Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
PySparkとGoogle Cloudを使った野球データ解析のきほん / PySpark, ...
Search
Sponsored
·
SiteGround - Reliable hosting with speed, security, and support you can count on.
→
Shinichi Nakagawa
PRO
September 28, 2022
Technology
700
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
PySparkとGoogle Cloudを使った野球データ解析のきほん / PySpark, Google Cloud and Baseball Data
#kwskrb #112 発表資料
PyCon JP 2022発表のプロトタイプ
Shinichi Nakagawa
PRO
September 28, 2022
More Decks by Shinichi Nakagawa
See All by Shinichi Nakagawa
LLMの出力を"いい感じに"する技術 / Taming LLM Output: AI Agent Design Patterns with FastAPI
shinyorke
PRO
4
1.9k
野球解説AI Agentを開発してみた - 2026/02/27 LayerX社内LT会資料
shinyorke
PRO
0
710
WBCの解説は生成AIにやらせよう - 生成AIで野球解説者AI Agentを実現する / Baseball Commentator AI Agent for Gemini
shinyorke
PRO
1
700
自らを強いエンジニアにするための3つの習慣 2025/ Fitter happier more productive
shinyorke
PRO
0
320
生成AI時代におけるSREの進化とキャリア戦略 / Building an Embedded SRE team and my career
shinyorke
PRO
0
190
生成AIを活用した野球データ分析 - メジャーリーグ編 / Baseball Analytics for Gen AI
shinyorke
PRO
1
6.7k
ゼロから始めるSREの事業貢献 - 生成AI時代のSRE成長戦略と実践 / Starting SRE from Day One
shinyorke
PRO
3
9.6k
AI・LLM事業部のSREとタスクの自動運転
shinyorke
PRO
0
590
実践Dash - 手を抜きながら本気で作るデータApplicationの基本と応用 / Dash for Python and Baseball
shinyorke
PRO
2
4.9k
Other Decks in Technology
See All in Technology
Amazon Quick on DesktopがIAM Identity Centerで動かない理由
yukiogawa
0
170
多層防御と最⼩権限で実現する、安全なAIエージェント設計パターン
lycorptech_jp
PRO
1
270
リージョンの壁を越える、 ちょっと変わったAWSサービスの話
falken
PRO
1
310
OpenTelemetryのメトリクスをCloudWatchに送ってPromQLで見てみた
ota1022
0
120
20260906 「AWS運用入門」著者が教える、運用業務への生成AI活用入門
masaruogura
0
260
2026/09/10 Spring Bootから Jakarta EE/MicroProfileへの移行
megascus
0
340
20260912_スクラムにジェネラリストは必要か
ryugen04
0
370
プロダクト思考 × 基盤思考を AIで実現する Compound Engineering
tkc66buzz
1
320
Amazon S3 Tablesに全部任せてみた結果——コンパクション/スナップショット管理は本当に手放せるか
shigeruoda
1
480
2026_devsumi_ozono.pdf
o3
3
410
登壇の自信を奪う3匹のオバケ / 3 Ghosts That Rob You of Your Confidence in Public Speaking
pauli
3
190
