History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"skewnesses"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
236 | 2024-04-16 03:22:55 | skewnesses | 4 | 161450 | 484 | 27.206 | 5934.4 |
235 | 2024-04-16 03:22:55 | skewnesses | 3 | 140603 | 72 | 16.330 | 8610.1 |
234 | 2024-04-15 12:39:33 | skewnesses | 4 | 161450 | 484 | 26.940 | 5992.9 |
233 | 2024-04-15 12:39:25 | skewnesses | 3 | 140603 | 72 | 9.236 | 15223.4 |
232 | 2024-04-15 07:36:02 | skewnesses | 4 | 161450 | 484 | 31.313 | 5156.0 |
231 | 2024-04-15 07:36:04 | skewnesses | 2 | 108824 | 6 | 8.520 | 12772.8 |
230 | 2024-04-15 07:28:37 | skewnesses | 4 | 161450 | 484 | 18.020 | 8959.5 |
229 | 2024-04-15 07:28:37 | skewnesses | 3 | 140603 | 72 | 15.686 | 8963.6 |
228 | 2024-04-15 07:28:50 | skewnesses | 1 | 67641 | 2 | 1.236 | 54725.7 |
227 | 2024-04-15 07:28:39 | skewnesses | 2 | 108824 | 6 | 7.250 | 15010.2 |
226 | 2024-04-15 07:28:17 | skewnesses | 1 | 67641 | 2 | 1.266 | 53428.9 |
225 | 2024-04-15 07:28:10 | skewnesses | 1 | 67641 | 2 | 1.250 | 54112.8 |
224 | 2024-03-25 23:43:18 | skewnesses | 2 | 108824 | 6 | 10.796 | 10080.0 |
223 | 2024-03-25 23:43:12 | skewnesses | 3 | 140603 | 72 | 16.360 | 8594.3 |
222 | 2024-03-25 23:30:17 | skewnesses | 1 | 67641 | 2 | 2.330 | 29030.5 |
221 | 2024-03-22 09:57:59 | skewnesses | 2 | 108824 | 6 | 10.546 | 10319.0 |
220 | 2024-03-21 06:27:33 | skewnesses | 4 | 161450 | 484 | 30.393 | 5312.1 |
219 | 2024-03-20 07:13:12 | skewnesses | 3 | 140603 | 72 | 21.046 | 6680.7 |
218 | 2024-03-20 07:13:12 | skewnesses | 4 | 161450 | 484 | 15.486 | 10425.5 |
217 | 2024-03-20 07:13:13 | skewnesses | 2 | 108824 | 6 | 3.673 | 29628.1 |
216 | 2024-03-20 05:02:05 | skewnesses | 1 | 67641 | 2 | 1.156 | 58513.0 |
215 | 2024-03-13 02:10:28 | skewnesses | 2 | 108824 | 6 | 9.703 | 11215.5 |
214 | 2024-03-13 02:09:29 | skewnesses | 1 | 67641 | 2 | 2.720 | 24868.0 |
213 | 2024-03-11 10:33:33 | skewnesses | 3 | 140603 | 72 | 13.673 | 10283.3 |
212 | 2024-03-11 10:31:21 | skewnesses | 1 | 67641 | 2 | 3.910 | 17299.5 |
211 | 2024-03-10 14:03:05 | skewnesses | 3 | 140603 | 72 | 16.390 | 8578.6 |
210 | 2024-03-10 04:22:25 | skewnesses | 4 | 161450 | 484 | 13.843 | 11662.9 |
209 | 2024-03-09 14:36:23 | skewnesses | 1 | 67641 | 2 | 1.030 | 65670.9 |
208 | 2024-03-09 14:34:33 | skewnesses | 3 | 140603 | 72 | 6.313 | 22272.0 |
207 | 2024-03-09 14:33:59 | skewnesses | 2 | 108824 | 6 | 3.170 | 34329.3 |
206 | 2024-03-09 14:32:59 | skewnesses | 4 | 161450 | 484 | 14.766 | 10933.9 |
205 | 2024-03-09 14:32:34 | skewnesses | 1 | 67641 | 2 | 1.000 | 67641.0 |
