History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"covariances"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 411 | 2026-01-10 23:14:45 | covariances | 1 | 49908 | 2 | 0.936 | 53320.5 |
| 410 | 2026-01-06 14:56:14 | covariances | 1 | 49908 | 2 | 0.923 | 54071.5 |
| 409 | 2026-01-04 13:38:32 | covariances | 4 | 148819 | 88 | 19.360 | 7686.9 |
| 408 | 2026-01-03 09:42:31 | covariances | 5 | 164607 | 618 | 29.063 | 5663.8 |
| 407 | 2025-12-22 21:51:41 | covariances | 4 | 148819 | 88 | 9.983 | 14907.2 |
| 406 | 2025-12-08 16:47:45 | covariances | 1 | 49908 | 2 | 0.810 | 61614.8 |
| 405 | 2025-11-22 19:15:56 | covariances | 4 | 148819 | 88 | 9.940 | 14971.7 |
| 404 | 2025-11-22 08:00:09 | covariances | 1 | 49908 | 2 | 1.063 | 46950.1 |
| 403 | 2025-11-21 17:09:11 | covariances | 1 | 49908 | 2 | 6.313 | 7905.6 |
| 402 | 2025-11-19 11:37:44 | covariances | 1 | 49908 | 2 | 0.970 | 51451.5 |
| 401 | 2025-11-19 11:36:42 | covariances | 1 | 49908 | 2 | 0.860 | 58032.6 |
| 400 | 2025-11-15 02:35:13 | covariances | 5 | 164607 | 618 | 15.033 | 10949.7 |
| 399 | 2025-11-14 14:56:54 | covariances | 5 | 164607 | 618 | 15.126 | 10882.4 |
| 398 | 2025-11-13 23:04:52 | covariances | 2 | 86808 | 5 | 3.843 | 22588.6 |
| 397 | 2025-11-04 01:19:59 | covariances | 1 | 49908 | 2 | 0.780 | 63984.6 |
| 396 | 2025-11-01 19:52:27 | covariances | 1 | 49908 | 2 | 0.906 | 55086.1 |
| 395 | 2025-10-28 09:48:40 | covariances | 4 | 148819 | 88 | 8.360 | 17801.3 |
| 394 | 2025-10-25 10:05:50 | covariances | 1 | 49908 | 2 | 0.783 | 63739.5 |
| 393 | 2025-10-24 21:00:19 | covariances | 1 | 49908 | 2 | 0.923 | 54071.5 |
| 392 | 2025-10-24 14:10:27 | covariances | 4 | 148819 | 88 | 9.563 | 15562.0 |
| 391 | 2025-10-23 14:23:50 | covariances | 1 | 49908 | 2 | 5.563 | 8971.4 |
| 390 | 2025-10-22 04:32:11 | covariances | 1 | 49908 | 2 | 2.563 | 19472.5 |
| 389 | 2025-10-21 09:07:43 | covariances | 1 | 49908 | 2 | 4.610 | 10826.0 |
| 388 | 2025-10-20 15:03:40 | covariances | 1 | 49908 | 2 | 6.093 | 8191.0 |
| 387 | 2025-10-20 15:03:12 | covariances | 1 | 49908 | 2 | 4.516 | 11051.4 |
| 386 | 2025-10-20 15:02:43 | covariances | 1 | 49908 | 2 | 6.063 | 8231.6 |
| 385 | 2025-10-20 15:02:05 | covariances | 1 | 49908 | 2 | 7.563 | 6599.0 |
| 384 | 2025-10-19 08:32:18 | covariances | 1 | 49908 | 2 | 1.436 | 34754.9 |
| 383 | 2025-10-14 19:14:00 | covariances | 1 | 49908 | 2 | 0.796 | 62698.5 |
| 382 | 2025-10-13 11:03:05 | covariances | 1 | 49908 | 2 | 0.783 | 63739.5 |
| 381 | 2025-10-04 13:53:34 | covariances | 5 | 164607 | 618 | 13.830 | 11902.2 |
| 380 | 2025-10-04 00:29:49 | covariances | 1 | 49908 | 2 | 0.876 | 56972.6 |
