History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"convexly"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
480 | 2025-08-05 01:27:38 | convexly | 1 | 82945 | 1 | 3.606 | 23001.9 |
479 | 2025-07-27 13:27:58 | convexly | 1 | 82945 | 1 | 3.763 | 22042.3 |
478 | 2025-07-24 21:21:24 | convexly | 1 | 82945 | 1 | 3.266 | 25396.5 |
477 | 2025-07-23 08:50:00 | convexly | 1 | 82945 | 1 | 3.593 | 23085.2 |
476 | 2025-07-22 15:39:17 | convexly | 1 | 82945 | 1 | 5.330 | 15561.9 |
475 | 2025-07-14 22:16:08 | convexly | 2 | 121059 | 5 | 27.266 | 4439.9 |
474 | 2025-07-14 04:21:30 | convexly | 4 | 161609 | 452 | 73.083 | 2211.3 |
473 | 2025-07-13 00:35:34 | convexly | 1 | 82945 | 1 | 1.593 | 52068.4 |
472 | 2025-07-09 21:12:43 | convexly | 4 | 161609 | 452 | 64.143 | 2519.5 |
471 | 2025-07-04 16:18:28 | convexly | 4 | 161609 | 452 | 62.363 | 2591.4 |
470 | 2025-07-02 17:52:05 | convexly | 4 | 161609 | 452 | 79.766 | 2026.0 |
469 | 2025-07-01 07:20:51 | convexly | 1 | 82945 | 1 | 1.390 | 59672.7 |
468 | 2025-06-26 12:21:21 | convexly | 1 | 82945 | 1 | 4.093 | 20265.1 |
467 | 2025-06-23 20:02:32 | convexly | 1 | 82945 | 1 | 7.160 | 11584.5 |
466 | 2025-06-17 08:38:20 | convexly | 3 | 146162 | 48 | 8.220 | 17781.3 |
465 | 2025-06-16 23:35:56 | convexly | 3 | 146162 | 48 | 43.000 | 3399.1 |
464 | 2025-06-16 21:27:14 | convexly | 2 | 121059 | 5 | 16.016 | 7558.6 |
463 | 2025-06-16 21:18:29 | convexly | 3 | 146162 | 48 | 31.780 | 4599.2 |
462 | 2025-06-15 07:08:26 | convexly | 3 | 146162 | 48 | 7.420 | 19698.4 |
461 | 2025-06-14 22:16:53 | convexly | 1 | 82945 | 1 | 1.563 | 53067.8 |
460 | 2025-06-14 03:46:12 | convexly | 3 | 146162 | 48 | 31.313 | 4667.8 |
459 | 2025-06-12 09:23:24 | convexly | 3 | 146162 | 48 | 40.690 | 3592.1 |
458 | 2025-06-12 07:52:52 | convexly | 4 | 161609 | 452 | 66.393 | 2434.1 |
457 | 2025-06-12 05:20:13 | convexly | 4 | 161609 | 452 | 48.360 | 3341.8 |
456 | 2025-06-10 08:02:20 | convexly | 3 | 146162 | 48 | 29.063 | 5029.1 |
455 | 2025-06-09 20:10:16 | convexly | 4 | 161609 | 452 | 77.450 | 2086.6 |
454 | 2025-06-08 23:35:04 | convexly | 1 | 82945 | 1 | 1.466 | 56579.1 |
453 | 2025-05-30 13:30:47 | convexly | 1 | 82945 | 1 | 7.266 | 11415.5 |
452 | 2025-05-28 02:07:26 | convexly | 4 | 161609 | 452 | 18.563 | 8706.0 |
451 | 2025-05-26 12:58:42 | convexly | 1 | 82945 | 1 | 10.093 | 8218.1 |
450 | 2025-05-23 06:43:12 | convexly | 4 | 161609 | 452 | 58.813 | 2747.8 |
449 | 2025-05-18 20:45:20 | convexly | 1 | 82945 | 1 | 2.486 | 33364.8 |