アプリログインとWeb認証基盤をつなぐ ASWebAuthenticationSession 作法
shimastripe
1
210
Featured
See All Featured
技術選定の審美眼(2025年版) / Understanding the Spiral of Technologies 2025 edition
twada
PRO
120
120k
A Soul's Torment
seathinner
7
3.6k
The SEO identity crisis: Don't let AI make you average
varn
0
550
From Legacy to Launchpad: Building Startup-Ready Communities
dugsong
0
330
Information Architects: The Missing Link in Design Systems
soysaucechin
1
1.1k
Have SEOs Ruined the Internet? - User Awareness of SEO in 2025
akashhashmi
0
500
How to Ace a Technical Interview
jacobian
281
24k
Google's AI Overviews - The New Search
badams
0
1.6k
Mozcon NYC 2025: Stop Losing SEO Traffic
samtorres
1
530
Mind Mapping
helmedeiros
1
350
Unlocking the hidden potential of vector embeddings in international SEO
frankvandijk
0
930
Max Prin - Stacking Signals: How International SEO Comes Together (And Falls Apart)
techseoconnect
PRO
0
460
Transcript
Python͍ͷͨΊͷ εϙʔπσʔλղੳͷ͖΄Μ β൛ʢ͘͠༧ࠂฤʣ PyCon JP 2022ͷຊ൪લʹͪΐͬͱ͓ͯ͠Έ·ͨ͠. Shinichi Nakagawa@shinyorke 2022/09/28 kawasaki.rb
#112
Onboardingʢ͜ͷࢿྉɾൃදͷҐஔ͚ͮʣ • PythonͱSparkͱύϒϦοΫΫϥυʢGoogle CloudʣͰ ඦGBҎ্ͷσʔλΛ͍͍ײ͡ʹॲཧͯ͠ѻ͓͏ͥʂ, ͱ͍͏. • PyCon JP
2022ʮPython͍ͷͨΊͷεϙʔπσʔλղੳͷ͖΄Μʯ ͰΔલʹͪΐͬͱ͚͓ͩ൸࿐͘͢࡞ͨ͠ͷͰ͢. • ݱ࣌Ͱͷ൛Ͱ, ຊ൪Ͱ༰͕มΘΔՄೳੑ͕͋Γ·͢.
Who am ɹ? ʢ͓લ୭Α?ʣ • Shinichi Nakagawa@shinyorke • ֎ࢿITίϯαϧاۀϚωʔδϟʔ •
ࣄΫϥυαʔϏεΛѻ͏ΤϯδχΞ. ʢͷνʔϜΛవΊΔϚωʔδϟʔʣ • ࠷ۙެࢲͱʹGoogle CloudΛ͏ਓ. • झຯͱ࣮ӹΛ݉Ͷͯݸਓ։ൃͯ͠·͢🍺 #Python #GoogleCloud #Baseball #DataScience #SABRmetrics
ຊͷελʔςΟϯάϝϯόʔ • ϝδϟʔϦʔάͷϏοάσʔλͰ༡΅͏ ※PyConʹ͢ • PythonͱGoogle CloudͰ࡞ΔαʔόϨεͰ͍͍ײ͡ͳσʔλج൫ • PySpark +
DataprocͰ࣮ݱ͢ΔαʔόϨεͳσʔλॲཧ • ࠓͷϝδϟʔϦʔάΛσʔλͰৼΓฦΔ ※PyConʹ͢
ࠓճѻ͏σʔλʮMLBͷϏοάσʔλʯ • ϝδϟʔϦʔάʮStatcastʯͱ͍͏γεςϜͰ৭ΜͳσʔλΛه͍ͯ͠·͢. ※ΧϝϥɾϨʔμʔͰه, Ұ෦౷ܭɾਓྗͰه • ྫ͑, ͜ͷลͷ࣮گͷݩωλͯ͢͜ͷʮStatcastʯͱ͍͏Ϗοάσʔλ͕ݩωλʹͳ͍ͬͯ·͢. •
ΦΦλχαϯʂ˓߸ຊྥଧʂଧٿ180km/h, ඈڑ130m • ΦΦλχαϯʂ162km/hͷਅ͙ͬͰݟಀ͠ࡾৼʂʂʂ • ٿͷҰڍखҰ, ͯ͢ͷٿɾଧٿσʔλ͕ه͞ΕΔ. • ϨΪϡϥʔγʔζϯʢ30νʔϜɾ162ࢼ߹ʣͰ͓͓Αͦ70ʙ80ສٿલޙ. ϙετγʔζϯɾय़Ωϟϯϓσʔλ͋Δ. • σʔλ91ݸͷ߲ʢ!?ʣͰߏ͞ΕΔ, ϨΪϡϥʔγʔζϯͰ͓͓Αͦ400MBʙ600MB͙Β͍ͷσʔλ. • baseballsavant.mlb.com ͱ͍͏αΠτͰ୭ͰӾཡɾμϯϩʔυʢCSV ϑΥʔϚοτʣͰ͖·͢.
