History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"predictor"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 469 | 2026-01-19 11:01:31 | predictor | 1 | 81242 | 2 | 1.656 | 49059.2 |
| 468 | 2026-01-17 02:28:38 | predictor | 3 | 148641 | 55 | 6.860 | 21667.8 |
| 467 | 2026-01-15 22:57:47 | predictor | 4 | 165329 | 311 | 42.223 | 3915.6 |
| 466 | 2026-01-10 12:09:10 | predictor | 1 | 81242 | 2 | 1.406 | 57782.4 |
| 465 | 2026-01-10 02:28:04 | predictor | 1 | 81242 | 2 | 3.016 | 26937.0 |
| 464 | 2026-01-09 23:34:30 | predictor | 1 | 81242 | 2 | 1.406 | 57782.4 |
| 463 | 2026-01-02 14:13:03 | predictor | 4 | 165329 | 311 | 28.313 | 5839.3 |
| 462 | 2026-01-02 14:12:52 | predictor | 4 | 165329 | 311 | 28.423 | 5816.7 |
| 461 | 2025-12-31 03:27:01 | predictor | 1 | 81242 | 2 | 1.593 | 50999.4 |
| 460 | 2025-12-29 06:52:02 | predictor | 1 | 81242 | 2 | 1.483 | 54782.2 |
| 459 | 2025-12-21 17:01:57 | predictor | 3 | 148641 | 55 | 6.420 | 23152.8 |
| 458 | 2025-12-20 17:37:37 | predictor | 4 | 165329 | 311 | 39.126 | 4225.6 |
| 457 | 2025-12-20 16:28:03 | predictor | 3 | 148641 | 55 | 7.783 | 19098.2 |
| 456 | 2025-12-19 23:26:34 | predictor | 2 | 121436 | 12 | 3.936 | 30852.6 |
| 455 | 2025-12-19 16:54:07 | predictor | 2 | 121436 | 12 | 3.906 | 31089.6 |
| 454 | 2025-12-19 11:35:43 | predictor | 2 | 121436 | 12 | 9.033 | 13443.6 |
| 453 | 2025-12-18 12:30:56 | predictor | 3 | 148641 | 55 | 7.310 | 20333.9 |
| 452 | 2025-12-18 04:24:40 | predictor | 1 | 81242 | 2 | 1.483 | 54782.2 |
| 451 | 2025-12-18 01:32:53 | predictor | 1 | 81242 | 2 | 1.893 | 42917.1 |
| 450 | 2025-12-17 13:12:34 | predictor | 3 | 148641 | 55 | 35.236 | 4218.4 |
| 449 | 2025-12-17 11:30:33 | predictor | 1 | 81242 | 2 | 1.576 | 51549.5 |
| 448 | 2025-12-17 10:01:08 | predictor | 2 | 121436 | 12 | 3.906 | 31089.6 |
| 447 | 2025-12-17 03:21:47 | predictor | 1 | 81242 | 2 | 1.453 | 55913.3 |
| 446 | 2025-12-17 02:16:12 | predictor | 2 | 121436 | 12 | 3.876 | 31330.2 |
| 445 | 2025-12-17 02:12:36 | predictor | 3 | 148641 | 55 | 7.733 | 19221.6 |
| 444 | 2025-12-16 22:18:53 | predictor | 2 | 121436 | 12 | 3.890 | 31217.5 |
| 443 | 2025-12-16 09:35:56 | predictor | 2 | 121436 | 12 | 7.750 | 15669.2 |
| 442 | 2025-12-15 23:52:18 | predictor | 2 | 121436 | 12 | 3.906 | 31089.6 |
| 441 | 2025-12-14 13:10:09 | predictor | 2 | 121436 | 12 | 3.873 | 31354.5 |
| 440 | 2025-12-12 22:04:53 | predictor | 1 | 81242 | 2 | 1.486 | 54671.6 |
| 439 | 2025-12-12 21:42:44 | predictor | 4 | 165329 | 311 | 13.390 | 12347.2 |
| 438 | 2025-12-11 15:48:37 | predictor | 4 | 165329 | 311 | 13.890 | 11902.7 |