| 379 | 2025-10-02 15:53:20 | covariances | 5 | 164607 | 618 | 15.516 | 10608.9 |
| 378 | 2025-09-26 17:23:55 | covariances | 1 | 49908 | 2 | 0.936 | 53320.5 |
| 377 | 2025-09-24 11:05:59 | covariances | 1 | 49908 | 2 | 0.763 | 65410.2 |
| 376 | 2025-09-23 06:56:52 | covariances | 2 | 86808 | 5 | 2.203 | 39404.4 |
| 375 | 2025-09-22 09:07:10 | covariances | 2 | 86808 | 5 | 2.126 | 40831.6 |
| 374 | 2025-09-21 08:15:07 | covariances | 1 | 49908 | 2 | 0.893 | 55888.0 |
| 373 | 2025-09-16 18:37:20 | covariances | 1 | 49908 | 2 | 0.890 | 56076.4 |
| 372 | 2025-08-30 05:47:10 | covariances | 2 | 86808 | 5 | 2.110 | 41141.2 |
| 371 | 2025-08-30 05:29:43 | covariances | 4 | 148819 | 88 | 8.330 | 17865.4 |
| 370 | 2025-08-30 05:06:26 | covariances | 5 | 164607 | 618 | 15.173 | 10848.7 |
| 369 | 2025-08-30 02:48:21 | covariances | 5 | 164607 | 618 | 14.530 | 11328.8 |
| 368 | 2025-08-30 02:43:48 | covariances | 2 | 86808 | 5 | 2.220 | 39102.7 |
| 367 | 2025-08-30 02:20:37 | covariances | 4 | 148819 | 88 | 12.313 | 12086.3 |
| 366 | 2025-08-29 15:42:58 | covariances | 4 | 148819 | 88 | 9.046 | 16451.4 |
| 365 | 2025-08-28 02:13:02 | covariances | 1 | 49908 | 2 | 0.890 | 56076.4 |
| 364 | 2025-08-19 13:08:34 | covariances | 1 | 49908 | 2 | 2.203 | 22654.6 |
| 363 | 2025-08-09 11:59:16 | covariances | 1 | 49908 | 2 | 2.656 | 18790.7 |
| 362 | 2025-08-08 21:57:54 | covariances | 1 | 49908 | 2 | 3.360 | 14853.6 |
| 361 | 2025-07-16 18:59:32 | covariances | 1 | 49908 | 2 | 0.876 | 56972.6 |
| 360 | 2025-07-13 19:31:50 | covariances | 3 | 121633 | 19 | 27.330 | 4450.5 |
| 359 | 2025-07-13 18:56:33 | covariances | 2 | 86808 | 5 | 6.096 | 14240.2 |
| 358 | 2025-07-11 12:23:11 | covariances | 2 | 86808 | 5 | 10.296 | 8431.2 |
| 357 | 2025-07-11 11:34:45 | covariances | 3 | 121633 | 19 | 26.500 | 4589.9 |
| 356 | 2025-07-11 08:07:54 | covariances | 3 | 121633 | 19 | 38.956 | 3122.3 |
| 355 | 2025-07-10 03:08:04 | covariances | 1 | 49908 | 2 | 2.703 | 18463.9 |
| 354 | 2025-07-04 13:33:55 | covariances | 1 | 49908 | 2 | 2.126 | 23475.1 |
| 353 | 2025-07-02 14:31:53 | covariances | 4 | 148819 | 88 | 45.676 | 3258.1 |
| 352 | 2025-07-01 21:46:10 | covariances | 1 | 49908 | 2 | 2.016 | 24756.0 |
| 351 | 2025-06-30 09:05:59 | covariances | 4 | 148819 | 88 | 35.093 | 4240.7 |
| 350 | 2025-06-18 21:44:50 | covariances | 1 | 49908 | 2 | 2.173 | 22967.3 |
| 349 | 2025-06-06 09:34:31 | covariances | 1 | 49908 | 2 | 4.410 | 11317.0 |
| 348 | 2025-05-29 02:26:15 | covariances | 1 | 49908 | 2 | 1.783 | 27991.0 |
| 347 | 2025-05-28 21:25:09 | covariances | 1 | 49908 | 2 | 4.483 | 11132.7 |
| 346 | 2025-05-27 09:19:55 | covariances | 1 | 49908 | 2 | 4.640 | 10756.0 |
