History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"sparseness"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 358 | 2025-10-13 19:14:33 | sparseness | 1 | 67641 | 2 | 1.203 | 56226.9 |
| 357 | 2025-10-09 15:55:33 | sparseness | 4 | 161450 | 385 | 11.656 | 13851.2 |
| 356 | 2025-10-03 02:55:33 | sparseness | 4 | 161450 | 385 | 11.830 | 13647.5 |
| 355 | 2025-09-16 09:36:39 | sparseness | 4 | 161450 | 385 | 10.610 | 15216.8 |
| 354 | 2025-09-15 22:54:05 | sparseness | 4 | 161450 | 385 | 17.406 | 9275.5 |
| 353 | 2025-09-05 15:56:29 | sparseness | 1 | 67641 | 2 | 1.186 | 57032.9 |
| 352 | 2025-09-04 11:20:31 | sparseness | 3 | 140603 | 55 | 6.373 | 22062.3 |
| 351 | 2025-09-01 22:03:08 | sparseness | 3 | 140603 | 55 | 6.826 | 20598.2 |
| 350 | 2025-08-26 19:58:16 | sparseness | 1 | 67641 | 2 | 1.233 | 54858.9 |
| 349 | 2025-08-25 09:12:48 | sparseness | 1 | 67641 | 2 | 4.500 | 15031.3 |
| 348 | 2025-08-22 18:50:20 | sparseness | 3 | 140603 | 55 | 30.550 | 4602.4 |
| 347 | 2025-08-19 05:59:27 | sparseness | 2 | 108824 | 7 | 9.826 | 11075.1 |
| 346 | 2025-08-19 00:30:33 | sparseness | 4 | 161450 | 385 | 32.846 | 4915.4 |
| 345 | 2025-08-17 01:35:59 | sparseness | 4 | 161450 | 385 | 31.410 | 5140.1 |
| 344 | 2025-08-16 20:30:19 | sparseness | 1 | 67641 | 2 | 4.970 | 13609.9 |
| 343 | 2025-08-14 06:56:04 | sparseness | 4 | 161450 | 385 | 19.203 | 8407.5 |
| 342 | 2025-08-14 05:22:04 | sparseness | 4 | 161450 | 385 | 11.610 | 13906.1 |
| 341 | 2025-08-12 13:37:44 | sparseness | 4 | 161450 | 385 | 42.733 | 3778.1 |
| 340 | 2025-08-09 14:43:36 | sparseness | 1 | 67641 | 2 | 5.156 | 13118.9 |
| 339 | 2025-08-02 07:11:38 | sparseness | 1 | 67641 | 2 | 2.500 | 27056.4 |
| 338 | 2025-07-27 14:55:01 | sparseness | 1 | 67641 | 2 | 2.780 | 24331.3 |
| 337 | 2025-07-25 08:58:06 | sparseness | 1 | 67641 | 2 | 2.906 | 23276.3 |
| 336 | 2025-07-20 21:18:03 | sparseness | 1 | 67641 | 2 | 1.220 | 55443.4 |
| 335 | 2025-07-19 02:06:05 | sparseness | 4 | 161450 | 385 | 11.220 | 14389.5 |
| 334 | 2025-07-16 19:14:54 | sparseness | 1 | 67641 | 2 | 1.326 | 51011.3 |
| 333 | 2025-07-11 12:04:27 | sparseness | 1 | 67641 | 2 | 2.766 | 24454.4 |
| 332 | 2025-07-06 08:56:23 | sparseness | 3 | 140603 | 55 | 5.700 | 24667.2 |
| 331 | 2025-07-06 00:24:33 | sparseness | 3 | 140603 | 55 | 6.890 | 20406.8 |
| 330 | 2025-07-05 04:34:19 | sparseness | 3 | 140603 | 55 | 19.330 | 7273.8 |
| 329 | 2025-07-02 12:13:52 | sparseness | 3 | 140603 | 55 | 26.686 | 5268.8 |
| 328 | 2025-06-30 12:25:38 | sparseness | 3 | 140603 | 55 | 47.640 | 2951.4 |
| 327 | 2025-06-30 02:09:52 | sparseness | 1 | 67641 | 2 | 5.233 | 12925.9 |
