Scio - A Scala API for Google Cloud Dataflow & Apache Beam
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This document summarizes Scio, a Scala API for Google Cloud Dataflow and Apache Beam. Scio provides a DSL for writing pipelines in Scala to process large datasets. It originated from Scalding and was moved to use Dataflow/Beam for its managed service, integration with Google Cloud Platform services, and unified batch and streaming model. Scio aims to make Beam concepts accessible from Scala and provides features like type-safe BigQuery and Bigtable access, distributed caching, and future-based job orchestration to make Scala pipelines on Dataflow/Beam more productive.
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Scio - A Scala API for Google Cloud Dataflow & Apache Beam
- 1. Scio A Scala API for Google Cloud Dataflow & Apache Beam Neville Li @sinisa_lyh
- 2. About Us ● 100M+ active users, 40M+ paying ● 30M+ songs, 20K new per day ● 2B+ playlists ● 60+ markets ● 2500+ node Hadoop cluster ● 50TB logs per day ● 10K+ jobs per day
- 3. Who am I? ● Spotify NYC since 2011 ● Formerly Yahoo! Search ● Music recommendations ● Data infrastructure ● Scala since 2013
- 4. Origin Story ● Python Luigi, circa 2011 ● Scalding, Spark and Storm, circa 2013 ● ML, recommendation, analytics ● 100+ Scala users, 500+ unique jobs
- 5. Moving to Google CloudEarly 2015 - Dataflow Scala hack project
- 7. The Evolution of Apache Beam MapReduce BigTable DremelColossus FlumeMegastoreSpanner PubSub Millwheel Apache Beam Google Cloud Dataflow
- 8. What is Apache Beam? 1. The Beam Programming Model 2. SDKs for writing Beam pipelines -- starting with Java 3. Runners for existing distributed processing backends ○ Apache Flink (thanks to data Artisans) ○ Apache Spark (thanks to Cloudera and PayPal) ○ Google Cloud Dataflow (fully managed service) ○ Local runner for testing
- 9. 9 The Beam Model: Asking the Right Questions What results are calculated? Where in event time are results calculated? When in processing time are results materialized? How do refinements of results relate?
- 10. 10 Customizing What Where When How 3 Streaming 4 Streaming + Accumulation 1 Classic Batch 2 Windowed Batch
- 11. 11 The Apache Beam Vision 1. End users: who want to write pipelines in a language that’s familiar. 2. SDK writers: who want to make Beam concepts available in new languages. 3. Runner writers: who have a distributed processing environment and want to support Beam pipelines Beam Model: Fn Runners Apache Flink Apache Spark Beam Model: Pipeline Construction Other LanguagesBeam Java Beam Python Execution Execution Cloud Dataflow Execution
- 12. Data model Spark ● RDD for batch, DStream for streaming ● Two sets of APIs ● Explicit caching semantics Dataflow / Beam ● PCollection for batch and streaming ● One unified API ● Windowed and timestamped values
- 13. Execution Spark ● One driver, n executors ● Dynamic execution from driver ● Transforms and actions Dataflow / Beam ● No master ● Static execution planning ● Transforms only, no actions
- 15. Scalding on Google Cloud Pros ● Community - Twitter, Stripe, Etsy, eBay ● Hadoop stable and proven Cons ● Cluster ops ● Multi-tenancy - resource contention and utilization ● No streaming (Summingbird?) ● Integration with GCP - BigQuery, Bigtable, Datastore, Pubsub
- 16. Spark on Google Cloud Pros ● Batch, streaming, interactive, SQL and MLLib ● Scala, Java, Python and R ● Zeppelin, spark-notebook Cons ● Cluster lifecycle management ● Hard to tune and scale ● Integration with GCP - BigQuery, Bigtable, Datastore, Pubsub
- 17. Dataflow ● Hosted, fully managed, no ops ● GCP ecosystem - BigQuery, Bigtable, Datastore, Pubsub ● Unified batch and streaming model Scala ● High level DSL ● Functional programming natural fit for data ● Numerical libraries - Breeze, Algebird Why Dataflow with Scala
- 18. Cloud Storage Pub/Sub Datastore BigtableBigQuery Batch Streaming Interactive REPL Scio Scala API Dataflow Java SDK Scala Libraries Extra features
- 19. Scio Ecclesiastical Latin IPA: /ˈʃi.o/, [ˈʃiː.o], [ˈʃi.i̯o] Verb: I can, know, understand, have knowledge.
