History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"granularity"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 356 | 2025-10-20 23:06:16 | granularity | 1 | 49908 | 1 | 4.143 | 12046.3 |
| 355 | 2025-10-20 23:06:11 | granularity | 1 | 49908 | 1 | 5.906 | 8450.4 |
| 354 | 2025-10-20 23:06:06 | granularity | 1 | 49908 | 1 | 3.750 | 13308.8 |
| 353 | 2025-10-20 23:06:01 | granularity | 1 | 49908 | 1 | 4.500 | 11090.7 |
| 352 | 2025-10-18 13:29:19 | granularity | 5 | 164607 | 187 | 15.456 | 10650.0 |
| 351 | 2025-09-28 13:35:00 | granularity | 5 | 164607 | 187 | 13.983 | 11771.9 |
| 350 | 2025-09-26 22:38:47 | granularity | 5 | 164607 | 187 | 16.203 | 10159.0 |
| 349 | 2025-09-17 23:57:55 | granularity | 5 | 164607 | 187 | 16.296 | 10101.1 |
| 348 | 2025-09-15 07:30:07 | granularity | 2 | 86808 | 2 | 2.470 | 35144.9 |
| 347 | 2025-09-07 14:31:26 | granularity | 5 | 164607 | 187 | 15.796 | 10420.8 |
| 346 | 2025-09-06 13:59:39 | granularity | 5 | 164607 | 187 | 13.550 | 12148.1 |
| 345 | 2025-09-06 13:28:52 | granularity | 4 | 148819 | 52 | 9.563 | 15562.0 |
| 344 | 2025-09-06 13:25:35 | granularity | 3 | 121633 | 11 | 4.720 | 25769.7 |
| 343 | 2025-09-06 12:16:56 | granularity | 2 | 86808 | 2 | 2.060 | 42139.8 |
| 342 | 2025-08-04 06:20:41 | granularity | 3 | 121633 | 11 | 4.390 | 27706.8 |
| 341 | 2025-07-27 00:12:28 | granularity | 1 | 49908 | 1 | 1.640 | 30431.7 |
| 340 | 2025-07-25 06:33:24 | granularity | 1 | 49908 | 1 | 5.063 | 9857.4 |
| 339 | 2025-07-24 06:23:56 | granularity | 1 | 49908 | 1 | 2.953 | 16900.8 |
| 338 | 2025-07-22 22:09:54 | granularity | 1 | 49908 | 1 | 2.130 | 23431.0 |
| 337 | 2025-07-10 18:19:52 | granularity | 1 | 49908 | 1 | 1.736 | 28748.8 |
| 336 | 2025-07-10 07:24:10 | granularity | 1 | 49908 | 1 | 3.530 | 14138.2 |
| 335 | 2025-07-07 21:39:32 | granularity | 4 | 148819 | 52 | 38.970 | 3818.8 |
| 334 | 2025-07-04 15:44:46 | granularity | 4 | 148819 | 52 | 41.716 | 3567.4 |
| 333 | 2025-07-02 09:02:08 | granularity | 1 | 49908 | 1 | 1.970 | 25334.0 |
| 332 | 2025-06-29 01:37:10 | granularity | 4 | 148819 | 52 | 42.250 | 3522.3 |
| 331 | 2025-06-10 06:27:38 | granularity | 1 | 49908 | 1 | 3.266 | 15281.1 |
| 330 | 2025-06-08 06:07:58 | granularity | 1 | 49908 | 1 | 4.826 | 10341.5 |
| 329 | 2025-05-28 06:11:21 | granularity | 1 | 49908 | 1 | 3.860 | 12929.5 |
| 328 | 2025-05-24 15:20:09 | granularity | 4 | 148819 | 52 | 63.920 | 2328.2 |
| 327 | 2025-05-20 15:18:21 | granularity | 1 | 49908 | 1 | 2.123 | 23508.2 |
| 326 | 2025-05-18 14:45:25 | granularity | 4 | 148819 | 52 | 62.586 | 2377.8 |
| 325 | 2025-05-16 15:20:17 | granularity | 3 | 121633 | 11 | 13.423 | 9061.5 |
