Lock in $30 Savings on PRO—Offer Ends Soon! ⏳
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
転置インデックスでどう検索しているか
Search
kotaroooo0
November 19, 2020
Technology
0
340
転置インデックスでどう検索しているか
kotaroooo0
November 19, 2020
Tweet
Share
More Decks by kotaroooo0
See All by kotaroooo0
データ鮮度を落とさずに安全にReindexしたい
kotaroooo0
0
91
検索エンジン自作入門 Go Conference 2021 Spring
kotaroooo0
17
7.4k
俺の全文検索エンジン(Go製)を作り始めた
kotaroooo0
0
110
ぼくのかんがえたさいきょうのDocker Build
kotaroooo0
0
91
Other Decks in Technology
See All in Technology
生成AI・AIエージェント時代、データサイエンティストは何をする人なのか?そして、今学生であるあなたは何を学ぶべきか?
kuri8ive
2
1.1k
AI開発の定着を推進するために揃えるべき前提
suguruooki
1
470
翻訳・対話・越境で強いチームワークを作ろう! / Building Strong Teamwork through Interpretation, Dialogue, and Border-Crossing
ar_tama
4
1.5k
Bill One 開発エンジニア 紹介資料
sansan33
PRO
4
16k
IaC を使いたくないけどポリシー管理をどうにかしたい
kazzpapa3
1
210
21st ACRi Webinar - AMD Presentation Slide (Nao Sumikawa)
nao_sumikawa
0
140
段階的に進める、 挫折しない自宅サーバ入門
yu_kod
5
2.2k
iRAFT法-他社事例を"自社仕様化"する技術 #pmconf2025
daichi_yamashita
0
200
.NET 10 のパフォーマンス改善
nenonaninu
2
4.4k
「え?!それ今ではHTMLだけでできるの!?」驚きの進化を遂げたモダンHTML
riyaamemiya
9
4.2k
事業部のプロジェクト進行と開発チームの改善の “時間軸" のすり合わせ
konifar
9
2.7k
MySQL AIとMySQL Studioを使ってみよう
ikomachi226
0
130
Featured
See All Featured
What’s in a name? Adding method to the madness
productmarketing
PRO
24
3.8k
Producing Creativity
orderedlist
PRO
348
40k
Mobile First: as difficult as doing things right
swwweet
225
10k
Save Time (by Creating Custom Rails Generators)
garrettdimon
PRO
32
1.8k
A designer walks into a library…
pauljervisheath
210
24k
Let's Do A Bunch of Simple Stuff to Make Websites Faster
chriscoyier
508
140k
Optimising Largest Contentful Paint
csswizardry
37
3.5k
Making Projects Easy
brettharned
120
6.5k
Navigating Team Friction
lara
191
16k
