Merge #639

639: Reduce the size of the word_pair_proximity database r=loiclec a=loiclec # Pull Request ## What does this PR do? Fixes #634 Now, the value corresponding to the key `prox word1 word2` in the `word_pair_proximity_docids` database contains the ids of the documents in which: - `word1` is followed by `word2` - the minimum number of words between `word1` and `word2` is `prox-1` Before this PR, the `word_pair_proximity_docids` had keys with the format `word1 word2 prox` and the value contained the ids of the documents in which either: - `word1` is followed by `word2` after a minimum of `prox-1` words in between them - `word2` is followed by `word1` after a minimum of `prox-2` words As a consequence of this change, calls such as: ``` let docids = word_pair_proximity_docids.get(rtxn, (word1, word2, prox)); ``` have to be replaced with: ``` let docids1 = word_pair_proximity_docids.get(rtxn, (prox, word1, word2)) ; let docids2 = word_pair_proximity_docids.get(rtxn, (prox-1, word2, word1)) ; let docids = docids1 | docids2; ``` ## Phrase search The PR also fixes two bugs in the `resolve_phrase` function. The first bug is that a phrase containing twice the same word would always return zero documents (e.g. `"dog eats dog"`). The second bug occurs with a phrase such as "fox is smarter than a dog"` and the document with the text: ``` fox or dog? a fox is smarter than a dog ``` In that case, the phrase search would not return the documents because: * we only have the key `fox dog 2` in `word_pair_proximity_docids` * but the implementation of `resolve_phrase` looks for `fox dog 5`, which returns 0 documents ### New implementation of `resolve_phrase` Given the phrase: ``` fox is smarter than a dog ``` We select the document ids corresponding to all of the following keys in `word_pair_proximity_docids`: - `1 fox is` - `1 is smarter` - `1 smarter than` - (etc.) - `1 fox smarter` OR `2 fox smarter` - `1 is than` OR `2 is than` - ... - `1 than dog` OR `2 than dog` ## Benchmark Results Indexing: ``` group indexing_main_d94339a8 indexing_word-pair-proximity-docids-refactor_2983dd8e ----- ---------------------- ----------------------------------------------------- indexing/-geo-delete-facetedNumber-facetedGeo-searchable- 1.19 40.7±11.28ms ? ?/sec 1.00 34.3±4.16ms ? ?/sec indexing/-movies-delete-facetedString-facetedNumber-searchable- 1.62 11.3±3.77ms ? ?/sec 1.00 7.0±1.56ms ? ?/sec indexing/-movies-delete-facetedString-facetedNumber-searchable-nested- 1.00 12.5±2.62ms ? ?/sec 1.07 13.4±4.24ms ? ?/sec indexing/-songs-delete-facetedString-facetedNumber-searchable- 1.26 50.2±12.63ms ? ?/sec 1.00 39.8±20.25ms ? ?/sec indexing/-wiki-delete-searchable- 1.83 269.1±16.11ms ? ?/sec 1.00 146.8±6.12ms ? ?/sec indexing/Indexing geo_point 1.00 47.2±0.46s ? ?/sec 1.00 47.3±0.56s ? ?/sec indexing/Indexing movies in three batches 1.42 12.7±0.13s ? ?/sec 1.00 9.0±0.07s ? ?/sec indexing/Indexing movies with default settings 1.40 10.2±0.07s ? ?/sec 1.00 7.3±0.06s ? ?/sec indexing/Indexing nested movies with default settings 1.22 7.8±0.11s ? ?/sec 1.00 6.4±0.13s ? ?/sec indexing/Indexing nested movies without any facets 1.24 7.3±0.07s ? ?/sec 1.00 5.9±0.06s ? ?/sec indexing/Indexing songs in three batches with default settings 1.14 47.6±0.67s ? ?/sec 1.00 41.8±0.63s ? ?/sec indexing/Indexing songs with default settings 1.13 44.1±0.74s ? ?/sec 1.00 38.9±0.76s ? ?/sec indexing/Indexing songs without any facets 1.19 42.0±0.66s ? ?/sec 1.00 35.2±0.48s ? ?/sec indexing/Indexing songs without faceted numbers 1.20 44.3±1.40s ? ?/sec 1.00 37.0±0.48s ? ?/sec indexing/Indexing wiki 1.39 862.9±9.95s ? ?/sec 1.00 622.6±27.11s ? ?/sec indexing/Indexing wiki in three batches 1.40 934.4±5.97s ? ?/sec 1.00 665.7±4.72s ? ?/sec indexing/Reindexing geo_point 1.01 15.9±0.39s ? ?/sec 1.00 15.7±0.28s ? ?/sec indexing/Reindexing movies with default settings 1.15 288.8±25.03ms ? ?/sec 1.00 250.4±2.23ms ? ?/sec indexing/Reindexing songs with default settings 1.01 4.1±0.06s ? ?/sec 1.00 4.1±0.03s ? ?/sec indexing/Reindexing wiki 1.41 1484.7±20.59s ? ?/sec 1.00 1052.0±19.89s ? ?/sec ``` Search Wiki: <details> <pre> group search_wiki_main_d94339a8 search_wiki_word-pair-proximity-docids-refactor_2983dd8e ----- ------------------------- -------------------------------------------------------- smol-wiki-articles.csv: basic placeholder/ 1.02 25.8±0.21µs ? ?/sec 1.00 25.4±0.19µs ? ?/sec smol-wiki-articles.csv: basic with quote/"film" 1.00 441.7±2.57µs ? ?/sec 1.00 442.3±2.41µs ? ?/sec smol-wiki-articles.csv: basic with quote/"france" 1.00 357.0±2.63µs ? ?/sec 1.00 358.3±2.65µs ? ?/sec smol-wiki-articles.csv: basic with quote/"japan" 1.00 239.4±2.24µs ? ?/sec 1.00 240.2±1.82µs ? ?/sec smol-wiki-articles.csv: basic with quote/"machine" 1.00 180.3±2.40µs ? ?/sec 1.00 180.0±1.08µs ? ?/sec smol-wiki-articles.csv: basic with quote/"miles" "davis" 1.00 9.1±0.03ms ? ?/sec 1.03 9.3±0.04ms ? ?/sec smol-wiki-articles.csv: basic with quote/"mingus" 1.00 3.6±0.01ms ? ?/sec 1.03 3.7±0.02ms ? ?/sec smol-wiki-articles.csv: basic with quote/"rock" "and" "roll" 1.00 34.0±0.11ms ? ?/sec 1.03 35.1±0.13ms ? ?/sec smol-wiki-articles.csv: basic with quote/"spain" 1.00 162.0±0.88µs ? ?/sec 1.00 161.9±0.98µs ? ?/sec smol-wiki-articles.csv: basic without quote/film 1.01 164.4±1.46µs ? ?/sec 1.00 163.1±1.58µs ? ?/sec smol-wiki-articles.csv: basic without quote/france 1.00 1698.3±7.37µs ? ?/sec 1.00 1697.7±11.53µs ? ?/sec smol-wiki-articles.csv: basic without quote/japan 1.00 1154.0±23.61µs ? ?/sec 1.00 1150.7±9.27µs ? ?/sec smol-wiki-articles.csv: basic without quote/machine 1.00 524.6±3.45µs ? ?/sec 1.01 528.1±4.56µs ? ?/sec smol-wiki-articles.csv: basic without quote/miles davis 1.00 13.5±0.05ms ? ?/sec 1.02 13.8±0.05ms ? ?/sec smol-wiki-articles.csv: basic without quote/mingus 1.00 4.1±0.02ms ? ?/sec 1.03 4.2±0.01ms ? ?/sec smol-wiki-articles.csv: basic without quote/rock and roll 1.00 49.0±0.19ms ? ?/sec 1.03 50.4±0.22ms ? ?/sec smol-wiki-articles.csv: basic without quote/spain 1.00 412.2±3.35µs ? ?/sec 1.00 412.9±2.81µs ? ?/sec smol-wiki-articles.csv: prefix search/c 1.00 383.9±2.53µs ? ?/sec 1.00 383.4±2.44µs ? ?/sec smol-wiki-articles.csv: prefix search/g 1.00 433.4±2.53µs ? ?/sec 1.00 432.8±2.52µs ? ?/sec smol-wiki-articles.csv: prefix search/j 1.00 424.3±2.05µs ? ?/sec 1.00 424.0±2.15µs ? ?/sec smol-wiki-articles.csv: prefix search/q 1.00 154.0±1.93µs ? ?/sec 1.00 153.5±1.04µs ? ?/sec smol-wiki-articles.csv: prefix search/t 1.04 658.5±91.93µs ? ?/sec 1.00 631.4±3.89µs ? ?/sec smol-wiki-articles.csv: prefix search/x 1.00 446.2±2.09µs ? ?/sec 1.00 445.6±3.13µs ? ?/sec smol-wiki-articles.csv: proximity/april paris 1.02 3.4±0.39ms ? ?/sec 1.00 3.3±0.01ms ? ?/sec smol-wiki-articles.csv: proximity/diesel engine 1.00 1022.1±17.52µs ? ?/sec 1.00 1017.7±8.16µs ? ?/sec smol-wiki-articles.csv: proximity/herald sings 1.01 1872.5±97.70µs ? ?/sec 1.00 1862.2±8.57µs ? ?/sec smol-wiki-articles.csv: proximity/tea two 1.00 295.2±34.91µs ? ?/sec 1.00 296.6±4.08µs ? ?/sec smol-wiki-articles.csv: typo/Disnaylande 1.00 3.4±0.51ms ? ?/sec 1.04 3.5±0.01ms ? ?/sec smol-wiki-articles.csv: typo/aritmetric 1.00 3.6±0.01ms ? ?/sec 1.00 3.7±0.01ms ? ?/sec smol-wiki-articles.csv: typo/linax 1.00 167.5±1.28µs ? ?/sec 1.00 167.1±2.65µs ? ?/sec smol-wiki-articles.csv: typo/migrosoft 1.01 217.9±1.84µs ? ?/sec 1.00 216.2±1.61µs ? ?/sec smol-wiki-articles.csv: typo/nympalidea 1.00 2.9±0.01ms ? ?/sec 1.10 3.1±0.01ms ? ?/sec smol-wiki-articles.csv: typo/phytogropher 1.00 3.0±0.23ms ? ?/sec 1.08 3.3±0.01ms ? ?/sec smol-wiki-articles.csv: typo/sisan 1.00 234.6±1.38µs ? ?/sec 1.01 235.8±1.67µs ? ?/sec smol-wiki-articles.csv: typo/the fronce 1.00 104.4±0.84µs ? ?/sec 1.00 103.9±0.81µs ? ?/sec smol-wiki-articles.csv: words/Abraham machin 1.02 675.5±4.74µs ? ?/sec 1.00 662.1±5.13µs ? ?/sec smol-wiki-articles.csv: words/Idaho Bellevue pizza 1.02 1004.5±11.07µs ? ?/sec 1.00 989.5±13.08µs ? ?/sec smol-wiki-articles.csv: words/Kameya Tokujirō mingus monk 1.00 1650.8±10.92µs ? ?/sec 1.00 1643.2±10.77µs ? ?/sec smol-wiki-articles.csv: words/Ulrich Hensel meilisearch milli 1.00 5.4±0.03ms ? ?/sec 1.00 5.4±0.02ms ? ?/sec smol-wiki-articles.csv: words/the black saint and the sinner lady and the good doggo 1.00 32.9±0.10ms ? ?/sec 1.00 32.8±0.10ms ? ?/sec </pre> </details> Search songs: <details> <pre> group search_songs_main_d94339a8 search_songs_word-pair-proximity-docids-refactor_2983dd8e ----- -------------------------- --------------------------------------------------------- smol-songs.csv: asc + default/Notstandskomitee 1.00 3.0±0.01ms ? ?/sec 1.01 3.0±0.04ms ? ?/sec smol-songs.csv: asc + default/charles 1.00 2.2±0.01ms ? ?/sec 1.01 2.2±0.01ms ? ?/sec smol-songs.csv: asc + default/charles mingus 1.00 3.1±0.01ms ? ?/sec 1.01 3.1±0.01ms ? ?/sec smol-songs.csv: asc + default/david 1.00 2.9±0.01ms ? ?/sec 1.00 2.9±0.01ms ? ?/sec smol-songs.csv: asc + default/david bowie 1.00 4.5±0.02ms ? ?/sec 1.00 4.5±0.02ms ? ?/sec smol-songs.csv: asc + default/john 1.00 3.1±0.01ms ? ?/sec 1.01 3.2±0.01ms ? ?/sec smol-songs.csv: asc + default/marcus miller 1.00 5.0±0.02ms ? ?/sec 1.00 5.0±0.02ms ? ?/sec smol-songs.csv: asc + default/michael jackson 1.00 4.7±0.02ms ? ?/sec 1.00 4.7±0.02ms ? ?/sec smol-songs.csv: asc + default/tamo 1.00 1463.4±12.17µs ? ?/sec 1.01 1481.5±8.83µs ? ?/sec smol-songs.csv: asc + default/thelonious monk 1.00 4.4±0.01ms ? ?/sec 1.00 4.4±0.02ms ? ?/sec smol-songs.csv: asc/Notstandskomitee 1.01 2.6±0.01ms ? ?/sec 1.00 2.6±0.01ms ? ?/sec smol-songs.csv: asc/charles 1.00 473.6±3.70µs ? ?/sec 1.01 476.8±22.17µs ? ?/sec smol-songs.csv: asc/charles mingus 1.01 780.1±3.90µs ? ?/sec 1.00 773.6±4.60µs ? ?/sec smol-songs.csv: asc/david 1.00 757.6±4.50µs ? ?/sec 1.00 760.7±5.20µs ? ?/sec smol-songs.csv: asc/david bowie 1.00 1131.2±8.68µs ? ?/sec 1.00 1130.7±8.36µs ? ?/sec smol-songs.csv: asc/john 1.00 668.9±6.48µs ? ?/sec 1.00 669.9±2.78µs ? ?/sec smol-songs.csv: asc/marcus miller 1.00 959.8±7.10µs ? ?/sec 1.00 958.9±4.72µs ? ?/sec smol-songs.csv: asc/michael jackson 1.01 1076.7±16.73µs ? ?/sec 1.00 1070.8±7.34µs ? ?/sec smol-songs.csv: asc/tamo 1.00 70.4±0.55µs ? ?/sec 1.00 70.5±0.51µs ? ?/sec smol-songs.csv: asc/thelonious monk 1.01 2.9±0.01ms ? ?/sec 1.00 2.9±0.01ms ? ?/sec smol-songs.csv: basic filter: <=/Notstandskomitee 1.00 162.0±0.91µs ? ?/sec 1.01 163.6±1.72µs ? ?/sec smol-songs.csv: basic filter: <=/charles 1.00 38.3±0.24µs ? ?/sec 1.01 38.7±0.31µs ? ?/sec smol-songs.csv: basic filter: <=/charles mingus 1.01 85.3±0.44µs ? ?/sec 1.00 84.6±0.47µs ? ?/sec smol-songs.csv: basic filter: <=/david 1.01 32.4±0.25µs ? ?/sec 1.00 32.1±0.24µs ? ?/sec smol-songs.csv: basic filter: <=/david bowie 1.00 68.6±0.99µs ? ?/sec 1.01 68.9±0.88µs ? ?/sec smol-songs.csv: basic filter: <=/john 1.04 26.1±0.37µs ? ?/sec 1.00 25.1±0.22µs ? ?/sec smol-songs.csv: basic filter: <=/marcus miller 1.00 76.7±0.39µs ? ?/sec 1.01 77.3±0.61µs ? ?/sec smol-songs.csv: basic filter: <=/michael jackson 1.00 95.5±0.66µs ? ?/sec 1.01 96.3±0.79µs ? ?/sec smol-songs.csv: basic filter: <=/tamo 1.03 26.2±0.36µs ? ?/sec 1.00 25.3±0.23µs ? ?/sec smol-songs.csv: basic filter: <=/thelonious monk 1.00 140.7±1.36µs ? ?/sec 1.01 142.7±0.88µs ? ?/sec smol-songs.csv: basic filter: TO/Notstandskomitee 1.00 165.4±1.25µs ? ?/sec 1.00 165.7±1.72µs ? ?/sec smol-songs.csv: basic filter: TO/charles 1.01 40.6±0.57µs ? ?/sec 1.00 40.1±0.54µs ? ?/sec smol-songs.csv: basic filter: TO/charles mingus 1.01 87.1±0.80µs ? ?/sec 1.00 86.3±0.61µs ? ?/sec smol-songs.csv: basic filter: TO/david 1.02 34.5±0.26µs ? ?/sec 1.00 33.7±0.24µs ? ?