| 324 | 2025-05-16 14:04:57 | granularity | 2 | 86808 | 2 | 7.486 | 11596.0 |
| 323 | 2025-05-16 04:45:38 | granularity | 4 | 148819 | 52 | 44.640 | 3333.8 |
| 322 | 2025-05-16 03:31:41 | granularity | 4 | 148819 | 52 | 57.676 | 2580.3 |
| 321 | 2025-05-15 20:54:16 | granularity | 4 | 148819 | 52 | 48.283 | 3082.2 |
| 320 | 2025-05-15 01:55:19 | granularity | 1 | 49908 | 1 | 0.906 | 55086.1 |
| 319 | 2025-05-08 14:40:23 | granularity | 3 | 121633 | 11 | 21.390 | 5686.4 |
| 318 | 2025-05-08 11:18:26 | granularity | 2 | 86808 | 2 | 15.046 | 5769.5 |
| 317 | 2025-05-08 05:40:37 | granularity | 1 | 49908 | 1 | 1.810 | 27573.5 |
| 316 | 2025-05-07 20:30:05 | granularity | 4 | 148819 | 52 | 28.346 | 5250.1 |
| 315 | 2025-05-07 12:34:02 | granularity | 1 | 49908 | 1 | 0.873 | 57168.4 |
| 314 | 2025-05-04 15:13:13 | granularity | 3 | 121633 | 11 | 13.923 | 8736.1 |
| 313 | 2025-04-24 21:21:24 | granularity | 3 | 121633 | 11 | 5.013 | 24263.5 |
| 312 | 2025-04-11 01:05:34 | granularity | 1 | 49908 | 1 | 1.953 | 25554.5 |
| 311 | 2025-03-29 03:53:01 | granularity | 1 | 49908 | 1 | 5.360 | 9311.2 |
| 310 | 2025-03-27 07:08:55 | granularity | 1 | 49908 | 1 | 2.156 | 23148.4 |
| 309 | 2025-03-18 02:07:03 | granularity | 3 | 121633 | 11 | 25.173 | 4831.9 |
| 308 | 2025-03-16 14:04:44 | granularity | 3 | 121633 | 11 | 41.203 | 2952.0 |
| 307 | 2025-03-16 14:04:34 | granularity | 2 | 86808 | 2 | 17.613 | 4928.6 |
| 306 | 2025-03-12 22:08:16 | granularity | 1 | 49908 | 1 | 3.623 | 13775.3 |
| 305 | 2025-03-11 01:39:31 | granularity | 3 | 121633 | 11 | 5.266 | 23097.8 |
| 304 | 2025-03-10 15:22:47 | granularity | 1 | 49908 | 1 | 1.953 | 25554.5 |
| 303 | 2025-03-10 15:06:05 | granularity | 1 | 49908 | 1 | 4.233 | 11790.2 |
| 302 | 2025-03-06 03:46:00 | granularity | 3 | 121633 | 11 | 26.720 | 4552.1 |
| 301 | 2025-03-06 03:45:57 | granularity | 2 | 86808 | 2 | 9.780 | 8876.1 |
| 300 | 2025-03-06 03:45:35 | granularity | 1 | 49908 | 1 | 3.046 | 16384.8 |
| 299 | 2025-03-06 03:36:13 | granularity | 3 | 121633 | 11 | 21.780 | 5584.6 |
| 298 | 2025-02-27 13:54:24 | granularity | 3 | 121633 | 11 | 29.970 | 4058.5 |
| 297 | 2025-02-19 13:53:30 | granularity | 1 | 49908 | 1 | 5.453 | 9152.4 |
| 296 | 2025-02-11 04:50:17 | granularity | 3 | 121633 | 11 | 29.330 | 4147.1 |
| 295 | 2025-02-10 07:41:08 | granularity | 1 | 49908 | 1 | 3.856 | 12942.9 |
| 294 | 2025-02-09 02:27:48 | granularity | 3 | 121633 | 11 | 27.546 | 4415.6 |
| 293 | 2025-02-08 15:06:30 | granularity | 3 | 121633 | 11 | 26.563 | 4579.0 |
| 292 | 2025-02-06 02:07:48 | granularity | 4 | 148819 | 52 | 40.516 | 3673.1 |
| 291 | 2025-02-02 08:36:43 | granularity | 4 | 148819 | 52 | 53.626 | 2775.1 |