ϫΠʮຖຖࢼ߹ݟΔΈཉ͍͠ʯ ຖσʔλऩू -> BigQueryʹอଘ -> ՄࢹԽ, తͳج൫࡞ͬͪΌ͑ʂ
ͱ͍͏Θ͚Ͱ, ͪΐͬ͜ͱ࡞ͬͯΈ·ͨ͠.
PythonͱGoogle CloudͰ࡞Δ αʔόϨεͰ͍͍ײ͡ͳ σʔλج൫ʢٿฤʣ
ΞʔΩςΫνϟͷશମ૾
None
ΞʔΩςΫνϟղઆʢ㲈ͩ͜ΘΓϙΠϯτʣ • ຖσʔλ֬ೝɾຖσʔλߋ৽Λ͍͍ײ͡ʹ࣮ݱ͢ΔͨΊ, ʮϑϧϚωʔδυͳαʔόϨεܥΫϥυαʔϏεʯΛશ໘తʹ׆༻ͯ͠ߏஙɾӡ༻. • ʮϑϧϚωʔδυͳαʔόϨεܥΫϥυαʔϏεʯ #ͱ • CLIίϯιʔϧͰϙνϙν͢Δ͚ͩͰͻͱ·্ཱ͕ͣͪΔ
• Πϯϑϥɾαʔόʔͷϝϯςφϯε͕ෆཁʢࣗ͡Όͳͯ͘, ΫϥυαʔϏεଆ͕Δʣ • ΑΓ۩ମతʹ, ࣗͰK8sΫϥελVMΛݐͯͳͯ͘ྑ͍ʢωοτϫʔΫͷઃఆൃੜʣ • GitHub ActionsͷCI/CDͷύΠϓϥΠϯʹΈࠐΜͰσϓϩΠɾεέʔϧͰ͖ͨΓ جຊతʹʮ͚ͬͨͩ՝ۚʯʹͳΔͷͰ͓ࡒʹ༏͍͠👛
ϢʔεέʔεͱͬͨαʔϏε
None
μογϡϘʔυΞϓϦ • ΞϓϦຊମCloud RunͰϗεςΟϯά, API GatewayΛ௨ͯ͠όοΫΤϯυʢCloud FunctionsʣʹΞΫηε • Firestore͕ϝΠϯͷDB, CacheͷMemoryStoreʢRedisʣΛஔ͍͍ͯΔ
• ͜͜ͰSparkʢPySparkʣొ͠·ͤΜ
None
σʔλऩू&BigQueryอଘ • σʔλݩαΠτʢBaseball Savantʣ͔Βఆظతʹσʔλऩू͢ΔΫϩʔϥʔʢCloud Functionsʣ࣮ߦ • ࣮ߦ݁ՌGoogle Cloud StorageʢGCSʣʹCSVͱͯ͠อଘ. ͜Ε͕ݯઘͷσʔλʢDatalakeʣ
• GCS্ͷCSVΛαϚϦʔ͍͍ͯ͠ײ͡ʹͯ͠BigQueryʹอଘ͢ΔPySparkεΫϦϓτΛDataproc Serverless্Ͱ࣮ߦ
None
FirestoreೖʢDatabaseʹσʔλҠૹʣ • BigQueryσʔλΛμογϡϘʔυ༻σʔλͷܗࣜʢJSONʣʹม͢ΔPySparkεΫϦϓτΛDataproc Serverless্Ͱ࣮ߦ • ࣮ߦ݁ՌʢGCS্ʹJSONܗࣜͰอଘʣΛFirestoreʹೖΕΔͨΊͷPythonεΫϦϓτΛ࣮ߦ • ͳ͓͍ͣΕखಈͰͷ࣮ߦʢཧ༝&ରԠࡦޙ΄Ͳʣ
PySpark + DataprocͰ࣮ݱ͢Δ αʔόϨεͳσʔλॲཧ ※͕͜͜͜ͷτʔΫͷຊͱͳΓ·͢.