| 345 | 2025-05-27 03:40:11 | covariances | 1 | 49908 | 2 | 4.453 | 11207.7 |
| 344 | 2025-05-26 06:25:59 | covariances | 1 | 49908 | 2 | 4.700 | 10618.7 |
| 343 | 2025-05-25 04:56:50 | covariances | 4 | 148819 | 88 | 30.346 | 4904.1 |
| 342 | 2025-05-24 01:27:58 | covariances | 4 | 148819 | 88 | 52.923 | 2812.0 |
| 341 | 2025-05-23 16:51:23 | covariances | 4 | 148819 | 88 | 58.126 | 2560.3 |
| 340 | 2025-05-23 13:04:17 | covariances | 1 | 49908 | 2 | 4.826 | 10341.5 |
| 339 | 2025-05-23 10:33:12 | covariances | 3 | 121633 | 19 | 28.206 | 4312.3 |
| 338 | 2025-05-21 23:00:44 | covariances | 4 | 148819 | 88 | 29.706 | 5009.7 |
| 337 | 2025-05-21 22:17:42 | covariances | 4 | 148819 | 88 | 59.156 | 2515.7 |
| 336 | 2025-05-20 01:57:32 | covariances | 1 | 49908 | 2 | 4.393 | 11360.8 |
| 335 | 2025-05-19 21:27:43 | covariances | 3 | 121633 | 19 | 26.313 | 4622.5 |
| 334 | 2025-05-16 13:03:03 | covariances | 4 | 148819 | 88 | 33.906 | 4389.2 |
| 333 | 2025-05-15 19:06:38 | covariances | 2 | 86808 | 5 | 10.270 | 8452.6 |
| 332 | 2025-05-14 19:38:12 | covariances | 3 | 121633 | 19 | 5.143 | 23650.2 |
| 331 | 2025-05-10 15:19:36 | covariances | 3 | 121633 | 19 | 16.690 | 7287.8 |
| 330 | 2025-05-10 01:48:14 | covariances | 2 | 86808 | 5 | 9.610 | 9033.1 |
| 329 | 2025-05-05 22:47:15 | covariances | 2 | 86808 | 5 | 13.360 | 6497.6 |
| 328 | 2025-05-05 15:26:42 | covariances | 3 | 121633 | 19 | 5.530 | 21995.1 |
| 327 | 2025-05-02 22:28:43 | covariances | 3 | 121633 | 19 | 32.533 | 3738.8 |
| 326 | 2025-04-27 20:55:43 | covariances | 1 | 49908 | 2 | 3.423 | 14580.2 |
| 325 | 2025-04-27 18:07:43 | covariances | 3 | 121633 | 19 | 8.670 | 14029.2 |
| 324 | 2025-04-24 05:54:14 | covariances | 4 | 148819 | 88 | 48.720 | 3054.6 |
| 323 | 2025-04-23 12:35:58 | covariances | 1 | 49908 | 2 | 4.280 | 11660.7 |
| 322 | 2025-04-19 20:51:06 | covariances | 4 | 148819 | 88 | 47.490 | 3133.7 |
| 321 | 2025-04-15 15:53:33 | covariances | 4 | 148819 | 88 | 10.110 | 14720.0 |
| 320 | 2025-04-02 01:35:28 | covariances | 1 | 49908 | 2 | 4.500 | 11090.7 |
| 319 | 2025-03-29 11:31:56 | covariances | 1 | 49908 | 2 | 4.203 | 11874.4 |
| 318 | 2025-02-26 18:08:08 | covariances | 1 | 49908 | 2 | 3.263 | 15295.1 |
| 317 | 2025-02-23 11:52:59 | covariances | 3 | 121633 | 19 | 26.830 | 4533.5 |
| 316 | 2025-02-22 20:56:02 | covariances | 1 | 49908 | 2 | 2.810 | 17760.9 |
| 315 | 2025-02-22 19:25:58 | covariances | 1 | 49908 | 2 | 3.750 | 13308.8 |
| 314 | 2025-02-22 13:29:26 | covariances | 3 | 121633 | 19 | 30.110 | 4039.6 |
| 313 | 2025-02-22 13:29:19 | covariances | 2 | 86808 | 5 | 12.263 | 7078.9 |
| 312 | 2025-02-22 08:44:17 | covariances | 3 | 121633 | 19 | 20.486 | 5937.4 |