/sec smol-songs.csv: basic filter: TO/david bowie 1.00 70.6±0.38µs ? ?/sec 1.00 70.6±0.68µs ? ?/sec smol-songs.csv: basic filter: TO/john 1.02 27.5±0.77µs ? ?/sec 1.00 26.9±0.21µs ? ?/sec smol-songs.csv: basic filter: TO/marcus miller 1.01 79.8±0.76µs ? ?/sec 1.00 79.3±1.27µs ? ?/sec smol-songs.csv: basic filter: TO/michael jackson 1.00 98.3±0.54µs ? ?/sec 1.00 98.0±0.88µs ? ?/sec smol-songs.csv: basic filter: TO/tamo 1.03 27.9±0.23µs ? ?/sec 1.00 27.1±0.32µs ? ?/sec smol-songs.csv: basic filter: TO/thelonious monk 1.00 142.5±1.36µs ? ?/sec 1.02 145.2±0.98µs ? ?/sec smol-songs.csv: basic placeholder/ 1.00 49.4±0.34µs ? ?/sec 1.00 49.3±0.45µs ? ?/sec smol-songs.csv: basic with quote/"Notstandskomitee" 1.00 190.5±1.60µs ? ?/sec 1.01 191.8±2.10µs ? ?/sec smol-songs.csv: basic with quote/"charles" 1.00 165.0±1.13µs ? ?/sec 1.01 166.0±1.39µs ? ?/sec smol-songs.csv: basic with quote/"charles" "mingus" 1.00 1149.4±15.78µs ? ?/sec 1.02 1171.1±9.95µs ? ?/sec smol-songs.csv: basic with quote/"david" 1.00 236.5±1.61µs ? ?/sec 1.00 236.9±1.73µs ? ?/sec smol-songs.csv: basic with quote/"david" "bowie" 1.00 1384.8±9.02µs ? ?/sec 1.01 1393.8±11.39µs ? ?/sec smol-songs.csv: basic with quote/"john" 1.00 358.3±4.85µs ? ?/sec 1.00 358.9±1.75µs ? ?/sec smol-songs.csv: basic with quote/"marcus" "miller" 1.00 281.4±1.79µs ? ?/sec 1.01 285.6±3.24µs ? ?/sec smol-songs.csv: basic with quote/"michael" "jackson" 1.00 1328.4±8.01µs ? ?/sec 1.00 1334.6±8.00µs ? ?/sec smol-songs.csv: basic with quote/"tamo" 1.00 528.7±3.72µs ? ?/sec 1.01 533.4±5.31µs ? ?/sec smol-songs.csv: basic with quote/"thelonious" "monk" 1.00 1223.0±7.24µs ? ?/sec 1.02 1245.7±12.04µs ? ?/sec smol-songs.csv: basic without quote/Notstandskomitee 1.00 2.8±0.01ms ? ?/sec 1.00 2.8±0.01ms ? ?/sec smol-songs.csv: basic without quote/charles 1.00 273.3±2.06µs ? ?/sec 1.01 275.9±1.76µs ? ?/sec smol-songs.csv: basic without quote/charles mingus 1.00 2.3±0.01ms ? ?/sec 1.02 2.4±0.01ms ? ?/sec smol-songs.csv: basic without quote/david 1.00 434.3±3.86µs ? ?/sec 1.01 436.7±2.47µs ? ?/sec smol-songs.csv: basic without quote/david bowie 1.00 5.6±0.02ms ? ?/sec 1.01 5.7±0.02ms ? ?/sec smol-songs.csv: basic without quote/john 1.00 1322.5±9.98µs ? ?/sec 1.00 1321.2±17.40µs ? ?/sec smol-songs.csv: basic without quote/marcus miller 1.02 2.4±0.02ms ? ?/sec 1.00 2.4±0.01ms ? ?/sec smol-songs.csv: basic without quote/michael jackson 1.00 3.8±0.02ms ? ?/sec 1.01 3.9±0.01ms ? ?/sec smol-songs.csv: basic without quote/tamo 1.00 809.0±4.01µs ? ?/sec 1.01 819.0±6.22µs ? ?/sec smol-songs.csv: basic without quote/thelonious monk 1.00 3.8±0.02ms ? ?/sec 1.02 3.9±0.02ms ? ?/sec smol-songs.csv: big filter/Notstandskomitee 1.00 2.7±0.01ms ? ?/sec 1.01 2.8±0.01ms ? ?/sec smol-songs.csv: big filter/charles 1.00 266.5±1.34µs ? ?/sec 1.01 270.1±8.17µs ? ?/sec smol-songs.csv: big filter/charles mingus 1.00 651.0±5.40µs ? ?/sec 1.00 651.0±2.73µs ? ?/sec smol-songs.csv: big filter/david 1.00 1018.1±11.16µs ? ?/sec 1.00 1022.3±8.94µs ? ?/sec smol-songs.csv: big filter/david bowie 1.00 1912.2±11.13µs ? ?/sec 1.00 1919.8±8.30µs ? ?/sec smol-songs.csv: big filter/john 1.00 867.2±6.66µs ? ?/sec 1.01 873.3±3.44µs ? ?/sec smol-songs.csv: big filter/marcus miller 1.00 717.7±2.86µs ? ?/sec 1.01 721.5±3.89µs ? ?/sec smol-songs.csv: big filter/michael jackson 1.00 1668.4±16.76µs ? ?/sec 1.00 1667.9±10.11µs ? ?/sec smol-songs.csv: big filter/tamo 1.01 136.7±0.88µs ? ?/sec 1.00 135.5±1.22µs ? ?/sec smol-songs.csv: big filter/thelonious monk 1.03 3.1±0.02ms ? ?/sec 1.00 3.0±0.01ms ? ?/sec smol-songs.csv: desc + default/Notstandskomitee 1.00 3.0±0.01ms ? ?/sec 1.00 3.0±0.01ms ? ?/sec smol-songs.csv: desc + default/charles 1.00 1599.5±13.07µs ? ?/sec 1.01 1622.9±22.43µs ? ?/sec smol-songs.csv: desc + default/charles mingus 1.00 2.3±0.01ms ? ?/sec 1.01 2.4±0.03ms ? ?/sec smol-songs.csv: desc + default/david 1.00 5.7±0.02ms ? ?/sec 1.00 5.7±0.02ms ? ?/sec smol-songs.csv: desc + default/david bowie 1.00 9.0±0.04ms ? ?/sec 1.00 9.0±0.03ms ? ?/sec smol-songs.csv: desc + default/john 1.00 4.5±0.01ms ? ?/sec 1.00 4.5±0.02ms ? ?/sec smol-songs.csv: desc + default/marcus miller 1.00 3.9±0.01ms ? ?/sec 1.00 3.9±0.02ms ? ?/sec smol-songs.csv: desc + default/michael jackson 1.00 6.6±0.03ms ? ?/sec 1.00 6.6±0.03ms ? ?/sec smol-songs.csv: desc + default/tamo 1.00 1472.4±10.38µs ? ?/sec 1.01 1484.2±8.07µs ? ?/sec smol-songs.csv: desc + default/thelonious monk 1.00 4.4±0.02ms ? ?/sec 1.00 4.4±0.05ms ? ?/sec smol-songs.csv: desc/Notstandskomitee 1.01 2.6±0.01ms ? ?/sec 1.00 2.6±0.01ms ? ?/sec smol-songs.csv: desc/charles 1.00 475.9±3.38µs ? ?/sec 1.00 475.9±2.64µs ? ?/sec smol-songs.csv: desc/charles mingus 1.00 775.3±4.30µs ? ?/sec 1.00 778.9±3.52µs ? ?/sec smol-songs.csv: desc/david 1.00 757.9±4.10µs ? ?/sec 1.01 763.4±3.27µs ? ?/sec smol-songs.csv: desc/david bowie 1.00 1129.0±11.87µs ? ?/sec 1.01 1135.1±8.86µs ? ?/sec smol-songs.csv: desc/john 1.00 670.2±4.38µs ? ?/sec 1.00 670.2±3.46µs ? ?/sec smol-songs.csv: desc/marcus miller 1.00 961.2±4.47µs ? ?/sec 1.00 961.9±4.03µs ? ?/sec smol-songs.csv: desc/michael jackson 1.00 1076.5±6.61µs ? ?/sec 1.00 1077.9±7.11µs ? ?/sec smol-songs.csv: desc/tamo 1.00 70.6±0.57µs ? ?/sec 1.01 71.3±0.48µs ? ?/sec smol-songs.csv: desc/thelonious monk 1.01 2.9±0.01ms ? ?/sec 1.00 2.9±0.01ms ? ?/sec smol-songs.csv: prefix search/a 1.00 1236.2±9.43µs ? ?/sec 1.00 1232.0±12.07µs ? ?/sec smol-songs.csv: prefix search/b 1.00 1090.8±9.89µs ? ?/sec 1.00 1090.8±9.43µs ? ?/sec smol-songs.csv: prefix search/i 1.00 1333.9±8.28µs ? ?/sec 1.00 1334.2±11.21µs ? ?/sec smol-songs.csv: prefix search/s 1.00 810.5±3.69µs ? ?/sec 1.00 806.6±3.50µs ? ?/sec smol-songs.csv: prefix search/x 1.00 290.5±1.88µs ? ?/sec 1.00 291.0±1.85µs ? ?/sec smol-songs.csv: proximity/7000 Danses Un Jour Dans Notre Vie 1.00 4.7±0.02ms ? ?/sec 1.00 4.7±0.02ms ? ?/sec smol-songs.csv: proximity/The Disneyland Sing-Along Chorus 1.01 5.6±0.02ms ? ?/sec 1.00 5.6±0.03ms ? ?/sec smol-songs.csv: proximity/Under Great Northern Lights 1.00 2.5±0.01ms ? ?/sec 1.00 2.5±0.01ms ? ?/sec smol-songs.csv: proximity/black saint sinner lady 1.00 4.8±0.02ms ? ?/sec 1.00 4.8±0.02ms ? ?/sec smol-songs.csv: proximity/les dangeureuses 1960 1.00 3.2±0.01ms ? ?/sec 1.01 3.2±0.01ms ? ?/sec smol-songs.csv: typo/Arethla Franklin 1.00 388.7±5.16µs ? ?/sec 1.00 390.0±2.11µs ? ?/sec smol-songs.csv: typo/Disnaylande 1.01 2.6±0.01ms ? ?/sec 1.00 2.6±0.01ms ? ?/sec smol-songs.csv: typo/dire straights 1.00 125.9±1.22µs ? ?/sec 1.00 126.0±0.71µs ? ?/sec smol-songs.csv: typo/fear of the duck 1.00 373.7±4.25µs ? ?/sec 1.01 375.7±14.17µs ? ?/sec smol-songs.csv: typo/indochie 1.00 103.6±0.94µs ? ?/sec 1.00 103.4±0.74µs ? ?/sec smol-songs.csv: typo/indochien 1.00 155.6±1.14µs ? ?/sec 1.01 157.5±1.75µs ? ?/sec smol-songs.csv: typo/klub des loopers 1.00 160.6±2.98µs ? ?/sec 1.01 161.7±1.96µs ? ?/sec smol-songs.csv: typo/michel depech 1.00 79.4±0.54µs ? ?/sec 1.01 79.9±0.60µs ? ?/sec smol-songs.csv: typo/mongus 1.00 126.7±1.85µs ? ?/sec 1.00 126.1±0.74µs ? ?/sec smol-songs.csv: typo/stromal 1.01 132.9±0.99µs ? ?/sec 1.00 131.9±1.09µs ? ?/sec smol-songs.csv: typo/the white striper 1.00 287.8±2.88µs ? ?/sec 1.00 286.5±1.91µs ? ?/sec smol-songs.csv: typo/thelonius monk 1.00 304.2±1.49µs ? ?/sec 1.01 306.5±1.50µs ? ?/sec smol-songs.csv: words/7000 Danses / Le Baiser / je me trompe de mots 1.01 20.9±0.08ms ? ?/sec 1.00 20.7±0.07ms ? ?/sec smol-songs.csv: words/Bring Your Daughter To The Slaughter but now this is not part of the title 1.00 48.9±0.13ms ? ?/sec 1.00 48.9±0.11ms ? ?/sec smol-songs.csv: words/The Disneyland Children's Sing-Alone song 1.01 13.9±0.06ms ? ?/sec 1.00 13.8±0.07ms ? ?/sec smol-songs.csv: words/les liaisons dangeureuses 1793 1.01 3.7±0.01ms ? ?/sec 1.00 3.6±0.02ms ? ?/sec smol-songs.csv: words/seven nation mummy 1.00 1054.2±14.49µs ? ?/sec 1.00 1056.6±10.53µs ? ?/sec smol-songs.csv: words/the black saint and the sinner lady and the good doggo 1.00 58.2±0.29ms ? ?/sec 1.00 57.9±0.21ms ? ?/sec smol-songs.csv: words/whathavenotnsuchforth and a good amount of words to pop to match the first one 1.00 66.1±0.21ms ? ?/sec 1.00 66.0±0.24ms ? ?/sec </code> </details> Co-authored-by: Loïc Lecrenier <loic@meilisearch.com> Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
2024-11-23 10:37:41 +08:00 · 2022-10-25 10:42:04 +00:00 · 2022-10-25 10:42:04 +00:00 · d11a6e187f
commit d11a6e187f
parent 2bf867982a 9a569d73d1
22 changed files with 1002 additions and 622 deletions
--- a/milli/src/heed_codec/mod.rs
+++ b/milli/src/heed_codec/mod.rs
@ -15,4 +15,4 @@ pub use self::roaring_bitmap_length::{
    BoRoaringBitmapLenCodec, CboRoaringBitmapLenCodec, RoaringBitmapLenCodec,
 };
 pub use self::str_beu32_codec::StrBEU32Codec;
-pub use self::str_str_u8_codec::{StrStrU8Codec, UncheckedStrStrU8Codec};
+pub use self::str_str_u8_codec::{U8StrStrCodec, UncheckedU8StrStrCodec};
--- a/milli/src/heed_codec/str_str_u8_codec.rs
+++ b/milli/src/heed_codec/str_str_u8_codec.rs
@ -1,61 +1,57 @@
 use std::borrow::Cow;
 use std::str;
-pub struct StrStrU8Codec;
+pub struct U8StrStrCodec;
-impl<'a> heed::BytesDecode<'a> for StrStrU8Codec {
+impl<'a> heed::BytesDecode<'a> for U8StrStrCodec {
-    type DItem = (&'a str, &'a str, u8);
+    type DItem = (u8, &'a str, &'a str);
    fn bytes_decode(bytes: &'a [u8]) -> Option<Self::DItem> {
-        let (n, bytes) = bytes.split_last()?;
+        let (n, bytes) = bytes.split_first()?;
        let s1_end = bytes.iter().position(|b| *b == 0)?;
        let (s1_bytes, rest) = bytes.split_at(s1_end);
-        let rest = &rest[1..];
+        let s2_bytes = &rest[1..];
        let s1 = str::from_utf8(s1_bytes).ok()?;
        let (_, s2_bytes) = rest.split_last()?;
        let s2 = str::from_utf8(s2_bytes).ok()?;
-        Some((s1, s2, *n))
+        Some((*n, s1, s2))
    }
 }
-impl<'a> heed::BytesEncode<'a> for StrStrU8Codec {
+impl<'a> heed::BytesEncode<'a> for U8StrStrCodec {
-    type EItem = (&'a str, &'a str, u8);
+    type EItem = (u8, &'a str, &'a str);
-    fn bytes_encode((s1, s2, n): &Self::EItem) -> Option<Cow<[u8]>> {
+    fn bytes_encode((n, s1, s2): &Self::EItem) -> Option<Cow<[u8]>> {
-        let mut bytes = Vec::with_capacity(s1.len() + s2.len() + 1 + 1);
+        let mut bytes = Vec::with_capacity(s1.len() + s2.len() + 1);
        bytes.push(*n);
        bytes.extend_from_slice(s1.as_bytes());
        bytes.push(0);
        bytes.extend_from_slice(s2.as_bytes());
        bytes.push(0);
        bytes.push(*n);
        Some(Cow::Owned(bytes))
    }
 }
-pub struct UncheckedStrStrU8Codec;
+pub struct UncheckedU8StrStrCodec;
-impl<'a> heed::BytesDecode<'a> for UncheckedStrStrU8Codec {
+impl<'a> heed::BytesDecode<'a> for UncheckedU8StrStrCodec {
-    type DItem = (&'a [u8], &'a [u8], u8);
+    type DItem = (u8, &'a [u8], &'a [u8]);
    fn bytes_decode(bytes: &'a [u8]) -> Option<Self::DItem> {
-        let (n, bytes) = bytes.split_last()?;
+        let (n, bytes) = bytes.split_first()?;
        let s1_end = bytes.iter().position(|b| *b == 0)?;
        let (s1_bytes, rest) = bytes.split_at(s1_end);
-        let rest = &rest[1..];
+        let s2_bytes = &rest[1..];
-        let (_, s2_bytes) = rest.split_last()?;
+        Some((*n, s1_bytes, s2_bytes))
        Some((s1_bytes, s2_bytes, *n))
    }
 }
-impl<'a> heed::BytesEncode<'a> for UncheckedStrStrU8Codec {
+impl<'a> heed::BytesEncode<'a> for UncheckedU8StrStrCodec {
-    type EItem = (&'a [u8], &'a [u8], u8);
+    type EItem = (u8, &'a [u8], &'a [u8]);
-    fn bytes_encode((s1, s2, n): &Self::EItem) -> Option<Cow<[u8]>> {
