History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"forecaster"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 411 | 2026-02-09 23:51:09 | forecaster | 1 | 67641 | 3 | 1.610 | 42013.0 |
| 410 | 2026-02-08 17:48:00 | forecaster | 1 | 67641 | 3 | 1.203 | 56226.9 |
| 409 | 2026-02-07 10:11:37 | forecaster | 1 | 67641 | 3 | 2.516 | 26884.3 |
| 408 | 2026-02-06 05:28:01 | forecaster | 1 | 67641 | 3 | 1.203 | 56226.9 |
| 407 | 2026-02-05 04:58:39 | forecaster | 1 | 67641 | 3 | 1.186 | 57032.9 |
| 406 | 2026-02-04 11:42:04 | forecaster | 1 | 67641 | 3 | 1.143 | 59178.5 |
| 405 | 2026-02-03 17:29:35 | forecaster | 4 | 161450 | 227 | 12.513 | 12902.6 |
| 404 | 2026-02-03 00:13:09 | forecaster | 3 | 140603 | 35 | 24.983 | 5627.9 |
| 403 | 2026-02-02 05:32:50 | forecaster | 1 | 67641 | 3 | 2.080 | 32519.7 |
| 402 | 2026-02-01 19:59:23 | forecaster | 3 | 140603 | 35 | 7.516 | 18707.2 |
| 401 | 2026-01-31 20:31:14 | forecaster | 3 | 140603 | 35 | 10.906 | 12892.3 |
| 400 | 2026-01-30 23:25:23 | forecaster | 2 | 108824 | 11 | 2.906 | 37448.0 |
| 399 | 2026-01-30 08:34:23 | forecaster | 2 | 108824 | 11 | 5.250 | 20728.4 |
| 398 | 2026-01-26 13:16:19 | forecaster | 4 | 161450 | 227 | 16.546 | 9757.6 |
| 397 | 2026-01-24 00:03:47 | forecaster | 4 | 161450 | 227 | 12.080 | 13365.1 |
| 396 | 2026-01-22 02:46:21 | forecaster | 2 | 108824 | 11 | 15.190 | 7164.2 |
| 395 | 2026-01-21 03:08:40 | forecaster | 4 | 161450 | 227 | 11.640 | 13870.3 |
| 394 | 2026-01-20 17:13:36 | forecaster | 4 | 161450 | 227 | 15.123 | 10675.8 |
| 393 | 2026-01-19 20:11:48 | forecaster | 3 | 140603 | 35 | 7.780 | 18072.4 |
| 392 | 2026-01-09 09:17:03 | forecaster | 2 | 108824 | 11 | 14.360 | 7578.3 |
| 391 | 2026-01-08 12:37:41 | forecaster | 2 | 108824 | 11 | 3.393 | 32073.1 |
| 390 | 2026-01-06 17:07:17 | forecaster | 3 | 140603 | 35 | 6.766 | 20780.8 |
| 389 | 2026-01-06 04:04:43 | forecaster | 4 | 161450 | 227 | 11.970 | 13487.9 |
| 388 | 2026-01-02 16:16:44 | forecaster | 1 | 67641 | 3 | 1.640 | 41244.5 |
| 387 | 2025-12-28 17:39:16 | forecaster | 1 | 67641 | 3 | 2.470 | 27385.0 |
| 386 | 2025-12-28 05:56:27 | forecaster | 1 | 67641 | 3 | 1.186 | 57032.9 |
| 385 | 2025-12-27 20:31:46 | forecaster | 1 | 67641 | 3 | 6.360 | 10635.4 |
| 384 | 2025-12-26 12:06:42 | forecaster | 1 | 67641 | 3 | 1.126 | 60071.9 |
| 383 | 2025-12-26 10:24:57 | forecaster | 1 | 67641 | 3 | 1.156 | 58513.0 |
| 382 | 2025-12-22 22:39:07 | forecaster | 3 | 140603 | 35 | 7.703 | 18253.0 |
| 381 | 2025-12-15 01:38:39 | forecaster | 1 | 67641 | 3 | 1.063 | 63632.2 |
| 380 | 2025-11-23 08:54:53 | forecaster | 3 | 140603 | 35 | 18.766 | 7492.4 |
