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
Sparkによる分散処理 / 2015-01-16 PyData.Tokyo#3
Search
shunsukeaihara
January 17, 2015
Technology
11
3.5k
Sparkによる分散処理 / 2015-01-16 PyData.Tokyo#3
shunsukeaihara
January 17, 2015
Tweet
Share
More Decks by shunsukeaihara
See All by shunsukeaihara
BONXを支える技術:発話区間検出(VAD)の話/Akerun & BONX Tech Talk
shunsukeaihara
4
7.6k
Goのnet.TCPConnの話/shibuya.go01
shunsukeaihara
3
800
Norikra in Gunosy Network Ads@Norikra meetup #2
shunsukeaihara
1
6k
LevelDB on S3 As A KVS
shunsukeaihara
1
2.8k
色恒常性仮説に基づく色補正ライブラリcolorcorrect / 2015-01-31-kantocv27
shunsukeaihara
3
2.4k
ゼロから始めた Gunosyアドサーバ開発運用記 / 2014-12-16-dots
shunsukeaihara
6
1.2k
Gunosy.Go#5 index/io/log
shunsukeaihara
0
160
Gunosy.go#2 package/compress
shunsukeaihara
0
110
Other Decks in Technology
See All in Technology
Web Intelligence and Visual Media Analytics
weblyzard
PRO
1
6.1k
うちの会社の評判は?SNSの投稿分析にAIを使ってみた
doumae
0
610
SwiftUI Transaction を徹底活用!ZOZOTOWN UI開発での活用事例
tsuzuki817
1
140
AIコーディング新時代を生き残るための試行錯誤 / AI Coding Survival Guide
tomohisa
5
6k
Sansan Engineering Unit 紹介資料
sansan33
PRO
1
2k
Javaアプリケーションの配布とパッケージング / Distribution and packaging of Java applications
hogelog
2
520
Applied NLP in the Age of Generative AI: Future-Proof Strategies for Banking and Finance
inesmontani
PRO
0
210
Eight Engineering Unit 紹介資料
sansan33
PRO
0
3.4k
Data Observability:企業資料管理技術的未來顯學
cheng_wei_chen
0
320
dbt Cloudの新機能を紹介!データエンジニアリングの民主化:GUIで操作、SQLで管理する新時代のdbt Cloud
sagara
0
110
Tensix Core アーキテクチャ解説
tenstorrent_japan
0
180
AIとSREの未来 / AI and SRE
ymotongpoo
2
1.8k
Featured
See All Featured
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
30
2.4k
Large-scale JavaScript Application Architecture
addyosmani
512
110k
YesSQL, Process and Tooling at Scale
rocio
172
14k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
47
2.8k
What’s in a name? Adding method to the madness
productmarketing
PRO
22
3.5k
No one is an island. Learnings from fostering a developers community.
thoeni
21
3.3k
Reflections from 52 weeks, 52 projects
jeffersonlam
349
20k
How To Stay Up To Date on Web Technology
chriscoyier
790
250k
Side Projects
sachag
454
42k
The Web Performance Landscape in 2024 [PerfNow 2024]
