History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"accuracy"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 502 | 2025-12-24 03:46:25 | accuracy | 1 | 82945 | 1 | 1.470 | 56425.2 |
| 501 | 2025-12-10 13:47:27 | accuracy | 1 | 82945 | 1 | 1.580 | 52496.8 |
| 500 | 2025-12-08 12:29:25 | accuracy | 1 | 82945 | 1 | 1.343 | 61761.0 |
| 499 | 2025-11-30 21:22:27 | accuracy | 1 | 82945 | 1 | 1.500 | 55296.7 |
| 498 | 2025-11-23 03:49:27 | accuracy | 1 | 82945 | 1 | 1.890 | 43886.2 |
| 497 | 2025-11-21 04:31:37 | accuracy | 2 | 121059 | 4 | 7.063 | 17139.9 |
| 496 | 2025-11-16 04:08:12 | accuracy | 1 | 82945 | 1 | 2.673 | 31030.7 |
| 495 | 2025-11-13 02:16:03 | accuracy | 1 | 82945 | 1 | 1.466 | 56579.1 |
| 494 | 2025-11-05 05:49:36 | accuracy | 1 | 82945 | 1 | 1.470 | 56425.2 |
| 493 | 2025-11-01 01:29:11 | accuracy | 1 | 82945 | 1 | 2.843 | 29175.2 |
| 492 | 2025-10-05 22:49:52 | accuracy | 1 | 82945 | 1 | 1.516 | 54713.1 |
| 491 | 2025-09-08 05:31:04 | accuracy | 1 | 82945 | 1 | 1.436 | 57761.1 |
| 490 | 2025-07-25 21:27:20 | accuracy | 1 | 82945 | 1 | 1.390 | 59672.7 |
| 489 | 2025-07-24 03:02:25 | accuracy | 1 | 82945 | 1 | 7.563 | 10967.2 |
| 488 | 2025-07-23 01:17:26 | accuracy | 1 | 82945 | 1 | 4.563 | 18177.7 |
| 487 | 2025-07-22 16:14:34 | accuracy | 1 | 82945 | 1 | 6.720 | 12343.0 |
| 486 | 2025-07-18 10:30:16 | accuracy | 1 | 82945 | 1 | 3.343 | 24811.5 |
| 485 | 2025-07-07 05:02:27 | accuracy | 1 | 82945 | 1 | 7.390 | 11224.0 |
| 484 | 2025-06-21 23:22:40 | accuracy | 1 | 82945 | 1 | 4.780 | 17352.5 |
| 483 | 2025-06-04 08:02:03 | accuracy | 3 | 146162 | 17 | 26.770 | 5459.9 |
| 482 | 2025-06-02 22:29:10 | accuracy | 3 | 146162 | 17 | 30.876 | 4733.8 |
| 481 | 2025-05-25 14:32:32 | accuracy | 2 | 121059 | 4 | 4.126 | 29340.5 |
| 480 | 2025-05-14 11:24:35 | accuracy | 1 | 82945 | 1 | 9.050 | 9165.2 |
| 479 | 2025-05-08 22:07:46 | accuracy | 1 | 82945 | 1 | 2.766 | 29987.3 |
| 478 | 2025-05-05 20:23:46 | accuracy | 3 | 146162 | 17 | 30.956 | 4721.6 |
| 477 | 2025-05-04 02:11:36 | accuracy | 1 | 82945 | 1 | 10.516 | 7887.5 |
| 476 | 2025-04-29 08:46:02 | accuracy | 1 | 82945 | 1 | 1.453 | 57085.3 |
| 475 | 2025-04-26 13:52:50 | accuracy | 3 | 146162 | 17 | 37.640 | 3883.2 |
| 474 | 2025-04-26 12:15:46 | accuracy | 4 | 161609 | 177 | 37.330 | 4329.2 |
| 473 | 2025-04-26 12:07:55 | accuracy | 4 | 161609 | 177 | 38.813 | 4163.8 |
| 472 | 2025-04-26 11:40:25 | accuracy | 4 | 161609 | 177 | 36.850 | 4385.6 |
| 471 | 2025-04-26 03:09:09 | accuracy | 2 | 121059 | 4 | 24.143 | 5014.2 |
| 470 | 2025-04-24 22:29:15 | accuracy | 1 | 82945 | 1 | 7.030 | 11798.7 |