448 | 2025-05-18 18:47:31 | convexly | 4 | 161609 | 452 | 77.613 | 2082.2 |
447 | 2025-05-17 20:00:22 | convexly | 1 | 82945 | 1 | 6.703 | 12374.3 |
446 | 2025-05-16 18:14:27 | convexly | 4 | 161609 | 452 | 71.330 | 2265.7 |
445 | 2025-05-14 15:29:55 | convexly | 4 | 161609 | 452 | 74.226 | 2177.3 |
444 | 2025-05-14 10:10:17 | convexly | 1 | 82945 | 1 | 9.920 | 8361.4 |
443 | 2025-05-14 03:05:35 | convexly | 4 | 161609 | 452 | 50.643 | 3191.1 |
442 | 2025-05-11 05:06:19 | convexly | 4 | 161609 | 452 | 56.660 | 2852.3 |
441 | 2025-05-11 02:37:55 | convexly | 1 | 82945 | 1 | 7.046 | 11771.9 |
440 | 2025-05-09 20:45:48 | convexly | 4 | 161609 | 452 | 68.630 | 2354.8 |
439 | 2025-05-08 23:13:19 | convexly | 1 | 82945 | 1 | 1.670 | 49667.7 |
438 | 2025-04-28 07:59:07 | convexly | 1 | 82945 | 1 | 9.063 | 9152.0 |
437 | 2025-04-25 11:10:36 | convexly | 1 | 82945 | 1 | 1.516 | 54713.1 |
436 | 2025-04-17 20:01:12 | convexly | 3 | 146162 | 48 | 27.903 | 5238.2 |
435 | 2025-04-16 12:46:29 | convexly | 1 | 82945 | 1 | 3.576 | 23194.9 |
434 | 2025-04-15 20:06:58 | convexly | 1 | 82945 | 1 | 4.046 | 20500.5 |
433 | 2025-04-12 08:11:07 | convexly | 3 | 146162 | 48 | 26.136 | 5592.4 |
432 | 2025-04-11 06:30:18 | convexly | 3 | 146162 | 48 | 23.300 | 6273.0 |
431 | 2025-04-10 14:45:34 | convexly | 3 | 146162 | 48 | 7.593 | 19249.6 |
430 | 2025-04-08 01:08:39 | convexly | 1 | 82945 | 1 | 7.390 | 11224.0 |
429 | 2025-04-05 12:29:06 | convexly | 3 | 146162 | 48 | 7.563 | 19325.9 |
428 | 2025-03-29 00:08:53 | convexly | 1 | 82945 | 1 | 7.453 | 11129.1 |
427 | 2025-03-27 17:48:52 | convexly | 3 | 146162 | 48 | 47.626 | 3069.0 |
426 | 2025-03-26 20:43:58 | convexly | 3 | 146162 | 48 | 39.580 | 3692.8 |
425 | 2025-03-25 11:44:35 | convexly | 3 | 146162 | 48 | 47.293 | 3090.6 |
424 | 2025-03-25 02:40:42 | convexly | 2 | 121059 | 5 | 15.813 | 7655.7 |
423 | 2025-03-24 00:22:43 | convexly | 1 | 82945 | 1 | 1.783 | 46519.9 |
422 | 2025-03-21 21:58:35 | convexly | 3 | 146162 | 48 | 32.313 | 4523.3 |
421 | 2025-03-20 06:03:49 | convexly | 2 | 121059 | 5 | 11.000 | 11005.4 |
420 | 2025-03-18 19:20:16 | convexly | 3 | 146162 | 48 | 7.940 | 18408.3 |
419 | 2025-03-17 11:33:38 | convexly | 3 | 146162 | 48 | 41.313 | 3537.9 |
418 | 2025-03-16 21:22:04 | convexly | 3 | 146162 | 48 | 39.610 | 3690.0 |
417 | 2025-03-16 17:31:23 | convexly | 3 | 146162 | 48 | 34.530 | 4232.9 |
416 | 2025-03-16 17:31:22 | convexly | 2 | 121059 | 5 | 13.033 | 9288.7 |
415 | 2025-03-16 17:31:04 | convexly | 1 | 82945 | 1 | 3.453 | 24021.1 |