tammyeverts
7
640
The Power of CSS Pseudo Elements
geoffreycrofte
76
5.8k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
248
1.3M
Transcript
SparkʹΑΔࢄॲཧ (ͱPythonͰͷࢄॲཧ) Gunosy Inc. Shunsuke Aihara
ࣗݾհ • ҄൧ݪढ़հ (http://argmax.jp) @shunsukeaihara • GunosyͷϚωʔδϟʔ • ࠂ৴γεςϜͷ։ൃશମͱR&DܥΛ୲ •
ઐ: ܭࢉݴޠֶ • PythonͱඇಉظࢄγεςϜΛΉ • ը૾ॲཧɾԻ৴߸ॲཧͰ͍Ζ͍ΖϥΠϒϥϦ࡞ͬͯΔ • https://bitbucket.org/aihara
Agenda • Spark֓ཁ • ࢄॲཧ(ͱSpark)ͷ • GunosyͰͷSparkͷϢʔεέʔε • PythonͰͷࢄॲཧΤίγεςϜ
Sparkʹ͍ͭͯ(1) • HadoopͷΤίγεςϜ(HDFS, MESOS, YARN)ͱ࿈ܞ͢ΔΦϯϝϞ Ϧࢄॲཧܥ • Resillient Distributed Datasetsͱ͍͏োੑΛ࣋ͬͨࢄσʔλߏ
ʹର͢Δࢄϓϩάϥϛϯάڥ • RDDʹద༻͢ΔฒྻܭࢉΛɺߴ֊ؔͷνΣΠϯͷܗͰScalaɺ PythonͰ࣮ߦ • immutableͳσʔλߏ • RDDͷཁૉΫϥελͷΦϯϝϞϦʹࢄɾϨϓϦέʔγϣϯ • ഁଛɾϩετͨ͠σʔλӬଓԽͨ͠ݩσʔλ͔Β෮ݩ
Sparkʹ͍ͭͯ(2) • RDDʹର͢Δࢄॲཧج൫ͷ্ʹҎԼΛ࣮ • σʔλετϦʔϜॲཧ(Spark Streaming) • ࢄSQL(SparkSQL) • ࢄػցֶशϥΠϒϥϦ(Mllib)
• ࢄάϥϑॲཧϥΠϒϥϦ(GraphX)
ࢄॲཧ(ͱSpark)ͷ
େنσʔλࢄॲཧͷ؊ • ΫϥελϚωʔδϝϯτ • σʔλͷࢄஔͷࣗಈԽ • σʔλଟॏԽ/ฒྻReadʹΑΔߴԽ • σʔλϩʔΧϦςΟΛอͬͨܭࢉ •
োੑ / ࠶ૹɾ࠶ܭࢉॲཧ
HadoopʹࢸΔ·Ͱ • ෳࡶͳฒྻॲཧϝοηʔδύογϯάͰಠࣗʹ࣮͢Δͱେม • εέϧτϯฒྻϓϩάϥϛϯά(Cole, 1989) • සग़͢ΔฒྻܭࢉύλʔϯͷΈ߹ΘͤͰɺ༷ʑͳฒྻॲཧΛߏతʹߏங ͢ΔؔϓϩάϥϛϯάͷΈͱෳͷ࣮ •
σʔλฒྻεέϧτϯ(map, fold/reduce, filter, zip…) • σʔλͷҟͳΔ෦ʹɼಉ࣌ʹಉ͡ૢ࡞Λߦ͏ܭࢉύλʔϯ • λεΫฒྻεέϧτϯ(pipe, farm…) • σʔλͷετϦʔϜʹରͯ͠ɼͦΕͧΕܭࢉΛద༻ͨ͠σʔλετϦʔ ϜΛฦ͢ύλʔϯ
εέϧτϯฒྻϓϩάϥϛϯά މৼߐ ؠ࡚ӳ࠸ εέϧτϯฒྻϓϩάϥϛϯάใॲཧ 7PM /P QQ
HadoopҎલͷࢄॲཧ • MPI άϦουγΣϧΛ༻͍࣮ͯ • σʔλͷஔࣗͰϚωʔδ • ڞ༗ϝϞϦ͔ڞ༗FSʹࣗͰஔ͕લఏ • ڊେσʔλͷஔͱͯ໘
• োੑಠ࣮ࣗͰอূ • ϝϞϦʹࡌΓΒͳ͍σʔλΛѻ͏ͷ͍͠
T-shirts message@WOMPAT2001 “Life is too short for MPI.”
Hadoop͕ղܾͨ͠ͷ • Պֶܭࢉ͚Ͱͳ͘େنσʔλʹಛԽ • ڊେσʔλͷஔͱॲཧͷ࣮ߦΛࣗಈཧ • HDFSͰͷࣗಈࢄஔͱɺஔॴͰMAPॲཧ
HadoopҎ߱ͷ৽ͨͳχʔζ • Hadoop / Hiveεϧʔϓοτॏࢹͷόονܥ • σʔλαΠΤϯςΟετͷχʔζΠϯλϥΫςΟϒͳ ੳɾϦΞϧλΠϜॲཧ • ॲཧֻ͚ͯ࣌ؒͪݫ͍͠
• Hadoop, Hiveߴ৴པੑͷ֬อͱҾ͖͑ʹதؒσʔλ ͷDisk I/O͕ϘτϧωοΫʹ • αʔόͨΓͷϝϞϦ༰ྔ૿େ
HadoopޙͷϓϩμΫτ • HiveͷΦϯϝϞϦߴԽ • ϦΞϧλΠϜͷετϦʔ Ϝσʔλॲཧ • ෳͷσʔλιʔε / DB
ʹ·͕ͨͬͯͷߴूܭ • λεΫ࣮ߦΛ࠷దԽ͠ϨΠςϯγΛ࣮ݱ
Spark • ൚༻ͷࢄϓϩάϥϛϯάڥ • RDDΛجૅʹ͓͍ͨεέϧτϯฒྻϓϩάϥϛϯάڥ • ΦϯϝϞϦͷRDDΛ༻͍Δ͜ͱͰɺϨΠςϯγʔͷ ࢄܭࢉΛ࣮ݱ • ϝϞϦʹΒͳ͍ͷDiskʹอଘ
• RDDʹର͢Δૢ࡞ΛΈ߹ΘͤΔ͜ͱͰɺػցֶशε τϦʔϜσʔλॲཧΛ࣮ݱ
RDDʹର͢Δجຊԋࢉ • ScalaͷSeqॲཧͷߴ֊ؔ+α͕ࢄ࣮ߦ • map, flatMap, filter, sort, union, zip
• reduce, fold, reduceByKey, groupBy, groupByKey, count cogroup, cross • join, leftOuterJoin, rightOuterJoin • sample, take, first, partitionBy, mapWith, pipe, save • etc….
