History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"preciseness"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 564 | 2025-09-10 05:50:10 | preciseness | 1 | 49908 | 1 | 0.906 | 55086.1 |
| 563 | 2025-09-04 12:33:22 | preciseness | 1 | 49908 | 1 | 0.920 | 54247.8 |
| 562 | 2025-08-24 20:10:00 | preciseness | 1 | 49908 | 1 | 4.140 | 12055.1 |
| 561 | 2025-08-10 23:28:26 | preciseness | 1 | 49908 | 1 | 2.283 | 21860.7 |
| 560 | 2025-08-10 12:07:09 | preciseness | 1 | 49908 | 1 | 4.000 | 12477.0 |
| 559 | 2025-08-04 23:26:19 | preciseness | 3 | 121633 | 14 | 20.546 | 5920.0 |
| 558 | 2025-08-04 00:05:37 | preciseness | 1 | 49908 | 1 | 2.593 | 19247.2 |
| 557 | 2025-08-02 18:01:38 | preciseness | 3 | 121633 | 14 | 13.656 | 8906.9 |
| 556 | 2025-07-29 21:37:16 | preciseness | 3 | 121633 | 14 | 21.766 | 5588.2 |
| 555 | 2025-07-28 06:03:01 | preciseness | 1 | 49908 | 1 | 4.860 | 10269.1 |
| 554 | 2025-07-25 18:11:25 | preciseness | 1 | 49908 | 1 | 5.813 | 8585.6 |
| 553 | 2025-07-24 11:16:17 | preciseness | 1 | 49908 | 1 | 5.873 | 8497.9 |
| 552 | 2025-07-24 07:43:37 | preciseness | 1 | 49908 | 1 | 3.343 | 14929.1 |
| 551 | 2025-07-23 02:18:15 | preciseness | 1 | 49908 | 1 | 2.000 | 24954.0 |
| 550 | 2025-07-22 18:11:36 | preciseness | 1 | 49908 | 1 | 2.653 | 18811.9 |
| 549 | 2025-07-20 14:36:23 | preciseness | 3 | 121633 | 14 | 5.016 | 24249.0 |
| 548 | 2025-07-18 14:02:16 | preciseness | 3 | 121633 | 14 | 19.953 | 6096.0 |
| 547 | 2025-07-18 13:27:39 | preciseness | 3 | 121633 | 14 | 33.363 | 3645.7 |
| 546 | 2025-07-14 06:48:15 | preciseness | 1 | 49908 | 1 | 2.326 | 21456.6 |
| 545 | 2025-07-02 00:08:39 | preciseness | 1 | 49908 | 1 | 4.033 | 12374.9 |
| 544 | 2025-06-23 06:29:44 | preciseness | 1 | 49908 | 1 | 4.046 | 12335.1 |
| 543 | 2025-06-23 03:46:09 | preciseness | 2 | 86808 | 3 | 13.673 | 6348.9 |
| 542 | 2025-06-22 22:30:42 | preciseness | 3 | 121633 | 14 | 9.123 | 13332.6 |
| 541 | 2025-06-21 13:53:17 | preciseness | 2 | 86808 | 3 | 13.123 | 6615.0 |
| 540 | 2025-06-21 04:19:47 | preciseness | 3 | 121633 | 14 | 33.940 | 3583.8 |
| 539 | 2025-06-19 20:11:33 | preciseness | 1 | 49908 | 1 | 6.983 | 7147.1 |
| 538 | 2025-06-16 04:52:31 | preciseness | 2 | 86808 | 3 | 18.173 | 4776.8 |
| 537 | 2025-06-14 23:57:07 | preciseness | 3 | 121633 | 14 | 5.393 | 22553.9 |
| 536 | 2025-06-14 18:04:43 | preciseness | 4 | 148819 | 139 | 53.173 | 2798.8 |
| 535 | 2025-06-14 15:00:13 | preciseness | 2 | 86808 | 3 | 8.640 | 10047.2 |
| 534 | 2025-06-13 10:16:21 | preciseness | 1 | 49908 | 1 | 0.813 | 61387.5 |
| 533 | 2025-06-12 13:45:18 | preciseness | 3 | 121633 | 14 | 28.626 | 4249.0 |
