History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"accuracies"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 786 | 2026-01-03 02:36:03 | accuracies | 1 | 67641 | 1 | 1.216 | 55625.8 |
| 785 | 2025-12-31 10:51:21 | accuracies | 5 | 173545 | 998 | 17.266 | 10051.3 |
| 784 | 2025-12-28 01:29:20 | accuracies | 5 | 173545 | 998 | 26.706 | 6498.4 |
| 783 | 2025-12-28 01:29:10 | accuracies | 5 | 173545 | 998 | 16.830 | 10311.6 |
| 782 | 2025-12-22 14:59:58 | accuracies | 4 | 161450 | 95 | 18.296 | 8824.3 |
| 781 | 2025-12-18 21:06:48 | accuracies | 4 | 161450 | 95 | 20.470 | 7887.2 |
| 780 | 2025-11-29 10:44:55 | accuracies | 4 | 161450 | 95 | 11.546 | 13983.2 |
| 779 | 2025-11-25 16:05:10 | accuracies | 4 | 161450 | 95 | 10.923 | 14780.7 |
| 778 | 2025-11-23 19:17:46 | accuracies | 4 | 161450 | 95 | 10.436 | 15470.5 |
| 777 | 2025-11-20 02:33:49 | accuracies | 5 | 173545 | 998 | 34.360 | 5050.8 |
| 776 | 2025-11-20 00:04:25 | accuracies | 1 | 67641 | 1 | 1.203 | 56226.9 |
| 775 | 2025-11-20 00:03:26 | accuracies | 1 | 67641 | 1 | 1.110 | 60937.8 |
| 774 | 2025-11-19 14:04:00 | accuracies | 5 | 173545 | 998 | 17.360 | 9996.8 |
| 773 | 2025-11-11 13:32:39 | accuracies | 1 | 67641 | 1 | 1.110 | 60937.8 |
| 772 | 2025-11-08 20:03:19 | accuracies | 1 | 67641 | 1 | 1.216 | 55625.8 |
| 771 | 2025-11-07 20:00:09 | accuracies | 1 | 67641 | 1 | 5.733 | 11798.5 |
| 770 | 2025-11-07 17:33:09 | accuracies | 1 | 67641 | 1 | 1.236 | 54725.7 |
| 769 | 2025-11-06 07:07:11 | accuracies | 1 | 67641 | 1 | 1.173 | 57665.0 |
| 768 | 2025-11-05 19:26:15 | accuracies | 1 | 67641 | 1 | 1.170 | 57812.8 |
| 767 | 2025-11-04 20:25:37 | accuracies | 5 | 173545 | 998 | 16.623 | 10440.1 |
| 766 | 2025-10-30 21:32:12 | accuracies | 1 | 67641 | 1 | 1.126 | 60071.9 |
| 765 | 2025-10-28 08:16:30 | accuracies | 4 | 161450 | 95 | 10.673 | 15127.0 |
| 764 | 2025-10-27 22:17:22 | accuracies | 5 | 173545 | 998 | 18.236 | 9516.6 |
| 763 | 2025-10-17 03:22:15 | accuracies | 4 | 161450 | 95 | 10.813 | 14931.1 |
| 762 | 2025-10-05 11:27:38 | accuracies | 1 | 67641 | 1 | 1.170 | 57812.8 |
| 761 | 2025-09-11 22:04:02 | accuracies | 4 | 161450 | 95 | 11.296 | 14292.7 |
| 760 | 2025-09-11 21:49:15 | accuracies | 3 | 140603 | 9 | 5.983 | 23500.4 |
| 759 | 2025-09-11 21:36:05 | accuracies | 2 | 108824 | 3 | 2.970 | 36641.1 |
| 758 | 2025-09-11 18:47:55 | accuracies | 1 | 67641 | 1 | 1.080 | 62630.6 |
| 757 | 2025-09-03 02:37:19 | accuracies | 3 | 140603 | 9 | 5.890 | 23871.5 |
| 756 | 2025-08-27 07:42:48 | accuracies | 1 | 67641 | 1 | 1.140 | 59334.2 |
| 755 | 2025-08-10 07:41:56 | accuracies | 2 | 108824 | 3 | 7.310 | 14887.0 |
