History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"predictors"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 384 | 2025-10-29 14:53:49 | predictors | 4 | 161450 | 286 | 11.283 | 14309.1 |
| 383 | 2025-10-29 05:24:34 | predictors | 4 | 161450 | 286 | 11.706 | 13792.1 |
| 382 | 2025-10-26 18:59:08 | predictors | 4 | 161450 | 286 | 25.610 | 6304.2 |
| 381 | 2025-10-26 02:07:48 | predictors | 1 | 67641 | 2 | 1.173 | 57665.0 |
| 380 | 2025-10-24 17:03:35 | predictors | 1 | 67641 | 2 | 1.156 | 58513.0 |
| 379 | 2025-10-20 04:56:08 | predictors | 1 | 67641 | 2 | 7.016 | 9641.0 |
| 378 | 2025-10-18 10:30:29 | predictors | 1 | 67641 | 2 | 1.356 | 49882.7 |
| 377 | 2025-10-18 07:21:32 | predictors | 2 | 108824 | 10 | 3.033 | 35880.0 |
| 376 | 2025-10-16 23:22:36 | predictors | 2 | 108824 | 10 | 3.216 | 33838.3 |
| 375 | 2025-10-13 12:55:55 | predictors | 1 | 67641 | 2 | 2.343 | 28869.4 |
| 374 | 2025-10-12 14:41:55 | predictors | 1 | 67641 | 2 | 1.200 | 56367.5 |
| 373 | 2025-10-12 10:20:03 | predictors | 3 | 140603 | 54 | 9.783 | 14372.2 |
| 372 | 2025-10-11 18:57:38 | predictors | 4 | 161450 | 286 | 11.423 | 14133.8 |
| 371 | 2025-10-11 06:03:25 | predictors | 2 | 108824 | 10 | 6.640 | 16389.2 |
| 370 | 2025-10-06 22:32:54 | predictors | 1 | 67641 | 2 | 1.283 | 52721.0 |
| 369 | 2025-10-06 02:43:15 | predictors | 1 | 67641 | 2 | 1.126 | 60071.9 |
| 368 | 2025-10-02 23:10:57 | predictors | 1 | 67641 | 2 | 1.186 | 57032.9 |
| 367 | 2025-09-30 22:07:17 | predictors | 1 | 67641 | 2 | 1.140 | 59334.2 |
| 366 | 2025-09-30 19:39:54 | predictors | 1 | 67641 | 2 | 1.080 | 62630.6 |
| 365 | 2025-09-28 18:31:00 | predictors | 4 | 161450 | 286 | 12.610 | 12803.3 |
| 364 | 2025-09-22 05:23:54 | predictors | 1 | 67641 | 2 | 1.233 | 54858.9 |
| 363 | 2025-09-19 07:33:40 | predictors | 1 | 67641 | 2 | 1.076 | 62863.4 |
| 362 | 2025-09-16 00:57:25 | predictors | 1 | 67641 | 2 | 1.110 | 60937.8 |
| 361 | 2025-09-11 04:00:55 | predictors | 1 | 67641 | 2 | 1.203 | 56226.9 |
| 360 | 2025-09-11 02:19:06 | predictors | 2 | 108824 | 10 | 3.376 | 32234.6 |
| 359 | 2025-09-10 16:56:28 | predictors | 1 | 67641 | 2 | 1.156 | 58513.0 |
| 358 | 2025-09-09 08:21:09 | predictors | 1 | 67641 | 2 | 1.156 | 58513.0 |
| 357 | 2025-09-06 17:10:48 | predictors | 4 | 161450 | 286 | 11.936 | 13526.3 |
| 356 | 2025-09-04 21:47:55 | predictors | 1 | 67641 | 2 | 1.216 | 55625.8 |
| 355 | 2025-09-04 06:34:44 | predictors | 1 | 67641 | 2 | 1.076 | 62863.4 |
| 354 | 2025-08-25 01:43:54 | predictors | 1 | 67641 | 2 | 5.953 | 11362.5 |
| 353 | 2025-08-21 21:05:10 | predictors | 1 | 67641 | 2 | 2.516 | 26884.3 |
