History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"nanometers"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
453 | 2024-05-01 06:35:35 | nanometers | 1 | 67641 | 4 | 1.170 | 57812.8 |
452 | 2024-04-27 15:31:40 | nanometers | 3 | 140603 | 61 | 9.596 | 14652.3 |
451 | 2024-04-27 14:33:58 | nanometers | 4 | 161450 | 260 | 32.050 | 5037.4 |
450 | 2024-04-27 14:16:52 | nanometers | 2 | 108824 | 11 | 12.406 | 8771.9 |
449 | 2024-04-27 14:09:12 | nanometers | 4 | 161450 | 260 | 34.410 | 4692.0 |
448 | 2024-04-27 13:00:06 | nanometers | 1 | 67641 | 4 | 1.873 | 36113.7 |
447 | 2024-04-27 10:44:27 | nanometers | 2 | 108824 | 11 | 3.436 | 31671.7 |
446 | 2024-04-25 07:08:49 | nanometers | 3 | 140603 | 61 | 22.140 | 6350.6 |
445 | 2024-04-25 07:08:51 | nanometers | 2 | 108824 | 11 | 8.440 | 12893.8 |
444 | 2024-04-25 06:12:09 | nanometers | 1 | 67641 | 4 | 1.030 | 65670.9 |
443 | 2024-04-17 23:07:20 | nanometers | 2 | 108824 | 11 | 12.470 | 8726.9 |
442 | 2024-04-17 23:06:29 | nanometers | 3 | 140603 | 61 | 30.393 | 4626.2 |
441 | 2024-04-17 23:03:16 | nanometers | 1 | 67641 | 4 | 2.326 | 29080.4 |
440 | 2024-04-17 14:47:28 | nanometers | 2 | 108824 | 11 | 9.923 | 10966.8 |
439 | 2024-04-17 13:53:46 | nanometers | 1 | 67641 | 4 | 1.046 | 64666.3 |
438 | 2024-04-17 10:01:46 | nanometers | 1 | 67641 | 4 | 1.173 | 57665.0 |
437 | 2024-04-06 03:33:16 | nanometers | 3 | 140603 | 61 | 11.656 | 12062.7 |
436 | 2024-04-05 16:00:50 | nanometers | 4 | 161450 | 260 | 33.096 | 4878.2 |
435 | 2024-04-05 16:00:47 | nanometers | 3 | 140603 | 61 | 17.500 | 8034.5 |
434 | 2024-04-05 16:00:46 | nanometers | 2 | 108824 | 11 | 17.983 | 6051.5 |
433 | 2024-04-05 15:33:32 | nanometers | 1 | 67641 | 4 | 1.606 | 42117.7 |
432 | 2024-03-30 22:14:56 | nanometers | 1 | 67641 | 4 | 1.533 | 44123.3 |
431 | 2024-03-21 18:57:13 | nanometers | 4 | 161450 | 260 | 17.203 | 9385.0 |
430 | 2024-03-21 18:57:07 | nanometers | 3 | 140603 | 61 | 15.436 | 9108.8 |
429 | 2024-03-21 18:57:10 | nanometers | 2 | 108824 | 11 | 7.453 | 14601.4 |
428 | 2024-03-21 18:37:35 | nanometers | 1 | 67641 | 4 | 1.173 | 57665.0 |
427 | 2024-03-18 20:59:26 | nanometers | 1 | 67641 | 4 | 1.016 | 66575.8 |
426 | 2024-03-15 12:35:51 | nanometers | 4 | 161450 | 260 | 28.126 | 5740.2 |
425 | 2024-03-15 12:35:51 | nanometers | 2 | 108824 | 11 | 7.140 | 15241.5 |
424 | 2024-03-15 12:31:53 | nanometers | 1 | 67641 | 4 | 1.280 | 52844.5 |
423 | 2024-03-12 07:41:23 | nanometers | 1 | 67641 | 4 | 2.580 | 26217.4 |
422 | 2024-03-05 23:01:19 | nanometers | 2 | 108824 | 11 | 5.250 | 20728.4 |