+    fn bytes_encode((n, s1, s2): &Self::EItem) -> Option<Cow<[u8]>> {
-        let mut bytes = Vec::with_capacity(s1.len() + s2.len() + 1 + 1);
+        let mut bytes = Vec::with_capacity(s1.len() + s2.len() + 1);
        bytes.push(*n);
        bytes.extend_from_slice(s1);
        bytes.push(0);
        bytes.extend_from_slice(s2);
        bytes.push(0);
        bytes.push(*n);
        Some(Cow::Owned(bytes))
    }
 }
--- a/milli/src/index.rs
+++ b/milli/src/index.rs
@ -21,7 +21,7 @@ use crate::{
    default_criteria, BEU32StrCodec, BoRoaringBitmapCodec, CboRoaringBitmapCodec, Criterion,
    DocumentId, ExternalDocumentsIds, FacetDistribution, FieldDistribution, FieldId,
    FieldIdWordCountCodec, GeoPoint, ObkvCodec, Result, RoaringBitmapCodec, RoaringBitmapLenCodec,
-    Search, StrBEU32Codec, StrStrU8Codec, BEU16, BEU32,
+    Search, StrBEU32Codec, U8StrStrCodec, BEU16, BEU32,
 };
 pub const DEFAULT_MIN_WORD_LEN_ONE_TYPO: u8 = 5;
@ -71,6 +71,7 @@ pub mod db_name {
    pub const DOCID_WORD_POSITIONS: &str = "docid-word-positions";
    pub const WORD_PAIR_PROXIMITY_DOCIDS: &str = "word-pair-proximity-docids";
    pub const WORD_PREFIX_PAIR_PROXIMITY_DOCIDS: &str = "word-prefix-pair-proximity-docids";
    pub const PREFIX_WORD_PAIR_PROXIMITY_DOCIDS: &str = "prefix-word-pair-proximity-docids";
    pub const WORD_POSITION_DOCIDS: &str = "word-position-docids";
    pub const WORD_PREFIX_POSITION_DOCIDS: &str = "word-prefix-position-docids";
    pub const FIELD_ID_WORD_COUNT_DOCIDS: &str = "field-id-word-count-docids";
@ -106,9 +107,11 @@ pub struct Index {
    pub docid_word_positions: Database<BEU32StrCodec, BoRoaringBitmapCodec>,
    /// Maps the proximity between a pair of words with all the docids where this relation appears.
-    pub word_pair_proximity_docids: Database<StrStrU8Codec, CboRoaringBitmapCodec>,
+    pub word_pair_proximity_docids: Database<U8StrStrCodec, CboRoaringBitmapCodec>,
    /// Maps the proximity between a pair of word and prefix with all the docids where this relation appears.
-    pub word_prefix_pair_proximity_docids: Database<StrStrU8Codec, CboRoaringBitmapCodec>,
+    pub word_prefix_pair_proximity_docids: Database<U8StrStrCodec, CboRoaringBitmapCodec>,
    /// Maps the proximity between a pair of prefix and word with all the docids where this relation appears.
    pub prefix_word_pair_proximity_docids: Database<U8StrStrCodec, CboRoaringBitmapCodec>,
    /// Maps the word and the position with the docids that corresponds to it.
    pub word_position_docids: Database<StrBEU32Codec, CboRoaringBitmapCodec>,
@ -138,7 +141,7 @@ impl Index {
    pub fn new<P: AsRef<Path>>(mut options: heed::EnvOpenOptions, path: P) -> Result<Index> {
        use db_name::*;
-        options.max_dbs(17);
+        options.max_dbs(18);
        unsafe { options.flag(Flags::MdbAlwaysFreePages) };
        let env = options.open(path)?;
@ -151,6 +154,8 @@ impl Index {
        let word_pair_proximity_docids = env.create_database(Some(WORD_PAIR_PROXIMITY_DOCIDS))?;
        let word_prefix_pair_proximity_docids =
            env.create_database(Some(WORD_PREFIX_PAIR_PROXIMITY_DOCIDS))?;
        let prefix_word_pair_proximity_docids =
            env.create_database(Some(PREFIX_WORD_PAIR_PROXIMITY_DOCIDS))?;
        let word_position_docids = env.create_database(Some(WORD_POSITION_DOCIDS))?;
        let field_id_word_count_docids = env.create_database(Some(FIELD_ID_WORD_COUNT_DOCIDS))?;
        let word_prefix_position_docids = env.create_database(Some(WORD_PREFIX_POSITION_DOCIDS))?;
@ -175,6 +180,7 @@ impl Index {
            docid_word_positions,
            word_pair_proximity_docids,
            word_prefix_pair_proximity_docids,
            prefix_word_pair_proximity_docids,
            word_position_docids,
            word_prefix_position_docids,
            field_id_word_count_docids,
--- a/milli/src/lib.rs
+++ b/milli/src/lib.rs
@ -37,7 +37,7 @@ pub use self::fields_ids_map::FieldsIdsMap;
 pub use self::heed_codec::{
    BEU32StrCodec, BoRoaringBitmapCodec, BoRoaringBitmapLenCodec, CboRoaringBitmapCodec,
    CboRoaringBitmapLenCodec, FieldIdWordCountCodec, ObkvCodec, RoaringBitmapCodec,
-    RoaringBitmapLenCodec, StrBEU32Codec, StrStrU8Codec, UncheckedStrStrU8Codec,
+    RoaringBitmapLenCodec, StrBEU32Codec, U8StrStrCodec, UncheckedU8StrStrCodec,
 };
 pub use self::index::Index;
 pub use self::search::{
--- a/milli/src/search/criteria/exactness.rs
+++ b/milli/src/search/criteria/exactness.rs
@ -7,7 +7,7 @@ use log::debug;
 use roaring::RoaringBitmap;
 use crate::search::criteria::{
-    resolve_query_tree, Context, Criterion, CriterionParameters, CriterionResult,
+    resolve_phrase, resolve_query_tree, Context, Criterion, CriterionParameters, CriterionResult,
 };
 use crate::search::query_tree::{Operation, PrimitiveQueryPart};
 use crate::{absolute_from_relative_position, FieldId, Result};
@ -226,19 +226,7 @@ fn resolve_state(
                    }
                    // compute intersection on pair of words with a proximity of 0.
                    Phrase(phrase) => {
-                        let mut bitmaps = Vec::with_capacity(phrase.len().saturating_sub(1));
+                        candidates |= resolve_phrase(ctx, phrase)?;
                        for words in phrase.windows(2) {
                            if let [left, right] = words {
                                match ctx.word_pair_proximity_docids(left, right, 0)? {
                                    Some(docids) => bitmaps.push(docids),
                                    None => {
                                        bitmaps.clear();
                                        break;
                                    }
                                }
                            }
                        }
                        candidates |= intersection_of(bitmaps.iter().collect());
                    }
                }
                parts_candidates_array.push(candidates);
--- a/milli/src/search/criteria/mod.rs
+++ b/milli/src/search/criteria/mod.rs
@ -71,6 +71,7 @@ pub trait Context<'c> {
    fn exact_word_docids(&self, word: &str) -> heed::Result<Option<RoaringBitmap>>;
    fn word_prefix_docids(&self, word: &str) -> heed::Result<Option<RoaringBitmap>>;
    fn exact_word_prefix_docids(&self, word: &str) -> heed::Result<Option<RoaringBitmap>>;
    fn word_pair_proximity_docids(
        &self,
        left: &str,
@ -83,6 +84,12 @@ pub trait Context<'c> {
        right: &str,
        proximity: u8,
    ) -> heed::Result<Option<RoaringBitmap>>;
    fn prefix_word_pair_proximity_docids(
        &self,
        prefix: &str,
        right: &str,
        proximity: u8,
    ) -> heed::Result<Option<RoaringBitmap>>;
    fn words_fst<'t>(&self) -> &'t fst::Set<Cow<[u8]>>;
    fn in_prefix_cache(&self, word: &str) -> bool;
    fn docid_words_positions(
@ -111,6 +118,68 @@ pub struct CriteriaBuilder<'t> {
    words_prefixes_fst: fst::Set<Cow<'t, [u8]>>,
 }
 /// Return the docids for the following word pairs and proximities using [`Context::word_pair_proximity_docids`].
 /// * `left, right, prox`   (leftward proximity)
 /// * `right, left, prox-1` (rightward proximity)
 ///
 /// ## Example
 /// For a document with the text `the good fox eats the apple`, we have:
 /// * `rightward_proximity(the, eats) = 3`
 /// * `leftward_proximity(eats, the) = 1`
 ///
 /// So both the expressions `word_pair_overall_proximity_docids(ctx, the, eats, 3)`
 /// and `word_pair_overall_proximity_docids(ctx, the, eats, 2)` would return a bitmap containing
 /// the id of this document.
 fn word_pair_overall_proximity_docids(
    ctx: &dyn Context,
    left: &str,
    right: &str,
    prox: u8,
 ) -> heed::Result<Option<RoaringBitmap>> {
    let rightward = ctx.word_pair_proximity_docids(left, right, prox)?;
    let leftward =
        if prox > 1 { ctx.word_pair_proximity_docids(right, left, prox - 1)? } else { None };
    if let Some(mut all) = rightward {
        if let Some(leftward) = leftward {
            all |= leftward;
        }
        Ok(Some(all))
    } else {
        Ok(leftward)
    }
 }
 /// This function works identically to [`word_pair_overall_proximity_docids`] except that the
 /// right word is replaced by a prefix string.
 ///
 /// It will return None if no documents were found or if the prefix does not exist in the
 /// `word_prefix_pair_proximity_docids` database.
 fn word_prefix_pair_overall_proximity_docids(
    ctx: &dyn Context,
    left: &str,
    prefix: &str,
    proximity: u8,
 ) -> heed::Result<Option<RoaringBitmap>> {
    // We retrieve the docids for the original and swapped word pairs:
    // A: word1 prefix2 proximity
    // B: prefix2 word1 proximity-1
    let rightward = ctx.word_prefix_pair_proximity_docids(left, prefix, proximity)?;
    let leftward = if proximity > 1 {
        ctx.prefix_word_pair_proximity_docids(prefix, left, proximity - 1)?
    } else {
        None
    };
    if let Some(mut all) = rightward {
        if let Some(leftward) = leftward {
            all |= leftward;
        }
        Ok(Some(all))
    } else {
        Ok(leftward)
    }
 }
 impl<'c> Context<'c> for CriteriaBuilder<'c> {
    fn documents_ids(&self) -> heed::Result<RoaringBitmap> {
        self.index.documents_ids(self.rtxn)
@ -138,18 +207,24 @@ impl<'c> Context<'c> for CriteriaBuilder<'c> {
        right: &str,
        proximity: u8,
    ) -> heed::Result<Option<RoaringBitmap>> {
-        let key = (left, right, proximity);
+        self.index.word_pair_proximity_docids.get(self.rtxn, &(proximity, left, right))
        self.index.word_pair_proximity_docids.get(self.rtxn, &key)
    }
    fn word_prefix_pair_proximity_docids(
        &self,
        left: &str,
        prefix: &str,
        proximity: u8,
    ) -> heed::Result<Option<RoaringBitmap>> {
        self.index.word_prefix_pair_proximity_docids.get(self.rtxn, &(proximity, left, prefix))
    }
    fn prefix_word_pair_proximity_docids(
        &self,
        prefix: &str,
        right: &str,
        proximity: u8,
    ) -> heed::Result<Option<RoaringBitmap>> {
-        let key = (left, right, proximity);
+        self.index.prefix_word_pair_proximity_docids.get(self.rtxn, &(proximity, prefix, right))
        self.index.word_prefix_pair_proximity_docids.get(self.rtxn, &key)
    }
    fn words_fst<'t>(&self) -> &'t fst::Set<Cow<[u8]>> {
@ -352,18 +427,31 @@ pub fn resolve_query_tree(
 pub fn resolve_phrase(ctx: &dyn Context, phrase: &[String]) -> Result<RoaringBitmap> {
    let mut candidates = RoaringBitmap::new();
    let mut first_iter = true;
-    let winsize = phrase.len().min(7);
+    let winsize = phrase.len().min(3);
    for win in phrase.windows(winsize) {
        // Get all the documents with the matching distance for each word pairs.
        let mut bitmaps = Vec::with_capacity(winsize.pow(2));
        for (offset, s1) in win.iter().enumerate() {
            for (dist, s2) in win.iter().skip(offset + 1).enumerate() {
-                match ctx.word_pair_proximity_docids(s1, s2, dist as u8 + 1)? {
+                if dist == 0 {
-                    Some(m) => bitmaps.push(m),
+                    match ctx.word_pair_proximity_docids(s1, s2, 1)? {
-                    // If there are no document for this distance, there will be no
+                        Some(m) => bitmaps.push(m),
-                    // results for the phrase query.
+                        // If there are no document for this pair, there will be no
-                    None => return Ok(RoaringBitmap::new()),
+                        // results for the phrase query.
