History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"cohort"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
874 | 2025-08-27 04:36:01 | cohort | 1 | 48755 | 3 | 0.936 | 52088.7 |
873 | 2025-08-26 09:29:05 | cohort | 3 | 112716 | 391 | 16.703 | 6748.2 |
872 | 2025-08-26 01:36:28 | cohort | 2 | 82551 | 32 | 2.343 | 35233.0 |
871 | 2025-08-25 09:53:33 | cohort | 1 | 48755 | 3 | 2.656 | 18356.6 |
870 | 2025-08-25 02:29:14 | cohort | 1 | 48755 | 3 | 5.860 | 8320.0 |
869 | 2025-08-23 13:59:54 | cohort | 2 | 82551 | 32 | 9.266 | 8909.0 |
868 | 2025-08-22 01:25:17 | cohort | 3 | 112716 | 391 | 5.593 | 20153.0 |
867 | 2025-08-21 08:37:06 | cohort | 3 | 112716 | 391 | 20.050 | 5621.7 |
866 | 2025-08-19 17:01:39 | cohort | 3 | 112716 | 391 | 26.703 | 4221.1 |
865 | 2025-08-19 04:34:53 | cohort | 3 | 112716 | 391 | 12.170 | 9261.8 |
864 | 2025-08-17 08:14:58 | cohort | 3 | 112716 | 391 | 13.910 | 8103.2 |
863 | 2025-08-17 02:50:35 | cohort | 2 | 82551 | 32 | 11.783 | 7005.9 |
862 | 2025-08-16 12:18:03 | cohort | 1 | 48755 | 3 | 0.780 | 62506.4 |
861 | 2025-08-14 08:52:47 | cohort | 1 | 48755 | 3 | 0.843 | 57835.1 |
860 | 2025-08-05 02:05:44 | cohort | 1 | 48755 | 3 | 2.203 | 22131.2 |
859 | 2025-08-01 09:08:11 | cohort | 1 | 48755 | 3 | 4.110 | 11862.5 |
858 | 2025-07-28 00:13:55 | cohort | 2 | 82551 | 32 | 17.380 | 4749.8 |
857 | 2025-07-23 13:23:30 | cohort | 1 | 48755 | 3 | 6.313 | 7723.0 |
856 | 2025-07-23 10:10:15 | cohort | 1 | 48755 | 3 | 7.530 | 6474.8 |
855 | 2025-07-23 08:09:56 | cohort | 1 | 48755 | 3 | 3.733 | 13060.5 |
854 | 2025-07-23 05:22:03 | cohort | 1 | 48755 | 3 | 2.096 | 23261.0 |
853 | 2025-07-22 22:30:20 | cohort | 1 | 48755 | 3 | 2.203 | 22131.2 |
852 | 2025-07-22 19:10:23 | cohort | 1 | 48755 | 3 | 3.593 | 13569.4 |
851 | 2025-07-22 15:37:07 | cohort | 1 | 48755 | 3 | 2.563 | 19022.6 |
850 | 2025-07-13 13:22:39 | cohort | 1 | 48755 | 3 | 0.766 | 63648.8 |
849 | 2025-07-08 07:44:01 | cohort | 3 | 112716 | 391 | 23.470 | 4802.6 |
848 | 2025-07-07 08:43:32 | cohort | 3 | 112716 | 391 | 33.330 | 3381.8 |
847 | 2025-07-07 06:17:32 | cohort | 1 | 48755 | 3 | 4.376 | 11141.5 |
846 | 2025-07-06 02:07:04 | cohort | 3 | 112716 | 391 | 11.703 | 9631.4 |
845 | 2025-06-25 12:26:43 | cohort | 1 | 48755 | 3 | 0.813 | 59969.2 |
844 | 2025-06-24 01:25:22 | cohort | 1 | 48755 | 3 | 4.250 | 11471.8 |
843 | 2025-06-20 13:51:51 | cohort | 1 | 48755 | 3 | 1.750 | 27860.0 |
