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 |
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 |
256 | 2024-11-17 15:42:45 | granularity | 5 | 164607 | 187 | 85.746 | 1919.7 |
255 | 2024-11-17 15:42:25 | granularity | 4 | 148819 | 52 | 71.396 | 2084.4 |
254 | 2024-11-14 11:18:03 | granularity | 2 | 86808 | 2 | 9.736 | 8916.2 |
253 | 2024-11-03 05:17:57 | granularity | 3 | 121633 | 11 | 13.973 | 8704.9 |
252 | 2024-10-22 13:06:33 | granularity | 2 | 86808 | 2 | 10.016 | 8666.9 |
251 | 2024-10-22 13:05:54 | granularity | 1 | 49908 | 1 | 5.906 | 8450.4 |
250 | 2024-10-22 12:41:38 | granularity | 3 | 121633 | 11 | 27.063 | 4494.4 |
249 | 2024-10-19 08:47:39 | granularity | 5 | 164607 | 187 | 86.986 | 1892.3 |
248 | 2024-10-18 22:49:53 | granularity | 5 | 164607 | 187 | 69.613 | 2364.6 |
247 | 2024-10-17 14:19:50 | granularity | 5 | 164607 | 187 | 76.660 | 2147.2 |
246 | 2024-10-17 12:53:04 | granularity | 5 | 164607 | 187 | 76.330 | 2156.5 |
245 | 2024-10-17 12:53:43 | granularity | 3 | 121633 | 11 | 25.440 | 4781.2 |
244 | 2024-10-17 12:53:13 | granularity | 2 | 86808 | 2 | 11.580 | 7496.4 |
243 | 2024-10-17 12:50:33 | granularity | 1 | 49908 | 1 | 5.686 | 8777.3 |
242 | 2024-10-14 14:02:09 | granularity | 4 | 148819 | 52 | 26.156 | 5689.7 |
241 | 2024-10-12 20:30:09 | granularity | 4 | 148819 | 52 | 50.206 | 2964.2 |
240 | 2024-10-12 07:32:14 | granularity | 1 | 49908 | 1 | 4.233 | 11790.2 |
239 | 2024-10-12 07:01:33 | granularity | 4 | 148819 | 52 | 45.673 | 3258.4 |
238 | 2024-10-11 20:52:37 | granularity | 4 | 148819 | 52 | 46.003 | 3235.0 |