                        None => return Ok(RoaringBitmap::new()),
                    }
                } else {
                    let mut bitmap = RoaringBitmap::new();
                    for dist in 0..=dist {
                        if let Some(m) = ctx.word_pair_proximity_docids(s1, s2, dist as u8 + 1)? {
                            bitmap |= m
                        }
                    }
                    if bitmap.is_empty() {
                        return Ok(bitmap);
                    } else {
                        bitmaps.push(bitmap);
                    }
                }
            }
        }
@ -387,7 +475,7 @@ pub fn resolve_phrase(ctx: &dyn Context, phrase: &[String]) -> Result<RoaringBit
    Ok(candidates)
 }
-fn all_word_pair_proximity_docids<T: AsRef<str>, U: AsRef<str>>(
+fn all_word_pair_overall_proximity_docids<T: AsRef<str>, U: AsRef<str>>(
    ctx: &dyn Context,
    left_words: &[(T, u8)],
    right_words: &[(U, u8)],
@ -396,9 +484,9 @@ fn all_word_pair_proximity_docids<T: AsRef<str>, U: AsRef<str>>(
    let mut docids = RoaringBitmap::new();
    for (left, _l_typo) in left_words {
        for (right, _r_typo) in right_words {
-            let current_docids = ctx
+            let current_docids =
-                .word_pair_proximity_docids(left.as_ref(), right.as_ref(), proximity)?
+                word_pair_overall_proximity_docids(ctx, left.as_ref(), right.as_ref(), proximity)?
-                .unwrap_or_default();
+                    .unwrap_or_default();
            docids |= current_docids;
        }
    }
@ -472,7 +560,8 @@ fn query_pair_proximity_docids(
    match (&left.kind, &right.kind) {
        (QueryKind::Exact { word: left, .. }, QueryKind::Exact { word: right, .. }) => {
            if prefix {
-                match ctx.word_prefix_pair_proximity_docids(
+                match word_prefix_pair_overall_proximity_docids(
                    ctx,
                    left.as_str(),
                    right.as_str(),
                    proximity,
@ -480,7 +569,12 @@ fn query_pair_proximity_docids(
                    Some(docids) => Ok(docids),
                    None => {
                        let r_words = word_derivations(&right, true, 0, ctx.words_fst(), wdcache)?;
-                        all_word_pair_proximity_docids(ctx, &[(left, 0)], &r_words, proximity)
+                        all_word_pair_overall_proximity_docids(
                            ctx,
                            &[(left, 0)],
                            &r_words,
                            proximity,
                        )
                    }
                }
            } else {
@ -495,7 +589,8 @@ fn query_pair_proximity_docids(
            if prefix {
                let mut docids = RoaringBitmap::new();
                for (left, _) in l_words {
-                    let current_docids = match ctx.word_prefix_pair_proximity_docids(
+                    let current_docids = match word_prefix_pair_overall_proximity_docids(
                        ctx,
                        left.as_str(),
                        right.as_str(),
                        proximity,
@ -504,19 +599,24 @@ fn query_pair_proximity_docids(
                        None => {
                            let r_words =
                                word_derivations(&right, true, 0, ctx.words_fst(), wdcache)?;
-                            all_word_pair_proximity_docids(ctx, &[(left, 0)], &r_words, proximity)
+                            all_word_pair_overall_proximity_docids(
                                ctx,
                                &[(left, 0)],
                                &r_words,
                                proximity,
                            )
                        }
                    }?;
                    docids |= current_docids;
                }
                Ok(docids)
            } else {
-                all_word_pair_proximity_docids(ctx, &l_words, &[(right, 0)], proximity)
+                all_word_pair_overall_proximity_docids(ctx, &l_words, &[(right, 0)], proximity)
            }
        }
        (QueryKind::Exact { word: left, .. }, QueryKind::Tolerant { typo, word: right }) => {
            let r_words = word_derivations(&right, prefix, *typo, ctx.words_fst(), wdcache)?;
-            all_word_pair_proximity_docids(ctx, &[(left, 0)], &r_words, proximity)
+            all_word_pair_overall_proximity_docids(ctx, &[(left, 0)], &r_words, proximity)
        }
        (
            QueryKind::Tolerant { typo: l_typo, word: left },
@ -525,7 +625,7 @@ fn query_pair_proximity_docids(
            let l_words =
                word_derivations(&left, false, *l_typo, ctx.words_fst(), wdcache)?.to_owned();
            let r_words = word_derivations(&right, prefix, *r_typo, ctx.words_fst(), wdcache)?;
-            all_word_pair_proximity_docids(ctx, &l_words, &r_words, proximity)
+            all_word_pair_overall_proximity_docids(ctx, &l_words, &r_words, proximity)
        }
    }
 }
@ -552,6 +652,7 @@ pub mod test {
        exact_word_prefix_docids: HashMap<String, RoaringBitmap>,
        word_pair_proximity_docids: HashMap<(String, String, i32), RoaringBitmap>,
        word_prefix_pair_proximity_docids: HashMap<(String, String, i32), RoaringBitmap>,
        prefix_word_pair_proximity_docids: HashMap<(String, String, i32), RoaringBitmap>,
        docid_words: HashMap<u32, Vec<String>>,
    }
@ -588,13 +689,22 @@ pub mod test {
        fn word_prefix_pair_proximity_docids(
            &self,
-            left: &str,
+            word: &str,
-            right: &str,
+            prefix: &str,
            proximity: u8,
        ) -> heed::Result<Option<RoaringBitmap>> {
-            let key = (left.to_string(), right.to_string(), proximity.into());
+            let key = (word.to_string(), prefix.to_string(), proximity.into());
            Ok(self.word_prefix_pair_proximity_docids.get(&key).cloned())
        }
        fn prefix_word_pair_proximity_docids(
            &self,
            prefix: &str,
            word: &str,
            proximity: u8,
        ) -> heed::Result<Option<RoaringBitmap>> {
            let key = (prefix.to_string(), word.to_string(), proximity.into());
            Ok(self.prefix_word_pair_proximity_docids.get(&key).cloned())
        }
        fn words_fst<'t>(&self) -> &'t fst::Set<Cow<[u8]>> {
            &self.words_fst
@ -708,6 +818,8 @@ pub mod test {
            let mut word_pair_proximity_docids = HashMap::new();
            let mut word_prefix_pair_proximity_docids = HashMap::new();
            let mut prefix_word_pair_proximity_docids = HashMap::new();
            for (lword, lcandidates) in &word_docids {
                for (rword, rcandidates) in &word_docids {
                    if lword == rword {
@ -740,15 +852,19 @@ pub mod test {
                            let lposition = docid_words.iter().position(|w| w == lword).unwrap();
                            let rposition =
                                docid_words.iter().position(|w| w.starts_with(pword)).unwrap();
-                            let key = if lposition < rposition {
+                            if lposition < rposition {
-                                (s(lword), s(pword), (rposition - lposition) as i32)
+                                let key = (s(lword), s(pword), (rposition - lposition) as i32);
                                let docids = word_prefix_pair_proximity_docids
                                    .entry(key)
                                    .or_insert(RoaringBitmap::new());
                                docids.push(candidate);
                            } else {
-                                (s(lword), s(pword), (lposition - rposition + 1) as i32)
+                                let key = (s(lword), s(pword), (lposition - rposition) as i32);
                                let docids = prefix_word_pair_proximity_docids
                                    .entry(key)
                                    .or_insert(RoaringBitmap::new());
                                docids.push(candidate);
                            };
                            let docids = word_prefix_pair_proximity_docids
                                .entry(key)
                                .or_insert(RoaringBitmap::new());
                            docids.push(candidate);
                        }
                    }
                }
@ -766,6 +882,7 @@ pub mod test {
                exact_word_prefix_docids,
                word_pair_proximity_docids,
                word_prefix_pair_proximity_docids,
                prefix_word_pair_proximity_docids,
                docid_words,
            }
        }
--- a/milli/src/search/query_tree.rs
+++ b/milli/src/search/query_tree.rs
@ -203,7 +203,7 @@ impl<'a> Context for QueryTreeBuilder<'a> {
        right_word: &str,
        proximity: u8,
    ) -> heed::Result<Option<u64>> {
-        let key = (left_word, right_word, proximity);
+        let key = (proximity, left_word, right_word);
        self.index
            .word_pair_proximity_docids
            .remap_data_type::<CboRoaringBitmapLenCodec>()
--- a/milli/src/snapshot_tests.rs
+++ b/milli/src/snapshot_tests.rs
@ -182,19 +182,28 @@ pub fn snap_docid_word_positions(index: &Index) -> String {
 }
 pub fn snap_word_pair_proximity_docids(index: &Index) -> String {
    let snap = make_db_snap_from_iter!(index, word_pair_proximity_docids, |(
-        (word1, word2, proximity),
+        (proximity, word1, word2),
        b,
    )| {
-        &format!("{word1:<16} {word2:<16} {proximity:<2} {}", display_bitmap(&b))
+        &format!("{proximity:<2} {word1:<16} {word2:<16} {}", display_bitmap(&b))
    });
    snap
 }
 pub fn snap_word_prefix_pair_proximity_docids(index: &Index) -> String {
    let snap = make_db_snap_from_iter!(index, word_prefix_pair_proximity_docids, |(
-        (word1, prefix, proximity),
+        (proximity, word1, prefix),
        b,
    )| {
-        &format!("{word1:<16} {prefix:<4} {proximity:<2} {}", display_bitmap(&b))
+        &format!("{proximity:<2} {word1:<16} {prefix:<4} {}", display_bitmap(&b))
    });
    snap
 }
 pub fn snap_prefix_word_pair_proximity_docids(index: &Index) -> String {
    let snap = make_db_snap_from_iter!(index, prefix_word_pair_proximity_docids, |(
        (proximity, prefix, word2),
        b,
    )| {
        &format!("{proximity:<2} {prefix:<4} {word2:<16} {}", display_bitmap(&b))
    });
    snap
 }
@ -427,6 +436,9 @@ macro_rules! full_snap_of_db {
    ($index:ident, word_prefix_pair_proximity_docids) => {{
        $crate::snapshot_tests::snap_word_prefix_pair_proximity_docids(&$index)
    }};
    ($index:ident, prefix_word_pair_proximity_docids) => {{
        $crate::snapshot_tests::snap_prefix_word_pair_proximity_docids(&$index)
    }};
    ($index:ident, word_position_docids) => {{
        $crate::snapshot_tests::snap_word_position_docids(&$index)
    }};
--- a/milli/src/update/clear_documents.rs
+++ b/milli/src/update/clear_documents.rs
@ -25,6 +25,7 @@ impl<'t, 'u, 'i> ClearDocuments<'t, 'u, 'i> {
            docid_word_positions,
            word_pair_proximity_docids,
            word_prefix_pair_proximity_docids,
            prefix_word_pair_proximity_docids,
            word_position_docids,
            field_id_word_count_docids,
            word_prefix_position_docids,
@ -66,6 +67,7 @@ impl<'t, 'u, 'i> ClearDocuments<'t, 'u, 'i> {
        docid_word_positions.clear(self.wtxn)?;
        word_pair_proximity_docids.clear(self.wtxn)?;
        word_prefix_pair_proximity_docids.clear(self.wtxn)?;
        prefix_word_pair_proximity_docids.clear(self.wtxn)?;
        word_position_docids.clear(self.wtxn)?;
        field_id_word_count_docids.clear(self.wtxn)?;
        word_prefix_position_docids.clear(self.wtxn)?;
--- a/milli/src/update/delete_documents.rs
+++ b/milli/src/update/delete_documents.rs
@ -183,6 +183,7 @@ impl<'t, 'u, 'i> DeleteDocuments<'t, 'u, 'i> {
            word_pair_proximity_docids,
            field_id_word_count_docids,
            word_prefix_pair_proximity_docids,
            prefix_word_pair_proximity_docids,
            word_position_docids,
            word_prefix_position_docids,
            facet_id_f64_docids,
@ -327,26 +328,26 @@ impl<'t, 'u, 'i> DeleteDocuments<'t, 'u, 'i> {
            self.index.put_words_prefixes_fst(self.wtxn, &new_words_prefixes_fst)?;
        }
-        // We delete the documents ids from the word prefix pair proximity database docids
+        for db in [word_prefix_pair_proximity_docids, prefix_word_pair_proximity_docids] {
-        // and remove the empty pairs too.
+            // We delete the documents ids from the word prefix pair proximity database docids
-        let db = word_prefix_pair_proximity_docids.remap_key_type::<ByteSlice>();
+            // and remove the empty pairs too.
-        let mut iter = db.iter_mut(self.wtxn)?;
+            let db = db.remap_key_type::<ByteSlice>();
-        while let Some(result) = iter.next() {
+            let mut iter = db.iter_mut(self.wtxn)?;
-            let (key, mut docids) = result?;
+            while let Some(result) = iter.next() {
-            let previous_len = docids.len();
+                let (key, mut docids) = result?;
-            docids -= &self.to_delete_docids;
+                let previous_len = docids.len();
-            if docids.is_empty() {
+                docids -= &self.to_delete_docids;
-                // safety: we don't keep references from inside the LMDB database.
+                if docids.is_empty() {
-                unsafe { iter.del_current()? };
+                    // safety: we don't keep references from inside the LMDB database.
-            } else if docids.len() != previous_len {
+                    unsafe { iter.del_current()? };
-                let key = key.to_owned();
+                } else if docids.len() != previous_len {
-                // safety: we don't keep references from inside the LMDB database.
+                    let key = key.to_owned();
-                unsafe { iter.put_current(&key, &docids)? };
+                    // safety: we don't keep references from inside the LMDB database.
                    unsafe { iter.put_current(&key, &docids)? };
                }
            }
        }
        drop(iter);
        // We delete the documents ids that are under the pairs of words,
        // it is faster and use no memory to iterate over all the words pairs than
        // to compute the cartesian product of every words of the deleted documents.
--- a/milli/src/update/index_documents/extract/extract_word_pair_proximity_docids.rs
+++ b/milli/src/update/index_documents/extract/extract_word_pair_proximity_docids.rs
@ -106,17 +106,6 @@ fn document_word_positions_into_sorter(
                            *p = cmp::min(*p, prox);
                        })
                        .or_insert(prox);
                    // We also compute the inverse proximity.
                    let prox = prox + 1;
                    if prox < MAX_DISTANCE {
                        word_pair_proximity
                            .entry((word.clone(), head.word.clone()))
                            .and_modify(|p| {
                                *p = cmp::min(*p, prox);
                            })
                            .or_insert(prox);
                    }
                }
            }
@ -151,11 +140,10 @@ fn document_word_positions_into_sorter(
    let mut key_buffer = Vec::new();
    for ((w1, w2), prox) in word_pair_proximity {
        key_buffer.clear();
        key_buffer.push(prox as u8);
        key_buffer.extend_from_slice(w1.as_bytes());
        key_buffer.push(0);
        key_buffer.extend_from_slice(w2.as_bytes());
        key_buffer.push(0);
        key_buffer.push(prox as u8);
        word_pair_proximity_docids_sorter.insert(&key_buffer, &document_id.to_ne_bytes())?;
    }
--- a/milli/src/update/index_documents/mod.rs
+++ b/milli/src/update/index_documents/mod.rs
@ -36,8 +36,8 @@ use crate::documents::{obkv_to_object, DocumentsBatchReader};
 use crate::error::UserError;
 pub use crate::update::index_documents::helpers::CursorClonableMmap;
 use crate::update::{
-    self, Facets, IndexerConfig, UpdateIndexingStep, WordPrefixDocids,
+    self, Facets, IndexerConfig, PrefixWordPairsProximityDocids, UpdateIndexingStep,
-    WordPrefixPairProximityDocids, WordPrefixPositionDocids, WordsPrefixesFst,
+    WordPrefixDocids, WordPrefixPositionDocids, WordsPrefixesFst,
 };
 use crate::{Index, Result, RoaringBitmapCodec};
@ -522,12 +522,13 @@ where
        if let Some(word_pair_proximity_docids) = word_pair_proximity_docids {
            // Run the word prefix pair proximity docids update operation.