204 | 2024-03-07 09:05:16 | skewnesses | 1 | 67641 | 2 | 3.623 | 18669.9 |
203 | 2024-02-13 14:15:55 | skewnesses | 1 | 67641 | 2 | 4.030 | 16784.4 |
202 | 2024-02-13 10:02:57 | skewnesses | 3 | 140603 | 72 | 10.143 | 13862.1 |
201 | 2024-02-13 06:51:10 | skewnesses | 2 | 108824 | 6 | 2.626 | 41441.0 |
200 | 2024-02-13 06:18:29 | skewnesses | 1 | 67641 | 2 | 1.030 | 65670.9 |
199 | 2024-02-12 14:17:08 | skewnesses | 3 | 140603 | 72 | 14.656 | 9593.5 |
198 | 2024-02-12 10:30:23 | skewnesses | 4 | 161450 | 484 | 28.390 | 5686.9 |
197 | 2024-02-10 22:46:43 | skewnesses | 3 | 140603 | 72 | 16.656 | 8441.6 |
196 | 2024-02-10 21:24:24 | skewnesses | 4 | 161450 | 484 | 24.970 | 6465.8 |
195 | 2024-02-10 02:00:24 | skewnesses | 4 | 161450 | 484 | 34.910 | 4624.7 |
194 | 2024-02-10 02:00:23 | skewnesses | 3 | 140603 | 72 | 18.953 | 7418.5 |
193 | 2024-02-10 02:00:23 | skewnesses | 2 | 108824 | 6 | 8.750 | 12437.0 |
192 | 2024-02-10 00:59:56 | skewnesses | 1 | 67641 | 2 | 1.266 | 53428.9 |
191 | 2024-02-07 18:27:04 | skewnesses | 1 | 67641 | 2 | 1.250 | 54112.8 |
190 | 2024-02-05 23:38:13 | skewnesses | 1 | 67641 | 2 | 1.296 | 52192.1 |
189 | 2024-01-29 04:41:15 | skewnesses | 1 | 67641 | 2 | 2.016 | 33552.1 |
188 | 2024-01-26 02:08:30 | skewnesses | 1 | 67641 | 2 | 1.186 | 57032.9 |
187 | 2023-12-28 21:48:26 | skewnesses | 1 | 67641 | 2 | 1.126 | 60071.9 |
186 | 2023-12-17 03:13:59 | skewnesses | 1 | 67641 | 2 | 1.123 | 60232.4 |
185 | 2023-12-08 20:57:36 | skewnesses | 3 | 140603 | 72 | 5.516 | 25490.0 |
184 | 2023-12-08 20:57:35 | skewnesses | 2 | 108824 | 6 | 2.830 | 38453.7 |
183 | 2023-11-22 19:15:23 | skewnesses | 1 | 67641 | 2 | 1.016 | 66575.8 |
182 | 2023-11-10 17:20:40 | skewnesses | 1 | 67641 | 2 | 1.033 | 65480.2 |
181 | 2023-10-28 09:30:37 | skewnesses | 1 | 67641 | 2 | 1.000 | 67641.0 |
180 | 2023-09-08 01:19:37 | skewnesses | 1 | 67641 | 2 | 1.000 | 67641.0 |
179 | 2023-07-30 14:19:01 | skewnesses | 1 | 67641 | 2 | 1.016 | 66575.8 |
178 | 2023-07-26 18:46:30 | skewnesses | 1 | 67641 | 2 | 1.436 | 47103.8 |
177 | 2023-06-14 04:44:50 | skewnesses | 1 | 67641 | 2 | 1.013 | 66773.0 |
176 | 2023-03-30 22:13:02 | skewnesses | 1 | 67641 | 2 | 1.123 | 60232.4 |
175 | 2023-03-23 21:16:10 | skewnesses | 1 | 67641 | 2 | 1.093 | 61885.6 |
174 | 2023-01-13 01:37:22 | skewnesses | 1 | 67641 | 2 | 1.093 | 61885.6 |
173 | 2022-11-11 17:01:41 | skewnesses | 1 | 67641 | 2 | 0.983 | 68810.8 |
172 | 2022-10-27 00:55:21 | skewnesses | 1 | 67641 | 2 | 0.966 | 70021.7 |
171 | 2022-07-05 08:05:35 | skewnesses | 1 | 67641 | 2 | 1.000 | 67641.0 |