͜ͷͷείʔϓ
None
33.4ඵͰΘ͔ͬͨʢؾʹͳΔʣ🐯 SparkͱPySpark
SparkͱPySpark • ʮେ͖͍σʔλΛ͍͍ײ͡ʹࢄͯ͠ॲཧ͢ΔʯͨΊͷFramework • ྺ࢙తͳͰ͍͑HadoopͷޙܧͰ, ར༻ࣄྫ݁ߏ͋Δ. • ϥΠϒϥϦɾAPIͷ࣮Java͕ͩ, ʮPySparkʯͱ͍͏
PythonͷInterface͕͋Δ. • DataFrame APIͱ͍͏, PandasͰ͓ೃછΈͷDataFrameͳInterface ͕͋Γ, ͜ΕΛϝΠϯʹ͏ͱ͍͍ײ͡ʹ͑Δ.
SparkʢͱPySparkʣͷ͍ํ • σʔληοτ • େݩͷσʔλRDDʢResilient Distributed DatasetʣͰߏɾཧ. • RDDΛ͍͍ײ͡ʹѻ͏Interfaceͱͯ͠DataFrame API͕ଘࡏ͢Δ.
• DataFramePandasͬΆ͍͍ํ͕ग़དྷΔ. • ͍ํ • εΫϦϓτΛॻ͍ͯSparkڥ্Ͱ࣮ߦ. • Jupyter Lab, Zeppelin notebookͰΞυϗοΫʹ͏.
ڥΛͲ͜Ͱߏஙɾӡ༻͢Δ͔🤔 ڥɾखஈ ߏஙͷखؒ ӡ༻͢͠͞ උߟ ΦϯϓϨϛεͰ શͯࣗલߏஙɾӡ༻ શͯࣗલͰઃఆ͢Δ ඞཁ͕͋Δ Կ͔ΒԿ·Ͱ
ࣗͰݟΔඞཁ͕͋Δ Ұ൪େมͳύλʔϯ ຊ৬ͷΠϯϑϥΤϯδχΞ Ͱ͖͍ͭࣄ Ϋϥυ্ͷ7.,Tʹ ࣗલͰߏஙɾӡ༻ શͯࣗલͰઃఆ͢Δ ඞཁ͕͋Δ ͋ΔఔΫϥυαʔϏε ͷԸܙʹत͔ΕΔ 4QBSLڥͷࣗલߏங ׂͱқ͕ߴ͍ ΫϥυαʔϏεఏڙͷ ϚωʔδυαʔϏεΛ͏ ˞࠷ਪ͢Δํࣜ (6*Ͱϙνϙν͢Δ ͘͠$-*"1*Ͱ ͍͍ײ͡ʹ࣮ߦ $16ͷϦιʔεΛࢹ ঢ়گʹԠͯ͡ϝϯςφϯε ࠷ָ͔ͭεϚʔτͳํ๏ "84 (PPHMF$MPVEଞ ֤ࣾαʔϏε༗
Google Cloudʹ͓͚ΔSparkӡ༻ͷબࢶ ڥɾखஈ ߏங ӡ༻ ͑Δػೳ උߟ ($&PS(,&ʹ ڥΛ࡞ͬͯӡ༻ ࣗલͰߏஙޙ
4QBSLΛಋೖ શͯࣗલͰӡ༻ ໘ΛݟΔඞཁ༗ શͯͷػೳ ݁ہͷॴ%BUBQSPDͰ ग़དྷΔ͜ͱͳͷͰ ͓͢͢Ί͠ͳ͍ %BUBQSPD HDMPVEίϚϯυ "1* ίϯιʔϧͷ ͲΕ͔Ͱߏங %BUBQSPD͕࡞ͬͨ (,&PS($&ڥ Λࢹɾӡ༻ શͯͷػೳ Ұ൪ඪ४తͳߏ %BUBQSPD 4FSWFSMFTT HDMPVEίϚϯυ "1* ίϯιʔϧ ্هͷͲΕ͔Ͱߏங ࣮ߦதͷࢹͷΈ ڥॲཧޙʹ ࣗಈআ όονॲཧͷΈରԠ OPUFCPPL͑ͳ͍ ఆظతͳόονॲཧ ͜Ε͕Ұ൪͍͍
