Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
OSC-Hokkaido-2018-hayabusa
Search
Hiroshi
July 07, 2018
Research
0
690
OSC-Hokkaido-2018-hayabusa
This is the presentation material for OSC Hokkaido 2018
Hiroshi
July 07, 2018
Tweet
Share
More Decks by Hiroshi
See All by Hiroshi
pepacon night : log research working group report
hirolovesbeer
0
1.4k
イベントネットワークにおけるsyslog分析でのElasticsearchの利用
hirolovesbeer
1
1.2k
Other Decks in Research
See All in Research
Introduction of NII S. Koyama's Lab (AY2025)
skoyamalab
0
430
利用シーンを意識した推薦システム〜SpotifyとAmazonの事例から〜
kuri8ive
1
180
Computational OT #1 - Monge and Kantorovitch
gpeyre
0
150
2025年度 生成AIの使い方/接し方
hkefka385
1
660
A multimodal data fusion model for accurate and interpretable urban land use mapping with uncertainty analysis
satai
3
160
SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery
satai
3
160
Generative Models 2025
takahashihiroshi
19
7.5k
データサイエンティストの採用に関するアンケート
datascientistsociety
PRO
0
780
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
satai
3
160
データサイエンティストの就労意識~2015→2024 一般(個人)会員アンケートより
datascientistsociety
PRO
0
570
VAGeo: View-specific Attention for Cross-View Object Geo-Localization
satai
3
290
公立高校入試等に対する受入保留アルゴリズム(DA)導入の提言
shunyanoda
0
4.9k
Featured
See All Featured
For a Future-Friendly Web
brad_frost
178
9.7k
How STYLIGHT went responsive
nonsquared
100
5.6k
How To Stay Up To Date on Web Technology
chriscoyier
790
250k
Refactoring Trust on Your Teams (GOTO; Chicago 2020)
rmw
34
3k
Fight the Zombie Pattern Library - RWD Summit 2016
marcelosomers
233