-            let mut builder = WordPrefixPairProximityDocids::new(self.wtxn, self.index);
+            PrefixWordPairsProximityDocids::new(
-            builder.chunk_compression_type = self.indexer_config.chunk_compression_type;
+                self.wtxn,
-            builder.chunk_compression_level = self.indexer_config.chunk_compression_level;
+                self.index,
-            builder.max_nb_chunks = self.indexer_config.max_nb_chunks;
+                self.indexer_config.chunk_compression_type,
-            builder.max_memory = self.indexer_config.max_memory;
+                self.indexer_config.chunk_compression_level,
-            builder.execute(
+            )
            .execute(
                word_pair_proximity_docids,
                &new_prefix_fst_words,
                &common_prefix_fst_words,
--- a/milli/src/update/mod.rs
+++ b/milli/src/update/mod.rs
@ -6,10 +6,10 @@ pub use self::index_documents::{
    DocumentAdditionResult, DocumentId, IndexDocuments, IndexDocumentsConfig, IndexDocumentsMethod,
 };
 pub use self::indexer_config::IndexerConfig;
 pub use self::prefix_word_pairs::PrefixWordPairsProximityDocids;
 pub use self::settings::{Setting, Settings};
 pub use self::update_step::UpdateIndexingStep;
 pub use self::word_prefix_docids::WordPrefixDocids;
 pub use self::word_prefix_pair_proximity_docids::WordPrefixPairProximityDocids;
 pub use self::words_prefix_position_docids::WordPrefixPositionDocids;
 pub use self::words_prefixes_fst::WordsPrefixesFst;
@ -19,9 +19,9 @@ mod delete_documents;
 mod facets;
 mod index_documents;
 mod indexer_config;
 mod prefix_word_pairs;
 mod settings;
 mod update_step;
 mod word_prefix_docids;
 mod word_prefix_pair_proximity_docids;
 mod words_prefix_position_docids;
 mod words_prefixes_fst;
--- a/milli/src/update/prefix_word_pairs/mod.rs
+++ b/milli/src/update/prefix_word_pairs/mod.rs
@ -0,0 +1,241 @@
 use std::borrow::Cow;
 use std::collections::HashSet;
 use std::io::BufReader;
 use grenad::CompressionType;
 use heed::types::ByteSlice;
 use super::index_documents::{merge_cbo_roaring_bitmaps, CursorClonableMmap};
 use crate::{Index, Result};
 mod prefix_word;
 mod word_prefix;
 pub use prefix_word::index_prefix_word_database;
 pub use word_prefix::index_word_prefix_database;
 pub struct PrefixWordPairsProximityDocids<'t, 'u, 'i> {
    wtxn: &'t mut heed::RwTxn<'i, 'u>,
    index: &'i Index,
    max_proximity: u8,
    max_prefix_length: usize,
    chunk_compression_type: CompressionType,
    chunk_compression_level: Option<u32>,
 }
 impl<'t, 'u, 'i> PrefixWordPairsProximityDocids<'t, 'u, 'i> {
    pub fn new(
        wtxn: &'t mut heed::RwTxn<'i, 'u>,
        index: &'i Index,
        chunk_compression_type: CompressionType,
        chunk_compression_level: Option<u32>,
    ) -> Self {
        Self {
            wtxn,
            index,
            max_proximity: 4,
            max_prefix_length: 2,
            chunk_compression_type,
            chunk_compression_level,
        }
    }
    /// Set the maximum proximity required to make a prefix be part of the words prefixes
    /// database. If two words are too far from the threshold the associated documents will
    /// not be part of the prefix database.
    ///
    /// Default value is 4. This value must be lower or equal than 7 and will be clamped
    /// to this bound otherwise.
    pub fn max_proximity(&mut self, value: u8) -> &mut Self {
        self.max_proximity = value.max(7);
        self
    }
    /// Set the maximum length the prefix of a word pair is allowed to have to be part of the words
    /// prefixes database. If the prefix length is higher than the threshold, the associated documents
    /// will not be part of the prefix database.
    ///
    /// Default value is 2.
    pub fn max_prefix_length(&mut self, value: usize) -> &mut Self {
        self.max_prefix_length = value;
        self
    }
    #[logging_timer::time("WordPrefixPairProximityDocids::{}")]
    pub fn execute<'a>(
        self,
        new_word_pair_proximity_docids: grenad::Reader<CursorClonableMmap>,
        new_prefix_fst_words: &'a [String],
        common_prefix_fst_words: &[&'a [String]],
        del_prefix_fst_words: &HashSet<Vec<u8>>,
    ) -> Result<()> {
        index_word_prefix_database(
            self.wtxn,
            self.index.word_pair_proximity_docids,
            self.index.word_prefix_pair_proximity_docids,
            self.max_proximity,
            self.max_prefix_length,
            new_word_pair_proximity_docids.clone(),
            new_prefix_fst_words,
            common_prefix_fst_words,
            del_prefix_fst_words,
            self.chunk_compression_type,
            self.chunk_compression_level,
        )?;
        index_prefix_word_database(
            self.wtxn,
            self.index.word_pair_proximity_docids,
            self.index.prefix_word_pair_proximity_docids,
            self.max_proximity,
            self.max_prefix_length,
            new_word_pair_proximity_docids,
            new_prefix_fst_words,
            common_prefix_fst_words,
            del_prefix_fst_words,
            self.chunk_compression_type,
            self.chunk_compression_level,
        )?;
        Ok(())
    }
 }
 // This is adapted from `sorter_into_lmdb_database`
 pub fn insert_into_database(
    wtxn: &mut heed::RwTxn,
    database: heed::PolyDatabase,
    new_key: &[u8],
    new_value: &[u8],
 ) -> Result<()> {
    let mut iter = database.prefix_iter_mut::<_, ByteSlice, ByteSlice>(wtxn, new_key)?;
    match iter.next().transpose()? {
        Some((key, old_val)) if new_key == key => {
            let val =
                merge_cbo_roaring_bitmaps(key, &[Cow::Borrowed(old_val), Cow::Borrowed(new_value)])
                    .map_err(|_| {
                        // TODO just wrap this error?
                        crate::error::InternalError::IndexingMergingKeys {
                            process: "get-put-merge",
                        }
                    })?;
            // safety: we use the new_key, not the one from the database iterator, to avoid undefined behaviour
            unsafe { iter.put_current(new_key, &val)? };
        }
        _ => {
            drop(iter);
            database.put::<_, ByteSlice, ByteSlice>(wtxn, new_key, new_value)?;
        }
    }
    Ok(())
 }
 // This is adapted from `sorter_into_lmdb_database` and `write_into_lmdb_database`,
 // but it uses `append` if the database is empty, and it assumes that the values in the
 // writer don't conflict with values in the database.
 pub fn write_into_lmdb_database_without_merging(
    wtxn: &mut heed::RwTxn,
    database: heed::PolyDatabase,
    writer: grenad::Writer<std::fs::File>,
 ) -> Result<()> {
    let file = writer.into_inner()?;
    let reader = grenad::Reader::new(BufReader::new(file))?;
    if database.is_empty(wtxn)? {
        let mut out_iter = database.iter_mut::<_, ByteSlice, ByteSlice>(wtxn)?;
        let mut cursor = reader.into_cursor()?;
        while let Some((k, v)) = cursor.move_on_next()? {
            // safety: the key comes from the grenad reader, not the database
            unsafe { out_iter.append(k, v)? };
        }
    } else {
        let mut cursor = reader.into_cursor()?;
        while let Some((k, v)) = cursor.move_on_next()? {
            database.put::<_, ByteSlice, ByteSlice>(wtxn, k, v)?;
        }
    }
    Ok(())
 }
 #[cfg(test)]
 mod tests {
    use std::io::Cursor;
    use crate::db_snap;
    use crate::documents::{DocumentsBatchBuilder, DocumentsBatchReader};
    use crate::index::tests::TempIndex;
    fn documents_with_enough_different_words_for_prefixes(prefixes: &[&str]) -> Vec<crate::Object> {
        let mut documents = Vec::new();
        for prefix in prefixes {
            for i in 0..50 {
                documents.push(
                    serde_json::json!({
                        "text": format!("{prefix}{i:x}"),
                    })
                    .as_object()
                    .unwrap()
                    .clone(),
                )
            }
        }
        documents
    }
    #[test]
    fn test_update() {
        let mut index = TempIndex::new();
        index.index_documents_config.words_prefix_threshold = Some(50);
        index.index_documents_config.autogenerate_docids = true;
        index
            .update_settings(|settings| {
                settings.set_searchable_fields(vec!["text".to_owned()]);
            })
            .unwrap();
        let batch_reader_from_documents = |documents| {
            let mut builder = DocumentsBatchBuilder::new(Vec::new());
            for object in documents {
                builder.append_json_object(&object).unwrap();
            }
            DocumentsBatchReader::from_reader(Cursor::new(builder.into_inner().unwrap())).unwrap()
        };
        let mut documents = documents_with_enough_different_words_for_prefixes(&["a", "be"]);
        // now we add some documents where the text should populate the word_prefix_pair_proximity_docids database
        documents.push(
            serde_json::json!({
                "text": "At an amazing and beautiful house"
            })
            .as_object()
            .unwrap()
            .clone(),
        );
        documents.push(
            serde_json::json!({
                "text": "The bell rings at 5 am"
            })
            .as_object()
            .unwrap()
            .clone(),
        );
        let documents = batch_reader_from_documents(documents);
        index.add_documents(documents).unwrap();
        db_snap!(index, word_prefix_pair_proximity_docids, "initial");
        let mut documents = documents_with_enough_different_words_for_prefixes(&["am", "an"]);
        documents.push(
            serde_json::json!({
                "text": "At an extraordinary house"
            })
            .as_object()
            .unwrap()
            .clone(),
        );
        let documents = batch_reader_from_documents(documents);
        index.add_documents(documents).unwrap();
        db_snap!(index, word_pair_proximity_docids, "update");
        db_snap!(index, word_prefix_pair_proximity_docids, "update");
        db_snap!(index, prefix_word_pair_proximity_docids, "update");
    }
 }
--- a/milli/src/update/prefix_word_pairs/prefix_word.rs
+++ b/milli/src/update/prefix_word_pairs/prefix_word.rs
@ -0,0 +1,182 @@
 use std::borrow::Cow;
 use std::collections::{BTreeMap, HashSet};
 use grenad::CompressionType;
 use heed::types::ByteSlice;
 use heed::BytesDecode;
 use log::debug;
 use crate::update::index_documents::{create_writer, CursorClonableMmap};
 use crate::update::prefix_word_pairs::{
    insert_into_database, write_into_lmdb_database_without_merging,
 };
 use crate::{CboRoaringBitmapCodec, Result, U8StrStrCodec, UncheckedU8StrStrCodec};
 #[logging_timer::time]
 pub fn index_prefix_word_database(
    wtxn: &mut heed::RwTxn,
    word_pair_proximity_docids: heed::Database<U8StrStrCodec, CboRoaringBitmapCodec>,
    prefix_word_pair_proximity_docids: heed::Database<U8StrStrCodec, CboRoaringBitmapCodec>,
    max_proximity: u8,
    max_prefix_length: usize,
    new_word_pair_proximity_docids: grenad::Reader<CursorClonableMmap>,
    new_prefix_fst_words: &[String],
    common_prefix_fst_words: &[&[String]],
    del_prefix_fst_words: &HashSet<Vec<u8>>,
    chunk_compression_type: CompressionType,
    chunk_compression_level: Option<u32>,
 ) -> Result<()> {
    let max_proximity = max_proximity - 1;
    debug!("Computing and writing the word prefix pair proximity docids into LMDB on disk...");
    let common_prefixes: Vec<_> = common_prefix_fst_words
        .into_iter()
        .map(|s| s.into_iter())
        .flatten()
        .map(|s| s.as_str())
        .filter(|s| s.len() <= max_prefix_length)
        .collect();
    for proximity in 1..max_proximity {
        for prefix in common_prefixes.iter() {
            let mut prefix_key = vec![];
            prefix_key.push(proximity);
            prefix_key.extend_from_slice(prefix.as_bytes());
            let mut cursor = new_word_pair_proximity_docids.clone().into_prefix_iter(prefix_key)?;
            // This is the core of the algorithm
            execute_on_word_pairs_and_prefixes(
                proximity,
                prefix.as_bytes(),
                // the next two arguments tell how to iterate over the new word pairs
                &mut cursor,
                |cursor| {
                    if let Some((key, value)) = cursor.next()? {
                        let (_, _, word2) = UncheckedU8StrStrCodec::bytes_decode(key)
                            .ok_or(heed::Error::Decoding)?;
                        Ok(Some((word2, value)))
                    } else {
                        Ok(None)
                    }
                },
                // and this argument tells what to do with each new key (proximity, prefix, word2) and value (roaring bitmap)
                |key, value| {
                    insert_into_database(
                        wtxn,
                        *prefix_word_pair_proximity_docids.as_polymorph(),
                        key,
                        value,
                    )
                },
            )?;
        }
    }
    // Now we do the same thing with the new prefixes and all word pairs in the DB
    let new_prefixes: Vec<_> = new_prefix_fst_words
        .into_iter()
        .map(|s| s.as_str())
        .filter(|s| s.len() <= max_prefix_length)
        .collect();
    // Since we read the DB, we can't write to it directly, so we add each new (word1, prefix, proximity)
    // element in an intermediary grenad
    let mut writer =
        create_writer(chunk_compression_type, chunk_compression_level, tempfile::tempfile()?);
    for proximity in 1..max_proximity {
        for prefix in new_prefixes.iter() {
            let mut prefix_key = vec![];
            prefix_key.push(proximity);
            prefix_key.extend_from_slice(prefix.as_bytes());
            let mut db_iter = word_pair_proximity_docids
                .as_polymorph()
                .prefix_iter::<_, ByteSlice, ByteSlice>(wtxn, prefix_key.as_slice())?
                .remap_key_type::<UncheckedU8StrStrCodec>();
            execute_on_word_pairs_and_prefixes(
                proximity,
                prefix.as_bytes(),
                &mut db_iter,
                |db_iter| {
                    db_iter
                        .next()
                        .transpose()
                        .map(|x| x.map(|((_, _, word2), value)| (word2, value)))
                        .map_err(|e| e.into())
                },
                |key, value| writer.insert(key, value).map_err(|e| e.into()),
            )?;
            drop(db_iter);
        }
    }
    // and then we write the grenad into the DB
    // Since the grenad contains only new prefixes, we know in advance that none
    // of its elements already exist in the DB, thus there is no need to specify
    // how to merge conflicting elements
    write_into_lmdb_database_without_merging(
        wtxn,
        *prefix_word_pair_proximity_docids.as_polymorph(),
        writer,
    )?;
    // All of the word prefix pairs in the database that have a w2
    // that is contained in the `suppr_pw` set must be removed as well.
    if !del_prefix_fst_words.is_empty() {
        let mut iter =
            prefix_word_pair_proximity_docids.remap_data_type::<ByteSlice>().iter_mut(wtxn)?;
        while let Some(((_, prefix, _), _)) = iter.next().transpose()? {
            if del_prefix_fst_words.contains(prefix.as_bytes()) {
                // Delete this entry as the w2 prefix is no more in the words prefix fst.
                unsafe { iter.del_current()? };
            }
        }
    }
    Ok(())
 }
 /// This is the core of the algorithm to initialise the Prefix Word Pair Proximity Docids database.