| 532 | 2025-06-12 07:49:03 | preciseness | 4 | 148819 | 139 | 36.173 | 4114.1 |
| 531 | 2025-06-12 05:19:22 | preciseness | 1 | 49908 | 1 | 4.250 | 11743.1 |
| 530 | 2025-06-06 03:02:57 | preciseness | 1 | 49908 | 1 | 2.123 | 23508.2 |
| 529 | 2025-06-01 23:27:40 | preciseness | 1 | 49908 | 1 | 2.966 | 16826.7 |
| 528 | 2025-05-23 00:36:02 | preciseness | 4 | 148819 | 139 | 49.933 | 2980.4 |
| 527 | 2025-05-19 20:01:19 | preciseness | 1 | 49908 | 1 | 2.123 | 23508.2 |
| 526 | 2025-05-15 03:08:56 | preciseness | 2 | 86808 | 3 | 7.973 | 10887.7 |
| 525 | 2025-05-14 22:43:00 | preciseness | 3 | 121633 | 14 | 26.720 | 4552.1 |
| 524 | 2025-05-12 13:57:57 | preciseness | 3 | 121633 | 14 | 29.296 | 4151.9 |
| 523 | 2025-05-12 06:30:15 | preciseness | 1 | 49908 | 1 | 3.123 | 15980.8 |
| 522 | 2025-05-12 02:17:13 | preciseness | 2 | 86808 | 3 | 13.486 | 6436.9 |
| 521 | 2025-05-11 11:16:37 | preciseness | 1 | 49908 | 1 | 0.970 | 51451.5 |
| 520 | 2025-05-11 05:01:04 | preciseness | 1 | 49908 | 1 | 0.846 | 58992.9 |
| 519 | 2025-05-02 12:07:52 | preciseness | 1 | 49908 | 1 | 4.513 | 11058.7 |
| 518 | 2025-05-01 06:41:16 | preciseness | 4 | 148819 | 139 | 35.483 | 4194.1 |
| 517 | 2025-04-30 22:13:43 | preciseness | 4 | 148819 | 139 | 50.610 | 2940.5 |
| 516 | 2025-04-30 10:55:47 | preciseness | 1 | 49908 | 1 | 3.093 | 16135.8 |
| 515 | 2025-04-27 21:17:39 | preciseness | 4 | 148819 | 139 | 52.143 | 2854.1 |
| 514 | 2025-04-26 06:47:40 | preciseness | 4 | 148819 | 139 | 45.753 | 3252.7 |
| 513 | 2025-04-25 12:25:56 | preciseness | 4 | 148819 | 139 | 42.596 | 3493.7 |
| 512 | 2025-04-25 01:04:44 | preciseness | 4 | 148819 | 139 | 9.813 | 15165.5 |
| 511 | 2025-04-24 22:59:28 | preciseness | 1 | 49908 | 1 | 4.093 | 12193.5 |
| 510 | 2025-04-24 14:17:30 | preciseness | 1 | 49908 | 1 | 6.360 | 7847.2 |
| 509 | 2025-04-24 11:52:45 | preciseness | 1 | 49908 | 1 | 0.893 | 55888.0 |
| 508 | 2025-04-23 02:44:22 | preciseness | 1 | 49908 | 1 | 1.906 | 26184.7 |
| 507 | 2025-04-23 01:06:28 | preciseness | 4 | 148819 | 139 | 59.660 | 2494.5 |
| 506 | 2025-04-19 15:18:24 | preciseness | 3 | 121633 | 14 | 5.016 | 24249.0 |
| 505 | 2025-04-15 14:44:34 | preciseness | 2 | 86808 | 3 | 12.393 | 7004.6 |
| 504 | 2025-04-15 04:25:06 | preciseness | 3 | 121633 | 14 | 35.313 | 3444.4 |
| 503 | 2025-04-13 20:03:46 | preciseness | 1 | 49908 | 1 | 1.923 | 25953.2 |
| 502 | 2025-04-11 06:29:16 | preciseness | 1 | 49908 | 1 | 5.233 | 9537.2 |
| 501 | 2025-04-09 21:40:47 | preciseness | 3 | 121633 | 14 | 5.920 | 20546.1 |
| 500 | 2025-04-09 17:53:57 | preciseness | 3 | 121633 | 14 | 29.833 | 4077.1 |
| 499 | 2025-04-09 17:35:26 | preciseness | 2 | 86808 | 3 | 6.436 | 13487.9 |
