How Spotify's big data ecosystem has evolved through Google Cloud Platform building blocks to leverage actionable insights

1. Shortening the
Feedback Loop
HowSpotify’sBigDataEcosystemHas
EvolvedtoLeverageActionableInsights
Josh Baer (jbx@spotify.com)
Note: opinions expressed in these slides are the authors and not necessarilythose of Spotify

2. Who am I?
• Technical Product Owner at Spotify
• Working with fast processing infrastructure
• Previously, building out Spotify’s 2500 node
Hadoop cluster
@l_phant

3. • Spotify Launches
• Access to a gigantic catalog
of music
• Click to play instantaneous!
In 2008

4. Behind the Scenes:
Days to Insights

5. Behind the Scenes

6. Behind the Scenes
Minutes to
transfer
Hours to Clean
and Bucket
Hours to Run
Jobs or Ad Hoc
Queries
DAYS TO
INSIGHTS

7. “Continuous Analytics: Stream Query Processing in Practice”, Michael J Franklin, Professor, UC Berkley, Dec 2009

8. Real-time
Processing
Batch Processing
(Hadoop, Hive, BigQuery)
“Continuous Analytics: Stream Query Processing in Practice”, Michael J Franklin, Professor, UC Berkley, Dec 2009

9. To leverage actionable
insights, we need a
faster feedback loop!

10. • Music Streaming Service
• Launched in 2008
• Premium and FreeTiers
• Available in 60 Countries
What is Spotify?

11. Over 100 Million Active Users

12. Over 30 Million Songs

13. Over 1 Billion Plays Per Day

14. And we have Data

15. Hadoop at Spotify
• ~2,500 Nodes
• >100 PB Capacity
• >100 TB Memory accessible by jobs
• 20KJobs/Day

16. Apache Kafka at Spotify
• 500 Kafka-related machines
• 40TB/day from logs

17. Real-Time at Spotify
• StormTopologies fed via Kafka
• Mostly used for hack ideas or proof of concepts

18. Migratingto
theCloud

19. In the Beginning…
• Spotifywas almost completely on-premise/bare metal
• Grew to 2,500 node Hadoop cluster and over 10K
total machines in production at four globally
distributed data centers
• “Flirted” with cloud providers at various times

20. In 2014
• Maybe we should trythis cloud thing for real

21. Why Move to the Cloud?
• Cloud Providers have matured, decreasing in costs
and increasing in reliability and variety of service
offered
• Owning and operating physical machines is not a
competitive advantage for Spotify

22. Why Google’s Cloud?
• We believe Google’s industry leading background
in Big Data technologies will give us a data
processing advantage

23. Google
Cloud Data
Building Blocks

24. BigQuery
• Ad-hoc and interactive querying service for massive datasets
• Like Hive, but without needing to manage Hadoop and servers
• Leverages Google’s internal tech
• Dremel (query execution engine)
• Colossus (distributed storage)
• Borg (distributed compute)
• Jupiter (network)
Source: https://cloud.google.com/blog/big-data/2016/01/bigquery-under-the-hood

25. BigQuery vs. Hive
• Example Queries:
• What are the top 10 songs by popularity in Spain
during October 2016?
• How many hours did users in Spain spend
listening to Spotify during October?

26. BigQuery vs. Hive
• What are the top 10 songs by popularity in Spain during October 2016?
• Hive
• 2647s (44min, 7sec)
• 15.5TB processed
• BigQuery
• 108s (1min, 48sec)
• 1.50TB processed
Note: Hive performance unoptimized.Version used (0.14), input format (Avro), run on
a ~2500 nodeYarn cluster.This is not considered to be a thorough benchmark

27. Top 10 Tracks in Spain during October 2016
Rank Artist(s) Track Name
1 J Balvin Safari
2 DJ Snake Let Me Love You
3 Ricky Mar8n Vente Pa' Ca
4 Sebas8an Yatra Traicionera
5 Zion & Lennox (feat. J Balvin) Otra Vez
6 Carlos Vives, Shakira La Bicicleta
7 The Chainsmokers Closer
8 Major Lazer (feat. Jus8n Bieber & MØ) Cold Water
9 Sia The Greatest
10 IAmChino (feat. Pitbull, Yandel & Chacal) Ay MI Dios

28. BigQuery vs. Hive
• How much time did users in Spain spend listening to Spotify during October?
• Hive
• 969s (16min, 9 sec)
• 15.5TB processed
• BigQuery
• 33s
• 780 GB processed
Note: Hive performance unoptimized.Version used (0.14), input format (Avro), run on
a ~2500 nodeYarn cluster.This is not considered to be a thorough benchmark

29. Nearly 10,000 Years!

30. BigQuery at Spotify
• Interactive and ad-hoc querying immediately
started to transferto BQ once the data was
available on the cloud
• Pace of learning increases as friction to question
decreases