RDDͷσʔλϩʔΧϦςΟ • λεΫͷ࣮ߦॴɾॱংσʔλɾιʔεͷ ஔॴΛݩʹ࠷దͳDAGදݱͰཧ )%'4 3%% 3%% NBQ NBQ NBQ
NBQ 3%% 3FEVDF
RDDͷোੑ • RDDͷ֤ཁૉ͕ࣗͲͷΑ͏ͳܦ࿏Ͱੜ ͞Ε͔ͨه )%'4 NBQ NBQ ☓ഁଛ )%'4 NBQ
NBQ NBQ ࠶ඞཁʹͳͬͨ࣌ɺσʔλɾιʔε͔Β࠶ੜ
Sparkʹ͍ͭͯ(2) • RDDʹର͢Δࢄॲཧج൫ͷ্ʹҎԼΛ࣮ • σʔλετϦʔϜॲཧ(Spark Streaming) • ࢄSQL(SparkSQL) • ࢄػցֶशϥΠϒϥϦ(Mllib)
• ࢄάϥϑॲཧϥΠϒϥϦ(GraphX)
PySpark + IPython Notebook • PySparkIPython্Ͱ࣮ߦՄೳ • AWSͳΒɺίϚϯυϥΠϯ1ൃͰΫϥελߏஙՄೳ • Spark
on EMR(YARNରԠ)Λಈ͔͢ • http://qiita.com/shunsukeaihara/items/1524b66579e91d1cf7cf
• ఆظόονܥfluentd -> RedshiftͰॲཧ • ΞυϗοΫͳϩάੳFluentd -> S3 -> Spark
• S3্ͷେྔͷϑΝΠϧΛखܰʹॲཧՄೳ GunosyͷSparkϢʔεέʔε "1*αʔό 4QBSLPO"84&.3 3FETIJGU$MVTUFS
GunosyͷSparkϢʔεέʔε(1) • CloudTrailsͷϩά͔ΒΘΕ͍ͯΔCredentialΛ୳ͯ͠ ௵͢ͱ͔… • େྔͷJSONϑΝΠϧΛಡΈࠐΜͰHiveQLΛ࣮ߦ EBUBTDUFYU'JMF TCVDLFU@OBNFQBUI H[
IJWFQZTQBSLTRM)JWF$POUFYU TD IUIJWFKTPO3%% EBUB IUSFHJTUFS5FNQ5BCMF USBJMMT IUDBDIF5BCMF USBJMMT IJWFTRM 4&-&$5%*45*/$5SFDPSEVTFS*EFOUJUZBDDFTT,FZ*E '30.USBJMMT-"5&3"-7*&8FYQMPEF 3FDPSET TBTSFDPSE
GunosyͷSparkϢʔεέʔε(2) • Ϣʔβͷهࣄϩά͔Βͷੑผྨ • Ϣʔβຖʹclickͨ͠هࣄͷidΛListΛcsvͰS3ʹอଘ • TF-IDFͰॏΈ͚ͭ TD4QBSL$POUFYU NBMFTDUFYU'JMF
lTCVDLFUQBUINBMF@ H[l GFNBMFTDUFYU'JMF lTCVDLFUQBUINBMF@ H[l UG)BTIJOH5' OVN'FBUVSFT NBMFNBMFNBQ MBNCEBYUGUSBOTGPSN YTQMJU l z GFNBMFNBMFNBQ MBNCEBYUGUSBOTGPSN YTQMJU l z JEG*%' JEG@NPEFMJEGpU NBMFVOJPO GFNBMF NBMFJEG@NPEFMUSBOTGPSN NBMF GFNBMFJEG@NPEFMUSBOTGPSN GFNBMF
GunosyͷSparkϢʔεέʔε(2) • Ϣʔβͷهࣄϩά͔Βͷੑผྨ • LabeledPointʹม͠ϩδεςΟοΫճؼͰֶश/ ྨ NBMFNBMFNBQ MBNCEBY-BCFMFE1PJOU Y
