History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"coarseness"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 459 | 2025-09-24 17:47:50 | coarseness | 3 | 140603 | 51 | 5.966 | 23567.4 |
| 458 | 2025-09-24 03:41:25 | coarseness | 1 | 67641 | 2 | 1.063 | 63632.2 |
| 457 | 2025-09-18 16:05:42 | coarseness | 4 | 161450 | 396 | 10.966 | 14722.8 |
| 456 | 2025-09-08 13:36:43 | coarseness | 4 | 161450 | 396 | 10.220 | 15797.5 |
| 455 | 2025-09-08 00:08:21 | coarseness | 4 | 161450 | 396 | 11.596 | 13922.9 |
| 454 | 2025-08-31 06:00:06 | coarseness | 4 | 161450 | 396 | 11.906 | 13560.4 |
| 453 | 2025-08-17 08:06:00 | coarseness | 1 | 67641 | 2 | 2.906 | 23276.3 |
| 452 | 2025-08-16 23:45:11 | coarseness | 3 | 140603 | 51 | 20.220 | 6953.7 |
| 451 | 2025-08-16 13:41:27 | coarseness | 1 | 67641 | 2 | 1.216 | 55625.8 |
| 450 | 2025-08-15 13:20:00 | coarseness | 1 | 67641 | 2 | 5.703 | 11860.6 |
| 449 | 2025-08-15 05:29:17 | coarseness | 3 | 140603 | 51 | 14.453 | 9728.3 |
| 448 | 2025-08-12 22:26:06 | coarseness | 2 | 108824 | 6 | 8.453 | 12874.0 |
| 447 | 2025-08-11 12:53:38 | coarseness | 3 | 140603 | 51 | 15.156 | 9277.1 |
| 446 | 2025-08-09 21:43:34 | coarseness | 1 | 67641 | 2 | 2.780 | 24331.3 |
| 445 | 2025-08-08 22:13:46 | coarseness | 1 | 67641 | 2 | 1.233 | 54858.9 |
| 444 | 2025-08-06 21:53:00 | coarseness | 2 | 108824 | 6 | 8.030 | 13552.2 |
| 443 | 2025-08-04 05:50:24 | coarseness | 1 | 67641 | 2 | 1.186 | 57032.9 |
| 442 | 2025-07-25 05:48:41 | coarseness | 1 | 67641 | 2 | 3.423 | 19760.7 |
| 441 | 2025-07-22 12:19:29 | coarseness | 1 | 67641 | 2 | 2.750 | 24596.7 |
| 440 | 2025-07-22 11:39:38 | coarseness | 1 | 67641 | 2 | 2.766 | 24454.4 |
| 439 | 2025-07-10 16:35:43 | coarseness | 1 | 67641 | 2 | 3.703 | 18266.5 |
| 438 | 2025-06-30 05:55:52 | coarseness | 1 | 67641 | 2 | 4.906 | 13787.4 |
| 437 | 2025-06-29 06:46:48 | coarseness | 3 | 140603 | 51 | 34.570 | 4067.2 |
| 436 | 2025-06-26 17:07:10 | coarseness | 3 | 140603 | 51 | 34.500 | 4075.4 |
| 435 | 2025-06-26 09:22:43 | coarseness | 2 | 108824 | 6 | 16.890 | 6443.1 |
| 434 | 2025-06-26 08:46:35 | coarseness | 3 | 140603 | 51 | 33.893 | 4148.4 |
| 433 | 2025-06-25 09:09:17 | coarseness | 1 | 67641 | 2 | 6.030 | 11217.4 |
| 432 | 2025-06-20 11:37:37 | coarseness | 1 | 67641 | 2 | 6.140 | 11016.4 |
| 431 | 2025-06-07 21:16:20 | coarseness | 1 | 67641 | 2 | 2.733 | 24749.7 |
| 430 | 2025-06-04 05:49:30 | coarseness | 1 | 67641 | 2 | 2.750 | 24596.7 |
| 429 | 2025-06-01 10:37:07 | coarseness | 1 | 67641 | 2 | 5.563 | 12159.1 |
| 428 | 2025-05-30 01:25:01 | coarseness | 1 | 67641 | 2 | 1.173 | 57665.0 |
