History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"predictor"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
408 | 2025-09-29 13:28:16 | predictor | 1 | 81242 | 2 | 1.500 | 54161.3 |
407 | 2025-09-29 11:52:14 | predictor | 1 | 81242 | 2 | 1.313 | 61875.1 |
406 | 2025-09-27 23:23:52 | predictor | 1 | 81242 | 2 | 1.470 | 55266.7 |
405 | 2025-09-24 00:12:33 | predictor | 1 | 81242 | 2 | 1.420 | 57212.7 |
404 | 2025-09-22 09:41:16 | predictor | 4 | 165329 | 311 | 15.406 | 10731.5 |
403 | 2025-09-22 04:31:30 | predictor | 4 | 165329 | 311 | 13.283 | 12446.7 |
402 | 2025-09-18 02:25:34 | predictor | 4 | 165329 | 311 | 11.046 | 14967.3 |
401 | 2025-09-11 20:49:40 | predictor | 2 | 121436 | 12 | 4.470 | 27166.9 |
400 | 2025-09-11 11:50:45 | predictor | 4 | 165329 | 311 | 12.126 | 13634.3 |
399 | 2025-09-10 05:35:52 | predictor | 1 | 81242 | 2 | 1.483 | 54782.2 |
398 | 2025-09-03 23:25:08 | predictor | 2 | 121436 | 12 | 3.220 | 37713.0 |
397 | 2025-09-02 21:58:39 | predictor | 1 | 81242 | 2 | 1.313 | 61875.1 |
396 | 2025-09-02 07:26:28 | predictor | 1 | 81242 | 2 | 1.296 | 62686.7 |
395 | 2025-09-01 16:28:44 | predictor | 1 | 81242 | 2 | 1.843 | 44081.4 |
394 | 2025-09-01 14:16:58 | predictor | 4 | 165329 | 311 | 12.893 | 12823.2 |
393 | 2025-08-31 14:42:54 | predictor | 1 | 81242 | 2 | 1.343 | 60492.9 |
392 | 2025-08-29 14:44:55 | predictor | 1 | 81242 | 2 | 1.390 | 58447.5 |
391 | 2025-08-29 12:47:01 | predictor | 1 | 81242 | 2 | 1.500 | 54161.3 |
390 | 2025-08-29 05:00:22 | predictor | 1 | 81242 | 2 | 1.530 | 53099.3 |
389 | 2025-08-27 23:27:50 | predictor | 4 | 165329 | 311 | 11.813 | 13995.5 |
388 | 2025-08-27 23:09:04 | predictor | 1 | 81242 | 2 | 1.593 | 50999.4 |
387 | 2025-08-25 18:52:09 | predictor | 4 | 165329 | 311 | 12.016 | 13759.1 |
386 | 2025-08-25 05:31:58 | predictor | 4 | 165329 | 311 | 53.766 | 3075.0 |
385 | 2025-08-24 23:46:15 | predictor | 1 | 81242 | 2 | 7.503 | 10827.9 |
384 | 2025-08-24 13:22:49 | predictor | 4 | 165329 | 311 | 12.906 | 12810.2 |
383 | 2025-08-23 06:21:35 | predictor | 4 | 165329 | 311 | 34.550 | 4785.2 |
382 | 2025-08-21 00:22:56 | predictor | 3 | 148641 | 55 | 21.860 | 6799.7 |
381 | 2025-08-20 21:23:21 | predictor | 4 | 165329 | 311 | 60.576 | 2729.3 |
380 | 2025-08-20 12:16:52 | predictor | 3 | 148641 | 55 | 8.110 | 18328.1 |
379 | 2025-08-20 11:32:24 | predictor | 4 | 165329 | 311 | 13.093 | 12627.3 |
378 | 2025-08-20 03:47:37 | predictor | 1 | 81242 | 2 | 1.470 | 55266.7 |
377 | 2025-08-18 12:37:49 | predictor | 3 | 148641 | 55 | 6.500 | 22867.8 |
