History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"precision"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 960 | 2025-07-18 03:10:04 | precision | 3 | 148641 | 80 | 43.343 | 3429.4 |
| 959 | 2025-07-17 22:58:32 | precision | 2 | 121436 | 17 | 21.876 | 5551.1 |
| 958 | 2025-07-16 06:37:03 | precision | 1 | 81242 | 5 | 2.936 | 27671.0 |
| 957 | 2025-07-15 21:28:52 | precision | 1 | 81242 | 5 | 3.173 | 25604.2 |
| 956 | 2025-07-12 09:16:33 | precision | 1 | 81242 | 5 | 1.486 | 54671.6 |
| 955 | 2025-07-11 04:30:34 | precision | 2 | 121436 | 17 | 18.860 | 6438.8 |
| 954 | 2025-07-09 15:40:41 | precision | 1 | 81242 | 5 | 1.436 | 56575.2 |
| 953 | 2025-07-02 12:26:24 | precision | 1 | 81242 | 5 | 3.830 | 21212.0 |
| 952 | 2025-06-07 15:24:19 | precision | 1 | 81242 | 5 | 3.156 | 25742.1 |
| 951 | 2025-06-06 09:08:21 | precision | 2 | 121436 | 17 | 16.436 | 7388.4 |
| 950 | 2025-06-06 04:55:00 | precision | 1 | 81242 | 5 | 5.330 | 15242.4 |
| 949 | 2025-06-04 15:26:20 | precision | 2 | 121436 | 17 | 24.123 | 5034.0 |
| 948 | 2025-06-04 02:04:47 | precision | 2 | 121436 | 17 | 9.080 | 13374.0 |
| 947 | 2025-06-03 02:29:16 | precision | 1 | 81242 | 5 | 4.920 | 16512.6 |
| 946 | 2025-06-03 02:09:03 | precision | 1 | 81242 | 5 | 7.393 | 10989.0 |
| 945 | 2025-06-03 01:49:08 | precision | 1 | 81242 | 5 | 7.343 | 11063.9 |
| 944 | 2025-06-02 03:59:05 | precision | 2 | 121436 | 17 | 20.330 | 5973.2 |
| 943 | 2025-06-01 11:10:47 | precision | 1 | 81242 | 5 | 1.486 | 54671.6 |
| 942 | 2025-05-30 17:53:34 | precision | 1 | 81242 | 5 | 1.296 | 62686.7 |
| 941 | 2025-05-27 13:49:59 | precision | 1 | 81242 | 5 | 7.873 | 10319.1 |
| 940 | 2025-05-21 08:12:42 | precision | 1 | 81242 | 5 | 1.546 | 52549.8 |
| 939 | 2025-05-02 16:00:18 | precision | 1 | 81242 | 5 | 10.876 | 7469.8 |
| 938 | 2025-04-28 11:24:18 | precision | 1 | 81242 | 5 | 6.780 | 11982.6 |
| 937 | 2025-04-10 23:48:03 | precision | 1 | 81242 | 5 | 6.000 | 13540.3 |
| 936 | 2025-04-04 04:41:35 | precision | 1 | 81242 | 5 | 4.296 | 18911.1 |
| 935 | 2025-04-02 15:16:19 | precision | 1 | 81242 | 5 | 10.703 | 7590.6 |
| 934 | 2025-03-21 11:36:50 | precision | 3 | 148641 | 80 | 34.706 | 4282.9 |
| 933 | 2025-03-21 07:24:06 | precision | 1 | 81242 | 5 | 6.533 | 12435.6 |
| 932 | 2025-03-21 04:32:56 | precision | 3 | 148641 | 80 | 24.923 | 5964.0 |
| 931 | 2025-03-20 10:25:45 | precision | 1 | 81242 | 5 | 6.016 | 13504.3 |
| 930 | 2025-03-20 09:12:20 | precision | 3 | 148641 | 80 | 27.126 | 5479.7 |
| 929 | 2025-03-19 05:32:20 | precision | 3 | 148641 | 80 | 6.843 | 21721.6 |