| 326 | 2025-06-28 23:04:06 | sparseness | 1 | 67641 | 2 | 5.846 | 11570.5 |
| 325 | 2025-06-28 05:05:18 | sparseness | 4 | 161450 | 385 | 59.350 | 2720.3 |
| 324 | 2025-06-27 01:27:16 | sparseness | 4 | 161450 | 385 | 56.740 | 2845.4 |
| 323 | 2025-06-26 15:07:33 | sparseness | 1 | 67641 | 2 | 7.690 | 8796.0 |
| 322 | 2025-06-26 05:56:11 | sparseness | 4 | 161450 | 385 | 59.143 | 2729.8 |
| 321 | 2025-06-25 11:30:51 | sparseness | 2 | 108824 | 7 | 3.343 | 32552.8 |
| 320 | 2025-06-25 09:51:57 | sparseness | 4 | 161450 | 385 | 30.656 | 5266.5 |
| 319 | 2025-06-24 09:55:19 | sparseness | 1 | 67641 | 2 | 3.653 | 18516.6 |
| 318 | 2025-06-16 14:58:14 | sparseness | 3 | 140603 | 55 | 24.126 | 5827.9 |
| 317 | 2025-06-15 22:03:17 | sparseness | 3 | 140603 | 55 | 24.186 | 5813.4 |
| 316 | 2025-06-15 21:16:27 | sparseness | 1 | 67641 | 2 | 3.266 | 20710.7 |
| 315 | 2025-06-14 09:56:45 | sparseness | 3 | 140603 | 55 | 28.766 | 4887.8 |
| 314 | 2025-06-13 08:37:32 | sparseness | 1 | 67641 | 2 | 1.236 | 54725.7 |
| 313 | 2025-06-09 21:56:05 | sparseness | 1 | 67641 | 2 | 4.096 | 16513.9 |
| 312 | 2025-06-03 18:44:32 | sparseness | 1 | 67641 | 2 | 1.090 | 62056.0 |
| 311 | 2025-05-29 03:14:30 | sparseness | 1 | 67641 | 2 | 5.703 | 11860.6 |
| 310 | 2025-05-27 01:27:48 | sparseness | 4 | 161450 | 385 | 49.330 | 3272.9 |
| 309 | 2025-05-24 06:01:18 | sparseness | 4 | 161450 | 385 | 52.033 | 3102.8 |
| 308 | 2025-05-24 05:13:30 | sparseness | 4 | 161450 | 385 | 71.413 | 2260.8 |
| 307 | 2025-05-22 06:24:19 | sparseness | 4 | 161450 | 385 | 58.520 | 2758.9 |
| 306 | 2025-05-20 10:36:23 | sparseness | 1 | 67641 | 2 | 4.843 | 13966.8 |
| 305 | 2025-05-19 15:12:46 | sparseness | 4 | 161450 | 385 | 10.513 | 15357.2 |
| 304 | 2025-05-12 14:41:42 | sparseness | 3 | 140603 | 55 | 29.313 | 4796.6 |
| 303 | 2025-05-09 22:36:34 | sparseness | 3 | 140603 | 55 | 27.363 | 5138.4 |
| 302 | 2025-05-06 09:18:37 | sparseness | 3 | 140603 | 55 | 51.816 | 2713.5 |
| 301 | 2025-05-02 20:35:29 | sparseness | 3 | 140603 | 55 | 22.580 | 6226.9 |
| 300 | 2025-05-02 09:54:43 | sparseness | 1 | 67641 | 2 | 2.890 | 23405.2 |
| 299 | 2025-04-30 12:23:46 | sparseness | 3 | 140603 | 55 | 33.986 | 4137.1 |
| 298 | 2025-04-29 21:33:01 | sparseness | 1 | 67641 | 2 | 5.436 | 12443.2 |
| 297 | 2025-04-26 01:41:49 | sparseness | 4 | 161450 | 385 | 40.736 | 3963.3 |
| 296 | 2025-04-25 14:24:36 | sparseness | 1 | 67641 | 2 | 2.326 | 29080.4 |
| 295 | 2025-04-21 10:43:23 | sparseness | 2 | 108824 | 7 | 7.563 | 14389.0 |
| 294 | 2025-04-21 10:24:25 | sparseness | 3 | 140603 | 55 | 25.280 | 5561.8 |
| 293 | 2025-04-20 08:43:58 | sparseness | 1 | 67641 | 2 | 3.233 | 20922.1 |