376 | 2025-08-11 13:34:51 | predictor | 1 | 81242 | 2 | 3.233 | 25129.0 |
375 | 2025-08-08 13:09:53 | predictor | 1 | 81242 | 2 | 2.890 | 28111.4 |
374 | 2025-08-04 18:40:37 | predictor | 3 | 148641 | 55 | 23.843 | 6234.2 |
373 | 2025-08-01 15:59:51 | predictor | 1 | 81242 | 2 | 2.890 | 28111.4 |
372 | 2025-07-27 02:41:30 | predictor | 3 | 148641 | 55 | 19.160 | 7757.9 |
371 | 2025-07-25 11:01:23 | predictor | 3 | 148641 | 55 | 40.750 | 3647.6 |
370 | 2025-07-25 08:17:37 | predictor | 3 | 148641 | 55 | 25.330 | 5868.2 |
369 | 2025-07-25 04:09:46 | predictor | 1 | 81242 | 2 | 7.410 | 10963.8 |
368 | 2025-07-25 00:28:07 | predictor | 1 | 81242 | 2 | 6.716 | 12096.8 |
367 | 2025-07-24 00:48:16 | predictor | 1 | 81242 | 2 | 7.720 | 10523.6 |
366 | 2025-07-22 23:11:35 | predictor | 1 | 81242 | 2 | 3.623 | 22424.0 |
365 | 2025-07-22 05:26:24 | predictor | 1 | 81242 | 2 | 1.326 | 61268.5 |
364 | 2025-07-20 02:25:57 | predictor | 3 | 148641 | 55 | 7.500 | 19818.8 |
363 | 2025-07-19 12:47:20 | predictor | 1 | 81242 | 2 | 1.466 | 55417.5 |
362 | 2025-07-13 12:43:34 | predictor | 1 | 81242 | 2 | 1.483 | 54782.2 |
361 | 2025-07-10 14:12:18 | predictor | 1 | 81242 | 2 | 3.686 | 22040.7 |
360 | 2025-07-09 07:38:49 | predictor | 1 | 81242 | 2 | 1.716 | 47343.8 |
359 | 2025-06-25 23:44:24 | predictor | 1 | 81242 | 2 | 4.096 | 19834.5 |
358 | 2025-06-18 02:34:58 | predictor | 3 | 148641 | 55 | 36.046 | 4123.6 |
357 | 2025-06-17 18:39:54 | predictor | 1 | 81242 | 2 | 1.516 | 53589.7 |
356 | 2025-06-16 01:07:59 | predictor | 2 | 121436 | 12 | 12.236 | 9924.5 |
355 | 2025-06-15 15:49:33 | predictor | 3 | 148641 | 55 | 6.780 | 21923.5 |
354 | 2025-06-14 14:41:00 | predictor | 1 | 81242 | 2 | 9.503 | 8549.1 |
353 | 2025-06-10 11:06:41 | predictor | 3 | 148641 | 55 | 34.706 | 4282.9 |
352 | 2025-06-09 22:03:30 | predictor | 2 | 121436 | 12 | 17.126 | 7090.7 |
351 | 2025-06-08 01:28:18 | predictor | 2 | 121436 | 12 | 15.046 | 8071.0 |
350 | 2025-06-07 22:42:27 | predictor | 3 | 148641 | 55 | 19.296 | 7703.2 |
349 | 2025-06-07 11:44:56 | predictor | 1 | 81242 | 2 | 8.843 | 9187.2 |
348 | 2025-06-01 05:39:25 | predictor | 3 | 148641 | 55 | 39.686 | 3745.4 |
347 | 2025-05-29 01:21:08 | predictor | 1 | 81242 | 2 | 6.953 | 11684.5 |
346 | 2025-05-26 00:09:21 | predictor | 1 | 81242 | 2 | 6.766 | 12007.4 |
345 | 2025-05-18 03:59:24 | predictor | 1 | 81242 | 2 | 3.610 | 22504.7 |
344 | 2025-05-15 17:42:33 | predictor | 1 | 81242 | 2 | 1.533 | 52995.4 |
343 | 2025-05-13 22:56:33 | predictor | 1 | 81242 | 2 | 10.360 | 7841.9 |