| 754 | 2025-08-10 01:49:34 | accuracies | 2 | 108824 | 3 | 13.436 | 8099.4 |
| 753 | 2025-08-09 16:31:51 | accuracies | 1 | 67641 | 1 | 2.033 | 33271.5 |
| 752 | 2025-08-09 13:22:42 | accuracies | 1 | 67641 | 1 | 4.110 | 16457.7 |
| 751 | 2025-07-30 19:13:12 | accuracies | 1 | 67641 | 1 | 2.440 | 27721.7 |
| 750 | 2025-07-24 23:26:56 | accuracies | 1 | 67641 | 1 | 3.983 | 16982.4 |
| 749 | 2025-07-24 00:22:55 | accuracies | 1 | 67641 | 1 | 2.656 | 25467.2 |
| 748 | 2025-07-23 23:56:41 | accuracies | 1 | 67641 | 1 | 6.820 | 9918.0 |
| 747 | 2025-07-14 01:55:54 | accuracies | 3 | 140603 | 9 | 26.110 | 5385.0 |
| 746 | 2025-07-14 00:00:28 | accuracies | 3 | 140603 | 9 | 41.596 | 3380.2 |
| 745 | 2025-07-13 07:33:24 | accuracies | 1 | 67641 | 1 | 1.203 | 56226.9 |
| 744 | 2025-07-12 04:25:15 | accuracies | 1 | 67641 | 1 | 1.203 | 56226.9 |
| 743 | 2025-06-24 15:01:16 | accuracies | 1 | 67641 | 1 | 2.873 | 23543.7 |
| 742 | 2025-06-09 21:18:30 | accuracies | 1 | 67641 | 1 | 3.093 | 21869.1 |
| 741 | 2025-05-27 08:59:22 | accuracies | 4 | 161450 | 95 | 42.703 | 3780.8 |
| 740 | 2025-05-22 07:44:59 | accuracies | 1 | 67641 | 1 | 6.080 | 11125.2 |
| 739 | 2025-05-20 13:54:23 | accuracies | 4 | 161450 | 95 | 27.530 | 5864.5 |
| 738 | 2025-05-19 22:30:45 | accuracies | 4 | 161450 | 95 | 48.143 | 3353.6 |
| 737 | 2025-05-19 05:14:26 | accuracies | 1 | 67641 | 1 | 2.453 | 27574.8 |
| 736 | 2025-05-18 00:36:22 | accuracies | 4 | 161450 | 95 | 38.456 | 4198.3 |
| 735 | 2025-04-27 21:22:00 | accuracies | 1 | 67641 | 1 | 2.560 | 26422.3 |
| 734 | 2025-04-27 07:55:36 | accuracies | 4 | 161450 | 95 | 11.623 | 13890.6 |
| 733 | 2025-04-22 09:41:35 | accuracies | 4 | 161450 | 95 | 9.376 | 17219.5 |
| 732 | 2025-03-27 10:11:47 | accuracies | 4 | 161450 | 95 | 51.723 | 3121.4 |
| 731 | 2025-03-27 03:39:47 | accuracies | 2 | 108824 | 3 | 12.076 | 9011.6 |
| 730 | 2025-03-25 22:32:57 | accuracies | 1 | 67641 | 1 | 0.986 | 68601.4 |
| 729 | 2025-03-16 06:05:23 | accuracies | 1 | 67641 | 1 | 6.983 | 9686.5 |
| 728 | 2025-03-12 15:42:41 | accuracies | 2 | 108824 | 3 | 21.220 | 5128.4 |
| 727 | 2025-03-12 14:43:57 | accuracies | 1 | 67641 | 1 | 4.876 | 13872.2 |
| 726 | 2025-03-10 13:07:54 | accuracies | 1 | 67641 | 1 | 6.453 | 10482.1 |
| 725 | 2025-03-09 19:40:29 | accuracies | 1 | 67641 | 1 | 5.690 | 11887.7 |
| 724 | 2025-03-09 19:40:06 | accuracies | 3 | 140603 | 9 | 24.283 | 5790.2 |
| 723 | 2025-03-07 07:43:18 | accuracies | 3 | 140603 | 9 | 37.456 | 3753.8 |
| 722 | 2025-02-16 20:22:48 | accuracies | 3 | 140603 | 9 | 32.080 | 4382.9 |
| 721 | 2025-02-08 16:59:48 | accuracies | 4 | 161450 | 95 | 65.283 | 2473.1 |