GFNBMFGFNBMFNBQ MBNCEBY-BCFMFE1PJOU Y USBJOJOHNBMFVOJPO GFNBMF USBJOJOHDBDIF NPEFM-PHJTUJD3FHSFTTJPO8JUI4(%USBJO USBJOJOH
GunosyͷSparkϢʔεέʔε(2) • Ϣʔβͷهࣄϩά͔Βͷੑผྨ • ઌ಄͕ϢʔβID, ͦΕҎ͕߱هࣄIDͷϦετ͔Βਪఆ EFGQBSTF Y EBUB<JOU
J GPSJJOYTQMJU l z > SFUVSO-BCFMFE1PJOU EBUB<> EBUB<> VOLOPXOTDUFYU'JMF lTCVDLFUQBUIVOLOPXO@ H[l VOLOPXOVOLOPXONBQ MBNCEBYUGUSBOTGPSN YTQMJU l z VOLOPXOVOLOPXONBQ MBNCEBY Y<> JEG@NPEFMUPSBOTGPSN UGUSBOTGPSNY<> VOLOPXONBQ MBNCEBY Y<> NPEFMQSFEJDU Y<> DPMMFDU
Pyspark͓ख͚ܰͩͲ… • PythonͷؔΛPickleͯ͠ࢄ࣮ߦ͢ΔͷͰ͍Ζ͍Ζ͍ • JavaͷϥΠϒϥϦ(kuromoji)Λར༻͍ͨ͠߹Scala ͷϥούʔ + py4jͷϥούʔ͕ඞཁ • Scala͔ΒͳΒͦͷ··͑Δ
• ؤுͬͯΈ͚ͨͲ࠳ંɻpy4jͱʹ͔ͭ͘Β͍ • Spark༻్ఔͳΒScalaͷֶशίετ͍ • ͱ͍͑sbt໘͚ͩͲ…
Pythonͷࢄॲཧڥ
PythonͷࢄॲཧϥΠϒϥϦ • Ϋϥελܭࢉ༻ • PyRC, dispy, Pyro4(GensimͷLSI, LDAͷࢄԽόοΫΤϯυʹར༻) • ࢄλεΫΩϡʔ
• Celery : σίϨʔλΛ͚ͭΔ͚ͩͰؔ୯ҐͰඇಉظࢄԽ • IPython Cluster: ؆୯ͳλεΫࢄ༻ • Spartan: Numpy arrayͷZeroMQʹΑΔࢄԽ(SparkͷRDDΠϯεύΠΞ) • Disco: PythonMapReduceϑϨʔϜϫʔΫ
GunosyͷPythonࢄॲཧڥ • ػցֶशͷαʔϏε࿈ܞλεΫฒྻ(ฒྻετϦʔϜॲཧ)͕ॏ ཁͰφΠʔϒͳࢄॲཧͰ͍͍ͨͯͳ͍(ex. Jubatus) • aws্ͩͱجຊσʔλશͯS3ʹूੵ • λεΫཧͱϦτϥΠCelery(AMQP)ʹͤΔ •
ϫʔΧʔͷσϓϩΠChef + OpsworksͰશࣗಈԽ • ΦϯϥΠϯֶशͷࢄԽparameter iterative mixing • EMΞϧΰϦζϜͷࢄԽσʔλΛਫฏࢄͯ͠ಠཱʹܭࢉͨ͠ ύϥϝʔλͷฏۉΛऔΔ
• هࣄऩूϢʔβຖͷਪનΛϫʔΧʔʹόϥϚΩ GunosyͷPythonࢄॲཧڥ هࣄΫϩʔϥʔ DFMFSZXPSLFS ਪનΤϯδϯ DFMFSZXPSLFS هࣄΫϦοΫϩά ίϯτϩʔϥ EKBOHPDFMFSZ
·ͱΊ • Sparkͷ؊RDDͱ͍͏σʔλߏͱεέϧτϯฒྻϕʔ εͷ൚༻తͳฒྻϓϩάϥϛϯάڥ • Python͔Βͷखܰʹࢄॲཧͱࢄػցֶश͕͑ͯศར • ͰPython͔Βෳࡶͳ͜ͱΛ͠Α͏ͱ͢ΔͱຊʹΩπΠ ͷͰScalaͰॻ͖·͠ΐ͏ •
Ͳ͏ͯ͠Python͕ྑ͍ͳΒଞͷPythonͷࢄॲཧΤ ίγεςϜΛݕ౼͠·͠ΐ͏