| 290 | 2025-02-02 08:36:56 | granularity | 3 | 121633 | 11 | 20.253 | 6005.7 |
| 289 | 2025-02-02 08:36:59 | granularity | 2 | 86808 | 2 | 11.703 | 7417.6 |
| 288 | 2025-02-02 08:33:20 | granularity | 1 | 49908 | 1 | 3.156 | 15813.7 |
| 287 | 2025-01-23 00:57:33 | granularity | 4 | 148819 | 52 | 48.190 | 3088.2 |
| 286 | 2025-01-14 15:26:27 | granularity | 2 | 86808 | 2 | 11.720 | 7406.8 |
| 285 | 2025-01-14 15:26:14 | granularity | 1 | 49908 | 1 | 3.703 | 13477.7 |
| 284 | 2025-01-08 06:29:09 | granularity | 3 | 121633 | 11 | 26.656 | 4563.1 |
| 283 | 2025-01-08 06:26:33 | granularity | 2 | 86808 | 2 | 18.543 | 4681.4 |
| 282 | 2025-01-08 06:23:13 | granularity | 1 | 49908 | 1 | 3.923 | 12721.9 |
| 281 | 2025-01-03 12:20:40 | granularity | 4 | 148819 | 52 | 51.360 | 2897.6 |
| 280 | 2024-12-29 20:08:13 | granularity | 4 | 148819 | 52 | 39.566 | 3761.3 |
| 279 | 2024-12-29 20:07:22 | granularity | 3 | 121633 | 11 | 25.910 | 4694.4 |
| 278 | 2024-12-29 20:07:34 | granularity | 2 | 86808 | 2 | 8.673 | 10009.0 |
| 277 | 2024-12-29 20:07:00 | granularity | 1 | 49908 | 1 | 5.440 | 9174.3 |
| 276 | 2024-12-26 03:01:24 | granularity | 4 | 148819 | 52 | 39.163 | 3800.0 |
| 275 | 2024-12-25 14:55:11 | granularity | 3 | 121633 | 11 | 24.393 | 4986.4 |
| 274 | 2024-12-22 19:29:23 | granularity | 3 | 121633 | 11 | 31.763 | 3829.4 |
| 273 | 2024-12-20 01:07:39 | granularity | 3 | 121633 | 11 | 22.656 | 5368.7 |
| 272 | 2024-12-18 09:05:53 | granularity | 3 | 121633 | 11 | 26.126 | 4655.6 |
| 271 | 2024-12-18 09:05:51 | granularity | 3 | 121633 | 11 | 24.296 | 5006.3 |
| 270 | 2024-12-13 02:52:35 | granularity | 1 | 49908 | 1 | 4.576 | 10906.5 |
| 269 | 2024-12-01 09:03:58 | granularity | 3 | 121633 | 11 | 29.470 | 4127.3 |
| 268 | 2024-12-01 09:03:58 | granularity | 2 | 86808 | 2 | 12.406 | 6997.3 |
| 267 | 2024-11-24 20:16:37 | granularity | 1 | 49908 | 1 | 3.876 | 12876.2 |
| 266 | 2024-11-24 18:56:51 | granularity | 3 | 121633 | 11 | 38.283 | 3177.2 |
| 265 | 2024-11-24 18:56:54 | granularity | 2 | 86808 | 2 | 12.076 | 7188.5 |
| 264 | 2024-11-24 18:54:40 | granularity | 1 | 49908 | 1 | 2.936 | 16998.6 |
| 263 | 2024-11-23 10:29:00 | granularity | 4 | 148819 | 52 | 48.096 | 3094.2 |
| 262 | 2024-11-22 16:13:47 | granularity | 1 | 49908 | 1 | 3.936 | 12679.9 |
| 261 | 2024-11-19 21:34:43 | granularity | 4 | 148819 | 52 | 39.253 | 3791.3 |
| 260 | 2024-11-19 04:12:54 | granularity | 4 | 148819 | 52 | 61.283 | 2428.4 |
| 259 | 2024-11-19 04:12:58 | granularity | 4 | 148819 | 52 | 49.526 | 3004.9 |
| 258 | 2024-11-18 08:07:25 | granularity | 4 | 148819 | 52 | 36.656 | 4059.9 |
| 257 | 2024-11-17 17:14:34 | granularity | 4 | 148819 | 52 | 47.580 | 3127.8 |