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 |
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 |
284 | 2025-03-27 02:49:07 | predictors | 3 | 140603 | 54 | 19.313 | 7280.2 |
283 | 2025-03-26 16:43:29 | predictors | 1 | 67641 | 2 | 5.550 | 12187.6 |
282 | 2025-03-24 03:56:18 | predictors | 3 | 140603 | 54 | 29.610 | 4748.5 |
281 | 2025-03-23 12:46:03 | predictors | 3 | 140603 | 54 | 17.266 | 8143.3 |
280 | 2025-03-23 08:00:30 | predictors | 3 | 140603 | 54 | 43.520 | 3230.8 |
279 | 2025-03-23 05:25:25 | predictors | 3 | 140603 | 54 | 32.753 | 4292.8 |
278 | 2025-03-22 16:37:04 | predictors | 2 | 108824 | 10 | 3.233 | 33660.4 |
277 | 2025-03-22 12:58:31 | predictors | 3 | 140603 | 54 | 37.656 | 3733.9 |
276 | 2025-03-21 22:38:15 | predictors | 2 | 108824 | 10 | 15.000 | 7254.9 |
275 | 2025-03-21 22:08:40 | predictors | 4 | 161450 | 286 | 13.343 | 12100.0 |
274 | 2025-03-21 03:10:06 | predictors | 3 | 140603 | 54 | 19.656 | 7153.2 |
273 | 2025-03-20 20:37:18 | predictors | 1 | 67641 | 2 | 1.016 | 66575.8 |
272 | 2025-03-16 14:08:02 | predictors | 1 | 67641 | 2 | 7.440 | 9091.5 |
271 | 2025-03-14 08:38:14 | predictors | 3 | 140603 | 54 | 47.380 | 2967.6 |
270 | 2025-03-13 15:18:39 | predictors | 3 | 140603 | 54 | 36.796 | 3821.1 |
269 | 2025-03-13 15:18:39 | predictors | 3 | 140603 | 54 | 36.783 | 3822.5 |
268 | 2025-03-13 15:18:36 | predictors | 2 | 108824 | 10 | 10.893 | 9990.3 |
267 | 2025-03-13 15:14:34 | predictors | 1 | 67641 | 2 | 2.890 | 23405.2 |
266 | 2025-02-23 15:51:48 | predictors | 2 | 108824 | 10 | 16.126 | 6748.4 |
265 | 2025-02-23 15:50:36 | predictors | 3 | 140603 | 54 | 34.563 | 4068.0 |
264 | 2025-02-23 15:44:57 | predictors | 1 | 67641 | 2 | 6.170 | 10962.9 |
263 | 2025-02-19 22:30:02 | predictors | 3 | 140603 | 54 | 32.783 | 4288.9 |
262 | 2025-02-18 07:57:55 | predictors | 3 | 140603 | 54 | 19.623 | 7165.2 |
261 | 2025-02-09 11:29:04 | predictors | 1 | 67641 | 2 | 6.003 | 11267.9 |
260 | 2025-02-08 07:27:21 | predictors | 1 | 67641 | 2 | 4.483 | 15088.3 |
259 | 2025-02-02 02:45:23 | predictors | 3 | 140603 | 54 | 34.596 | 4064.1 |
258 | 2025-02-01 16:17:55 | predictors | 1 | 67641 | 2 | 10.283 | 6577.9 |
257 | 2025-02-01 03:38:01 | predictors | 3 | 140603 | 54 | 40.456 | 3475.5 |
256 | 2025-01-31 00:25:03 | predictors | 3 | 140603 | 54 | 30.813 | 4563.1 |
255 | 2025-01-27 02:28:59 | predictors | 1 | 67641 | 2 | 1.263 | 53555.8 |