History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"crispness"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 523 | 2026-04-07 14:40:28 | crispness | 1 | 81242 | 2 | 3.016 | 26937.0 |
| 522 | 2026-04-07 07:52:42 | crispness | 2 | 121436 | 10 | 12.220 | 9937.5 |
| 521 | 2026-04-07 07:51:59 | crispness | 2 | 121436 | 10 | 27.566 | 4405.3 |
| 520 | 2026-04-07 06:19:21 | crispness | 4 | 165329 | 659 | 41.893 | 3946.5 |
| 519 | 2026-04-06 20:43:40 | crispness | 4 | 165329 | 659 | 37.563 | 4401.4 |
| 518 | 2026-04-06 15:28:52 | crispness | 4 | 165329 | 659 | 57.036 | 2898.7 |
| 517 | 2026-04-06 14:25:22 | crispness | 3 | 148641 | 80 | 7.126 | 20859.0 |
| 516 | 2026-04-06 05:31:00 | crispness | 2 | 121436 | 10 | 7.436 | 16330.8 |
| 515 | 2026-03-31 00:03:05 | crispness | 1 | 81242 | 2 | 5.970 | 13608.4 |
| 514 | 2026-03-30 07:33:42 | crispness | 1 | 81242 | 2 | 10.393 | 7817.0 |
| 513 | 2026-03-30 03:17:40 | crispness | 1 | 81242 | 2 | 3.780 | 21492.6 |
| 512 | 2026-03-24 21:16:23 | crispness | 4 | 165329 | 659 | 48.320 | 3421.5 |
| 511 | 2026-03-24 10:28:42 | crispness | 1 | 81242 | 2 | 7.373 | 11018.9 |
| 510 | 2026-03-22 16:48:13 | crispness | 3 | 148641 | 80 | 21.126 | 7035.9 |
| 509 | 2026-03-01 17:12:33 | crispness | 1 | 81242 | 2 | 1.466 | 55417.5 |
| 508 | 2026-02-27 11:51:26 | crispness | 1 | 81242 | 2 | 10.500 | 7737.3 |
| 507 | 2026-02-20 12:49:09 | crispness | 1 | 81242 | 2 | 1.390 | 58447.5 |
| 506 | 2026-02-13 21:57:04 | crispness | 1 | 81242 | 2 | 6.736 | 12060.9 |
| 505 | 2026-02-13 21:56:54 | crispness | 1 | 81242 | 2 | 5.983 | 13578.8 |
| 504 | 2026-01-19 07:13:03 | crispness | 1 | 81242 | 2 | 1.640 | 49537.8 |
| 503 | 2026-01-03 21:05:44 | crispness | 1 | 81242 | 2 | 1.500 | 54161.3 |
| 502 | 2026-01-02 12:34:43 | crispness | 1 | 81242 | 2 | 1.563 | 51978.2 |
| 501 | 2026-01-01 01:46:45 | crispness | 1 | 81242 | 2 | 1.440 | 56418.1 |
| 500 | 2025-12-27 03:09:58 | crispness | 1 | 81242 | 2 | 1.440 | 56418.1 |
| 499 | 2025-11-28 13:32:47 | crispness | 3 | 148641 | 80 | 7.140 | 20818.1 |
| 498 | 2025-11-25 05:29:17 | crispness | 2 | 121436 | 10 | 9.936 | 12221.8 |
| 497 | 2025-11-24 18:33:44 | crispness | 1 | 81242 | 2 | 1.486 | 54671.6 |
| 496 | 2025-11-22 13:26:39 | crispness | 1 | 81242 | 2 | 1.406 | 57782.4 |
| 495 | 2025-11-16 02:52:11 | crispness | 4 | 165329 | 659 | 12.923 | 12793.4 |
| 494 | 2025-11-15 08:56:26 | crispness | 4 | 165329 | 659 | 12.640 | 13079.8 |
| 493 | 2025-11-12 04:16:27 | crispness | 4 | 165329 | 659 | 10.906 | 15159.5 |
| 492 | 2025-10-20 06:53:39 | crispness | 1 | 81242 | 2 | 1.563 | 51978.2 |