421 | 2024-03-05 22:58:15 | nanometers | 1 | 67641 | 4 | 3.890 | 17388.4 |
420 | 2024-03-05 07:18:46 | nanometers | 3 | 140603 | 61 | 22.936 | 6130.2 |
419 | 2024-02-29 22:05:50 | nanometers | 3 | 140603 | 61 | 6.893 | 20397.9 |
418 | 2024-02-29 01:37:50 | nanometers | 1 | 67641 | 4 | 1.203 | 56226.9 |
417 | 2024-02-21 12:35:57 | nanometers | 4 | 161450 | 260 | 14.173 | 11391.4 |
416 | 2024-02-21 04:16:57 | nanometers | 4 | 161450 | 260 | 27.563 | 5857.5 |
415 | 2024-02-20 01:50:01 | nanometers | 3 | 140603 | 61 | 17.580 | 7997.9 |
414 | 2024-02-20 01:50:01 | nanometers | 4 | 161450 | 260 | 14.860 | 10864.7 |
413 | 2024-02-20 01:49:58 | nanometers | 2 | 108824 | 11 | 5.063 | 21494.0 |
412 | 2024-02-19 18:50:37 | nanometers | 1 | 67641 | 4 | 1.033 | 65480.2 |
411 | 2024-02-17 01:55:49 | nanometers | 1 | 67641 | 4 | 2.686 | 25182.8 |
410 | 2024-02-02 04:42:51 | nanometers | 1 | 67641 | 4 | 1.080 | 62630.6 |
409 | 2024-01-30 01:03:24 | nanometers | 1 | 67641 | 4 | 1.483 | 45610.9 |
408 | 2024-01-30 01:03:03 | nanometers | 1 | 67641 | 4 | 1.110 | 60937.8 |
407 | 2024-01-17 15:45:37 | nanometers | 1 | 67641 | 4 | 1.030 | 65670.9 |
406 | 2023-12-31 18:33:06 | nanometers | 1 | 67641 | 4 | 1.033 | 65480.2 |
405 | 2023-12-08 04:44:58 | nanometers | 4 | 161450 | 260 | 13.860 | 11648.6 |
404 | 2023-12-08 04:44:54 | nanometers | 3 | 140603 | 61 | 6.016 | 23371.5 |
403 | 2023-12-08 04:44:56 | nanometers | 2 | 108824 | 11 | 3.016 | 36082.2 |
402 | 2023-12-02 10:17:38 | nanometers | 3 | 140603 | 61 | 6.030 | 23317.2 |
401 | 2023-12-02 10:17:29 | nanometers | 4 | 161450 | 260 | 10.906 | 14803.8 |
400 | 2023-12-02 10:17:31 | nanometers | 2 | 108824 | 11 | 3.233 | 33660.4 |
399 | 2023-11-22 17:04:40 | nanometers | 3 | 140603 | 61 | 6.126 | 22951.8 |
398 | 2023-11-14 04:35:41 | nanometers | 4 | 161450 | 260 | 11.283 | 14309.1 |
397 | 2023-11-14 04:35:39 | nanometers | 3 | 140603 | 61 | 5.936 | 23686.5 |
396 | 2023-11-14 04:35:36 | nanometers | 2 | 108824 | 11 | 2.593 | 41968.4 |
395 | 2023-11-11 01:41:47 | nanometers | 1 | 67641 | 4 | 1.110 | 60937.8 |
394 | 2023-11-10 04:50:31 | nanometers | 1 | 67641 | 4 | 1.000 | 67641.0 |
393 | 2023-10-26 19:34:04 | nanometers | 1 | 67641 | 4 | 1.000 | 67641.0 |
392 | 2023-10-21 08:04:18 | nanometers | 1 | 67641 | 4 | 0.986 | 68601.4 |
391 | 2023-10-17 11:21:17 | nanometers | 1 | 67641 | 4 | 1.203 | 56226.9 |
390 | 2023-10-11 10:29:03 | nanometers | 1 | 67641 | 4 | 0.983 | 68810.8 |
389 | 2023-09-24 00:45:30 | nanometers | 1 | 67641 | 4 | 1.106 | 61158.2 |
388 | 2023-09-23 06:18:09 | nanometers | 1 | 67641 | 4 | 1.093 | 61885.6 |