| 469 | 2025-04-24 14:20:23 | accuracy | 4 | 161609 | 177 | 62.766 | 2574.8 |
| 468 | 2025-04-23 10:57:08 | accuracy | 4 | 161609 | 177 | 42.320 | 3818.7 |
| 467 | 2025-04-22 12:02:06 | accuracy | 2 | 121059 | 4 | 4.906 | 24675.7 |
| 466 | 2025-04-21 03:37:45 | accuracy | 4 | 161609 | 177 | 66.736 | 2421.6 |
| 465 | 2025-04-20 04:58:26 | accuracy | 1 | 82945 | 1 | 5.920 | 14011.0 |
| 464 | 2025-03-29 05:55:32 | accuracy | 1 | 82945 | 1 | 10.236 | 8103.3 |
| 463 | 2025-03-24 19:53:40 | accuracy | 3 | 146162 | 17 | 6.970 | 20970.2 |
| 462 | 2025-03-24 09:37:41 | accuracy | 2 | 121059 | 4 | 26.893 | 4501.5 |
| 461 | 2025-03-24 07:18:23 | accuracy | 3 | 146162 | 17 | 40.580 | 3601.8 |
| 460 | 2025-03-21 23:17:51 | accuracy | 1 | 82945 | 1 | 1.470 | 56425.2 |
| 459 | 2025-03-20 06:36:46 | accuracy | 1 | 82945 | 1 | 4.486 | 18489.7 |
| 458 | 2025-03-15 02:37:26 | accuracy | 3 | 146162 | 17 | 32.863 | 4447.6 |
| 457 | 2025-03-11 06:12:16 | accuracy | 3 | 146162 | 17 | 55.503 | 2633.4 |
| 456 | 2025-03-10 20:57:04 | accuracy | 3 | 146162 | 17 | 25.893 | 5644.8 |
| 455 | 2025-03-09 18:49:17 | accuracy | 3 | 146162 | 17 | 20.170 | 7246.5 |
| 454 | 2025-02-22 13:19:17 | accuracy | 1 | 82945 | 1 | 7.610 | 10899.5 |
| 453 | 2025-02-15 12:50:48 | accuracy | 2 | 121059 | 4 | 15.063 | 8036.8 |
| 452 | 2025-02-10 23:41:31 | accuracy | 1 | 82945 | 1 | 9.610 | 8631.1 |
| 451 | 2025-02-10 01:01:22 | accuracy | 4 | 161609 | 177 | 56.783 | 2846.1 |
| 450 | 2025-02-10 00:30:17 | accuracy | 4 | 161609 | 177 | 65.286 | 2475.4 |
| 449 | 2025-02-09 22:26:47 | accuracy | 4 | 161609 | 177 | 64.596 | 2501.8 |
| 448 | 2025-02-09 09:53:53 | accuracy | 4 | 161609 | 177 | 50.643 | 3191.1 |
| 447 | 2025-02-09 09:53:49 | accuracy | 2 | 121059 | 4 | 16.983 | 7128.2 |
| 446 | 2025-02-06 22:24:50 | accuracy | 3 | 146162 | 17 | 27.923 | 5234.5 |
| 445 | 2025-02-06 20:35:04 | accuracy | 3 | 146162 | 17 | 23.923 | 6109.7 |
| 444 | 2025-01-31 07:00:24 | accuracy | 1 | 82945 | 1 | 10.720 | 7737.4 |
| 443 | 2025-01-28 17:40:44 | accuracy | 3 | 146162 | 17 | 25.780 | 5669.6 |
| 442 | 2025-01-26 19:53:01 | accuracy | 3 | 146162 | 17 | 33.876 | 4314.6 |
| 441 | 2025-01-16 02:13:23 | accuracy | 2 | 121059 | 4 | 16.160 | 7491.3 |
| 440 | 2025-01-05 23:07:17 | accuracy | 1 | 82945 | 1 | 3.656 | 22687.4 |
| 439 | 2025-01-05 04:28:03 | accuracy | 1 | 82945 | 1 | 4.046 | 20500.5 |
| 438 | 2025-01-05 04:23:27 | accuracy | 2 | 121059 | 4 | 19.203 | 6304.2 |
| 437 | 2025-01-03 15:05:51 | accuracy | 3 | 146162 | 17 | 36.626 | 3990.7 |