| 928 | 2025-03-06 17:36:54 | precision | 1 | 81242 | 5 | 1.576 | 51549.5 |
| 927 | 2025-03-02 17:45:57 | precision | 3 | 148641 | 80 | 38.050 | 3906.5 |
| 926 | 2025-03-01 21:14:39 | precision | 2 | 121436 | 17 | 19.266 | 6303.1 |
| 925 | 2025-02-25 13:56:00 | precision | 1 | 81242 | 5 | 1.280 | 63470.3 |
| 924 | 2025-02-21 04:26:57 | precision | 3 | 148641 | 80 | 37.940 | 3917.8 |
| 923 | 2025-02-21 04:24:55 | precision | 1 | 81242 | 5 | 6.923 | 11735.1 |
| 922 | 2025-02-20 06:20:23 | precision | 3 | 148641 | 80 | 38.796 | 3831.3 |
| 921 | 2025-02-20 06:20:10 | precision | 2 | 121436 | 17 | 28.670 | 4235.6 |
| 920 | 2025-02-18 21:09:05 | precision | 3 | 148641 | 80 | 42.093 | 3531.3 |
| 919 | 2025-02-18 21:09:03 | precision | 2 | 121436 | 17 | 12.360 | 9824.9 |
| 918 | 2025-02-17 22:00:07 | precision | 3 | 148641 | 80 | 39.346 | 3777.8 |
| 917 | 2025-02-15 17:58:46 | precision | 3 | 148641 | 80 | 35.956 | 4134.0 |
| 916 | 2025-02-15 17:56:01 | precision | 1 | 81242 | 5 | 10.106 | 8039.0 |
| 915 | 2025-02-13 12:38:53 | precision | 3 | 148641 | 80 | 38.910 | 3820.1 |
| 914 | 2025-02-10 05:04:39 | precision | 3 | 148641 | 80 | 36.250 | 4100.4 |
| 913 | 2025-02-10 05:04:30 | precision | 2 | 121436 | 17 | 10.983 | 11056.7 |
| 912 | 2025-02-10 04:12:34 | precision | 1 | 81242 | 5 | 1.516 | 53589.7 |
| 911 | 2025-02-05 03:17:26 | precision | 1 | 81242 | 5 | 4.576 | 17753.9 |
| 910 | 2025-01-19 04:24:39 | precision | 1 | 81242 | 5 | 10.063 | 8073.3 |
| 909 | 2025-01-17 05:41:21 | precision | 3 | 148641 | 80 | 36.110 | 4116.3 |
| 908 | 2025-01-14 05:49:58 | precision | 3 | 148641 | 80 | 48.656 | 3054.9 |
| 907 | 2025-01-14 05:49:54 | precision | 3 | 148641 | 80 | 44.343 | 3352.1 |
| 906 | 2025-01-14 05:49:58 | precision | 2 | 121436 | 17 | 9.720 | 12493.4 |
| 905 | 2025-01-13 04:32:00 | precision | 1 | 81242 | 5 | 7.330 | 11083.5 |
| 904 | 2025-01-09 19:46:33 | precision | 1 | 81242 | 5 | 1.500 | 54161.3 |
| 903 | 2025-01-03 15:50:54 | precision | 1 | 81242 | 5 | 4.736 | 17154.1 |
| 902 | 2024-12-16 04:59:17 | precision | 1 | 81242 | 5 | 7.300 | 11129.0 |
| 901 | 2024-12-12 18:55:08 | precision | 2 | 121436 | 17 | 24.330 | 4991.2 |
| 900 | 2024-12-10 17:24:25 | precision | 3 | 148641 | 80 | 37.820 | 3930.2 |
| 899 | 2024-12-10 04:32:09 | precision | 1 | 81242 | 5 | 5.796 | 14016.9 |
| 898 | 2024-12-10 01:30:36 | precision | 1 | 81242 | 5 | 3.906 | 20799.3 |
| 897 | 2024-12-09 20:03:13 | precision | 3 | 148641 | 80 | 36.203 | 4105.8 |
| 896 | 2024-12-09 20:03:14 | precision | 3 | 148641 | 80 | 18.296 | 8124.2 |
| 895 | 2024-12-09 20:03:15 | precision | 2 | 121436 | 17 | 8.500 | 14286.6 |
