History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"crispnesses"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
660 | 2025-08-04 10:50:29 | crispnesses | 1 | 49908 | 2 | 5.846 | 8537.1 |
659 | 2025-08-01 18:49:24 | crispnesses | 2 | 86808 | 6 | 9.640 | 9005.0 |
658 | 2025-07-31 02:33:34 | crispnesses | 1 | 49908 | 2 | 2.080 | 23994.2 |
657 | 2025-07-30 23:28:14 | crispnesses | 1 | 49908 | 2 | 3.936 | 12679.9 |
656 | 2025-07-30 13:36:17 | crispnesses | 4 | 148819 | 367 | 47.143 | 3156.8 |
655 | 2025-07-21 22:06:33 | crispnesses | 1 | 49908 | 2 | 1.016 | 49122.0 |
654 | 2025-07-21 11:41:08 | crispnesses | 4 | 148819 | 367 | 30.443 | 4888.4 |
653 | 2025-07-20 10:29:52 | crispnesses | 4 | 148819 | 367 | 43.273 | 3439.1 |
652 | 2025-07-20 06:07:07 | crispnesses | 2 | 86808 | 6 | 2.640 | 32881.8 |
651 | 2025-07-14 23:50:55 | crispnesses | 3 | 121633 | 53 | 24.813 | 4902.0 |
650 | 2025-07-13 07:24:26 | crispnesses | 2 | 86808 | 6 | 2.096 | 41416.0 |
649 | 2025-07-13 06:44:03 | crispnesses | 1 | 49908 | 2 | 0.873 | 57168.4 |
648 | 2025-07-12 21:04:38 | crispnesses | 4 | 148819 | 367 | 29.986 | 4962.9 |
647 | 2025-07-12 15:46:25 | crispnesses | 1 | 49908 | 2 | 0.936 | 53320.5 |
646 | 2025-07-12 09:57:33 | crispnesses | 1 | 49908 | 2 | 0.906 | 55086.1 |
645 | 2025-07-10 22:36:47 | crispnesses | 1 | 49908 | 2 | 1.750 | 28518.9 |
644 | 2025-07-09 20:10:04 | crispnesses | 1 | 49908 | 2 | 2.170 | 22999.1 |
643 | 2025-07-02 22:13:26 | crispnesses | 4 | 148819 | 367 | 57.346 | 2595.1 |
642 | 2025-07-02 04:10:50 | crispnesses | 1 | 49908 | 2 | 4.203 | 11874.4 |
641 | 2025-06-22 12:37:30 | crispnesses | 2 | 86808 | 6 | 18.313 | 4740.2 |
640 | 2025-06-15 13:52:12 | crispnesses | 1 | 49908 | 2 | 2.220 | 22481.1 |
639 | 2025-06-08 20:08:45 | crispnesses | 1 | 49908 | 2 | 3.326 | 15005.4 |
638 | 2025-06-07 21:31:58 | crispnesses | 2 | 86808 | 6 | 13.483 | 6438.3 |
637 | 2025-06-06 23:12:35 | crispnesses | 1 | 49908 | 2 | 4.436 | 11250.7 |
636 | 2025-06-06 19:51:26 | crispnesses | 1 | 49908 | 2 | 2.046 | 24393.0 |
635 | 2025-06-03 10:37:41 | crispnesses | 3 | 121633 | 53 | 14.860 | 8185.3 |
634 | 2025-06-03 04:13:11 | crispnesses | 1 | 49908 | 2 | 0.876 | 56972.6 |
633 | 2025-06-03 00:03:36 | crispnesses | 2 | 86808 | 6 | 2.296 | 37808.4 |
632 | 2025-05-31 01:07:34 | crispnesses | 1 | 49908 | 2 | 4.420 | 11291.4 |
631 | 2025-05-15 02:30:06 | crispnesses | 1 | 49908 | 2 | 0.873 | 57168.4 |
630 | 2025-05-12 02:34:59 | crispnesses | 4 | 148819 | 367 | 38.046 | 3911.6 |
629 | 2025-05-10 22:13:32 | crispnesses | 4 | 148819 | 367 | 53.876 | 2762.3 |