| 352 | 2025-08-20 19:29:48 | predictors | 1 | 67641 | 2 | 3.420 | 19778.1 |
| 351 | 2025-08-20 15:49:36 | predictors | 1 | 67641 | 2 | 1.173 | 57665.0 |
| 350 | 2025-08-19 06:27:41 | predictors | 1 | 67641 | 2 | 2.826 | 23935.2 |
| 349 | 2025-08-19 04:13:02 | predictors | 1 | 67641 | 2 | 3.156 | 21432.5 |
| 348 | 2025-08-16 01:31:41 | predictors | 1 | 67641 | 2 | 1.170 | 57812.8 |
| 347 | 2025-08-09 22:30:08 | predictors | 1 | 67641 | 2 | 3.016 | 22427.4 |
| 346 | 2025-07-25 18:03:51 | predictors | 1 | 67641 | 2 | 6.013 | 11249.1 |
| 345 | 2025-07-25 08:59:17 | predictors | 1 | 67641 | 2 | 4.876 | 13872.2 |
| 344 | 2025-07-24 21:43:53 | predictors | 1 | 67641 | 2 | 6.970 | 9704.6 |
| 343 | 2025-07-24 21:16:38 | predictors | 1 | 67641 | 2 | 4.423 | 15293.0 |
| 342 | 2025-07-22 14:04:14 | predictors | 1 | 67641 | 2 | 5.376 | 12582.0 |
| 341 | 2025-07-16 19:16:56 | predictors | 1 | 67641 | 2 | 1.280 | 52844.5 |
| 340 | 2025-07-14 07:15:32 | predictors | 1 | 67641 | 2 | 3.046 | 22206.5 |
| 339 | 2025-07-13 01:47:18 | predictors | 1 | 67641 | 2 | 2.936 | 23038.5 |
| 338 | 2025-07-07 11:12:29 | predictors | 1 | 67641 | 2 | 3.470 | 19493.1 |
| 337 | 2025-06-30 23:50:46 | predictors | 3 | 140603 | 54 | 26.406 | 5324.7 |
| 336 | 2025-06-30 17:22:56 | predictors | 1 | 67641 | 2 | 3.906 | 17317.2 |
| 335 | 2025-06-30 07:16:20 | predictors | 3 | 140603 | 54 | 28.953 | 4856.2 |
| 334 | 2025-06-29 14:44:26 | predictors | 1 | 67641 | 2 | 1.236 | 54725.7 |
| 333 | 2025-06-27 09:23:59 | predictors | 1 | 67641 | 2 | 5.393 | 12542.4 |
| 332 | 2025-06-27 04:54:13 | predictors | 1 | 67641 | 2 | 3.956 | 17098.3 |
| 331 | 2025-06-27 03:50:43 | predictors | 3 | 140603 | 54 | 33.483 | 4199.2 |
| 330 | 2025-06-26 10:19:21 | predictors | 3 | 140603 | 54 | 20.490 | 6862.0 |
| 329 | 2025-06-26 09:17:35 | predictors | 3 | 140603 | 54 | 34.453 | 4081.0 |
| 328 | 2025-06-26 03:51:44 | predictors | 3 | 140603 | 54 | 33.203 | 4234.6 |
| 327 | 2025-06-26 00:05:53 | predictors | 4 | 161450 | 286 | 39.940 | 4042.3 |
| 326 | 2025-06-25 11:40:09 | predictors | 4 | 161450 | 286 | 14.956 | 10795.0 |
| 325 | 2025-06-25 10:47:22 | predictors | 3 | 140603 | 54 | 6.156 | 22840.0 |
| 324 | 2025-06-24 22:07:42 | predictors | 2 | 108824 | 10 | 3.560 | 30568.5 |
| 323 | 2025-06-24 13:33:35 | predictors | 2 | 108824 | 10 | 19.346 | 5625.1 |
| 322 | 2025-06-23 19:25:39 | predictors | 1 | 67641 | 2 | 2.716 | 24904.6 |
| 321 | 2025-06-23 02:08:30 | predictors | 1 | 67641 | 2 | 3.390 | 19953.1 |
| 320 | 2025-06-21 15:44:37 | predictors | 1 | 67641 | 2 | 1.360 | 49736.0 |
| 319 | 2025-06-20 14:55:10 | predictors | 1 | 67641 | 2 | 5.906 | 11452.9 |