| 491 | 2025-10-17 00:09:12 | crispness | 1 | 81242 | 2 | 1.283 | 63321.9 |
| 490 | 2025-10-13 05:43:33 | crispness | 1 | 81242 | 2 | 1.283 | 63321.9 |
| 489 | 2025-10-09 09:54:49 | crispness | 1 | 81242 | 2 | 1.296 | 62686.7 |
| 488 | 2025-10-05 10:55:40 | crispness | 1 | 81242 | 2 | 1.453 | 55913.3 |
| 487 | 2025-10-01 10:27:04 | crispness | 1 | 81242 | 2 | 1.513 | 53696.0 |
| 486 | 2025-08-19 16:19:05 | crispness | 3 | 148641 | 80 | 16.563 | 8974.3 |
| 485 | 2025-08-16 07:39:44 | crispness | 1 | 81242 | 2 | 1.470 | 55266.7 |
| 484 | 2025-08-11 07:35:41 | crispness | 4 | 165329 | 659 | 63.736 | 2594.0 |
| 483 | 2025-08-08 01:00:18 | crispness | 4 | 165329 | 659 | 12.783 | 12933.5 |
| 482 | 2025-08-04 09:12:43 | crispness | 1 | 81242 | 2 | 7.156 | 11353.0 |
| 481 | 2025-08-04 02:27:10 | crispness | 1 | 81242 | 2 | 1.436 | 56575.2 |
| 480 | 2025-08-02 23:15:04 | crispness | 4 | 165329 | 659 | 38.093 | 4340.1 |
| 479 | 2025-07-30 23:05:44 | crispness | 4 | 165329 | 659 | 70.080 | 2359.1 |
| 478 | 2025-07-30 18:57:40 | crispness | 1 | 81242 | 2 | 1.503 | 54053.2 |
| 477 | 2025-07-30 16:59:59 | crispness | 4 | 165329 | 659 | 66.173 | 2498.4 |
| 476 | 2025-07-30 05:56:37 | crispness | 4 | 165329 | 659 | 56.426 | 2930.0 |
| 475 | 2025-07-24 18:49:09 | crispness | 1 | 81242 | 2 | 3.843 | 21140.3 |
| 474 | 2025-07-20 22:12:18 | crispness | 4 | 165329 | 659 | 13.690 | 12076.6 |
| 473 | 2025-07-20 15:43:37 | crispness | 2 | 121436 | 10 | 3.966 | 30619.3 |
| 472 | 2025-07-20 15:11:35 | crispness | 3 | 148641 | 80 | 9.063 | 16400.9 |
| 471 | 2025-07-17 00:38:31 | crispness | 1 | 81242 | 2 | 5.906 | 13755.8 |
| 470 | 2025-07-16 09:47:37 | crispness | 1 | 81242 | 2 | 4.530 | 17934.2 |
| 469 | 2025-07-13 09:11:53 | crispness | 1 | 81242 | 2 | 1.483 | 54782.2 |
| 468 | 2025-07-13 04:36:34 | crispness | 1 | 81242 | 2 | 1.466 | 55417.5 |
| 467 | 2025-07-10 13:25:48 | crispness | 3 | 148641 | 80 | 36.140 | 4112.9 |
| 466 | 2025-07-10 09:52:07 | crispness | 3 | 148641 | 80 | 37.130 | 4003.3 |
| 465 | 2025-07-10 02:24:55 | crispness | 3 | 148641 | 80 | 32.750 | 4538.7 |
| 464 | 2025-07-09 07:04:13 | crispness | 3 | 148641 | 80 | 38.126 | 3898.7 |
| 463 | 2025-07-07 17:34:32 | crispness | 2 | 121436 | 10 | 17.843 | 6805.8 |
| 462 | 2025-07-07 16:51:33 | crispness | 3 | 148641 | 80 | 34.316 | 4331.5 |
| 461 | 2025-07-06 07:13:28 | crispness | 1 | 81242 | 2 | 1.423 | 57092.1 |
| 460 | 2025-07-02 09:03:04 | crispness | 1 | 81242 | 2 | 6.623 | 12266.6 |
| 459 | 2025-06-26 07:04:16 | crispness | 1 | 81242 | 2 | 7.190 | 11299.3 |
| 458 | 2025-06-16 16:12:18 | crispness | 1 | 81242 | 2 | 7.626 | 10653.3 |