| 436 | 2025-01-02 23:46:15 | accuracy | 3 | 146162 | 17 | 38.800 | 3767.1 |
| 435 | 2025-01-02 19:07:14 | accuracy | 3 | 146162 | 17 | 32.766 | 4460.8 |
| 434 | 2024-12-30 18:52:37 | accuracy | 3 | 146162 | 17 | 36.590 | 3994.6 |
| 433 | 2024-12-30 08:55:26 | accuracy | 4 | 161609 | 177 | 64.410 | 2509.1 |
| 432 | 2024-12-30 08:55:46 | accuracy | 2 | 121059 | 4 | 27.063 | 4473.2 |
| 431 | 2024-12-30 08:55:41 | accuracy | 3 | 146162 | 17 | 30.876 | 4733.8 |
| 430 | 2024-12-30 08:54:29 | accuracy | 1 | 82945 | 1 | 7.546 | 10991.9 |
| 429 | 2024-12-20 13:41:11 | accuracy | 3 | 146162 | 17 | 28.873 | 5062.2 |
| 428 | 2024-12-20 13:41:10 | accuracy | 2 | 121059 | 4 | 13.233 | 9148.3 |
| 427 | 2024-12-19 23:03:42 | accuracy | 3 | 146162 | 17 | 29.330 | 4983.4 |
| 426 | 2024-12-03 10:30:33 | accuracy | 3 | 146162 | 17 | 47.236 | 3094.3 |
| 425 | 2024-12-03 10:30:35 | accuracy | 2 | 121059 | 4 | 27.940 | 4332.8 |
| 424 | 2024-12-03 10:30:30 | accuracy | 3 | 146162 | 17 | 18.796 | 7776.2 |
| 423 | 2024-11-26 00:26:53 | accuracy | 1 | 82945 | 1 | 7.030 | 11798.7 |
| 422 | 2024-11-20 19:08:16 | accuracy | 1 | 82945 | 1 | 3.670 | 22600.8 |
| 421 | 2024-11-16 08:36:36 | accuracy | 2 | 121059 | 4 | 27.653 | 4377.8 |
| 420 | 2024-11-16 08:36:13 | accuracy | 1 | 82945 | 1 | 3.296 | 25165.4 |
| 419 | 2024-11-01 08:54:46 | accuracy | 3 | 146162 | 17 | 45.266 | 3229.0 |
| 418 | 2024-11-01 08:54:42 | accuracy | 2 | 121059 | 4 | 19.216 | 6299.9 |
| 417 | 2024-10-29 10:19:58 | accuracy | 3 | 146162 | 17 | 32.470 | 4501.4 |
| 416 | 2024-10-29 10:19:52 | accuracy | 3 | 146162 | 17 | 34.983 | 4178.1 |
| 415 | 2024-10-29 10:19:53 | accuracy | 2 | 121059 | 4 | 18.640 | 6494.6 |
| 414 | 2024-10-29 10:19:40 | accuracy | 1 | 82945 | 1 | 1.453 | 57085.3 |
| 413 | 2024-10-21 01:48:23 | accuracy | 3 | 146162 | 17 | 40.140 | 3641.3 |
| 412 | 2024-10-21 01:48:30 | accuracy | 3 | 146162 | 17 | 27.720 | 5272.8 |
| 411 | 2024-10-20 09:25:10 | accuracy | 3 | 146162 | 17 | 29.706 | 4920.3 |
| 410 | 2024-10-20 09:25:13 | accuracy | 2 | 121059 | 4 | 13.640 | 8875.3 |
| 409 | 2024-10-20 09:25:02 | accuracy | 1 | 82945 | 1 | 3.860 | 21488.3 |
| 408 | 2024-10-20 09:13:43 | accuracy | 4 | 161609 | 177 | 51.533 | 3136.0 |
| 407 | 2024-10-20 04:11:25 | accuracy | 4 | 161609 | 177 | 72.003 | 2244.5 |
| 406 | 2024-10-13 01:16:22 | accuracy | 1 | 82945 | 1 | 10.250 | 8092.2 |
| 405 | 2024-09-26 08:00:13 | accuracy | 4 | 161609 | 177 | 92.086 | 1755.0 |
| 404 | 2024-09-20 16:06:08 | accuracy | 1 | 82945 | 1 | 6.983 | 11878.1 |
| 403 | 2024-09-19 23:47:36 | accuracy | 4 | 161609 | 177 | 153.430 | 1053.3 |