17k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
280
13k
Sharpening the Axe: The Primacy of Toolmaking
bcantrill
42
2.3k
Build The Right Thing And Hit Your Dates
maggiecrowley
35
2.7k
Testing 201, or: Great Expectations
jmmastey
42
7.5k
"I'm Feeling Lucky" - Building Great Search Experiences for Today's Users (#IAC19)
danielanewman
228
22k
Let's Do A Bunch of Simple Stuff to Make Websites Faster
chriscoyier
507
140k
We Have a Design System, Now What?
morganepeng
52
7.6k
Transcript
Hayabusa ߴʹશจݕࡧՄೳͳ OSSϩάݕࡧΤϯδϯͷ͝հ Ѩ෦ തɿגࣜձࣾϨϐμϜ ݚڀһ OSC 2018 Hokkaido 2018/07/08
ࣗݾհ • ໊લɿѨ෦ ത • ॴଐɿגࣜձࣾϨϐμϜʢݚڀһʣɺίίϯגࣜձࣾʢࣾิࠤ/ٕज़ݚڀ ॴ ݚڀһʣɺใ௨৴ݚڀػߏʢڠྗݚڀһʣɺઌՊֶٕज़େֶ Ӄେֶʢത࢜ޙظ՝ఔʣ •
ͦͷଞɿInterop Tokyo ShowNet NOCϝϯόʔ
࣍ • എܠͱత • Hayabusaʹ͍ͭͯ • ࢄHayabusaͷఏҊʢઃܭͱ࣮ʣ • ධՁ •
ߟ • ·ͱΊͱࠓޙͷ՝ !3
എܠͱత !4
Interop Tokyo ShowNet 2018 • 900Λ͑ΔཧɾԾػث܈ • ΄΅શͯͷػث͕syslogΛૹ৴ • ߏஙظؒʹड৴͢Δsyslogྔ
• 2ສ݅/ඵʢ20k/secʣ • 1ԯ̓ઍສ݅/ !5
ShowNetʹ͓͚Δϩάͷӡ༻ • େྔͷϩάΛੵ͢Δ • େྔͷϩά͔Βݕࡧ͢Δ • ΠϯγσϯτରԠͷͨΊʹϩάΛݕࡧ͢Δ • τϥϒϧγϡʔτͷͨΊʹϩάΛݕࡧ͢Δ •
ϩά͔Β౷ܭใΛऔಘ͢Δ • ߜΓࠐΜͩݕࡧใΛ౷ܭใͱͯ͠දࣔ͢Δ !6
طଘͷղܾࡦ • HadoopΤίγεςϜʢSpark, Impala, Hive, …ʣ • OSSʢElasticsearch + Kibana,
fluentd, …ʣ • ༻ϓϩμΫτʢSplunk, VMware Loginsight, …ʣ • ΫϥυαʔϏεʢGoogle BigQuery, Treasure Data, …ʣ !7
େ͖ͳ • ϩάͷߏԽ͕Ͱ͖ͳ͍ • ػࡐʹ౷Ұੑ͕ͳ͍ɾ࠷৽ͷϑΝʔϜ͗ͯ͢ใ͕ͳ͍ • ετϦʔϛϯάॲཧ͕͍͠ྲྀྔ • ϩάͷྲྀྔ͕ଟ͗ͯ͢ॲཧ͕͍͔ͭͳ͍ •
όονॲཧ͕͍͔ͭͳ͍ • όονॲཧ͕ࢦఆ࣌ؒʹऴΘΒͳ͍ • ࢄॲཧγεςϜ͕ෳࡶ͗͢Δ • ཧίετ͕ലେ !8
త • ܰྔʹߏஙɾӡ༻͕ߦ͑ΔγεςϜͷ࣮ݱ • γϯϓϧͰεέʔϧΞοϓՄೳͳγεςϜͷ࣮ݱ • ݕࡧੑೳ͕CPUʢίΞʣੑೳʹൺྫͯ͠ૣ͘ͳΔ • ෳࡶͳཧػߏΛඋ͑ͳ͍ !9
)BZBCVTBʹ͍ͭͯ !10
Hayabusaͱʁ • େྔͷϩάΛߴʹݕࡧ͢Δʢ17ԯϨίʔυͷશจݕࡧ͕5ඵʣ • ελϯυΞϩϯαʔόͰಈ࡞͢Δ • ϚϧνίΞΛ༗ޮʹ͍ɺߴͳฒྻݕࡧॲཧΛ࣮ݱ͢Δ
