History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"meat"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 1702 | 2026-02-18 07:07:04 | meat | 2 | 29872 | 357 | 0.860 | 34734.9 |
| 1701 | 2026-02-17 17:27:10 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1700 | 2026-02-13 14:30:36 | meat | 1 | 13983 | 21 | 0.263 | 53167.3 |
| 1699 | 2026-02-13 08:19:16 | meat | 1 | 13983 | 21 | 0.236 | 59250.0 |
| 1698 | 2026-02-12 11:36:19 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1697 | 2026-02-11 20:29:22 | meat | 1 | 13983 | 21 | 0.266 | 52567.7 |
| 1696 | 2026-02-08 13:48:08 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1695 | 2026-02-06 04:34:53 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1694 | 2026-02-02 06:10:10 | meat | 1 | 13983 | 21 | 0.263 | 53167.3 |
| 1693 | 2026-02-01 15:36:23 | meat | 2 | 29872 | 357 | 0.780 | 38297.4 |
| 1692 | 2026-02-01 05:11:29 | meat | 1 | 13983 | 21 | 0.236 | 59250.0 |
| 1691 | 2026-01-31 14:44:44 | meat | 2 | 29872 | 357 | 3.796 | 7869.3 |
| 1690 | 2026-01-29 21:23:42 | meat | 1 | 13983 | 21 | 1.576 | 8872.5 |
| 1689 | 2026-01-28 20:43:01 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1688 | 2026-01-28 11:54:11 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1687 | 2026-01-28 06:46:15 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1686 | 2026-01-27 22:03:51 | meat | 1 | 13983 | 21 | 0.920 | 15198.9 |
| 1685 | 2026-01-27 00:47:08 | meat | 1 | 13983 | 21 | 0.220 | 63559.1 |
| 1684 | 2026-01-26 22:15:20 | meat | 1 | 13983 | 21 | 0.953 | 14672.6 |
| 1683 | 2026-01-26 20:33:50 | meat | 1 | 13983 | 21 | 0.280 | 49939.3 |
| 1682 | 2026-01-26 13:10:57 | meat | 1 | 13983 | 21 | 0.220 | 63559.1 |
| 1681 | 2026-01-23 17:40:50 | meat | 1 | 13983 | 21 | 0.266 | 52567.7 |
| 1680 | 2026-01-22 06:11:08 | meat | 1 | 13983 | 21 | 1.593 | 8777.8 |
| 1679 | 2026-01-21 11:29:44 | meat | 2 | 29872 | 357 | 2.250 | 13276.4 |
| 1678 | 2026-01-20 22:44:37 | meat | 1 | 13983 | 21 | 1.626 | 8599.6 |
| 1677 | 2026-01-19 12:28:00 | meat | 1 | 13983 | 21 | 1.110 | 12597.3 |
| 1676 | 2026-01-19 03:27:34 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1675 | 2026-01-18 05:05:38 | meat | 1 | 13983 | 21 | 1.860 | 7517.7 |
| 1674 | 2026-01-16 19:15:12 | meat | 1 | 13983 | 21 | 1.830 | 7641.0 |
| 1673 | 2026-01-15 11:46:44 | meat | 1 | 13983 | 21 | 1.356 | 10311.9 |
| 1672 | 2026-01-11 11:53:40 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1671 | 2026-01-10 10:53:23 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1670 | 2026-01-10 06:49:57 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1669 | 2026-01-09 02:14:35 | meat | 1 | 13983 | 21 | 0.656 | 21315.5 |
| 1668 | 2026-01-05 17:27:06 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1667 | 2026-01-03 19:44:56 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1666 | 2025-12-29 06:17:58 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1665 | 2025-12-28 23:04:38 | meat | 1 | 13983 | 21 | 0.220 | 63559.1 |
| 1664 | 2025-12-27 12:25:29 | meat | 1 | 13983 | 21 | 0.220 | 63559.1 |
| 1663 | 2025-12-27 12:22:13 | meat | 1 | 13983 | 21 | 0.500 | 27966.0 |
| 1662 | 2025-12-25 11:35:14 | meat | 1 | 13983 | 21 | 0.513 | 27257.3 |
| 1661 | 2025-12-24 10:52:43 | meat | 1 | 13983 | 21 | 0.420 | 33292.9 |
| 1660 | 2025-12-24 08:09:23 | meat | 1 | 13983 | 21 | 0.266 | 52567.7 |
| 1659 | 2025-12-23 10:23:08 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1658 | 2025-12-22 09:57:39 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1657 | 2025-12-13 13:53:18 | meat | 1 | 13983 | 21 | 0.923 | 15149.5 |
| 1656 | 2025-12-06 22:19:40 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1655 | 2025-12-03 16:23:48 | meat | 2 | 29872 | 357 | 0.906 | 32971.3 |
| 1654 | 2025-12-02 04:35:27 | meat | 1 | 13983 | 21 | 0.326 | 42892.6 |