- 21. WordCount val sc = ScioContext() sc.textFile("shakespeare.txt") .flatMap { _ .split("[^a-zA-Z']+") .filter(_.nonEmpty) } .countByValue .saveAsTextFile("wordcount.txt") sc.close()
- 22. PageRank def pageRank(in: SCollection[(String, String)]) = { val links = in.groupByKey() var ranks = links.mapValues(_ => 1.0) for (i <- 1 to 10) { val contribs = links.join(ranks).values .flatMap { case (urls, rank) => val size = urls.size urls.map((_, rank / size)) } ranks = contribs.sumByKey.mapValues((1 - 0.85) + 0.85 * _) } ranks }
- 23. Why Scio?
- 24. Type safe BigQuery Macro generated case classes, schemas and converters @BigQuery.fromQuery("SELECT id, name FROM [users] WHERE ...") class User // look mom no code! sc.typedBigQuery[User]().map(u => (u.id, u.name)) @BigQuery.toTable case class Score(id: String, score: Double) data.map(kv => Score(kv._1, kv._2)).saveAsTypedBigQuery("table")
- 25. REPL $ scio-repl Welcome to _____ ________________(_)_____ __ ___/ ___/_ /_ __ _(__ )/ /__ _ / / /_/ / /____/ ___/ /_/ ____/ version 0.2.5 Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_11) Type in expressions to have them evaluated. Type :help for more information. Using 'scio-test' as your BigQuery project. BigQuery client available as 'bq' Scio context available as 'sc' scio> _ Available in github.com/spotify/homebrew-public
- 26. Future based orchestration // Job 1 val f: Future[Tap[String]] = data1.saveAsTextFile("output") sc1.close() // submit job val t: Tap[String] = Await.result(f) t.value.foreach(println) // Iterator[String] // Job 2 val sc2 = ScioContext(options) val data2: SCollection[String] = t.open(sc2)
- 27. DistCache val sw = sc.distCache("gs://bucket/stopwords.txt") { f => Source.fromFile(f).getLines().toSet } sc.textFile("gs://bucket/shakespeare.txt") .flatMap { _ .split("[^a-zA-Z']+") .filter(w => w.nonEmpty && !sw().contains(w)) } .countByValue .saveAsTextFile("wordcount.txt")
- 28. ● DAG visualization & source code mapping ● BigQuery caching, legacy & SQL 2011 support ● HDFS Source/Sink, Protobuf & object file I/O ● Job metrics, e.g. accumulators ○ Programmatic access ○ Persist to file ● Bigtable ○ Multi-table write ○ Cluster scaling for bulk I/O Other goodies
- 29. Demo Time!