387 | 2023-09-11 02:54:41 | nanometers | 1 | 67641 | 4 | 1.123 | 60232.4 |
386 | 2023-08-23 18:08:59 | nanometers | 3 | 140603 | 61 | 5.893 | 23859.3 |
385 | 2023-08-17 21:20:40 | nanometers | 1 | 67641 | 4 | 1.013 | 66773.0 |
384 | 2023-06-03 16:14:30 | nanometers | 1 | 67641 | 4 | 0.983 | 68810.8 |
383 | 2023-05-20 03:50:06 | nanometers | 1 | 67641 | 4 | 1.000 | 67641.0 |
382 | 2023-05-07 14:16:16 | nanometers | 3 | 140603 | 61 | 5.483 | 25643.4 |
381 | 2023-03-15 10:37:05 | nanometers | 1 | 67641 | 4 | 1.140 | 59334.2 |
380 | 2023-02-13 12:52:21 | nanometers | 3 | 140603 | 61 | 6.250 | 22496.5 |
379 | 2023-02-11 22:18:21 | nanometers | 1 | 67641 | 4 | 1.093 | 61885.6 |
378 | 2023-01-05 11:38:24 | nanometers | 1 | 67641 | 4 | 0.983 | 68810.8 |
377 | 2022-11-24 23:07:52 | nanometers | 3 | 140603 | 61 | 6.140 | 22899.5 |
376 | 2022-10-25 10:36:22 | nanometers | 1 | 67641 | 4 | 1.076 | 62863.4 |
375 | 2022-10-02 11:06:58 | nanometers | 1 | 67641 | 4 | 1.033 | 65480.2 |
374 | 2022-07-05 19:06:06 | nanometers | 1 | 67641 | 4 | 0.983 | 68810.8 |
373 | 2022-06-30 10:48:58 | nanometers | 3 | 140603 | 61 | 5.486 | 25629.4 |
372 | 2022-05-14 10:54:05 | nanometers | 1 | 67641 | 4 | 1.016 | 66575.8 |
371 | 2022-04-30 16:50:37 | nanometers | 3 | 140603 | 61 | 5.406 | 26008.7 |
370 | 2022-03-31 12:02:07 | nanometers | 3 | 140603 | 61 | 7.186 | 19566.2 |
369 | 2022-03-11 13:15:13 | nanometers | 1 | 67641 | 4 | 1.216 | 55625.8 |
368 | 2022-01-31 06:46:24 | nanometers | 1 | 67641 | 4 | 1.190 | 56841.2 |
367 | 2022-01-29 02:33:14 | nanometers | 1 | 67641 | 4 | 1.000 | 67641.0 |
366 | 2022-01-26 12:59:15 | nanometers | 3 | 140603 | 61 | 6.266 | 22439.0 |
365 | 2021-12-31 11:36:02 | nanometers | 3 | 140603 | 61 | 6.080 | 23125.5 |
364 | 2021-11-24 12:55:41 | nanometers | 1 | 67641 | 4 | 1.296 | 52192.1 |
363 | 2021-11-24 07:29:01 | nanometers | 1 | 67641 | 4 | 1.156 | 58513.0 |
362 | 2021-11-22 05:06:39 | nanometers | 1 | 67641 | 4 | 1.030 | 65670.9 |
361 | 2021-11-20 07:31:09 | nanometers | 3 | 140603 | 61 | 5.453 | 25784.5 |
360 | 2021-10-05 21:22:54 | nanometers | 1 | 67641 | 4 | 1.000 | 67641.0 |
359 | 2021-09-14 15:16:16 | nanometers | 1 | 67641 | 4 | 1.106 | 61158.2 |
358 | 2021-08-18 05:06:39 | nanometers | 1 | 67641 | 4 | 1.016 | 66575.8 |
357 | 2021-08-14 21:40:48 | nanometers | 1 | 67641 | 4 | 1.110 | 60937.8 |
356 | 2021-07-17 19:43:03 | nanometers | 1 | 67641 | 4 | 1.110 | 60937.8 |
355 | 2021-07-06 23:12:45 | nanometers | 3 | 140603 | 61 | 5.576 | 25215.7 |
354 | 2021-06-14 07:13:45 | nanometers | 3 | 140603 | 61 | 5.656 | 24859.1 |