| 437 | 2025-12-10 12:28:10 | predictor | 4 | 165329 | 311 | 12.546 | 13177.8 |
| 436 | 2025-12-09 17:24:24 | predictor | 1 | 81242 | 2 | 1.483 | 54782.2 |
| 435 | 2025-12-07 22:11:42 | predictor | 1 | 81242 | 2 | 1.810 | 44885.1 |
| 434 | 2025-12-07 21:33:03 | predictor | 1 | 81242 | 2 | 1.653 | 49148.2 |
| 433 | 2025-12-05 05:45:01 | predictor | 2 | 121436 | 12 | 3.220 | 37713.0 |
| 432 | 2025-12-05 05:08:33 | predictor | 1 | 81242 | 2 | 1.500 | 54161.3 |
| 431 | 2025-11-30 20:37:02 | predictor | 3 | 148641 | 55 | 6.953 | 21378.0 |
| 430 | 2025-11-28 10:35:54 | predictor | 1 | 81242 | 2 | 1.406 | 57782.4 |
| 429 | 2025-11-26 08:10:12 | predictor | 3 | 148641 | 55 | 7.923 | 18760.7 |
| 428 | 2025-11-24 00:00:58 | predictor | 1 | 81242 | 2 | 1.500 | 54161.3 |
| 427 | 2025-11-23 18:27:08 | predictor | 3 | 148641 | 55 | 7.283 | 20409.3 |
| 426 | 2025-11-23 05:56:22 | predictor | 3 | 148641 | 55 | 6.753 | 22011.1 |
| 425 | 2025-11-23 02:08:54 | predictor | 3 | 148641 | 55 | 6.466 | 22988.1 |
| 424 | 2025-11-22 14:31:34 | predictor | 1 | 81242 | 2 | 1.690 | 48072.2 |
| 423 | 2025-11-22 08:34:10 | predictor | 1 | 81242 | 2 | 1.593 | 50999.4 |
| 422 | 2025-11-21 00:00:56 | predictor | 3 | 148641 | 55 | 18.593 | 7994.5 |
| 421 | 2025-11-20 13:11:18 | predictor | 1 | 81242 | 2 | 3.343 | 24302.1 |
| 420 | 2025-11-12 12:31:21 | predictor | 1 | 81242 | 2 | 1.313 | 61875.1 |
| 419 | 2025-11-11 05:22:23 | predictor | 1 | 81242 | 2 | 1.423 | 57092.1 |
| 418 | 2025-11-01 11:07:34 | predictor | 4 | 165329 | 311 | 12.500 | 13226.3 |
| 417 | 2025-10-27 11:59:55 | predictor | 1 | 81242 | 2 | 1.346 | 60358.1 |
| 416 | 2025-10-24 23:09:06 | predictor | 1 | 81242 | 2 | 1.533 | 52995.4 |
| 415 | 2025-10-18 23:45:41 | predictor | 1 | 81242 | 2 | 1.360 | 59736.8 |
| 414 | 2025-10-18 17:21:05 | predictor | 1 | 81242 | 2 | 1.423 | 57092.1 |
| 413 | 2025-10-18 16:13:31 | predictor | 1 | 81242 | 2 | 6.346 | 12802.1 |
| 412 | 2025-10-16 22:56:07 | predictor | 1 | 81242 | 2 | 1.483 | 54782.2 |
| 411 | 2025-10-14 21:28:46 | predictor | 4 | 165329 | 311 | 11.923 | 13866.4 |
| 410 | 2025-10-11 06:04:40 | predictor | 1 | 81242 | 2 | 1.346 | 60358.1 |
| 409 | 2025-10-06 14:46:50 | predictor | 1 | 81242 | 2 | 1.423 | 57092.1 |
| 408 | 2025-09-29 13:28:16 | predictor | 1 | 81242 | 2 | 1.500 | 54161.3 |
| 407 | 2025-09-29 11:52:14 | predictor | 1 | 81242 | 2 | 1.313 | 61875.1 |
| 406 | 2025-09-27 23:23:52 | predictor | 1 | 81242 | 2 | 1.470 | 55266.7 |
| 405 | 2025-09-24 00:12:33 | predictor | 1 | 81242 | 2 | 1.420 | 57212.7 |
| 404 | 2025-09-22 09:41:16 | predictor | 4 | 165329 | 311 | 15.406 | 10731.5 |