| 379 | 2025-11-16 21:42:01 | forecaster | 4 | 161450 | 227 | 53.643 | 3009.7 |
| 378 | 2025-11-16 01:10:05 | forecaster | 3 | 140603 | 35 | 15.203 | 9248.4 |
| 377 | 2025-11-13 21:41:19 | forecaster | 4 | 161450 | 227 | 11.706 | 13792.1 |
| 376 | 2025-11-08 09:26:04 | forecaster | 1 | 67641 | 3 | 1.063 | 63632.2 |
| 375 | 2025-11-05 18:20:41 | forecaster | 2 | 108824 | 11 | 2.923 | 37230.2 |
| 374 | 2025-11-04 20:42:08 | forecaster | 2 | 108824 | 11 | 3.330 | 32679.9 |
| 373 | 2025-11-03 04:47:44 | forecaster | 2 | 108824 | 11 | 3.530 | 30828.3 |
| 372 | 2025-10-26 12:30:31 | forecaster | 1 | 67641 | 3 | 1.140 | 59334.2 |
| 371 | 2025-10-25 13:57:30 | forecaster | 1 | 67641 | 3 | 1.186 | 57032.9 |
| 370 | 2025-10-18 15:54:43 | forecaster | 3 | 140603 | 35 | 7.063 | 19907.0 |
| 369 | 2025-10-08 15:59:54 | forecaster | 2 | 108824 | 11 | 2.763 | 39386.2 |
| 368 | 2025-09-15 15:03:19 | forecaster | 4 | 161450 | 227 | 11.326 | 14254.8 |
| 367 | 2025-09-05 16:15:13 | forecaster | 3 | 140603 | 35 | 6.706 | 20966.7 |
| 366 | 2025-08-31 15:21:55 | forecaster | 1 | 67641 | 3 | 1.110 | 60937.8 |
| 365 | 2025-08-28 13:29:58 | forecaster | 1 | 67641 | 3 | 1.173 | 57665.0 |
| 364 | 2025-08-27 16:11:55 | forecaster | 3 | 140603 | 35 | 6.250 | 22496.5 |
| 363 | 2025-08-27 05:48:19 | forecaster | 2 | 108824 | 11 | 3.266 | 33320.3 |
| 362 | 2025-08-26 19:07:29 | forecaster | 4 | 161450 | 227 | 12.000 | 13454.2 |
| 361 | 2025-08-25 21:21:23 | forecaster | 1 | 67641 | 3 | 2.283 | 29628.1 |
| 360 | 2025-08-18 23:47:52 | forecaster | 3 | 140603 | 35 | 14.610 | 9623.8 |
| 359 | 2025-08-16 22:21:21 | forecaster | 1 | 67641 | 3 | 2.186 | 30942.8 |
| 358 | 2025-08-15 04:58:08 | forecaster | 3 | 140603 | 35 | 29.096 | 4832.4 |
| 357 | 2025-08-12 18:08:09 | forecaster | 4 | 161450 | 227 | 29.316 | 5507.2 |
| 356 | 2025-08-12 11:08:17 | forecaster | 3 | 140603 | 35 | 16.936 | 8302.0 |
| 355 | 2025-08-09 20:09:07 | forecaster | 1 | 67641 | 3 | 3.656 | 18501.4 |
| 354 | 2025-08-09 16:15:45 | forecaster | 1 | 67641 | 3 | 2.873 | 23543.7 |
| 353 | 2025-08-08 23:35:03 | forecaster | 1 | 67641 | 3 | 4.830 | 14004.3 |
| 352 | 2025-07-24 23:49:13 | forecaster | 1 | 67641 | 3 | 6.563 | 10306.4 |
| 351 | 2025-07-24 20:26:07 | forecaster | 1 | 67641 | 3 | 4.170 | 16220.9 |
| 350 | 2025-07-23 03:18:45 | forecaster | 1 | 67641 | 3 | 6.266 | 10794.9 |
| 349 | 2025-07-10 17:53:58 | forecaster | 1 | 67641 | 3 | 5.906 | 11452.9 |
| 348 | 2025-07-09 12:46:18 | forecaster | 1 | 67641 | 3 | 5.343 | 12659.7 |
| 347 | 2025-07-06 23:13:05 | forecaster | 1 | 67641 | 3 | 5.140 | 13159.7 |
| 346 | 2025-06-30 11:20:32 | forecaster | 1 | 67641 | 3 | 2.483 | 27241.6 |