| 427 | 2025-05-29 05:34:33 | coarseness | 1 | 67641 | 2 | 3.046 | 22206.5 |
| 426 | 2025-05-20 07:25:07 | coarseness | 1 | 67641 | 2 | 3.156 | 21432.5 |
| 425 | 2025-05-02 20:55:23 | coarseness | 1 | 67641 | 2 | 2.593 | 26086.0 |
| 424 | 2025-04-28 06:00:17 | coarseness | 1 | 67641 | 2 | 2.783 | 24305.1 |
| 423 | 2025-04-26 10:40:06 | coarseness | 1 | 67641 | 2 | 5.140 | 13159.7 |
| 422 | 2025-04-26 08:17:43 | coarseness | 1 | 67641 | 2 | 3.096 | 21847.9 |
| 421 | 2025-04-20 06:33:03 | coarseness | 1 | 67641 | 2 | 6.406 | 10559.0 |
| 420 | 2025-04-02 19:20:53 | coarseness | 4 | 161450 | 396 | 76.020 | 2123.8 |
| 419 | 2025-03-29 07:32:07 | coarseness | 1 | 67641 | 2 | 5.796 | 11670.3 |
| 418 | 2025-03-28 18:01:33 | coarseness | 4 | 161450 | 396 | 11.360 | 14212.1 |
| 417 | 2025-03-25 22:03:53 | coarseness | 2 | 108824 | 6 | 2.873 | 37878.2 |
| 416 | 2025-03-23 15:50:51 | coarseness | 3 | 140603 | 51 | 24.156 | 5820.6 |
| 415 | 2025-03-23 07:29:16 | coarseness | 3 | 140603 | 51 | 32.830 | 4282.8 |
| 414 | 2025-03-22 19:49:18 | coarseness | 3 | 140603 | 51 | 8.906 | 15787.4 |
| 413 | 2025-03-21 09:37:50 | coarseness | 1 | 67641 | 2 | 5.923 | 11420.1 |
| 412 | 2025-03-20 10:08:40 | coarseness | 4 | 161450 | 396 | 53.440 | 3021.1 |
| 411 | 2025-03-20 08:38:40 | coarseness | 1 | 67641 | 2 | 2.843 | 23792.1 |
| 410 | 2025-03-19 09:00:51 | coarseness | 3 | 140603 | 51 | 6.486 | 21677.9 |
| 409 | 2025-03-17 08:25:11 | coarseness | 1 | 67641 | 2 | 4.736 | 14282.3 |
| 408 | 2025-03-17 08:24:24 | coarseness | 3 | 140603 | 51 | 45.656 | 3079.6 |
| 407 | 2025-03-17 08:24:24 | coarseness | 3 | 140603 | 51 | 22.360 | 6288.1 |
| 406 | 2025-03-17 08:24:25 | coarseness | 2 | 108824 | 6 | 15.453 | 7042.3 |
| 405 | 2025-03-17 05:01:52 | coarseness | 3 | 140603 | 51 | 32.610 | 4311.7 |
| 404 | 2025-03-14 09:14:57 | coarseness | 3 | 140603 | 51 | 34.920 | 4026.4 |
| 403 | 2025-03-11 13:59:49 | coarseness | 1 | 67641 | 2 | 7.753 | 8724.5 |
| 402 | 2025-02-28 20:05:30 | coarseness | 3 | 140603 | 51 | 34.960 | 4021.8 |
| 401 | 2025-02-28 20:05:20 | coarseness | 2 | 108824 | 6 | 14.250 | 7636.8 |
| 400 | 2025-02-26 21:53:12 | coarseness | 3 | 140603 | 51 | 30.690 | 4581.4 |
| 399 | 2025-02-24 02:40:25 | coarseness | 3 | 140603 | 51 | 33.923 | 4144.8 |
| 398 | 2025-02-22 10:11:11 | coarseness | 3 | 140603 | 51 | 28.626 | 4911.7 |
| 397 | 2025-02-22 09:29:06 | coarseness | 4 | 161450 | 396 | 49.440 | 3265.6 |
| 396 | 2025-02-21 15:05:57 | coarseness | 4 | 161450 | 396 | 49.283 | 3276.0 |
| 395 | 2025-02-21 02:18:45 | coarseness | 4 | 161450 | 396 | 52.566 | 3071.4 |
| 394 | 2025-02-21 02:18:39 | coarseness | 3 | 140603 | 51 | 24.923 | 5641.5 |