842 | 2025-06-20 07:21:59 | cohort | 1 | 48755 | 3 | 5.966 | 8172.1 |
841 | 2025-06-16 13:15:43 | cohort | 2 | 82551 | 32 | 8.906 | 9269.1 |
840 | 2025-06-15 02:06:33 | cohort | 1 | 48755 | 3 | 1.030 | 47335.0 |
839 | 2025-06-14 02:57:34 | cohort | 2 | 82551 | 32 | 8.516 | 9693.6 |
838 | 2025-06-13 12:18:24 | cohort | 1 | 48755 | 3 | 2.893 | 16852.7 |
837 | 2025-06-12 03:07:12 | cohort | 1 | 48755 | 3 | 4.550 | 10715.4 |
836 | 2025-06-10 08:09:06 | cohort | 3 | 112716 | 391 | 17.500 | 6440.9 |
835 | 2025-06-08 10:39:39 | cohort | 3 | 112716 | 391 | 19.563 | 5761.7 |
834 | 2025-06-06 14:22:17 | cohort | 2 | 82551 | 32 | 9.220 | 8953.5 |
833 | 2025-06-06 06:25:10 | cohort | 1 | 48755 | 3 | 4.076 | 11961.5 |
832 | 2025-06-06 01:44:44 | cohort | 3 | 112716 | 391 | 47.876 | 2354.3 |
831 | 2025-06-04 17:41:44 | cohort | 3 | 112716 | 391 | 38.643 | 2916.9 |
830 | 2025-06-04 08:59:04 | cohort | 2 | 82551 | 32 | 12.563 | 6571.0 |
829 | 2025-06-01 03:48:44 | cohort | 1 | 48755 | 3 | 1.796 | 27146.4 |
828 | 2025-05-20 09:38:27 | cohort | 1 | 48755 | 3 | 4.610 | 10575.9 |
827 | 2025-05-18 06:09:31 | cohort | 1 | 48755 | 3 | 6.343 | 7686.4 |
826 | 2025-05-17 00:18:24 | cohort | 1 | 48755 | 3 | 0.763 | 63899.1 |
825 | 2025-05-12 03:06:38 | cohort | 1 | 48755 | 3 | 2.013 | 24220.1 |
824 | 2025-05-10 02:49:25 | cohort | 2 | 82551 | 32 | 16.846 | 4900.3 |
823 | 2025-05-09 19:05:36 | cohort | 3 | 112716 | 391 | 6.093 | 18499.3 |
822 | 2025-05-09 12:43:47 | cohort | 1 | 48755 | 3 | 1.563 | 31193.2 |
821 | 2025-04-22 12:31:33 | cohort | 2 | 82551 | 32 | 2.873 | 28733.4 |
820 | 2025-04-20 15:45:14 | cohort | 2 | 82551 | 32 | 2.860 | 28864.0 |
819 | 2025-04-20 07:59:42 | cohort | 3 | 112716 | 391 | 19.236 | 5859.6 |
818 | 2025-04-19 09:00:33 | cohort | 1 | 48755 | 3 | 0.763 | 63899.1 |
817 | 2025-04-18 10:16:09 | cohort | 3 | 112716 | 391 | 25.656 | 4393.4 |
816 | 2025-04-18 05:09:59 | cohort | 1 | 48755 | 3 | 4.283 | 11383.4 |
815 | 2025-04-16 06:14:22 | cohort | 3 | 112716 | 391 | 29.876 | 3772.8 |
814 | 2025-04-14 08:32:11 | cohort | 3 | 112716 | 391 | 20.313 | 5549.0 |
813 | 2025-04-11 18:22:19 | cohort | 3 | 112716 | 391 | 15.283 | 7375.3 |
812 | 2025-04-11 02:40:52 | cohort | 1 | 48755 | 3 | 4.423 | 11023.1 |
811 | 2025-04-10 02:39:43 | cohort | 1 | 48755 | 3 | 0.766 | 63648.8 |
810 | 2025-04-09 11:57:16 | cohort | 1 | 48755 | 3 | 2.626 | 18566.3 |
809 | 2025-03-28 18:17:25 | cohort | 1 | 48755 | 3 | 0.843 | 57835.1 |