| 457 | 2025-06-11 03:39:46 | crispness | 1 | 81242 | 2 | 10.423 | 7794.5 |
| 456 | 2025-05-29 16:51:21 | crispness | 1 | 81242 | 2 | 3.626 | 22405.4 |
| 455 | 2025-05-22 07:49:19 | crispness | 1 | 81242 | 2 | 6.720 | 12089.6 |
| 454 | 2025-05-20 23:22:50 | crispness | 1 | 81242 | 2 | 1.326 | 61268.5 |
| 453 | 2025-05-12 23:19:25 | crispness | 1 | 81242 | 2 | 3.593 | 22611.2 |
| 452 | 2025-05-09 08:37:26 | crispness | 1 | 81242 | 2 | 1.486 | 54671.6 |
| 451 | 2025-05-02 23:55:16 | crispness | 4 | 165329 | 659 | 67.270 | 2457.7 |
| 450 | 2025-04-30 16:13:54 | crispness | 4 | 165329 | 659 | 73.236 | 2257.5 |
| 449 | 2025-04-27 08:51:13 | crispness | 4 | 165329 | 659 | 51.876 | 3187.0 |
| 448 | 2025-04-24 16:19:53 | crispness | 4 | 165329 | 659 | 11.640 | 14203.5 |
| 447 | 2025-04-23 15:29:12 | crispness | 1 | 81242 | 2 | 9.953 | 8162.6 |
| 446 | 2025-04-22 06:10:24 | crispness | 1 | 81242 | 2 | 1.486 | 54671.6 |
| 445 | 2025-04-17 11:53:21 | crispness | 2 | 121436 | 10 | 3.190 | 38067.7 |
| 444 | 2025-04-17 07:19:16 | crispness | 3 | 148641 | 80 | 7.096 | 20947.2 |
| 443 | 2025-04-15 12:24:42 | crispness | 4 | 165329 | 659 | 21.580 | 7661.2 |
| 442 | 2025-04-14 07:50:29 | crispness | 4 | 165329 | 659 | 42.833 | 3859.9 |
| 441 | 2025-04-12 02:01:48 | crispness | 4 | 165329 | 659 | 44.813 | 3689.3 |
| 440 | 2025-04-10 16:23:21 | crispness | 4 | 165329 | 659 | 12.376 | 13358.8 |
| 439 | 2025-04-10 02:26:10 | crispness | 2 | 121436 | 10 | 5.390 | 22529.9 |
| 438 | 2025-04-10 00:31:17 | crispness | 3 | 148641 | 80 | 7.780 | 19105.5 |
| 437 | 2025-04-06 03:26:43 | crispness | 1 | 81242 | 2 | 5.610 | 14481.6 |
| 436 | 2025-04-01 23:34:46 | crispness | 1 | 81242 | 2 | 10.016 | 8111.2 |
| 435 | 2025-03-22 07:57:41 | crispness | 1 | 81242 | 2 | 7.563 | 10742.0 |
| 434 | 2025-03-09 12:58:28 | crispness | 2 | 121436 | 10 | 19.890 | 6105.4 |
| 433 | 2025-03-09 12:56:06 | crispness | 1 | 81242 | 2 | 4.546 | 17871.1 |
| 432 | 2025-03-07 13:27:06 | crispness | 3 | 148641 | 80 | 49.816 | 2983.8 |
| 431 | 2025-03-07 13:25:15 | crispness | 2 | 121436 | 10 | 13.266 | 9153.9 |
| 430 | 2025-03-07 13:25:01 | crispness | 1 | 81242 | 2 | 3.063 | 26523.7 |
| 429 | 2025-03-06 00:14:30 | crispness | 4 | 165329 | 659 | 36.813 | 4491.0 |
| 428 | 2025-03-02 20:13:09 | crispness | 4 | 165329 | 659 | 72.626 | 2276.4 |
| 427 | 2025-03-02 05:40:56 | crispness | 3 | 148641 | 80 | 21.716 | 6844.8 |
| 426 | 2025-02-26 23:26:49 | crispness | 1 | 81242 | 2 | 6.953 | 11684.5 |
| 425 | 2025-02-23 00:25:49 | crispness | 4 | 165329 | 659 | 59.986 | 2756.1 |
| 424 | 2025-02-22 05:11:37 | crispness | 4 | 165329 | 659 | 61.706 | 2679.3 |