DataprocͱDataproc Serverless • Google CloudʹDataprocͱ͍͏SparkʢHadoopʣͷ ϚωʔδυαʔϏε͕ଘࡏ͢Δ. • ࠓ·ͰGCEGKEʢK8sʣͰʮϗετɾCluster͕ଘࡏʯલఏͷ
ӡ༻͔͠Ͱ͖ͳ͔͕ͬͨ, ͍ͭ࠷ۙServerlessͱ͍͏બࢶ͕ര • ʮ11ճʯʮ30͓͖ʯΈ͍ͨͳόονӡ༻Ͱ͋Ε Serverless͕͑Δʂͨͩ͠notebookܥͷ࣮ߦʢJupyterͳͲʣະରԠ • Serverless͚ͬͨͩ՝ۚͳͷͰ͓ࡒʹ༏͍͠👛
PySparkΛͬͯͬͨλεΫΛհ • σʔλऩू&BigQueryͷσʔλೖ • μογϡϘʔυΞϓϦ༻DBʢFirestoreʣͷσʔλೖ
ʲ࠶ܝʳσʔλऩू&BigQueryอଘ • σʔλݩαΠτʢBaseball Savantʣ͔Βఆظతʹσʔλऩू͢ΔΫϩʔϥʔʢCloud Functionsʣ࣮ߦ • ࣮ߦ݁ՌGoogle Cloud StorageʢGCSʣʹCSVͱͯ͠อଘ. ͜Ε͕ݯઘͷσʔλʢDatalakeʣ
• GCS্ͷCSVΛαϚϦʔ͍͍ͯ͠ײ͡ʹͯ͠BigQueryʹอଘ͢ΔPySparkεΫϦϓτΛDataproc Serverless্Ͱ࣮ߦ
σʔλऩू ʢnot Sparkʣ • WebεΫϨΠϐϯάSparkͰ Δ͖͜ͱͰͳ͍. • λεΫΛrequests-htmlͰ࣮,
Cloud FunctionsͰӡ༻ͯ͠ରॲ. • Cloud SchedulerͷCronઃఆͰ ఆظ࣮ߦ, GCSʹอଘ
CSVσʔλΛ BigQueryʹೖ • Dataproc্ͰΔλεΫͱͯ͠ దͳൣғɾॲཧͷҰͭ • GCSͷύε͔ΒϑΝΠϧநग़ Spark
SQLͰॲཧͯ͠BigQuery • DataFrameͱSQL͕Θ͔Ε ͍͍ײ͡ʹ࣮ɾӡ༻Մೳ
DataprocΛ͓͏ • Google CloudͷυΩϡϝϯτɾαϯϓϧΛࣸܦ͠ͳ͕ΒΔͱྑ͖ • https://cloud.google.com/dataproc • https://cloud.google.com/dataproc-serverless/docs • https://github.com/GoogleCloudDataproc/cloud-dataproc
• Serverlessͷ߹, ࣄલʹVPC subnetΛ࡞Δඞཁ͋Γ • ࣍ϖʔδ͔Β, PySparkΛͬͯΔ߹ͷαϯϓϧΛগ͠հ • αϯϓϧίʔυType Hints࣮ͬͯͯ͠·͕͢ผʹແ͍͍ͯ͘Ͱ͢. ʢͲ͜ͷInterfaceͳͷ͔Θ͔Γ͘͢͢ΔͨΊ, ͑ͯType HintsΛͬͯॻ͍͍ͯ·͢ʣ.