StoreEngine • σΟεΫʹॻ͖ࠐ·ΕͨϩάΛߴʹಡΈࠐΉ • ಡΈࠐΜͩϩάΛSQLite3ͷϑΝΠϧͱมʢ1ߦ1Ϩίʔυʣ • SQLite3ͷશจݕࡧʹಛԽͨ͠FTS(Full Text Search)ܗࣜͰinsert •
࣌ؒσΟϨΫτϦߏʹରԠ : /targetdir/yyyy/mm/dd/hh/min.db StoreEngine
SearchEngine • GNU ParallelΛ༻͍ͯSQLite3ϑΝΠϧฒྻݕࡧΛ͔͚Δ $ parallel sqlite3 ::: target files
::: “select count(*) from xxx where logs match ‘keyword’;” • ݕࡧ݁ՌΛUNIXύΠϓϥΠϯΛ༻͍ͯɺawkcountίϚϯυͰूܭ $ parallel sqlite3 ::: target files ::: “select count(*) from xxx where logs match ‘keyword’;” | awk ‘{m+=$1} END{print m;}’ SeachEngine !13
શจݕࡧੑೳ • Apache SparkͱͷൺֱʢελϯυΞϩϯڥʣ • Apache SparkͱͷൺֱʢSpark x 3 +
HDFS vs Hayabusa x 1ʣ Hayabusa͕ ̐ഒߴ Hayabusa͕ 27ഒߴ
OSSͱͯ͠ެ։ • GitHubʹͯެ։ • https://github.com/hirolovesbeer/hayabusa !15
Hayabusaͷ • ελϯυΞϩϯڥ • ੑೳΛ্͛ΔʹεέʔϧΞοϓ͔͠ͳ͍ • εέʔϧΞοϓίετ • ࢄॲཧγεςϜͱͷࠩ •
ن͕େ͖͘ͳΕࢄॲཧγεςϜͷॲཧ͘ͳΔ • Hayabusa͍͔ͭੑೳ͕ൈ͔ΕΔ !16
ࢄ)BZBCVTBͷఏҊʢઃܭͱ࣮ʣ !17
త • HayabusaΛࢄॲཧγεςϜͱਐԽͤ͞ॲཧΛεέʔϧΞτͤ͞Δ • ελϯυΞϩϯͷੑೳੜ͔͠ଓ͚Δ • ࢄॲཧγεςϜͰ͋Δ͕γϯϓϧͳઃܭΛࢤ͢ • σʔλΛෳ͢Δ͜ͱͰোੑΛߴΊΔ !18
GNU ParallelͷϦϞʔτ࣮ߦ • ཧ : GNU ParallelͷϦϞʔτ࣮ߦΛར༻͢Εࢄ࣮ߦՄೳ $ time parallel
—controlmaster -S host1,host2,host3 sqlite3 ::: … • ݱ࣮ : sshͷΦʔόϔου͕͔͔Γॲཧ͕Ԇ ϗετ͕૿͑Δͱॲཧ͕࣌ؒ૿͑Δ
ఏҊख๏ • ࢄݕࡧ • ࣮ߦ͢ΔݕࡧॲཧΛRPCͱͯ͠HayabusaૹΓࠐΉ • ݁ՌΛRPCͷϨεϙϯεͱͯ͠ड͚औΓूܭ͢Δ • ฒྻੵ •
શͯͷϗετಉҰͷϦΫΤετ͕ಧ͍ͯಉ݁͡ՌΛฦ͢Α͏ʹ͢Δ • ࣄલʹશॲཧϗετͱϩάσʔλΛෳ͢Δ !20
ࢄHayabusaΞʔΩςΫνϟશ༰
ฒྻੵ • syslogΛෳϗετͱෳ͢Δ • શϗετͰಉҰͷsyslogΛड৴ • UDP SamplicatorʢOSSʣͷར༻ • syslogύέοτͷෳͱసૹ
• ෳॲཧͷίΞεέʔϧԽ • UDP SmaplicatorͷϚϧνϓϩηεԽ !22 syslogͷෳ
UDP SamplicatorͷϚϧνϓϩηεԽ • ϘτϧωοΫʹͳΓ͕ͪͳϓϩηεΛίΞεέʔϧ • SO_REUSEPORTΛར༻ͨ͠ϚϧνϓϩηεԽ • ͜ΕʹΑΓUDP 514ϙʔτ͕ෳϓϩηεͰγΣΞ͞ΕΔ socketΦϓγϣϯͷՃ
ۉʹsyslogసૹͷෛՙ͕ όϥϯε͞ΕΔ !23
ࢄݕࡧ • RPC • Producer / ConsumerϞσϧͷ࠾༻ • ࣮ •