628 | 2025-05-10 04:21:12 | crispnesses | 2 | 86808 | 6 | 11.876 | 7309.5 |
627 | 2025-05-09 00:34:32 | crispnesses | 1 | 49908 | 2 | 1.483 | 33653.4 |
626 | 2025-05-08 21:07:28 | crispnesses | 3 | 121633 | 53 | 20.063 | 6062.6 |
625 | 2025-04-29 20:02:21 | crispnesses | 1 | 49908 | 2 | 2.953 | 16900.8 |
624 | 2025-04-23 00:39:48 | crispnesses | 1 | 49908 | 2 | 4.780 | 10441.0 |
623 | 2025-04-13 14:44:36 | crispnesses | 3 | 121633 | 53 | 12.216 | 9956.9 |
622 | 2025-04-13 14:20:55 | crispnesses | 2 | 86808 | 6 | 2.376 | 36535.4 |
621 | 2025-04-08 16:29:19 | crispnesses | 4 | 148819 | 367 | 10.343 | 14388.4 |
620 | 2025-04-08 04:17:29 | crispnesses | 3 | 121633 | 53 | 25.500 | 4769.9 |
619 | 2025-04-08 01:31:54 | crispnesses | 2 | 86808 | 6 | 10.970 | 7913.2 |
618 | 2025-04-07 00:30:19 | crispnesses | 1 | 49908 | 2 | 6.063 | 8231.6 |
617 | 2025-04-04 17:27:54 | crispnesses | 1 | 49908 | 2 | 5.533 | 9020.1 |
616 | 2025-04-03 19:49:13 | crispnesses | 1 | 49908 | 2 | 1.830 | 27272.1 |
615 | 2025-04-02 21:22:04 | crispnesses | 1 | 49908 | 2 | 4.076 | 12244.4 |
614 | 2025-03-29 05:53:19 | crispnesses | 1 | 49908 | 2 | 4.686 | 10650.4 |
613 | 2025-03-23 21:18:07 | crispnesses | 1 | 49908 | 2 | 4.653 | 10726.0 |
612 | 2025-03-23 02:10:55 | crispnesses | 1 | 49908 | 2 | 3.936 | 12679.9 |
611 | 2025-03-09 18:55:56 | crispnesses | 3 | 121633 | 53 | 26.140 | 4653.1 |
610 | 2025-03-09 12:59:02 | crispnesses | 3 | 121633 | 53 | 38.410 | 3166.7 |
609 | 2025-03-09 12:58:54 | crispnesses | 2 | 86808 | 6 | 12.313 | 7050.1 |
608 | 2025-03-07 13:27:41 | crispnesses | 3 | 121633 | 53 | 27.050 | 4496.6 |
607 | 2025-03-07 13:27:21 | crispnesses | 2 | 86808 | 6 | 12.330 | 7040.4 |
606 | 2025-03-06 00:48:14 | crispnesses | 4 | 148819 | 367 | 31.643 | 4703.1 |
605 | 2025-03-05 15:06:30 | crispnesses | 4 | 148819 | 367 | 40.286 | 3694.1 |
604 | 2025-03-05 15:06:11 | crispnesses | 4 | 148819 | 367 | 50.783 | 2930.5 |
603 | 2025-03-05 13:54:07 | crispnesses | 4 | 148819 | 367 | 52.800 | 2818.5 |
602 | 2025-03-05 13:54:02 | crispnesses | 3 | 121633 | 53 | 25.173 | 4831.9 |
601 | 2025-03-05 13:54:01 | crispnesses | 2 | 86808 | 6 | 8.046 | 10789.0 |
600 | 2025-03-05 13:53:42 | crispnesses | 1 | 49908 | 2 | 6.626 | 7532.1 |
599 | 2025-02-22 23:44:50 | crispnesses | 1 | 49908 | 2 | 2.190 | 22789.0 |
598 | 2025-02-20 06:37:32 | crispnesses | 1 | 49908 | 2 | 3.656 | 13651.0 |
597 | 2025-02-06 17:23:37 | crispnesses | 2 | 86808 | 6 | 12.220 | 7103.8 |
596 | 2025-02-05 09:43:17 | crispnesses | 2 | 86808 | 6 | 15.016 | 5781.0 |
595 | 2025-01-31 11:54:55 | crispnesses | 3 | 121633 | 53 | 27.266 | 4461.0 |