ͻͱ·࣮ͣ 1. SessionΛ࡞Δ from pyspark.sql import SparkSession # BigQueryΛ͏&StringͷൣғΛ͛Δ spark:
SparkSession = SparkSession \ .builder \ .appName(‘app_yakiu')\ .config('spark.jars', 'gs://spark-lib/bigquery/spark- bigquery-with-dependencies_2.12-0.25.2.jar') \ .config('spark.sql.debug.maxToStringFields', 2000) \ .getOrCreate() # ͜ΕDataprocಛ༗ͷఆٛ, ࣮ߦ࣌ͷtemporaryྖҬ spark.conf.set('temporaryGcsBucket', 'GCSͷόέοτ໊') • దͳ.pyϑΝΠϧΛ࡞Δ • SparkSessionΛੜ͢Δ • ඞཁͳJARఆٛΛઃఆ BigQueryΛ͏࣌ JARͷࢦఆ͕ඞਢ
ͻͱ·࣮ͣ 2. SchemaΛ࡞Δ from pyspark.sql.types import StructType, StructField, DoubleType, DateType,
StringType, LongType # schemaઃఆʢͪΐͬͱ͍ʣ STATCAST_SCHEMA: StructType = StructType( [ StructField("pitch_type", StringType(), False), StructField("game_date", DateType(), False), StructField("release_speed", DoubleType(), False), StructField("release_pos_x", DoubleType(), False), StructField("release_pos_z", DoubleType(), False), # ͗͢ΔͷͰলུʢ91߲͋Δʣ StructField("spin_axis", DoubleType(), False), StructField("delta_home_win_exp", DoubleType(), False), StructField("delta_run_exp", DoubleType(), False) ] ) • CSVͷ߹SchemaΛ࡞Δ • ͜Ε͕ແ͍ͱҙਤ௨Γʹ ಈ͔ͳ͍ • 91߲ͷSchema ؤுͬͯॻ͖·ͨ͠ྦ
ͻͱ·࣮ͣ 3. CSVಡΈࠐΉ from pyspark.sql import DataFrame as SparkDataFrame from
schema import STATCAST_SCHEMA # ઌ΄ͲͷεΩʔϚఆٛ # ࠷ॳʹੜͨ͠sparkηογϣϯ͔ΒreadؔΛCSV FormatࢦఆͰಈ͔͢ def read_csv(date: str, filename: str, schema: StructType = None) -> SparkDataFrame: try: return spark.read \ .format('csv') \ .options(header="true", inferSchema="true") \ .load(f'gs://your-bucket-name/path/{date}/ {filename}', schema=schema) except AnalysisException: return None # type: ignore sdf: SparkDataFrame = read_csv('2022-10-15', 'batter.csv', schema=STATCAST_SCHEMA) • sparkηογϣϯͷreadΛ ͏, formatʹCSVΛࢦఆ • ϔομʔͱͯ͠ઌ΄Ͳͷ SchemaΛࢦఆ • GCSͷϑϧύεΛࢦఆ