- 30. Adoption ● At Spotify ○ 20+ teams, 80+ users, 70+ production pipelines ○ Most of them new to Scala and Scio ● Open source model ○ Discussion on Slack, mailing list ○ Issue tracking on public Github ○ Community driven - type safe BigQuery, Bigtable, Datastore, Protobuf
- 31. Release Radar ● 50 n1-standard-1 workers ● 1 core 3.75GB RAM ● 130GB in - Avro & Bigtable ● 130GB out x 2 - Bigtable in US+EU ● 110M Bigtable mutations ● 120 LOC
- 32. Fan Insights ● Listener stats [artist|track] × [context|geography|demography] × [day|week|month] ● BigQuery, GCS, Datastore ● TBs daily ● 150+ Java jobs to < 10 Scio jobs
- 33. Master Metadata ● n1-standard-1 workers ● 1 core 3.75GB RAM ● Autoscaling 2-35 workers ● 26 Avro sources - artist, album, track, disc, cover art, ... ● 120GB out, 70M records ● 200 LOC vs original Java 600 LOC
- 34. And we broke Google
- 35. BigDiffy ● Pairwise field-level statistical diff ● Diff 2 SCollection[T] given keyFn: T => String ● T: Avro, BigQuery, Protobuf ● Field level Δ - numeric, string, vector ● Δ statistics - min, max, μ, σ, etc. ● Non-deterministic fields ○ ignore field ○ treat "repeated" field as unordered list Part of github.com/spotify/ratatool
- 36. Dataset Diff ● Diff stats ○ Global: # of SAME, DIFF, MISSING LHS/RHS ○ Key: key → SAME, DIFF, MISSING LHS/RHS ○ Field: field → min, max, μ, σ, etc. ● Use cases ○ Validating pipeline migration ○ Sanity checking ML models
- 37. Pairwise field-level deltas val lKeyed = lhs.keyBy(keyFn) val rKeyed = rhs.keyBy(keyFn) val deltas = (lKeyed outerJoin rKeyed).map { case (k, (lOpt, rOpt)) => (lOpt, rOpt) match { case (Some(l), Some(r)) => val ds = diffy(l, r) // Seq[Delta] val dt = if (ds.isEmpty) SAME else DIFFERENT (k, (ds, dt)) case (_, _) => val dt = if (lOpt.isDefined) MISSING_RHS else MISSING_LHS (k, (Nil, dt)) } }
- 38. Summing deltas import com.twitter.algebird._ // convert deltas to map of (field → summable stats) def deltasToMap(ds: Seq[Delta], dt: DeltaType) : Map[String, (Long, Option[(DeltaType, Min[Double], Max[Double], Moments)])] = { // ... } deltas .map { case (_, (ds, dt)) => deltasToMap(ds, dt) } .sum // Semigroup!
- 39. Other uses ● AB testing ○ Statistical analysis with bootstrap and DimSum ○ BigQuery, Datastore, TBs in/out ● Monetization ○ Ads targeting ○ User conversion analysis ○ BigQuery, TBs in/out ● User understanding ○ Diversity ○ Session analysis ○ Behavior analysis ● Home page ranking ● Audio fingerprint analysis
- 41. Serialization ● Data ser/de ○ Scalding, Spark and Storm uses Kryo and Chill ○ Dataflow/Beam requires explicit Coder[T] Sometimes inferable via Guava TypeToken ○ ClassTag to the rescue, fallback to Kryo/Chill ● Lambda ser/de ○ ClosureCleaner ○ Serializable and @transient lazy val
- 42. REPL ● Spark REPL transports lambda bytecode via HTTP ● Dataflow requires job jar for execution (no master) ● Custom class loader and ILoop ● Interpreted classes → job jar → job submission ● SCollection[T]#closeAndCollect(): Iterator[T] to mimic Spark actions
- 43. Macros and IntelliJ IDEA ● IntelliJ IDEA does not see macro expanded classes https://youtrack.jetbrains.com/issue/SCL-8834 ● @BigQueryType.{fromTable, fromQuery} class MyRecord ● Scio IDEA plugin https://github.com/spotify/scio-idea-plugin
- 44. Scio in Apache Zeppelin Local Zeppelin server, remote managed Dataflow cluster, NO OPS
- 45. Experimental ● github.com/nevillelyh/shapeless-datatype ○ Case class ↔ BigQuery TableRow & Datastore Entity ○ Generic mapper between case class types ○ Type and lens based record matcher ● github.com/nevillelyh/protobuf-generic ○ Generic Protobuf manipulation similar to Avro GenericRecord ○ Protobuf type T → JSON schema ○ Bytes ↔ JSON given JSON schema
- 46. What's Next? ● Better streaming support [#163] ● Support Beam 0.3.0-incubating ● Support other runners ● Donate to Beam as Scala DSL [BEAM-302]
- 47. The End Thank You Neville Li @sinisa_lyh