| 498 | 2025-04-03 02:40:35 | preciseness | 3 | 121633 | 14 | 11.063 | 10994.6 |
| 497 | 2025-03-31 21:20:47 | preciseness | 3 | 121633 | 14 | 33.550 | 3625.4 |
| 496 | 2025-03-31 18:58:07 | preciseness | 2 | 86808 | 3 | 7.750 | 11201.0 |
| 495 | 2025-03-25 22:37:36 | preciseness | 4 | 148819 | 139 | 55.440 | 2684.3 |
| 494 | 2025-03-23 23:45:24 | preciseness | 1 | 49908 | 1 | 2.440 | 20454.1 |
| 493 | 2025-03-21 04:25:21 | preciseness | 1 | 49908 | 1 | 4.623 | 10795.6 |
| 492 | 2025-03-20 08:11:17 | preciseness | 4 | 148819 | 139 | 39.720 | 3746.7 |
| 491 | 2025-03-18 07:35:48 | preciseness | 4 | 148819 | 139 | 45.653 | 3259.8 |
| 490 | 2025-03-12 15:44:03 | preciseness | 2 | 86808 | 3 | 17.453 | 4973.8 |
| 489 | 2025-03-12 14:39:54 | preciseness | 1 | 49908 | 1 | 4.720 | 10573.7 |
| 488 | 2025-03-10 23:41:32 | preciseness | 4 | 148819 | 139 | 53.596 | 2776.7 |
| 487 | 2025-03-09 12:58:29 | preciseness | 3 | 121633 | 14 | 23.736 | 5124.4 |
| 486 | 2025-03-07 23:28:25 | preciseness | 3 | 121633 | 14 | 29.720 | 4092.6 |
| 485 | 2025-03-07 23:28:17 | preciseness | 2 | 86808 | 3 | 6.626 | 13101.1 |
| 484 | 2025-03-07 23:26:26 | preciseness | 1 | 49908 | 1 | 4.700 | 10618.7 |
| 483 | 2025-03-07 14:59:08 | preciseness | 1 | 49908 | 1 | 4.093 | 12193.5 |
| 482 | 2025-03-02 12:25:03 | preciseness | 1 | 49908 | 1 | 4.563 | 10937.5 |
| 481 | 2025-03-02 11:53:26 | preciseness | 4 | 148819 | 139 | 68.316 | 2178.4 |
| 480 | 2025-02-23 08:07:01 | preciseness | 4 | 148819 | 139 | 43.753 | 3401.3 |
| 479 | 2025-02-23 00:15:47 | preciseness | 4 | 148819 | 139 | 55.520 | 2680.5 |
| 478 | 2025-02-22 22:42:19 | preciseness | 4 | 148819 | 139 | 40.906 | 3638.1 |
| 477 | 2025-02-22 22:42:14 | preciseness | 3 | 121633 | 14 | 17.220 | 7063.5 |
| 476 | 2025-02-22 22:42:11 | preciseness | 2 | 86808 | 3 | 7.686 | 11294.3 |
| 475 | 2025-02-14 17:26:40 | preciseness | 1 | 49908 | 1 | 4.110 | 12143.1 |
| 474 | 2025-01-25 08:15:48 | preciseness | 1 | 49908 | 1 | 5.736 | 8700.8 |
| 473 | 2025-01-20 06:16:15 | preciseness | 1 | 49908 | 1 | 3.780 | 13203.2 |
| 472 | 2025-01-18 07:30:21 | preciseness | 1 | 49908 | 1 | 6.483 | 7698.3 |
| 471 | 2024-12-22 00:08:33 | preciseness | 2 | 86808 | 3 | 12.656 | 6859.0 |
| 470 | 2024-12-22 00:06:38 | preciseness | 1 | 49908 | 1 | 2.110 | 23653.1 |
| 469 | 2024-12-19 03:19:23 | preciseness | 3 | 121633 | 14 | 25.223 | 4822.3 |
| 468 | 2024-12-17 19:07:05 | preciseness | 3 | 121633 | 14 | 25.783 | 4717.6 |
| 467 | 2024-12-17 19:07:07 | preciseness | 2 | 86808 | 3 | 6.376 | 13614.8 |
| 466 | 2024-12-17 19:05:35 | preciseness | 1 | 49908 | 1 | 4.673 | 10680.1 |
| 465 | 2024-12-15 22:46:57 | preciseness | 4 | 148819 | 139 | 51.236 | 2904.6 |