342 | 2025-05-12 20:34:10 | predictor | 3 | 148641 | 55 | 7.640 | 19455.6 |
341 | 2025-05-12 06:59:39 | predictor | 2 | 121436 | 12 | 18.486 | 6569.1 |
340 | 2025-05-04 11:36:10 | predictor | 1 | 81242 | 2 | 3.530 | 23014.7 |
339 | 2025-05-04 05:50:10 | predictor | 1 | 81242 | 2 | 6.623 | 12266.6 |
338 | 2025-04-30 13:52:13 | predictor | 1 | 81242 | 2 | 5.500 | 14771.3 |
337 | 2025-04-29 13:24:04 | predictor | 1 | 81242 | 2 | 5.500 | 14771.3 |
336 | 2025-04-26 01:50:34 | predictor | 3 | 148641 | 55 | 41.970 | 3541.6 |
335 | 2025-04-25 20:16:52 | predictor | 3 | 148641 | 55 | 7.763 | 19147.4 |
334 | 2025-04-23 08:06:36 | predictor | 3 | 148641 | 55 | 37.000 | 4017.3 |
333 | 2025-04-23 00:03:51 | predictor | 1 | 81242 | 2 | 6.453 | 12589.8 |
332 | 2025-04-21 00:31:23 | predictor | 3 | 148641 | 55 | 37.190 | 3996.8 |
331 | 2025-04-13 17:30:35 | predictor | 3 | 148641 | 55 | 8.360 | 17780.0 |
330 | 2025-03-23 16:23:03 | predictor | 3 | 148641 | 55 | 52.296 | 2842.3 |
329 | 2025-03-23 15:03:40 | predictor | 2 | 121436 | 12 | 24.906 | 4875.8 |
328 | 2025-03-22 23:27:34 | predictor | 2 | 121436 | 12 | 18.453 | 6580.8 |
327 | 2025-03-22 15:39:44 | predictor | 1 | 81242 | 2 | 6.796 | 11954.4 |
326 | 2025-03-22 06:34:35 | predictor | 1 | 81242 | 2 | 1.546 | 52549.8 |
325 | 2025-03-22 01:00:11 | predictor | 1 | 81242 | 2 | 3.983 | 20397.2 |
324 | 2025-03-21 18:38:07 | predictor | 2 | 121436 | 12 | 18.160 | 6687.0 |
323 | 2025-03-18 02:38:35 | predictor | 2 | 121436 | 12 | 15.156 | 8012.4 |
322 | 2025-03-14 08:37:58 | predictor | 3 | 148641 | 55 | 35.143 | 4229.6 |
321 | 2025-03-13 15:18:11 | predictor | 2 | 121436 | 12 | 21.346 | 5688.9 |
320 | 2025-03-13 15:17:52 | predictor | 3 | 148641 | 55 | 35.546 | 4181.7 |
319 | 2025-03-13 15:14:18 | predictor | 1 | 81242 | 2 | 6.076 | 13371.0 |
318 | 2025-03-11 12:55:11 | predictor | 3 | 148641 | 55 | 62.436 | 2380.7 |
317 | 2025-03-09 21:57:17 | predictor | 3 | 148641 | 55 | 55.750 | 2666.2 |
316 | 2025-03-06 14:04:32 | predictor | 3 | 148641 | 55 | 32.690 | 4547.0 |
315 | 2025-02-25 08:31:45 | predictor | 1 | 81242 | 2 | 1.423 | 57092.1 |
314 | 2025-02-23 15:45:55 | predictor | 1 | 81242 | 2 | 6.890 | 11791.3 |
313 | 2025-02-05 04:44:04 | predictor | 1 | 81242 | 2 | 6.580 | 12346.8 |
312 | 2025-02-02 23:43:55 | predictor | 3 | 148641 | 55 | 32.253 | 4608.6 |
311 | 2025-01-28 05:12:22 | predictor | 2 | 121436 | 12 | 23.003 | 5279.1 |
310 | 2025-01-27 05:19:56 | predictor | 3 | 148641 | 55 | 42.970 | 3459.2 |
309 | 2025-01-27 05:20:00 | predictor | 2 | 121436 | 12 | 24.673 | 4921.8 |