 ///
 /// Its arguments are:
 /// - an iterator over the words following the given `prefix` with the given `proximity`
 /// - a closure to describe how to handle the new computed (proximity, prefix, word2) elements
 fn execute_on_word_pairs_and_prefixes<I>(
    proximity: u8,
    prefix: &[u8],
    iter: &mut I,
    mut next_word2_and_docids: impl for<'a> FnMut(&'a mut I) -> Result<Option<(&'a [u8], &'a [u8])>>,
    mut insert: impl for<'a> FnMut(&'a [u8], &'a [u8]) -> Result<()>,
 ) -> Result<()> {
    let mut batch: BTreeMap<Vec<u8>, Vec<Cow<'static, [u8]>>> = BTreeMap::default();
    // Memory usage check:
    // The content of the loop will be called for each `word2` that follows a word beginning
    // with `prefix` with the given proximity.
    // In practice, I don't think the batch can ever get too big.
    while let Some((word2, docids)) = next_word2_and_docids(iter)? {
        let entry = batch.entry(word2.to_owned()).or_default();
        entry.push(Cow::Owned(docids.to_owned()));
    }
    let mut key_buffer = Vec::with_capacity(512);
    key_buffer.push(proximity);
    key_buffer.extend_from_slice(prefix);
    key_buffer.push(0);
    let mut value_buffer = Vec::with_capacity(65_536);
    for (word2, docids) in batch {
        key_buffer.truncate(prefix.len() + 2);
        value_buffer.clear();
        key_buffer.extend_from_slice(&word2);
        let data = if docids.len() > 1 {
            CboRoaringBitmapCodec::merge_into(&docids, &mut value_buffer)?;
            value_buffer.as_slice()
        } else {
            &docids[0]
        };
        insert(key_buffer.as_slice(), data)?;
    }
    Ok(())
 }
--- a/milli/src/update/prefix_word_pairs/snapshots/mod.rs/test_update/initial/word_prefix_pair_proximity_docids.snap
+++ b/milli/src/update/prefix_word_pairs/snapshots/mod.rs/test_update/initial/word_prefix_pair_proximity_docids.snap
@ -0,0 +1,26 @@
 ---
 source: milli/src/update/prefix_word_pairs/mod.rs
 ---
 1  5                a    [101, ]
 1  amazing          a    [100, ]
 1  an               a    [100, ]
 1  and              b    [100, ]
 1  and              be   [100, ]
 1  at               a    [100, ]
 1  rings            a    [101, ]
 1  the              b    [101, ]
 1  the              be   [101, ]
 2  amazing          b    [100, ]
 2  amazing          be   [100, ]
 2  an               a    [100, ]
 2  at               a    [100, 101, ]
 2  bell             a    [101, ]
 3  an               b    [100, ]
 3  an               be   [100, ]
 3  at               a    [100, ]
 3  rings            a    [101, ]
 3  the              a    [101, ]
 4  at               b    [100, ]
 4  at               be   [100, ]
 4  bell             a    [101, ]
--- a/milli/src/update/prefix_word_pairs/snapshots/mod.rs/test_update/update/prefix_word_pair_proximity_docids.snap
+++ b/milli/src/update/prefix_word_pairs/snapshots/mod.rs/test_update/update/prefix_word_pair_proximity_docids.snap
@ -0,0 +1,29 @@
 ---
 source: milli/src/update/prefix_word_pairs/mod.rs
 ---
 1  a    5                [101, ]
 1  a    amazing          [100, ]
 1  a    an               [100, 202, ]
 1  a    and              [100, ]
 1  a    beautiful        [100, ]
 1  a    extraordinary    [202, ]
 1  am   and              [100, ]
 1  an   amazing          [100, ]
 1  an   beautiful        [100, ]
 1  an   extraordinary    [202, ]
 1  b    house            [100, ]
 1  b    rings            [101, ]
 1  be   house            [100, ]
 1  be   rings            [101, ]
 2  a    am               [101, ]
 2  a    amazing          [100, ]
 2  a    and              [100, ]
 2  a    beautiful        [100, ]
 2  a    extraordinary    [202, ]
 2  a    house            [100, 202, ]
 2  am   beautiful        [100, ]
 2  an   and              [100, ]
 2  an   house            [100, 202, ]
 2  b    at               [101, ]
 2  be   at               [101, ]
--- a/milli/src/update/prefix_word_pairs/snapshots/mod.rs/test_update/update/word_pair_proximity_docids.snap
+++ b/milli/src/update/prefix_word_pairs/snapshots/mod.rs/test_update/update/word_pair_proximity_docids.snap
@ -0,0 +1,39 @@
 ---
 source: milli/src/update/prefix_word_pairs/mod.rs
 ---
 1  5                am               [101, ]
 1  amazing          and              [100, ]
 1  an               amazing          [100, ]
 1  an               extraordinary    [202, ]
 1  and              beautiful        [100, ]
 1  at               5                [101, ]
 1  at               an               [100, 202, ]
 1  beautiful        house            [100, ]
 1  bell             rings            [101, ]
 1  extraordinary    house            [202, ]
 1  rings            at               [101, ]
 1  the              bell             [101, ]
 2  amazing          beautiful        [100, ]
 2  an               and              [100, ]
 2  an               house            [202, ]
 2  and              house            [100, ]
 2  at               am               [101, ]
 2  at               amazing          [100, ]
 2  at               extraordinary    [202, ]
 2  bell             at               [101, ]
 2  rings            5                [101, ]
 2  the              rings            [101, ]
 3  amazing          house            [100, ]
 3  an               beautiful        [100, ]
 3  at               and              [100, ]
 3  at               house            [202, ]
 3  bell             5                [101, ]
 3  rings            am               [101, ]
 3  the              at               [101, ]
 4  an               house            [100, ]
 4  at               beautiful        [100, ]
 4  bell             am               [101, ]
 4  the              5                [101, ]
 5  at               house            [100, ]
 5  the              am               [101, ]
--- a/milli/src/update/prefix_word_pairs/snapshots/mod.rs/test_update/update/word_prefix_pair_proximity_docids.snap
+++ b/milli/src/update/prefix_word_pairs/snapshots/mod.rs/test_update/update/word_prefix_pair_proximity_docids.snap
@ -0,0 +1,35 @@
 ---
 source: milli/src/update/prefix_word_pairs/mod.rs
 ---
 1  5                a    [101, ]
 1  5                am   [101, ]
 1  amazing          a    [100, ]
 1  amazing          an   [100, ]
 1  an               a    [100, ]
 1  an               am   [100, ]
 1  and              b    [100, ]
 1  and              be   [100, ]
 1  at               a    [100, 202, ]
 1  at               an   [100, 202, ]
 1  rings            a    [101, ]
 1  the              b    [101, ]
 1  the              be   [101, ]
 2  amazing          b    [100, ]
 2  amazing          be   [100, ]
 2  an               a    [100, ]
 2  an               an   [100, ]
 2  at               a    [100, 101, ]
 2  at               am   [100, 101, ]
 2  bell             a    [101, ]
 3  an               b    [100, ]
 3  an               be   [100, ]
 3  at               a    [100, ]
 3  at               an   [100, ]
 3  rings            a    [101, ]
 3  rings            am   [101, ]
 3  the              a    [101, ]
 4  at               b    [100, ]
 4  at               be   [100, ]
 4  bell             a    [101, ]
 4  bell             am   [101, ]
--- a/milli/src/update/word_prefix_pair_proximity_docids.rs
+++ b/milli/src/update/word_prefix_pair_proximity_docids.rs
@ -1,7 +1,6 @@
 /*!
 ## What is WordPrefixPairProximityDocids?
 The word-prefix-pair-proximity-docids database is a database whose keys are of
-the form (`word`, `prefix`, `proximity`) and the values are roaring bitmaps of
+the form `(proximity, word, prefix)` and the values are roaring bitmaps of
 the documents which contain `word` followed by another word starting with
 `prefix` at a distance of `proximity`.
@ -23,127 +22,100 @@ dog
 Note that only prefixes which correspond to more than a certain number of
 different words from the database are included in this list.
-* a sorted list of word pairs and the distance between them (i.e. proximity),
+* a sorted list of proximities and word pairs (the proximity is the distance between the two words),
-* associated with a roaring bitmap, such as:
+associated with a roaring bitmap, such as:
 ```text
-good dog   3         -> docids1: [2, 5, 6]
+1 good doggo         -> docids1: [8]
-good doggo 1         -> docids2: [8]
+1 good door          -> docids2: [7, 19, 20]
-good dogma 1         -> docids3: [7, 19, 20]
+1 good ghost         -> docids3: [1]
-good ghost 2         -> docids4: [1]
+2 good dog           -> docids4: [2, 5, 6]
-horror cathedral 4   -> docids5: [1, 2]
+2 horror cathedral   -> docids5: [1, 2]
 ```
 I illustrate a simplified version of the algorithm to create the word-prefix
 pair-proximity database below:
-1. **Outer loop:** First, we iterate over each word pair and its proximity:
+1. **Outer loop:** First, we iterate over each proximity and word pair:
 ```text
 proximity: 1
 word1    : good
-word2    : dog
+word2    : doggo
 proximity: 3
 ```
 2. **Inner loop:** Then, we iterate over all the prefixes of `word2` that are
-in the list of sorted prefixes. And we insert the key (`prefix`, `proximity`)
+in the list of sorted prefixes. And we insert the key `prefix`
 and the value (`docids`) to a sorted map which we call the “batch”. For example,
 at the end of the first inner loop, we may have:
 ```text
 Outer loop 1:
 ------------------------------
 proximity: 1
 word1    : good
-word2    : dog
+word2    : doggo
 proximity: 3
 docids   : docids1
 prefixes: [d, do, dog]
 batch: [
-    (d, 3)   -> [docids1]
+    d,   -> [docids1]
-    (do, 3)  -> [docids1]
+    do   -> [docids1]
-    (dog, 3) -> [docids1]
+    dog  -> [docids1]
 ]
 ```
 3. For illustration purpose, let's run through a second iteration of the outer loop:
 ```text
 Outer loop 2:
 ------------------------------
 word1    : good
 word2    : doggo
 proximity: 1
 word1    : good
 word2    : door
 docids   : docids2
-prefixes: [d, do, dog]
+prefixes: [d, do, doo]
 batch: [
-    (d, 1)   -> [docids2]
+    d   -> [docids1, docids2]
-    (d, 3)   -> [docids1]
+    do  -> [docids1, docids2]
-    (do, 1)  -> [docids2]
+    dog -> [docids1]
-    (do, 3)  -> [docids1]
+    doo -> [docids2]
    (dog, 1) -> [docids2]
    (dog, 3) -> [docids1]
 ]
 ```
 Notice that the batch had to re-order some (`prefix`, `proximity`) keys: some
 of the elements inserted in the second iteration of the outer loop appear
 *before* elements from the first iteration.
 4. And a third:
 ```text
 Outer loop 3:
 ------------------------------
 word1    : good
 word2    : dogma
 proximity: 1
 docids   : docids3
 prefixes: [d, do, dog]
 batch: [
    (d, 1)   -> [docids2, docids3]
    (d, 3)   -> [docids1]
    (do, 1)  -> [docids2, docids3]
    (do, 3)  -> [docids1]
    (dog, 1) -> [docids2, docids3]
    (dog, 3) -> [docids1]
 ]
 ```
 Notice that there were some conflicts which were resolved by merging the
-conflicting values together.
+conflicting values together. Also, an additional prefix was added at the
 end of the batch.
-5. On the fourth iteration of the outer loop, we have:
+4. On the third iteration of the outer loop, we have:
 ```text
 Outer loop 4:
 ------------------------------
 proximity: 1
 word1    : good
 word2    : ghost
 proximity: 2
 ```
 Because `word2` begins with a different letter than the previous `word2`,
-we know that:
+we know that all the prefixes of `word2` are greater than the prefixes of the previous word2
 1. All the prefixes of `word2` are greater than the prefixes of the previous word2
 2. And therefore, every instance of (`word2`, `prefix`) will be greater than
 any element in the batch.
 Therefore, we know that we can insert every element from the batch into the
 database before proceeding any further. This operation is called
-“flushing the batch”. Flushing the batch should also be done whenever `word1`
+“flushing the batch”. Flushing the batch should also be done whenever:
-is different than the previous `word1`.
+* `proximity` is different than the previous `proximity`.
 * `word1` is different than the previous `word1`.
 * `word2` starts with a different letter than the previous word2
-6. **Flushing the batch:** to flush the batch, we look at the `word1` and
+6. **Flushing the batch:** to flush the batch, we iterate over its elements:
 iterate over the elements of the batch in sorted order:
 ```text
 Flushing Batch loop 1:
 ------------------------------
-word1    : good
+proximity  : 1
-word2    : d
+word1      : good
-proximity: 1
+prefix     : d
 docids   : [docids2, docids3]
 ```
 We then merge the array of `docids` (of type `Vec<Vec<u8>>`) using
 `merge_cbo_roaring_bitmap` in order to get a single byte vector representing a
 roaring bitmap of all the document ids where `word1` is followed by `prefix`
 at a distance of `proximity`.
-Once we have done that, we insert (`word1`, `prefix`, `proximity`) -> `merged_docids`
+Once we have done that, we insert `(proximity, word1, prefix) -> merged_docids`
 into the database.
 7. That's it! ... except...
@ -166,7 +138,7 @@ inputs described above, which come from different places:
    2. `word_pairs_db`, which is the list of word pairs from the database.
    This list includes all elements in `new_word_pairs` since `new_word_pairs`
-    was added to the database prior to calling the `WordPrefixPairProximityDocIds::execute`
+    was added to the database prior to calling the `WordPrefix::execute`
    function.
 To update the prefix database correctly, we call the algorithm described earlier first
@ -184,199 +156,146 @@ Note, also, that since we read data from the database when iterating over
 `word_pairs_db`, we cannot insert the computed word-prefix-pair-proximity-
 docids from the batch directly into the database (we would have a concurrent
 reader and writer). Therefore, when calling the algorithm on
-(`new_prefixes`, `word_pairs_db`), we insert the computed
+`(new_prefixes, word_pairs_db)`, we insert the computed
-((`word`, `prefix`, `proximity`), `docids`) elements in an intermediary grenad
+`((proximity, word, prefix), docids)` elements in an intermediary grenad
 Writer instead of the DB. At the end of the outer loop, we finally read from
 the grenad and insert its elements in the database.