Statistics for Hackers
jakevdp
799
230k
How To Stay Up To Date on Web Technology
chriscoyier
791
250k
Designing Experiences People Love
moore
142
24k
Transcript
2020/11/19 @kotaroooo0 సஔΠϯσοΫεͰ Ͳ͏ݕࡧ͍ͯ͠Δ͔
ࣗݾհ
సஔΠϯσοΫεɺͪΖΜͬͯΔΑ? grepΈͨ͘ஞ࣍ݕࡧͯ͠ΔͱΊͪΌͪ͘Ό͕͔͔࣌ؒΔ͔ ΒɺݕࡧΛૣ͘͢ΔͨΊʹࣄલʹຊͷ࣍Έ͍ͨͳͷΛ ࡞͓ͬͯ͘ΜͰ͠ΐ? ͰɺͲ͏ͬͯݕࡧ͍ͯ͠Δ͔·Ͱ… ఆฉ͖ख
సஔΠϯσοΫεΛ Δ
సஔΠϯσοΫε netflix prime amazon - సஔΠϯσοΫε = ࣙॻ + సஔϦετ
1 1 2 3 4 5 5 ࣙॻ సஔϦετ ϙεςΟϯάϦετ
సஔΠϯσοΫε netflix prime amazon - Word-level inverted list ͱݺΕɺ୯ޠ͕จॻͷԿ୯ޠ͔อଘ͢Δ͜ͱ͋Δ -
DocID;offset1,ofset2… 1;2 1;3 2;3 3;5 4;1 5;2 5;3 2;5
୯ޠͷҐஔใͳʹʹ͏? - ϑϨʔζΛ୳͢߹ - ʮAmazon Primeʯͱݕࡧͨ͠߹ - D1: “a prime
concern of Amazon” - D2: “Amazon Prime movies” - Ґஔใ͕͋Ε୯ޠͷॱংΛߟྀ͢Δ͜ͱ͕Ͱ͖ΔͷͰɺD2ͷΈΛώοτ͞ ͤΔ͜ͱ͕Ͱ͖ͨΓɺD2ͷείΞΛେ͖ͨ͘͠Γ͢Δ͜ͱ͕Ͱ͖Δ
ݕࡧ͢Δ
ANDݕࡧͱORݕࡧ -ΫΤϦ: “pink orange blue” -ANDݕࡧ: 3 -ORݕࡧ: 1,2,3,4,5,6 pink
Orange blue 6 3 4 5 2 1
φΠʔϒͳݕࡧઓུ - ϙεςΟϯάϦετΛࠪ͢Δํࣜ - TAAT(Term At A Time) - ϙεςΟϯάϦετΛ̍ͭͣͭॲཧ͢Δɻಉ࣌ʹ։͘ϙεςΟϯάϦετͷΧʔι
ϧ͚̍ͭͩɻ - ୯ޠ͝ͱʹࠪ͢Δ - DAAT(Document At A Time) - શ୯ޠͷϙεςΟϯάϦετΛಉ࣌ʹॲཧ͢ΔɻΫΤϦʹؚ·ΕΔ୯ޠͷϙε ςΟϯάϦετͷΧʔιϧΛͯ͢։͖ɼಉ࣌ʹਐΊ͍ͯ͘ɻ - υΩϡϝϯτ͝ͱʹࠪ͢Δ
TAATͰͷANDݕࡧ 1. ϙεςΟϯάϦετͷαΠζ͕࠷খͷͷ(prime)Λબ͠ΛɺAccumulator࡞ [2, 5] 2. amazonͷϦετΛࠪ ɾ2ؚ·Ε͍ͯΔ͔?5ؚ·Ε͍ͯΔ͔?ͷΈͷνΣοΫͰOK netflix prime
amazon 1;2 1;3 2;3 4;1 5;2 5;3 2;5
TAATͰͷORݕࡧ 1. ͲͷΩʔͰྑ͍ͷͰAccumlatorΛ࡞ [1,2,5] (amazon) 2. ॏෳ͠ͳ͍શͯͷΩʔΛݟͯϚʔδ ✌(‘ω'✌ ) ݪ࢝త
( ✌'ω')✌ netflix prime amazon 1;2 1;3 2;3 4;1 5;2 5;3 2;5
DAATͰͷANDݕࡧ - Amazon AND prime - Accumulated = [] netflix
prime amazon 1;2 1;3 2;3 4;1 5;2 5;3 2;5
DAATͰͷANDݕࡧ - Amazon AND prime - Accumulater = [2] netflix
prime amazon 1;2 1;3 2;3 4;1 5;2 5;3 2;5
DAATͰͷANDݕࡧ - Amazon AND prime - Accumulate = [2, 5]
netflix prime amazon 1;2 1;3 2;3 4;1 5;2 5;3 2;5
DAATͰͷORݕࡧ - ΧʔιϧΛಈ͔ͯ͠ɺશͯͷཁૉΛॏෳͳ͘AccumulatorʹՃ ✌(‘ω'✌ ) ݪ࢝త ( ✌'ω')✌ netflix prime
amazon 1;2 1;3 2;3 4;1 5;2 5;3 2;5