| 393 | 2025-02-21 02:18:33 | coarseness | 2 | 108824 | 6 | 15.936 | 6828.8 |
| 392 | 2025-02-17 21:31:33 | coarseness | 1 | 67641 | 2 | 6.376 | 10608.7 |
| 391 | 2025-02-15 00:48:49 | coarseness | 1 | 67641 | 2 | 7.203 | 9390.7 |
| 390 | 2025-02-14 20:02:08 | coarseness | 1 | 67641 | 2 | 5.500 | 12298.4 |
| 389 | 2025-01-29 19:29:52 | coarseness | 1 | 67641 | 2 | 1.360 | 49736.0 |
| 388 | 2025-01-25 22:15:29 | coarseness | 1 | 67641 | 2 | 7.373 | 9174.1 |
| 387 | 2025-01-13 19:23:32 | coarseness | 1 | 67641 | 2 | 5.686 | 11896.1 |
| 386 | 2025-01-08 10:03:03 | coarseness | 3 | 140603 | 51 | 25.393 | 5537.1 |
| 385 | 2025-01-07 17:55:55 | coarseness | 3 | 140603 | 51 | 31.206 | 4505.6 |
| 384 | 2025-01-07 17:55:52 | coarseness | 2 | 108824 | 6 | 16.296 | 6678.0 |
| 383 | 2025-01-06 18:45:52 | coarseness | 3 | 140603 | 51 | 42.330 | 3321.6 |
| 382 | 2025-01-06 18:45:55 | coarseness | 2 | 108824 | 6 | 23.580 | 4615.1 |
| 381 | 2025-01-06 18:44:33 | coarseness | 1 | 67641 | 2 | 3.280 | 20622.3 |
| 380 | 2024-12-30 05:08:52 | coarseness | 3 | 140603 | 51 | 44.596 | 3152.8 |
| 379 | 2024-12-29 21:54:35 | coarseness | 3 | 140603 | 51 | 39.846 | 3528.7 |
| 378 | 2024-12-29 21:54:39 | coarseness | 2 | 108824 | 6 | 14.456 | 7527.9 |
| 377 | 2024-12-29 21:52:48 | coarseness | 1 | 67641 | 2 | 4.766 | 14192.4 |
| 376 | 2024-12-08 23:23:57 | coarseness | 1 | 67641 | 2 | 2.610 | 25916.1 |
| 375 | 2024-12-07 09:50:43 | coarseness | 1 | 67641 | 2 | 6.063 | 11156.4 |
| 374 | 2024-11-25 09:01:49 | coarseness | 1 | 67641 | 2 | 2.516 | 26884.3 |
| 373 | 2024-11-09 17:27:36 | coarseness | 2 | 108824 | 6 | 15.436 | 7050.0 |
| 372 | 2024-11-04 21:29:09 | coarseness | 3 | 140603 | 51 | 34.173 | 4114.4 |
| 371 | 2024-11-02 02:40:38 | coarseness | 2 | 108824 | 6 | 19.500 | 5580.7 |
| 370 | 2024-11-02 02:40:01 | coarseness | 3 | 140603 | 51 | 24.363 | 5771.2 |
| 369 | 2024-11-02 02:37:50 | coarseness | 1 | 67641 | 2 | 1.610 | 42013.0 |
| 368 | 2024-10-24 22:19:18 | coarseness | 3 | 140603 | 51 | 35.690 | 3939.6 |
| 367 | 2024-10-22 15:39:56 | coarseness | 1 | 67641 | 2 | 4.110 | 16457.7 |
| 366 | 2024-10-22 13:32:43 | coarseness | 2 | 108824 | 6 | 14.906 | 7300.7 |
| 365 | 2024-10-22 13:30:04 | coarseness | 1 | 67641 | 2 | 2.750 | 24596.7 |
| 364 | 2024-10-06 03:02:43 | coarseness | 3 | 140603 | 51 | 67.023 | 2097.8 |
| 363 | 2024-10-05 07:42:36 | coarseness | 3 | 140603 | 51 | 33.566 | 4188.9 |
| 362 | 2024-10-01 11:13:45 | coarseness | 3 | 140603 | 51 | 48.566 | 2895.1 |
| 361 | 2024-09-20 20:06:41 | coarseness | 1 | 67641 | 2 | 11.766 | 5748.9 |
| 360 | 2024-09-19 12:46:42 | coarseness | 1 | 67641 | 2 | 9.966 | 6787.2 |