170 | 2022-06-16 13:13:41 | skewnesses | 1 | 67641 | 2 | 1.000 | 67641.0 |
169 | 2022-06-14 07:41:33 | skewnesses | 1 | 67641 | 2 | 1.050 | 64420.0 |
168 | 2022-03-04 08:48:20 | skewnesses | 1 | 67641 | 2 | 1.250 | 54112.8 |
167 | 2022-02-28 22:46:03 | skewnesses | 1 | 67641 | 2 | 1.126 | 60071.9 |
166 | 2022-02-24 01:28:08 | skewnesses | 1 | 67641 | 2 | 1.343 | 50365.6 |
165 | 2022-01-01 03:03:28 | skewnesses | 1 | 67641 | 2 | 1.093 | 61885.6 |
164 | 2021-12-30 00:32:03 | skewnesses | 1 | 67641 | 2 | 1.013 | 66773.0 |
163 | 2021-10-24 23:31:25 | skewnesses | 1 | 67641 | 2 | 1.000 | 67641.0 |
162 | 2021-07-10 16:21:25 | skewnesses | 1 | 67641 | 2 | 1.173 | 57665.0 |
161 | 2021-04-11 04:13:27 | skewnesses | 1 | 67641 | 2 | 1.326 | 51011.3 |
160 | 2021-02-23 00:03:18 | skewnesses | 1 | 67641 | 2 | 2.080 | 32519.7 |
159 | 2020-12-28 20:54:52 | skewnesses | 1 | 67641 | 2 | 1.033 | 65480.2 |
158 | 2020-09-21 11:45:24 | skewnesses | 1 | 67641 | 2 | 0.970 | 69733.0 |
157 | 2020-08-13 19:00:45 | skewnesses | 1 | 67641 | 2 | 1.000 | 67641.0 |
156 | 2020-05-29 18:33:39 | skewnesses | 1 | 67641 | 2 | 1.000 | 67641.0 |
155 | 2020-03-26 01:50:38 | skewnesses | 1 | 67641 | 2 | 1.156 | 58513.0 |
154 | 2020-02-23 11:57:32 | skewnesses | 1 | 67641 | 2 | 2.013 | 33602.1 |
153 | 2020-02-11 23:08:47 | skewnesses | 1 | 67641 | 2 | 2.610 | 25916.1 |
152 | 2020-02-09 15:15:27 | skewnesses | 1 | 67641 | 2 | 1.250 | 54112.8 |
151 | 2020-02-09 10:52:09 | skewnesses | 1 | 67641 | 2 | 2.610 | 25916.1 |
150 | 2020-02-01 09:34:21 | skewnesses | 1 | 67641 | 2 | 2.530 | 26735.6 |
149 | 2020-01-29 23:23:05 | skewnesses | 1 | 67641 | 2 | 1.266 | 53428.9 |
148 | 2020-01-29 06:57:50 | skewnesses | 1 | 67641 | 2 | 1.186 | 57032.9 |
147 | 2020-01-14 12:52:30 | skewnesses | 1 | 67641 | 2 | 1.160 | 58311.2 |
146 | 2020-01-13 20:55:22 | skewnesses | 1 | 67641 | 2 | 2.216 | 30523.9 |
145 | 2020-01-06 15:21:23 | skewnesses | 1 | 67641 | 2 | 1.173 | 57665.0 |
144 | 2019-11-24 15:26:27 | skewnesses | 1 | 67641 | 2 | 1.013 | 66773.0 |
143 | 2019-08-25 20:42:34 | skewnesses | 1 | 67641 | 2 | 0.946 | 71502.1 |
142 | 2019-07-23 22:57:14 | skewnesses | 1 | 67641 | 2 | 1.070 | 63215.9 |
141 | 2019-05-16 19:03:25 | skewnesses | 1 | 67641 | 2 | 1.096 | 61716.2 |
140 | 2019-01-25 17:22:52 | skewnesses | 1 | 67641 | 2 | 1.113 | 60773.6 |
139 | 2018-09-25 18:13:17 | skewnesses | 1 | 67641 | 2 | 1.020 | 66314.7 |
138 | 2018-07-01 09:07:46 | skewnesses | 1 | 67641 | 2 | 1.003 | 67438.7 |
137 | 2018-06-25 22:03:17 | skewnesses | 1 | 67641 | 2 | 1.316 | 51398.9 |