| 292 | 2025-04-18 02:57:38 | sparseness | 1 | 67641 | 2 | 2.840 | 23817.3 |
| 291 | 2025-04-13 08:12:10 | sparseness | 1 | 67641 | 2 | 1.046 | 64666.3 |
| 290 | 2025-04-04 14:08:51 | sparseness | 3 | 140603 | 55 | 34.830 | 4036.8 |
| 289 | 2025-04-04 08:15:48 | sparseness | 1 | 67641 | 2 | 5.390 | 12549.4 |
| 288 | 2025-04-03 22:05:31 | sparseness | 3 | 140603 | 55 | 37.533 | 3746.1 |
| 287 | 2025-03-31 23:23:42 | sparseness | 3 | 140603 | 55 | 39.456 | 3563.5 |
| 286 | 2025-03-29 04:52:18 | sparseness | 3 | 140603 | 55 | 30.580 | 4597.9 |
| 285 | 2025-03-29 01:35:12 | sparseness | 2 | 108824 | 7 | 16.890 | 6443.1 |
| 284 | 2025-03-28 22:34:17 | sparseness | 1 | 67641 | 2 | 2.983 | 22675.5 |
| 283 | 2025-03-23 17:05:15 | sparseness | 1 | 67641 | 2 | 7.670 | 8818.9 |
| 282 | 2025-03-23 11:41:10 | sparseness | 1 | 67641 | 2 | 3.203 | 21118.0 |
| 281 | 2025-03-20 10:42:22 | sparseness | 1 | 67641 | 2 | 6.766 | 9997.2 |
| 280 | 2025-03-15 05:21:04 | sparseness | 1 | 67641 | 2 | 2.766 | 24454.4 |
| 279 | 2025-02-11 02:36:17 | sparseness | 1 | 67641 | 2 | 8.416 | 8037.2 |
| 278 | 2025-02-07 10:02:07 | sparseness | 3 | 140603 | 55 | 24.593 | 5717.2 |
| 277 | 2025-02-07 10:02:06 | sparseness | 2 | 108824 | 7 | 14.253 | 7635.2 |
| 276 | 2025-02-07 10:00:42 | sparseness | 3 | 140603 | 55 | 52.160 | 2695.6 |
| 275 | 2025-02-07 09:00:37 | sparseness | 1 | 67641 | 2 | 1.190 | 56841.2 |
| 274 | 2025-01-30 07:48:45 | sparseness | 3 | 140603 | 55 | 37.550 | 3744.4 |
| 273 | 2025-01-30 03:18:34 | sparseness | 3 | 140603 | 55 | 29.110 | 4830.1 |
| 272 | 2025-01-28 18:05:33 | sparseness | 3 | 140603 | 55 | 33.656 | 4177.7 |
| 271 | 2025-01-28 18:05:27 | sparseness | 3 | 140603 | 55 | 31.673 | 4439.2 |
| 270 | 2025-01-28 18:05:28 | sparseness | 2 | 108824 | 7 | 19.123 | 5690.7 |
| 269 | 2025-01-28 17:02:24 | sparseness | 1 | 67641 | 2 | 1.076 | 62863.4 |
| 268 | 2025-01-24 06:09:06 | sparseness | 1 | 67641 | 2 | 5.453 | 12404.4 |
| 267 | 2025-01-07 02:29:47 | sparseness | 1 | 67641 | 2 | 8.016 | 8438.2 |
| 266 | 2025-01-06 14:08:11 | sparseness | 3 | 140603 | 55 | 30.593 | 4595.9 |
| 265 | 2024-12-30 08:51:59 | sparseness | 3 | 140603 | 55 | 29.563 | 4756.0 |
| 264 | 2024-12-28 13:00:10 | sparseness | 1 | 67641 | 2 | 1.076 | 62863.4 |
| 263 | 2024-12-25 05:21:03 | sparseness | 1 | 67641 | 2 | 5.546 | 12196.4 |
| 262 | 2024-12-18 04:03:27 | sparseness | 3 | 140603 | 55 | 30.580 | 4597.9 |
| 261 | 2024-12-18 04:03:32 | sparseness | 2 | 108824 | 7 | 7.873 | 13822.4 |
| 260 | 2024-12-18 04:01:03 | sparseness | 1 | 67641 | 2 | 4.280 | 15804.0 |
| 259 | 2024-11-29 00:12:11 | sparseness | 3 | 140603 | 55 | 32.313 | 4351.3 |