ZeroMQͷPush / Pullύλʔϯ • ϦΫΤετͷϩʔυόϥϯε • Push / PullύλʔϯۉҰʹϦΫΤετΛϗετ͢Δ ZeroMQͷPush / Pullύλʔϯ !24
ࢄݕࡧ • ZeroMQΫϥΠΞϯτ • VentilatorͱSinkͷׂ • ZeroMQϫʔΧ • ड͚औͬͨॲཧϦΫΤετ Λ࣮ߦͯ݁͠ՌΛฦ͢
!25
ॲཧϦΫΤετ • ϦΫΤετ $ parallel sqlite3 ::: target files :::
“select count(*) from xxx where logs match ‘keyword’;” | awk ‘{m+=$1} END{print m;}’ ੨ࣈ : GNU ParallelͷίϚϯυΛ֤ॲཧϗετૹΓࠐΉ ࣈ : ΫϥΠΞϯτϗετͰ·ͱΊ͋͛Δ !26
΄΅ຊͳٙࣅίʔυ • ΫϥΠΞϯτ • Worker ࣮ߦίϚϯυ ίϚϯυΛ ϫʔΧૹ৴ ίϚϯυΛ࣮ߦ ݁ՌΛΫϥΠΞϯτૹ৴
݁ՌΛड͚λʔϛφϧදࣔ !27
ධՁ !28
࣮ݧڥ • Amazon Web Service (AWS) • EC2Πϯελϯε : c4.4xlarge
• vCPU : Xeon E5-2666 v3 @ 2.90GHz x 16 cores • ϝϞϦ : 30GB • σΟεΫʢEBSʣ : SSD 8GB (OS) + SSD 50GB (Data) • OS : Ubuntu 16.04.3 LTS (Xenial Xerus) !29
ࢄݕࡧ • ݕࡧͷ݅ • 1ͷσʔλʹରͯ͠100ճϦΫΤετΛ࣮ߦ͢Δ • 1ͷσʔλϑΝΠϧ60ʢ60ϑΝΠϧʣ x 24࣌ؒ =
1,440ϑΝΠϧ • 1ϑΝΠϧ͋ͨΓͷϨίʔυ10ສ݅ʢ1,440 x 10ສʹ1ԯ4400ສϨίʔυʣ • 100ճͷϦΫΤετͰ144ԯϨίʔυ͕ରͱͳΔ • ࣮ߦ͢ΔSQLจҎԼͰશจݕࡧͱΧϯτ • select count(*) from syslog where logs match ‘keyword’; !30
ࢄݕࡧʢϗετεέʔϧΞτʣ • ϗετΛ1͔Β10૿Ճͤ͞Δ • 1Ͱ249ඵ͔Β10Ͱ39ඵ·Ͱॖʢ10ճࢼߦฏۉʣ
ࢄݕࡧʢϗετεέʔϧΞτʣ • ϗετΛ1͔Β10૿Ճͤ͞Δ • 1Ͱ249ඵ͔Β10Ͱ39ඵ·Ͱॖʢ10ճࢼߦฏۉʣ Ϋϥυڥෆ҆ఆ ʢϕετΤϑΥʔτʣ
ࢄݕࡧʢWorkerεέʔϧΞτʣ • ϗετ10ɺ͔ͭ1͋ͨΓͷϫʔΧΛ1͔Β16·Ͱ૿Ճͤ͞Δ • 1ϗετ1 worker 249ඵ͔Β10ϗετ10 workerͰ6.8ඵ·Ͱॖ ͜ͷลΓ͕࠷ *0ڝ߹͕ى͖Δ͔Β͔
͔ΘΒͣ
݁Ռͷ·ͱΊ • ॲཧੑೳ • ϗετ10ͷ߹ : ϗετ1ͷ10ഒૣ͘ͳΔʢ249ඵ -> 39ඵʣ •
ϗετ10ͰϫʔΧΛ૿Ճ : ૯ϫʔΧ10ʙ160Ͱ 249ඵ -> 6.8ඵ • ϨίʔυΛϑϧεΩϟϯˍશจݕࡧͨ݁͠Ռ • 144ԯϨίʔυ͔ΒඞཁͳσʔλΛൈ͖ग़͢ͷʹ6.8ඵ·ͰߴԽ • 10ͷϗετͰ36ഒͷߴԽΛ࣮ݱ !34
Amazon Elastic MapReduceͱͷൺֱ • Amazon EMR : ΠϯελϯεHayabusaͱಉ͡c4.4xlarge • ߏ1Ϛελʔϊʔυ