| 345 | 2025-06-28 15:30:53 | forecaster | 1 | 67641 | 3 | 3.140 | 21541.7 |
| 344 | 2025-06-14 11:23:07 | forecaster | 3 | 140603 | 35 | 34.110 | 4122.0 |
| 343 | 2025-06-13 20:48:39 | forecaster | 3 | 140603 | 35 | 43.723 | 3215.8 |
| 342 | 2025-06-06 14:03:31 | forecaster | 1 | 67641 | 3 | 3.233 | 20922.1 |
| 341 | 2025-06-04 05:14:59 | forecaster | 1 | 67641 | 3 | 3.080 | 21961.4 |
| 340 | 2025-06-01 19:11:02 | forecaster | 3 | 140603 | 35 | 13.876 | 10132.8 |
| 339 | 2025-05-30 23:25:11 | forecaster | 1 | 67641 | 3 | 7.033 | 9617.7 |
| 338 | 2025-05-26 16:01:02 | forecaster | 1 | 67641 | 3 | 5.390 | 12549.4 |
| 337 | 2025-05-26 04:45:15 | forecaster | 3 | 140603 | 35 | 17.923 | 7844.8 |
| 336 | 2025-05-24 16:56:38 | forecaster | 3 | 140603 | 35 | 18.313 | 7677.8 |
| 335 | 2025-05-22 17:27:34 | forecaster | 1 | 67641 | 3 | 8.003 | 8452.0 |
| 334 | 2025-05-22 15:30:55 | forecaster | 3 | 140603 | 35 | 35.140 | 4001.2 |
| 333 | 2025-05-18 10:09:19 | forecaster | 1 | 67641 | 3 | 5.843 | 11576.4 |
| 332 | 2025-05-16 05:15:44 | forecaster | 1 | 67641 | 3 | 5.550 | 12187.6 |
| 331 | 2025-05-15 20:14:53 | forecaster | 4 | 161450 | 227 | 80.723 | 2000.0 |
| 330 | 2025-05-14 17:55:52 | forecaster | 4 | 161450 | 227 | 54.993 | 2935.8 |
| 329 | 2025-05-13 22:55:47 | forecaster | 1 | 67641 | 3 | 5.000 | 13528.2 |
| 328 | 2025-05-11 09:01:49 | forecaster | 1 | 67641 | 3 | 2.076 | 32582.4 |
| 327 | 2025-05-07 23:00:51 | forecaster | 1 | 67641 | 3 | 3.046 | 22206.5 |
| 326 | 2025-05-06 14:32:16 | forecaster | 1 | 67641 | 3 | 7.406 | 9133.3 |
| 325 | 2025-05-05 19:43:51 | forecaster | 4 | 161450 | 227 | 35.423 | 4557.8 |
| 324 | 2025-05-04 15:55:14 | forecaster | 4 | 161450 | 227 | 45.156 | 3575.4 |
| 323 | 2025-05-03 10:07:15 | forecaster | 4 | 161450 | 227 | 19.466 | 8293.9 |
| 322 | 2025-04-27 05:58:31 | forecaster | 4 | 161450 | 227 | 11.656 | 13851.2 |
| 321 | 2025-04-19 08:17:29 | forecaster | 1 | 67641 | 3 | 1.203 | 56226.9 |
| 320 | 2025-04-04 10:11:35 | forecaster | 1 | 67641 | 3 | 5.670 | 11929.6 |
| 319 | 2025-04-01 20:02:05 | forecaster | 4 | 161450 | 227 | 31.050 | 5199.7 |
| 318 | 2025-03-30 22:48:37 | forecaster | 1 | 67641 | 3 | 6.076 | 11132.5 |
| 317 | 2025-03-29 00:50:23 | forecaster | 1 | 67641 | 3 | 7.263 | 9313.1 |
| 316 | 2025-03-27 13:18:13 | forecaster | 4 | 161450 | 227 | 60.050 | 2688.6 |
| 315 | 2025-03-25 18:05:37 | forecaster | 4 | 161450 | 227 | 40.913 | 3946.2 |
| 314 | 2025-03-23 10:56:36 | forecaster | 2 | 108824 | 11 | 14.720 | 7392.9 |
| 313 | 2025-03-22 21:29:09 | forecaster | 4 | 161450 | 227 | 38.923 | 4147.9 |
| 312 | 2025-03-22 17:04:02 | forecaster | 3 | 140603 | 35 | 25.673 | 5476.7 |