31. Cloud Pub/Sub
• At least once globally distributed message queue
• For high volume, low topic (<10,000) publish
subscribe behavior
• Like Kafka, but without needing to operate servers
and supporting services (zookeeper)

32. Cloud Pub/Sub at Spotify
• 800K events/second? No problem
• P99 Latency of ingestions into ES: 500ms
• Ingestion from globally distributed non-GCP
datacenters is painless

33. • Managed Service for running batch and streaming jobs
• UnifiedAPI for batch and streaming mode
• Inspired by internal Google tools like FlumeJava and
Millwheel
• Programming model open-sourced asApache Beam
(currently incubating)
Cloud Dataflow

34. • Usually run via Scio: https://github.com/spotify/scio
• Scio provides a scalaAPI for running Dataflow jobs
and provides easy integrations with BigQuery
• New batch processing jobs at Spotify are being
written in Scio/Dataflow
Cloud Dataflow (Batch) at Spotify

35. • Exactly-once stream processing framework
• Areplacement for Spark/Flink streaming and
Storm workloads at Spotify
• Optimizes for consistencywhich can complicate
real-time workloads
Cloud Dataflow (Streaming) at Spotify

37. Spotify + Google Cloud Timeline
2015 2016
Beginning of Google
Cloud evaluation
BigQuery begins
to replace Hive
Cloud Pub/Sub begins
to replace Kafka
Dataflow (streaming)
begins to replace Storm
Dataflow (batch)
replacing Map/Reduce
Note: Dates are approximations

38. Putting ItAll
Together

39. The Problem
• We want to detect within minutes ifwe’ve
introduced a bug in a client release that affects
important event logging behavior

40. Before…
Minutes to
transfer
Hours to Clean
and Bucket
Hours to Run
Jobs or Ad Hoc
Queries
DAYS TO
INSIGHTS

41. Getting Data from Clients to Pub/Sub
• Built Pulsar, a simple service aggregating data from
Access Points and feeding it into Cloud Pub/Sub
• Replaces the Kafka real-time event feed

42. Pulsar

43. Dataflow
• Subscribes to important event Pub/Sub topics
• Aggregate events into minute windows
• Always running, no need to schedule orwait for
results

44. BigQuery
• Receives aggregates from Dataflow
• Allows for ad-hoc inspection or slicing on different
dimensions

45. Tableau
• DataVisualizationTool that integrates nicelywith
BigQuery
• Pulls data from BigQuery periodically and caches for
quick inspection

47. Milliseconds
to transfer
Milliseconds
to process
Seconds to
Query
SECONDS TO
INSIGHTS

49. FasterInsights
toClient
Behavior

50. Problem
As a developer, I want to be able to instantly explore
data being logged bythe clients.

51. Solution
• Produce a topic for all employee client events
• Store in Elasticsearch
• Visualize in Kibana

54. Benefits
• Able to understand what’s being sent bythe client
as it happens
• Exploring events, visualizing distribution (i.e. does
this field actually get populated)
• Prototyping analysis based on a sample
• Dashboards for Employee Releases

55. OtherUses

56. Ad Targeting
• Real-time genre targeting
• Session insights — explicit filter

57. Real-time Recommendations

58. Live Results for X-Factor
• X-Factor: music competition
• Songs available on Spotify
immediately after show airs
• Listener behavior determines the
order of contestants on the playlist

59. Review

60. Real-time
Processing
Batch Processing
(Hadoop, Hive, BigQuery)
“Continuous Analytics: Stream Query Processing in Practice”, Michael J Franklin, Professor, UC Berkley, Dec 2009

61. Behind the Scenes
Minutes to
transfer
Hours to Clean
and Bucket
Hours to Run
Jobs or Ad Hoc
Queries
DAYS TO
INSIGHTS

62. To leverage actionable
insights, we need a
faster feedback loop!

63. Putting it all together
Milliseconds
to transfer
Milliseconds
to process
Seconds to
Query
SECONDS TO
INSIGHTS

64. TheValue of a Fast Feedback Loop
• Detecting problems early in data avoids long backfills or
long term data loss
• Instant insights on newly developed features allows
teams to iterate quicker and take risks
• Providing a quicker ad-hoc querying engine allows teams
to ask more questions and learn faster

65. UseAnything and Everything
• Spotify has leveraged Google Cloud tools, such as Pub/
Sub, Dataflow and BigQuery
• Opensource and other cloud providers offer many
alternatives to this stack
• Opensource tools (Elasticsearch/Kibana) and proprietary
solutions (Tableau) have also been useful additions

66. WhereAre We Going?
• The real-time mission is in the early stages at
Spotify

67. Stream Processing First
• The sun never sets on Spotify, why impose
boundaries on our datasets?
• What’s the shortest distance between two points?
Zero!
• Can we reduce the feedback cycle to zero?

68. We’reHiring!
Engineers, Managers, Product Owners
needed in NYC and Stockholm
https://www.spotify.com/jobs

69. Thanks! BigDataSpain!