databases 1502:Functions 1437:Partition 1264:In-memory 1222:Key–value 1038:ignored ( 1028:cite book 760:leaf node 589:databases 471:customers 426:customers 391:last_name 292:Clustered 167:last_name 1608:Category 1524:Sharding 1380:Relation 1354:Superkey 1309:Database 1302:Concepts 922:See also 873:my_table 867:my_index 745:B+ trees 677:record. 605:a record 597:pointers 577:B+ trees 452:sargable 446:wildcard 192:database 110:May 2024 1618:Outline 1417:Trigger 1373:Objects 885:INCLUDE 546:⁠ 517:⁠ 492:reverse 477:reverse 311:cluster 305:Cluster 213:(N) or 151:lookups 94:scholar 1432:Cursor 1390:column 1259:NewSQL 1016:  909:CREATE 861:CREATE 758:, the 749:hashes 601:record 548:time. 462:SELECT 417:SELECT 409:B-tree 385:people 376:SELECT 315:B-Tree 199:lookup 96:  89:  82:  75:  67:  1422:Index 1385:table 1288:Cloud 1254:NoSQL 1249:Graph 1186:Types 912:INDEX 864:INDEX 838:Fuse 827:Lamp 816:Plug 805:Name 621:block 591:is a 474:WHERE 429:WHERE 388:WHERE 349:and 190:Most 181:Usage 136:is a 101:JSTOR 87:books 1473:ODBC 1463:JDBC 1402:View 1339:Null 1334:CRUD 1314:ACID 1269:list 1232:list 1210:list 1040:help 1014:ISBN 891:name 841:... 830:... 819:... 747:and 593:file 489:LIKE 468:FROM 450:not 435:LIKE 423:FROM 382:FROM 355:city 347:city 248:and 73:news 1468:XQJ 1395:row 835:14 824:13 813:12 802:ID 754:In 706:. 371:SQL 56:by 1645:: 1130:. 1104:. 1075:. 1056:. 1032:: 1030:}} 1026:{{ 1012:. 1008:. 989:, 985:, 897:. 894:); 879:id 870:ON 751:. 743:, 731:. 501:); 132:A 1171:e 1164:t 1157:v 1140:. 1115:. 1042:) 1022:. 888:( 882:) 876:( 713:) 709:( 534:) 531:N 528:( 525:O 495:( 486:) 480:( 441:; 400:; 394:= 230:O 226:O 211:O 123:) 117:( 112:) 108:( 98:· 91:· 84:· 77:· 50:. 20:)

Index

Index file

verification
improve this article
adding citations to reliable sources
"Database index"
news
newspapers
books
scholar
JSTOR
Learn how and when to remove this message
data structure
database table
columns of a database table
lookups
expressions
partial index
user-defined functions
database
sub-linear time
lookup
linear search
O
linear time
data structures
O
O
database constraints
PRIMARY KEY

Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.

↑