DAATͱTAAT - DAATͷϝϦοτ - DAATͷํ͕ɺলϝϞϦͰࡁΉ(ྫ: τοϓ10݅ݕࡧ) - DAATͷํ͕ɺΫΤϦ༻ޠ͕จॻͷಛఆͷ݅Λຬ͍ͨͯ͠Δ͔Ͳ͏͔Λ؆୯ʹࣝ ผͰ͖Δ(ྫ: ϑϨʔζݕࡧɺϑΟϧλϦϯά)
- ElasticsearchͰར༻͞Ε͍ͯΔLuceneDAATํࣜ - ORݕࡧݪ࢝తͳΈͰ͋ΓɺANDݕࡧΑΓଟ͘ͷυΩϡϝϯτΛࠪ͢ΔͨΊɺ ॏ͍ͨ - ݕࡧΤϯδϯORݕࡧʹ࠷దԽ͞Ε͍ͯΔ
ORݕࡧͷ ࠷దԽ४උ
Ͳ͏ߴԽ͢Δ͔ - DAATΛϕʔεʹվળ͢Δ - ݕࡧ݁Ռ্͕Ґ͚݅ͩඞཁͰ͋Δɺ্ҐʹདྷΔՄೳੑ͕ͳ͍จষͷධՁΛεΩοϓ ͢Δ͜ͱʹΑΓɺॲཧͷߴԽ͕Մೳ - ͍߹Θͤʹର্ͯ͠Ґk݅ͷΈΛऔΓग़͢͜ͱΛtop-k query processingͱݺͿ
จষͷϥϯΫ͚ - ্Ґk݅Λग़ྗ͢ΔͨΊʹɺΫΤϦʹରͯ͠ͲͷจষͷॱҐ͕ߴ͍͔Λܾఆ͢Δඞཁ͕͋ Δ - TF-IDF, Okapi BM25
సஔΠϯσοΫεͷ֦ு - సஔΠϯσοΫεʹରͯ͠ɺ֤୯ޠͷείΞ࠷େͱυΩϡϝϯτ͝ͱͷ୯ޠͷείΞ Λ༩͢Δ - ܗࣜ DocID;Score netflix prime amazon
1;5 1;3 2;1 3;2 4;1 5;2 5;1 2;3 max_score=5 max_score=3 max_score=2
Top-k Query ɾmax-score ɾinterval-based running
max-score - ্ҐkҐʹೖΔͨΊͷείΞ5ඞཁͱ͢Δͱɺ”amazon”,”prime”ͷmax_score5ະຬͳͷ Ͱɺ”amazon”ͷΈ”prime”ͷΈΛؚΉจষީิ͔Β֎ΕΔ netflix prime amazon max_score=5 max_score=3 max_score=2
1;5 1;1 2;1 4;1 5;1 2;3 5;2 5;1 3;3
max-score - ্Ґ1݅Λऔಘ͍ͨ͠ - จॻ1: score6 - ඞͣ”netflix”ΛؚΉυΩϡϝϯτ͡Όͳ͍ͱμϝ netflix prime
amazon max_score=5 max_score=3 max_score=2 1;5 1;1 2;1 4;1 5;1 2;3 5;2 5;1 3;3
max-score - “netflix”ΛؚΉจॻ5·ͰΧʔιϧΛඈ͢ - จॻ5είΞ4 - จॻ1,5ͷΈΛධՁ͢Δ͚ͩͰऴྃ netflix prime amazon
max_score=5 max_score=3 max_score=2 1;5 1;1 2;1 4;1 2;3 5;2 3;3 5;1 5;1
Interval-base - max_scoreΑΓ͞ΒʹεΩοϓͰ͖Δ - ͕͜͜ΒΜͰྗਚ͖ͨ࣌ؒͪ͠ΐ͏Ͳ͍͍ͩΖ͏…
LuceneͰ - Lucent 8Ͱmax-scoreͷൃలܗͰ͋ΔWAND͕ΘΕ͍ͯ·͢ - Ding, Shuai, and Torsten Suel.
"Faster top-k document retrieval using block-max indexes." Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 2011. - 2019/3/14ϦϦʔεͷLucene 8Ͱ্ͷΞϧΰϦζϜ͕࣮͞ΕΔͳͲࠓͰޮతͳΞϧ ΰϦζϜͷݚڀ͕ଓ͍͍ͯΔ APA
సஔΠϯσοΫεͷ ࣮
·ͱΊ - సஔΠϯσοΫε༷ʑͳϝλใΛՃ͢Δ͜ͱͰ֦ு͞ΕΔ(୯ޠͷΦϑηοτɺε ίΞɺϙΠϯλ) - సஔΠϯσοΫεʹରͯ͠ANDݕࡧɺORݕࡧ͢ΔࡍͷφΠʔϒͳํ๏ - TAAT: ୯ޠ͝ͱʹࠪ͢Δ -
DAAT: จॻ͝ͱʹࠪ͢Δ - DAATʹର͢ΔORݕࡧʹ࠷దԽ - max-score: ୯ޠ͝ͱͷ࠷େείΞͱݱࡏͷ࠷େείΞΛอ࣋͢Δ͜ͱͰࢬמΓΛߦ ͍୳ࡧΛεΩοϓ͢Δ - LuceneͰDAAT͕࠾༻͞Ε͓ͯΓɺݱࡏͰΞϧΰϦζϜ͕վળ͞Ε͍ͯΔ