| 318 | 2025-06-17 02:44:16 | predictors | 1 | 67641 | 2 | 5.953 | 11362.5 |
| 317 | 2025-06-05 00:08:41 | predictors | 1 | 67641 | 2 | 3.953 | 17111.3 |
| 316 | 2025-05-31 09:16:05 | predictors | 1 | 67641 | 2 | 8.080 | 8371.4 |
| 315 | 2025-05-30 06:05:51 | predictors | 4 | 161450 | 286 | 18.050 | 8944.6 |
| 314 | 2025-05-26 10:18:34 | predictors | 4 | 161450 | 286 | 57.830 | 2791.8 |
| 313 | 2025-05-26 08:51:30 | predictors | 2 | 108824 | 10 | 17.156 | 6343.2 |
| 312 | 2025-05-25 17:05:44 | predictors | 1 | 67641 | 2 | 1.203 | 56226.9 |
| 311 | 2025-05-24 07:00:03 | predictors | 1 | 67641 | 2 | 4.063 | 16648.0 |
| 310 | 2025-05-24 00:58:57 | predictors | 1 | 67641 | 2 | 5.876 | 11511.4 |
| 309 | 2025-05-23 11:17:34 | predictors | 3 | 140603 | 54 | 25.220 | 5575.1 |
| 308 | 2025-05-23 11:02:10 | predictors | 3 | 140603 | 54 | 34.816 | 4038.5 |
| 307 | 2025-05-23 02:48:32 | predictors | 1 | 67641 | 2 | 8.423 | 8030.5 |
| 306 | 2025-05-22 23:31:39 | predictors | 4 | 161450 | 286 | 50.520 | 3195.8 |
| 305 | 2025-05-22 21:59:53 | predictors | 2 | 108824 | 10 | 13.563 | 8023.6 |
| 304 | 2025-05-22 19:31:09 | predictors | 2 | 108824 | 10 | 3.580 | 30397.8 |
| 303 | 2025-05-21 19:34:02 | predictors | 1 | 67641 | 2 | 1.296 | 52192.1 |
| 302 | 2025-05-21 03:54:56 | predictors | 3 | 140603 | 54 | 6.876 | 20448.4 |
| 301 | 2025-05-20 18:19:59 | predictors | 3 | 140603 | 54 | 31.843 | 4415.5 |
| 300 | 2025-05-20 10:42:09 | predictors | 2 | 108824 | 10 | 12.876 | 8451.7 |
| 299 | 2025-05-20 03:13:55 | predictors | 3 | 140603 | 54 | 32.973 | 4264.2 |
| 298 | 2025-05-19 07:43:33 | predictors | 1 | 67641 | 2 | 1.110 | 60937.8 |
| 297 | 2025-05-19 03:24:28 | predictors | 3 | 140603 | 54 | 8.800 | 15977.6 |
| 296 | 2025-05-14 22:52:26 | predictors | 3 | 140603 | 54 | 38.046 | 3695.6 |
| 295 | 2025-05-08 14:09:02 | predictors | 3 | 140603 | 54 | 30.846 | 4558.2 |
| 294 | 2025-05-02 04:06:29 | predictors | 3 | 140603 | 54 | 24.440 | 5753.0 |
| 293 | 2025-04-30 02:44:30 | predictors | 3 | 140603 | 54 | 33.876 | 4150.5 |
| 292 | 2025-04-28 20:00:40 | predictors | 3 | 140603 | 54 | 6.500 | 21631.2 |
| 291 | 2025-04-28 19:25:46 | predictors | 1 | 67641 | 2 | 2.436 | 27767.2 |
| 290 | 2025-04-25 04:12:50 | predictors | 1 | 67641 | 2 | 1.483 | 45610.9 |
| 289 | 2025-04-23 06:26:28 | predictors | 1 | 67641 | 2 | 2.876 | 23519.1 |
| 288 | 2025-04-20 19:36:40 | predictors | 1 | 67641 | 2 | 6.796 | 9953.1 |
| 287 | 2025-04-18 15:59:48 | predictors | 1 | 67641 | 2 | 2.796 | 24192.1 |
| 286 | 2025-04-17 13:41:39 | predictors | 1 | 67641 | 2 | 1.186 | 57032.9 |
| 285 | 2025-04-14 09:33:25 | predictors | 1 | 67641 | 2 | 5.750 | 11763.7 |