414 | 2025-03-16 10:35:41 | convexly | 1 | 82945 | 1 | 8.360 | 9921.7 |
413 | 2025-03-16 04:59:39 | convexly | 4 | 161609 | 452 | 62.996 | 2565.4 |
412 | 2025-03-15 11:29:48 | convexly | 4 | 161609 | 452 | 50.373 | 3208.2 |
411 | 2025-03-15 03:28:11 | convexly | 4 | 161609 | 452 | 77.313 | 2090.3 |
410 | 2025-03-14 21:44:03 | convexly | 4 | 161609 | 452 | 72.206 | 2238.2 |
409 | 2025-03-07 21:42:29 | convexly | 1 | 82945 | 1 | 7.516 | 11035.8 |
408 | 2025-03-07 16:48:43 | convexly | 4 | 161609 | 452 | 53.393 | 3026.8 |
407 | 2025-02-16 14:24:51 | convexly | 1 | 82945 | 1 | 5.796 | 14310.7 |
406 | 2025-02-13 08:34:51 | convexly | 4 | 161609 | 452 | 51.266 | 3152.4 |
405 | 2025-02-12 13:33:13 | convexly | 4 | 161609 | 452 | 55.853 | 2893.5 |
404 | 2025-02-12 10:04:53 | convexly | 4 | 161609 | 452 | 41.550 | 3889.5 |
403 | 2025-02-12 03:20:42 | convexly | 4 | 161609 | 452 | 70.423 | 2294.8 |
402 | 2025-01-28 09:07:43 | convexly | 1 | 82945 | 1 | 23.453 | 3536.6 |
401 | 2025-01-28 03:34:12 | convexly | 1 | 82945 | 1 | 6.563 | 12638.3 |
400 | 2025-01-09 19:30:30 | convexly | 2 | 121059 | 5 | 3.626 | 33386.4 |
399 | 2025-01-09 19:30:14 | convexly | 3 | 146162 | 48 | 8.220 | 17781.3 |
398 | 2025-01-05 13:37:29 | convexly | 2 | 121059 | 5 | 16.203 | 7471.4 |
397 | 2025-01-04 01:36:12 | convexly | 3 | 146162 | 48 | 13.906 | 10510.7 |
396 | 2024-12-30 22:21:33 | convexly | 1 | 82945 | 1 | 10.986 | 7550.1 |
395 | 2024-12-30 21:59:01 | convexly | 2 | 121059 | 5 | 3.406 | 35542.9 |
394 | 2024-12-30 21:58:41 | convexly | 3 | 146162 | 48 | 7.000 | 20880.3 |
393 | 2024-12-21 09:07:07 | convexly | 3 | 146162 | 48 | 9.406 | 15539.2 |
392 | 2024-12-21 09:07:05 | convexly | 2 | 121059 | 5 | 5.093 | 23769.7 |
391 | 2024-12-21 09:07:04 | convexly | 1 | 82945 | 1 | 3.076 | 26965.2 |
390 | 2024-12-18 23:52:20 | convexly | 1 | 82945 | 1 | 6.940 | 11951.7 |
389 | 2024-12-17 12:08:54 | convexly | 4 | 161609 | 452 | 71.860 | 2248.9 |
388 | 2024-12-16 20:26:02 | convexly | 4 | 161609 | 452 | 54.000 | 2992.8 |
387 | 2024-12-15 20:19:23 | convexly | 4 | 161609 | 452 | 64.396 | 2509.6 |
386 | 2024-12-15 19:15:51 | convexly | 4 | 161609 | 452 | 74.376 | 2172.9 |
385 | 2024-12-05 08:47:56 | convexly | 1 | 82945 | 1 | 3.750 | 22118.7 |
384 | 2024-11-27 12:21:03 | convexly | 3 | 146162 | 48 | 6.843 | 21359.3 |
383 | 2024-11-27 12:21:01 | convexly | 2 | 121059 | 5 | 3.500 | 34588.3 |
382 | 2024-11-26 16:05:13 | convexly | 3 | 146162 | 48 | 26.390 | 5538.5 |
381 | 2024-11-25 08:46:31 | convexly | 1 | 82945 | 1 | 5.423 | 15295.0 |