 */
 use std::borrow::Cow;
 use std::collections::HashSet;
 use std::io::BufReader;
 use grenad::CompressionType;
 use heed::types::ByteSlice;
 use heed::BytesDecode;
 use log::debug;
-use crate::update::index_documents::{
+use crate::update::index_documents::{create_writer, CursorClonableMmap};
-    create_writer, merge_cbo_roaring_bitmaps, CursorClonableMmap,
+use crate::update::prefix_word_pairs::{
    insert_into_database, write_into_lmdb_database_without_merging,
 };
-use crate::{CboRoaringBitmapCodec, Index, Result, UncheckedStrStrU8Codec};
+use crate::{CboRoaringBitmapCodec, Result, U8StrStrCodec, UncheckedU8StrStrCodec};
-pub struct WordPrefixPairProximityDocids<'t, 'u, 'i> {
+#[logging_timer::time]
-    wtxn: &'t mut heed::RwTxn<'i, 'u>,
+pub fn index_word_prefix_database(
-    index: &'i Index,
+    wtxn: &mut heed::RwTxn,
-    pub(crate) chunk_compression_type: CompressionType,
+    word_pair_proximity_docids: heed::Database<U8StrStrCodec, CboRoaringBitmapCodec>,
-    pub(crate) chunk_compression_level: Option<u32>,
+    word_prefix_pair_proximity_docids: heed::Database<U8StrStrCodec, CboRoaringBitmapCodec>,
    pub(crate) max_nb_chunks: Option<usize>,
    pub(crate) max_memory: Option<usize>,
    max_proximity: u8,
    max_prefix_length: usize,
-}
+    new_word_pair_proximity_docids: grenad::Reader<CursorClonableMmap>,
    new_prefix_fst_words: &[String],
    common_prefix_fst_words: &[&[String]],
    del_prefix_fst_words: &HashSet<Vec<u8>>,
    chunk_compression_type: CompressionType,
    chunk_compression_level: Option<u32>,
 ) -> Result<()> {
    debug!("Computing and writing the word prefix pair proximity docids into LMDB on disk...");
-impl<'t, 'u, 'i> WordPrefixPairProximityDocids<'t, 'u, 'i> {
+    // Make a prefix trie from the common prefixes that are shorter than self.max_prefix_length
-    pub fn new(
+    let prefixes = PrefixTrieNode::from_sorted_prefixes(
-        wtxn: &'t mut heed::RwTxn<'i, 'u>,
+        common_prefix_fst_words
-        index: &'i Index,
+            .into_iter()
-    ) -> WordPrefixPairProximityDocids<'t, 'u, 'i> {
+            .map(|s| s.into_iter())
-        WordPrefixPairProximityDocids {
+            .flatten()
-            wtxn,
+            .map(|s| s.as_str())
-            index,
+            .filter(|s| s.len() <= max_prefix_length),
-            chunk_compression_type: CompressionType::None,
+    );
            chunk_compression_level: None,
            max_nb_chunks: None,
            max_memory: None,
            max_proximity: 4,
            max_prefix_length: 2,
        }
    }
-    /// Set the maximum proximity required to make a prefix be part of the words prefixes
+    // If the prefix trie is not empty, then we can iterate over all new
-    /// database. If two words are too far from the threshold the associated documents will
+    // word pairs to look for new (proximity, word1, common_prefix) elements
-    /// not be part of the prefix database.
+    // to insert in the DB
-    ///
+    if !prefixes.is_empty() {
-    /// Default value is 4. This value must be lower or equal than 7 and will be clamped
+        let mut cursor = new_word_pair_proximity_docids.into_cursor()?;
-    /// to this bound otherwise.
+        // This is the core of the algorithm
-    pub fn max_proximity(&mut self, value: u8) -> &mut Self {
+        execute_on_word_pairs_and_prefixes(
-        self.max_proximity = value.max(7);
+            // the first two arguments tell how to iterate over the new word pairs
-        self
+            &mut cursor,
-    }
+            |cursor| {
-
+                if let Some((key, value)) = cursor.move_on_next()? {
-    /// Set the maximum length the prefix of a word pair is allowed to have to be part of the words
+                    let (proximity, word1, word2) =
-    /// prefixes database. If the prefix length is higher than the threshold, the associated documents
+                        UncheckedU8StrStrCodec::bytes_decode(key).ok_or(heed::Error::Decoding)?;
-    /// will not be part of the prefix database.
+                    Ok(Some(((proximity, word1, word2), value)))
-    ///
+                } else {
-    /// Default value is 2.
+                    Ok(None)
    pub fn max_prefix_length(&mut self, value: usize) -> &mut Self {
        self.max_prefix_length = value;
        self
    }
    #[logging_timer::time("WordPrefixPairProximityDocids::{}")]
    pub fn execute<'a>(
        self,
        new_word_pair_proximity_docids: grenad::Reader<CursorClonableMmap>,
        new_prefix_fst_words: &'a [String],
        common_prefix_fst_words: &[&'a [String]],
        del_prefix_fst_words: &HashSet<Vec<u8>>,
    ) -> Result<()> {
        debug!("Computing and writing the word prefix pair proximity docids into LMDB on disk...");
        // Make a prefix trie from the common prefixes that are shorter than self.max_prefix_length
        let prefixes = PrefixTrieNode::from_sorted_prefixes(
            common_prefix_fst_words
                .iter()
                .flat_map(|s| s.iter())
                .map(|s| s.as_str())
                .filter(|s| s.len() <= self.max_prefix_length),
        );
        // If the prefix trie is not empty, then we can iterate over all new
        // word pairs to look for new (word1, common_prefix, proximity) elements
        // to insert in the DB
        if !prefixes.is_empty() {
            let mut cursor = new_word_pair_proximity_docids.into_cursor()?;
            // This is the core of the algorithm
            execute_on_word_pairs_and_prefixes(
                // the first two arguments tell how to iterate over the new word pairs
                &mut cursor,
                |cursor| {
                    if let Some((key, value)) = cursor.move_on_next()? {
                        let (word1, word2, proximity) = UncheckedStrStrU8Codec::bytes_decode(key)
                            .ok_or(heed::Error::Decoding)?;
                        Ok(Some(((word1, word2, proximity), value)))
                    } else {
                        Ok(None)
                    }
                },
                &prefixes,
                self.max_proximity,
                // and this argument tells what to do with each new key (word1, prefix, proximity) and value (roaring bitmap)
                |key, value| {
                    insert_into_database(
                        self.wtxn,
                        *self.index.word_prefix_pair_proximity_docids.as_polymorph(),
                        key,
                        value,
                    )
                },
            )?;
        }
        // Now we do the same thing with the new prefixes and all word pairs in the DB
        let prefixes = PrefixTrieNode::from_sorted_prefixes(
            new_prefix_fst_words
                .iter()
                .map(|s| s.as_str())
                .filter(|s| s.len() <= self.max_prefix_length),
        );
        if !prefixes.is_empty() {
            let mut db_iter = self
                .index
                .word_pair_proximity_docids
                .remap_key_type::<UncheckedStrStrU8Codec>()
                .remap_data_type::<ByteSlice>()
                .iter(self.wtxn)?;
            // Since we read the DB, we can't write to it directly, so we add each new (word1, prefix, proximity)
            // element in an intermediary grenad
            let mut writer = create_writer(
                self.chunk_compression_type,
                self.chunk_compression_level,
                tempfile::tempfile()?,
            );
            execute_on_word_pairs_and_prefixes(
                &mut db_iter,
                |db_iter| db_iter.next().transpose().map_err(|e| e.into()),
                &prefixes,
                self.max_proximity,
                |key, value| writer.insert(key, value).map_err(|e| e.into()),
            )?;
            drop(db_iter);
            // and then we write the grenad into the DB
            // Since the grenad contains only new prefixes, we know in advance that none
            // of its elements already exist in the DB, thus there is no need to specify
            // how to merge conflicting elements
            write_into_lmdb_database_without_merging(
                self.wtxn,
                *self.index.word_prefix_pair_proximity_docids.as_polymorph(),
                writer,
            )?;
        }
        // All of the word prefix pairs in the database that have a w2
        // that is contained in the `suppr_pw` set must be removed as well.
        if !del_prefix_fst_words.is_empty() {
            let mut iter = self
                .index
                .word_prefix_pair_proximity_docids
                .remap_data_type::<ByteSlice>()
                .iter_mut(self.wtxn)?;
            while let Some(((_, w2, _), _)) = iter.next().transpose()? {
                if del_prefix_fst_words.contains(w2.as_bytes()) {
                    // Delete this entry as the w2 prefix is no more in the words prefix fst.
                    unsafe { iter.del_current()? };
                }
            },
            &prefixes,
            max_proximity,
            // and this argument tells what to do with each new key (proximity, word1, prefix) and value (roaring bitmap)
            |key, value| {
                insert_into_database(
                    wtxn,
                    *word_prefix_pair_proximity_docids.as_polymorph(),
                    key,
                    value,
                )
            },
        )?;
    }
    // Now we do the same thing with the new prefixes and all word pairs in the DB
    let prefixes = PrefixTrieNode::from_sorted_prefixes(
        new_prefix_fst_words
            .into_iter()
            .map(|s| s.as_str())
            .filter(|s| s.len() <= max_prefix_length),
    );
    if !prefixes.is_empty() {
        let mut db_iter = word_pair_proximity_docids
            .remap_key_type::<UncheckedU8StrStrCodec>()
            .remap_data_type::<ByteSlice>()
            .iter(wtxn)?;
        // Since we read the DB, we can't write to it directly, so we add each new (proximity, word1, prefix)
        // element in an intermediary grenad
        let mut writer =
            create_writer(chunk_compression_type, chunk_compression_level, tempfile::tempfile()?);
        execute_on_word_pairs_and_prefixes(
            &mut db_iter,
            |db_iter| db_iter.next().transpose().map_err(|e| e.into()),
            &prefixes,
            max_proximity,
            |key, value| writer.insert(key, value).map_err(|e| e.into()),
        )?;
        drop(db_iter);
        // and then we write the grenad into the DB
        // Since the grenad contains only new prefixes, we know in advance that none
        // of its elements already exist in the DB, thus there is no need to specify
        // how to merge conflicting elements
        write_into_lmdb_database_without_merging(
            wtxn,
            *word_prefix_pair_proximity_docids.as_polymorph(),
            writer,
        )?;
    }
    // All of the word prefix pairs in the database that have a w2
    // that is contained in the `suppr_pw` set must be removed as well.
    if !del_prefix_fst_words.is_empty() {
        let mut iter =
            word_prefix_pair_proximity_docids.remap_data_type::<ByteSlice>().iter_mut(wtxn)?;
        while let Some(((_, _, prefix), _)) = iter.next().transpose()? {
            if del_prefix_fst_words.contains(prefix.as_bytes()) {
                // Delete this entry as the w2 prefix is no more in the words prefix fst.
                unsafe { iter.del_current()? };
            }
        }
        Ok(())
    }
    Ok(())
 }
 /// This is the core of the algorithm to initialise the Word Prefix Pair Proximity Docids database.
 ///
 /// Its main arguments are:
-/// 1. a sorted iterator over ((word1, word2, proximity), docids) elements
+/// 1. a sorted iterator over ((proximity, word1, word2), docids) elements
 /// 2. a prefix trie
-/// 3. a closure to describe how to handle the new computed (word1, prefix, proximity) elements
+/// 3. a closure to describe how to handle the new computed (proximity, word1, prefix) elements
 ///
 /// For more information about what this function does, read the module documentation.
 fn execute_on_word_pairs_and_prefixes<I>(
@ -384,7 +303,7 @@ fn execute_on_word_pairs_and_prefixes<I>(
    mut next_word_pair_proximity: impl for<'a> FnMut(
        &'a mut I,
    ) -> Result<
-        Option<((&'a [u8], &'a [u8], u8), &'a [u8])>,
+        Option<((u8, &'a [u8], &'a [u8]), &'a [u8])>,
    >,
    prefixes: &PrefixTrieNode,
    max_proximity: u8,
@ -402,10 +321,10 @@ fn execute_on_word_pairs_and_prefixes<I>(
    let mut prefix_buffer = Vec::with_capacity(8);
    let mut merge_buffer = Vec::with_capacity(65_536);
-    while let Some(((word1, word2, proximity), data)) = next_word_pair_proximity(iter)? {
+    while let Some(((proximity, word1, word2), data)) = next_word_pair_proximity(iter)? {
-        // skip this iteration if the proximity is over the threshold
+        // stop indexing if the proximity is over the threshold
        if proximity > max_proximity {
-            continue;
+            break;
        };
        let word2_start_different_than_prev = word2[0] != prev_word2_start;
        // if there were no potential prefixes for the previous word2 based on its first letter,
@ -415,16 +334,21 @@ fn execute_on_word_pairs_and_prefixes<I>(
            continue;
        }
-        // if word1 is different than the previous word1 OR if the start of word2 is different
+        // if the proximity is different to the previous one, OR
-        // than the previous start of word2, then we'll need to flush the batch
+        // if word1 is different than the previous word1, OR
        // if the start of word2 is different than the previous start of word2,
        // THEN we'll need to flush the batch
        let prox_different_than_prev = proximity != batch.proximity;
        let word1_different_than_prev = word1 != batch.word1;
-        if word1_different_than_prev || word2_start_different_than_prev {
+        if prox_different_than_prev || word1_different_than_prev || word2_start_different_than_prev
        {
            batch.flush(&mut merge_buffer, &mut insert)?;
            // don't forget to reset the value of batch.word1 and prev_word2_start
            if word1_different_than_prev {
                prefix_search_start.0 = 0;
                batch.word1.clear();
                batch.word1.extend_from_slice(word1);
                batch.proximity = proximity;
            }
            if word2_start_different_than_prev {
                // word2_start_different_than_prev == true
@ -436,74 +360,70 @@ fn execute_on_word_pairs_and_prefixes<I>(
        if !empty_prefixes {
            // All conditions are satisfied, we can now insert each new prefix of word2 into the batch
            prefix_buffer.clear();
            prefixes.for_each_prefix_of(
                word2,
                &mut prefix_buffer,
                &prefix_search_start,
                |prefix_buffer| {
-                    let prefix_len = prefix_buffer.len();
+                    batch.insert(&prefix_buffer, data.to_vec());
                    prefix_buffer.push(0);
                    prefix_buffer.push(proximity);
                    batch.insert(prefix_buffer, data.to_vec());
                    prefix_buffer.truncate(prefix_len);
                },
            );
            prefix_buffer.clear();
        }
    }
    batch.flush(&mut merge_buffer, &mut insert)?;
    Ok(())
 }
 /**
-A map structure whose keys are (prefix, proximity) and whose values are vectors of bitstrings (serialized roaring bitmaps).
+A map structure whose keys are prefixes and whose values are vectors of bitstrings (serialized roaring bitmaps).
 The keys are sorted and conflicts are resolved by merging the vectors of bitstrings together.
-It is used to ensure that all ((word1, prefix, proximity), docids) are inserted into the database in sorted order and efficiently.
+It is used to ensure that all ((proximity, word1, prefix), docids) are inserted into the database in sorted order and efficiently.
-The batch is flushed as often as possible, when we are sure that every (word1, prefix, proximity) key derived from its content
+The batch is flushed as often as possible, when we are sure that every (proximity, word1, prefix) key derived from its content
 can be inserted into the database in sorted order. When it is flushed, it calls a user-provided closure with the following arguments:
- key   : (word1, prefix, proximity) as bytes
+- key   : (proximity, word1, prefix) as bytes
- value : merged roaring bitmaps from all values associated with (prefix, proximity) in the batch, serialised to bytes
+- value : merged roaring bitmaps from all values associated with prefix in the batch, serialised to bytes
 */
 #[derive(Default)]
 struct PrefixAndProximityBatch {
    proximity: u8,
    word1: Vec<u8>,
    batch: Vec<(Vec<u8>, Vec<Cow<'static, [u8]>>)>,
 }
 impl PrefixAndProximityBatch {
    /// Insert the new key and value into the batch
    ///
    /// The key must either exist in the batch or be greater than all existing keys
    fn insert(&mut self, new_key: &[u8], new_value: Vec<u8>) {
-        match self.batch.binary_search_by_key(&new_key, |(k, _)| k.as_slice()) {
+        match self.batch.iter_mut().find(|el| el.0 == new_key) {
-            Ok(position) => {
+            Some((_prefix, docids)) => docids.push(Cow::Owned(new_value)),
-                self.batch[position].1.push(Cow::Owned(new_value));
+            None => self.batch.push((new_key.to_vec(), vec![Cow::Owned(new_value)])),
            }
            Err(position) => {
                self.batch.insert(position, (new_key.to_vec(), vec![Cow::Owned(new_value)]));
            }
        }
    }
    /// Empties the batch, calling `insert` on each element.