ͻͱ·࣮ͣ 4. BigQueryอଘ from pyspark.sql import DataFrame as SparkDataFrame #
BigQueryʹอଘ͢Δؔ def save_bigquery(sdf: SparkDataFrame, table_name: str) -> None: sdf.write\ .mode('append') \ .format('bigquery') \ .option('project', 'your project name') \ .option('table', f'dataset.{table_name}') \ .option('temporaryGcsBucket', 'GCSͷόέοτ໊') \ .option('createDisposition', 'CREATE_NEVER') \ .save() # Spark DataFrameͱBigQueryͷςʔϒϧ໊Λࢦఆ save_bigquery(sdf, 'batting_data') • DataFrameͷwriteؔ • bigqueryΛࢦఆ • ྫطଘςʔϒϧͷ هॻ͖ࠐΈ
Dataproc ServerlessΛ࣮ͬͯߦ # ࣮ߦϑΝΠϧΛGCSʹΞοϓϩʔυ gsutil -m cp -r app gs://your-gcs-bucket/script
# bata optionΛ͚ͭΔͱαʔόϨε࣮ߦʹͳΓ·͢ gcloud beta dataproc batches submit \ --project your-project \ --region asia-northeast1 pyspark \ --batch hello-world gs://your-gcs-bucket/script/app/csv2bq_daily.py \ --jars gs://spark-lib/bigquery/spark-bigquery-with-dependencies_2.12-0.25.2.jar \ --subnet your-subnet \ --py-files gs://your-gcs-bucket/script/app/environment.py,gs://your-gcs-bucket/script/app/query.py,gs://your-gcs- bucket/script/app/schema.py
ͬͯΈͨײ • ͻͱ·ͣ࡞Γ͍ͨͷ͕εϚʔτʹ࡞ΕͨͷͰྑ͔ͬͨ. • PySparkͷ࣮ͦͷͷDataprocͷํݴ͕গͳͯͦ͘͜ྑ͖. • ࣮ߦݖݶʢService Account & IAMʣσϑΥϧτͰ͍͍ײ͡ʹߦ͚ͨ.
• BigQueryGCS࠷ॳ͔ΒϑϧͰ৮ΕΔ. • ͨͩ, ݖݶڧ͗͢Δઆ͋ΔͷͰ࣮ͷ࣌νϡʔχϯά͕ඞཁ͔. • ͋ΔఔSparkͱGoogle Cloudʹ׳ΕͯΔਓ͡Όͳ͍ͱΩπΠઆ? SparkΛॳΊͯ৮Γ·͢ʂͱ͍͏ํͪΐͬͱ֮ޛ͕ඞཁ͔͠Εͳ͍.
ʲ࠶ܝʳFirestoreೖʢDatabaseʹσʔλҠૹʣ • BigQueryσʔλΛμογϡϘʔυ༻σʔλͷܗࣜʢJSONʣʹม͢ΔPySparkεΫϦϓτΛDataproc Serverless্Ͱ࣮ߦ • ࣮ߦ݁ՌʢGCS্ʹJSONܗࣜͰอଘʣΛFirestoreʹೖΕΔͨΊͷPythonεΫϦϓτΛ࣮ߦ • ͳ͓͍ͣΕखಈͰͷ࣮ߦʢཧ༝&ରԠࡦޙ΄Ͳʣ
ʲ࠶ܝʳFirestoreೖʢDatabaseʹσʔλҠૹʣ • BigQueryσʔλΛμογϡϘʔυ༻σʔλͷܗࣜʢJSONʣʹม͢ΔPySparkεΫϦϓτΛDataproc Serverless্Ͱ࣮ߦ • ࣮ߦ݁ՌʢGCS্ʹJSONܗࣜͰอଘʣΛFirestoreʹೖΕΔͨΊͷPythonεΫϦϓτΛ࣮ߦ • ͳ͓͍ͣΕखಈͰͷ࣮ߦʢཧ༝&ରԠࡦޙ΄Ͳʣ
BigQuery͔ΒGCSʹϑΝΠϧग़ྗ for Dataproc • BigQueryͷσʔλΛSpark DataFrameʹ • Spark DataFrameΛϑΝΠϧग़ྗ ͪͳΈʹ࣮ߦํ๏ʢgcloud
CLIʣมΘΒͳ͍ͷͰׂѪ͠·͢.