| 1653 | 2025-11-28 03:45:16 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1652 | 2025-11-23 23:56:40 | meat | 1 | 13983 | 21 | 0.236 | 59250.0 |
| 1651 | 2025-11-22 13:55:01 | meat | 2 | 29872 | 357 | 1.780 | 16782.0 |
| 1650 | 2025-11-20 23:13:30 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1649 | 2025-11-20 23:12:52 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1648 | 2025-11-20 07:52:53 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1647 | 2025-11-18 06:08:06 | meat | 1 | 13983 | 21 | 0.220 | 63559.1 |
| 1646 | 2025-11-14 23:21:01 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1645 | 2025-11-14 23:19:15 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1644 | 2025-11-14 21:32:00 | meat | 1 | 13983 | 21 | 0.263 | 53167.3 |
| 1643 | 2025-11-14 12:58:35 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1642 | 2025-11-14 05:25:13 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1641 | 2025-11-13 18:40:03 | meat | 1 | 13983 | 21 | 0.266 | 52567.7 |
| 1640 | 2025-11-13 15:19:37 | meat | 1 | 13983 | 21 | 0.236 | 59250.0 |
| 1639 | 2025-11-11 05:48:38 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1638 | 2025-11-10 22:00:50 | meat | 1 | 13983 | 21 | 0.263 | 53167.3 |
| 1637 | 2025-11-10 19:09:52 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1636 | 2025-11-08 05:42:49 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1635 | 2025-10-26 02:35:20 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1634 | 2025-10-21 14:07:44 | meat | 2 | 29872 | 357 | 4.500 | 6638.2 |
| 1633 | 2025-10-21 14:07:38 | meat | 2 | 29872 | 357 | 6.406 | 4663.1 |
| 1632 | 2025-10-21 14:07:31 | meat | 2 | 29872 | 357 | 2.093 | 14272.3 |
| 1631 | 2025-10-21 14:07:25 | meat | 2 | 29872 | 357 | 4.233 | 7056.9 |
| 1630 | 2025-10-19 13:29:13 | meat | 1 | 13983 | 21 | 0.500 | 27966.0 |
| 1629 | 2025-10-18 23:12:11 | meat | 1 | 13983 | 21 | 0.236 | 59250.0 |
| 1628 | 2025-10-15 06:43:06 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1627 | 2025-10-12 10:55:35 | meat | 1 | 13983 | 21 | 0.530 | 26383.0 |
| 1626 | 2025-10-11 08:08:50 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1625 | 2025-10-10 12:34:02 | meat | 1 | 13983 | 21 | 0.296 | 47239.9 |
| 1624 | 2025-10-10 12:01:55 | meat | 1 | 13983 | 21 | 0.263 | 53167.3 |
| 1623 | 2025-10-08 17:04:21 | meat | 2 | 29872 | 357 | 0.766 | 38997.4 |
| 1622 | 2025-09-28 19:03:15 | meat | 1 | 13983 | 21 | 0.280 | 49939.3 |
| 1621 | 2025-09-20 01:30:48 | meat | 2 | 29872 | 357 | 0.796 | 37527.6 |
| 1620 | 2025-09-19 20:16:03 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1619 | 2025-09-11 23:39:40 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1618 | 2025-09-04 13:03:32 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1617 | 2025-09-03 06:51:59 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1616 | 2025-08-27 16:50:50 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1615 | 2025-08-27 07:50:13 | meat | 1 | 13983 | 21 | 0.266 | 52567.7 |
| 1614 | 2025-08-25 14:11:43 | meat | 1 | 13983 | 21 | 0.580 | 24108.6 |
| 1613 | 2025-08-23 07:26:58 | meat | 1 | 13983 | 21 | 0.283 | 49409.9 |
| 1612 | 2025-08-16 10:09:09 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1611 | 2025-07-31 23:01:26 | meat | 1 | 13983 | 21 | 1.813 | 7712.6 |
| 1610 | 2025-07-28 06:38:44 | meat | 2 | 29872 | 357 | 1.796 | 16632.5 |
| 1609 | 2025-07-27 10:13:50 | meat | 1 | 13983 | 21 | 0.263 | 53167.3 |
| 1608 | 2025-07-26 23:38:29 | meat | 1 | 13983 | 21 | 0.860 | 16259.3 |
| 1607 | 2025-07-24 16:29:30 | meat | 1 | 13983 | 21 | 1.813 | 7712.6 |
| 1606 | 2025-07-23 16:56:07 | meat | 1 | 13983 | 21 | 1.703 | 8210.8 |
| 1605 | 2025-07-22 19:55:03 | meat | 1 | 13983 | 21 | 0.610 | 22923.0 |
| 1604 | 2025-07-22 17:15:49 | meat | 1 | 13983 | 21 | 0.486 | 28771.6 |
| 1603 | 2025-07-19 04:47:17 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |