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 |
| 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 |
| 1602 | 2025-07-18 06:57:14 | meat | 1 | 13983 | 21 | 0.593 | 23580.1 |
| 1601 | 2025-07-15 23:24:28 | meat | 1 | 13983 | 21 | 0.453 | 30867.5 |
| 1600 | 2025-07-13 20:56:54 | meat | 1 | 13983 | 21 | 2.123 | 6586.4 |
| 1599 | 2025-07-13 05:28:22 | meat | 1 | 13983 | 21 | 0.263 | 53167.3 |
| 1598 | 2025-07-12 14:21:29 | meat | 1 | 13983 | 21 | 0.546 | 25609.9 |
| 1597 | 2025-07-01 22:22:10 | meat | 1 | 13983 | 21 | 0.640 | 21848.4 |
| 1596 | 2025-07-01 00:06:42 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1595 | 2025-06-29 14:31:09 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1594 | 2025-06-27 10:56:33 | meat | 1 | 13983 | 21 | 0.953 | 14672.6 |
| 1593 | 2025-06-27 08:55:57 | meat | 1 | 13983 | 21 | 0.643 | 21746.5 |
| 1592 | 2025-06-22 16:25:26 | meat | 1 | 13983 | 21 | 0.763 | 18326.3 |
| 1591 | 2025-06-17 09:56:23 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1590 | 2025-06-15 10:52:13 | meat | 2 | 29872 | 357 | 0.846 | 35309.7 |
| 1589 | 2025-06-13 08:03:21 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1588 | 2025-06-13 04:40:27 | meat | 1 | 13983 | 21 | 1.423 | 9826.4 |
| 1587 | 2025-06-12 03:20:53 | meat | 1 | 13983 | 21 | 0.783 | 17858.2 |
| 1586 | 2025-06-12 03:20:39 | meat | 1 | 13983 | 21 | 1.313 | 10649.7 |
| 1585 | 2025-06-08 05:24:08 | meat | 1 | 13983 | 21 | 0.563 | 24836.6 |
| 1584 | 2025-06-07 07:22:58 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1583 | 2025-06-01 21:19:21 | meat | 1 | 13983 | 21 | 0.983 | 14224.8 |
| 1582 | 2025-05-30 14:46:34 | meat | 1 | 13983 | 21 | 2.046 | 6834.3 |
| 1581 | 2025-05-30 06:57:49 | meat | 1 | 13983 | 21 | 0.453 | 30867.5 |
| 1580 | 2025-05-29 09:55:43 | meat | 1 | 13983 | 21 | 0.686 | 20383.4 |
| 1579 | 2025-05-27 09:37:34 | meat | 1 | 13983 | 21 | 1.080 | 12947.2 |
| 1578 | 2025-05-23 03:19:46 | meat | 1 | 13983 | 21 | 0.906 | 15433.8 |
| 1577 | 2025-05-16 19:28:34 | meat | 1 | 13983 | 21 | 0.283 | 49409.9 |
| 1576 | 2025-05-16 10:22:24 | meat | 1 | 13983 | 21 | 1.923 | 7271.5 |
| 1575 | 2025-05-12 16:33:19 | meat | 1 | 13983 | 21 | 1.046 | 13368.1 |
| 1574 | 2025-05-12 08:56:01 | meat | 1 | 13983 | 21 | 1.266 | 11045.0 |
| 1573 | 2025-05-09 02:25:57 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1572 | 2025-05-08 05:05:05 | meat | 1 | 13983 | 21 | 1.780 | 7855.6 |
| 1571 | 2025-05-08 03:31:12 | meat | 2 | 29872 | 357 | 6.266 | 4767.3 |
| 1570 | 2025-05-02 20:58:54 | meat | 1 | 13983 | 21 | 1.436 | 9737.5 |
| 1569 | 2025-05-02 14:56:02 | meat | 1 | 13983 | 21 | 1.250 | 11186.4 |
| 1568 | 2025-05-01 12:34:45 | meat | 1 | 13983 | 21 | 1.266 | 11045.0 |
| 1567 | 2025-04-30 00:04:34 | meat | 1 | 13983 | 21 | 1.420 | 9847.2 |
| 1566 | 2025-04-29 01:29:47 | meat | 2 | 29872 | 357 | 0.890 | 33564.0 |
| 1565 | 2025-04-28 02:43:42 | meat | 2 | 29872 | 357 | 4.500 | 6638.2 |
| 1564 | 2025-04-27 09:37:51 | meat | 2 | 29872 | 357 | 0.860 | 34734.9 |
| 1563 | 2025-04-27 09:15:28 | meat | 2 | 29872 | 357 | 0.966 | 30923.4 |
| 1562 | 2025-04-27 07:56:38 | meat | 1 | 13983 | 21 | 0.266 | 52567.7 |
| 1561 | 2025-04-26 20:42:13 | meat | 1 | 13983 | 21 | 1.623 | 8615.5 |
| 1560 | 2025-04-26 20:37:35 | meat | 1 | 13983 | 21 | 0.686 | 20383.4 |
| 1559 | 2025-04-25 16:02:58 | meat | 1 | 13983 | 21 | 0.250 | 55932.0 |
| 1558 | 2025-04-14 10:19:28 | meat | 1 | 13983 | 21 | 0.656 | 21315.5 |
| 1557 | 2025-04-13 18:32:21 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1556 | 2025-04-08 17:47:25 | meat | 2 | 29872 | 357 | 0.826 | 36164.6 |
| 1555 | 2025-04-08 08:54:16 | meat | 2 | 29872 | 357 | 4.296 | 6953.4 |
| 1554 | 2025-04-07 04:43:15 | meat | 1 | 13983 | 21 | 0.763 | 18326.3 |
| 1553 | 2025-04-01 20:47:37 | meat | 1 | 13983 | 21 | 0.623 | 22444.6 |
| 1552 | 2025-03-14 21:04:41 | meat | 1 | 13983 | 21 | 1.170 | 11951.3 |
| 1551 | 2025-03-10 08:31:04 | meat | 1 | 13983 | 21 | 0.233 | 60012.9 |
| 1550 | 2025-03-07 18:36:06 | meat | 2 | 29872 | 357 | 4.530 | 6594.3 |
| 1549 | 2025-03-07 15:51:17 | meat | 2 | 29872 | 357 | 4.513 | 6619.1 |
| 1548 | 2025-03-07 05:39:13 | meat | 2 | 29872 | 357 | 2.233 | 13377.5 |
| 1547 | 2025-03-07 03:03:39 | meat | 2 | 29872 | 357 | 5.936 | 5032.3 |
| 1546 | 2025-03-07 02:28:55 | meat | 1 | 13983 | 21 | 0.346 | 40413.3 |
| 1545 | 2025-02-26 04:37:44 | meat | 2 | 29872 | 357 | 2.826 | 10570.4 |
| 1544 | 2025-02-26 01:45:23 | meat | 2 | 29872 | 357 | 4.173 | 7158.4 |
| 1543 | 2025-02-25 08:34:19 | meat | 1 | 13983 | 21 | 0.220 | 63559.1 |
| 1542 | 2025-02-24 08:09:07 | meat | 1 | 13983 | 21 | 0.893 | 15658.5 |
| 1541 | 2025-02-23 18:42:39 | meat | 1 | 13983 | 21 | 1.560 | 8963.5 |
| 1540 | 2025-02-05 00:36:22 | meat | 2 | 29872 | 357 | 6.456 | 4627.0 |
| 1539 | 2025-02-04 23:10:35 | meat | 2 | 29872 | 357 | 2.110 | 14157.3 |
| 1538 | 2025-02-04 18:55:46 | meat | 2 | 29872 | 357 | 4.360 | 6851.4 |
| 1537 | 2025-02-04 18:48:12 | meat | 1 | 13983 | 21 | 1.890 | 7398.4 |
| 1536 | 2025-01-26 08:10:56 | meat | 1 | 13983 | 21 | 0.686 | 20383.4 |