594 | 2025-01-31 07:36:09 | crispnesses | 2 | 86808 | 6 | 14.283 | 6077.7 |
593 | 2025-01-31 07:33:45 | crispnesses | 1 | 49908 | 2 | 4.186 | 11922.6 |
592 | 2025-01-30 10:49:27 | crispnesses | 4 | 148819 | 367 | 57.860 | 2572.1 |
591 | 2025-01-30 10:49:26 | crispnesses | 3 | 121633 | 53 | 25.126 | 4840.9 |
590 | 2025-01-30 10:37:52 | crispnesses | 4 | 148819 | 367 | 47.126 | 3157.9 |
589 | 2025-01-30 07:03:02 | crispnesses | 4 | 148819 | 367 | 80.816 | 1841.5 |
588 | 2025-01-30 07:02:50 | crispnesses | 3 | 121633 | 53 | 26.786 | 4540.9 |
587 | 2025-01-30 02:51:48 | crispnesses | 4 | 148819 | 367 | 51.566 | 2886.0 |
586 | 2025-01-30 02:51:50 | crispnesses | 3 | 121633 | 53 | 22.953 | 5299.2 |
585 | 2025-01-30 02:51:50 | crispnesses | 2 | 86808 | 6 | 13.373 | 6491.3 |
584 | 2025-01-30 02:50:58 | crispnesses | 1 | 49908 | 2 | 1.763 | 28308.6 |
583 | 2025-01-22 11:41:15 | crispnesses | 1 | 49908 | 2 | 4.106 | 12154.9 |
582 | 2025-01-03 17:00:43 | crispnesses | 2 | 86808 | 6 | 5.813 | 14933.4 |
581 | 2025-01-03 16:59:37 | crispnesses | 1 | 49908 | 2 | 2.250 | 22181.3 |
580 | 2024-12-20 21:21:08 | crispnesses | 2 | 86808 | 6 | 6.733 | 12892.9 |
579 | 2024-12-20 21:18:25 | crispnesses | 1 | 49908 | 2 | 1.313 | 38010.7 |
578 | 2024-12-20 17:32:27 | crispnesses | 2 | 86808 | 6 | 12.690 | 6840.7 |
577 | 2024-12-20 17:32:21 | crispnesses | 1 | 49908 | 2 | 1.580 | 31587.3 |
576 | 2024-12-20 12:41:25 | crispnesses | 3 | 121633 | 53 | 13.330 | 9124.8 |
575 | 2024-12-20 06:34:30 | crispnesses | 3 | 121633 | 53 | 19.003 | 6400.7 |
574 | 2024-12-18 22:02:14 | crispnesses | 4 | 148819 | 367 | 53.516 | 2780.8 |
573 | 2024-12-18 21:06:24 | crispnesses | 4 | 148819 | 367 | 61.410 | 2423.4 |
572 | 2024-12-18 21:06:19 | crispnesses | 3 | 121633 | 53 | 25.813 | 4712.1 |
571 | 2024-12-18 20:47:39 | crispnesses | 4 | 148819 | 367 | 49.300 | 3018.6 |
570 | 2024-12-18 18:22:08 | crispnesses | 3 | 121633 | 53 | 28.313 | 4296.0 |
569 | 2024-12-18 18:21:36 | crispnesses | 4 | 148819 | 367 | 48.800 | 3049.6 |
568 | 2024-12-18 18:21:55 | crispnesses | 2 | 86808 | 6 | 12.016 | 7224.4 |
567 | 2024-12-12 22:44:00 | crispnesses | 1 | 49908 | 2 | 1.983 | 25167.9 |
566 | 2024-12-04 09:56:36 | crispnesses | 1 | 49908 | 2 | 6.063 | 8231.6 |
565 | 2024-11-17 05:44:25 | crispnesses | 3 | 121633 | 53 | 21.156 | 5749.3 |
564 | 2024-11-17 05:44:25 | crispnesses | 2 | 86808 | 6 | 11.796 | 7359.1 |
563 | 2024-11-17 05:44:12 | crispnesses | 1 | 49908 | 2 | 4.500 | 11090.7 |
562 | 2024-11-16 22:57:13 | crispnesses | 4 | 148819 | 367 | 45.986 | 3236.2 |
561 | 2024-11-15 20:57:57 | crispnesses | 4 | 148819 | 367 | 49.973 | 2978.0 |