| 720 | 2025-02-06 22:24:55 | accuracies | 4 | 161450 | 95 | 46.613 | 3463.6 |
| 719 | 2025-02-06 22:24:42 | accuracies | 1 | 67641 | 1 | 2.266 | 29850.4 |
| 718 | 2025-02-03 23:18:24 | accuracies | 4 | 161450 | 95 | 33.360 | 4839.6 |
| 717 | 2025-02-02 18:37:31 | accuracies | 4 | 161450 | 95 | 55.123 | 2928.9 |
| 716 | 2025-02-02 05:59:37 | accuracies | 4 | 161450 | 95 | 75.893 | 2127.3 |
| 715 | 2025-02-02 05:59:39 | accuracies | 4 | 161450 | 95 | 59.300 | 2722.6 |
| 714 | 2025-02-02 05:58:58 | accuracies | 3 | 140603 | 9 | 18.750 | 7498.8 |
| 713 | 2025-02-02 05:58:59 | accuracies | 2 | 108824 | 3 | 17.703 | 6147.2 |
| 712 | 2025-02-02 05:58:43 | accuracies | 1 | 67641 | 1 | 3.420 | 19778.1 |
| 711 | 2025-01-29 20:32:17 | accuracies | 3 | 140603 | 9 | 31.236 | 4501.3 |
| 710 | 2025-01-29 20:32:16 | accuracies | 2 | 108824 | 3 | 15.393 | 7069.7 |
| 709 | 2025-01-22 03:14:42 | accuracies | 1 | 67641 | 1 | 5.533 | 12225.0 |
| 708 | 2025-01-22 03:01:06 | accuracies | 3 | 140603 | 9 | 42.913 | 3276.5 |
| 707 | 2025-01-22 03:01:06 | accuracies | 2 | 108824 | 3 | 11.190 | 9725.1 |
| 706 | 2025-01-22 02:58:17 | accuracies | 1 | 67641 | 1 | 3.720 | 18183.1 |
| 705 | 2025-01-19 19:45:42 | accuracies | 1 | 67641 | 1 | 5.343 | 12659.7 |
| 704 | 2024-12-25 22:05:08 | accuracies | 1 | 67641 | 1 | 8.390 | 8062.1 |
| 703 | 2024-12-11 14:22:53 | accuracies | 1 | 67641 | 1 | 1.170 | 57812.8 |
| 702 | 2024-12-08 20:53:15 | accuracies | 1 | 67641 | 1 | 1.013 | 66773.0 |
| 701 | 2024-12-06 20:18:56 | accuracies | 1 | 67641 | 1 | 2.890 | 23405.2 |
| 700 | 2024-11-17 03:52:54 | accuracies | 3 | 140603 | 9 | 36.440 | 3858.5 |
| 699 | 2024-11-17 03:52:56 | accuracies | 3 | 140603 | 9 | 26.390 | 5327.9 |
| 698 | 2024-11-17 03:51:22 | accuracies | 1 | 67641 | 1 | 5.546 | 12196.4 |
| 697 | 2024-11-08 16:41:34 | accuracies | 1 | 67641 | 1 | 8.326 | 8124.1 |
| 696 | 2024-11-05 13:16:09 | accuracies | 3 | 140603 | 9 | 35.986 | 3907.2 |
| 695 | 2024-11-05 13:16:19 | accuracies | 2 | 108824 | 3 | 18.296 | 5948.0 |
| 694 | 2024-11-05 13:15:12 | accuracies | 3 | 140603 | 9 | 31.783 | 4423.8 |
| 693 | 2024-11-05 13:15:13 | accuracies | 3 | 140603 | 9 | 29.126 | 4827.4 |
| 692 | 2024-11-05 13:13:56 | accuracies | 1 | 67641 | 1 | 2.876 | 23519.1 |
| 691 | 2024-11-02 17:34:56 | accuracies | 3 | 140603 | 9 | 40.570 | 3465.7 |
| 690 | 2024-11-02 17:34:45 | accuracies | 2 | 108824 | 3 | 14.893 | 7307.1 |
| 689 | 2024-10-31 16:53:22 | accuracies | 1 | 67641 | 1 | 5.126 | 13195.7 |
| 688 | 2024-10-20 05:03:49 | accuracies | 1 | 67641 | 1 | 5.810 | 11642.2 |
| 687 | 2024-10-05 18:51:13 | accuracies | 1 | 67641 | 1 | 8.686 | 7787.4 |