| 894 | 2024-12-01 18:10:07 | precision | 1 | 81242 | 5 | 6.843 | 11872.3 |
| 893 | 2024-11-08 22:14:52 | precision | 1 | 81242 | 5 | 7.156 | 11353.0 |
| 892 | 2024-11-06 19:12:31 | precision | 1 | 81242 | 5 | 7.220 | 11252.4 |
| 891 | 2024-10-31 13:05:12 | precision | 1 | 81242 | 5 | 6.733 | 12066.2 |
| 890 | 2024-10-23 01:58:50 | precision | 1 | 81242 | 5 | 3.546 | 22910.9 |
| 889 | 2024-10-13 01:30:22 | precision | 2 | 121436 | 17 | 18.346 | 6619.2 |
| 888 | 2024-10-09 16:00:18 | precision | 1 | 81242 | 5 | 7.110 | 11426.4 |
| 887 | 2024-10-09 02:45:00 | precision | 3 | 148641 | 80 | 45.780 | 3246.9 |
| 886 | 2024-10-09 00:30:59 | precision | 3 | 148641 | 80 | 36.813 | 4037.7 |
| 885 | 2024-10-09 00:31:03 | precision | 2 | 121436 | 17 | 27.813 | 4366.2 |
| 884 | 2024-10-09 00:26:59 | precision | 1 | 81242 | 5 | 6.640 | 12235.2 |
| 883 | 2024-10-07 18:26:40 | precision | 1 | 81242 | 5 | 10.766 | 7546.2 |
| 882 | 2024-09-22 07:04:22 | precision | 3 | 148641 | 80 | 75.693 | 1963.7 |
| 881 | 2024-09-08 09:46:23 | precision | 3 | 148641 | 80 | 80.283 | 1851.5 |
| 880 | 2024-09-08 09:44:08 | precision | 1 | 81242 | 5 | 15.453 | 5257.4 |
| 879 | 2024-09-07 22:49:18 | precision | 2 | 121436 | 17 | 28.206 | 4305.3 |
| 878 | 2024-09-06 21:09:32 | precision | 3 | 148641 | 80 | 96.410 | 1541.8 |
| 877 | 2024-09-06 21:07:58 | precision | 2 | 121436 | 17 | 13.986 | 8682.7 |
| 876 | 2024-09-06 21:00:57 | precision | 3 | 148641 | 80 | 29.876 | 4975.3 |
| 875 | 2024-09-06 20:21:50 | precision | 1 | 81242 | 5 | 1.516 | 53589.7 |
| 874 | 2024-09-06 18:22:36 | precision | 3 | 148641 | 80 | 42.456 | 3501.1 |
| 873 | 2024-09-05 01:29:14 | precision | 1 | 81242 | 5 | 10.640 | 7635.5 |
| 872 | 2024-09-02 13:53:09 | precision | 3 | 148641 | 80 | 70.673 | 2103.2 |
| 871 | 2024-08-25 17:40:09 | precision | 3 | 148641 | 80 | 58.833 | 2526.5 |
| 870 | 2024-08-14 06:42:36 | precision | 3 | 148641 | 80 | 58.990 | 2519.8 |
| 869 | 2024-08-13 15:28:38 | precision | 3 | 148641 | 80 | 43.470 | 3419.4 |
| 868 | 2024-08-13 14:44:21 | precision | 1 | 81242 | 5 | 13.220 | 6145.4 |
| 867 | 2024-08-07 17:46:50 | precision | 1 | 81242 | 5 | 6.516 | 12468.1 |
| 866 | 2024-08-04 11:12:05 | precision | 3 | 148641 | 80 | 34.893 | 4259.9 |
| 865 | 2024-08-04 11:12:00 | precision | 2 | 121436 | 17 | 14.220 | 8539.8 |
| 864 | 2024-08-04 11:11:52 | precision | 1 | 81242 | 5 | 5.470 | 14852.3 |
| 863 | 2024-08-04 05:30:32 | precision | 3 | 148641 | 80 | 52.066 | 2854.9 |
| 862 | 2024-08-03 13:18:01 | precision | 4 | 165329 | 417 | 45.736 | 3614.9 |
| 861 | 2024-08-03 13:06:00 | precision | 3 | 148641 | 80 | 14.940 | 9949.2 |