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 |
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 |
311 | 2025-02-22 08:44:21 | covariances | 2 | 86808 | 5 | 11.576 | 7499.0 |
310 | 2025-02-22 08:41:48 | covariances | 1 | 49908 | 2 | 3.830 | 13030.8 |
309 | 2025-02-22 04:24:42 | covariances | 4 | 148819 | 88 | 51.660 | 2880.7 |
308 | 2025-02-20 08:52:12 | covariances | 4 | 148819 | 88 | 47.906 | 3106.5 |
307 | 2025-02-18 18:40:02 | covariances | 4 | 148819 | 88 | 47.363 | 3142.1 |
306 | 2025-02-18 13:47:25 | covariances | 4 | 148819 | 88 | 44.330 | 3357.1 |
305 | 2025-02-18 03:56:23 | covariances | 4 | 148819 | 88 | 63.626 | 2339.0 |
304 | 2025-02-18 03:12:51 | covariances | 4 | 148819 | 88 | 42.030 | 3540.8 |
303 | 2025-02-17 21:43:09 | covariances | 3 | 121633 | 19 | 23.986 | 5071.0 |
302 | 2025-02-17 21:13:30 | covariances | 4 | 148819 | 88 | 66.160 | 2249.4 |
301 | 2025-02-17 21:13:32 | covariances | 3 | 121633 | 19 | 41.596 | 2924.2 |
300 | 2025-02-17 21:13:19 | covariances | 2 | 86808 | 5 | 12.623 | 6877.0 |
299 | 2025-02-17 21:12:28 | covariances | 1 | 49908 | 2 | 2.076 | 24040.5 |
298 | 2025-02-15 19:08:54 | covariances | 5 | 164607 | 618 | 94.100 | 1749.3 |
297 | 2025-02-11 09:35:56 | covariances | 4 | 148819 | 88 | 9.346 | 15923.3 |
296 | 2025-01-22 14:46:06 | covariances | 1 | 49908 | 2 | 2.720 | 18348.5 |
295 | 2025-01-22 13:03:02 | covariances | 2 | 86808 | 5 | 12.610 | 6884.1 |
294 | 2025-01-22 13:01:38 | covariances | 1 | 49908 | 2 | 1.796 | 27788.4 |
293 | 2025-01-21 17:52:28 | covariances | 2 | 86808 | 5 | 8.266 | 10501.8 |
292 | 2025-01-21 17:51:06 | covariances | 1 | 49908 | 2 | 2.920 | 17091.8 |
291 | 2025-01-21 16:37:29 | covariances | 3 | 121633 | 19 | 25.673 | 4737.8 |
290 | 2025-01-19 21:06:37 | covariances | 3 | 121633 | 19 | 23.596 | 5154.8 |
289 | 2025-01-19 17:19:28 | covariances | 4 | 148819 | 88 | 45.143 | 3296.6 |
288 | 2025-01-19 07:45:31 | covariances | 4 | 148819 | 88 | 55.800 | 2667.0 |
287 | 2025-01-19 07:45:41 | covariances | 4 | 148819 | 88 | 36.656 | 4059.9 |
286 | 2025-01-19 07:45:32 | covariances | 3 | 121633 | 19 | 21.296 | 5711.5 |
285 | 2025-01-19 00:27:08 | covariances | 4 | 148819 | 88 | 49.610 | 2999.8 |
284 | 2025-01-18 21:52:41 | covariances | 4 | 148819 | 88 | 54.236 | 2743.9 |
283 | 2025-01-18 21:14:31 | covariances | 4 | 148819 | 88 | 48.143 | 3091.2 |
282 | 2025-01-18 21:12:43 | covariances | 4 | 148819 | 88 | 43.423 | 3427.2 |
281 | 2025-01-18 21:12:48 | covariances | 3 | 121633 | 19 | 29.596 | 4109.8 |
280 | 2025-01-18 21:12:54 | covariances | 2 | 86808 | 5 | 12.126 | 7158.8 |
279 | 2025-01-18 21:11:40 | covariances | 5 | 164607 | 618 | 74.566 | 2207.5 |
278 | 2025-01-18 21:10:56 | covariances | 1 | 49908 | 2 | 3.533 | 14126.2 |
277 | 2024-12-23 20:26:50 | covariances | 2 | 86808 | 5 | 11.860 | 7319.4 |
276 | 2024-12-21 22:55:15 | covariances | 1 | 49908 | 2 | 4.626 | 10788.6 |
275 | 2024-12-14 09:30:17 | covariances | 2 | 86808 | 5 | 13.000 | 6677.5 |
274 | 2024-12-14 09:29:39 | covariances | 1 | 49908 | 2 | 0.906 | 55086.1 |
273 | 2024-12-13 01:46:10 | covariances | 5 | 164607 | 618 | 81.250 | 2025.9 |