Lock in $30 Savings on PRO—Offer Ends Soon! ⏳
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
7.7k
Goのnet.TCPConnの話/shibuya.go01
shunsukeaihara
3
830
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
2.5k
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
170
Gunosy.go#2 package/compress
shunsukeaihara
0
110
Other Decks in Technology
See All in Technology
プロダクトマネージャーが押さえておくべき、ソフトウェア資産とAIエージェント投資効果 / pmconf2025
i35_267
2
340
AI駆動開発によるDDDの実践
dip_tech
PRO
0
290
Digitization部 紹介資料
sansan33
PRO
1
6.1k
freeeにおけるファンクションを超えた一気通貫でのAI活用
jaxx2104
3
600
AI 時代のデータ戦略
na0
8
3.2k
Contract One Engineering Unit 紹介資料
sansan33
PRO
0
9.9k
Capture Checking / Separation Checking 入門
tanishiking
0
110
M5UnifiedとPicoRubyで楽しむM5シリーズ
kishima
0
110
【5分でわかる】セーフィー エンジニア向け会社紹介
safie_recruit
0
37k
Eight Engineering Unit 紹介資料
sansan33
PRO
0
5.7k
Sansan Engineering Unit 紹介資料
sansan33
PRO
1
3.3k
Active Directory 勉強会 第 6 回目 Active Directory セキュリティについて学ぶ回
eurekaberry
16
5.9k
Featured
See All Featured
YesSQL, Process and Tooling at Scale
rocio
174
15k
RailsConf 2023
tenderlove
30
1.3k
Six Lessons from altMBA
skipperchong
29
4.1k
Git: the NoSQL Database
bkeepers
PRO
432
66k
Improving Core Web Vitals using Speculation Rules API
sergeychernyshev
21
1.3k
Navigating Team Friction
lara
191
16k
Imperfection Machines: The Place of Print at Facebook
scottboms
269
13k
Refactoring Trust on Your Teams (GOTO; Chicago 2020)
rmw
35
3.3k
Why Our Code Smells
bkeepers
PRO
340
57k
Optimising Largest Contentful Paint
csswizardry
37
3.5k
4 Signs Your Business is Dying
shpigford
186
22k
RailsConf & Balkan Ruby 2019: The Past, Present, and Future of Rails at GitHub
eileencodes
140
34k
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