| 403 | 2025-09-22 04:31:30 | predictor | 4 | 165329 | 311 | 13.283 | 12446.7 |
| 402 | 2025-09-18 02:25:34 | predictor | 4 | 165329 | 311 | 11.046 | 14967.3 |
| 401 | 2025-09-11 20:49:40 | predictor | 2 | 121436 | 12 | 4.470 | 27166.9 |
| 400 | 2025-09-11 11:50:45 | predictor | 4 | 165329 | 311 | 12.126 | 13634.3 |
| 399 | 2025-09-10 05:35:52 | predictor | 1 | 81242 | 2 | 1.483 | 54782.2 |
| 398 | 2025-09-03 23:25:08 | predictor | 2 | 121436 | 12 | 3.220 | 37713.0 |
| 397 | 2025-09-02 21:58:39 | predictor | 1 | 81242 | 2 | 1.313 | 61875.1 |
| 396 | 2025-09-02 07:26:28 | predictor | 1 | 81242 | 2 | 1.296 | 62686.7 |
| 395 | 2025-09-01 16:28:44 | predictor | 1 | 81242 | 2 | 1.843 | 44081.4 |
| 394 | 2025-09-01 14:16:58 | predictor | 4 | 165329 | 311 | 12.893 | 12823.2 |
| 393 | 2025-08-31 14:42:54 | predictor | 1 | 81242 | 2 | 1.343 | 60492.9 |
| 392 | 2025-08-29 14:44:55 | predictor | 1 | 81242 | 2 | 1.390 | 58447.5 |
| 391 | 2025-08-29 12:47:01 | predictor | 1 | 81242 | 2 | 1.500 | 54161.3 |
| 390 | 2025-08-29 05:00:22 | predictor | 1 | 81242 | 2 | 1.530 | 53099.3 |
| 389 | 2025-08-27 23:27:50 | predictor | 4 | 165329 | 311 | 11.813 | 13995.5 |
| 388 | 2025-08-27 23:09:04 | predictor | 1 | 81242 | 2 | 1.593 | 50999.4 |
| 387 | 2025-08-25 18:52:09 | predictor | 4 | 165329 | 311 | 12.016 | 13759.1 |
| 386 | 2025-08-25 05:31:58 | predictor | 4 | 165329 | 311 | 53.766 | 3075.0 |
| 385 | 2025-08-24 23:46:15 | predictor | 1 | 81242 | 2 | 7.503 | 10827.9 |
| 384 | 2025-08-24 13:22:49 | predictor | 4 | 165329 | 311 | 12.906 | 12810.2 |
| 383 | 2025-08-23 06:21:35 | predictor | 4 | 165329 | 311 | 34.550 | 4785.2 |
| 382 | 2025-08-21 00:22:56 | predictor | 3 | 148641 | 55 | 21.860 | 6799.7 |
| 381 | 2025-08-20 21:23:21 | predictor | 4 | 165329 | 311 | 60.576 | 2729.3 |
| 380 | 2025-08-20 12:16:52 | predictor | 3 | 148641 | 55 | 8.110 | 18328.1 |
| 379 | 2025-08-20 11:32:24 | predictor | 4 | 165329 | 311 | 13.093 | 12627.3 |
| 378 | 2025-08-20 03:47:37 | predictor | 1 | 81242 | 2 | 1.470 | 55266.7 |
| 377 | 2025-08-18 12:37:49 | predictor | 3 | 148641 | 55 | 6.500 | 22867.8 |
| 376 | 2025-08-11 13:34:51 | predictor | 1 | 81242 | 2 | 3.233 | 25129.0 |
| 375 | 2025-08-08 13:09:53 | predictor | 1 | 81242 | 2 | 2.890 | 28111.4 |
| 374 | 2025-08-04 18:40:37 | predictor | 3 | 148641 | 55 | 23.843 | 6234.2 |
| 373 | 2025-08-01 15:59:51 | predictor | 1 | 81242 | 2 | 2.890 | 28111.4 |
| 372 | 2025-07-27 02:41:30 | predictor | 3 | 148641 | 55 | 19.160 | 7757.9 |
| 371 | 2025-07-25 11:01:23 | predictor | 3 | 148641 | 55 | 40.750 | 3647.6 |
| 370 | 2025-07-25 08:17:37 | predictor | 3 | 148641 | 55 | 25.330 | 5868.2 |