808 | 2025-03-24 16:25:17 | cohort | 1 | 48755 | 3 | 1.766 | 27607.6 |
807 | 2025-03-20 22:41:28 | cohort | 2 | 82551 | 32 | 10.516 | 7850.0 |
806 | 2025-03-20 21:45:01 | cohort | 3 | 112716 | 391 | 23.660 | 4764.0 |
805 | 2025-03-19 11:20:15 | cohort | 1 | 48755 | 3 | 0.876 | 55656.4 |
804 | 2025-03-18 13:08:56 | cohort | 2 | 82551 | 32 | 12.360 | 6678.9 |
803 | 2025-03-18 13:08:54 | cohort | 1 | 48755 | 3 | 2.220 | 21961.7 |
802 | 2025-03-10 18:20:43 | cohort | 2 | 82551 | 32 | 15.906 | 5189.9 |
801 | 2025-03-10 17:56:20 | cohort | 1 | 48755 | 3 | 0.923 | 52822.3 |
800 | 2025-03-05 13:49:24 | cohort | 3 | 112716 | 391 | 18.906 | 5961.9 |
799 | 2025-03-05 11:14:10 | cohort | 1 | 48755 | 3 | 3.423 | 14243.4 |
798 | 2025-03-04 23:23:08 | cohort | 3 | 112716 | 391 | 24.093 | 4678.4 |
797 | 2025-02-28 03:09:44 | cohort | 3 | 112716 | 391 | 11.466 | 9830.5 |
796 | 2025-02-26 23:01:48 | cohort | 1 | 48755 | 3 | 1.893 | 25755.4 |
795 | 2025-02-25 10:10:10 | cohort | 1 | 48755 | 3 | 0.876 | 55656.4 |
794 | 2025-02-25 04:18:49 | cohort | 3 | 112716 | 391 | 25.970 | 4340.2 |
793 | 2025-02-25 02:20:31 | cohort | 3 | 112716 | 391 | 28.390 | 3970.3 |
792 | 2025-02-25 01:40:40 | cohort | 3 | 112716 | 391 | 29.470 | 3824.8 |
791 | 2025-02-25 00:58:02 | cohort | 3 | 112716 | 391 | 31.186 | 3614.3 |
790 | 2025-02-24 22:39:33 | cohort | 3 | 112716 | 391 | 21.186 | 5320.3 |
789 | 2025-02-15 15:37:07 | cohort | 3 | 112716 | 391 | 26.986 | 4176.8 |
788 | 2025-02-11 03:24:58 | cohort | 1 | 48755 | 3 | 2.873 | 16970.1 |
787 | 2025-02-04 22:30:42 | cohort | 2 | 82551 | 32 | 9.923 | 8319.2 |
786 | 2025-02-04 22:30:36 | cohort | 1 | 48755 | 3 | 2.813 | 17332.0 |
785 | 2025-01-27 20:03:14 | cohort | 1 | 48755 | 3 | 3.470 | 14050.4 |
784 | 2025-01-26 00:35:06 | cohort | 1 | 48755 | 3 | 0.903 | 53992.2 |
783 | 2025-01-24 20:13:45 | cohort | 3 | 112716 | 391 | 28.016 | 4023.3 |
782 | 2025-01-24 19:34:41 | cohort | 3 | 112716 | 391 | 29.986 | 3759.0 |
781 | 2025-01-21 13:04:12 | cohort | 3 | 112716 | 391 | 14.313 | 7875.1 |
780 | 2025-01-20 08:16:44 | cohort | 3 | 112716 | 391 | 23.546 | 4787.1 |
779 | 2025-01-19 13:23:52 | cohort | 3 | 112716 | 391 | 22.126 | 5094.3 |
778 | 2025-01-19 13:23:49 | cohort | 2 | 82551 | 32 | 21.876 | 3773.6 |
777 | 2025-01-19 13:22:43 | cohort | 1 | 48755 | 3 | 3.970 | 12280.9 |
776 | 2025-01-14 12:45:55 | cohort | 3 | 112716 | 391 | 28.783 | 3916.1 |
775 | 2025-01-07 14:25:41 | cohort | 1 | 48755 | 3 | 6.140 | 7940.6 |