+ 10 ίΞϊʔυ • σʔλͷΞΫηε • EMR͔ΒAmazon S3μΠϨΫτʹ ΞΫηε • શจݕࡧͷํ๏ • ϚελʔϊʔυͷPySpark͔Βߦ͏ JNQPSUUJNF GSPNQZTQBSLTRMJNQPSU42-$POUFYU TRM$POUFYU42-$POUFYU TD MJOFTTDUFYU'JMF TBCFXPSLTTECFODINBSLMPH pMFTLL MPH MJOFTDBDIF GPSJJOSBOHF TUBSUUJNFUJNF <MJOFTpMUFS MBNCEBTOPDJO T DPVOU GPSJJOSBOHF >FMBQTFE@UJNFUJNFUJNF TUBSUQSJOUFMBQTFE@UJNF 1Z4QBSLͰ࣮ߦ͢Δίʔυ
Amazon Elastic Mapreduceͱͷൺֱ • ࣮ߦ݁Ռ • 10ͷߏͰ17ഒHayabusaͷํ͕ߴʹಈ࡞
ߟ !37
ݕࡧͷεέʔϧΞτ • 144ԯ͔ΒඞཁͳσʔλΛൈ͖ग़͢ͷʹ6.8ඵ·ͰߴԽ • 2લͷBigQueryͷϑϧεΩϟϯ͕120ԯϨίʔυͰ5ඵ • 10ͷϗετͰ36ഒͷߴԽΛ࣮ݱ • BigQueryԿඦɺԿઍͷϗετ͕ಉ࣌ʹಈ͍͍ͯΔ͔ෆ໌ •
Amazon Elastic MapReduceͱͷൺֱ • 10ͷߏͰ17ഒHayabusaͷํ͕ߴʹશจݕࡧՄೳ • γεςϜͷίετΛߟ͑ͨ߹ • ϦʔζφϒϧͰߴੑೳͳࢄݕࡧॲཧ͕࣮ݱͰ͖ͨ !38
ੵͷฒྻԽ • syslogͷෳͷ • େྔͷσʔλʢύέοτʣͷෳͰଳҬΛѹഭ͢Δ • ຊདྷͰ͋ΕHDFSͷΑ͏ʹࢄϑΝΠϧγεςϜΛ͏͖ • ϝλσʔλػߏΛܦ༝ͯ͠σʔλʹΞΫηε͢ΔͨΊຊ࣭తʹ͘ͳΔ •
ࢄϑΝΠϧγεςϜͱ͍ͯ͠ʢҰͭͷݚڀʣ • γϯϓϧ͞ͷٻͷ݁Ռ • อ࣋σʔλ͕ػثͷނোͰফࣦͨ͠ͱͯ͠ෳ͕ΔɾނোػΛ֎͚ͩ͢ • ࢄϑΝΠϧγεςϜͷΑ͏ʹ࠶ஔॲཧ͕ෆཁ !39
γϯϓϧͳઃܭʹΑΔӡ༻ͷ؆ུԽ • ࢄݕࡧ • Procedure / ConsumerϞσϧͰ࣮ݱ • ϓϩηε࣮ߦεέδϡʔϥGNU Parallelʹґଘ
• ෳࡶͳࢄγεςϜΛΘͳ͍ར • τϥϒϧѲͷߴԽ • γεςϜӡ༻ෛՙͷܰݮ !40
ߴԽͷ؊ • ׂΓΓઃܭ • ϦτϥΠॲཧ/Τϥʔॲཧະ࣮ • εέδϡʔϥ • ZeroMQͱGnu Parallelʹ͓ͤ
• ετϨʔδ • ࢄอଘͤͣ͞ʹෳΛอ࣋
ϋʔυΣΞʹґଘ͢Δ • CPU Core • ૣ͚Εૣ͍΄Ͳྑ͍ • CoreͷΑΓΫϩοΫ͕ͦͦ͜͜ૣ͍ํ͕͕ग़Δ͜ͱ͋Δ • σΟεΫ
• SSDNVMeʢͦΓΌૣ͍ʹܾ·͍ͬͯΔʣ • I/OੑೳΛҾ͖ग़͢
ଞͷγεςϜͱͷൺֱ • શจݕࡧͰApache Sparkͱൺֱͨ͠ • Elasticsearchͱͷൺֱʁ • Ͳ͏ͬͯൺΔʁ • ΤϯδϯͷʁʢElasticsearchͱͯૣ͍ʣ
• ݺͼग़͠APIͷՃຯ͢ΔʁʢREST APIݺͼग़͠ͱ͍ͯʣ • Write & Read • ॻ͖ͳ͕ΒಡΈࠐΜͩ߹ʁ
·ͱΊͱࠓޙͷ՝ !44
·ͱΊ • HayabusaͷࢄγεςϜԽͷઃܭͱ࣮ • 144ԯϨίʔυͷsyslogϑϧεΩϟϯˍશจݕࡧΛ6.8ඵͰ࣮ݱ • ϚϧνϕϯμػثΛରͱͨ͠ɺେྔͷෆἧ͍ͳϩάΛߴʹݕࡧՄೳ • τϥϒϧγϡʔτɾΠϯγσϯτϨεϙϯεΛஶ͘͠ॖ͢ΔՄೳੑ •
γϯϓϧͳࢄॲཧߏʹΑΔཧͷ༰қੑ !45
ࠓޙͷ՝ • ଞͷιϑτΣΞͱͷൺֱʢBigQuery, ElasticSearch, Splunkʣ • HayabusaͱଞͷΞϓϦέʔγϣϯͱͷ༥߹ʢΞϊϚϦݕͳͲʣ • Hayabusaͱ౷ܭॲཧϥΠϒϥϦػցֶशϥΠϒϥϦͱͷ݁߹ •
ࢄϑΝΠϧγεςϜɾࢄετϨʔδͷ࣮ !46
ँࣙ • ຊݚڀͷҰ෦ɺࠃཱݚڀ։ൃ๏ਓՊֶٕज़ৼڵػߏʢJSTʣͷݚڀՌ ൃలࣄۀʮઓུతݚڀਪਐࣄۀʢCRESTʣJPMJCR1783ʯͷࢧԉʹ ΑͬͯߦΘΕͨ
None