    ///
-    /// The key given to `insert` is `(word1, prefix, proximity)` and the value is the associated merged roaring bitmap.
+    /// The key given to `insert` is `(proximity, word1, prefix)` and the value is the associated merged roaring bitmap.
    fn flush(
        &mut self,
        merge_buffer: &mut Vec<u8>,
        insert: &mut impl for<'buffer> FnMut(&'buffer [u8], &'buffer [u8]) -> Result<()>,
    ) -> Result<()> {
-        let PrefixAndProximityBatch { word1, batch } = self;
+        let PrefixAndProximityBatch { proximity, word1, batch } = self;
        if batch.is_empty() {
            return Ok(());
        }
        merge_buffer.clear();
-        let mut buffer = Vec::with_capacity(word1.len() + 1 + 6 + 1);
+        let mut buffer = Vec::with_capacity(word1.len() + 1 + 6);
        buffer.push(*proximity);
        buffer.extend_from_slice(word1);
        buffer.push(0);
        for (key, mergeable_data) in batch.drain(..) {
-            buffer.truncate(word1.len() + 1);
+            buffer.truncate(1 + word1.len() + 1);
            buffer.extend_from_slice(key.as_slice());
            let data = if mergeable_data.len() > 1 {
@ -520,61 +440,6 @@ impl PrefixAndProximityBatch {
    }
 }
 // This is adapted from `sorter_into_lmdb_database`
 fn insert_into_database(
    wtxn: &mut heed::RwTxn,
    database: heed::PolyDatabase,
    new_key: &[u8],
    new_value: &[u8],
 ) -> Result<()> {
    let mut iter = database.prefix_iter_mut::<_, ByteSlice, ByteSlice>(wtxn, new_key)?;
    match iter.next().transpose()? {
        Some((key, old_val)) if new_key == key => {
            let val =
                merge_cbo_roaring_bitmaps(key, &[Cow::Borrowed(old_val), Cow::Borrowed(new_value)])
                    .map_err(|_| {
                        // TODO just wrap this error?
                        crate::error::InternalError::IndexingMergingKeys {
                            process: "get-put-merge",
                        }
                    })?;
            // safety: we use the new_key, not the one from the database iterator, to avoid undefined behaviour
            unsafe { iter.put_current(new_key, &val)? };
        }
        _ => {
            drop(iter);
            database.put::<_, ByteSlice, ByteSlice>(wtxn, new_key, new_value)?;
        }
    }
    Ok(())
 }
 // This is adapted from `sorter_into_lmdb_database` and `write_into_lmdb_database`,
 // but it uses `append` if the database is empty, and it assumes that the values in the
 // writer don't conflict with values in the database.
 pub fn write_into_lmdb_database_without_merging(
    wtxn: &mut heed::RwTxn,
    database: heed::PolyDatabase,
    writer: grenad::Writer<std::fs::File>,
 ) -> Result<()> {
    let file = writer.into_inner()?;
    let reader = grenad::Reader::new(BufReader::new(file))?;
    if database.is_empty(wtxn)? {
        let mut out_iter = database.iter_mut::<_, ByteSlice, ByteSlice>(wtxn)?;
        let mut cursor = reader.into_cursor()?;
        while let Some((k, v)) = cursor.move_on_next()? {
            // safety: the key comes from the grenad reader, not the database
            unsafe { out_iter.append(k, v)? };
        }
    } else {
        let mut cursor = reader.into_cursor()?;
        while let Some((k, v)) = cursor.move_on_next()? {
            database.put::<_, ByteSlice, ByteSlice>(wtxn, k, v)?;
        }
    }
    Ok(())
 }
 /** A prefix trie. Used to iterate quickly over the prefixes of a word that are
 within a set.
@ -619,7 +484,7 @@ impl PrefixTrieNode {
    fn set_search_start(&self, word: &[u8], search_start: &mut PrefixTrieNodeSearchStart) -> bool {
        let byte = word[0];
        if self.children[search_start.0].1 == byte {
-            true
+            return true;
        } else {
            match self.children[search_start.0..].binary_search_by_key(&byte, |x| x.1) {
                Ok(position) => {
@ -637,7 +502,7 @@ impl PrefixTrieNode {
    fn from_sorted_prefixes<'a>(prefixes: impl Iterator<Item = &'a str>) -> Self {
        let mut node = PrefixTrieNode::default();
        for prefix in prefixes {
-            node.insert_sorted_prefix(prefix.as_bytes().iter());
+            node.insert_sorted_prefix(prefix.as_bytes().into_iter());
        }
        node
    }
@ -701,90 +566,10 @@ impl PrefixTrieNode {
 }
 #[cfg(test)]
 mod tests {
    use std::io::Cursor;
    use roaring::RoaringBitmap;
    use super::*;
-    use crate::documents::{DocumentsBatchBuilder, DocumentsBatchReader};
+    use crate::{CboRoaringBitmapCodec, U8StrStrCodec};
    use crate::index::tests::TempIndex;
    use crate::{db_snap, CboRoaringBitmapCodec, StrStrU8Codec};
    fn documents_with_enough_different_words_for_prefixes(prefixes: &[&str]) -> Vec<crate::Object> {
        let mut documents = Vec::new();
        for prefix in prefixes {
            for i in 0..50 {
                documents.push(
                    serde_json::json!({
                        "text": format!("{prefix}{i:x}"),
                    })
                    .as_object()
                    .unwrap()
                    .clone(),
                )
            }
        }
        documents
    }
    #[test]
    fn test_update() {
        let mut index = TempIndex::new();
        index.index_documents_config.words_prefix_threshold = Some(50);
        index.index_documents_config.autogenerate_docids = true;
        index
            .update_settings(|settings| {
                settings.set_searchable_fields(vec!["text".to_owned()]);
            })
            .unwrap();
        let batch_reader_from_documents = |documents| {
            let mut builder = DocumentsBatchBuilder::new(Vec::new());
            for object in documents {
                builder.append_json_object(&object).unwrap();
            }
            DocumentsBatchReader::from_reader(Cursor::new(builder.into_inner().unwrap())).unwrap()
        };
        let mut documents = documents_with_enough_different_words_for_prefixes(&["a", "be"]);
        // now we add some documents where the text should populate the word_prefix_pair_proximity_docids database
        documents.push(
            serde_json::json!({
                "text": "At an amazing and beautiful house"
            })
            .as_object()
            .unwrap()
            .clone(),
        );
        documents.push(
            serde_json::json!({
                "text": "The bell rings at 5 am"
            })
            .as_object()
            .unwrap()
            .clone(),
        );
        let documents = batch_reader_from_documents(documents);
        index.add_documents(documents).unwrap();
        db_snap!(index, word_prefix_pair_proximity_docids, "initial");
        let mut documents = documents_with_enough_different_words_for_prefixes(&["am", "an"]);
        documents.push(
            serde_json::json!({
                "text": "At an extraordinary house"
            })
            .as_object()
            .unwrap()
            .clone(),
        );
        let documents = batch_reader_from_documents(documents);
        index.add_documents(documents).unwrap();
        db_snap!(index, word_prefix_pair_proximity_docids, "update");
    }
    fn check_prefixes(
        trie: &PrefixTrieNode,
@ -883,58 +668,40 @@ mod tests {
        CboRoaringBitmapCodec::serialize_into(&bitmap_ranges, &mut serialised_bitmap_ranges);
        let word_pairs = [
-            // 1, 3:  (healthy arb 2) and (healthy arbre 2) with (bitmap123 | bitmap456)
+            ((1, "healthy", "arbres"), &serialised_bitmap123),
-            (("healthy", "arbre", 2), &serialised_bitmap123),
+            ((1, "healthy", "boat"), &serialised_bitmap123),
-            //          not inserted because 3 > max_proximity
+            ((1, "healthy", "ca"), &serialised_bitmap123),
-            (("healthy", "arbre", 3), &serialised_bitmap456),
+            ((1, "healthy", "cats"), &serialised_bitmap456),
-            // 0, 2:  (healthy arb 1) and (healthy arbre 1) with (bitmap123)
+            ((1, "healthy", "cattos"), &serialised_bitmap123),
-            (("healthy", "arbres", 1), &serialised_bitmap123),
+            ((1, "jittery", "cat"), &serialised_bitmap123),
-            // 1, 3:
+            ((1, "jittery", "cata"), &serialised_bitmap456),
-            (("healthy", "arbres", 2), &serialised_bitmap456),
+            ((1, "jittery", "catb"), &serialised_bitmap789),
-            //          not be inserted because 3 > max_proximity
+            ((1, "jittery", "catc"), &serialised_bitmap_ranges),
-            (("healthy", "arbres", 3), &serialised_bitmap789),
+            ((2, "healthy", "arbre"), &serialised_bitmap123),
-            //          not inserted because no prefixes for boat
+            ((2, "healthy", "arbres"), &serialised_bitmap456),
-            (("healthy", "boat", 1), &serialised_bitmap123),
+            ((2, "healthy", "cats"), &serialised_bitmap789),
-            //          not inserted because no prefixes for ca
+            ((2, "healthy", "cattos"), &serialised_bitmap_ranges),
-            (("healthy", "ca", 1), &serialised_bitmap123),
+            ((3, "healthy", "arbre"), &serialised_bitmap456),
-            // 4: (healthy cat 1) with (bitmap456 + bitmap123)
+            ((3, "healthy", "arbres"), &serialised_bitmap789),
            (("healthy", "cats", 1), &serialised_bitmap456),
            // 5: (healthy cat 2) with (bitmap789 + bitmap_ranges)
            (("healthy", "cats", 2), &serialised_bitmap789),
            // 4 + 6: (healthy catto 1) with (bitmap123)
            (("healthy", "cattos", 1), &serialised_bitmap123),
            // 5 + 7: (healthy catto 2) with (bitmap_ranges)
            (("healthy", "cattos", 2), &serialised_bitmap_ranges),
            // 8: (jittery cat 1) with (bitmap123 | bitmap456 | bitmap789 | bitmap_ranges)
            (("jittery", "cat", 1), &serialised_bitmap123),
            // 8:
            (("jittery", "cata", 1), &serialised_bitmap456),
            // 8:
            (("jittery", "catb", 1), &serialised_bitmap789),
            // 8:
            (("jittery", "catc", 1), &serialised_bitmap_ranges),
        ];
        let expected_result = [
-            // first batch:
+            ((1, "healthy", "arb"), bitmap123.clone()),
-            (("healthy", "arb", 1), bitmap123.clone()),
+            ((1, "healthy", "arbre"), bitmap123.clone()),
-            (("healthy", "arb", 2), &bitmap123 | &bitmap456),
+            ((1, "healthy", "cat"), &bitmap456 | &bitmap123),
-            (("healthy", "arbre", 1), bitmap123.clone()),
+            ((1, "healthy", "catto"), bitmap123.clone()),
-            (("healthy", "arbre", 2), &bitmap123 | &bitmap456),
+            ((1, "jittery", "cat"), (&bitmap123 | &bitmap456 | &bitmap789 | &bitmap_ranges)),
-            // second batch:
+            ((2, "healthy", "arb"), &bitmap123 | &bitmap456),
-            (("healthy", "cat", 1), &bitmap456 | &bitmap123),
+            ((2, "healthy", "arbre"), &bitmap123 | &bitmap456),
-            (("healthy", "cat", 2), &bitmap789 | &bitmap_ranges),
+            ((2, "healthy", "cat"), &bitmap789 | &bitmap_ranges),
-            (("healthy", "catto", 1), bitmap123.clone()),
+            ((2, "healthy", "catto"), bitmap_ranges.clone()),
            (("healthy", "catto", 2), bitmap_ranges.clone()),
            // third batch
            (("jittery", "cat", 1), (&bitmap123 | &bitmap456 | &bitmap789 | &bitmap_ranges)),
        ];
        let mut result = vec![];
        let mut iter =
-            IntoIterator::into_iter(word_pairs).map(|((word1, word2, proximity), data)| {
+            IntoIterator::into_iter(word_pairs).map(|((proximity, word1, word2), data)| {
-                ((word1.as_bytes(), word2.as_bytes(), proximity), data.as_slice())
+                ((proximity, word1.as_bytes(), word2.as_bytes()), data.as_slice())
            });
        execute_on_word_pairs_and_prefixes(
            &mut iter,
@ -942,17 +709,17 @@ mod tests {
            &prefixes,
            2,
            |k, v| {
-                let (word1, prefix, proximity) = StrStrU8Codec::bytes_decode(k).unwrap();
+                let (proximity, word1, prefix) = U8StrStrCodec::bytes_decode(k).unwrap();
                let bitmap = CboRoaringBitmapCodec::bytes_decode(v).unwrap();
-                result.push(((word1.to_owned(), prefix.to_owned(), proximity.to_owned()), bitmap));
+                result.push(((proximity.to_owned(), word1.to_owned(), prefix.to_owned()), bitmap));
                Ok(())
            },
        )
        .unwrap();
        for (x, y) in result.into_iter().zip(IntoIterator::into_iter(expected_result)) {
-            let ((actual_word1, actual_prefix, actual_proximity), actual_bitmap) = x;
+            let ((actual_proximity, actual_word1, actual_prefix), actual_bitmap) = x;
-            let ((expected_word1, expected_prefix, expected_proximity), expected_bitmap) = y;
+            let ((expected_proximity, expected_word1, expected_prefix), expected_bitmap) = y;
            assert_eq!(actual_word1, expected_word1);
            assert_eq!(actual_prefix, expected_prefix);
--- a/milli/src/update/snapshots/word_prefix_pair_proximity_docids.rs/test_update/initial/word_prefix_pair_proximity_docids.snap
+++ b/milli/src/update/snapshots/word_prefix_pair_proximity_docids.rs/test_update/initial/word_prefix_pair_proximity_docids.snap
@ -1,46 +0,0 @@
 ---
 source: milli/src/update/word_prefix_pair_proximity_docids.rs
 ---
 5                a    1  [101, ]
 5                a    2  [101, ]
 5                b    4  [101, ]
 5                be   4  [101, ]
 am               a    3  [101, ]
 amazing          a    1  [100, ]
 amazing          a    2  [100, ]
 amazing          a    3  [100, ]
 amazing          b    2  [100, ]
 amazing          be   2  [100, ]
 an               a    1  [100, ]
 an               a    2  [100, ]
 an               b    3  [100, ]
 an               be   3  [100, ]
 and              a    2  [100, ]
 and              a    3  [100, ]
 and              a    4  [100, ]
 and              b    1  [100, ]
 and              be   1  [100, ]
 at               a    1  [100, ]
 at               a    2  [100, 101, ]
 at               a    3  [100, ]
 at               b    3  [101, ]
 at               b    4  [100, ]
 at               be   3  [101, ]
 at               be   4  [100, ]
 beautiful        a    2  [100, ]
 beautiful        a    3  [100, ]
 beautiful        a    4  [100, ]
 bell             a    2  [101, ]
 bell             a    4  [101, ]
 house            a    3  [100, ]
 house            a    4  [100, ]
 house            b    2  [100, ]
 house            be   2  [100, ]
 rings            a    1  [101, ]
 rings            a    3  [101, ]
 rings            b    2  [101, ]
 rings            be   2  [101, ]
 the              a    3  [101, ]
 the              b    1  [101, ]
 the              be   1  [101, ]
--- a/milli/src/update/snapshots/word_prefix_pair_proximity_docids.rs/test_update/update/word_prefix_pair_proximity_docids.hash.snap
+++ b/milli/src/update/snapshots/word_prefix_pair_proximity_docids.rs/test_update/update/word_prefix_pair_proximity_docids.hash.snap
@ -1,4 +0,0 @@
 ---
 source: milli/src/update/word_prefix_pair_proximity_docids.rs
 ---
 5ed4bf83317b10962a55ade353427bdd