ͻͱ·࣮ͣ 5. BigQueryಡࠐ from pyspark.sql import SparkSession from pyspark.sql import
DataFrame as SparkDataFrame from pyspark.sql.utils import AnalysisException # ͜͜ಉ͡ spark: SparkSession = SparkSession \ .builder \ .appName('your app')\ .config('spark.jars', 'gs://spark-lib/bigquery/spark-bigquery-with- dependencies_2.12-0.25.2.jar') \ .config('spark.sql.debug.maxToStringFields', 2000) \ .getOrCreate() spark.conf.set('temporaryGcsBucket', GCS_BUCKET) # ͜͜ͰViewಡΈऔΓΛ༗ޮԽ͠ͳ͍ͱΤϥʔʹͳΔͷͰҙ spark.conf.set("viewsEnabled", "true") def read_bq() -> SparkDataFrame: """ Read Dashboard data BigQuery View to SparkDataFrame """ try: df: SparkDataFrame = spark.read.format('bigquery') \ .option('project', 'your project') \ .option('table', f'your_project.view_baseball') \ .load() return df except AnalysisException: return None # type: ignore sdf: SparkDataFrame = read_bq() • อଘͱಉ͘͡BigQueryͷ JARΛࢦఆ • spark readͰBigQueryΛࢦఆ • BigQueryͷViewʹରͯ͠ ߦ͏߹, Φϓγϣϯ͕ඞཁ
ͻͱ·࣮ͣ 6. GCSอଘ from pyspark.sql import DataFrame as SparkDataFrame def
save_json(sdf: SparkDataFrame) -> None: """ Save as JSON dataset SparkDataFrame to GCS Bucket """ sdf.write \ .format("json") \ .mode("overwrite") \ .option("path", "gs://your-gcs-bucket/filepath/hoge") \ .save() save_json(sdf) • DataFrameͷwriteؔ • jsonΛࢦఆ • ࠷ऴతͳύεΛࢦఆ
PySparkͱDataproc Serverless • ʮ͍͍ͨͱ͖͚ͩSparkΛ͏ʯͱ͍͏ϢʔεέʔεΛ࣮ݱՄೳ. ͜Ε͕αʔόϨεαʔϏεΛ͏͖࠷େͷཧ༝. • ࠓճͷΞϓϦέʔγϣϯͷσʔλαΠζʢ1Ͱ1GB͍͔ͳ͍ʣͩͱ Ըܙʹत͔Εͳ͍͕,
ʮGB/ఔͷσʔλΛαΫοͱόονॲཧʯ Έ͍ͨͳϢʔεέʔεʹͳΔͱ݁ߏศརͳؾ͕͠·͢ʢલॲཧɾΫϨϯδϯά͢Δͱ͔ʣ. • ʮॲཧ͢Δͱ͖͚ͩಈ͔͢ʯͱ͍͏ײ͡ͷ͍ܰίʔυͳͷͰPySparkͱ૬ੑόπάϯ. • ͳ͓, ॲཧͷࣗಈԽͪΐͬͱบ͕͋Γ·͢, खஈ੍͕ݶ͞ΕΔʢͷͰखಈʹͨ͠ʣ. ࣗಈԽCloud ComposerʢAir fl owʣͰΔͷ͕ࠓͷॴͷϕεϓϥͬΆ͍.
ࠓͷͷ·ͱΊ • PythonͰ͍͍ײ͡ʹσʔλॲཧΛ͢ΔͷʹPySparkྑ͍ͧ. • PySparkΫϥυͰಈ͔ͤ·͢, ࠓDataprocΛհ͠·ͨ͠. • αʔόϨεʹΫϥυΛ͑ΔΑ͏ʹͳΔͱ,৭ʑͱָ ͨͩ͠,
্༷ͷ੍ݶ͋ΔͷͰ༰ྔ༻๏Λਖ਼͘͠ཧղ͓ͯ͠͏.
Done. ͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠⽁